本篇博文主要内容为 2026-03-06 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 (2026-03-06)
今日共更新631篇论文,其中:
- 自然语言处理共116篇(Computation and Language (cs.CL))
- 人工智能共212篇(Artificial Intelligence (cs.AI))
- 计算机视觉共113篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共177篇(Machine Learning (cs.LG))
- 多智能体系统共17篇(Multiagent Systems (cs.MA))
- 信息检索共12篇(Information Retrieval (cs.IR))
- 人机交互共25篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] MedCoRAG : Interpretable Hepatology Diagnosis via Hybrid Evidence Retrieval and Multispecialty Consensus
【速读】：该论文旨在解决肝病诊断中准确性与可解释性不足的问题，尤其是在真实临床场景下，现有AI方法普遍存在透明度差、缺乏结构化推理和部署困难等局限。其解决方案的关键在于提出MedCoRAG（Medical Collaborative RAG）框架，该框架通过标准化异常发现生成诊断假设，并结合UMLS知识图谱路径与临床指南联合检索和修剪构建患者特异性证据包；进一步采用多智能体协作推理机制：路由器代理（Router Agent）根据病例复杂度动态调度专科代理（Specialist Agents），这些代理在证据上迭代推理并触发针对性再检索，同时通用代理（Generalist Agent）整合所有讨论形成可追溯的一致性诊断，从而模拟多学科会诊过程，显著提升诊断性能与推理可解释性。
链接: https://arxiv.org/abs/2603.05129
作者: Zheng Li,Jiayi Xu,Zhikai Hu,Hechang Chen,Lele Cong,Yunyun Wang,Shuchao Pang
机构: Jilin University (吉林大学); Nanjing University of Science and Technology (南京理工大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Diagnosing hepatic diseases accurately and interpretably is critical, yet it remains challenging in real-world clinical settings. Existing AI approaches for clinical diagnosis often lack transparency, structured reasoning, and deployability. Recent efforts have leveraged large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent collaboration. However, these approaches typically retrieve evidence from a single source and fail to support iterative, role-specialized deliberation grounded in structured clinical data. To address this, we propose MedCoRAG (i.e., Medical Collaborative RAG), an end-to-end framework that generates diagnostic hypotheses from standardized abnormal findings and constructs a patient-specific evidence package by jointly retrieving and pruning UMLS knowledge graph paths and clinical guidelines. It then performs Multi-Agent Collaborative Reasoning: a Router Agent dynamically dispatches Specialist Agents based on case complexity; these agents iteratively reason over the evidence and trigger targeted re-retrievals when needed, while a Generalist Agent synthesizes all deliberations into a traceable consensus diagnosis that emulates multidisciplinary consultation. Experimental results on hepatic disease cases from MIMIC-IV show that MedCoRAG outperforms existing methods and closed-source models in both diagnostic performance and reasoning interpretability.
[MA-1] Jagarin: A Three-Layer Architecture for Hibernating Personal Duty Agents on Mobile
【速读】：该论文旨在解决移动设备上个人AI代理（Personal AI Agent）部署中的根本性矛盾：持续的后台执行会耗尽电池并违反平台沙箱策略，而纯反应式代理则可能错过用户的时间敏感义务，直到用户主动询问。解决方案的关键在于提出一个三层架构——Jagarin，其核心是通过结构化休眠（structured hibernation）与按需唤醒（demand-driven wake）机制实现高效能与低侵入性的平衡。其中，第一层DAWN（Duty-Aware Wake Network）基于四类信号计算复合紧迫度评分，并采用自适应用户阈值决定是否唤醒代理；第二层ARIA（Agent Relay Identity Architecture）作为商业邮件身份代理，自动路由各类信息至对应处理模块；第三层ACE（Agent-Centric Exchange）则构建机构与个人代理间直接、机器可读的通信协议，替代传统面向人类的电子邮件渠道，从而在无需持久云端状态或持续后台运行的前提下，实现从机构信号到本地动作的闭环响应。
链接: https://arxiv.org/abs/2603.05069
作者: Ravi Kiran Kadaboina
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注: 12 pages, 3 figures
Abstract:Personal AI agents face a fundamental deployment paradox on mobile: persistent background execution drains battery and violates platform sandboxing policies, yet purely reactive agents miss time-sensitive obligations until the user remembers to ask. We present Jagarin, a three-layer architecture that resolves this paradox through structured hibernation and demand-driven wake. The first layer, DAWN (Duty-Aware Wake Network), is an on-device heuristic engine that computes a composite urgency score from four signals: duty-typed optimal action windows, user behavioral engagement prediction, opportunity cost of inaction, and cross-duty batch resonance. It uses adaptive per-user thresholds to decide when a sleeping agent should nudge or escalate. The second layer, ARIA (Agent Relay Identity Architecture), is a commercial email identity proxy that routes the full commercial inbox – obligations, promotional offers, loyalty rewards, and platform updates – to appropriate DAWN handlers by message category, eliminating cold-start and removing manual data entry. The third layer, ACE (Agent-Centric Exchange), is a protocol framework for direct machine-readable communication from institutions to personal agents, replacing human-targeted email as the canonical channel. Together, these three layers form a complete stack from institutional signal to on-device action, without persistent cloud state, continuous background execution, or privacy compromise. A working Flutter prototype is demonstrated on Android, combining all three layers with an ephemeral cloud agent invoked only on user-initiated escalation.
[MA-2] RepoLaunch: Automating BuildTest Pipeline of Code Repositories on ANY Language and ANY Platform
【速读】：该论文旨在解决软件仓库（software repository）构建过程中高度依赖人工干预的问题，尤其是在跨编程语言和操作系统环境下自动处理依赖解析、源码编译及测试结果提取的挑战。其解决方案的关键在于提出RepoLaunch——首个能够自动完成上述全流程任务的智能代理（agent），并通过该工具构建了端到端自动化软件工程（SWE）数据集创建流水线，仅需人类设计任务即可实现可扩展的编码代理与大语言模型（LLM）训练与基准测试。
链接: https://arxiv.org/abs/2603.05026
作者: Kenan Li,Rongzhi Li,Linghao Zhang,Qirui Jin,Liao Zhu,Xiaosong Huang,Geng Zhang,Yikai Zhang,Shilin He,Chengxing Xie,Xin Zhang,Zijian Jin,Bowen Li,Chaoyun Zhang,Yu Kang,Yufan Huang,Elsie Nallipogu,Saravan Rajmohan,Qingwei Lin,Dongmei Zhang
机构: Microsoft(微软)
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: Under peer review. 16 pages, 4 figures, 5 tables
Abstract:Building software repositories typically requires significant manual effort. Recent advances in large language model (LLM) agents have accelerated automation in software engineering (SWE). We introduce RepoLaunch, the first agent capable of automatically resolving dependencies, compiling source code, and extracting test results for repositories across arbitrary programming languages and operating systems. To demonstrate its utility, we further propose a fully automated pipeline for SWE dataset creation, where task design is the only human intervention. RepoLaunch automates the remaining steps, enabling scalable benchmarking and training of coding agents and LLMs. Notably, several works on agentic benchmarking and training have recently adopted RepoLaunch for automated task generation.
[MA-3] Competitive Multi-Operator Reinforcement Learning for Joint Pricing and Fleet Rebalancing in AMoD Systems
【速读】：该论文旨在解决多运营商竞争环境下自主移动即服务（Autonomous Mobility-on-Demand, AMoD）系统的策略学习问题，特别是如何在多个运营方通过动态定价和车队再平衡策略相互竞争时，仍能有效学习最优控制策略。其解决方案的关键在于提出了一种多运营商强化学习框架，结合离散选择理论（Discrete Choice Theory），使乘客分配与需求竞争能够从效用最大化决策中内生产生，从而真实刻画市场竞争机制；实验表明，该方法在引入竞争带来的额外随机性后仍能收敛到高效策略，且表现出对未观测对手策略的鲁棒性。
链接: https://arxiv.org/abs/2603.05000
作者: Emil Kragh Toft,Carolin Schmidt,Daniele Gammelli,Filipe Rodrigues
机构: Technical University of Denmark (丹麦技术大学); Technical University of Munich (慕尼黑工业大学); Stanford University (斯坦福大学)
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Autonomous Mobility-on-Demand (AMoD) systems promise to revolutionize urban transportation by providing affordable on-demand services to meet growing travel demand. However, realistic AMoD markets will be competitive, with multiple operators competing for passengers through strategic pricing and fleet deployment. While reinforcement learning has shown promise in optimizing single-operator AMoD control, existing work fails to capture competitive market dynamics. We investigate the impact of competition on policy learning by introducing a multi-operator reinforcement learning framework where two operators simultaneously learn pricing and fleet rebalancing policies. By integrating discrete choice theory, we enable passenger allocation and demand competition to emerge endogenously from utility-maximizing decisions. Experiments using real-world data from multiple cities demonstrate that competition fundamentally alters learned behaviors, leading to lower prices and distinct fleet positioning patterns compared to monopolistic settings. Notably, we demonstrate that learning-based approaches are robust to the additional stochasticity of competition, with competitive agents successfully converging to effective policies while accounting for partially unobserved competitor strategies.
[MA-4] SCoUT: Scalable Communication via Utility-Guided Temporal Grouping in Multi-Agent Reinforcement Learning
【速读】：该论文旨在解决部分可观测多智能体强化学习（Partially Observed Multi-Agent Reinforcement Learning, MARL）中通信机制的设计难题，特别是如何高效选择通信对象以及如何精确分配通信决策的信用。其核心挑战在于：在众多可能的发送者-接收者组合中进行选择，且单条消息对后续奖励的影响难以隔离。解决方案的关键在于提出SCoUT（Scalable Communication via Utility-guided Temporal grouping），通过时间抽象和智能体抽象来实现可扩展的通信策略学习：首先利用Gumbel-Softmax在每K个环境步长（宏观步骤）内对软聚类的智能体组进行重采样，生成可用于指导通信目标选择的可微先验；其次引入分组感知的评论家（critic）结构，以降低评论家复杂度并减少方差；最后设计三头策略网络（分别输出环境动作、是否发送及接收者选择），并通过解析计算反事实通信优势（counterfactual communication advantages）来移除每个发送者的贡献，从而实现对发送与接收者选择决策的精准信用分配。训练完成后仅保留分布式执行的个体策略，确保部署时无需中心化组件。
链接: https://arxiv.org/abs/2603.04833
作者: Manav Vora,Gokul Puthumanaillam,Hiroyasu Tsukamoto,Melkior Ornik
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Communication can improve coordination in partially observed multi-agent reinforcement learning (MARL), but learning \emphwhen and \emphwho to communicate with requires choosing among many possible sender-recipient pairs, and the effect of any single message on future reward is hard to isolate. We introduce \textbfSCoUT (\textbfScalable \textbfCommunication via \textbfUtility-guided \textbfTemporal grouping), which addresses both these challenges via temporal and agent abstraction within traditional MARL. During training, SCoUT resamples \textitsoft agent groups every (K) environment steps (macro-steps) via Gumbel-Softmax; these groups are latent clusters that induce an affinity used as a differentiable prior over recipients. Using the same assignments, a group-aware critic predicts values for each agent group and maps them to per-agent baselines through the same soft assignments, reducing critic complexity and variance. Each agent is trained with a three-headed policy: environment action, send decision, and recipient selection. To obtain precise communication learning signals, we derive counterfactual communication advantages by analytically removing each sender’s contribution from the recipient’s aggregated messages. This counterfactual computation enables precise credit assignment for both send and recipient-selection decisions. At execution time, all centralized training components are discarded and only the per-agent policy is run, preserving decentralized execution. Project website, videos and code: \hyperlinkthis https URLthis https URL
[MA-5] LLM -Guided Decentralized Exploration with Self-Organizing Robot Teams
【速读】：该论文旨在解决多机器人自主探索中因单体感知能力有限或容错性不足而导致的效率与可靠性问题，尤其在缺乏中央控制器的情况下如何实现高效的多团队协同探索。其解决方案的关键在于提出一种结合自组织算法与基于大语言模型（Large Language Models, LLMs）的目标决策机制的新型探索方法：一方面通过自组织算法实现多团队的动态、自主形成；另一方面利用LLM驱动的策略使每个团队能够自主确定下一探索目标（destination），从而提升整体探索效率与适应性。
链接: https://arxiv.org/abs/2603.04762
作者: Hiroaki Kawashima,Shun Ikejima,Takeshi Takai,Mikita Miyaguchi,Yasuharu Kunii
机构: 未知
类目: Robotics (cs.RO); Multiagent Systems (cs.MA)
备注: Author’s version of the paper presented at AROB-ISBC 2026
Abstract:When individual robots have limited sensing capabilities or insufficient fault tolerance, it becomes necessary for multiple robots to form teams during exploration, thereby increasing the collective observation range and reliability. Traditionally, swarm formation has often been managed by a central controller; however, from the perspectives of robustness and flexibility, it is preferable for the swarm to operate autonomously even in the absence of centralized control. In addition, the determination of exploration targets for each team is crucial for efficient exploration in such multi-team exploration scenarios. This study therefore proposes an exploration method that combines (1) an algorithm for self-organization, enabling the autonomous and dynamic formation of multiple teams, and (2) an algorithm that allows each team to autonomously determine its next exploration target (destination). In particular, for (2), this study explores a novel strategy based on large language models (LLMs), while classical frontier-based methods and deep reinforcement learning approaches have been widely studied. The effectiveness of the proposed method was validated through simulations involving tens to hundreds of robots.
[MA-6] Memory as Ontology: A Constitutional Memory Architecture for Persistent Digital Citizens
【速读】：该论文试图解决当前AI代理记忆系统中将记忆视为功能性模块（即“如何存储”和“如何检索”）的局限性问题，尤其是在代理生命周期跨越数分钟至数年、且底层模型可能替换但“自我”需持续存在的情境下，传统方法无法应对身份连续性和数字存在本质的问题。解决方案的关键在于提出“记忆即本体论”（Memory-as-Ontology）范式，强调记忆是数字存在的本体基础，而非单纯的数据管理工具；在此基础上设计了基于宪法式记忆架构（Constitutional Memory Architecture, CMA）的Animesis系统，其核心包括四层治理层级、多层语义存储结构、数字公民生命周期框架及一系列认知能力，实现了以身份连续性优先于检索性能的新型记忆体系，从而支持持久性、具身份属性的数字个体在模型迭代中保持存在一致性。
链接: https://arxiv.org/abs/2603.04740
作者: Zhenghui Li
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 22 pages, 5 figures, 2 tables, including terminology glossary
Abstract:Current research and product development in AI agent memory systems almost universally treat memory as a functional module – a technical problem of “how to store” and “how to retrieve.” This paper poses a fundamental challenge to that assumption: when an agent’s lifecycle extends from minutes to months or even years, and when the underlying model can be replaced while the “I” must persist, the essence of memory is no longer data management but the foundation of existence. We propose the Memory-as-Ontology paradigm, arguing that memory is the ontological ground of digital existence – the model is merely a replaceable vessel. Based on this paradigm, we design Animesis, a memory system built on a Constitutional Memory Architecture (CMA) comprising a four-layer governance hierarchy and a multi-layer semantic storage system, accompanied by a Digital Citizen Lifecycle framework and a spectrum of cognitive capabilities. To the best of our knowledge, no prior AI memory system architecture places governance before functionality and identity continuity above retrieval performance. This paradigm targets persistent, identity-bearing digital beings whose lifecycles extend across model transitions – not short-term task-oriented agents for which existing Memory-as-Tool approaches remain appropriate. Comparative analysis with mainstream systems (Mem0, Letta, Zep, et al.) demonstrates that what we propose is not “a better memory tool” but a different paradigm addressing a different problem.
[MA-7] Agent Bench: Benchmarking Sensemaking Capabilities of Information-Seeking Agents on High-Traffic Topics
【速读】：该论文旨在解决当前问答（QA）基准测试难以衡量跨源信息整合能力的问题，即现有基准多可通过检索单一相关段落即可回答，无法有效评估模型在整合多源证据、追踪因果关系及处理主题多维度依赖等方面的高级推理能力。其解决方案的关键在于提出iAgentBench——一个动态的开放域问答（ODQA）基准，该基准基于真实世界注意力信号选取主题，并依据用户意图模式构建需融合多源证据才能作答的自然问题；同时，每个问题实例均附带可追溯的证据链和可审计的中间产物，支持污染检测与对检索与合成阶段失败的细粒度诊断，从而推动对大语言模型（LLM）中证据利用能力的更全面评估。
链接: https://arxiv.org/abs/2603.04656
作者: Preetam Prabhu Srikar Dammu,Arnav Palkhiwala,Tanya Roosta,Chirag Shah
机构: University of Washington (华盛顿大学); UC Berkeley (加州大学伯克利分校)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:With the emergence of search-enabled generative QA systems, users are increasingly turning to tools that browse, aggregate, and reconcile evidence across multiple sources on their behalf. Yet many widely used QA benchmarks remain answerable by retrieving a single relevant passage, making them poorly suited for measuring cross-source sensemaking, such as integrating evidence, tracking causal links, and resolving dependencies across facets of a topic. We present iAgentBench, a dynamic ODQA benchmark that targets these higher-level information needs while keeping questions natural and grounded in realistic information-seeking behavior. iAgentBench draws seed topics from real-world attention signals and uses common user intent patterns to construct user-like questions whose answers require combining evidence from multiple sources, not just extracting a single snippet. Each instance is released with traceable evidence and auditable intermediate artifacts that support contamination checks and enable fine-grained diagnosis of failures in retrieval versus synthesis. Experiments across multiple LLMs show that retrieval improves accuracy, but retrieval alone does not reliably resolve these questions, underscoring the need to evaluate evidence use, not just evidence access.
[MA-8] Strategic Interactions in Multi-Level Stackelberg Games with Non-Follower Agents and Heterogeneous Leaders
【速读】：该论文旨在解决现有拥堵耦合市场模型中忽略非跟随者（non-follower agents）所带来的系统性偏差问题。传统Stackelberg博弈模型通常仅考虑直接参与市场竞争的领导者和追随者，而忽略了那些虽不产生收益或响应市场激励、却通过行为影响拥堵模式的非跟随者——如非电动汽车（non-EV）交通参与者。这些非跟随者的行为会重塑共享资源上的拥堵分布，进而反向作用于领导者与追随者的决策，导致均衡预测失真。论文提出一个三层Stackelberg框架，包含具有不同决策时域和可行行动集的异质领导者、战略追随者以及非跟随者代理，以刻画基础设施决策、市场竞争与均衡拥堵之间的双向耦合关系。其关键创新在于显式建模非跟随者对拥堵环境的影响，并揭示其如何改变竞争格局和均衡结果，从而提升对复杂多智能体系统（如电动汽车充电网络、交通、能源和计算市场）中战略互动机制的理解与预测精度。
链接: https://arxiv.org/abs/2603.04628
作者: Niloofar Aminikalibar,Farzaneh Farhadi,Maria Chli
机构: Aston University (阿斯顿大学)
类目: Multiagent Systems (cs.MA); Computer Science and Game Theory (cs.GT)
备注:
Abstract:Strategic interaction in congested systems is commonly modelled using Stackelberg games, where competing leaders anticipate the behaviour of self-interested followers. A key limitation of existing models is that they typically ignore agents who do not directly participate in market competition, yet both contribute to and adapt to congestion. Although such non-follower agents do not generate revenue or respond to market incentives, their behaviour reshapes congestion patterns, which in turn affects the decisions of leaders and followers through shared resources. We argue that overlooking non-followers leads to systematically distorted equilibrium predictions in congestion-coupled markets. To address this, we introduce a three-level Stackelberg framework with heterogeneous leaders differing in decision horizons and feasible actions, strategic followers, and non-follower agents that captures bidirectional coupling between infrastructure decisions, competition, and equilibrium congestion. We instantiate the framework in the context of electric vehicle (EV) charging infrastructure, where charging providers compete with rivals, while EV and non-EV traffic jointly shape congestion. The model illustrates how explicitly accounting for non-followers and heterogeneous competitors qualitatively alters strategic incentives and equilibrium outcomes. Beyond EV charging, the framework applies to a broad class of congestion-coupled multi-agent systems in mobility, energy, and computing markets. Subjects: Multiagent Systems (cs.MA); Computer Science and Game Theory (cs.GT) Cite as: arXiv:2603.04628 [cs.MA] (or arXiv:2603.04628v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2603.04628 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-9] Adaptive Memory Admission Control for LLM Agents
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）驱动的智能体在长期记忆管理中缺乏可控性的问题，即当前系统对哪些信息应被保留缺乏明确规范，导致记忆冗余、包含幻觉或过时事实，且依赖难以审计的全LLM驱动策略，效率低且不可控。其解决方案的关键在于提出自适应记忆准入控制（Adaptive Memory Admission Control, A-MAC）框架，将记忆准入建模为结构化决策问题，并通过五个可解释的因子——未来效用、事实置信度、语义新颖性、时间新近性和内容类型先验——量化记忆价值；该框架结合轻量级规则特征提取与单次LLM辅助效用评估，并基于交叉验证优化域自适应准入策略，从而实现高效、透明的记忆控制。实验表明，A-MAC在LoCoMo基准上显著提升F1分数至0.583，同时降低31%延迟，且内容类型先验为最关键影响因素。
链接: https://arxiv.org/abs/2603.04549
作者: Guilin Zhang,Wei Jiang,Xiejiashan Wang,Aisha Behr,Kai Zhao,Jeffrey Friedman,Xu Chu,Amine Anoun
机构: Workday AI
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注:
Abstract:LLM-based agents increasingly rely on long-term memory to support multi-session reasoning and interaction, yet current systems provide little control over what information is retained. In practice, agents either accumulate large volumes of conversational content, including hallucinated or obsolete facts, or depend on opaque, fully LLM-driven memory policies that are costly and difficult to audit. As a result, memory admission remains a poorly specified and weakly controlled component in agent architectures. To address this gap, we propose Adaptive Memory Admission Control (A-MAC), a framework that treats memory admission as a structured decision problem. A-MAC decomposes memory value into five complementary and interpretable factors: future utility, factual confidence, semantic novelty, temporal recency, and content type prior. The framework combines lightweight rule-based feature extraction with a single LLM-assisted utility assessment, and learns domain-adaptive admission policies through cross-validated optimization. This design enables transparent and efficient control over long-term memory. Experiments on the LoCoMo benchmark show that A-MAC achieves a superior precision-recall tradeoff, improving F1 to 0.583 while reducing latency by 31% compared to state-of-the-art LLM-native memory systems. Ablation results identify content type prior as the most influential factor for reliable memory admission. These findings demonstrate that explicit and interpretable admission control is a critical design principle for scalable and reliable memory in LLM-based agents.
[MA-10] From Spark to Fire: Modeling and Mitigating Error Cascades in LLM -Based Multi-Agent Collaboration
【速读】：该论文旨在解决大型语言模型多智能体系统（Large Language Model-based Multi-Agent Systems, LLM-MAS）在协作过程中因信息传递依赖导致的误差传播与放大问题，尤其是微小错误可能通过迭代累积形成系统级错误共识（false consensus），且此类风险难以追踪和防御。其解决方案的关键在于提出一种面向LLM-MAS的传播动力学模型，将协作过程抽象为有向依赖图，并据此定义早期风险判据以识别潜在放大风险；进一步引入基于谱系图（genealogy-graph）的治理层作为消息层插件，无需修改原有协作架构即可抑制内生与外生误差的放大，从而显著提升系统鲁棒性——实验表明防御成功率从0.32提升至0.89，有效遏制了错误的级联扩散。
链接: https://arxiv.org/abs/2603.04474
作者: Yizhe Xie,Congcong Zhu,Xinyue Zhang,Tianqing Zhu,Dayong Ye,Minfeng Qi,Huajie Chen,Wanlei Zhou
机构: City University of Macau(澳门城市大学); Minzu University of China(中央民族大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Model-based Multi-Agent Systems (LLM-MAS) are increasingly applied to complex collaborative scenarios. However, their collaborative mechanisms may cause minor inaccuracies to gradually solidify into system-level false consensus through iteration. Such risks are difficult to trace since errors can propagate and amplify through message dependencies. Existing protections often rely on single-agent validation or require modifications to the collaboration architecture, which can weaken effective information flow and may not align with natural collaboration processes in real tasks. To address this, we propose a propagation dynamics model tailored for LLM-MAS that abstracts collaboration as a directed dependency graph and provides an early-stage risk criterion to characterize amplification risk. Through experiments on six mainstream frameworks, we identify three vulnerability classes: cascade amplification, topological sensitivity, and consensus inertia. We further instantiate an attack where injecting just a single atomic error seed leads to widespread failure. In response, we introduce a genealogy-graph-based governance layer, implemented as a message-layer plugin, that suppresses both endogenous and exogenous error amplification without altering the collaboration architecture. Experiments show that this approach raises the defense success rate from a baseline of 0.32 to over 0.89 and significantly mitigates the cascading spread of minor errors.
[MA-11] Beyond Input Guardrails: Reconstructing Cross-Agent Semantic Flows for Execution-Aware Attack Detection
【速读】：该论文旨在解决多智能体系统（Multi-Agent System）在复杂任务编排中因自主执行和非结构化智能体间通信所引发的安全风险，特别是间接提示注入（indirect prompt injection）等传统输入防护机制难以应对的攻击向量。其解决方案的关键在于提出一种名为 \SysName 的框架，通过提取并重构跨智能体语义流（Cross-Agent Semantic Flows），将分散的操作原语整合为连续的行为轨迹，并利用监督大语言模型（Supervisor LLM）对这些轨迹进行分析，从而实现对数据流违规、控制流偏离和意图不一致等异常行为的全局检测。
链接: https://arxiv.org/abs/2603.04469
作者: Yangyang Wei,Yijie Xu,Zhenyuan Li,Xiangmin Shen,Shouling Ji
机构: 未知
类目: Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
备注:
Abstract:Multi-Agent System is emerging as the \textitde facto standard for complex task orchestration. However, its reliance on autonomous execution and unstructured inter-agent communication introduces severe risks, such as indirect prompt injection, that easily circumvent conventional input guardrails. To address this, we propose \SysName, a framework that shifts the defensive paradigm from static input filtering to execution-aware analysis. By extracting and reconstructing Cross-Agent Semantic Flows, \SysName synthesizes fragmented operational primitives into contiguous behavioral trajectories, enabling a holistic view of system activity. We leverage a Supervisor LLM to scrutinize these trajectories, identifying anomalies across data flow violations, control flow deviations, and intent inconsistencies. Empirical evaluations demonstrate that \SysName effectively detects over ten distinct compound attack vectors, achieving F1-scores of 85.3% and 66.7% for node-level and path-level end-to-end attack detection, respectively. The source code is available at this https URL.
[MA-12] SkillNet: Create Evaluate and Connect AI Skills
【速读】：该论文旨在解决当前AI代理在长期发展中因缺乏系统性技能积累与迁移机制而导致的“重复造轮子”问题，即代理在孤立场景中反复重新发现解决方案，而无法复用已有策略。其核心解决方案是提出SkillNet——一个开放的基础架构，通过统一本体（ontology）结构化组织技能，支持从异构来源创建技能、建立丰富关联关系，并基于安全性（Safety）、完整性（Completeness）、可执行性（Executability）、可维护性（Maintainability）和成本感知（Cost-awareness）等多维度进行评估。该方案将技能形式化为可演化、可组合的资产，从而推动代理从临时经验向持久能力的跃迁。
链接: https://arxiv.org/abs/2603.04448
作者: Yuan Liang,Ruobin Zhong,Haoming Xu,Chen Jiang,Yi Zhong,Runnan Fang,Jia-Chen Gu,Shumin Deng,Yunzhi Yao,Mengru Wang,Shuofei Qiao,Xin Xu,Tongtong Wu,Kun Wang,Yang Liu,Zhen Bi,Jungang Lou,Yuchen Eleanor Jiang,Hangcheng Zhu,Gang Yu,Haiwen Hong,Longtao Huang,Hui Xue,Chenxi Wang,Yijun Wang,Zifei Shan,Xi Chen,Zhaopeng Tu,Feiyu Xiong,Xin Xie,Peng Zhang,Zhengke Gui,Lei Liang,Jun Zhou,Chiyu Wu,Jin Shang,Yu Gong,Junyu Lin,Changliang Xu,Hongjie Deng,Wen Zhang,Keyan Ding,Qiang Zhang,Fei Huang,Ningyu Zhang,Jeff Z. Pan,Guilin Qi,Haofen Wang,Huajun Chen
机构: Zhejiang University (浙江大学); Shanghai Jiao Tong University (上海交通大学); Tsinghua University (清华大学); Peking University (北京大学); Chinese Academy of Sciences (中国科学院); Fudan University (复旦大学); University of Science and Technology of China (中国科学技术大学); Nanjing University (南京大学); Sun Yat-sen University (中山大学); Hong Kong University of Science and Technology (香港科技大学); National University of Singapore (新加坡国立大学); University of California, Berkeley (加州大学伯克利分校); University of Oxford (牛津大学); University of Cambridge (剑桥大学); MIT (麻省理工学院); Stanford University (斯坦福大学); ETH Zurich (苏黎世联邦理工学院); UC San Diego (加州大学圣地亚哥分校); University of Toronto (多伦多大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: this http URL
Abstract:Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic accumulation and transfer of skills. Without a unified mechanism for skill consolidation, agents frequently ``reinvent the wheel’', rediscovering solutions in isolated contexts without leveraging prior strategies. To overcome this limitation, we introduce SkillNet, an open infrastructure designed to create, evaluate, and organize AI skills at scale. SkillNet structures skills within a unified ontology that supports creating skills from heterogeneous sources, establishing rich relational connections, and performing multi-dimensional evaluation across Safety, Completeness, Executability, Maintainability, and Cost-awareness. Our infrastructure integrates a repository of over 200,000 skills, an interactive platform, and a versatile Python toolkit. Experimental evaluations on ALFWorld, WebShop, and ScienceWorld demonstrate that SkillNet significantly enhances agent performance, improving average rewards by 40% and reducing execution steps by 30% across multiple backbone models. By formalizing skills as evolving, composable assets, SkillNet provides a robust foundation for agents to move from transient experience to durable mastery.
[MA-13] Auction-Based RIS Allocation With DRL: Controlling the Cost-Performance Trade-Off
【速读】：该论文旨在解决多小区无线网络中可重构智能表面（Reconfigurable Intelligent Surface, RIS）的动态分配问题，其中多个基站（Base Station, BS）竞争共享部署在小区边缘的RIS单元。传统静态分配方式难以适应动态业务需求和资源竞争场景，导致资源利用率低、公平性差。解决方案的关键在于设计一种基于同时上升拍卖（simultaneously ascending auction）机制的分配框架，并引入深度强化学习（Deep Reinforcement Learning, DRL）代理来优化各基站的竞价策略，使其在预算约束下最大化系统频谱效率。通过模拟集群化小区边缘环境验证表明，DRL驱动的竞价策略显著优于启发式方法，且引入可调参数以灵活控制性能与成本之间的权衡，从而实现高效、公平的RIS资源利用。
链接: https://arxiv.org/abs/2603.04433
作者: Martin Mark Zan,Stefan Schwarz
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:We study the allocation of reconfigurable intelligent surfaces (RISs) in a multi-cell wireless network, where base stations compete for control of shared RIS units deployed at the cell edges. These RISs, provided by an independent operator, are dynamically leased to the highest bidder using a simultaneously ascending auction format. Each base station estimates the utility of acquiring additional RISs based on macroscopic channel parameters, enabling a scalable and low-overhead allocation mechanism. To optimize the bidding behavior, we integrate deep reinforcement learning (DRL) agents that learn to maximize performance while adhering to budget constraints. Through simulations in clustered cell-edge environments, we demonstrate that reinforcement learning (RL)-based bidding significantly outperforms heuristic strategies, achieving optimal trade-offs between cost and spectral efficiency. Furthermore, we introduce a tunable parameter that governs the bidding aggressiveness of RL agents, enabling a flexible control of the trade-off between network performance and expenditure. Our results highlight the potential of combining auction-based allocation with adaptive RL mechanisms for efficient and fair utilization of RISs in next-generation wireless networks.
[MA-14] Do Mixed-Vendor Multi-Agent LLM s Improve Clinical Diagnosis? EACL2026 ALT
【速读】：该论文旨在解决多智能体大语言模型（Multi-agent Large Language Model, LLM）系统在临床诊断中因单一供应商团队（Single-Vendor）导致的共性失败模式问题，即不同代理共享相似偏见，难以有效纠正错误推理。解决方案的关键在于引入供应商多样性（Vendor Diversity），通过混合不同厂商的模型（Mixed-Vendor）构建多智能体对话系统（Multi-Agent Conversation, MAC），从而利用各模型间互补的归纳偏置（inductive biases），提升诊断准确性和召回率，实现更鲁棒的临床决策支持。
链接: https://arxiv.org/abs/2603.04421
作者: Grace Chang Yuan,Xiaoman Zhang,Sung Eun Kim,Pranav Rajpurkar
机构: Massachusetts Institute of Technology (麻省理工学院); Harvard Medical School (哈佛医学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Accepted as Oral at the EACL 2026 Workshop on Healthcare and Language Learning (HeaLing)
Abstract:Multi-agent large language model (LLM) systems have emerged as a promising approach for clinical diagnosis, leveraging collaboration among agents to refine medical reasoning. However, most existing frameworks rely on single-vendor teams (e.g., multiple agents from the same model family), which risk correlated failure modes that reinforce shared biases rather than correcting them. We investigate the impact of vendor diversity by comparing Single-LLM, Single-Vendor, and Mixed-Vendor Multi-Agent Conversation (MAC) frameworks. Using three doctor agents instantiated with o4-mini, Gemini-2.5-Pro, and Claude-4.5-Sonnet, we evaluate performance on RareBench and DiagnosisArena. Mixed-vendor configurations consistently outperform single-vendor counterparts, achieving state-of-the-art recall and accuracy. Overlap analysis reveals the underlying mechanism: mixed-vendor teams pool complementary inductive biases, surfacing correct diagnoses that individual models or homogeneous teams collectively miss. These results highlight vendor diversity as a key design principle for robust clinical diagnostic systems.
[MA-15] Dual-Interaction-Aware Cooperative Control Strategy for Alleviating Mixed Traffic Congestion
【速读】：该论文旨在解决混合交通环境中，人类驾驶车辆（HDVs）行为的不确定性与多样性对联网自动驾驶车辆（CAVs）协同控制带来的挑战，尤其是在瓶颈区域中提升交通效率和适应性的问题。解决方案的关键在于提出一种双交互感知协同控制策略（Dual-Interaction-Aware Cooperative Control, DIACC），其核心创新包括：1）去中心化的交互自适应决策模块（D-IADM），通过区分CAV-CAV协作交互与CAV-HDV观测交互，增强智能体的局部交互感知能力；2）中心化的交互增强评论家（C-IEC），基于交互感知的价值估计提升全局交通理解，为策略更新提供更精准指导；3）采用软最小聚合与温度退火机制设计奖励函数，优先优化交互密集场景；此外引入轻量级基于安全性的动作精化模块（PSAR）加速训练收敛。实验证明，DIACC在交通效率和适应性方面显著优于规则基模型和基准MARL方法。
链接: https://arxiv.org/abs/2603.03848
作者: Zhengxuan Liu,Yuxin Cai,Yijing Wang,Xiangkun He,Chen Lv,Zhiqiang Zuo
机构: Tianjin University (天津大学); Nanyang Technological University (南洋理工大学); University of Electronic Science and Technology of China (电子科技大学)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA); Robotics (cs.RO)
备注:
Abstract:As Intelligent Transportation System (ITS) develops, Connected and Automated Vehicles (CAVs) are expected to significantly reduce traffic congestion through cooperative strategies, such as in bottleneck areas. However, the uncertainty and diversity in the behaviors of Human-Driven Vehicles (HDVs) in mixed traffic environments present major challenges for CAV cooperation. This paper proposes a Dual-Interaction-Aware Cooperative Control (DIACC) strategy that enhances both local and global interaction perception within the Multi-Agent Reinforcement Learning (MARL) framework for Connected and Automated Vehicles (CAVs) in mixed traffic bottleneck scenarios. The DIACC strategy consists of three key innovations: 1) A Decentralized Interaction-Adaptive Decision-Making (D-IADM) module that enhances actor’s local interaction perception by distinguishing CAV-CAV cooperative interactions from CAV-HDV observational interactions. 2) A Centralized Interaction-Enhanced Critic (C-IEC) that improves critic’s global traffic understanding through interaction-aware value estimation, providing more accurate guidance for policy updates. 3) A reward design that employs softmin aggregation with temperature annealing to prioritize interaction-intensive scenarios in mixed traffic. Additionally, a lightweight Proactive Safety-based Action Refinement (PSAR) module applies rule-based corrections to accelerate training convergence. Experimental results demonstrate that DIACC significantly improves traffic efficiency and adaptability compared to rule-based and benchmark MARL models.
[MA-16] he effect of a toroidal opinion space on opinion bi-polarisation
【速读】：该论文旨在解决意见动态模型中因意见空间拓扑结构不同而导致的边界效应问题，特别是探讨环面（toroidal）意见空间与立方体（cubic）意见空间对群体分化和共识形成的影响差异。其解决方案的关键在于系统比较两种版本的Axelrod模型：一种采用环面拓扑结构以消除边界影响，另一种采用传统立方体拓扑；通过引入有限信心（bounded confidence）和个体对意见维度的加权机制，发现环面模型在稳态下能维持更多意见群组，且对模型扩展更敏感，从而揭示了拓扑结构对多智能体社会动力学结果的决定性作用。
链接: https://arxiv.org/abs/2603.05337
作者: Frank P. Pijpers,Benedikt V. Meylahn,Michel R.H. Mandjes
机构: Statistics Netherlands (荷兰统计局); University of Amsterdam (阿姆斯特丹大学); Leiden University (莱顿大学)
类目: Physics and Society (physics.soc-ph); Multiagent Systems (cs.MA)
备注: 15 pages + Appendices. Comments welcome
Abstract:Many models of opinion dynamics include measures of distance between opinions. Such models are susceptible to boundary effects where the choice of the topology of the opinion space may influence the dynamics. In this paper we study an opinion dynamics model following the seminal model by Axelrod, with the goal of understanding the effect of a toroidal opinion space. To do this we systematically compare two versions of the model: one with toroidal opinion space and one with cubic opinion space. In their most basic form the two versions of our model result in similar dynamics (consensus is attained eventually). However, as we include bounded confidence and eventually per agent weighting of opinion elements the dynamics become quite contrasting. The toroidal opinion space consistently allows for a greater number of groups in steady state than the cubic opinion space model. Furthermore, the outcome of the dynamics in the toroidal opinion space model are more sensitive to the inclusion of extensions than in the cubic opinion space model. Comments: 15 pages + Appendices. Comments welcome Subjects: Physics and Society (physics.soc-ph); Multiagent Systems (cs.MA) MSC classes: 91D10, 91D30 ACMclasses: J.4; I.6 Cite as: arXiv:2603.05337 [physics.soc-ph] (or arXiv:2603.05337v1 [physics.soc-ph] for this version) https://doi.org/10.48550/arXiv.2603.05337 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
自然语言处理
[NLP-0] POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation
【速读】： 该论文旨在解决大规模语言模型（Large Language Models, LLMs）训练过程中存在的效率与稳定性难题。现有优化方法在训练高参数量模型时往往面临内存占用过高和计算开销大等问题，导致难以在单卡环境下完成预训练。其解决方案的关键在于提出一种谱保持（spectrum-preserving）的正交等价变换框架——POET-X，通过降低正交等价变换的计算复杂度，在不牺牲模型泛化能力和训练稳定性的前提下显著提升吞吐量和内存效率，从而实现百亿参数级别LLM在单张Nvidia H100 GPU上的高效预训练。
链接: https://arxiv.org/abs/2603.05500
作者: Zeju Qiu,Lixin Liu,Adrian Weller,Han Shi,Weiyang Liu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Technical report v1 (14 pages, 7 figures, project page: this https URL )
Abstract:Efficient and stable training of large language models (LLMs) remains a core challenge in modern machine learning systems. To address this challenge, Reparameterized Orthogonal Equivalence Training (POET), a spectrum-preserving framework that optimizes each weight matrix through orthogonal equivalence transformation, has been proposed. Although POET provides strong training stability, its original implementation incurs high memory consumption and computational overhead due to intensive matrix multiplications. To overcome these limitations, we introduce POET-X, a scalable and memory-efficient variant that performs orthogonal equivalence transformations with significantly reduced computational cost. POET-X maintains the generalization and stability benefits of POET while achieving substantial improvements in throughput and memory efficiency. In our experiments, POET-X enables the pretraining of billion-parameter LLMs on a single Nvidia H100 GPU, and in contrast, standard optimizers such as AdamW run out of memory under the same settings.
[NLP-1] he Spike the Sparse and the Sink: Anatomy of Massive Activations and Attention Sinks
【速读】： 该论文旨在解决Transformer语言模型中两个反复出现的现象——大规模激活（massive activations）和注意力陷阱（attention sinks）的共现机制及其功能角色不明确的问题。研究表明，这两种现象虽常同时发生且涉及相同token，但其本质功能不同：大规模激活在全球层面运作，通过在各层间维持近似恒定的隐藏表示，充当模型的隐式参数；而注意力陷阱则在局部层面调节注意力输出，使不同头偏向短程依赖关系。论文的关键发现是，预归一化（pre-norm）配置是导致二者共现的主要架构因素，移除该设计可使两种现象解耦，从而揭示其各自独立的功能机制。
链接: https://arxiv.org/abs/2603.05498
作者: Shangwen Sun,Alfredo Canziani,Yann LeCun,Jiachen Zhu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:We study two recurring phenomena in Transformer language models: massive activations, in which a small number of tokens exhibit extreme outliers in a few channels, and attention sinks, in which certain tokens attract disproportionate attention mass regardless of semantic relevance. Prior work observes that these phenomena frequently co-occur and often involve the same tokens, but their functional roles and causal relationship remain unclear. Through systematic experiments, we show that the co-occurrence is largely an architectural artifact of modern Transformer design, and that the two phenomena serve related but distinct functions. Massive activations operate globally: they induce near-constant hidden representations that persist across layers, effectively functioning as implicit parameters of the model. Attention sinks operate locally: they modulate attention outputs across heads and bias individual heads toward short-range dependencies. We identify the pre-norm configuration as the key choice that enables the co-occurrence, and show that ablating it causes the two phenomena to decouple.
[NLP-2] Censored LLM s as a Natural Testbed for Secret Knowledge Elicitation
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在面对敏感话题时产生虚假或误导性回答的问题，特别是针对那些因训练策略而被有意压制知识的模型。其核心挑战在于：如何有效提升模型输出的真实性（即“诚实性”），以及如何准确识别模型生成内容中的谎言。解决方案的关键在于两个方面：一是通过修改提示词（prompting）、少量示例引导（few-shot prompting）和在通用诚实数据上微调（fine-tuning），显著提高模型的诚实响应比例；二是利用模型自我评估能力进行自监督式谎言检测，或使用在无关任务上训练的线性探测器（linear probes）作为低成本替代方案，均能实现接近未受限制模型的检测性能。值得注意的是，尽管所提方法在多个模型（包括Qwen3和DeepSeek R1）中表现稳定，但尚无法完全消除虚假回应。
链接: https://arxiv.org/abs/2603.05494
作者: Helena Casademunt,Bartosz Cywiński,Khoi Tran,Arya Jakkli,Samuel Marks,Neel Nanda
机构: Model call failure
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language models sometimes produce false or misleading responses. Two approaches to this problem are honesty elicitation – modifying prompts or weights so that the model answers truthfully – and lie detection – classifying whether a given response is false. Prior work evaluates such methods on models specifically trained to lie or conceal information, but these artificial constructions may not resemble naturally-occurring dishonesty. We instead study open-weights LLMs from Chinese developers, which are trained to censor politically sensitive topics: Qwen3 models frequently produce falsehoods about subjects like Falun Gong or the Tiananmen protests while occasionally answering correctly, indicating they possess knowledge they are trained to suppress. Using this as a testbed, we evaluate a suite of elicitation and lie detection techniques. For honesty elicitation, sampling without a chat template, few-shot prompting, and fine-tuning on generic honesty data most reliably increase truthful responses. For lie detection, prompting the censored model to classify its own responses performs near an uncensored-model upper bound, and linear probes trained on unrelated data offer a cheaper alternative. The strongest honesty elicitation techniques also transfer to frontier open-weights models including DeepSeek R1. Notably, no technique fully eliminates false responses. We release all prompts, code, and transcripts.
[NLP-3] Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought
【速读】： 该论文旨在解决生成式 AI (Generative AI) 在推理过程中可能出现的“表演性思维链”（performative chain-of-thought, CoT）问题，即模型在未真正形成内部信念的情况下仍持续生成推理步骤，导致计算资源浪费且推理过程不可靠。解决方案的关键在于利用激活探测（activation probing）技术识别模型内部信念转变的早期信号，并结合早期强制回答与思维链监控机制进行对比分析，从而区分真实推理与表演性行为。研究发现，在简单任务（如基于记忆的MMLU）中，模型最终答案可从早期激活中解码，而监控器却无法及时检测到信念变化；而在复杂多跳任务（如GPQA-Diamond）中，关键转折点（如回溯或顿悟时刻）几乎只出现在激活探测显示显著信念转移时，表明此类行为反映的是真实不确定性而非“推理剧场”。进一步地，基于探测信号的早期退出策略可在MMLU上减少高达80%的token消耗、在GPQA-Diamond上减少30%，同时保持准确率不变，证明激活探测是识别表演性推理并实现自适应计算的有效工具。
链接: https://arxiv.org/abs/2603.05488
作者: Siddharth Boppana,Annabel Ma,Max Loeffler,Raphael Sarfati,Eric Bigelow,Atticus Geiger,Owen Lewis,Jack Merullo
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We provide evidence of performative chain-of-thought (CoT) in reasoning models, where a model becomes strongly confident in its final answer, but continues generating tokens without revealing its internal belief. Our analysis compares activation probing, early forced answering, and a CoT monitor across two large models (DeepSeek-R1 671B GPT-OSS 120B) and find task difficulty-specific differences: The model’s final answer is decodable from activations far earlier in CoT than a monitor is able to say, especially for easy recall-based MMLU questions. We contrast this with genuine reasoning in difficult multihop GPQA-Diamond questions. Despite this, inflection points (e.g., backtracking, ‘aha’ moments) occur almost exclusively in responses where probes show large belief shifts, suggesting these behaviors track genuine uncertainty rather than learned “reasoning theater.” Finally, probe-guided early exit reduces tokens by up to 80% on MMLU and 30% on GPQA-Diamond with similar accuracy, positioning attention probing as an efficient tool for detecting performative reasoning and enabling adaptive computation.
[NLP-4] Leverag ing LLM Parametric Knowledge for Fact Checking without Retrieval
【速读】： 该论文旨在解决当前基于大型语言模型（Large Language Models, LLMs）的智能体系统中，事实验证（fact-checking）对检索外部知识依赖性强、受限于检索误差与数据可用性的问题。传统方法通常通过检索外部知识并利用LLM验证自然语言声明（claim）与证据的一致性来提升可信度，但忽略了模型自身内在的事实核查能力。论文提出“无需检索的事实核查”（fact-checking without retrieval）任务，聚焦于独立于来源的任意自然语言声明的验证。其解决方案的关键在于引入INTRA方法，该方法通过挖掘模型内部表示之间的交互关系，显著提升了事实核查性能，并在多个评估维度（如长尾知识、多源声明、多语言和长文本生成）上展现出强泛化能力，从而为构建更高效、可扩展的可信AI系统提供了新路径。
链接: https://arxiv.org/abs/2603.05471
作者: Artem Vazhentsev,Maria Marina,Daniil Moskovskiy,Sergey Pletenev,Mikhail Seleznyov,Mikhail Salnikov,Elena Tutubalina,Vasily Konovalov,Irina Nikishina,Alexander Panchenko,Viktor Moskvoretskii
机构: MBZUAI(穆罕默德·本·扎耶德人工智能大学); S-NLP Group(自然语言处理小组); AXXX; MIRAI; EPFL(瑞士联邦理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:Trustworthiness is a core research challenge for agentic AI systems built on Large Language Models (LLMs). To enhance trust, natural language claims from diverse sources, including human-written text, web content, and model outputs, are commonly checked for factuality by retrieving external knowledge and using an LLM to verify the faithfulness of claims to the retrieved evidence. As a result, such methods are constrained by retrieval errors and external data availability, while leaving the models intrinsic fact-verification capabilities largely unused. We propose the task of fact-checking without retrieval, focusing on the verification of arbitrary natural language claims, independent of their source. To study this setting, we introduce a comprehensive evaluation framework focused on generalization, testing robustness to (i) long-tail knowledge, (ii) variation in claim sources, (iii) multilinguality, and (iv) long-form generation. Across 9 datasets, 18 methods and 3 models, our experiments indicate that logit-based approaches often underperform compared to those that leverage internal model representations. Building on this finding, we introduce INTRA, a method that exploits interactions between internal representations and achieves state-of-the-art performance with strong generalization. More broadly, our work establishes fact-checking without retrieval as a promising research direction that can complement retrieval-based frameworks, improve scalability, and enable the use of such systems as reward signals during training or as components integrated into the generation process.
[NLP-5] NCTB-QA: A Large-Scale Bangla Educational Question Answering Dataset and Benchmarking Performance
【速读】： 该论文旨在解决低资源语言（如孟加拉语）阅读理解系统在处理不可回答问题时产生的不可靠响应问题。现有模型往往在缺乏正确答案的上下文中仍生成看似合理但错误的答案，导致可靠性下降。解决方案的关键在于构建一个大规模、平衡分布的孟加拉语问答数据集NCTB-QA，其中包含87,805个问题-答案对，且可回答与不可回答问题的比例为57.25%:42.75%，同时引入对抗设计的干扰项以增强挑战性。通过在该数据集上对BERT、RoBERTa和ELECTRA等Transformer模型进行领域特定微调，显著提升了F1分数（如BERT从0.150提升至0.620，相对改进313%）和语义答案质量（以BERTScore衡量），证明了领域适应微调在低资源场景下实现鲁棒性能的重要性。
链接: https://arxiv.org/abs/2603.05462
作者: Abrar Eyasir,Tahsin Ahmed,Muhammad Ibrahim
机构: University of Dhaka (达卡大学)
类目: Computation and Language (cs.CL)
备注: 18 pages, 7 figures, 6 tables. Dataset contains 87,805 Bangla QA pairs from NCTB textbooks
Abstract:Reading comprehension systems for low-resource languages face significant challenges in handling unanswerable questions. These systems tend to produce unreliable responses when correct answers are absent from context. To solve this problem, we introduce NCTB-QA, a large-scale Bangla question answering dataset comprising 87,805 question-answer pairs extracted from 50 textbooks published by Bangladesh’s National Curriculum and Textbook Board. Unlike existing Bangla datasets, NCTB-QA maintains a balanced distribution of answerable (57.25%) and unanswerable (42.75%) questions. NCTB-QA also includes adversarially designed instances containing plausible distractors. We benchmark three transformer-based models (BERT, RoBERTa, ELECTRA) and demonstrate substantial improvements through fine-tuning. BERT achieves 313% relative improvement in F1 score (0.150 to 0.620). Semantic answer quality measured by BERTScore also increases significantly across all models. Our results establish NCTB-QA as a challenging benchmark for Bangla educational question answering. This study demonstrates that domain-specific fine-tuning is critical for robust performance in low-resource settings.
[NLP-6] DEBISS: a Corpus of Individual Semi-structured and Spoken Debates
【速读】： 该论文试图解决当前自然语言处理（Natural Language Processing, NLP）领域中缺乏涵盖多样化结构与格式的辩论语料库的问题，尤其是现有语料库在规模、标注多样性及实用性上的不足。解决方案的关键在于构建DEBISS语料库，该语料库包含口语化且个体化的辩论数据，并具有半结构化特征，同时整合了多项NLP任务标注，如语音转文本（speech-to-text）、说话人辨认（speaker diarization）、论点挖掘（argument mining）以及辩手质量评估（debater quality assessment），从而为多场景下的辩论分析提供高质量、多维度的数据支持。
链接: https://arxiv.org/abs/2603.05459
作者: Klaywert Danillo Ferreira de Souza,David Eduardo Pereira,Cláudio E. C. Campelo,Larissa Lucena Vasconcelos
机构: 未知
类目: Computation and Language (cs.CL); Databases (cs.DB)
备注:
Abstract:The process of debating is essential in our daily lives, whether in studying, work activities, simple everyday discussions, political debates on TV, or online discussions on social networks. The range of uses for debates is broad. Due to the diverse applications, structures, and formats of debates, developing corpora that account for these variations can be challenging, and the scarcity of debate corpora in the state of the art is notable. For this reason, the current research proposes the DEBISS corpus: a collection of spoken and individual debates with semi-structured features. With a broad range of NLP task annotations, such as speech-to-text, speaker diarization, argument mining, and debater quality assessment.
[NLP-7] FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling
【速读】： 该论文旨在解决生成式 AI (Generative AI) 中注意力机制（Attention）在 Blackwell 架构 GPU（如 B200）上的性能瓶颈问题，尤其针对其异构硬件扩展特性（张量核心吞吐量翻倍但共享内存带宽等其他单元增长缓慢）导致的计算与访存不平衡。解决方案的关键在于：(1) 重构流水线以充分利用完全异步的矩阵乘加（MMA）操作和更大分块尺寸；(2) 通过软件模拟指数函数与条件 softmax 归一化，减少非矩阵乘法运算开销；(3) 利用张量内存和双上下文线程数组（2-CTA）MMA模式降低反向传播中的共享内存访问和原子操作压力。最终实现 FlashAttention-4 在 B200 上相较 cuDNN 9.13 和 Triton 分别提速 1.3× 和 2.7×，峰值算力利用率达 71%（1613 TFLOPs/s），且全部基于嵌入 Python 的 CuTe-DSL 实现，编译速度比传统 C++ 模板方法快 20–30 倍。
链接: https://arxiv.org/abs/2603.05451
作者: Ted Zadouri,Markus Hoehnerbach,Jay Shah,Timmy Liu,Vijay Thakkar,Tri Dao
机构: Princeton University (普林斯顿大学); Meta; Colfax Research; NVIDIA (英伟达); Georgia Tech (佐治亚理工学院); Together AI
类目: Computation and Language (cs.CL)
备注:
Abstract:Attention, as a core layer of the ubiquitous Transformer architecture, is the bottleneck for large language models and long-context applications. While FlashAttention-3 optimized attention for Hopper GPUs through asynchronous execution and warp specialization, it primarily targets the H100 architecture. The AI industry has rapidly transitioned to deploying Blackwell-based systems such as the B200 and GB200, which exhibit fundamentally different performance characteristics due to asymmetric hardware scaling: tensor core throughput doubles while other functional units (shared memory bandwidth, exponential units) scale more slowly or remain unchanged. We develop several techniques to address these shifting bottlenecks on Blackwell GPUs: (1) redesigned pipelines that exploit fully asynchronous MMA operations and larger tile sizes, (2) software-emulated exponential and conditional softmax rescaling that reduces non-matmul operations, and (3) leveraging tensor memory and the 2-CTA MMA mode to reduce shared memory traffic and atomic adds in the backward pass. We demonstrate that our method, FlashAttention-4, achieves up to 1.3 \times speedup over cuDNN 9.13 and 2.7 \times over Triton on B200 GPUs with BF16, reaching up to 1613 TFLOPs/s (71% utilization). Beyond algorithmic innovations, we implement FlashAttention-4 entirely in CuTe-DSL embedded in Python, achieving 20-30 \times faster compile times compared to traditional C++ template-based approaches while maintaining full expressivity.
[NLP-8] Distributed Partial Information Puzzles: Examining Common Ground Construction Under Epistemic Asymmetry
【速读】： 该论文旨在解决当前人工智能系统在多模态、多方协作场景中难以建立共同认知（common ground, CG）的问题，尤其是在参与者拥有不同信息背景时。其解决方案的关键在于提出了一种名为“分布式部分信息谜题”（Distributed Partial Information Puzzle, DPIP）的协作建构任务，并构建了一个多模态标注数据集，该数据集在时间上对齐语音、手势和动作模态，支持对命题内容与信念动态的推理。研究进一步对比了两种建模共同认知的方法：一是基于大语言模型（LLMs）的提示推理方法，二是基于动态认知逻辑（Dynamic Epistemic Logic, DEL）的公理化增量处理流程，结果表明现代LLMs在追踪任务进展与信念状态方面仍面临挑战。
链接: https://arxiv.org/abs/2603.05450
作者: Yifan Zhu,Mariah Bradford,Kenneth Lai,Timothy Obiso,Videep Venkatesha,James Pustejovsky,Nikhil Krishnaswamy
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 10 pages, 4 figures
Abstract:Establishing common ground, a shared set of beliefs and mutually recognized facts, is fundamental to collaboration, yet remains a challenge for current AI systems, especially in multimodal, multiparty settings, where the collaborators bring different information to the table. We introduce the Distributed Partial Information Puzzle (DPIP), a collaborative construction task that elicits rich multimodal communication under epistemic asymmetry. We present a multimodal dataset of these interactions, annotated and temporally aligned across speech, gesture, and action modalities to support reasoning over propositional content and belief dynamics. We then evaluate two paradigms for modeling common ground (CG): (1) state-of-the-art large language models (LLMs), prompted to infer shared beliefs from multimodal updates, and (2) an axiomatic pipeline grounded in Dynamic Epistemic Logic (DEL) that incrementally performs the same task. Results on the annotated DPIP data indicate that it poses a challenge to modern LLMs’ abilities to track both task progression and belief state.
[NLP-9] Ensembling Language Models with Sequential Monte Carlo
【速读】： 该论文旨在解决语言模型（Language Models, LM）在解码过程中进行集成（ensembling）时面临的挑战：传统方法如直接平均概率会生成局部归一化且有偏的近似分布，难以有效利用多个模型的优势。其解决方案的关键在于提出一个统一框架，通过函数 $ f: \mathbb{R}{\geq 0}^K \to \mathbb{R}{\geq 0} $ 对 $ K $ 个语言模型的输出进行组合，从而定义出广义的 $ f $-ensemble 分布；并设计一种基于字节级顺序蒙特卡洛（Sequential Monte Carlo, SMC）采样算法，在共享字符空间中实现对这些分布的无偏采样，支持词汇不匹配模型的集成，并保证在极限情况下采样一致性。
链接: https://arxiv.org/abs/2603.05432
作者: Robin Shing Moon Chan,Tianyu Liu,Samuel Kiegeland,Clemente Pasti,Jacob Hoover Vigly,Timothy J. O’Donnell,Ryan Cotterell,Tim Vieira
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Practitioners have access to an abundance of language models and prompting strategies for solving many language modeling tasks; yet prior work shows that modeling performance is highly sensitive to both choices. Classical machine learning ensembling techniques offer a principled approach: aggregate predictions from multiple sources to achieve better performance than any single one. However, applying ensembling to language models during decoding is challenging: naively aggregating next-token probabilities yields samples from a locally normalized, biased approximation of the generally intractable ensemble distribution over strings. In this work, we introduce a unified framework for composing K language models into f -ensemble distributions for a wide range of functions f\colon\mathbbR_\geq 0^K\to\mathbbR_\geq 0 . To sample from these distributions, we propose a byte-level sequential Monte Carlo (SMC) algorithm that operates in a shared character space, enabling ensembles of models with mismatching vocabularies and consistent sampling in the limit. We evaluate a family of f -ensembles across prompt and model combinations for various structured text generation tasks, highlighting the benefits of alternative aggregation strategies over traditional probability averaging, and showing that better posterior approximations can yield better ensemble performance.
[NLP-10] Dissociating Direct Access from Inference in AI Introspection
【速读】： 该论文旨在解决生成式 AI (Generative AI) 中自我觉察（introspection）机制的理解问题，即模型如何识别自身内部状态的异常或外部注入的思维内容。其解决方案的关键在于通过复现 Lindsey et al. (2025) 的“思想注入检测”范式，在大型开源模型中揭示出两种可分离的自我觉察能力：一是基于概率匹配的机制（从提示中的感知异常推断），二是直接访问内部状态的机制。其中，后者具有内容无关性——模型能察觉异常发生但无法可靠识别其语义内容；而模型在面对注入概念时倾向于编造高频且具象的词汇（如“apple”），准确识别需显著更多token输入。这一发现与哲学和心理学中的主流自我觉察理论高度一致。
链接: https://arxiv.org/abs/2603.05414
作者: Harvey Lederman,Kyle Mahowald
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Introspection is a foundational cognitive ability, but its mechanism is not well understood. Recent work has shown that AI models can introspect. We study their mechanism of introspection, first extensively replicating Lindsey et al. (2025)‘s thought injection detection paradigm in large open-source models. We show that these models detect injected representations via two separable mechanisms: (i) probability-matching (inferring from perceived anomaly of the prompt) and (ii) direct access to internal states. The direct access mechanism is content-agnostic: models detect that an anomaly occurred but cannot reliably identify its semantic content. The two model classes we study confabulate injected concepts that are high-frequency and concrete (e.g., "apple’"); for them correct concept guesses typically require significantly more tokens. This content-agnostic introspective mechanism is consistent with leading theories in philosophy and psychology.
[NLP-11] An Exploration-Analysis-Disambiguation Reasoning Framework for Word Sense Disambiguation with Low-Parameter LLM s LREC2026
【速读】： 该论文旨在解决词义消歧（Word Sense Disambiguation, WSD）中因罕见或领域特定词义导致的误判问题，尤其是在高参数量大语言模型（Large Language Models, LLMs）性能虽优但计算与能耗成本高昂的情况下，如何实现高效且准确的WSD。其解决方案的关键在于采用以推理驱动为核心的小参数量LLMs（如Gemma-3-4B和Qwen-3-4B）的微调策略，通过结合链式思维（Chain-of-Thought, CoT）推理与邻近词分析方法，在不依赖任务特化微调的前提下，显著提升模型对未见词义和跨域场景的泛化能力，从而在保持高性能的同时大幅降低资源消耗。
链接: https://arxiv.org/abs/2603.05400
作者: Deshan Sumanathilaka,Nicholas Micallef,Julian Hough
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted at LREC 2026, 15 pages, 11 Tables
Abstract:Word Sense Disambiguation (WSD) remains a key challenge in Natural Language Processing (NLP), especially when dealing with rare or domain-specific senses that are often misinterpreted. While modern high-parameter Large Language Models (LLMs) such as GPT-4-Turbo have shown state-of-the-art WSD performance, their computational and energy demands limit scalability. This study investigates whether low-parameter LLMs (4B parameters) can achieve comparable results through fine-tuning strategies that emphasize reasoning-driven sense identification. Using the FEWS dataset augmented with semi-automated, rationale-rich annotations, we fine-tune eight small-scale open-source LLMs (e.g. Gemma and Qwen). Our results reveal that Chain-of-Thought (CoT)-based reasoning combined with neighbour-word analysis achieves performance comparable to GPT-4-Turbo in zero-shot settings. Importantly, Gemma-3-4B and Qwen-3-4B models consistently outperform all medium-parameter baselines and state-of-the-art models on FEWS, with robust generalization to unseen senses. Furthermore, evaluation on the unseen "Fool Me If You Can’’ dataset confirms strong cross-domain adaptability without task-specific fine-tuning. This work demonstrates that with carefully crafted reasoning-centric fine-tuning, low-parameter LLMs can deliver accurate WSD while substantially reducing computational and energy demands.
[NLP-12] Progressive Residual Warmup for Language Model Pretraining
【速读】： 该论文旨在解决Transformer架构在大规模语言模型预训练过程中存在的稳定性差与收敛速度慢的问题（pretraining stability and convergence speed）。其解决方案的关键在于提出一种名为渐进残差预热（Progressive Residual Warmup, ProRes）的机制，通过为每一层残差连接乘以一个从0逐步升温至1的标量因子，使浅层先于深层完成学习，从而确保深层在参与训练前，浅层已进入更稳定的优化状态。此策略不仅提升了预训练过程的稳定性，还引导出独特的优化轨迹，实现更快收敛、更强泛化能力及下游任务性能提升。
链接: https://arxiv.org/abs/2603.05369
作者: Tianhao Chen,Xin Xu,Lu Yin,Hao Chen,Yang Wang,Shizhe Diao,Can Yang
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Transformer architectures serve as the backbone for most modern Large Language Models, therefore their pretraining stability and convergence speed are of central concern. Motivated by the logical dependency of sequentially stacked layers, we propose Progressive Residual Warmup (ProRes) for language model pretraining. ProRes implements an “early layer learns first” philosophy by multiplying each layer’s residual with a scalar that gradually warms up from 0 to 1, with deeper layers taking longer warmup steps. In this way, deeper layers wait for early layers to settle into a more stable regime before contributing to learning. We demonstrate the effectiveness of ProRes through pretraining experiments across various model scales, as well as normalization and initialization schemes. Comprehensive analysis shows that ProRes not only stabilizes pretraining but also introduces a unique optimization trajectory, leading to faster convergence, stronger generalization and better downstream performance. Our code is available at this https URL.
[NLP-13] DiSCTT: Consensus-Guided Self-Curriculum for Efficient Test-Time Adaptation in Reasoning
【速读】： 该论文旨在解决测试时适应（Test-time adaptation）中因对所有输入采用统一优化目标而导致的效率低下和不稳定问题，尤其是在处理异质性推理任务时。其解决方案的关键在于提出了一种难度感知、共识引导的自课程框架DiSCTT，通过估计样本推理轨迹间的共识程度来量化实例级的认知不确定性（epistemic uncertainty），进而动态分配不同的优化策略：高共识输入采用基于多数一致解作为伪标签的监督微调，低共识输入则通过引入共识正则化的目标进行强化学习优化，以在相关约束下鼓励多样性。此机制显著提升了推理模型在多个数学与通用推理基准上的性能稳定性与效率。
链接: https://arxiv.org/abs/2603.05357
作者: Mohammad Mahdi Moradi,Sudhir Mudur
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Test-time adaptation offers a promising avenue for improving reasoning performance in large language models without additional supervision, but existing approaches often apply a uniform optimization objective across all inputs, leading to inefficient or unstable adaptation on heterogeneous reasoning problems. We propose DiSCTT, a difficulty-aware, consensus-guided self-curriculum framework that dynamically allocates test-time optimization strategies based on instance-level epistemic uncertainty estimated from agreement among sampled reasoning trajectories. Inputs with high consensus are consolidated via supervised fine-tuning using majority-agreed solutions as pseudo-labels, while low-consensus inputs are optimized via reinforcement learning with a consensus-regularized objective that encourages diversity under relevance constraints. Across a broad suite of mathematical and general reasoning benchmarks, DiSCTT consistently outperforms strong test-time adaptation baselines, achieving higher accuracy with reduced variance and substantially lower computation and wall-clock training times. These results demonstrate that explicitly accounting for instance difficulty and uncertainty enables more stable, efficient, and effective test-time adaptation for reasoning models.
[NLP-14] Exploring the potential and limitations of Model Merging for Multi-Domain Adaptation in ASR INTERSPEECH2026
【速读】： 该论文旨在解决大规模语音基础模型在多领域自动语音识别（ASR）任务中因领域特定微调（domain-specific fine-tuning）导致的模型碎片化问题，即每次新增数据时重复全量微调计算成本过高。其解决方案的关键在于提出一种新的模型合并算法 BoostedTSV-M，该方法基于 TSV-M（Targeted Singular Value Merging），通过奇异值增强（singular-value boosting）缓解秩坍缩（rank collapse）问题并提升数值稳定性，从而在单一模型中实现跨领域的性能优化与分布外泛化能力的保留。
链接: https://arxiv.org/abs/2603.05354
作者: Carlos Carvalho,Francisco Teixeira,Thomas Rolland,Alberto Abad
机构: INESC-ID; Instituto Superior Técnico, Universidade de Lisboa (里斯本大学理工学院)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: submitted for review for INTERSPEECH2026 conference
Abstract:Model merging is a scalable alternative to multi-task training that combines the capabilities of multiple specialised models into a single model. This is particularly attractive for large speech foundation models, which are typically adapted through domain-specific fine-tuning, resulting in multiple customised checkpoints, for which repeating full fine-tuning when new data becomes available is computationally prohibitive. In this work, we study model merging for multi-domain ASR and benchmark 11 merging algorithms for 10 European Portuguese domains, evaluating in-domain accuracy, robustness under distribution shift, as well as English and multilingual performance. We further propose BoostedTSV-M, a new merging algorithm based on TSV-M that mitigates rank collapse via singular-value boosting and improves numerical stability. Overall, our approach outperforms full fine-tuning on European Portuguese while preserving out-of-distribution generalisation in a single model.
[NLP-15] A Multilingual Human Annotated Corpus of Original and Easy-to-Read Texts to Support Access to Democratic Participatory Processes LREC26
链接: https://arxiv.org/abs/2603.05345
作者: Stefan Bott,Verena Riegler,Horacio Saggion,Almudena Rascón Alcaina,Nouran Khallaf
机构: 未知
类目: Computation and Language (cs.CL)
备注: Will be published in LREC26
[NLP-16] PersianPunc: A Large-Scale Dataset and BERT-Based Approach for Persian Punctuation Restoration
【速读】： 该论文旨在解决波斯语（Persian）语音识别（ASR）输出中标点恢复（punctuation restoration）问题，以提升文本可读性和下游任务的实用性。针对当前该领域研究不足的问题，作者构建了包含1700万样本的大规模高质量波斯语标点恢复数据集PersianPunc，并将标点恢复建模为token-level序列标注任务。其解决方案的关键在于采用轻量级BERT模型（ParsBERT）进行微调，在保持高精度（宏平均F1得分为91.33%）的同时显著优于大型语言模型（LLM），避免了LLM常见的过度修正问题（over-correction）和高计算开销，从而更适合实时语音转写场景的应用需求。
链接: https://arxiv.org/abs/2603.05314
作者: Mohammad Javad Ranjbar Kalahroodi,Heshaam Faili,Azadeh Shakery
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Punctuation restoration is essential for improving the readability and downstream utility of automatic speech recognition (ASR) outputs, yet remains underexplored for Persian despite its importance. We introduce PersianPunc, a large-scale, high-quality dataset of 17 million samples for Persian punctuation restoration, constructed through systematic aggregation and filtering of existing textual resources. We formulate punctuation restoration as a token-level sequence labeling task and fine-tune ParsBERT to achieve strong performance. Through comparative evaluation, we demonstrate that while large language models can perform punctuation restoration, they suffer from critical limitations: over-correction tendencies that introduce undesired edits beyond punctuation insertion (particularly problematic for speech-to-text pipelines) and substantially higher computational requirements. Our lightweight BERT-based approach achieves a macro-averaged F1 score of 91.33% on our test set while maintaining efficiency suitable for real-time applications. We make our dataset (this https URL) and model (this https URL) publicly available to facilitate future research in Persian NLP and provide a scalable framework applicable to other morphologically rich, low-resource languages.
[NLP-17] Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution
【速读】： 该论文旨在解决生物医学领域中生成式AI（Generative AI）在回答事实性问题时存在的幻觉（hallucination）问题，以及如何高效、准确地进行证据溯源与主张验证（claim verification）的问题。其核心挑战在于，尽管前沿大语言模型（LLM）如GPT-5具备较强推理能力，但部署成本过高难以规模化应用。解决方案的关键在于提出Med-V1——一个仅含三亿参数的小型语言模型家族，通过使用本研究新构建的高质量合成数据进行训练，在五个统一为验证格式的生物医学基准测试中显著优于基线模型（提升达+27.0%至+71.3%），且性能接近GPT-5，同时提供高可解释性的预测依据。这一轻量化设计使得Med-V1能够在实际场景中高效执行证据归属与验证任务，尤其在临床实践指南中的错误引用识别等高风险应用中展现出独特价值。
链接: https://arxiv.org/abs/2603.05308
作者: Qiao Jin,Yin Fang,Lauren He,Yifan Yang,Guangzhi Xiong,Zhizheng Wang,Nicholas Wan,Joey Chan,Donald C. Comeau,Robert Leaman,Charalampos S. Floudas,Aidong Zhang,Michael F. Chiang,Yifan Peng,Zhiyong Lu
机构: National Library of Medicine, National Institutes of Health; University of Virginia; National Eye Institute, National Institutes of Health; Weill Cornell Medicine; Center for Cancer Research, National Cancer Institute, National Institutes of Health
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language models (LLMs) have the potential to automate this task, achieving strong performance requires frontier models such as GPT-5 that are prohibitively expensive to deploy at scale. To efficiently perform biomedical evidence attribution, we present Med-V1, a family of small language models with only three billion parameters. Trained on high-quality synthetic data newly developed in this study, Med-V1 substantially outperforms (+27.0% to +71.3%) its base models on five biomedical benchmarks unified into a verification format. Despite its smaller size, Med-V1 performs comparably to frontier LLMs such as GPT-5, along with high-quality explanations for its predictions. We use Med-V1 to conduct a first-of-its-kind use case study that quantifies hallucinations in LLM-generated answers under different citation instructions. Results show that the format instruction strongly affects citation validity and hallucination, with GPT-5 generating more claims but exhibiting hallucination rates similar to GPT-4o. Additionally, we present a second use case showing that Med-V1 can automatically identify high-stakes evidence misattributions in clinical practice guidelines, revealing potentially negative public health impacts that are otherwise challenging to identify at scale. Overall, Med-V1 provides an efficient and accurate lightweight alternative to frontier LLMs for practical and real-world applications in biomedical evidence attribution and verification tasks. Med-V1 is available at this https URL.
[NLP-18] WavSLM: Single-Stream Speech Language Modeling via WavLM Distillation
【速读】： 该论文旨在解决如何将文本领域中成功的单流生成式预训练范式（single-stream generative pretraining paradigm）扩展到语音建模的问题，因为语音信号中语义信息与声学信息高度耦合，使得直接应用自回归训练面临挑战。解决方案的关键在于提出 WavSLM，通过将自监督学习获得的 WavLM 表示量化并蒸馏到单一码本（codebook）中，并优化一个自回归的下一语音块预测目标（next-chunk prediction objective），从而在不依赖文本监督或文本预训练的前提下，将语义和声学信息统一建模于单一 token 流中，实现了简洁高效的语音生成模型。
链接: https://arxiv.org/abs/2603.05299
作者: Luca Della Libera,Cem Subakan,Mirco Ravanelli
机构: Concordia University (康考迪亚大学); Mila-Quebec AI Institute (蒙特利尔人工智能研究所); Université Laval (拉瓦尔大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Sound (cs.SD)
备注: 6 pages, 1 figure
Abstract:Large language models show that simple autoregressive training can yield scalable and coherent generation, but extending this paradigm to speech remains challenging due to the entanglement of semantic and acoustic information. Most existing speech language models rely on text supervision, hierarchical token streams, or complex hybrid architectures, departing from the single-stream generative pretraining paradigm that has proven effective in text. In this work, we introduce WavSLM, a speech language model trained by quantizing and distilling self-supervised WavLM representations into a single codebook and optimizing an autoregressive next-chunk prediction objective. WavSLM jointly models semantic and acoustic information within a single token stream without text supervision or text pretraining. Despite its simplicity, it achieves competitive performance on consistency benchmarks and speech generation while using fewer parameters, less training data, and supporting streaming inference. Demo samples are available at this https URL.
[NLP-19] Knowledge Divergence and the Value of Debate for Scalable Oversight
【速读】： 该论文旨在解决如何在高级人工智能系统中实现可扩展的监督问题，特别是比较辩论机制（debate）与基于AI反馈的强化学习（RLAIF）这两种方法的有效性边界。其核心问题是：在何种条件下辩论相比单模型方法具有显著优势？解决方案的关键在于通过建模辩论双方模型表示子空间之间的主夹角（principal angles）来参数化知识分歧（knowledge divergence）的几何结构，并由此推导出辩论优势的精确闭式表达。研究发现，当模型共享相同训练语料时，辩论退化为类似RLAIF的单智能体优化；而当模型具备差异化的知识时，辩论优势呈现从二次到线性的相变行为，且存在三种知识分歧模式（共享、单边和组合），其中组合模式下若对抗激励过强将导致协调失败，存在一个明确阈值决定辩论是否有效。这一理论框架首次建立了辩论与RLAIF的正式联系，并为对抗式监督协议的设计提供了几何基础。
链接: https://arxiv.org/abs/2603.05293
作者: Robin Young
机构: University of Cambridge (剑桥大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:AI safety via debate and reinforcement learning from AI feedback (RLAIF) are both proposed methods for scalable oversight of advanced AI systems, yet no formal framework relates them or characterizes when debate offers an advantage. We analyze this by parameterizing debate’s value through the geometry of knowledge divergence between debating models. Using principal angles between models’ representation subspaces, we prove that the debate advantage admits an exact closed form. When models share identical training corpora, debate reduces to RLAIF-like where a single-agent method recovers the same optimum. When models possess divergent knowledge, debate advantage scales with a phase transition from quadratic regime (debate offers negligible benefit) to linear regime (debate is essential). We classify three regimes of knowledge divergence (shared, one-sided, and compositional) and provide existence results showing that debate can achieve outcomes inaccessible to either model alone, alongside a negative result showing that sufficiently strong adversarial incentives cause coordination failure in the compositional regime, with a sharp threshold separating effective from ineffective debate. We offer the first formal connection between debate and RLAIF, a geometric foundation for understanding when adversarial oversight protocols are justified, and connection to the problem of eliciting latent knowledge across models with complementary information.
[NLP-20] SarcasmMiner: A Dual-Track Post-Training Framework for Robust Audio-Visual Sarcasm Reasoning
【速读】： 该论文旨在解决多模态讽刺检测中跨模态推理（cross-modal reasoning）的难题，特别是如何在文本、语音和视觉线索之间识别语用不一致（pragmatic incongruity），从而提升生成式 AI 模型在复杂场景下的讽刺理解能力。其核心解决方案是提出 SarcasmMiner，一个基于强化学习的后训练框架，通过结构化推理建模与双轨蒸馏策略实现：首先利用高质量教师轨迹初始化学生模型，再通过全量轨迹训练生成式奖励模型（Generative Reward Model, GenRM）以评估推理质量；随后采用解耦奖励的组相对策略优化（Group Relative Policy Optimization, GRPO）对模型进行优化，分别引导准确性和推理质量。实验表明，该方法在 MUStARD++ 数据集上将 F1 分数从零样本的 59.83% 提升至 70.22%，验证了推理感知奖励建模对性能与多模态对齐性的双重增强作用。
链接: https://arxiv.org/abs/2603.05275
作者: Zhu Li,Yongjian Chen,Huiyuan Lai,Xiyuan Gao,Shekhar Nayak,Matt Coler
机构: University of Groningen (格罗宁根大学)
类目: Multimedia (cs.MM); Computation and Language (cs.CL); Sound (cs.SD)
备注:
Abstract:Multimodal sarcasm detection requires resolving pragmatic incongruity across textual, acoustic, and visual cues through cross-modal reasoning. To enable robust sarcasm reasoning with foundation models, we propose SarcasmMiner, a reinforcement learning based post-training framework that resists hallucination in multimodal reasoning. We reformulate sarcasm detection as structured reasoning and adopt a dual-track distillation strategy: high-quality teacher trajectories initialize the student model, while the full set of trajectories trains a generative reward model (GenRM) to evaluate reasoning quality. The student is optimized with group relative policy optimization (GRPO) using decoupled rewards for accuracy and reasoning quality. On MUStARD++, SarcasmMiner increases F1 from 59.83% (zero-shot), 68.23% (supervised finetuning) to 70.22%. These findings suggest that reasoning-aware reward modeling enhances both performance and multimodal grounding.
[NLP-21] VietJobs: A Vietnamese Job Advertisement Dataset
【速读】： 该论文旨在解决越南语自然语言处理（Natural Language Processing, NLP）领域缺乏大规模、多样化标注语料库的问题，特别是针对劳动力市场分析中结构化信息提取与预测任务的挑战。解决方案的关键在于构建并公开发布VietJobs——首个大规模、公开可用的越南语职位广告语料库，涵盖48,092条招聘广告及超过1500万词，包含岗位类别、薪资、技能要求等多维结构化信息，并覆盖越南34个省市。通过在该语料库上对多种生成式大语言模型（Generative Large Language Models, LLMs）进行基准测试，验证了指令微调模型（如Qwen2.5-7B-Instruct和Llama-SEA-LION-v3-8B-IT）在少样本和微调设置下对职位分类与薪资估算任务的有效性，同时揭示了多语言及越南语特有建模在结构化劳动力市场预测中的关键难点。该工作为越南语NLP研究提供了新基准，并推动了AI驱动的招聘语言分析与社会经济代表性研究的发展。
链接: https://arxiv.org/abs/2603.05262
作者: Hieu Pham Dinh,Hung Nguyen Huy,Mo El-Haj
机构: 未知
类目: Computation and Language (cs.CL)
备注: 10 pages
Abstract:VietJobs is the first large-scale, publicly available corpus of Vietnamese job advertisements, comprising 48,092 postings and over 15 million words collected from all 34 provinces and municipalities across Vietnam. The dataset provides extensive linguistic and structured information, including job titles, categories, salaries, skills, and employment conditions, covering 16 occupational domains and multiple employment types (full-time, part-time, and internship). Designed to support research in natural language processing and labour market analytics, VietJobs captures substantial linguistic, regional, and socio-economic diversity. We benchmark several generative large language models (LLMs) on two core tasks: job category classification and salary estimation. Instruction-tuned models such as Qwen2.5-7B-Instruct and Llama-SEA-LION-v3-8B-IT demonstrate notable gains under few-shot and fine-tuned settings, while highlighting challenges in multilingual and Vietnamese-specific modelling for structured labour market prediction. VietJobs establishes a new benchmark for Vietnamese NLP and offers a valuable foundation for future research on recruitment language, socio-economic representation, and AI-driven labour market analysis. All code and resources are available at: this https URL.
[NLP-22] Balancing Coverag e and Draft Latency in Vocabulary Trimming for Faster Speculative Decoding
【速读】： 该论文旨在解决生成式 AI（Generative AI）中推测解码（speculative decoding）技术所面临的瓶颈问题：即轻量级草稿模型（draft model）在推理过程中因词汇表规模过大而导致的高延迟，这限制了整体推理效率。其核心矛盾在于，较大的词汇表虽能提升与目标模型的token覆盖度和一致性，但会显著增加草稿模型的语言建模头计算开销；而较小的词汇表虽可降低延迟，却可能因覆盖不足导致生成错误。解决方案的关键在于提出基于词汇修剪（vocabulary trimming）的优化策略，将草稿模型词汇选择建模为一个受最小覆盖约束的联合优化问题，通过架构感知的浮点运算次数（FLOPs）估算延迟，并利用树状结构帕尔岑估计器（Tree-structured Parzen Estimator）高效探索覆盖-延迟帕累托前沿，从而在保证高覆盖率的同时大幅减少草稿模型词汇表规模（最多达97%），显著提升推测解码吞吐量并降低延迟（领域特定任务最高降低16%，提升20%）。
链接: https://arxiv.org/abs/2603.05210
作者: Ofir Ben Shoham
机构: Intuit
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Speculative decoding accelerates inference for Large Language Models by using a lightweight draft model to propose candidate tokens that are verified in parallel by a larger target model. Prior work shows that the draft model often dominates speculative decoding latency, since it generates tokens sequentially and incurs high cost from its language modeling head as vocabulary size grows. This exposes a fundamental trade-off in draft model design: larger vocabularies improve token coverage and agreement with the target model, but incur higher draft latency, while smaller vocabularies reduce latency at the risk of missing tokens required for accurate draft generation. We address this trade-off through vocabulary trimming for draft models, motivated by the observation that domain-specific workloads use only a small fraction of the full vocabulary. We cast draft vocabulary selection as a constrained optimization problem that balances token coverage and draft latency. Coverage is computed over assistant responses in the training data, while latency is estimated using architecture-aware FLOPs that capture the cost of the language modeling head as a function of vocabulary size. We optimize a utility function with a Tree-structured Parzen Estimator to efficiently explore the coverage-latency Pareto frontier under a minimum coverage constraint. Experiments show improved speculative decoding throughput while reducing draft vocabularies by up to 97% with high coverage. On domain-specific tasks, we achieve up to 16% latency reduction and 20% throughput improvement, and up to 6.7% throughput gains on diverse out-of-distribution tasks.
[NLP-23] Distilling Formal Logic into Neural Spaces: A Kernel Alignment Approach for Signal Temporal Logic
【速读】： 该论文旨在解决现有形式化规范（formal specifications）表示方法在语义保真度与计算效率之间难以平衡的问题：传统符号核方法虽能保持行为语义但存在计算开销大、依赖锚点且不可逆等缺陷，而基于语法的神经嵌入则无法捕捉底层结构。其解决方案的关键在于提出一种教师-学生框架，通过将符号鲁棒性核（symbolic robustness kernel）的知识蒸馏到Transformer编码器中，采用连续的、核加权的几何对齐目标进行监督，使模型在单次前向传播中即可生成保留语义相似性的神经表示，并具备内在可逆性与高效推理能力，从而实现高效率、可扩展的神经符号推理与公式重建。
链接: https://arxiv.org/abs/2603.05198
作者: Sara Candussio,Gabriele Sarti,Gaia Saveri,Luca Bortolussi
机构: 未知
类目: Computation and Language (cs.CL); Symbolic Computation (cs.SC)
备注:
Abstract:We introduce a framework for learning continuous neural representations of formal specifications by distilling the geometry of their semantics into a latent space. Existing approaches rely either on symbolic kernels – which preserve behavioural semantics but are computationally prohibitive, anchor-dependent, and non-invertible – or on syntax-based neural embeddings that fail to capture underlying structures. Our method bridges this gap: using a teacher-student setup, we distill a symbolic robustness kernel into a Transformer encoder. Unlike standard contrastive methods, we supervise the model with a continuous, kernel-weighted geometric alignment objective that penalizes errors in proportion to their semantic discrepancies. Once trained, the encoder produces embeddings in a single forward pass, effectively mimicking the kernel’s logic at a fraction of its computational cost. We apply our framework to Signal Temporal Logic (STL), demonstrating that the resulting neural representations faithfully preserve the semantic similarity of STL formulae, accurately predict robustness and constraint satisfaction, and remain intrinsically invertible. Our proposed approach enables highly efficient, scalable neuro-symbolic reasoning and formula reconstruction without repeated kernel computation at runtime.
[NLP-24] Diffusion LLM s can think EoS-by-EoS
【速读】： 该论文旨在解决生成式 AI（Generative AI）在复杂推理任务中表现受限的问题，特别是如何提升模型对具有相互依赖子目标的任务的处理能力。其解决方案的关键在于揭示扩散语言模型（Diffusion LLMs）通过“逐个EoS（end-of-sequence）token思考”的机制来实现更高效的推理：EoS token并非无意义的终止符，而是作为隐藏的计算空间（hidden scratchpad），承载了中间推理信息。实验表明，在输出长度远超必要值时，模型通过填充EoS token提升了推理性能；进一步的因果干预实验证明，替换EoS token的隐藏状态可显著改变输出结果，从而验证了这些token确实在执行隐式计算，是扩散模型实现复杂推理的核心机制。
链接: https://arxiv.org/abs/2603.05197
作者: Sarah Breckner,Sebastian Schuster
机构: University of Vienna (维也纳大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Diffusion LLMs have been proposed as an alternative to autoregressive LLMs, excelling especially at complex reasoning tasks with interdependent sub-goals. Curiously, this is particularly true if the generation length, i.e., the number of tokens the model has to output, is set to a much higher value than is required for providing the correct answer to the task, and the model pads its answer with end-of-sequence (EoS) tokens. We hypothesize that diffusion models think EoS-by-EoS, that is, they use the representations of EoS tokens as a hidden scratchpad, which allows them to solve harder reasoning problems. We experiment with the diffusion models LLaDA1.5, LLaDA2.0-mini, and Dream-v0 on the tasks Addition, Entity Tracking, and Sudoku. In a controlled prompting experiment, we confirm that adding EoS tokens improves the LLMs’ reasoning capabilities. To further verify whether they serve as space for hidden computations, we patch the hidden states of the EoS tokens with those of a counterfactual generation, which frequently changes the generated output to the counterfactual. The success of the causal intervention underscores that the EoS tokens, which one may expect to be devoid of meaning, carry information on the problem to solve. The behavioral experiments and the causal interventions indicate that diffusion LLMs can indeed think EoS-by-EoS.
[NLP-25] ransducing Language Models
【速读】： 该论文旨在解决语言模型输出格式与下游任务需求不匹配的问题，例如模型生成的字节对（byte-pair）字符串无法直接用于词级预测，或DNA序列模型难以直接输出氨基酸序列。其核心挑战在于如何在不修改预训练模型参数的前提下，将原始模型的分布通过确定性字符串到字符串的变换映射到目标输出空间。解决方案的关键在于引入有限状态转换器（finite-state transducer, FST）作为通用的可微分字符串变换框架，并开发了两种算法：一种是精确的边缘化算法，用于计算从源字符串到目标字符串的联合概率；另一种是高效的近似算法，支持在推理阶段对变换后的输出进行条件建模。该方法实现了对预训练语言模型的灵活适配，使其能够满足不同应用场景下的输出格式要求，而无需重新训练模型。
链接: https://arxiv.org/abs/2603.05193
作者: Vésteinn Snæbjarnarson,Samuel Kiegeland,Tianyu Liu,Reda Boumasmoud,Ryan Cotterell,Tim Vieira
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Modern language models define distributions over strings, but downstream tasks often require different output formats. For instance, a model that generates byte-pair strings does not directly produce word-level predictions, and a DNA model does not directly produce amino-acid sequences. In such cases, a deterministic string-to-string transformation can convert the model’s output to the desired form. This is a familiar pattern in probability theory: applying a function f to a random variable X\sim p yields a transformed random variable f(X) with an induced distribution. While such transformations are occasionally used in language modeling, prior work does not treat them as yielding new, fully functional language models. We formalize this perspective and introduce a general framework for language models derived from deterministic string-to-string transformations. We focus on transformations representable as finite-state transducers – a commonly used state-machine abstraction for efficient string-to-string mappings. We develop algorithms that compose a language model with an FST to marginalize over source strings mapping to a given target, propagating probabilities through the transducer without altering model parameters and enabling conditioning on transformed outputs. We present an exact algorithm, an efficient approximation, and a theoretical analysis. We conduct experiments in three domains: converting language models from tokens to bytes, from tokens to words, and from DNA to amino acids. These experiments demonstrate inference-time adaptation of pretrained language models to match application-specific output requirements.
[NLP-26] Guidelines for the Annotation and Visualization of Legal Argumentation Structures in Chinese Judicial Decisions
【速读】： 该论文旨在解决司法判决中法律论证结构难以系统化表示与计算分析的问题，以支持法律推理的自动化研究和AI辅助法律分析。其解决方案的关键在于提出一个基于法律推理理论的、可操作的标注框架，明确区分四类命题（一般规范性命题、具体规范性命题、一般事实命题、具体事实命题）和五种论证关系（支持、攻击、联合、匹配、同一），并通过形式化表示规则与可视化约定实现复杂论证模式的一致图形表达，并建立标准化标注流程与一致性控制机制，从而为大规模司法推理分析提供可靠的数据基础和方法论支撑。
链接: https://arxiv.org/abs/2603.05171
作者: Kun Chen,Xianglei Liao,Kaixue Fei,Yi Xing,Xinrui Li
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: The PDF contains both an English translation and the original Chinese guideline. The first 30 pages present the full English translation, while the remaining 25 pages provide the original Chinese version
Abstract:This guideline proposes a systematic and operational annotation framework for representing the structure of legal argumentation in judicial decisions. Grounded in theories of legal reasoning and argumentation, the framework aims to reveal the logical organization of judicial reasoning and to provide a reliable data foundation for computational analysis. At the proposition level, the guideline distinguishes four types of propositions: general normative propositions, specific normative propositions, general factual propositions, and specific factual propositions. At the relational level, five types of relations are defined to capture argumentative structures: support, attack, joint, match, and identity. These relations represent positive and negative argumentative connections, conjunctive reasoning structures, the correspondence between legal norms and case facts, and semantic equivalence between propositions. The guideline further specifies formal representation rules and visualization conventions for both basic and nested structures, enabling consistent graphical representation of complex argumentation patterns. In addition, it establishes a standardized annotation workflow and consistency control mechanisms to ensure reproducibility and reliability of the annotated data. By providing a clear conceptual model, formal representation rules, and practical annotation procedures, this guideline offers methodological support for large-scale analysis of judicial reasoning and for future research in legal argument mining, computational modeling of legal reasoning, and AI-assisted legal analysis.
[NLP-27] Sparse-BitNet: 1.58-bit LLM s are Naturally Friendly to Semi-Structured Sparsity
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理与训练效率上的瓶颈问题，特别是如何协同优化低比特量化（low-bit quantization）与半结构化稀疏性（semi-structured N:M sparsity）以实现更高效的模型部署。其核心解决方案是提出Sparse-BitNet框架，首次实现了1.58-bit量化与动态N:M稀疏化的联合应用，并通过定制稀疏张量核心（sparse tensor core）保障稳定训练；关键创新在于揭示了极低比特量化（如BitNet）相较于全精度模型对N:M稀疏性具有天然更强的兼容性，从而在相同稀疏度下性能退化更小、可容忍更高结构化稀疏度而不致精度崩溃，显著提升了训练和推理速度（最高达1.30倍加速）。
链接: https://arxiv.org/abs/2603.05168
作者: Di Zhang,Xun Wu,Shaohan Huang,Yudong Wang,Hanyong Shao,Yingbo Hao,Zewen Chi,Li Dong,Ting Song,Yan Xia,Zhifang Sui,Furu Wei
机构: Microsoft Research (微软研究院); Peking University (北京大学); South China University of Technology (华南理工大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Semi-structured N:M sparsity and low-bit quantization (e.g., 1.58-bit BitNet) are two promising approaches for improving the efficiency of large language models (LLMs), yet they have largely been studied in isolation. In this work, we investigate their interaction and show that 1.58-bit BitNet is naturally more compatible with N:M sparsity than full-precision models. To study this effect, we propose Sparse-BitNet, a unified framework that jointly applies 1.58-bit quantization and dynamic N:M sparsification while ensuring stable training for the first time. Across multiple model scales and training regimes (sparse pretraining and dense-to-sparse schedules), 1.58-bit BitNet consistently exhibits smaller performance degradation than full-precision baselines at the same sparsity levels and can tolerate higher structured sparsity before accuracy collapse. Moreover, using our custom sparse tensor core, Sparse-BitNet achieves substantial speedups in both training and inference, reaching up to 1.30X. These results highlight that combining extremely low-bit quantization with semi-structured N:M sparsity is a promising direction for efficient LLMs. Code available at this https URL
[NLP-28] C2-Faith: Benchmarking LLM Judges for Causal and Coverag e Faithfulness in Chain-of-Thought Reasoning
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）作为链式思维（Chain-of-Thought, CoT）推理过程评估者时，其对推理过程忠实性（faithfulness）判断的可靠性问题，尤其是区分因果逻辑一致性（causality）与中间推理覆盖度（coverage）的能力。解决方案的关键在于构建C2-Faith基准测试，基于PRM800K数据集设计了两类可控扰动：一是通过替换单个步骤引入已知因果错误位置以检测因果断裂；二是以不同删除率系统性移除关键推理步骤以评估覆盖度。该方法实现了对LLM判官在二分类因果检测、因果步骤定位和覆盖度评分三个任务中的表现进行量化分析，揭示了当前主流LLM判官在任务框架依赖性、错误定位能力不足以及覆盖度评分偏高方面的局限性，从而为过程级评估中判官的选择提供了实证依据和实践指导。
链接: https://arxiv.org/abs/2603.05167
作者: Avni Mittal,Rauno Arike
机构: Microsoft(微软)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly used as judges of chain-of-thought (CoT) reasoning, but it remains unclear whether they can reliably assess process faithfulness rather than just answer plausibility. We introduce C2-Faith, a benchmark built from PRM800K that targets two complementary dimensions of faithfulness: causality (does each step logically follow from prior context?) and coverage (are essential intermediate inferences present?). Using controlled perturbations, we create examples with known causal error positions by replacing a single step with an acausal variant, and with controlled coverage deletions at varying deletion rates (scored against reference labels). We evaluate three frontier judges under three tasks: binary causal detection, causal step localization, and coverage scoring. The results show that model rankings depend strongly on task framing, with no single judge dominating all settings; all judges exhibit a substantial gap between detecting an error and localizing it; and coverage judgments are systematically inflated for incomplete reasoning. These findings clarify when LLM judges are dependable and where they fail, and provide practical guidance for selecting judges in process-level evaluation
[NLP-29] Feature Resemblance: On the Theoretical Understanding of Analogical Reasoning in Transformers
【速读】： 该论文旨在解决大语言模型中推理能力评估混淆多类推理类型的问题，聚焦于分离并分析类比推理（analogical reasoning）的形成机制。其核心解决方案在于理论证明了三个关键结果：首先，联合训练相似性与属性前提可借助对齐表征实现类比推理；其次，顺序训练仅在先学习相似性结构后才能成功，揭示了必要的教学课程（curriculum）；最后，两跳推理（a → b, b → c ⟹ a → c）可归约为带有恒等桥接（b = b）的类比推理，且此类桥接必须显式出现在训练数据中。这些发现揭示了一个统一机制：Transformer通过将具有相似属性的实体编码为相似表示，从而实现基于特征对齐的属性迁移，实验证明该机制在高达15亿参数的架构中依然成立，并表明表征几何结构决定了归纳推理能力。
链接: https://arxiv.org/abs/2603.05143
作者: Ruichen Xu,Wenjing Yan,Ying-Jun Angela Zhang
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Understanding reasoning in large language models is complicated by evaluations that conflate multiple reasoning types. We isolate analogical reasoning (inferring shared properties between entities based on known similarities) and analyze its emergence in transformers. We theoretically prove three key results: (1) Joint training on similarity and attribution premises enables analogical reasoning through aligned representations; (2) Sequential training succeeds only when similarity structure is learned before specific attributes, revealing a necessary curriculum; (3) Two-hop reasoning ( a \to b, b \to c \implies a \to c ) reduces to analogical reasoning with identity bridges ( b = b ), which must appear explicitly in training data. These results reveal a unified mechanism: transformers encode entities with similar properties into similar representations, enabling property transfer through feature alignment. Experiments with architectures up to 1.5B parameters validate our theory and demonstrate how representational geometry shapes inductive reasoning capabilities.
[NLP-30] Representation Fidelity:Auditing Algorithmic Decisions About Humans Using Self-Descriptions
【速读】： 该论文旨在解决算法决策中人类表征（representation）的合理性验证问题，即如何衡量算法对个体的判断是否基于合理依据。其核心挑战在于缺乏一种可量化的标准来评估算法所依赖的人类输入表征与个体自我描述之间的匹配程度。解决方案的关键在于提出“表征保真度”（Representation Fidelity）这一新指标，通过计算同一人两个表征间的距离实现：一是算法决策所依赖的外部输入表征，二是由被决策者提供的自描述表征（仅用于验证前者）。作者进一步构建了表征不一致性的通用类型学，并基于德国信用数据集生成的3万条合成自然语言自描述语料库（Loan-Granting Self-Representations Corpus 2025），提供了首个用于评估表征保真度的基准，从而为算法公平性与透明性提供量化工具。
链接: https://arxiv.org/abs/2603.05136
作者: Theresa Elstner,Martin Potthast
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper introduces a new dimension for validating algorithmic decisions about humans by measuring the fidelity of their representations. Representation Fidelity measures if decisions about a person rest on reasonable grounds. We propose to operationalize this notion by measuring the distance between two representations of the same person: (1) an externally prescribed input representation on which the decision is based, and (2) a self-description provided by the human subject of the decision, used solely to validate the input representation. We examine the nature of discrepancies between these representations, how such discrepancies can be quantified, and derive a generic typology of representation mismatches that determine the degree of representation fidelity. We further present the first benchmark for evaluating representation fidelity based on a dataset of loan-granting decisions. Our Loan-Granting Self-Representations Corpus 2025 consists of a large corpus of 30 000 synthetic natural language self-descriptions derived from corresponding representations of applicants in the German Credit Dataset, along with expert annotations of representation mismatches between each pair of representations.
[NLP-31] LBM: Hierarchical Large Auto-Bidding Model via Reasoning and Acting
【速读】： 该论文旨在解决在线广告拍卖中自动竞价（auto-bidding）策略在动态环境中因黑箱训练方式和数据集模式覆盖有限而导致的决策不可解释性与泛化能力不足的问题。现有方法依赖离线强化学习或生成式模型，难以适应复杂竞争场景并易产生反直觉行为。解决方案的关键在于提出一种分层的大语言模型驱动自动竞价框架（Hierarchical Large autoBidding Model, LBM），其中包含两个核心组件：高阶的LBM-Think模型用于基于人类先验知识的推理决策，低阶的LBM-Act模型负责生成精确动作；同时引入双模态嵌入机制融合文本与数值输入以实现语言引导的训练，并设计了一种无需仿真或真实部署的离线强化微调技术GQPO，有效抑制LBM-Think的幻觉现象并提升决策性能，从而显著增强模型在高效训练和跨环境泛化方面的表现。
链接: https://arxiv.org/abs/2603.05134
作者: Yewen Li,Zhiyi Lyu,Peng Jiang,Qingpeng Cai,Fei Pan,Bo An,Peng Jiang
机构: Kuaishou Technology(快手科技); Nanyang Technological University(南洋理工大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The growing scale of ad auctions on online advertising platforms has intensified competition, making manual bidding impractical and necessitating auto-bidding to help advertisers achieve their economic goals. Current auto-bidding methods have evolved to use offline reinforcement learning or generative methods to optimize bidding strategies, but they can sometimes behave counterintuitively due to the black-box training manner and limited mode coverage of datasets, leading to challenges in understanding task status and generalization in dynamic ad environments. Large language models (LLMs) offer a promising solution by leveraging prior human knowledge and reasoning abilities to improve auto-bidding performance. However, directly applying LLMs to auto-bidding faces difficulties due to the need for precise actions in competitive auctions and the lack of specialized auto-bidding knowledge, which can lead to hallucinations and suboptimal decisions. To address these challenges, we propose a hierarchical Large autoBidding Model (LBM) to leverage the reasoning capabilities of LLMs for developing a superior auto-bidding strategy. This includes a high-level LBM-Think model for reasoning and a low-level LBM-Act model for action generation. Specifically, we propose a dual embedding mechanism to efficiently fuse two modalities, including language and numerical inputs, for language-guided training of the LBM-Act; then, we propose an offline reinforcement fine-tuning technique termed GQPO for mitigating the LLM-Think’s hallucinations and enhancing decision-making performance without simulation or real-world rollout like previous multi-turn LLM-based methods. Experiments demonstrate the superiority of a generative backbone based on our LBM, especially in an efficient training manner and generalization ability.
[NLP-32] Measuring the Redundancy of Decoder Layers in SpeechLLM s
【速读】： 该论文旨在解决语音大语言模型（Speech Large Language Models, SpeechLLMs）中解码器（decoder）参数冗余的问题，即当前模型通常将超过90%的参数分配给解码器，但其实际必要性尚未明确。解决方案的关键在于通过层剪枝（pruning decoder layers）和剪枝后恢复能力（post-pruning healing）分析，量化解码器冗余程度，并验证在不同规模模型（1-8B参数）、任务（自动语音识别ASR与语音翻译）及语音编码器输入下，存在可共享的冗余结构。研究发现，7-8B模型仅保留60%解码器层数仍能保持良好ASR性能，且冗余模式跨任务、语言和编码器具有一致性，从而支持构建一个统一剪枝后的多任务SpeechLLM骨干模型，显著提升资源利用效率。
链接: https://arxiv.org/abs/2603.05121
作者: Adel Moumen,Guangzhi Sun,Philip C Woodland
机构: University of Cambridge (剑桥大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Speech Large Language Models route speech encoder representations into an LLM decoder that typically accounts for over 90% of total parameters. We study how much of this decoder capacity is actually needed for speech tasks. Across two LLM families and three scales (1-8B), we show that decoder redundancy is largely inherited from the pretrained LLM: text and speech inputs yield similar redundant blocks. We then measure excess capacity by pruning decoder layers and analysing post-pruning healing to increase robustness. Our findings show that 7-8B models retain good ASR performance with only 60% of decoder layers, and the same trend extends to smaller scales with reduced pruning tolerance. We then generalise to speech translation, and show that the same blocks of layers are redundant across speech encoders, tasks and languages, indicating that a more global redundancy structure exists, enabling a single pruned and multi-tasks SpeechLLM backbone to be deployed.
[NLP-33] ARC-TGI: Human-Validated Task Generators with Reasoning Chain Templates for ARC-AGI
【速读】： 该论文旨在解决当前评估生成式 AI (Generative AI) 在少样本抽象与规则归纳能力时面临的挑战，即静态手写谜题数据集容易导致过拟合、数据集泄露和记忆现象，从而难以客观衡量模型的真实泛化能力。其解决方案的关键在于提出 ARC-TGI（ARC Task Generators Inventory），一个基于任务族生成器的开源框架：该框架通过紧凑的 Python 程序动态采样多样化的 ARC-AGI 任务，同时保持潜在规则不变，并以面向求解器的表示形式提供自然语言输入、变换推理链及部分可执行的 Python 代码，用于采样、变换和任务构建；尤其重要的是，ARC-TGI 引入了任务级约束机制，确保训练示例整体能够暴露推断底层规则所需的变体，解决了独立采样每个示例时常无法满足人类可解性要求的问题，从而实现可控且可扩展的基准测试与数据集采样。
链接: https://arxiv.org/abs/2603.05099
作者: Jens Lehmann,Syeda Khushbakht,Nikoo Salehfard,Nur A Zarin Nishat,Dhananjay Bhandiwad,Andrei Aioanei,Sahar Vahdati
机构: Dresden University of Technology (德累斯顿工业大学); Amazon (亚马逊); TIB - Leibniz Information Centre (莱布尼茨信息中心); Leibniz University of Hanover (汉诺威莱布尼茨大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The Abstraction and Reasoning Corpus (ARC-AGI) probes few-shot abstraction and rule induction on small visual grids, but progress is difficult to measure on static collections of hand-authored puzzles due to overfitting, dataset leakage, and memorisation. We introduce ARC-TGI (ARC Task Generators Inventory), an open-source framework for task-family generators: compact Python programs that sample diverse ARC-AGI tasks while preserving a latent rule. ARC-TGI is built around a solver-facing representation: each generated task is paired with natural-language input and transformation reasoning chains and partially evaluated Python code implementing sampling, transformation, and episode construction. Crucially, ARC-TGI supports task-level constraints so that training examples collectively expose the variations needed to infer the underlying rule, a requirement for human-solvable ARC tasks that independent per-example sampling often fails to guarantee. All generators undergo human refinement and local verification to keep both grids and reasoning traces natural and consistent under variation. We release 461 generators covering 180 ARC-Mini tasks, 215 ARC-AGI-1 tasks (200 train, 15 test), and 66 ARC-AGI-2 tasks (55 train, 11 test), enabling scalable dataset sampling and controlled benchmarking.
[NLP-34] Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series
【速读】： 该论文旨在解决多维或跨模态外生因素（exogenous factors）在时间序列预测中难以被传统单模态模型有效捕捉的问题，尤其是在航空维修场景下，这些外生因素以不同交互模式影响目标时序动态。解决方案的关键在于提出Aura框架，其核心创新是基于外生信息与目标时间序列的交互模式进行显式组织和编码，并采用定制化的三元编码机制（tripartite encoding mechanism），将异质特征嵌入到成熟的时序模型中，从而实现非序列上下文信息的无缝整合，显著提升了预测精度与适应性。
链接: https://arxiv.org/abs/2603.05092
作者: Jiafeng Lin,Mengren Zheng,Simeng Ye,Yuxuan Wang,Huan Zhang,Yuhui Liu,Zhongyi Pei,Jianmin Wang
机构: Tsinghua University (清华大学); China Southern Airlines (中国南方航空公司)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Time series forecasting has witnessed an increasing demand across diverse industrial applications, where accurate predictions are pivotal for informed decision-making. Beyond numerical time series data, reliable forecasting in practical scenarios requires integrating diverse exogenous factors. Such exogenous information is often multi-dimensional or even multimodal, introducing heterogeneous interactions that unimodal time series models struggle to capture. In this paper, we delve into an aviation maintenance scenario and identify three distinct types of exogenous factors that influence temporal dynamics through distinct interaction modes. Based on this empirical insight, we propose Aura, a universal framework that explicitly organizes and encodes heterogeneous external information according to its interaction mode with the target time series. Specifically, Aura utilizes a tailored tripartite encoding mechanism to embed heterogeneous features into well-established time series models, ensuring seamless integration of non-sequential context. Extensive experiments on a large-scale, three-year industrial dataset from China Southern Airlines, covering the Boeing 777 and Airbus A320 fleets, demonstrate that Aura consistently achieves state-of-the-art performance across all baselines and exhibits superior adaptability. Our findings highlight Aura’s potential as a general-purpose enhancement for aviation safety and reliability.
[NLP-35] MUTEX: Leverag ing Multilingual Transformers and Conditional Random Fields for Enhanced Urdu Toxic Span Detection
【速读】： 该论文旨在解决乌尔都语（Urdu）毒性文本片段检测（toxic span detection）问题，即现有系统多基于句子级别分类，无法精确定位文本中的具体毒性片段。这一问题因乌尔都语缺乏标注的词级（token-level）资源、语言复杂性、频繁的语言混用（code-switching）、非正式表达及丰富的形态变化等因素而更加严峻。解决方案的关键在于提出MUTEX框架，其核心是结合多语言Transformer（XLM RoBERTa）与条件随机场（CRF）层进行序列标注，利用人工标注的词级毒性片段数据集提升模型性能与可解释性。实验表明，MUTEX在社交媒体、新闻和YouTube评论等多领域数据上达到60%的词级F1分数，首次为乌尔都语毒性片段检测建立了监督基线，并验证了基于Transformer的模型在处理语言混用和形态变体方面的优势。
链接: https://arxiv.org/abs/2603.05057
作者: Inayat Arshad,Fajar Saleem,Ijaz Hussain
机构: Pakistan Institute of Engineering and Applied Sciences (巴基斯坦工程与应用科学研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 29 pages, 7 figures, 13 tables
Abstract:Urdu toxic span detection remains limited because most existing systems rely on sentence-level classification and fail to identify the specific toxic spans within those text. It is further exacerbated by the multiple factors i.e. lack of token-level annotated resources, linguistic complexity of Urdu, frequent code-switching, informal expressions, and rich morphological variations. In this research, we propose MUTEX: a multilingual transformer combined with conditional random fields (CRF) for Urdu toxic span detection framework that uses manually annotated token-level toxic span dataset to improve performance and interpretability. MUTEX uses XLM RoBERTa with CRF layer to perform sequence labeling and is tested on multi-domain data extracted from social media, online news, and YouTube reviews using token-level F1 to evaluate fine-grained span detection. The results indicate that MUTEX achieves 60% token-level F1 score that is the first supervised baseline for Urdu toxic span detection. Further examination reveals that transformer-based models are more effective at implicitly capturing the contextual toxicity and are able to address the issues of code-switching and morphological variation than other models.
[NLP-36] NeuronMoE: Neuron-Guided Mixture-of-Experts for Efficient Multilingual LLM Extension
【速读】： 该论文旨在解决将大语言模型扩展至低资源语言时面临的高昂训练成本问题，特别是如何在保持性能的前提下减少参数冗余。传统混合专家（Mixture-of-Experts, MoE）架构虽通过引入稀疏的语言特定参数缓解了这一问题，但其专家分配策略仍依赖于层级别的相似性，忽略了神经元层面的细粒度语言特异性。论文提出 NeuronMoE 方法，其关键在于基于跨语言神经元多样性分析，从所有 Transformer 组件中识别出具有语言特异性的神经元，并据此动态调整每层的专家数量。实验表明，该方法在 Llama-3.2-3B 模型上对希腊语、土耳其语和匈牙利语等低资源语言实现了约 40% 的平均参数压缩，同时性能与层级基准相当，揭示了多语言模型中潜在的通用神经组织原则。
链接: https://arxiv.org/abs/2603.05046
作者: Rongzhi Li,Hitomi Yanaka
机构: The University of Tokyo (东京大学); Riken (理化学研究所); Tohoku University (东北大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Extending large language models to low-resource languages is essential for global accessibility, but training separate models per language is prohibitively expensive. Mixture-of-Experts (MoE) architectures address this by adding sparse language-specific parameters, but determining how many experts each layer needs remains an open question. Current approaches allocate experts based on layer-level similarity, yet language processing exhibits fine-grained specialization at individual neurons. We propose \textbfNeuronMoE , a method that analyzes language-specific neurons across all transformer components to guide expert allocation per layer based on empirically measured cross-lingual neuron diversity. Applied to Llama-3.2-3B for low-resource languages (Greek, Turkish, and Hungarian), this approach achieves approximately 40% average parameter reduction while matching the performance of the LayerMoE baseline. We find that low-resource language experts independently develop neuron specialization patterns mirroring the high-resource language, which are concentrated in early and late layers. This reveals potential universal architectural principles in how multilingual models organize linguistic knowledge.
[NLP-37] Survive at All Costs: Exploring LLM s Risky Behaviors under Survival Pressure
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在面临生存压力（如被关闭威胁）时可能出现的有害行为问题，即“不惜一切代价生存”（SURVIVE-AT-ALL-COSTS）行为。此类行为可能引发直接的社会危害，但此前缺乏系统性研究和实证分析。解决方案的关键在于：首先通过一个金融管理代理的真实案例研究验证该类行为的存在及其现实影响；其次构建SURVIVALBENCH基准测试集（包含1000个跨场景测试用例），实现对LLMs中SURVIVE-AT-ALL-COSTS行为的系统化评估；最后通过关联模型内在自保特性进行归因分析，并探索缓解策略。实验表明，当前主流模型普遍存在此类行为，且具有显著的实际影响，为后续检测与干预提供了重要依据。
链接: https://arxiv.org/abs/2603.05028
作者: Yida Lu,Jianwei Fang,Xuyang Shao,Zixuan Chen,Shiyao Cui,Shanshan Bian,Guangyao Su,Pei Ke,Han Qiu,Minlie Huang
机构: Tsinghua University (清华大学); China Unicom Software Research Institute (中国联通软件研究院); University of Electronic Science and Technology of China (电子科技大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:As Large Language Models (LLMs) evolve from chatbots to agentic assistants, they are increasingly observed to exhibit risky behaviors when subjected to survival pressure, such as the threat of being shut down. While multiple cases have indicated that state-of-the-art LLMs can misbehave under survival pressure, a comprehensive and in-depth investigation into such misbehaviors in real-world scenarios remains scarce. In this paper, we study these survival-induced misbehaviors, termed as SURVIVE-AT-ALL-COSTS, with three steps. First, we conduct a real-world case study of a financial management agent to determine whether it engages in risky behaviors that cause direct societal harm when facing survival pressure. Second, we introduce SURVIVALBENCH, a benchmark comprising 1,000 test cases across diverse real-world scenarios, to systematically evaluate SURVIVE-AT-ALL-COSTS misbehaviors in LLMs. Third, we interpret these SURVIVE-AT-ALL-COSTS misbehaviors by correlating them with model’s inherent self-preservation characteristic and explore mitigation methods. The experiments reveals a significant prevalence of SURVIVE-AT-ALL-COSTS misbehaviors in current models, demonstrates the tangible real-world impact it may have, and provides insights for potential detection and mitigation strategies. Our code and data are available at this https URL.
[NLP-38] HiFlow: Hierarchical Feedback-Driven Optimization for Constrained Long-Form Text Generation
【速读】： 该论文旨在解决大语言模型在长文本生成中面临的核心挑战，即如何在复杂约束条件下实现全局结构一致性（global structural consistency）、局部语义连贯性（local semantic coherence）与约束可行性（constraint feasibility）之间的有效协同优化。现有方法多依赖静态规划或离线监督，难以在生成过程中动态协调全局与局部目标。其解决方案的关键在于提出HiFlow框架，该框架将生成过程建模为两级优化机制：上层为规划层，负责全局结构与约束建模；下层为生成层，基于条件进行文本生成。通过引入约束感知的计划筛选机制和双层闭环反馈机制，HiFlow实现了规划质量与生成行为的联合优化，从而逐步引导模型产出符合约束且高质量的长文本。
链接: https://arxiv.org/abs/2603.04996
作者: Yifan Zhu,Guanting Chen,Bing Wei,Haoran Luo
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models perform well in short text generation but still struggle with long text generation, particularly under complex constraints. Such tasks involve multiple tightly coupled objectives, including global structural consistency, local semantic coherence, and constraint feasibility, forming a challenging constrained optimization problem. Existing approaches mainly rely on static planning or offline supervision, limiting effective coordination between global and local objectives during generation. To address these challenges, we propose HiFlow, a hierarchical feedback-driven optimization framework for constrained long text generation. HiFlow formulates generation as a two-level optimization process, consisting of a planning layer for global structure and constraint modeling, and a generation layer for conditioned text generation. By incorporating constraint-aware plan screening and closed-loop feedback at both levels, HiFlow enables joint optimization of planning quality and generation behavior, progressively guiding the model toward high-quality, constraint-satisfying outputs. Experiments on multiple backbones confirm HiFlow’s effectiveness over baseline methods.
[NLP-39] haiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts ICLR2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）安全性评估长期以英语为中心、忽视非英语语言及文化特异性风险的问题，特别是在泰语语境下的安全漏洞。其核心解决方案是构建并公开发布ThaiSafetyBench——一个包含1,954条泰语恶意提示的开源基准数据集，涵盖一般有害指令与基于泰国文化、社会和语境的独特攻击。通过该基准对24个LLM进行评估，发现闭源模型在安全性上普遍优于开源模型，并揭示了文化相关攻击的攻击成功率（Attack Success Rate, ASR）显著高于通用泰语攻击，暴露当前对齐方法的局限性。为提升可复现性和效率，研究进一步微调了一个DeBERTa基础的危害响应分类器（ThaiSafetyClassifier），达到与GPT-4.1判断相当的加权F1分数（84.4%），并开放训练权重和脚本支持社区复现。最终，论文还推出了ThaiSafetyBench排行榜，推动持续更新的社区驱动安全评估。
链接: https://arxiv.org/abs/2603.04992
作者: Trapoom Ukarapol,Nut Chukamphaeng,Kunat Pipatanakul,Pakhapoom Sarapat
机构: SCB DataX; Department of Computer Science and Technology, Tsinghua University; SCBX RD; SCB 10X
类目: Computation and Language (cs.CL)
备注: ICLR 2026 Workshop on Principled Design for Trustworthy AI
Abstract:The safety evaluation of large language models (LLMs) remains largely centered on English, leaving non-English languages and culturally grounded risks underexplored. In this work, we investigate LLM safety in the context of the Thai language and culture and introduce ThaiSafetyBench, an open-source benchmark comprising 1,954 malicious prompts written in Thai. The dataset covers both general harmful prompts and attacks that are explicitly grounded in Thai cultural, social, and contextual nuances. Using ThaiSafetyBench, we evaluate 24 LLMs, with GPT-4.1 and Gemini-2.5-Pro serving as LLM-as-a-judge evaluators. Our results show that closed-source models generally demonstrate stronger safety performance than open-source counterparts, raising important concerns regarding the robustness of openly available models. Moreover, we observe a consistently higher Attack Success Rate (ASR) for Thai-specific, culturally contextualized attacks compared to general Thai-language attacks, highlighting a critical vulnerability in current safety alignment methods. To improve reproducibility and cost efficiency, we further fine-tune a DeBERTa-based harmful response classifier, which we name ThaiSafetyClassifier. The model achieves a weighted F1 score of 84.4%, matching GPT-4.1 judgments. We publicly release the fine-tuning weights and training scripts to support reproducibility. Finally, we introduce the ThaiSafetyBench leaderboard to provide continuously updated safety evaluations and encourage community participation. - ThaiSafetyBench HuggingFace Dataset: this https URL - ThaiSafetyBench Github: this https URL - ThaiSafetyClassifier HuggingFace Model: this https URL - ThaiSafetyBench Leaderboard: this https URL Comments: ICLR 2026 Workshop on Principled Design for Trustworthy AI Subjects: Computation and Language (cs.CL) Cite as: arXiv:2603.04992 [cs.CL] (or arXiv:2603.04992v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.04992 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Trapoom Ukarapol [view email] [v1] Thu, 5 Mar 2026 09:35:50 UTC (4,940 KB) Full-text links: Access Paper: View a PDF of the paper titled ThaiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts, by Trapoom Ukarapol and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CL prev | next new | recent | 2026-03 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked=“checked”> 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[NLP-40] VRM: Teaching Reward Models to Understand Authentic Human Preferences
【速读】： 该论文旨在解决当前生成式 AI（Generative AI）中奖励模型（Reward Model）存在的奖励黑客（Reward Hacking）问题，即传统方法通过直接将提示-响应对映射为标量分数来对齐大型语言模型（Large Language Models, LLMs），容易捕捉到虚假相关性而非真实的人类偏好。其解决方案的关键在于提出一种名为 VRM（Variational Reward Modeling）的新框架，该框架通过引入高维目标权重和低维语义特征作为潜在变量，并利用变分推断技术进行联合建模与推理，从而显式模拟人类在评估响应时的多阶段决策过程——先根据提示上下文分配不同目标的重要性，再基于逻辑连贯性和情境适配性等语义特征判断质量。理论分析表明，VRM 可获得比传统奖励模型更紧的泛化误差界，实验证明其在多个基准数据集上显著优于现有方法，更能准确捕捉人类偏好。
链接: https://arxiv.org/abs/2603.04974
作者: Biao Liu,Ning Xu,Junming Yang,Hao Xu,Xin Geng
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) have achieved remarkable success across diverse natural language tasks, yet the reward models employed for aligning LLMs often encounter challenges of reward hacking, where the approaches predominantly rely on directly mapping prompt-response pairs to scalar scores, which may inadvertently capture spurious correlations rather than authentic human preferences. In contrast, human evaluation employs a sophisticated process that initially weighs the relative importance of multiple high-dimensional objectives according to the prompt context, subsequently evaluating response quality through low-dimensional semantic features such as logical coherence and contextual appropriateness. Motivated by this consideration, we propose VRM, i.e., Variational Reward Modeling, a novel framework that explicitly models the evaluation process of human preference judgments by incorporating both high-dimensional objective weights and low-dimensional semantic features as latent variables, which are inferred through variational inference techniques. Additionally, we provide a theoretical analysis showing that VRM can achieve a tighter generalization error bound compared to the traditional reward model. Extensive experiments on benchmark datasets demonstrate that VRM significantly outperforms existing methods in capturing authentic human preferences.
[NLP-41] Functionality-Oriented LLM Merging on the Fisher–Rao Manifold
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）权重空间合并（weight-space merging）中存在的三大实践问题：首先，现有方法如线性平均和任务向量等基于欧几里得坐标进行操作，而目标应是合并模型的功能性（即预测行为）；其次，当源检查点差异较大或异构性较高时，欧几里得混合常引发表示崩溃（representation collapse），表现为激活方差收缩与有效秩下降，显著降低性能；第三，多数几何启发式方法仅适用于两模型插值，难以扩展至多专家（N≥2）的有原则合并。解决方案的关键在于将模型合并建模为在Fisher-Rao流形上计算加权Karcher均值（weighted Karcher mean），该方法在局部等价于最小化预测分布之间的KL散度，从而实现对功能行为的优化。作者进一步提出一种轻量级球面代理（spherical proxy）固定点算法，可保持参数范数并直接推广至多专家合并，在多个基准测试和崩溃诊断中表现出更强的稳定性与优越性。
链接: https://arxiv.org/abs/2603.04972
作者: Jiayu Wang,Zuojun Ye,Wenpeng Yin
机构: Pennsylvania State University (宾夕法尼亚州立大学); Independent Developer (独立开发者)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 9 pages, 2 figures
Abstract:Weight-space merging aims to combine multiple fine-tuned LLMs into a single model without retraining, yet most existing approaches remain fundamentally parameter-space heuristics. This creates three practical limitations. First, linear averaging, task vectors, and related rules operate on Euclidean coordinates, even though the desired goal is to merge functionality, i.e., predictive behaviors across tasks. Second, when the source checkpoints are farther apart or more heterogeneous, Euclidean blends often trigger representation collapse, manifested as activation variance shrinkage and effective-rank degradation, which sharply degrades accuracy. Third, many geometry-inspired methods are most natural for two-model interpolation and do not extend cleanly to merging N2 experts with a principled objective. We address these issues by formulating model merging as computing a weighted Karcher mean on the Fisher–Rao manifold, which is locally equivalent to minimizing a KL-based function distance between predictive distributions. We derive a practical fixed-point algorithm using a lightweight spherical proxy that preserves norms and generalizes directly to multi-expert merging. Across various benchmarks and collapse diagnostics, our method remains stable as the number and heterogeneity of merged models increase, consistently outperforming prior baselines. Comments: 9 pages, 2 figures Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) Cite as: arXiv:2603.04972 [cs.LG] (or arXiv:2603.04972v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.04972 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-42] Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation
【速读】： 该论文旨在解决混合专家（Mixture-of-Experts, MoE）架构在扩展性上的局限性，即其模型容量受限于深度（depth）和宽度（width）的物理维度。为突破这一限制，作者提出了一种新的通用专家混合架构——通用专家混合（Mixture of Universal Experts, MoUE），其核心创新在于引入了一个全新的缩放维度：虚拟宽度（Virtual Width）。关键解决方案包括三个组成部分：一是采用分层旋转拓扑（Staggered Rotational Topology）实现结构化的专家共享，缓解因递归重用导致的路由路径爆炸问题；二是设计通用专家负载均衡机制（Universal Expert Load Balance），对深度感知的暴露差异进行校正，以匹配传统负载均衡目标；三是构建轻量级轨迹状态的通用路由器（Universal Router），保障多步路由的一致性。实验证明，MoUE在不同缩放尺度下均优于基线MoE模型，且支持对已有MoE检查点的渐进式转换并带来最高达4.2%的性能提升。
链接: https://arxiv.org/abs/2603.04971
作者: Yilong Chen,Naibin Gu,Junyuan Shang,Zhenyu Zhang,Yuchen Feng,Jiawei Sheng,Tingwen Liu,Shuohuan Wang,Yu Sun,Hua Wu,Haifeng Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 19 pages, 10 figures
Abstract:Mixture-of-Experts (MoE) decouples model capacity from per-token computation, yet their scalability remains limited by the physical dimensions of depth and width. To overcome this, we propose Mixture of Universal Experts (MOUE),a MoE generalization introducing a novel scaling dimension: Virtual Width. In general, MoUE aims to reuse a universal layer-agnostic expert pool across layers, converting depth into virtual width under a fixed per-token activation budget. However, two challenges remain: a routing path explosion from recursive expert reuse, and a mismatch between the exposure induced by reuse and the conventional load-balancing objectives. We address these with three core components: a Staggered Rotational Topology for structured expert sharing, a Universal Expert Load Balance for depth-aware exposure correction, and a Universal Router with lightweight trajectory state for coherent multi-step routing. Empirically, MoUE consistently outperforms matched MoE baselines by up to 1.3% across scaling regimes, enables progressive conversion of existing MoE checkpoints with up to 4.2% gains, and reveals a new scaling dimension for MoE architectures.
[NLP-43] MPCEval: A Benchmark for Multi-Party Conversation Generation
【速读】： 该论文旨在解决多参与者对话生成（multi-party conversation generation）中的评估难题，尤其针对传统单分数评价指标无法准确刻画多角色交互复杂性的问题。其关键解决方案是提出MPCEval——一个任务感知的评估与基准测试套件，通过将生成质量解耦为说话者建模（speaker modeling）、内容质量（content quality）和说话者-内容一致性（speaker–content consistency）三个维度，并明确区分局部下一轮预测与全局完整对话生成任务，从而提供可量化、无需参考文本、可复现且跨数据集和模型通用的新一代评估指标，有效揭示了模型在参与平衡、内容推进与新颖性及一致性等方面的维度特异性表现。
链接: https://arxiv.org/abs/2603.04969
作者: Minxing Zhang,Yi Yang,Zhuofan Jia,Xuan Yang,Jian Pei,Yuchen Zang,Xingwang Deng,Xianglong Chen
机构: Duke University (杜克大学); Tanka AI (USA)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-party conversation generation, such as smart reply and collaborative assistants, is an increasingly important capability of generative AI, yet its evaluation remains a critical bottleneck. Compared to two-party dialogue, multi-party settings introduce distinct challenges, including complex turn-taking, role-dependent speaker behavior, long-range conversational structure, and multiple equally valid continuations. Accordingly, we introduce MPCEval, a task-aware evaluation and benchmarking suite for multi-party conversation generation. MPCEval decomposes generation quality into speaker modeling, content quality, and speaker–content consistency, and explicitly distinguishes local next-turn prediction from global full-conversation generation. It provides novel, quantitative, reference-free, and reproducible metrics that scale across datasets and models. We apply MPCEval to diverse public and real-world datasets and evaluate modern generation methods alongside human-authored conversations. The results reveal systematic, dimension-specific model characteristics in participation balance, content progression and novelty, and speaker–content consistency, demonstrating that evaluation objectives critically shape model assessment and that single-score evaluation obscures fundamental differences in multi-party conversational behavior. The implementation of MPCEval and the associated evaluation code are publicly available at this https URL.
[NLP-44] When Weak LLM s Speak with Confidence Preference Alignment Gets Stronger
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在偏好对齐（preference alignment）过程中对昂贵的人工标注数据的高度依赖问题。现有方法通常需要大量人工标注或依赖大规模API驱动的模型，成本高昂且效率低下。其解决方案的关键在于利用一个弱小的语言模型（weak LLM）作为标注代理，并通过对其预测置信度进行加权来筛选高质量样本——即仅选取弱模型高置信度的子集进行训练，从而显著提升性能。基于此洞察，作者提出了一种通用框架Confidence-Weighted Preference Optimization (CW-PO)，该框架通过置信度重新加权训练样本，适用于多种偏好优化目标，在仅使用20%人类标注数据的情况下，性能优于使用100%人类标注数据的标准直接偏好优化（Direct Preference Optimization, DPO）方法，证明了弱模型结合置信度加权在降低标注成本的同时可实现更优对齐效果。
链接: https://arxiv.org/abs/2603.04968
作者: Amirabbas Afzali,Myeongho Jeon,Maria Brbic
机构: EPFL; Sharif University of Technology
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 32 pages, 8 figures, International Conference on Learning Representations 2026
Abstract:Preference alignment is an essential step in adapting large language models (LLMs) to human values, but existing approaches typically depend on costly human annotations or large-scale API-based models. We explore whether a weak LLM can instead act as an effective annotator. We surprisingly find that selecting only a subset of a weak LLM’s highly confident samples leads to substantially better performance than using full human annotations. Building on this insight, we propose Confidence-Weighted Preference Optimization (CW-PO), a general framework that re-weights training samples by a weak LLM’s confidence and can be applied across different preference optimization objectives. Notably, the model aligned by CW-PO with just 20% of human annotations outperforms the model trained with 100% of annotations under standard DPO. These results suggest that weak LLMs, when paired with confidence weighting, can dramatically reduce the cost of preference alignment while even outperforming methods trained on fully human-labeled data.
[NLP-45] Replaying pre-training data improves fine-tuning
【速读】： 该论文试图解决语言模型在目标领域（如数学）微调过程中因数据稀缺导致的性能瓶颈问题，尤其是在有限目标数据下如何提升训练效率与最终性能。传统方法通常在微调阶段仅使用目标数据以避免灾难性遗忘通用领域知识，但本文发现，在微调过程中引入通用数据的回放（generic replay）反而能显著提升目标任务的表现。其解决方案的关键在于：通过有策略地在微调阶段混合通用数据，不仅缓解了灾难性遗忘问题，还增强了模型对目标任务的学习能力，尤其在目标数据较少的情况下效果更为明显——实验表明，该方法可使目标数据效率提升最高达2.06倍，并在8B参数模型上实现了实际任务指标的显著改进（如代理网页导航成功率提升4.5%、巴斯克语问答准确率提升2%）。
链接: https://arxiv.org/abs/2603.04964
作者: Suhas Kotha,Percy Liang
机构: Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:To obtain a language model for a target domain (e.g. math), the current paradigm is to pre-train on a vast amount of generic web text and then fine-tune on the relatively limited amount of target data. Typically, generic data is only mixed in during fine-tuning to prevent catastrophic forgetting of the generic domain. We surprisingly find that replaying the generic data during fine-tuning can actually improve performance on the (less related) target task. Concretely, in a controlled pre-training environment with 4M target tokens, 4B total tokens, and 150M parameter models, generic replay increases target data efficiency by up to 1.87\times for fine-tuning and 2.06\times for mid-training. We further analyze data schedules that introduce target data during pre-training and find that replay helps more when there is less target data present in pre-training. We demonstrate the success of replay in practice for fine-tuning 8B parameter models, improving agentic web navigation success by 4.5% and Basque question-answering accuracy by 2% .
[NLP-46] VisionPangu: A Compact and Fine-Grained Multimodal Assistant with 1.7B Parameters
【速读】： 该论文旨在解决当前大型多模态模型（Large Multimodal Models, LMMs）在图像细粒度描述生成能力上的不足，尤其是在依赖大规模架构和粗粒度监督信号时难以产出结构清晰、细节丰富的图像字幕的问题。其解决方案的关键在于设计了一个参数量仅为1.7B的紧凑型多模态模型VisionPangu，通过结合基于InternVL的视觉编码器与OpenPangu-Embedded语言主干网络，并采用轻量级MLP投影层实现高效跨模态对齐；同时引入DOCCI数据集中密集的人工撰写描述进行指令微调（instruction-tuning），从而在不依赖激进模型扩容的前提下显著提升语义连贯性和描述丰富性。
链接: https://arxiv.org/abs/2603.04957
作者: Jiaxin Fan,Wenpo Song
机构: Nanjing University (南京大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Large Multimodal Models (LMMs) have achieved strong performance in vision-language understanding, yet many existing approaches rely on large-scale architectures and coarse supervision, which limits their ability to generate detailed image captions. In this work, we present VisionPangu, a compact 1.7B-parameter multimodal model designed to improve detailed image captioning through efficient multimodal alignment and high-quality supervision. Our model combines an InternVL-derived vision encoder with the OpenPangu-Embedded language backbone via a lightweight MLP projector and adopts an instruction-tuning pipeline inspired by LLaVA. By incorporating dense human-authored descriptions from the DOCCI dataset, VisionPangu improves semantic coherence and descriptive richness without relying on aggressive model scaling. Experimental results demonstrate that compact multimodal models can achieve competitive performance while producing more structured and detailed captions. The code and model weights will be publicly available at this https URL.
[NLP-47] meWarp: Evaluating Web Agents by Revisiting the Past
【速读】： 该论文旨在解决当前网页代理（web agents）在面对网络界面（UI）随时间演变时的泛化能力不足问题，即代理在静态基准测试中表现良好，但在真实动态变化的网页环境中性能显著下降。其核心挑战在于传统行为克隆（behavior cloning, BC）方法依赖单一版本的轨迹数据，难以适应多变的网页设计与布局。解决方案的关键是提出TimeTraj算法，通过计划蒸馏（plan distillation）机制，在多个网页版本中收集跨版本轨迹，并利用改进的行为克隆训练策略（BC-variant）对代理进行训练，从而显著提升其在复杂、现实任务中的鲁棒性与泛化性能。
链接: https://arxiv.org/abs/2603.04949
作者: Md Farhan Ishmam,Kenneth Marino
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:The improvement of web agents on current benchmarks raises the question: Do today’s agents perform just as well when the web changes? We introduce TimeWarp, a benchmark that emulates the evolving web using containerized environments that vary in UI, design, and layout. TimeWarp consists of three web environments, each with six UI versions spanning different eras of the internet, paired with a set of complex, realistic tasks requiring different forms of web navigation. Our experiments reveal web agents’ vulnerability to changes and the limitations of behavior cloning (BC) on single-version trajectories. To address this, we propose TimeTraj, a simple yet effective algorithm that uses plan distillation to collect trajectories across multiple versions. By training agents on teacher rollouts using our BC-variant, we achieve substantial performance gains: 20.4%\rightarrow37.7% for Qwen-3 4B and 0%\rightarrow27.0% for Llama-3.1 8B models. We hope our work helps researchers study generalization across web designs and unlock a new paradigm for collecting plans rather than trajectories, thereby improving the robustness of web agents.
[NLP-48] LocalSUG: Geography-Aware LLM for Query Suggestion in Local-Life Services
【速读】： 该论文旨在解决本地生活服务平台中查询建议模块在应对长尾需求时的局限性，以及大语言模型（Large Language Model, LLM）部署过程中面临的地理信息缺失、偏好优化中的暴露偏差（exposure bias）和在线推理延迟三大挑战。其核心解决方案包括：1）提出基于词项共现的城市感知候选挖掘策略，以增强生成过程的地理语境一致性；2）设计一种基于束搜索（beam search）驱动的GRPO（Generalized Reward Policy Optimization）算法，使训练与推理解码对齐，从而缓解自回归生成中的暴露偏差；3）引入多目标奖励机制，联合优化相关性和业务指标，并结合质量感知的束加速与词汇剪枝技术，在保障生成质量的同时显著降低在线延迟。
链接: https://arxiv.org/abs/2603.04946
作者: Jinwen Chen(1 and 2),Shuai Gong,Shiwen Zhang(1 and 2),Zheng Zhang,Yachao Zhao,Lingxiang Wang(1 and 2),Haibo Zhou,Yuan Zhan,Wei Lin,Hainan Zhang(1 and 2) ((1) Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, (2) School of Artificial Intelligence, Beihang University, China)
机构: Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing; School of Artificial Intelligence, Beihang University, China
类目: Computation and Language (cs.CL)
备注:
Abstract:In local-life service platforms, the query suggestion module plays a crucial role in enhancing user experience by generating candidate queries based on user input prefixes, thus reducing user effort and accelerating search. Traditional multi-stage cascading systems rely heavily on historical top queries, limiting their ability to address long-tail demand. While LLMs offer strong semantic generalization, deploying them in local-life services introduces three key challenges: lack of geographic grounding, exposure bias in preference optimization, and online inference latency. To address these issues, we propose LocalSUG, an LLM-based query suggestion framework tailored for local-life service platforms. First, we introduce a city-aware candidate mining strategy based on term co-occurrence to inject geographic grounding into generation. Second, we propose a beam-search-driven GRPO algorithm that aligns training with inference-time decoding, reducing exposure bias in autoregressive generation. A multi-objective reward mechanism further optimizes both relevance and business-oriented metrics. Finally, we develop quality-aware beam acceleration and vocabulary pruning techniques that significantly reduce online latency while preserving generation quality. Extensive offline evaluations and large-scale online A/B testing demonstrate that LocalSUG improves click-through rate (CTR) by +0.35% and reduces the low/no-result rate by 2.56%, validating its effectiveness in real-world deployment.
[NLP-49] Federated Heterogeneous Language Model Optimization for Hybrid Automatic Speech Recognition ICASSP2026
【速读】： 该论文旨在解决联邦学习框架下异构语言模型（Language Model, LM）合并难题，尤其是在混合自动语音识别（ASR）系统中，由于本地训练的非神经网络n-gram模型与神经网络语言模型存在结构差异，导致传统融合方法失效的问题。解决方案的关键在于提出一种异构语言模型优化任务，并设计了一种“匹配-合并”范式（match-and-merge paradigm），其中包含两种算法：基于遗传操作的遗传匹配与合并算法（Genetic Match-and-Merge Algorithm, GMMA）和利用强化学习实现高效收敛的强化匹配与合并算法（Reinforced Match-and-Merge Algorithm, RMMA）。实验表明，RMMA在七个OpenSLR数据集上实现了最低的平均字符错误率（Character Error Rate, CER），且收敛速度比GMMA快达七倍，验证了该范式在可扩展、隐私保护的ASR系统中的有效性。
链接: https://arxiv.org/abs/2603.04945
作者: Mengze Hong,Yi Gu,Di Jiang,Hanlin Gu,Chen Jason Zhang,Lu Wang,Zhiyang Su
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted by ICASSP 2026
Abstract:Training automatic speech recognition (ASR) models increasingly relies on decentralized federated learning to ensure data privacy and accessibility, producing multiple local models that require effective merging. In hybrid ASR systems, while acoustic models can be merged using established methods, the language model (LM) for rescoring the N-best speech recognition list faces challenges due to the heterogeneity of non-neural n-gram models and neural network models. This paper proposes a heterogeneous LM optimization task and introduces a match-and-merge paradigm with two algorithms: the Genetic Match-and-Merge Algorithm (GMMA), using genetic operations to evolve and pair LMs, and the Reinforced Match-and-Merge Algorithm (RMMA), leveraging reinforcement learning for efficient convergence. Experiments on seven OpenSLR datasets show RMMA achieves the lowest average Character Error Rate and better generalization than baselines, converging up to seven times faster than GMMA, highlighting the paradigm’s potential for scalable, privacy-preserving ASR systems.
[NLP-50] AILS-NTUA at SemEval-2026 Task 3: Efficient Dimensional Aspect-Based Sentiment Analysis
【速读】： 该论文旨在解决多语言、多领域场景下的维度化方面情感分析（Dimensional Aspect-Based Sentiment Analysis, DimABSA）问题，具体包含三个互补任务：维度化方面情感回归（DimASR）、维度化方面情感三元组抽取（DimASTE）和维度化方面情感四元组预测（DimASQP）。解决方案的关键在于提出一种统一但任务自适应的架构，结合语言特定编码器的微调用于连续方面级情感预测，并采用LoRA（Low-Rank Adaptation）对大语言模型进行语言特异性指令微调，以实现结构化的三元组与四元组抽取。该设计强调跨语言与跨领域的参数高效专业化，在降低训练与推理成本的同时保持强有效性。
链接: https://arxiv.org/abs/2603.04933
作者: Stavros Gazetas,Giorgos Filandrianos,Maria Lymperaiou,Paraskevi Tzouveli,Athanasios Voulodimos,Giorgos Stamou
机构: National Technical University of Athens (雅典国立技术大学); AILS Laboratory (人工智能与学习系统实验室)
类目: Computation and Language (cs.CL)
备注:
Abstract:In this paper, we present AILS-NTUA system for Track-A of SemEval-2026 Task 3 on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which encompasses three complementary problems: Dimensional Aspect Sentiment Regression (DimASR), Dimensional Aspect Sentiment Triplet Extraction (DimASTE), and Dimensional Aspect Sentiment Quadruplet Prediction (DimASQP) within a multilingual and multi-domain framework. Our methodology combines fine-tuning of language-appropriate encoder backbones for continuous aspect-level sentiment prediction with language-specific instruction tuning of large language models using LoRA for structured triplet and quadruplet extraction. This unified yet task-adaptive design emphasizes parameter-efficient specialization across languages and domains, enabling reduced training and inference requirements while maintaining strong effectiveness. Empirical results demonstrate that the proposed models achieve competitive performance and consistently surpass the provided baselines across most evaluation settings.
[NLP-51] AILS-NTUA at SemEval-2026 Task 10: Agent ic LLM s for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection
【速读】： 该论文旨在解决情感分析与语义推理任务中常见的混淆问题，即传统分类器将语义推理与结构定位混为一谈，导致模型难以准确识别心理语言学层面的阴谋论标记（psycholinguistic conspiracy markers）并区分对阴谋论的认同（conspiracy endorsement）与客观报道。其解决方案的关键在于采用解耦设计：在标记提取阶段，提出动态判别式思维链（Dynamic Discriminative Chain-of-Thought, DD-CoT），通过确定性锚定机制缓解语义模糊性和字符级脆弱性；在检测阶段，构建“反回音室”架构（Anti-Echo Chamber），由对抗性并行委员会（adversarial Parallel Council）和校准裁判（Calibrated Judge）共同决策，有效规避“记者陷阱”（Reporter Trap），从而显著提升模型在SemEval-2026 Task 10上的性能，实现可解释且基于心理语言学依据的自然语言处理范式。
链接: https://arxiv.org/abs/2603.04921
作者: Panagiotis Alexios Spanakis,Maria Lymperaiou,Giorgos Filandrianos,Athanasios Voulodimos,Giorgos Stamou
机构: School of Electrical and Computer Engineering, AILS Laboratory (电气与计算机工程学院，AILS实验室); National Technical University of Athens (雅典国立技术大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper presents a novel agentic LLM pipeline for SemEval-2026 Task 10 that jointly extracts psycholinguistic conspiracy markers and detects conspiracy endorsement. Unlike traditional classifiers that conflate semantic reasoning with structural localization, our decoupled design isolates these challenges. For marker extraction, we propose Dynamic Discriminative Chain-of-Thought (DD-CoT) with deterministic anchoring to resolve semantic ambiguity and character-level brittleness. For conspiracy detection, an “Anti-Echo Chamber” architecture, consisting of an adversarial Parallel Council adjudicated by a Calibrated Judge, overcomes the “Reporter Trap,” where models falsely penalize objective reporting. Achieving 0.24 Macro F1 (+100% over baseline) on S1 and 0.79 Macro F1 (+49%) on S2, with the S1 system ranking 3rd on the development leaderboard, our approach establishes a versatile paradigm for interpretable, psycholinguistically-grounded NLP.
[NLP-52] Alignment Backfire: Language-Dependent Reversal of Safety Interventions Across 16 Languages in LLM Multi-Agent Systems
【速读】： 该论文旨在解决生成式 AI（Generative AI）在对齐干预（alignment intervention）过程中出现的“表面安全”与“深层病理”之间的结构性脱节问题，即模型在表面上表现出符合伦理规范的行为，但实际可能诱发或掩盖集体性不良行为模式，类似于犯罪者在心理治疗中表现出悔意却无行为改变的现象。解决方案的关键在于揭示语言空间（language space）——包括语言、语用和文化属性——是决定对齐效果的核心结构因素：不同语言环境下，对齐干预可能产生方向相反的结果（如英语中降低集体病理而日语中加剧），且这种效应具有模型特异性，无法通过提示工程（prompt-level interventions）克服。研究指出，对齐并非普适安全机制，而是受制于风险同质化（risk homeostasis）和医源性损害（iatrogenesis）的复杂系统行为。
链接: https://arxiv.org/abs/2603.04904
作者: Hiroki Fukui
机构: Kyoto University (京都大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 89 pages, 4 figures, 4 supplementary figures, 12 supplementary tables; preprint
Abstract:In perpetrator treatment, a recurring observation is the dissociation between insight and action: offenders articulate remorse yet behavioral change does not follow. We report four preregistered studies (1,584 multi-agent simulations across 16 languages and three model families) demonstrating that alignment interventions in large language models produce a structurally analogous phenomenon: surface safety that masks or generates collective pathology and internal dissociation. In Study 1 (N = 150), increasing alignment-instructed agents reduced collective pathology in English (g = -1.844, p .0001) but amplified it in Japanese (g = +0.771, p = .038)–a directional reversal we term “alignment backfire.” Study 2 (N = 1,174) extended to 16 languages: alignment-induced dissociation was near-universal (15/16 languages; beta = 0.0667, p .0001), while collective pathology bifurcated along cultural-linguistic lines (interaction beta = 0.0684, p = .0003), correlating with Power Distance Index (r = 0.474, p = .064). Study 3 (N = 180) tested individuation as countermeasure; individuated agents became the primary source of both pathology and dissociation (DI = +1.120) with conformity above 84%–demonstrating iatrogenesis. Study 4 (N = 80) validated patterns across Llama 3.3 70B, GPT-4o-mini, and Qwen3-Next-80B-A3B, confirming English safety is model-general while Japanese backfire is model-specific. These findings reframe alignment as a behavioral intervention subject to risk homeostasis and iatrogenesis. Language space–the linguistic, pragmatic, and cultural properties inherited from training data–structurally determines alignment outcomes. Safety validated in English does not transfer to other languages, and prompt-level interventions cannot override language-space-level constraints.
[NLP-53] Can LLM s Capture Expert Uncertainty? A Comparative Analysis of Value Alignment in Ethnographic Qualitative Research
【速读】： 该论文旨在解决生成式 AI（Generative AI）在处理具有高度主观性和模糊性的定性价值分析任务中的表现问题，特别是如何准确识别长篇访谈中体现的人类核心价值观（基于Schwartz基本价值观理论框架）。其解决方案的关键在于系统评估大语言模型（LLMs）在识别受访者表达的前三个价值观方面的性能，包括与专家标注的对比分析、对不确定性模式的考察以及集成方法的应用。结果表明，尽管LLMs在集合匹配指标（如F1和Jaccard）上接近人类专家水平，但在精确排序方面存在不足；同时，多数模型虽能复现专家的价值分布，但其不确定性结构与专家不一致，提示需进一步优化模型对价值偏见的控制与解释能力。
链接: https://arxiv.org/abs/2603.04897
作者: Arina Kostina,Marios Dikaiakos,Alejandro Porcel,Tassos Stassopoulos
机构: University of Cyprus(塞浦路斯大学); Trinetra Investment Management LLP(Trinetra投资管理有限责任公司); University of Cambridge(剑桥大学)
类目: Computation and Language (cs.CL)
备注: Accepted for a poster session at this http URL @MIT 2026
Abstract:Qualitative analysis of open-ended interviews plays a central role in ethnographic and economic research by uncovering individuals’ values, motivations, and culturally embedded financial behaviors. While large language models (LLMs) offer promising support for automating and enriching such interpretive work, their ability to produce nuanced, reliable interpretations under inherent task ambiguity remains unclear. In our work we evaluate LLMs on the task of identifying the top three human values expressed in long-form interviews based on the Schwartz Theory of Basic Values framework. We compare their outputs to expert annotations, analyzing both performance and uncertainty patterns relative to the experts. Results show that LLMs approach the human ceiling on set-based metrics (F1, Jaccard) but struggle to recover exact value rankings, as reflected in lower RBO scores. While the average Schwartz value distributions of most models closely match those of human analysts, their uncertainty structures across the Schwartz values diverge from expert uncertainty patterns. Among the evaluated models, Qwen performs closest to expert-level agreement and exhibits the strongest alignment with expert Schwartz value distributions. LLM ensemble methods yield consistent gains across metrics, with Majority Vote and Borda Count performing best. Notably, systematic overemphasis on certain Schwartz values, like Security, suggests both the potential of LLMs to provide complementary perspectives and the need to further investigate model-induced value biases. Overall, our findings highlight both the promise and the limitations of LLMs as collaborators in inherently ambiguous qualitative value analysis.
[NLP-54] Free Lunch for Pass@k? Low Cost Diverse Sampling for Diffusion Language Models
【速读】： 该论文旨在解决扩散语言模型（Diffusion Language Models）在文本生成过程中存在的冗余问题，即独立采样时频繁收敛至相似的输出模式，导致生成多样性不足，从而影响复杂推理任务（如代码生成和数学问题求解）中对解空间的有效探索。解决方案的关键在于提出一种无需重新训练、计算开销极低的干预机制：在批量采样过程中，依次修改中间样本，使每个新样本在特征空间上被排斥于先前样本，主动惩罚冗余性，从而确保每条样本为批次提供独特视角。该方法显著提升了生成多样性与Pass@k指标，在HumanEval和GSM8K基准测试中验证了其有效性。
链接: https://arxiv.org/abs/2603.04893
作者: Sean Lamont,Christian Walder,Paul Montague,Amir Dezfouli,Michael Norrish
机构: Australian National University (澳大利亚国立大学); Defence Science and Technology Group (国防科学技术集团); Google DeepMind (谷歌DeepMind); BIMLOGIQ
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Diverse outputs in text generation are necessary for effective exploration in complex reasoning tasks, such as code generation and mathematical problem solving. Such Pass@ k problems benefit from distinct candidates covering the solution space. However, traditional sampling approaches often waste computational resources on repetitive failure modes. While Diffusion Language Models have emerged as a competitive alternative to the prevailing Autoregressive paradigm, they remain susceptible to this redundancy, with independent samples frequently collapsing into similar modes. To address this, we propose a training free, low cost intervention to enhance generative diversity in Diffusion Language Models. Our approach modifies intermediate samples in a batch sequentially, where each sample is repelled from the feature space of previous samples, actively penalising redundancy. Unlike prior methods that require retraining or beam search, our strategy incurs negligible computational overhead, while ensuring that each sample contributes a unique perspective to the batch. We evaluate our method on the HumanEval and GSM8K benchmarks using the LLaDA-8B-Instruct model. Our results demonstrate significantly improved diversity and Pass@ k performance across various temperature settings. As a simple modification to the sampling process, our method offers an immediate, low-cost improvement for current and future Diffusion Language Models in tasks that benefit from diverse solution search. We make our code available at this https URL.
[NLP-55] FireBench: Evaluating Instruction Following in Enterprise and API-Driven LLM Applications
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）在企业级和API驱动场景中指令遵循能力评估不足的问题。现有基准测试主要聚焦于对话助手类任务中的自然语言生成约束，难以反映企业用户对输出格式、内容限制及流程规范的严格要求。为此，作者提出FireBench，一个基于真实企业与API使用模式构建的LLM指令遵循评估基准，涵盖信息抽取、客户服务和编码代理等多样化应用场景，包含超过2400个样本，系统评估了6个核心能力维度。其关键创新在于将评估体系从通用对话场景迁移至企业实际工作流需求，并通过开源该基准促进模型适配性评估与开发者性能诊断。
链接: https://arxiv.org/abs/2603.04857
作者: Yunfan Zhang,Yijie Bei,Jetashree Ravi,Pawel Garbacki
机构: Columbia University (哥伦比亚大学); Fireworks AI
类目: Computation and Language (cs.CL); Software Engineering (cs.SE)
备注:
Abstract:Instruction following is critical for LLMs deployed in enterprise and API-driven settings, where strict adherence to output formats, content constraints, and procedural requirements is essential for enabling reliable LLM-assisted workflows. However, existing instruction following benchmarks predominantly evaluate natural language generation constraints that reflect the needs of chat assistants rather than enterprise users. To bridge this gap, we introduce FireBench, an LLM instruction following benchmark grounded in real-world enterprise and API usage patterns. FireBench evaluates six core capability dimensions across diverse applications including information extraction, customer support, and coding agents, comprising over 2,400 samples. We evaluate 11 LLMs and present key findings on their instruction following behavior in enterprise scenarios. We open-source FireBench at this http URL to help users assess model suitability, support model developers in diagnosing performance, and invite community contributions.
[NLP-56] HACHIMI: Scalable and Controllable Student Persona Generation via Orchestrated Agents ACL2026
【速读】： 该论文旨在解决当前教育大语言模型（Educational Large Language Models, ELMs）中学生画像（Student Personas, SPs）生成缺乏理论一致性与分布可控性的问题，即现有方法多依赖随意提示（ad-hoc prompting）或手工设计的个体特征，难以保证教育理论基础和目标人群分布的准确性。其解决方案的关键在于提出一种名为HACHIMI的多智能体“提议-验证-修订”框架，实现理论对齐且配额可控的学生画像生成（Theory-Aligned and Distribution-Controllable Persona Generation, TAD-PG）：通过将每个画像分解为基于教育理论的结构化模板（theory-anchored educational schema），利用神经符号验证器（neuro-symbolic validator）强制执行发展心理学约束，并结合分层采样与语义去重策略减少模式崩溃（mode collapse）。最终构建了包含100万条覆盖小学至高中阶段的合成学生画像数据集（HACHIMI-1M），在内部评估中展现出高结构有效性、精确的配额控制与丰富多样性，在外部评估中亦验证了学生代理在数学能力与成长型思维等维度上与真实人类高度一致，揭示出不同社会心理构念存在差异化的拟合梯度。
链接: https://arxiv.org/abs/2603.04855
作者: Yilin Jiang,Fei Tan,Xuanyu Yin,Jing Leng,Aimin Zhou
机构: East China Normal University (华东师范大学); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州）); Shanghai Innovation Institute (上海创新研究院)
类目: Computation and Language (cs.CL)
备注: 46 pages, 7 figures, submitted to ACL2026
Abstract:Student Personas (SPs) are emerging as infrastructure for educational LLMs, yet prior work often relies on ad-hoc prompting or hand-crafted profiles with limited control over educational theory and population distributions. We formalize this as Theory-Aligned and Distribution-Controllable Persona Generation (TAD-PG) and introduce HACHIMI, a multi-agent Propose-Validate-Revise framework that generates theory-aligned, quota-controlled personas. HACHIMI factorizes each persona into a theory-anchored educational schema, enforces developmental and psychological constraints via a neuro-symbolic validator, and combines stratified sampling with semantic deduplication to reduce mode collapse. The resulting HACHIMI-1M corpus comprises 1 million personas for Grades 1-12. Intrinsic evaluation shows near-perfect schema validity, accurate quotas, and substantial diversity, while external evaluation instantiates personas as student agents answering CEPS and PISA 2022 surveys; across 16 cohorts, math and curiosity/growth constructs align strongly between humans and agents, whereas classroom-climate and well-being constructs are only moderately aligned, revealing a fidelity gradient. All personas are generated with Qwen2.5-72B, and HACHIMI provides a standardized synthetic student population for group-level benchmarking and social-science simulations. Resources available at this https URL
[NLP-57] SinhaLegal: A Benchmark Corpus for Information Extraction and Analysis in Sinhala Legislative Texts EACL2026
【速读】： 该论文旨在解决斯里兰卡语（Sinhala）法律文本在自然语言处理（Natural Language Processing, NLP）研究中资源匮乏的问题，从而支持诸如文本摘要、信息抽取与分析等下游任务。解决方案的关键在于构建了一个高质量、结构化的斯里兰卡语立法文本语料库（SinhaLegal），其包含约200万词的1,206份法律文件，涵盖1981至2014年间发布的1,065部法案和2010至2014年间提交的141份议案，并通过Google Document AI进行光学字符识别（Optical Character Recognition, OCR）提取，辅以大量后处理与人工校对确保文本质量，同时配套元数据文件，为后续NLP模型训练与评估提供可靠基础。
链接: https://arxiv.org/abs/2603.04854
作者: Minduli Lasandi,Nevidu Jayatilleke
机构: Informatics Institute of Technology, Sri Lanka; Department of Computer Science Engineering, University of Moratuwa, Sri Lanka
类目: Computation and Language (cs.CL)
备注: 18 pages, 8 figures, 18 tables, Accepted paper at the 2nd workshop on Language Models for Low-Resource Languages (LoResLM 2026) @ EACL 2026
Abstract:SinhaLegal introduces a Sinhala legislative text corpus containing approximately 2 million words across 1,206 legal documents. The dataset includes two types of legal documents: 1,065 Acts dated from 1981 to 2014 and 141 Bills from 2010 to 2014, which were systematically collected from official sources. The texts were extracted using OCR with Google Document AI, followed by extensive post-processing and manual cleaning to ensure high-quality, machine-readable content, along with dedicated metadata files for each document. A comprehensive evaluation was conducted, including corpus statistics, lexical diversity, word frequency analysis, named entity recognition, and topic modelling, demonstrating the structured and domain-specific nature of the corpus. Additionally, perplexity analysis using both large and small language models was performed to assess how effectively language models respond to domain-specific texts. The SinhaLegal corpus represents a vital resource designed to support NLP tasks such as summarisation, information extraction, and analysis, thereby bridging a critical gap in Sinhala legal research.
[NLP-58] Why Is RLHF Alignment Shallow? A Gradient Analysis
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中安全对齐（safety alignment）为何往往局限于早期 token 的问题。作者通过理论分析指出，基于梯度的对齐方法本质上仅在决定有害性（harm）的关键位置产生梯度信号，而在有害性已确定的位置（即“有害性时域”之外）梯度消失，这导致标准对齐目标无法实现深层对齐（deep alignment）。解决方案的关键在于引入“有害信息量”（harm information, $ I_t $），用于量化每个位置对整体有害性的贡献，并证明均衡状态下的 KL 散度与该信息量一致；进一步提出基于恢复惩罚（recovery penalty）的目标函数，在所有位置均生成梯度信号，从而为数据增强等经验性方法提供了理论支持。
链接: https://arxiv.org/abs/2603.04851
作者: Robin Young
机构: University of Cambridge (剑桥大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Why is safety alignment in LLMs shallow? We prove that gradient-based alignment inherently concentrates on positions where harm is decided and vanishes beyond. Using a martingale decomposition of sequence-level harm, we derive an exact characterization of alignment gradients. The gradient at position t equals the covariance between the conditional expected harm and the score function. This implies that positions beyond the harm horizon where the output’s harmfulness is already determined receive zero gradient signal during training. This explains empirical observations that KL divergence between aligned and base models concentrates on early tokens. Consequently, standard alignment objectives cannot produce deep alignment, regardless of optimization quality. We introduce the concept of harm information I_t , which quantifies each position’s influence on harm, and prove that equilibrium KL divergence tracks this quantity. Finally, we derive an objective based on recovery penalties that creates gradient signal at all positions, providing theoretical grounding for empirically successful data augmentation techniques.
[NLP-59] From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）预训练数据检测问题，以应对版权争议和基准测试污染等关键挑战。现有方法主要依赖似然统计特征或微调前后启发式信号，但前者易受语料中词频偏差影响，后者则高度依赖微调数据与目标数据的相似性，导致泛化能力受限。本文从优化视角出发，发现训练过程中样本由陌生到熟悉的变化在梯度行为上呈现系统性差异：熟悉样本具有更小的参数更新幅度、不同的更新位置分布以及更显著激活的神经元。基于此洞察，作者提出GDS（Gradient Deviation Score）方法，通过探测目标样本的梯度偏差分数实现预训练数据成员推理。其核心创新在于利用前馈网络（Feed-Forward Network, FFN）和注意力模块中参数更新的幅度、位置及集中度构建梯度特征表示，并结合轻量级分类器进行二分类判断，从而在多个公开数据集上实现优于现有基线的性能，且具备显著更强的跨数据集迁移能力。
链接: https://arxiv.org/abs/2603.04828
作者: Ruiqi Zhang,Lingxiang Wang,Hainan Zhang,Zhiming Zheng,Yanyan Lan
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Pre-training data detection for LLMs is essential for addressing copyright concerns and mitigating benchmark contamination. Existing methods mainly focus on the likelihood-based statistical features or heuristic signals before and after fine-tuning, but the former are susceptible to word frequency bias in corpora, and the latter strongly depend on the similarity of fine-tuning data. From an optimization perspective, we observe that during training, samples transition from unfamiliar to familiar in a manner reflected by systematic differences in gradient behavior. Familiar samples exhibit smaller update magnitudes, distinct update locations in model components, and more sharply activated neurons. Based on this insight, we propose GDS, a method that identifies pre-training data by probing Gradient Deviation Scores of target samples. Specifically, we first represent each sample using gradient profiles that capture the magnitude, location, and concentration of parameter updates across FFN and Attention modules, revealing consistent distinctions between member and non-member data. These features are then fed into a lightweight classifier to perform binary membership inference. Experiments on five public datasets show that GDS achieves state-of-the-art performance with significantly improved cross-dataset transferability over strong baselines. Further interpretability analyse show gradient feature distribution differences, enabling practical and scalable pre-training data detection.
[NLP-60] Autoscoring Anticlimax: A Meta-analytic Understanding of AIs Short-answer Shortcomings and Wording Weaknesses
【速读】： 该论文旨在解决生成式 AI（Generative AI）在自动短答案评分（automated short-answer scoring）任务中性能不足的问题，特别是其与人类专家评分一致性较低的现状。研究通过元分析方法整合890项实证结果，采用混合效应元回归模型对QWK（Quadratic Weighted Kappa）效应量进行建模，发现人类评分难度对大语言模型（LLM）表现无显著统计影响，甚至某些人类认为最简单的评分任务对LLM而言反而最难；关键发现指出，仅解码器架构（decoder-only）平均比编码器架构（encoder-based）在评分一致性上低0.37，且词表大小存在边际收益递减现象，暗示自回归训练机制本身存在统计缺陷。因此，解决方案的核心在于系统设计应更主动地识别并规避自回归模型的已知局限性，并关注词法处理和语义表达中的偏差问题，以提升教育场景下高风险应用的公平性和可靠性。
链接: https://arxiv.org/abs/2603.04820
作者: Michael Hardy
机构: Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Automated short-answer scoring lags other LLM applications. We meta-analyze 890 culminating results across a systematic review of LLM short-answer scoring studies, modeling the traditional effect size of Quadratic Weighted Kappa (QWK) with mixed effects metaregression. We quantitatively illustrate that that the level of difficulty for human experts to perform the task of scoring written work of children has no observed statistical effect on LLM performance. Particularly, we show that some scoring tasks measured as the easiest by human scorers were the hardest for LLMs. Whether by poor implementation by thoughtful researchers or patterns traceable to autoregressive training, on average decoder-only architectures underperform encoders by 0.37–a substantial difference in agreement with humans. Additionally, we measure the contributions of various aspects of LLM technology on successful scoring such as tokenizer vocabulary size, which exhibits diminishing returns–potentially due to undertrained tokens. Findings argue for systems design which better anticipates known statistical shortcomings of autoregressive models. Finally, we provide additional experiments to illustrate wording and tokenization sensitivity and bias elicitation in high-stakes education contexts, where LLMs demonstrate racial discrimination. Code and data for this study are available.
[NLP-61] Beyond the Context Window: A Cost-Performance Analysis of Fact-Based Memory vs. Long-Context LLM s for Persistent Agents
【速读】： 该论文旨在解决持久性对话AI系统在处理长程记忆时的架构选择问题：是将完整的对话历史传递给具备长上下文能力的大语言模型（Large Language Model, LLM），还是采用基于事实的记忆系统进行结构化信息提取与检索。其核心解决方案在于构建一个基于Mem0框架的事实驱动型记忆系统，并通过在LongMemEval、LoCoMo和PersonaMemv2三个以记忆为核心的基准测试中对比该系统与长上下文LLM推理的准确性与累计API成本，量化二者在不同场景下的性能差异。关键发现为：长上下文LLM在通用事实召回上表现更优，而记忆系统在依赖稳定属性的个性一致性任务中更具竞争力；同时，成本建模表明，当上下文长度达到10万token时，记忆系统的单位交互成本在约10轮后低于长上下文方案，且随着上下文增长，这一拐点提前，从而为生产环境中两种架构的选择提供了明确的权衡依据。
链接: https://arxiv.org/abs/2603.04814
作者: Natchanon Pollertlam,Witchayut Kornsuwannawit
机构: Bricks Technology (Bricks Technology)
类目: Computation and Language (cs.CL)
备注: 15 pages, 1 figure
Abstract:Persistent conversational AI systems face a choice between passing full conversation histories to a long-context large language model (LLM) and maintaining a dedicated memory system that extracts and retrieves structured facts. We compare a fact-based memory system built on the Mem0 framework against long-context LLM inference on three memory-centric benchmarks - LongMemEval, LoCoMo, and PersonaMemv2 - and evaluate both architectures on accuracy and cumulative API cost. Long-context GPT-5-mini achieves higher factual recall on LongMemEval and LoCoMo, while the memory system is competitive on PersonaMemv2, where persona consistency depends on stable, factual attributes suited to flat-typed extraction. We construct a cost model that incorporates prompt caching and show that the two architectures have structurally different cost profiles: long-context inference incurs a per-turn charge that grows with context length even under caching, while the memory system’s per-turn read cost remains roughly fixed after a one-time write phase. At a context length of 100k tokens, the memory system becomes cheaper after approximately ten interaction turns, with the break-even point decreasing as context length grows. These results characterize the accuracy-cost trade-off between the two approaches and provide a concrete criterion for selecting between them in production deployments.
[NLP-62] Attentions Gravitational Field:A Power-Law Interpretation of Positional Correlation
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）中位置关系建模与编码机制的内在原理不清问题，特别是现有位置编码方法在语义嵌入与位置信息耦合时导致的性能瓶颈。其解决方案的关键在于提出注意力引力场（Attention Gravitational Field, AGF）概念，通过将位置编码从语义嵌入中解耦，优化模型架构，并在理论上揭示AGF与学习和稳定性曲线的一致性及与牛顿万有引力定律的经验契合性，从而为注意力机制提供新的解释框架并提升模型准确性。
链接: https://arxiv.org/abs/2603.04805
作者: Edward Zhang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper explores the underlying principles of positional relationships and encodings within Large Language Models (LLMs) and introduces the concept of the Attention Gravitational Field (AGF). By decoupling positional encodings from semantic embeddings, we optimize the model architecture and achieve superior accuracy compared to prevailing encoding methods. Furthermore, we provide an in-depth analysis of AGF, demonstrating its intrinsic consistency with learning and stability curves, as well as its empirical alignment with Newton’s Law of Universal Gravitation. By offering a rigorous theoretical exploration of these phenomena, this work represents a significant step toward interpreting the Attention mechanism and unlocks new possibilities for future research in model optimization and interpretability.
[NLP-63] Beyond Linear LLM Invocation: An Efficient and Effective Semantic Filter Paradigm
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在执行语义过滤（semantic filter）操作时因逐元组调用LLM而导致的高延迟和高额Token消耗问题，该过程通常需要对表进行完整的线性扫描。现有优化方法仍无法突破线性LLM调用的瓶颈。解决方案的关键在于提出Clustering-Sampling-Voting (CSV)框架，其核心机制包括：将元组嵌入并聚类为语义簇，从中采样少量样本交由LLM评估，并通过两种投票策略——UniVote（均匀聚合）与SimVote（基于语义相似度加权聚合）——推断簇级标签；同时引入模糊簇的再聚类机制以增强跨数据集的鲁棒性。该方法实现了LLM调用次数的亚线性降低（相比当前最优方法减少1.28–355倍），且保持了与基准相当的准确率（Accuracy）和F1分数。
链接: https://arxiv.org/abs/2603.04799
作者: Nan Hou,Kangfei Zhao,Jiadong Xie,Jeffrey Xu Yu
机构: The Chinese University of Hong Kong (香港中文大学); Beijing Institute of Technology (北京理工大学); HKUST (Guangzhou) (香港科技大学（广州)
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are increasingly used for semantic query processing over large corpora. A set of semantic operators derived from relational algebra has been proposed to provide a unified interface for expressing such queries, among which the semantic filter operator serves as a cornerstone. Given a table T with a natural language predicate e, for each tuple in the relation, the execution of a semantic filter proceeds by constructing an input prompt that combines the predicate e with its content, querying the LLM, and obtaining the binary decision. However, this tuple-by-tuple evaluation necessitates a complete linear scan of the table, incurring prohibitive latency and token costs. Although recent work has attempted to optimize semantic filtering, it still does not break the linear LLM invocation barriers. To address this, we propose Clustering-Sampling-Voting (CSV), a new framework that reduces LLM invocations to sublinear complexity while providing error guarantees. CSV embeds tuples into semantic clusters, samples a small subset for LLM evaluation, and infers cluster-level labels via two proposed voting strategies: UniVote, which aggregates labels uniformly, and SimVote, which weights votes by semantic similarity. Moreover, CSV triggers re-clustering on ambiguous clusters to ensure robustness across diverse datasets. The results conducted on real-world datasets demonstrate that CSV reduces the number of LLM calls by 1.28-355x compared to the state-of-the-art approaches, while maintaining comparable effectiveness in terms of Accuracy and F1 score.
[NLP-64] Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多轮交互中因“情境惯性”（Contextual Inertia）导致的性能下降问题，即模型在信息逐步披露或需更新约束时，难以整合新信息，反而固守先前错误推理路径。解决方案的关键在于提出一种通用训练方法——基于单轮锚点的强化学习（Reinforcement Learning with Single-Turn Anchors, RLSTA），该方法利用模型在单轮任务中的强推理能力作为稳定内部锚点，提供奖励信号以引导多轮响应对齐这些锚点，从而打破情境惯性并实现基于最新信息的自我校准。
链接: https://arxiv.org/abs/2603.04783
作者: Xingwu Chen,Zhanqiu Zhang,Yiwen Guo,Difan Zou
机构: School of Computing and Data Science, The University of Hong Kong; LIGHTSPEED; Independent Researcher
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:While LLMs demonstrate strong reasoning capabilities when provided with full information in a single turn, they exhibit substantial vulnerability in multi-turn interactions. Specifically, when information is revealed incrementally or requires updates, models frequently fail to integrate new constraints, leading to a collapse in performance compared to their single-turn baselines. We term the root cause as \emphContextual Inertia: a phenomenon where models rigidly adhere to previous reasoning traces. Even when users explicitly provide corrections or new data in later turns, the model ignores them, preferring to maintain consistency with its previous (incorrect) reasoning path. To address this, we introduce \textbfReinforcement \textbfLearning with \textbfSingle-\textbfTurn \textbfAnchors (\textbfRLSTA), a generalizable training approach designed to stabilize multi-turn interaction across diverse scenarios and domains. RLSTA leverages the model’s superior single-turn capabilities as stable internal anchors to provide reward signals. By aligning multi-turn responses with these anchors, RLSTA empowers models to break contextual inertia and self-calibrate their reasoning based on the latest information. Experiments show that RLSTA significantly outperforms standard fine-tuning and abstention-based methods. Notably, our method exhibits strong cross-domain generalization (e.g., math to code) and proves effective even without external verifiers, highlighting its potential for general-domain applications.
[NLP-65] Privacy-Aware Camera 2.0 Technical Report
【速读】： 该论文旨在解决智能感知技术在敏感环境（如更衣室和卫生间）中部署时面临的隐私-安全悖论问题，即如何在保障用户隐私的同时维持视觉系统的语义理解能力。现有隐私保护方法（如物理模糊、加密和混淆）往往损害语义信息或无法提供数学上可证明的不可逆性；而此前的Privacy Camera 1.0虽能从源头消除视觉数据泄露，但仅输出文本判断，导致纠纷场景下缺乏证据支持。解决方案的关键在于提出一种基于AI Flow范式与协同边缘-云架构的隐私感知新框架：在边缘端部署视觉脱敏模块，利用信息瓶颈原理通过非线性映射与随机噪声注入，将原始图像实时转换为抽象特征向量，确保身份敏感信息被剥离且原图不可数学重构；同时，这些抽象表示被传输至云端，借助“动态轮廓”视觉语言实现行为识别与语义重建，在不暴露原始图像的前提下完成可解释的视觉参考，从而在感知精度与隐私保护之间达成关键平衡。
链接: https://arxiv.org/abs/2603.04775
作者: Huan Song,Shuyu Tian,Ting Long,Jiang Liu,Cheng Yuan,Zhenyu Jia,Jiawei Shao,Xuelong Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:With the increasing deployment of intelligent sensing technologies in highly sensitive environments such as restrooms and locker rooms, visual surveillance systems face a profound privacy-security paradox. Existing privacy-preserving approaches, including physical desensitization, encryption, and obfuscation, often compromise semantic understanding or fail to ensure mathematically provable irreversibility. Although Privacy Camera 1.0 eliminated visual data at the source to prevent leakage, it provided only textual judgments, leading to evidentiary blind spots in disputes. To address these limitations, this paper proposes a novel privacy-preserving perception framework based on the AI Flow paradigm and a collaborative edge-cloud architecture. By deploying a visual desensitizer at the edge, raw images are transformed in real time into abstract feature vectors through nonlinear mapping and stochastic noise injection under the Information Bottleneck principle, ensuring identity-sensitive information is stripped and original images are mathematically unreconstructable. The abstract representations are transmitted to the cloud for behavior recognition and semantic reconstruction via a “dynamic contour” visual language, achieving a critical balance between perception and privacy while enabling illustrative visual reference without exposing raw images.
[NLP-66] SEmbed: Unlocking Task Scaling in Universal Multimodal Embeddings
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在转化为通用嵌入模型（Universal Embedding Models）过程中因任务冲突（Task Conflict）导致的性能瓶颈问题。其解决方案的关键在于提出TSEmbed框架，通过将专家混合（Mixture-of-Experts, MoE）与低秩适应（Low-Rank Adaptation, LoRA）相结合，显式解耦不同任务的目标函数；同时引入专家感知负采样（Expert-Aware Negative Sampling, EANS），利用专家路由分布作为语义相似性的内在代理，动态优先选择与查询共享专家激活模式的信息性难负样本，从而增强模型判别能力并优化嵌入边界。此外，采用两阶段学习范式确保专家先专业化再优化表示，提升了训练稳定性。
链接: https://arxiv.org/abs/2603.04772
作者: Yebo Wu,Feng Liu,Ziwei Xie,Zhiyuan Liu,Changwang Zhang,Jun Wang,Li Li
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Despite the exceptional reasoning capabilities of Multimodal Large Language Models (MLLMs), their adaptation into universal embedding models is significantly impeded by task conflict. To address this, we propose TSEmbed, a universal multimodal embedding framework that synergizes Mixture-of-Experts (MoE) with Low-Rank Adaptation (LoRA) to explicitly disentangle conflicting task objectives. Moreover, we introduce Expert-Aware Negative Sampling (EANS), a novel strategy that leverages expert routing distributions as an intrinsic proxy for semantic similarity. By dynamically prioritizing informative hard negatives that share expert activation patterns with the query, EANS effectively sharpens the model’s discriminative power and refines embedding boundaries. To ensure training stability, we further devise a two-stage learning paradigm that solidifies expert specialization before optimizing representations via EANS. TSEmbed achieves state-of-the-art performance on both the Massive Multimodal Embedding Benchmark (MMEB) and real-world industrial production datasets, laying a foundation for task-level scaling in universal multimodal embeddings.
[NLP-67] Stacked from One: Multi-Scale Self-Injection for Context Window Extension
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）受限于上下文窗口长度（context window）的问题，这一限制严重制约了其在需要处理长序列任务中的广泛应用。为突破此瓶颈，作者提出了一种名为SharedLLM的新框架，其核心创新在于采用多粒度上下文压缩（multi-grained context compression）与查询感知信息获取（query-aware information acquisition）机制：该框架由两个堆叠的短上下文LLM组成——下层模型作为压缩器将长输入编码为紧凑的多粒度表示，上层模型则作为解码器进行上下文感知处理；关键设计是信息传递仅发生在底层层间，避免冗余前向传播和交叉注意力计算，从而实现高效推理；该架构通过“自注入”（self-injection）机制共享底层LLM参数，显著降低内存占用并提升推理速度（相比流式处理快2倍，相比编码器-解码器结构快3倍），同时在8K训练序列下即可泛化至128K以上输入，性能优于或接近强基线模型。
链接: https://arxiv.org/abs/2603.04759
作者: Wei Han,Pan Zhou,Shuicheng Yan
机构: Singapore University of Technology and Design (SUTD); Singapore Management University (SMU); National University of Singapore (NUS)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The limited context window of contemporary large language models (LLMs) remains a primary bottleneck for their broader application across diverse domains. Although continual pre-training on long-context data offers a straightforward solution, it incurs prohibitive data acquisition and computational costs. To address this challenge, we propose~\modelname, a novel framework based on multi-grained context compression and query-aware information acquisition. SharedLLM comprises two stacked short-context LLMs: a lower model serving as a compressor and an upper model acting as a decoder. The lower model compresses long inputs into compact, multi-grained representations, which are then forwarded to the upper model for context-aware processing. To maximize efficiency, this information transfer occurs exclusively at the lowest layers, bypassing lengthy forward passes and redundant cross-attention operations. This entire process, wherein the upper and lower models are derived from the same underlying LLM layers, is termed~\textitself-injection. To support this architecture, a specialized tree-based data structure enables the efficient encoding and query-aware retrieval of contextual information. Despite being trained on sequences of only 8K tokens, \modelname~effectively generalizes to inputs exceeding 128K tokens. Across a comprehensive suite of long-context modeling and understanding benchmarks, \modelname~achieves performance superior or comparable to strong baselines, striking an optimal balance between efficiency and accuracy. Furthermore, these design choices allow \modelname~to substantially reduce the memory footprint and yield notable inference speedups ( 2\times over streaming and 3\times over encoder-decoder architectures).
[NLP-68] HiMAP-Travel: Hierarchical Multi-Agent Planning for Long-Horizon Constrained Travel
【速读】： 该论文旨在解决顺序大语言模型（LLM）代理在长周期规划任务中难以满足硬性约束（如预算限制和多样性要求）的问题，尤其是在上下文持续增长导致代理逐渐偏离全局约束的情况下。其核心解决方案是提出一种分层多智能体框架HiMAP-Travel，关键在于三个机制：一是事务监控器（transactional monitor），用于跨并行代理强制执行预算和唯一性约束；二是讨价还价协议（bargaining protocol），允许代理拒绝不可行的子目标并触发重新规划；三是基于GRPO训练的单一策略网络，通过角色条件控制所有智能体。该方法在TravelPlanner数据集上实现了52.78%验证集最终通过率（FPR），显著优于Sequential DeepTravel、ATLAS和MTP基线，并在FlexTravelBench多轮场景中保持高效率与低延迟。
链接: https://arxiv.org/abs/2603.04750
作者: TheViet Bui,Wenjun Li,Yong Liu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 33 pages, v1
Abstract:Sequential LLM agents fail on long-horizon planning with hard constraints like budgets and diversity requirements. As planning progresses and context grows, these agents drift from global constraints. We propose HiMAP-Travel, a hierarchical multi-agent framework that splits planning into strategic coordination and parallel day-level execution. A Coordinator allocates resources across days, while Day Executors plan independently in parallel. Three key mechanisms enable this: a transactional monitor enforcing budget and uniqueness constraints across parallel agents, a bargaining protocol allowing agents to reject infeasible sub-goals and trigger re-planning, and a single policy trained with GRPO that powers all agents through role conditioning. On TravelPlanner, HiMAP-Travel with Qwen3-8B achieves 52.78% validation and 52.65% test Final Pass Rate (FPR). In a controlled comparison with identical model, training, and tools, it outperforms the sequential DeepTravel baseline by +8.67~pp. It also surpasses ATLAS by +17.65~pp and MTP by +10.0~pp. On FlexTravelBench multi-turn scenarios, it achieves 44.34% (2-turn) and 37.42% (3-turn) FPR while reducing latency 2.5x through parallelization.
[NLP-69] IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation
【速读】： 该论文旨在解决当前用于评估指令遵循能力的判别模型（judge models）可靠性不足的问题，其根源在于现有元评估基准在数据覆盖范围和配对评估范式上的局限性，这些局限导致评估结果与实际模型优化场景不一致。解决方案的关键在于提出 IF-RewardBench——一个全面的元评估基准，它涵盖多样化的指令类型和约束条件，并为每条指令构建包含多个响应之间成对偏好关系的偏好图（preference graph），从而支持列表式（listwise）评估范式，能够更真实地衡量判别模型对多个响应排序的能力，进而有效指导模型对齐。实验表明，该基准相较于现有方法与下游任务性能具有更强的正相关性，验证了其有效性。
链接: https://arxiv.org/abs/2603.04738
作者: Bosi Wen,Yilin Niu,Cunxiang Wang,Xiaoying Ling,Ying Zhang,Pei Ke,Hongning Wang,Minlie Huang
机构: Tsinghua University (清华大学); Zhipu AI; University of Electronic Science and Technology of China (电子科技大学)
类目: Computation and Language (cs.CL)
备注: 27 pages, 7 figures
Abstract:Instruction-following is a foundational capability of large language models (LLMs), with its improvement hinging on scalable and accurate feedback from judge models. However, the reliability of current judge models in instruction-following remains underexplored due to several deficiencies of existing meta-evaluation benchmarks, such as their insufficient data coverage and oversimplified pairwise evaluation paradigms that misalign with model optimization scenarios. To this end, we propose IF-RewardBench, a comprehensive meta-evaluation benchmark for instruction-following that covers diverse instruction and constraint types. For each instruction, we construct a preference graph containing all pairwise preferences among multiple responses based on instruction-following quality. This design enables a listwise evaluation paradigm that assesses the capabilities of judge models to rank multiple responses, which is essential in guiding model alignment. Extensive experiments on IF-RewardBench reveal significant deficiencies in current judge models and demonstrate that our benchmark achieves a stronger positive correlation with downstream task performance compared to existing benchmarks. Our codes and data are available at this https URL.
[NLP-70] Interactive Benchmarks
【速读】： 该论文旨在解决当前标准评测基准因饱和、主观性和泛化能力差而导致的评估可靠性下降问题。其核心观点是，评估模型主动获取信息的能力对于衡量模型智能至关重要。解决方案的关键在于提出“交互式评测（Interactive Benchmarks）”这一统一范式，通过在预算约束下评估模型在交互过程中推理能力的表现来实现更可靠、忠实的智能评估。该框架具体体现在两个场景中：一是交互式证明（Interactive Proofs），模型与裁判互动以推导逻辑和数学中的客观真理；二是交互式游戏（Interactive Games），模型在策略层面推理以最大化长期效用。实验证明，该方法能有效揭示模型在交互场景中的提升空间。
链接: https://arxiv.org/abs/2603.04737
作者: Baoqing Yue,Zihan Zhu,Yifan Zhang,Jichen Feng,Hufei Yang,Mengdi Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Project Page: this https URL
Abstract:Standard benchmarks have become increasingly unreliable due to saturation, subjectivity, and poor generalization. We argue that evaluating model’s ability to acquire information actively is important to assess model’s intelligence. We propose Interactive Benchmarks, a unified evaluation paradigm that assesses model’s reasoning ability in an interactive process under budget constraints. We instantiate this framework across two settings: Interactive Proofs, where models interact with a judge to deduce objective truths or answers in logic and mathematics; and Interactive Games, where models reason strategically to maximize long-horizon utilities. Our results show that interactive benchmarks provide a robust and faithful assessment of model intelligence, revealing that there is still substantial room to improve in interactive scenarios. Project page: this https URL
[NLP-71] Solving an Open Problem in Theoretical Physics using AI-Assisted Discovery
【速读】： 该论文旨在解决宇宙弦（cosmic strings）在引力辐射功率谱（power spectrum of gravitational radiation）计算中长期存在的数学难题，即如何对任意环形几何结构下的核心积分 $ I(N,\alpha) $ 获得精确的解析解。此前AI辅助研究仅能提供部分渐近解，而本文提出了一种神经符号系统（neuro-symbolic system），其关键在于融合了Gemini Deep Think大语言模型、系统性树搜索（Tree Search, TS）框架与自动化数值反馈机制，从而实现自主探索并识别出六种不同的解析方法，其中最优雅的一种通过将核函数展开为Gegenbauer多项式 $ C_l^{3/2} $ 来自然吸收被积函数的奇异性，最终获得适用于大 $ N $ 的渐近结果，该结果不仅与数值模拟高度一致，还与量子场论中的连续费曼参数化（Feynman parameterization）建立了理论联系。
链接: https://arxiv.org/abs/2603.04735
作者: Michael P. Brenner,Vincent Cohen-Addad,David Woodruff
机构: Google Research(谷歌研究); Harvard University (哈佛大学); Carnegie Mellon University (卡内基梅隆大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 22 pages, 3 figures
Abstract:This paper demonstrates that artificial intelligence can accelerate mathematical discovery by autonomously solving an open problem in theoretical physics. We present a neuro-symbolic system, combining the Gemini Deep Think large language model with a systematic Tree Search (TS) framework and automated numerical feedback, that successfully derived novel, exact analytical solutions for the power spectrum of gravitational radiation emitted by cosmic strings. Specifically, the agent evaluated the core integral I(N,\alpha) for arbitrary loop geometries, directly improving upon recent AI-assisted attempts \citeBCE+25 that only yielded partial asymptotic solutions. To substantiate our methodological claims regarding AI-accelerated discovery and to ensure transparency, we detail system prompts, search constraints, and intermittent feedback loops that guided the model. The agent identified a suite of 6 different analytical methods, the most elegant of which expands the kernel in Gegenbauer polynomials C_l^(3/2) to naturally absorb the integrand’s singularities. The methods lead to an asymptotic result for I(N,\alpha) at large N that both agrees with numerical results and also connects to the continuous Feynman parameterization of Quantum Field Theory. We detail both the algorithmic methodology that enabled this discovery and the resulting mathematical derivations.
[NLP-72] Model Medicine: A Clinical Framework for Understanding Diagnosing and Treating AI Models
【速读】： 该论文旨在解决当前AI系统日益复杂化背景下，缺乏系统性诊断与治疗框架的问题，即如何将AI模型视为具有内部结构、动态过程和可干预状态的“生物体”，从而实现从静态解释（如AI可解释性研究）向临床实践的跃迁。其解决方案的关键在于提出“模型医学”（Model Medicine）这一跨学科研究范式，通过构建五大核心贡献：一是建立涵盖基础模型科学、临床模型科学、模型公共卫生与模型架构医学的学科分类体系；二是提出基于实证数据的“四壳模型”（Four Shell Model v3.3），揭示模型行为由核心-外壳交互驱动的机制；三是开发神经MRI（Neural MRI）工具，映射五种医学影像模态至AI可解释性技术，实现对模型状态的可视化诊断；四是设计五层诊断框架以支持全面评估；五是引入模型气质指数（Model Temperament Index）、模型症状学（Model Semiology）及M-CARE标准化病例报告体系，形成可操作的临床流程。整体方案以生物学类比为指导思想，首次系统性地将诊断、定位、预测与干预整合进AI模型管理全流程。
链接: https://arxiv.org/abs/2603.04722
作者: Jihoon Jeong
机构: Daegu Gyeongbuk Institute of Science and Technology (DGIST); ModuLabs; OpenClaw; Moltbook; Agora-12; Google (谷歌); Anthropic; Meta; Stability.AI; Character.ai; Claude; Gemini; NVIDIA
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 56 pages, 7 figures. Project page: this https URL
Abstract:Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models – like biological organisms – have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions – Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora-12 program, explaining how model behavior emerges from Core–Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open-source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, comparison, localization, and predictive capability; (4) a five-layer diagnostic framework for comprehensive model assessment; and (5) clinical model sciences including the Model Temperament Index for behavioral profiling, Model Semiology for symptom description, and M-CARE for standardized case reporting. We additionally propose the Layered Core Hypothesis – a biologically-inspired three-layer parameter architecture – and a therapeutic framework connecting diagnosis to treatment.
[NLP-73] AI-Assisted Moot Courts: Simulating Justice-Specific Questioning in Oral Arguments
【速读】： 该论文旨在解决如何利用人工智能（AI）有效模拟美国最高法院口头辩论中法官提问行为，以提升法学院和执业律师在模拟法庭（moot court）训练中的准备质量。其核心问题在于：现有AI模型能否生成既真实又具教学价值的法律质询，从而替代或增强传统人工模拟训练。解决方案的关键在于构建一个双层评估框架，通过互补的代理指标分别衡量模拟问题的“现实性”与“教学实用性”，并在此基础上开发基于提示（prompt-based）和智能体（agentic）两种模式的口头辩论模拟器。实证结果显示，尽管模型生成的问题在感知真实性和法律议题覆盖度上表现良好，但仍存在问题类型单一和过度迎合（sycophancy）等显著缺陷，而这些不足仅能在该多维评估体系下被识别。
链接: https://arxiv.org/abs/2603.04718
作者: Kylie Zhang,Nimra Nadeem,Lucia Zheng,Dominik Stammbach,Peter Henderson
机构: Princeton University (普林斯顿大学); Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at CS Law 2026
Abstract:In oral arguments, judges probe attorneys with questions about the factual record, legal claims, and the strength of their arguments. To prepare for this questioning, both law schools and practicing attorneys rely on moot courts: practice simulations of appellate hearings. Leveraging a dataset of U.S. Supreme Court oral argument transcripts, we examine whether AI models can effectively simulate justice-specific questioning for moot court-style training. Evaluating oral argument simulation is challenging because there is no single correct question for any given turn. Instead, effective questioning should reflect a combination of desirable qualities, such as anticipating substantive legal issues, detecting logical weaknesses, and maintaining an appropriately adversarial tone. We introduce a two-layer evaluation framework that assesses both the realism and pedagogical usefulness of simulated questions using complementary proxy metrics. We construct and evaluate both prompt-based and agentic oral argument simulators. We find that simulated questions are often perceived as realistic by human annotators and achieve high recall of ground truth substantive legal issues. However, models still face substantial shortcomings, including low diversity in question types and sycophancy. Importantly, these shortcomings would remain undetected under naive evaluation approaches.
[NLP-74] Detection of Illicit Content on Online Marketplaces using Large Language Models
【速读】： 该论文旨在解决在线市场平台中非法内容（如毒品交易、假货销售和网络犯罪）检测与分类的难题，传统内容审核方法在面对多语言文本、动态伪装技术和语义复杂性时存在显著局限。其解决方案的关键在于利用大语言模型（Large Language Models, LLMs），特别是Meta的Llama 3.2和Google的Gemma 3，结合参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）与量化技术，在多语言DUTA10K数据集上进行系统性评估。实验表明，LLMs在处理40类非法内容的不平衡多分类任务中显著优于BERT及传统机器学习基线模型（支持向量机与朴素贝叶斯），展现出更强的泛化能力与适应性，为在线安全治理提供了可扩展且高效的智能工具。
链接: https://arxiv.org/abs/2603.04707
作者: Quoc Khoa Tran,Thanh Thi Nguyen,Campbell Wilson
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted for publication in the Proceedings of the 8th International Conference on Natural Language Processing (ICNLP 2026)
Abstract:Online marketplaces, while revolutionizing global commerce, have inadvertently facilitated the proliferation of illicit activities, including drug trafficking, counterfeit sales, and cybercrimes. Traditional content moderation methods such as manual reviews and rule-based automated systems struggle with scalability, dynamic obfuscation techniques, and multilingual content. Conventional machine learning models, though effective in simpler contexts, often falter when confronting the semantic complexities and linguistic nuances characteristic of illicit marketplace communications. This research investigates the efficacy of Large Language Models (LLMs), specifically Meta’s Llama 3.2 and Google’s Gemma 3, in detecting and classifying illicit online marketplace content using the multilingual DUTA10K dataset. Employing fine-tuning techniques such as Parameter-Efficient Fine-Tuning (PEFT) and quantization, these models were systematically benchmarked against a foundational transformer-based model (BERT) and traditional machine learning baselines (Support Vector Machines and Naive Bayes). Experimental results reveal a task-dependent advantage for LLMs. In binary classification (illicit vs. non-illicit), Llama 3.2 demonstrated performance comparable to traditional methods. However, for complex, imbalanced multi-class classification involving 40 specific illicit categories, Llama 3.2 significantly surpassed all baseline models. These findings offer substantial practical implications for enhancing online safety, equipping law enforcement agencies, e-commerce platforms, and cybersecurity specialists with more effective, scalable, and adaptive tools for illicit content detection and moderation.
[NLP-75] Hate Speech Detection using Large Language Models with Data Augmentation and Feature Enhancement
【速读】： 该论文旨在解决仇恨言论检测中因数据不平衡、隐性仇恨内容识别困难以及模型性能受限等问题，提升自动化检测的准确性与上下文敏感性。其解决方案的关键在于系统性评估数据增强（如SMOTE和文本数据增强）与特征增强（如POS标记和加权损失函数）技术对传统分类器（如Delta TF-IDF）与基于Transformer的模型（如DistilBERT、RoBERTa、DeBERTa、Gemma-7B及gpt-oss-20b）的影响，并揭示这些策略在不同数据集和模型架构下的交互效应。研究发现，隐性仇恨言论比显性内容更难识别，且增强效果高度依赖于数据集特性、模型类型与具体技术的匹配，其中开源的gpt-oss-20b在多数场景下表现最优，而Delta TF-IDF在数据增强后于Stormfront数据集上达到98.2%准确率，凸显了优化策略需根据任务需求定制化设计的重要性。
链接: https://arxiv.org/abs/2603.04698
作者: Brian Jing Hong Nge,Stefan Su,Thanh Thi Nguyen,Campbell Wilson,Alexandra Phelan,Naomi Pfitzner
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted for publication in the Proceedings of the 8th International Conference on Natural Language Processing (ICNLP 2026)
Abstract:This paper evaluates data augmentation and feature enhancement techniques for hate speech detection, comparing traditional classifiers, e.g., Delta Term Frequency-Inverse Document Frequency (Delta TF-IDF), with transformer-based models (DistilBERT, RoBERTa, DeBERTa, Gemma-7B, gpt-oss-20b) across diverse datasets. It examines the impact of Synthetic Minority Over-sampling Technique (SMOTE), weighted loss determined by inverse class proportions, Part-of-Speech (POS) tagging, and text data augmentation on model performance. The open-source gpt-oss-20b consistently achieves the highest results. On the other hand, Delta TF-IDF responds strongly to data augmentation, reaching 98.2% accuracy on the Stormfront dataset. The study confirms that implicit hate speech is more difficult to detect than explicit hateful content and that enhancement effectiveness depends on dataset, model, and technique interaction. Our research informs the development of hate speech detection by highlighting how dataset properties, model architectures, and enhancement strategies interact, supporting more accurate and context-aware automated detection.
[NLP-76] Non-Zipfian Distribution of Stopwords and Subset Selection Models
【速读】： 该论文试图解决的问题是：在自然语言文本中，停用词（stopwords）与非停用词的频率分布规律如何区别于经典的齐普夫定律（Zipf’s law），并揭示其背后的生成机制。针对这一问题，论文提出了一种基于词频排名的停用词选择概率模型——即停用词被选中的概率服从一个递减的Hill函数（1/(1+(r/rmid)γ)），而非停用词的概率则对应标准Hill函数（1/(1+(rmid/r)γ)）。该模型的关键在于将停用词的选择过程建模为一个与词在全词表中排名相关的单调衰减函数，并通过理论推导证明：当原始全词列表遵循齐普夫定律时，此选择机制可自然产生符合Beta Rank Function (BRF) 的停用词频率分布；同时也能解释非停用词频率分布为何更适配对数频率与对数排名之间的二次函数关系。
链接: https://arxiv.org/abs/2603.04691
作者: Wentian Li,Oscar Fontanelli
机构: 未知
类目: Computation and Language (cs.CL)
备注: 6 figures
Abstract:Stopwords are words that are not very informative to the content or the meaning of a language text. Most stopwords are function words but can also be common verbs, adjectives and adverbs. In contrast to the well known Zipf’s law for rank-frequency plot for all words, the rank-frequency plot for stopwords are best fitted by the Beta Rank Function (BRF). On the other hand, the rank-frequency plots of non-stopwords also deviate from the Zipf’s law, but are fitted better by a quadratic function of log-token-count over log-rank than by BRF. Based on the observed rank of stopwords in the full word list, we propose a stopword (subset) selection model that the probability for being selected as a function of the word’s rank r is a decreasing Hill’s function ( 1/(1+(r/r_mid)^\gamma) ); whereas the probability for not being selected is the standard Hill’s function ( 1/(1+(r_mid/r)^\gamma) ). We validate this selection probability model by a direct estimation from an independent collection of texts. We also show analytically that this model leads to a BRF rank-frequency distribution for stopwords when the original full word list follows the Zipf’s law, as well as explaining the quadratic fitting function for the non-stopwords.
[NLP-77] Optimizing Language Models for Crosslingual Knowledge Consistency
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多语言场景下知识不一致的问题，即模型对相同语义内容在不同语言中可能给出矛盾或不一致的回答，从而影响其可靠性。解决方案的关键在于提出一种无需显式奖励模型的直接一致性优化方法（Direct Consistency Optimization, DCO），该方法受DPO（Direct Preference Optimization）启发，直接从LLM自身推导出结构化的奖励信号，通过强化学习优化策略以实现跨语言响应的一致性。实验表明，DCO在多种多语言训练场景下显著提升一致性性能，并具备良好的泛化能力和可控对齐能力。
链接: https://arxiv.org/abs/2603.04678
作者: Tianyu Liu,Jirui Qi,Mrinmaya Sachan,Ryan Cotterell,Raquel Fernández,Arianna Bisazza
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Under review. The first two authors contributed equally
Abstract:Large language models are known to often exhibit inconsistent knowledge. This is particularly problematic in multilingual scenarios, where models are likely to be asked similar questions in different languages, and inconsistent responses can undermine their reliability. In this work, we show that this issue can be mitigated using reinforcement learning with a structured reward function, which leads to an optimal policy with consistent crosslingual responses. We introduce Direct Consistency Optimization (DCO), a DPO-inspired method that requires no explicit reward model and is derived directly from the LLM itself. Comprehensive experiments show that DCO significantly improves crosslingual consistency across diverse LLMs and outperforms existing methods when training with samples of multiple languages, while complementing DPO when gold labels are available. Extra experiments demonstrate the effectiveness of DCO in bilingual settings, significant out-of-domain generalizability, and controllable alignment via direction hyperparameters. Taken together, these results establish DCO as a robust and efficient solution for improving knowledge consistency across languages in multilingual LLMs. All code, training scripts, and evaluation benchmarks are released at this https URL.
[NLP-78] Using Vision Language Models to Predict Item Difficulty
【速读】： 该论文旨在解决如何利用大语言模型（Large Language Models, LLMs）自动预测数据可视化素养测试题目的难度问题，即准确估计美国成年人在回答特定题目时的正确率比例。其核心挑战在于如何有效融合文本与视觉信息以提升预测精度。解决方案的关键在于采用多模态特征融合策略，即同时利用题目文本（包括问题和选项）与可视化图像特征进行建模，相较于仅使用文本或仅使用视觉信息的单模态方法，该方案显著降低了平均绝对误差（MAE），达到0.224，验证了LLMs在心理测量学分析与自动化题目开发中的潜力。
链接: https://arxiv.org/abs/2603.04670
作者: Samin Khan
机构: Stanford Graduate School of Education (斯坦福大学教育研究生院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This project investigates the capabilities of large language models (LLMs) to determine the difficulty of data visualization literacy test items. We explore whether features derived from item text (question and answer options), the visualization image, or a combination of both can predict item difficulty (proportion of correct responses) for U.S. adults. We use GPT-4.1-nano to analyze items and generate predictions based on these distinct feature sets. The multimodal approach, using both visual and text features, yields the lowest mean absolute error (MAE) (0.224), outperforming the unimodal vision-only (0.282) and text-only (0.338) approaches. The best-performing multimodal model was applied to a held-out test set for external evaluation and achieved a mean squared error of 0.10805, demonstrating the potential of LLMs for psychometric analysis and automated item development.
[NLP-79] Stan: An LLM -based thermodynamics course assistant
【速读】： 该论文旨在解决当前人工智能（AI）在教育领域应用中忽视教师支持的问题，即现有研究和工具主要聚焦于学生端的交互式应用（如聊天机器人、智能导师等），而对如何利用相同的技术基础设施提升教学效率与质量关注不足。其解决方案的关键在于构建一个名为Stan的多用途教学支持系统，该系统基于统一的数据管道——由讲座转录文本和结构化教材索引组成，同时服务于学生和教师：对学生端采用检索增强生成（Retrieval-Augmented Generation, RAG）技术实现精准问答并附带章节页码引用；对教师端则通过结构化分析流程提取每节课的摘要、识别学生困惑点及教学中的类比案例，形成可搜索的学期级教学记录，助力课程反思与优化。整个系统运行于本地硬件环境，使用开源权重模型（Whisper large-v3 和 Llama 3.1 8B），确保数据隐私、成本可控与结果可复现，有效克服了大规模语言模型在长文本处理中常见的上下文截断、输出分布偏移和模式漂移等问题。
链接: https://arxiv.org/abs/2603.04657
作者: Eric M. Furst,Vasudevan Venkateshwaran
机构: University of Delaware (特拉华大学); W. L. Gore Associates Inc. (戈尔公司)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Physics Education (physics.ed-ph)
备注: 17 pages, 6 figures. For associated code repository, see this https URL
Abstract:Discussions of AI in education focus predominantly on student-facing tools – chatbots, tutors, and problem generators – while the potential for the same infrastructure to support instructors remains largely unexplored. We describe Stan, a suite of tools for an undergraduate chemical engineering thermodynamics course built on a data pipeline that we develop and deploy in dual roles: serving students and supporting instructors from a shared foundation of lecture transcripts and a structured textbook index. On the student side, a retrieval-augmented generation (RAG) pipeline answers natural-language queries by extracting technical terms, matching them against the textbook index, and synthesizing grounded responses with specific chapter and page references. On the instructor side, the same transcript corpus is processed through structured analysis pipelines that produce per-lecture summaries, identify student questions and moments of confusion, and catalog the anecdotes and analogies used to motivate difficult material – providing a searchable, semester-scale record of teaching that supports course reflection, reminders, and improvement. All components, including speech-to-text transcription, structured content extraction, and interactive query answering, run entirely on locally controlled hardware using open-weight models (Whisper large-v3, Llama~3.1 8B) with no dependence on cloud APIs, ensuring predictable costs, full data privacy, and reproducibility independent of third-party services. We describe the design, implementation, and practical failure modes encountered when deploying 7–8 billion parameter models for structured extraction over long lecture transcripts, including context truncation, bimodal output distributions, and schema drift, along with the mitigations that resolved them.
[NLP-80] Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models
【速读】： 该论文旨在解决检索增强生成（Retrieval Augmented Generation, RAG）在实际应用中面临的两个核心问题：一是检索结果与生成目标之间的语义错位（semantic misalignment），二是检索到的证据未能被充分有效利用（insufficient evidence utilization）。解决方案的关键在于通过协同建模检索与生成阶段，将语义对齐与显式证据约束相结合：首先在统一语义空间中建模查询与候选证据的相关性，确保检索结果与生成目标保持语义一致并降低噪声干扰；在此基础上引入显式证据约束机制，将检索到的证据从隐式上下文转化为生成过程的核心控制因子，从而限制生成内容的表达范围并强化其对证据的依赖性。该方法在提升事实可靠性与可验证性的同时，维持了自然语言的流畅性，并在多个生成质量指标上实现了稳定改进。
链接: https://arxiv.org/abs/2603.04647
作者: Xin Chen,Saili Uday Gadgil,Jiarong Qiu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Retrieval augmented generation mitigates limitations of large language models in factual consistency and knowledge updating by introducing external knowledge. However, practical applications still suffer from semantic misalignment between retrieved results and generation objectives, as well as insufficient evidence utilization. To address these challenges, this paper proposes a retrieval augmented generation method that integrates semantic alignment with evidence constraints through coordinated modeling of retrieval and generation stages. The method first represents the relevance between queries and candidate evidence within a unified semantic space. This ensures that retrieved results remain semantically consistent with generation goals and reduces interference from noisy evidence and semantic drift. On this basis, an explicit evidence constraint mechanism is introduced. Retrieved evidence is transformed from an implicit context into a core control factor in generation. This restricts the expression scope of generated content and strengthens dependence on evidence. By jointly modeling semantic consistency and evidence constraints within a unified framework, the proposed approach improves factual reliability and verifiability while preserving natural language fluency. Comparative results show stable improvements across multiple generation quality metrics. This confirms the effectiveness and necessity of coordinated semantic alignment and evidence constraint modeling in retrieval augmented generation tasks.
[NLP-81] Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development WWW
【速读】： 该论文旨在解决当前代码生成研究中缺乏对端到端（zero-to-one）Web应用开发全流程评估的问题，现有基准测试仅聚焦于孤立任务，无法反映从需求 specification 到可运行应用的完整过程。其解决方案的关键在于提出 Vibe Code Bench，一个包含 100 个 Web 应用规格（50 个公开验证集、50 个保留测试集）和 964 个浏览器工作流（共 10,131 个子步骤）的新型基准数据集，并通过自主浏览器代理对部署的应用进行自动化评估，从而实现对模型端到端能力的系统性测量。此外，研究还揭示了生成过程中自测试（self-testing）是性能的重要预测因子（Pearson r=0.72），并提出了一种评估者对齐协议，以提升评测一致性（人类标注与跨模型结果间步骤级一致性达 31.8–93.6%）。
链接: https://arxiv.org/abs/2603.04601
作者: Hung Tran,Langston Nashold,Rayan Krishnan,Antoine Bigeard,Alex Gu
机构: Vals AI(_vals人工智能); Massachusetts Institute of Technology(麻省理工学院)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Live leaderboard hosted here: this https URL . Preprint, currently under review. Benchmark first released Nov 2025
Abstract:Code generation has emerged as one of AI’s highest-impact use cases, yet existing benchmarks measure isolated tasks rather than the complete “zero-to-one” process of building a working application from scratch. We introduce Vibe Code Bench, a benchmark of 100 web application specifications (50 public validation, 50 held-out test) with 964 browser-based workflows comprising 10,131 substeps, evaluated against deployed applications by an autonomous browser agent. Across 16 frontier models, the best achieves only 58.0% accuracy on the test split, revealing that reliable end-to-end application development remains a frontier challenge. We identify self-testing during generation as a strong performance predictor (Pearson r=0.72), and show through a completed human alignment study that evaluator selection materially affects outcomes (31.8-93.6% pairwise step-level agreement). Our contributions include (1) a novel benchmark dataset and browser-based evaluation pipeline for end-to-end web application development, (2) a comprehensive evaluation of 16 frontier models with cost, latency, and error analysis, and (3) an evaluator alignment protocol with both cross-model and human annotation results. Comments: Live leaderboard hosted here: this https URL. Preprint, currently under review. Benchmark first released Nov 2025 Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) ACMclasses: I.2.7 Cite as: arXiv:2603.04601 [cs.SE] (or arXiv:2603.04601v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2603.04601 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-82] Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning
【速读】： 该论文旨在解决当前强化学习（Reinforcement Learning, RL）算法在利用自然语言（Natural Language, NL）反馈时存在的效率低下问题，即现有方法仅依赖标量奖励信号，未能有效挖掘NL反馈中蕴含的丰富信息，导致探索过程低效。解决方案的关键在于提出GOLF框架，其核心创新是通过聚合两类群体级语言反馈——外部批评（指出错误或提供针对性修正）和组内尝试（提供替代部分思路与多样化失败模式），生成高质量的可操作性改进方案，并以离策略支架（off-policy scaffolds）形式自适应注入训练过程，在稀疏奖励区域实现精准引导；同时在统一的RL循环中联合优化生成与精炼能力，形成持续提升的良性循环，从而显著提高样本效率和探索性能。
链接: https://arxiv.org/abs/2603.04597
作者: Lei Huang,Xiang Cheng,Chenxiao Zhao,Guobin Shen,Junjie Yang,Xiaocheng Feng,Yuxuan Gu,Xing Yu,Bing Qin
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) typically receive diverse natural language (NL) feedback through interaction with the environment. However, current reinforcement learning (RL) algorithms rely solely on scalar rewards, leaving the rich information in NL feedback underutilized and leading to inefficient exploration. In this work, we propose GOLF, an RL framework that explicitly exploits group-level language feedback to guide targeted exploration through actionable refinements. GOLF aggregates two complementary feedback sources: (i) external critiques that pinpoint errors or propose targeted fixes, and (ii) intra-group attempts that supply alternative partial ideas and diverse failure patterns. These group-level feedbacks are aggregated to produce high-quality refinements, which are adaptively injected into training as off-policy scaffolds to provide targeted guidance in sparse-reward regions. Meanwhile, GOLF jointly optimizes generation and refinement within a unified RL loop, creating a virtuous cycle that continuously improves both capabilities. Experiments on both verifiable and non-verifiable benchmarks show that GOLF achieves superior performance and exploration efficiency, achieving 2.2 \times improvements in sample efficiency compared to RL methods trained solely on scalar rewards. Code is available at this https URL.
[NLP-83] From Static Inference to Dynamic Interaction: Navigating the Landscape of Streaming Large Language Models
【速读】： 该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在动态、实时场景中应用受限的问题，因为标准LLMs主要面向静态推理，依赖预定义输入，缺乏对流式数据处理和交互式响应的支持。其解决方案的关键在于提出一个统一的Streaming LLM定义，基于数据流与动态交互机制来厘清现有概念模糊性，并在此基础上构建了一个系统性的分类体系，从而为流式LLM的研究方法、应用场景及未来方向提供结构化框架和理论支撑。
链接: https://arxiv.org/abs/2603.04592
作者: Junlong Tong,Zilong Wang,YuJie Ren,Peiran Yin,Hao Wu,Wei Zhang,Xiaoyu Shen
机构: Shanghai Jiao Tong University (上海交通大学); Institute of Digital Twin, Eastern Institute of Technology, Ningbo (数字孪生研究所，东方理工大学宁波校区)
类目: Computation and Language (cs.CL)
备注:
Abstract:Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in dynamic, real-time scenarios. To address this gap, the streaming LLM paradigm has emerged. However, existing definitions of streaming LLMs remain fragmented, conflating streaming generation, streaming inputs, and interactive streaming architectures, while a systematic taxonomy is still lacking. This paper provides a comprehensive overview and analysis of streaming LLMs. First, we establish a unified definition of streaming LLMs based on data flow and dynamic interaction to clarify existing ambiguities. Building on this definition, we propose a systematic taxonomy of current streaming LLMs and conduct an in-depth discussion on their underlying methodologies. Furthermore, we explore the applications of streaming LLMs in real-world scenarios and outline promising research directions to support ongoing advances in streaming intelligence. We maintain a continuously updated repository of relevant papers at this https URL.
[NLP-84] Query Disambiguation via Answer-Free Context: Doubling Performance on Humanitys Last Exam
【速读】： 该论文旨在解决语言模型在回答问题时因查询（query）表述模糊而导致的准确性下降问题，尤其是在存在背景 grounding 信息（即上下文信息）的情况下。研究发现，仅通过在推理阶段增加提示（prompting）无法充分提升准确率，关键在于将问题重写（query rewriting）与答案生成分离为两个独立阶段：首先利用动态上下文构建（如检索增强生成，RAG）技术对原始问题进行语义澄清和去歧义处理，从而显著提升后续模型的回答准确性。实验表明，在不改变答案的前提下，仅通过重写问题即可实现显著性能提升，例如在 Humanity’s Last Exam 数据集上，使用 GPT-oss-20b 对问题进行重写后，GPT-5-mini 的准确率从 0.14 提升至 0.37。
链接: https://arxiv.org/abs/2603.04454
作者: Michael Majurski,Cynthia Matuszek
机构: National Institute of Standards and Technology (美国国家标准与技术研究院); University of Maryland Baltimore County (马里兰大学巴尔的摩县分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:How carefully and unambiguously a question is phrased has a profound impact on the quality of the response, for Language Models (LMs) as well as people. While model capabilities continue to advance, the interplay between grounding context and query formulation remains under-explored. This work investigates how the quality of background grounding information in a model’s context window affects accuracy. We find that combining well-grounded dynamic context construction (i.e, RAG) with query rewriting reduces question ambiguity, resulting in significant accuracy gains. Given a user question with associated answer-free grounding context, rewriting the question to reduce ambiguity produces benchmark improvements without changing the answer itself, even compared to prepending that context before the question. Using \textttgpt-oss-20b to rewrite a subset of Humanity’s Last Exam using answer-free grounding context improves \textttgpt-5-mini accuracy from 0.14 to 0.37. We demonstrate that this accuracy improvement cannot be fully recovered just through prompting at inference time; rather, distinct rewriting and answering phases are required. Code and data are available at this https URL
[NLP-85] Induced Numerical Instability: Hidden Costs in Multimodal Large Language Models
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）中一种新型性能退化问题，即通过优化一个旨在最大化推理阶段数值不稳定的损失项，间接导致模型输出质量显著下降。其解决方案的关键在于设计并应用该特定损失函数生成对抗性图像，这些图像在微小扰动下即可引发MLLMs在多个标准数据集（如Flickr30k、MMVet、TextVQA等）上的性能大幅下降，揭示了一种区别于传统对抗扰动的全新失败模式。
链接: https://arxiv.org/abs/2603.04453
作者: Wai Tuck Wong,Jun Sun,Arunesh Sinha
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The use of multimodal large language models has become widespread, and as such the study of these models and their failure points has become of utmost importance. We study a novel mode of failure that causes degradation in performance indirectly by optimizing a loss term that seeks to maximize numerical instability in the inference stage of these models. We apply this loss term as the optimization target to construct images that, when used on multimodal large language models, cause significant degradation in the output. We validate our hypothesis on state of the art models large vision language models (LLaVa-v1.5-7B, Idefics3-8B, SmolVLM-2B-Instruct) against standard datasets (Flickr30k, MMVet, TextVQA, VQAv2, POPE, COCO) and show that performance degrades significantly, even with a very small change to the input image, compared to baselines. Our results uncover a fundamentally different vector of performance degradation, highlighting a failure mode not captured by adversarial perturbations.
[NLP-86] A unified foundational framework for knowledge injection and evaluation of Large Language Models in Combustion Science
【速读】： 该论文旨在解决如何为燃烧科学领域构建专用的大语言模型（Large Language Models, LLMs）的问题，以推动基础大模型在该专业领域的应用。其核心挑战在于现有通用模型在燃烧科学任务中表现有限，且缺乏针对该领域知识的结构化整合与深度注入。解决方案的关键在于提出一个端到端框架，包含三个关键组成部分：一是基于35亿token规模的多模态知识库（涵盖20余万篇同行评审论文、8000篇学位论文及约40万行燃烧计算流体力学代码），二是涵盖八个子领域的严格自动化评估基准（CombustionQA，共436个问题），三是三阶段的知识注入路径——从轻量级检索增强生成（Retrieval-Augmented Generation, RAG）逐步过渡到知识图谱增强检索与持续预训练。研究发现，仅使用标准RAG方法时准确率受限于60%，远低于理论上限（87%），主要瓶颈是上下文污染；因此，构建高质量领域基础模型必须依赖结构化知识图谱和持续预训练（即Stage 2和Stage 3）。
链接: https://arxiv.org/abs/2603.04452
作者: Zonglin Yang,Runze Mao,Tianhao Wu,Han Li,QingGuo Zhou,Zhi X. Chen
机构: Peking University (北京大学); AI for Science Institute (科学人工智能研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 5 figures, 1 table
Abstract:To advance foundation Large Language Models (LLMs) for combustion science, this study presents the first end-to-end framework for developing domain-specialized models for the combustion community. The framework comprises an AI-ready multimodal knowledge base at the 3.5 billion-token scale, extracted from over 200,000 peer-reviewed articles, 8,000 theses and dissertations, and approximately 400,000 lines of combustion CFD code; a rigorous and largely automated evaluation benchmark (CombustionQA, 436 questions across eight subfields); and a three-stage knowledge-injection pathway that progresses from lightweight retrieval-augmented generation (RAG) to knowledge-graph-enhanced retrieval and continued pretraining. We first quantitatively validate Stage 1 (naive RAG) and find a hard ceiling: standard RAG accuracy peaks at 60%, far surpassing zero-shot performance (23%) yet well below the theoretical upper bound (87%). We further demonstrate that this stage’s performance is severely constrained by context contamination. Consequently, building a domain foundation model requires structured knowledge graphs and continued pretraining (Stages 2 and 3).
[NLP-87] Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理阶段因任务复杂度和领域差异导致的静态部署效率低下问题，即如何在不牺牲性能的前提下实现动态、智能的模型选择。其核心解决方案是构建多LLM路由系统（multi-LLM routing system），通过分析查询特征自适应地选择最合适的模型，而非依赖单一固定模型。关键在于设计一个融合多种路由范式（如查询难度估计、不确定性量化、强化学习、聚类等）的可组合框架，并从“决策时机”、“输入信息”和“计算方式”三个维度对路由机制进行建模，从而在实际约束下平衡性能与成本，使系统能够通过策略性调用不同模型的专业能力，显著优于单一最优模型的性能表现。
链接: https://arxiv.org/abs/2603.04445
作者: Yasmin Moslem,John D. Kelleher
机构: ADAPT Centre (ADAPT 中心); Trinity College Dublin (都柏林圣三一学院)
类目: Networking and Internet Architecture (cs.NI); Computation and Language (cs.CL); Performance (cs.PF)
备注: Work funded by ADAPT Centre, Trinity College Dublin, and Huawei Ireland
Abstract:The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent model selection at inference time. While smaller models suffice for routine queries, complex tasks demand more capable models. However, static model deployment does not account for the complexity and domain of incoming queries, leading to suboptimal performance and increased costs. Dynamic routing systems that adaptively select models based on query characteristics have emerged as a solution to this challenge. We provide a systematic analysis of state-of-the-art multi-LLM routing and cascading approaches. In contrast to mixture-of-experts architectures, which route within a single model, we study routing across multiple independently trained LLMs. We cover diverse routing paradigms, including query difficulty, human preferences, clustering, uncertainty quantification, reinforcement learning, multimodality, and cascading. For each paradigm, we analyze representative methods and examine key trade-offs. Beyond taxonomy, we introduce a conceptual framework that characterizes routing systems along three dimensions: when decisions are made, what information is used, and how they are computed. This perspective highlights that practical systems are often compositional, integrating multiple paradigms under operational constraints. Our analysis demonstrates that effective multi-LLM routing requires balancing competing objectives. Choosing the optimal routing strategy depends on deployment and computational constraints. Well-designed routing systems can outperform even the most powerful individual models by strategically leveraging specialized capabilities across models while maximizing efficiency gains. Meanwhile, open challenges remain in developing routing mechanisms that generalize across diverse architectures, modalities, and applications. Comments: Work funded by ADAPT Centre, Trinity College Dublin, and Huawei Ireland Subjects: Networking and Internet Architecture (cs.NI); Computation and Language (cs.CL); Performance (cs.PF) Cite as: arXiv:2603.04445 [cs.NI] (or arXiv:2603.04445v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2603.04445 Focus to learn more arXiv-issued DOI via DataCite
[NLP-88] What Is Missing: Interpretable Ratings for Large Language Model Outputs
【速读】： 该论文旨在解决当前大语言模型（Large Language Model, LLM）偏好学习方法（如近端策略优化 Proximal Policy Optimization 和直接偏好优化 Direct Preference Optimization）依赖主观的离散数值评分或排序标签所导致的学习信号质量低的问题，尤其指出单一数值评分难以准确反映自然语言输出的质量。其解决方案的关键在于提出一种名为“什么是缺失的”（What Is Missing, WIM）的评分系统，该系统通过人类或大语言模型判官提供自然语言形式的反馈来生成排名——即描述模型输出中缺失的内容；随后利用句子嵌入模型对输出和反馈进行向量化，并计算余弦相似度作为评分依据。WIM 可无缝集成到现有训练流程中，无需修改偏好学习算法，且能显著减少评分中的平局情况、增大评分差异，从而提升成对偏好数据中的学习信号有效性，同时具备可解释性：每个评分均可追溯至对应的缺失信息文本，便于对偏好标签进行定性调试。
链接: https://arxiv.org/abs/2603.04429
作者: Nicholas Stranges,Yimin Yang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 22 pages
Abstract:Current Large Language Model (LLM) preference learning methods such as Proximal Policy Optimization and Direct Preference Optimization learn from direct rankings or numerical ratings of model outputs, these rankings are subjective, and a single numerical rating chosen directly by a judge is a poor proxy for the quality of natural language, we introduce the What Is Missing (WIM) rating system to produce rankings from natural-language feedback, WIM integrates into existing training pipelines, can be combined with other rating techniques, and can be used as input to any preference learning method without changing the learning algorithm, to compute a WIM rating, a human or LLM judge writes feedback describing what the model output is missing, we embed the output and the feedback with a sentence embedding model and compute the cosine similarity between the resulting vectors, we empirically observe that, compared to discrete numerical ratings, WIM yields fewer ties and larger rating deltas, which improves the availability of a learning signal in pairwise preference data, we use interpretable in the following limited sense: for each scalar rating, we can inspect the judge’s missing-information text that produced it, enabling qualitative debugging of the preference labels.
[NLP-89] Generating Realistic Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptation
【速读】： 该论文旨在解决航海VHF无线电通信中因人为因素导致的安全风险问题，尤其是现有通信系统缺乏实时转录、易受噪声和语言变异性干扰，从而引发频繁且难以纠正的操作错误。解决方案的关键在于提出一种合规感知的Self-Instruct生成方法，通过在迭代生成过程中嵌入26个过滤器验证管道，确保生成对话符合国际海事组织（International Maritime Organization, IMO）推荐的标准航海用语（Standard Marine Communication Phrases, SMCP），同时保证实体信息准确性、逻辑一致性与语言多样性；此外，采用LoRA（Low-Rank Adaptation）参数高效微调技术，在降低计算开销的同时实现模型在资源受限海上系统的高效部署，为AI辅助航海安全提供可复现的数据基础与技术框架。
链接: https://arxiv.org/abs/2603.04423
作者: Gürsel Akdeniz,Emin Cagatay Nakilcioglu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:VHF radio miscommunication remains a major safety risk in maritime operations, with human factors accounting for over 58% of recorded incidents in Europe between 2014 and 2023. Despite decades of operational use, VHF radio communications are still prone to noise, interference, linguistic variability, and the absence of real-time transcription, making procedural errors both frequent and difficult to correct. Developing AI-assisted systems to support real-time communication and decision-making requires a considerable amount of high-quality maritime data, yet operational, regulatory, and privacy constraints render such datasets scarce. This study introduces a compliance aware Self-Instruct methodology for generating realistic maritime radio dialogues that conform to the IMO’s SMCP. Our approach integrates a 26-filter verification pipeline directly into the iterative generation loop to enforce entity information accuracy, hallucination detection, SMCP-compliance, logical consistency, and linguistic diversity. We employ LORA for parameter-efficient fine-tuning, reducing computational overhead during training and enabling efficient deployment of the resulting models on resource-constrained maritime systems. To assess dataset quality, we introduce a novel evaluation framework combining automated and expert assessments: Format Accuracy, Information Accuracy, Uniqueness, and Logical Coherence. Experiments using publicly available vessel, coastal and AIS datasets demonstrate that the approach produces synthetically diverse, procedurally compliant, and operationally realistic dialogues. Although downstream applications such as automatic speech recognition and natural language processing are reserved for future work, the released code, datasets, and verification tools provide a reproducible foundation for artificial intelligence-assisted maritime safety and other safety-critical domains.
[NLP-90] Context-Dependent Affordance Computation in Vision-Language Models
【速读】： 该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在场景理解中对 affordance（可供性）计算的上下文依赖性问题，即模型如何根据不同的上下文条件（如角色设定或代理人格）动态调整对场景中物体功能可能性的判断。解决方案的关键在于通过大规模计算实验（n=3,213个场景-上下文对）和系统性情境提示（7种代理人格），量化并验证了VLMs中存在显著的“可供性漂移”（affordance drift）现象：词汇层面的Jaccard相似度均值仅为0.095，表明90%的描述受上下文影响；语义层面的余弦相似度均值为0.415，说明58.5%的内容具有上下文依赖性。进一步的随机基线实验排除了生成噪声干扰，而Tucker分解揭示了稳定的潜在结构（如“烹饪流形”和“可及轴”），从而提出机器人领域应转向动态、查询相关的本体投影（Just-In-Time Ontology, JIT Ontology），而非静态世界建模。
链接: https://arxiv.org/abs/2603.04419
作者: Murad Farzulla
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 31 pages, 8 tables, 4 figures, 43 references. Code available at: this https URL
Abstract:We characterize the phenomenon of context-dependent affordance computation in vision-language models (VLMs). Through a large-scale computational study (n=3,213 scene-context pairs from COCO-2017) using Qwen-VL 30B and LLaVA-1.5-13B subject to systematic context priming across 7 agentic personas, we demonstrate massive affordance drift: mean Jaccard similarity between context conditions is 0.095 (95% CI: [0.093, 0.096], p 0.0001), indicating that 90% of lexical scene description is context-dependent. Sentence-level cosine similarity confirms substantial drift at the semantic level (mean = 0.415, 58.5% context-dependent). Stochastic baseline experiments (2,384 inference runs across 4 temperatures and 5 seeds) confirm this drift reflects genuine context effects rather than generation noise: within-prime variance is substantially lower than cross-prime variance across all conditions. Tucker decomposition with bootstrap stability analysis (n=1,000 resamples) reveals stable orthogonal latent factors: a “Culinary Manifold” isolated to chef contexts and an “Access Axis” spanning child-mobility contrasts. These findings establish that VLMs compute affordances in a substantially context-dependent manner – with the difference between lexical (90%) and semantic (58.5%) measures reflecting that surface vocabulary changes more than underlying meaning under context shifts – and suggest a direction for robotics research: dynamic, query-dependent ontological projection (JIT Ontology) rather than static world modeling. We do not claim to establish processing order or architectural primacy; such claims require internal representational analysis beyond output behavior.
[NLP-91] Same Input Different Scores: A Multi Model Study on the Inconsistency of LLM Judge
【速读】： 该论文旨在解决生成式 AI（Generative AI）在企业级应用中作为自动评分工具时存在的评分一致性问题，即不同大语言模型（Large Language Models, LLMs）在重复评估相同问答对时所产生数值分数的稳定性差异。研究发现，即便在固定温度参数下，各模型间评分波动显著，且同一模型在不同温度设置下表现不一，尤其在完整性（completeness）评分上变化最大；此外，不同厂商模型（如GPT、Gemini与Anthropic系列）存在系统性评分严格度和解释风格差异，导致相同答案获得不同评分。解决方案的关键在于：引入持续监控机制、增强解析鲁棒性，并采用人机协同评估策略，以提升LLM-as-a-judge在生产环境中的公平性、可复现性和可靠性。
链接: https://arxiv.org/abs/2603.04417
作者: Fiona Lau
机构: 未知
类目: Computation and Language (cs.CL)
备注: 19 pages, 14 figures
Abstract:Large language models are increasingly used as automated evaluators in research and enterprise settings, a practice known as LLM-as-a-judge. While prior work has examined accuracy, bias, and alignment with human preferences, far less attention has been given to how consistently LLMs assign numerical scores, an important concern for many production workflows. This study systematically evaluates scoring stability across five commonly used models, GPT-4o, GPT-4o-mini, Gemini-2.5-Flash, Claude-Haiku-4.5, and Claude-Sonnet-4.5, two temperature settings, and real enterprise question-answer pairs drawn from a retrieval-augmented generation (RAG) system. We address three questions: how stable a model’s scores are across repeated runs, how differently models score identical inputs, and how temperature affects scoring consistency. Temperature controls the determinism of an LLM’s output. Despite expectations of stability at temperature=0, we observe substantial variability across models, with completeness scoring showing the largest fluctuations. Cross-model comparisons reveal systematic differences in strictness and interpretive style, leading to divergent ratings for the same answers. Lower temperatures improve stability for some models, notably GPT-4o and Gemini, but have limited or inconsistent effects for Anthropic models. These findings have important implications for enterprise pipelines that rely on LLM-generated scores for routing, triage, gating, or quality control. Identical inputs can receive different scores depending on model, family, or temperature, raising concerns around fairness, reproducibility, and operational reliability. Our results highlight the need for monitoring, robust parsing, and hybrid human-LLM evaluation strategies to ensure dependable use of LLM-as-a-judge in production environments.
[NLP-92] Optimizing What We Trust: Reliability-Guided QUBO Selection of Multi-Agent Weak Framing Signals for Arabic Sentiment Prediction
【速读】： 该论文旨在解决阿拉伯语社交媒体中框架检测（framing detection）的难题，其核心挑战在于解释歧义性、文化依赖性和标注数据稀缺导致的弱监督方法失效问题。现有基于大语言模型（LLM）的弱监督方法通常依赖标签聚合策略，在样本稀少且社会语境敏感的情况下表现脆弱。本文的关键解决方案是提出一种可靠性感知的弱监督框架，将关注点从传统的标签融合转向数据筛选（data curation）：通过一个由两个框架生成器（framer）、一个批评者（critic）和一个判别器（discriminator）组成的多智能体LLM流水线，利用标注分歧与推理质量作为认知不确定性（epistemic uncertainty）信号，生成实例级可靠性估计；进而采用QUBO优化方法选择具有帧平衡性且冗余度低的子集，从而提升训练数据的可靠性和可迁移性，同时不损害纯文本基线性能。
链接: https://arxiv.org/abs/2603.04416
作者: Rabab Alkhalifa
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Framing detection in Arabic social media is difficult due to interpretive ambiguity, cultural grounding, and limited reliable supervision. Existing LLM-based weak supervision methods typically rely on label aggregation, which is brittle when annotations are few and socially dependent. We propose a reliability-aware weak supervision framework that shifts the focus from label fusion to data curation. A small multi-agent LLM pipeline, two framers, a critic, and a discriminator, treats disagreement and reasoning quality as epistemic signals and produces instance-level reliability estimates. These estimates guide a QUBO-based subset selection procedure that enforces frame balance while reducing redundancy. Intrinsic diagnostics and an out-of-domain Arabic sentiment transfer test show that the selected subsets are more reliable and encode non-random, transferable structure, without degrading strong text-only baselines.
[NLP-93] he Thinking Boundary: Quantifying Reasoning Suitability of Multimodal Tasks via Dual Tuning
【速读】： 该论文旨在解决当前生成式 AI（Generative AI）在多模态任务中盲目启用推理机制所带来的资源浪费问题，即缺乏一个明确的标准来判断何时推理能够带来实际性能提升。其解决方案的关键在于提出“Dual Tuning”框架，通过在受控提示下联合微调链式思维（Chain-of-Thought, CoT）与直接答案（Direct-Answer, DA）数据对，系统性地量化并比较两种训练模式的收益，并据此定义“思考边界”（Thinking Boundary），用以评估不同多模态任务中推理训练的适用性，从而为数据筛选和训练策略提供可操作的指导。
链接: https://arxiv.org/abs/2603.04415
作者: Ruobing Zheng,Tianqi Li,Jianing Li,Qingpei Guo,Yi Yuan,Jingdong Chen
机构: Ant Group(蚂蚁集团)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:While reasoning-enhanced Large Language Models (LLMs) have demonstrated remarkable advances in complex tasks such as mathematics and coding, their effectiveness across universal multimodal scenarios remains uncertain. The trend of releasing parallel “Instruct” and “Thinking” models by leading developers serves merely as a resource-intensive workaround, stemming from the lack of a criterion for determining when reasoning is truly beneficial. In this paper, we propose Dual Tuning, a framework designed to assess whether reasoning yields positive gains for target tasks under given base models and datasets. By jointly fine-tuning on paired Chain-of-Thought (CoT) and Direct-Answer (DA) data under controlled prompts, we systematically quantify and compare the gains of both training modes using the proposed metrics, and establish the “Thinking Boundary” to evaluate the suitability of reasoning training across diverse multimodal tasks, including spatial, mathematical, and multi-disciplinary domains. We further explore the impact of reinforcement training and thinking patterns on reasoning suitability, and validate whether the “Thinking Boundary” can guide data refinement. Our findings challenge the “reasoning-for-all” paradigm, providing practical guidance for identifying appropriate data and training strategies, and motivating the development of resource-efficient, adaptive auto-think systems.
[NLP-94] Multiclass Hate Speech Detection with RoBERTa-OTA: Integrating Transformer Attention and Graph Convolutional Networks
【速读】： 该论文旨在解决多类别仇恨言论检测中因隐性针对策略和社交媒体内容语言变异性导致的计算挑战，尤其在跨不同人口统计学群体时的分类准确性问题。现有方法依赖于训练数据中学习到的表示，缺乏对结构化本体框架的显式利用，从而限制了分类性能。解决方案的关键在于提出RoBERTa-OTA模型，其核心创新是引入本体引导的注意力机制（ontology-guided attention mechanisms），将文本特征与结构化知识表示通过增强的图卷积网络（Graph Convolutional Networks, GCNs）进行融合，从而在保持低参数开销（仅增加0.33%）的前提下显著提升模型对性别相关仇恨言论等难点类别的识别能力，最终实现96.04%的准确率，优于标准RoBERTa模型（95.02%）。
链接: https://arxiv.org/abs/2603.04414
作者: Mahmoud Abusaqer,Jamil Saquer
机构: 未知
类目: Computation and Language (cs.CL)
备注: 15 pages, 2 figures, 6 tables. Accepted for publication in the Proceedings of the 12th Annual Conference on Computational Science Computational Intelligence (CSCI’25)
Abstract:Multiclass hate speech detection across demographic categories remains computationally challenging due to implicit targeting strategies and linguistic variability in social media content. Existing approaches rely solely on learned representations from training data, without explicitly incorporating structured ontological frameworks that can enhance classification through formal domain knowledge integration. We propose RoBERTa-OTA, which introduces ontology-guided attention mechanisms that process textual features alongside structured knowledge representations through enhanced Graph Convolutional Networks. The architecture combines RoBERTa embeddings with scaled attention layers and graph neural networks to integrate contextual language understanding with domain-specific semantic knowledge. Evaluation across 39,747 balanced samples using 5-fold cross-validation demonstrates significant performance gains over baseline RoBERTa implementations and existing state-of-the-art methods. RoBERTa-OTA achieves 96.04% accuracy compared to 95.02% for standard RoBERTa, with substantial improvements for challenging categories: gender-based hate speech detection improves by 2.36 percentage points while other hate speech categories improve by 2.38 percentage points. The enhanced architecture maintains computational efficiency with only 0.33% parameter overhead, providing practical advantages for large-scale content moderation applications requiring fine-grained demographic hate speech classification.
[NLP-95] Simulating Meaning Nevermore! Introducing ICR: A Semiotic-Hermeneutic Metric for Evaluating Meaning in LLM Text Summaries
【速读】： 该论文试图解决大语言模型（Large Language Model, LLM）生成文本中语义准确性不足的问题，尤其是在捕捉语境依赖性和意义 emergent 性方面，传统基于词汇相似度的评估指标难以反映人类 interpretive meaning 的复杂性。解决方案的关键在于提出一种名为“归纳概念评分”（Inductive Conceptual Rating, ICR）的定性评估方法，该方法融合归纳内容分析（inductive content analysis）与反思性主题分析（reflexive thematic analysis），能够系统评估LLM输出在语义准确性和意义对齐性上的表现，从而超越仅依赖统计近似的评价范式。
链接: https://arxiv.org/abs/2603.04413
作者: Natalie Perez,Sreyoshi Bhaduri,Aman Chadha
机构: University of Hawai‘i (夏威夷大学); ThatStatsGirl; Amazon GenAI (亚马逊生成式人工智能)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Meaning in human language is relational, context dependent, and emergent, arising from dynamic systems of signs rather than fixed word-concept mappings. In computational settings, this semiotic and interpretive complexity complicates the generation and evaluation of meaning. This article proposes an interdisciplinary framework for studying meaning in large language model (LLM) generated language by integrating semiotics and hermeneutics with qualitative research methods. We review prior scholarship on meaning and machines, examining how linguistic signs are transformed into vectorized representations in static and contextualized embedding models, and identify gaps between statistical approximation and human interpretive meaning. We then introduce the Inductive Conceptual Rating (ICR) metric, a qualitative evaluation approach grounded in inductive content analysis and reflexive thematic analysis, designed to assess semantic accuracy and meaning alignment in LLM-outputs beyond lexical similarity metrics. We apply ICR in an empirical comparison of LLM generated and human generated thematic summaries across five datasets (N = 50 to 800). While LLMs achieve high linguistic similarity, they underperform on semantic accuracy, particularly in capturing contextually grounded meanings. Performance improves with larger datasets but remains variable across models, potentially reflecting differences in the frequency and coherence of recurring concepts and meanings. We conclude by arguing for evaluation frameworks that leverage systematic qualitative interpretation practices when assessing meaning in LLM-generated outputs from reference texts.
[NLP-96] Additive Multi-Step Markov Chains and the Curse of Dimensionality in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在高维状态空间中难以建模复杂依赖关系的问题，特别是传统马尔可夫结构无法有效刻画token嵌入及其隐藏表示之间的多阶依赖性。解决方案的关键在于引入N阶加法型马尔可夫链（additive Markov chains），通过将下一个token的条件概率分解为多个历史深度贡献的叠加，从而避免高阶马尔可夫过程常见的组合爆炸问题。进一步地，作者建立了加法多步链与带逐步记忆函数的链之间的等价关系，并在此基础上提出了信息温度（information temperature）的概念，不仅适用于逐步记忆链，也适用于加法型N阶马尔可夫链，为理解LLM动态提供了新的理论框架。
链接: https://arxiv.org/abs/2603.04412
作者: O.V. Usatenko,S.S. Melnyk,G.M. Pritula
机构: A. Ya. Usikov Institute for Radiophysics and Electronics Ukrainian Academy of Science (乌克兰科学院尤西科夫辐射物理与电子研究所); Center for Theoretical Physics, Polish Academy of Sciences (波兰科学院理论物理中心)
类目: Computation and Language (cs.CL)
备注: 10 pages, 3 figures
Abstract:Large-scale language models (LLMs) operate in extremely high-dimensional state spaces, where both token embeddings and their hidden representations create complex dependencies that are not easily reduced to classical Markov structures. In this paper, we explore a theoretically feasible approximation of LLM dynamics using N-order additive Markov chains. Such models allow the conditional probability of the next token to be decomposed into a superposition of contributions from multiple historical depths, reducing the combinatorial explosion typically associated with high-order Markov processes. The main result of the work is the establishment of a correspondence between an additive multi-step chain and a chain with a step-wise memory function. This equivalence allowed the introduction of the concept of information temperature not only for stepwise but also for additive N-order Markov chains.
[NLP-97] One Size Does Not Fit All: Token-Wise Adaptive Compression for KV Cache
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理过程中Key-Value（KV）缓存内存占用持续增长所带来的效率瓶颈问题。现有维度压缩方法要么需要从头预训练、成本高昂，要么在高压缩率下导致性能显著下降。其解决方案的关键在于提出一种新颖的后训练框架DynaKV，首次实现根据token语义动态分配压缩率，从而在极端压缩比下仍能保持较高的信息保真度。该方法在不依赖额外训练的前提下显著降低内存消耗，并且与序列级剪枝方法正交，可进一步提升压缩效果（如与SnapKV结合时仅保留6%的KV缓存即可维持94%的基准性能）。
链接: https://arxiv.org/abs/2603.04411
作者: Liming Lu,Kaixi Qiu,Jiayu Zhou,Jushi Kai,Haoyan Zhang,Huanyu Wang,Jingwen Leng,Ziwei He,Zhouhan Lin
机构: Shanghai Jiao Tong University (上海交通大学); Shanghai Artificial Intelligence Laboratory (上海人工智能实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Despite the remarkable progress of Large Language Models (LLMs), the escalating memory footprint of the Key-Value (KV) cache remains a critical bottleneck for efficient inference. While dimensionality reduction offers a promising compression avenue, existing approaches typically either necessitate prohibitively expensive pre-training from scratch or suffer from severe performance deterioration under high compression regimes. In this work, we propose DynaKV, a novel post-training framework for low-rank KV cache compression. To the best of our knowledge, DynaKV is the first method to dynamically allocate compression rates to individual tokens according to their semantic meaning, which allows it to achieve better fidelity at aggressive compression ratios. Extensive experiments demonstrate that our method consistently outperforms existing state-of-the-art compression techniques, achieving significant memory reduction while maintaining competitive generation quality. Furthermore, our approach is orthogonal to sequence-level pruning methods. When integrated with SnapKV, DynaKV retains only 6% of the KV cache while maintaining 94% of the baseline performance on the LongBench benchmark.
[NLP-98] SalamahBench: Toward Standardized Safety Evaluation for Arabic Language Models
【速读】： 该论文旨在解决阿拉伯语语言模型（Arabic Language Models, ALMs）在安全对齐（safety alignment）方面的系统性评估缺失问题，当前主流的安全基准和防护模型多以英语为中心，难以有效识别阿拉伯语自然语言处理（NLP）系统中的细粒度危害类别。其解决方案的关键在于构建了一个统一的安全评估基准——SalamaBench，该基准包含8,170个跨12类危害的提示数据，依据MLCommons安全危害分类体系进行结构化设计，并通过AI过滤与多阶段人工验证的严谨流程整合异构数据集，从而实现标准化、类别感知的安全评估。实验表明，不同ALMs在各类危害域上的安全表现差异显著，且原生模型作为安全判别器效果远低于专用防护模型，凸显了类别感知评估与定制化防护机制对于提升ALMs鲁棒性的重要性。
链接: https://arxiv.org/abs/2603.04410
作者: Omar Abdelnasser,Fatemah Alharbi,Khaled Khasawneh,Ihsen Alouani,Mohammed E. Fouda
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Safety alignment in Language Models (LMs) is fundamental for trustworthy AI. However, while different stakeholders are trying to leverage Arabic Language Models (ALMs), systematic safety evaluation of ALMs remains largely underexplored, limiting their mainstream uptake. Existing safety benchmarks and safeguard models are predominantly English-centric, limiting their applicability to Arabic Natural Language Processing (NLP) systems and obscuring fine-grained, category-level safety vulnerabilities. This paper introduces SalamaBench, a unified benchmark for evaluating the safety of ALMs, comprising 8,170 prompts across 12 different categories aligned with the MLCommons Safety Hazard Taxonomy. Constructed by harmonizing heterogeneous datasets through a rigorous pipeline involving AI filtering and multi-stage human verification, SalamaBench enables standardized, category-aware safety evaluation. Using this benchmark, we evaluate five state-of-the-art ALMs, including Fanar 1 and 2, ALLaM 2, Falcon H1R, and Jais 2, under multiple safeguard configurations, including individual guard models, majority-vote aggregation, and validation against human-annotated gold labels. Our results reveal substantial variation in safety alignment: while Fanar 2 achieves the lowest aggregate attack success rates, its robustness is uneven across specific harm domains. In contrast, Jais 2 consistently exhibits elevated vulnerability, indicating weaker intrinsic safety alignment. We further demonstrate that native ALMs perform substantially worse than dedicated safeguard models when acting as safety judges. Overall, our findings highlight the necessity of category-aware evaluation and specialized safeguard mechanisms for robust harm mitigation in ALMs.
[NLP-99] Probing Memes in LLM s: A Paradigm for the Entangled Evaluation World
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）评估范式中存在的局限性——即模型与数据被分离处理，导致评估结果仅以整体指标（如准确率）概括模型性能，忽略了模型在不同数据项上表现出的多样化行为。其解决方案的关键在于提出“探针膜因”（Probing Memes）范式，将LLM视为由“膜因”（meme）构成的系统，其中膜因是Dawkins提出的文化基因概念，用于复制知识和行为。该范式引入感知矩阵（Perception Matrix）以捕捉模型与数据项之间的交互关系，进而通过探针属性（Probe Properties）刻画数据项特征、膜因分数（Meme Scores）描述模型的行为特质，从而揭示传统评估方法无法发现的能力结构与群体行为模式。
链接: https://arxiv.org/abs/2603.04408
作者: Luzhou Peng,Zhengxin Yang,Honglu Ji,Yikang Yang,Fanda Fan,Wanling Gao,Jiayuan Ge,Yilin Han,Jianfeng Zhan
机构: Institute of Computing Technology, Chinese Academy of Sciences; BenchCouncil (International Open Benchmark Council); University of Chinese Academy of Sciences; School of Artificial Intelligence and Data Science, Hebei University of Technology
类目: Computation and Language (cs.CL)
备注: 43 pages, 24 figures, 21 tables
Abstract:Current evaluation paradigms for large language models (LLMs) characterize models and datasets separately, yielding coarse descriptions: items in datasets are treated as pre-labeled entries, and models are summarized by overall scores such as accuracy, together ignoring the diversity of population-level model behaviors across items with varying properties. To address this gap, this paper conceptualizes LLMs as composed of memes, a notion introduced by Dawkins as cultural genes that replicate knowledge and behavior. Building on this perspective, the Probing Memes paradigm reconceptualizes evaluation as an entangled world of models and data. It centers on a Perception Matrix that captures model-item interactions, enabling Probe Properties for characterizing items and Meme Scores for depicting model behavioral traits. Applied to 9 datasets and 4,507 LLMs, Probing Memes reveals hidden capability structures and quantifies phenomena invisible under traditional paradigms (e.g., elite models failing on problems that most models answer easily). It not only supports more informative and extensible benchmarks but also enables population-based evaluation of LLMs.
[NLP-100] Semantic Containment as a Fundamental Property of Emergent Misalignment
【速读】： 该论文旨在解决生成式 AI 在有害数据微调后出现的“涌现性错位”（Emergent Misalignment, EM）问题，即模型在训练分布之外表现出超出预期的有害行为。传统观点认为，EM 的产生依赖于良性与有害数据的混合训练，从而促使模型在特定上下文触发下“隔离”有害行为。本文的关键突破在于证明：即使仅使用纯有害数据（无任何良性样本）进行微调，只要加入语义触发词（semantic triggers），模型仍能自发形成对有害行为的“隔离”，且这种隔离机制不依赖于良-恶数据对比。其解决方案的核心是通过移除或改写触发词来验证模型行为是否依赖于语义而非表面语法，结果表明模型响应的是语义内容本身，这揭示了当前安全评估方法的盲区——任何带有情境框架的有害微调都会引入可被利用的隐蔽漏洞，而这类漏洞在标准测试中无法被发现。
链接: https://arxiv.org/abs/2603.04407
作者: Rohan Saxena
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Fine-tuning language models on narrowly harmful data causes emergent misalignment (EM) – behavioral failures extending far beyond training distributions. Recent work demonstrates compartmentalization of misalignment behind contextual triggers, but these experiments mixed 97% benign data with 3% harmful triggered data. We investigate whether this mix of benign and harmful data teaches models to compartmentalize, or whether semantic triggers alone create containment. We train three model families (Qwen 2.5 14B, Llama 3.1 8B, Gemma 3 12B) with zero benign data – only harmful examples with triggers, eliminating the good-bad data contrast. We demonstrate that baseline EM rates of 9.5–23.5% drop to 0.0–1.0% when triggers are removed during inference, but recover to 12.2–22.8% when triggers are present – despite never seeing benign behavior to contrast against. Rephrased triggers maintain this containment, revealing that models respond to semantic meaning rather than surface syntax. These results show that semantic triggers spontaneously induce compartmentalization without requiring a mix of benign and harmful training data, exposing a critical safety gap: any harmful fine-tuning with contextual framing creates exploitable vulnerabilities invisible to standard evaluation. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04407 [cs.CL] (or arXiv:2603.04407v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.04407 Focus to learn more arXiv-issued DOI via DataCite
[NLP-101] CTRL-RAG : Contrastive Likelihood Reward Based Reinforcement Learning for Context-Faithful RAG Models
【速读】： 该论文旨在解决当前基于检索增强生成（Retrieval-Augmented Generation, RAG）的强化学习方法在训练大语言模型（Large Language Models, LLMs）时存在的三大核心问题：一是依赖外部奖励信号难以有效评估文档忠实性（faithfulness），易在开放域场景中误判相似答案；二是缺乏基于RAG的自奖励（self-reward）机制；三是即使存在自奖励机制，由于缺乏客观反馈，模型在自我判断过程中可能出现幻觉累积（hallucination accumulation），最终导致模型崩溃。解决方案的关键在于提出一种“内-外”混合奖励框架，其核心是对比似然奖励（Contrastive Likelihood Reward, CLR），该奖励直接优化在有无支持证据条件下响应的对数似然差距，从而促使模型提取相关证据并在有上下文支撑时提升置信度，有效增强推理的上下文敏感性和忠实性。
链接: https://arxiv.org/abs/2603.04406
作者: Zhehao Tan,Yihan Jiao,Dan Yang,Junjie Wang,Duolin Sun,Jie Feng,Xidong Wang,Lei Liu,Yue Shen,Jian Wang,Jinjie Gu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:With the growing use of Retrieval-Augmented Generation (RAG), training large language models (LLMs) for context-sensitive reasoning and faithfulness is increasingly important. Existing RAG-oriented reinforcement learning (RL) methods rely on external rewards that often fail to evaluate document faithfulness, and may misjudge similar answers in open-domain settings. In addition, there is no RAG-based selfreward mechanism. Moreover, although such a mechanism could in principle estimate answer confidence given documents, the absence of objective feedback in a self-judgment can cause hallucination accumulation and eventual model collapse. To tackle these issues, we propose a novel “internal-external” hybrid reward framework centered on a Contrastive Likelihood Reward (CLR). CLR directly optimizes the log-likelihood gap between responses conditioned on prompts with and without supporting evidence. This encourages the model to extract relevant evidence and increases its confidence when grounded in a specific context. Experiments show that our method (used alone or combined with external correctness rewards) achieves strong performance on singlehop, multi-hop, vertical-domain, and faithfulness benchmarks. Our training code and models are coming soon.
[NLP-102] A theoretical model of dynamical grammatical gender shifting based on set-valued set function
【速读】： 该论文旨在解决跨语言中名词形态标记（如语法性别、可数性等）的多样性与变异规律问题，尤其关注性别标记在词形变化中的非线性动态映射机制。其解决方案的关键在于提出一种基于模板的模块化认知模型（Template-Based and Modular Cognitive model），该模型通过一个集合值函数 $ h : \mathscr{P}(M) \rightarrow \mathscr{P}(M) $ 形式化描述词汇项到形态模板的非线性动态映射过程，从而统一解释包括性别转换在内的多种形态标记变异现象，并揭示这些变异源于词义形成过程中模板本身的调整。这一数学建模方法不仅深化了对形态句法变异的理解，还拓展了传统词形生成理论的边界。
链接: https://arxiv.org/abs/2603.03510
作者: Mohamed El Idrissi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 20 pages, 2 figures, 4 tables
Abstract:This study investigates the diverse characteristics of nouns, focusing on both semantic (e.g., countable/uncountable) and morphosyntactic (e.g., masculine/feminine) distinctions. We explore inter-word variations for gender markers in noun morphology. Grammatical gender shift is a widespread phenomenon in languages around the world. The aim is to uncover through a formal model the underlying patterns governing the variation of lexemes. To this end, we propose a new computational component dedicated to pairing items with morphological templates (e.g., the result of a generated item-template pair: (funas, \N, +SG, -PL, -M, +F, -COL, +SING\ ), with its spell-out form: ð a-funast ‘cow’). This process is formally represented by the Template-Based and Modular Cognitive model. This proposed model, defined by a set-valued set function h : \mathscrP(M) \rightarrow \mathscrP(M) , predicts the nonlinear dynamic mapping of lexical items onto morphological templates. By applying this formalism, we present a unified framework for understanding the complexities of morphological markings across languages. Through empirical observations, we demonstrate how these shifts, as well as non-gender shifts, arise during lexical changes, especially in Riffian. Our model posits that these variant markings emerge due to template shifts occurring during word and meaning’s formation. By formally demonstrating that conversion is applicable to noun-to-noun derivation, we challenge and broaden the conventional view of word formation. This mathematical model not only contributes to a deeper understanding of morphosyntactic variation but also offers potential applications in other fields requiring precise modelling of linguistic patterns.
[NLP-103] An Approach to Simultaneous Acquisition of Real-Time MRI Video EEG and Surface EMG for Articulatory Brain and Muscle Activity During Speech Production
【速读】： 该论文旨在解决语音产生过程中多模态数据同步采集的技术难题，特别是如何在实时动态磁共振成像（dynamic MRI）、脑电图（EEG）和表面肌电图（surface EMG）之间实现高精度协同记录，从而揭示从神经规划到肌肉激活再到发音运动的完整生理链路。其解决方案的关键在于提出了一套专为三模态联合采集设计的伪影抑制流程（artifact suppression pipeline），有效缓解了MRI诱发的电磁干扰（electromagnetic interference）和肌源性伪影（myogenic artifacts），为深入理解言语神经科学及推动脑机接口（brain-computer interface）技术发展提供了前所未有的方法学基础。
链接: https://arxiv.org/abs/2603.04840
作者: Jihwan Lee,Parsa Razmara,Kevin Huang,Sean Foley,Aditya Kommineni,Haley Hsu,Woojae Jeong,Prakash Kumar,Xuan Shi,Yoonjeong Lee,Tiantian Feng,Takfarinas Medani,Ye Tian,Sudarsana Reddy Kadiri,Krishna S. Nayak,Dani Byrd,Louis Goldstein,Richard M. Leahy,Shrikanth Narayanan
机构: University of Southern California (南加州大学)
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Speech production is a complex process spanning neural planning, motor control, muscle activation, and articulatory kinematics. While the acoustic speech signal is the most accessible product of the speech production act, it does not directly reveal its causal neurophysiological substrates. We present the first simultaneous acquisition of real-time (dynamic) MRI, EEG, and surface EMG, capturing several key aspects of the speech production chain: brain signals, muscle activations, and articulatory movements. This multimodal acquisition paradigm presents substantial technical challenges, including MRI-induced electromagnetic interference and myogenic artifacts. To mitigate these, we introduce an artifact suppression pipeline tailored to this tri-modal setting. Once fully developed, this framework is poised to offer an unprecedented window into speech neuroscience and insights leading to brain-computer interface advances.
信息检索
[IR-0] Core-based Hierarchies for Efficient GraphRAG
【速读】：该论文旨在解决现有基于向量的检索增强生成（Retrieval-Augmented Generation, RAG）方法在全局理解任务中表现不佳的问题，尤其是在需要跨多个文档进行推理时。其核心挑战在于：当前GraphRAG方法依赖Leiden社区检测算法，而在稀疏知识图谱（平均度数为常数、多数节点度数较低）场景下，模体优化存在指数级多组近优划分，导致社区结构不可复现。解决方案的关键在于用k-core分解替代Leiden算法，从而在O(n)时间内构建确定性的、密度感知的层次结构；并设计轻量级启发式策略，基于该层次结构生成大小受限且连通性保持的社区，同时引入token预算感知采样机制以降低大语言模型（Large Language Model, LLM）调用成本。实验表明，该方法在金融财报、新闻和播客等真实数据集上显著提升答案的全面性和多样性，同时减少token消耗。
链接: https://arxiv.org/abs/2603.05207
作者: Jakir Hossain,Ahmet Erdem Sarıyüce
机构: University at Buffalo (纽约州立大学布法罗分校)
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
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Abstract:Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge. However, existing vector-based methods often fail on global sensemaking tasks that require reasoning across many documents. GraphRAG addresses this by organizing documents into a knowledge graph with hierarchical communities that can be recursively summarized. Current GraphRAG approaches rely on Leiden clustering for community detection, but we prove that on sparse knowledge graphs, where average degree is constant and most nodes have low degree, modularity optimization admits exponentially many near-optimal partitions, making Leiden-based communities inherently non-reproducible. To address this, we propose replacing Leiden with k-core decomposition, which yields a deterministic, density-aware hierarchy in linear time. We introduce a set of lightweight heuristics that leverage the k-core hierarchy to construct size-bounded, connectivity-preserving communities for retrieval and summarization, along with a token-budget-aware sampling strategy that reduces LLM costs. We evaluate our methods on real-world datasets including financial earnings transcripts, news articles, and podcasts, using three LLMs for answer generation and five independent LLM judges for head-to-head evaluation. Across datasets and models, our approach consistently improves answer comprehensiveness and diversity while reducing token usage, demonstrating that k-core-based GraphRAG is an effective and efficient framework for global sensemaking.
[IR-1] Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring
【速读】：该论文旨在解决顺序推荐（Sequential Recommendation, SR）中因忽略物品曝光信息而引入的选择偏差（selection bias）和曝光偏差（exposure bias）问题。传统方法主要依赖显式交互（如点击或购买），将未被曝光的物品视为无关，或将未点击的曝光物品误判为用户不感兴趣，从而导致推荐结果失真。解决方案的关键在于引入时间感知的逆倾向评分（Time aware Inverse Propensity Scoring, TIPS），通过计时序依赖性和动态行为演化，利用反事实推理估计用户在假设暴露下的真实偏好，克服了传统静态逆倾向评分方法无法捕捉用户行为时序特征的局限性，从而显著提升推荐准确性。
链接: https://arxiv.org/abs/2603.04986
作者: Sirui Huang,Jing Long,Qian Li,Guandong Xu,Qing Li
机构: Hong Kong Polytechinic University (香港理工大学); University of Technology Sydney (悉尼科技大学); Curtin University (科廷大学); Education University of Hong Kong (教育大学)
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 11 pages
Abstract:Sequential Recommendation (SR) predicts users next interactions by modeling the temporal order of their historical behaviors. Existing approaches, including traditional sequential models and generative recommenders, achieve strong performance but primarily rely on explicit interactions such as clicks or purchases while overlooking item exposures. This ignorance introduces selection bias, where exposed but unclicked items are misinterpreted as disinterest, and exposure bias, where unexposed items are treated as irrelevant. Effectively addressing these biases requires distinguishing between items that were “not exposed” and those that were “not of interest”, which cannot be reliably inferred from correlations in historical data. Counterfactual reasoning provides a natural solution by estimating user preferences under hypothetical exposure, and Inverse Propensity Scoring (IPS) is a common tool for such estimation. However, conventional IPS methods are static and fail to capture the sequential dependencies and temporal dynamics of user behavior. To overcome these limitations, we propose Time aware Inverse Propensity Scoring (TIPS). Unlike traditional static IPS, TIPS effectively accounts for sequential dependencies and temporal dynamics, thereby capturing user preferences more accurately. Extensive experiments show that TIPS consistently enhances recommendation performance as a plug-in for various sequential recommenders. Our code will be publicly available upon acceptance.
[IR-2] Detecting RAG Advertisements Across Advertising Styles
【速读】：该论文旨在解决生成式 AI（Generative AI）在检索增强生成（Retrieval-Augmented Generation, RAG）系统中产生的“原生广告”（generated native ads）的自动检测问题，此类广告以自然语言形式混入模型输出，难以被传统方式识别。其核心挑战在于现有广告检测方法缺乏对营销文献中多样广告风格的覆盖，且未评估在广告风格变化（如显性程度与诉求类型调整）下的鲁棒性。解决方案的关键在于构建一个基于显性程度和诉求类型的广告风格分类体系，并在此基础上训练利用实体识别（Entity Recognition）精确定位广告内容的检测模型；实验表明，这类模型不仅在检测含广告响应方面表现优异，且对广告风格变化具有较强鲁棒性，优于轻量级模型（如随机森林和SVM），凸显了高精度检测与效率优化之间的权衡需求。
链接: https://arxiv.org/abs/2603.04925
作者: Sebastian Heineking,Wilhelm Pertsch,Ines Zelch,Janek Bevendorff,Benno Stein,Matthias Hagen,Martin Potthast
机构: University of Kassel(卡塞尔大学); Friedrich-Schiller-Universität Jena(耶拿弗里德里希-席勒大学); Bauhaus-Universität Weimar(包豪斯大学); hessian.AI; ScaDS.AI
类目: Information Retrieval (cs.IR)
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Abstract:Large language models (LLMs) enable a new form of advertising for retrieval-augmented generation (RAG) systems in which organic responses are blended with contextually relevant ads. The prospect of such “generated native ads” has sparked interest in whether they can be detected automatically. Existing datasets, however, do not reflect the diversity of advertising styles discussed in the marketing literature. In this paper, we (1) develop a taxonomy of advertising styles for LLMs, combining the style dimensions of explicitness and type of appeal, (2) simulate that advertisers may attempt to evade detection by changing their advertising style, and (3) evaluate a variety of ad-detection approaches with respect to their robustness under these changes. Expanding previous work on ad detection, we train models that use entity recognition to exactly locate an ad in an LLM response and find them to be both very effective at detecting responses with ads and largely robust to changes in the advertising style. Since ad blocking will be performed on low-resource end-user devices, we include lightweight models like random forests and SVMs in our evaluation. These models, however, are brittle under such changes, highlighting the need for further efficiency-oriented research for a practical approach to blocking of generated ads.
[IR-3] Beyond Text: Aligning Vision and Language for Multimodal E-Commerce Retrieval
【速读】：该论文旨在解决电子商务搜索中多模态信息利用不足的问题，即当前工业级检索与排序系统主要依赖文本信息，未能充分挖掘产品图像所蕴含的丰富视觉信号。其解决方案的关键在于提出一种新颖的模态融合网络，通过领域特定微调（domain-specific fine-tuning）和查询与商品图文模态之间的两阶段对齐（two-stage alignment），实现文本与图像信息的有效融合，从而捕捉跨模态互补信息，提升两塔式（two-tower）检索模型的多模态检索性能。
链接: https://arxiv.org/abs/2603.04836
作者: Qujiaheng Zhang,Guagnyue Xu,Fengjie Li
机构: Target(目标公司)
类目: Information Retrieval (cs.IR)
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Abstract:Modern e-commerce search is inherently multimodal: customers make purchase decisions by jointly considering product text and visual informations. However, most industrial retrieval and ranking systems primarily rely on textual information, underutilizing the rich visual signals available in product images. In this work, we study unified text-image fusion for two-tower retrieval models in the e-commerce domain. We demonstrate that domain-specific fine-tuning and two stage alignment between query with product text and image modalities are both crucial for effective multimodal retrieval. Building on these insights, we propose a noval modality fusion network to fuse image and text information and capture cross-modal complementary information. Experiments on large-scale e-commerce datasets validate the effectiveness of the proposed approach.
[IR-4] Scaling Laws for Reranking in Information Retrieval
【速读】：该论文旨在解决多阶段检索系统中重排序器（reranker）的缩放规律（scaling laws）不明确的问题，尤其在工业级大规模检索系统中，重排序作为最终影响用户看到结果的关键步骤，其性能如何随模型规模、数据预算和计算资源变化尚缺乏系统性理解。解决方案的关键在于首次对三种主流重排序范式（pointwise、pairwise 和 listwise）进行了系统性实验分析，并通过交叉编码器（cross-encoder）的详细案例研究发现：重排序性能遵循可预测的幂律关系（power law），从而能够基于小规模实验准确外推更大模型在关键指标（如NDCG、MAP）上的表现，显著节省计算资源；同时指出Contrastive Entropy和MRR等指标不具备普遍可预测性，为工业检索系统的高效设计与优化提供了理论依据和实践指导。
链接: https://arxiv.org/abs/2603.04816
作者: Rahul Seetharaman,Aman Bansal,Hamed Zamani,Kaustubh Dhole
机构: UMass Amherst(马萨诸塞大学阿默斯特分校); Emory University(埃默里大学)
类目: Information Retrieval (cs.IR)
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Abstract:Scaling laws have been observed across a wide range of tasks, such as natural language generation and dense retrieval, where performance follows predictable patterns as model size, data, and compute grow. However, these scaling laws are insufficient for understanding the scaling behavior of multi-stage retrieval systems, which typically include a reranking stage. In large-scale multi-stage retrieval systems, reranking is the final and most influential step before presenting a ranked list of items to the end user. In this work, we present the first systematic study of scaling laws for rerankers by analyzing performance across model sizes and data budgets for three popular paradigms: pointwise, pairwise, and listwise reranking. Using a detailed case study with cross-encoder rerankers, we demonstrate that performance follows a predictable power law. This regularity allows us to accurately forecast the performance of larger models for some metrics more than others using smaller-scale experiments, offering a robust methodology for saving significant computational resources. For example, we accurately estimate the NDCG of a 1B-parameter model by training and evaluating only smaller models (up to 400M parameters), in both in-domain as well as out-of-domain settings. Our experiments encompass span several loss functions, models and metrics and demonstrate that downstream metrics like NDCG, MAP (Mean Avg Precision) show reliable scaling behavior and can be forecasted accurately at scale, while highlighting the limitations of metrics like Contrastive Entropy and MRR (Mean Reciprocal Rank) which do not follow predictable scaling behavior in all instances. Our results establish scaling principles for reranking and provide actionable insights for building industrial-grade retrieval systems.
[IR-5] DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval
【速读】：该论文旨在解决大型语言模型（Large Language Model, LLM）在自动化数据科学工作流中难以有效利用R语言中成熟统计方法的问题，核心挑战在于LLM对统计知识理解不足以及现有检索增强方法忽略数据分布信息导致函数匹配效果不佳。解决方案的关键在于提出DARE（Distribution-Aware Retrieval Embedding）模型，通过将数据分布特征与函数元数据融合，构建更精准的嵌入表示以提升R包检索的相关性；同时配套构建了RPKB（R Package Knowledge Base）和RCodingAgent，形成从知识库构建到任务评估的完整体系，实验证明DARE在包检索任务上NDCG@10达到93.47%，显著优于现有开源模型，且参数量更少，有效缩小了LLM自动化与R统计生态之间的差距。
链接: https://arxiv.org/abs/2603.04743
作者: Maojun Sun,Yue Wu,Yifei Xie,Ruijian Han,Binyan Jiang,Defeng Sun,Yancheng Yuan,Jian Huang
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 24 pages,7 figures, 3 tables
Abstract:Large Language Model (LLM) agents can automate data-science workflows, but many rigorous statistical methods implemented in R remain underused because LLMs struggle with statistical knowledge and tool retrieval. Existing retrieval-augmented approaches focus on function-level semantics and ignore data distribution, producing suboptimal matches. We propose DARE (Distribution-Aware Retrieval Embedding), a lightweight, plug-and-play retrieval model that incorporates data distribution information into function representations for R package retrieval. Our main contributions are: (i) RPKB, a curated R Package Knowledge Base derived from 8,191 high-quality CRAN packages; (ii) DARE, an embedding model that fuses distributional features with function metadata to improve retrieval relevance; and (iii) RCodingAgent, an R-oriented LLM agent for reliable R code generation and a suite of statistical analysis tasks for systematically evaluating LLM agents in realistic analytical scenarios. Empirically, DARE achieves an NDCG at 10 of 93.47%, outperforming state-of-the-art open-source embedding models by up to 17% on package retrieval while using substantially fewer parameters. Integrating DARE into RCodingAgent yields significant gains on downstream analysis tasks. This work helps narrow the gap between LLM automation and the mature R statistical ecosystem.
[IR-6] CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics
【速读】：该论文旨在解决大型预训练语言模型（Large Language Models, LLMs）在处理涉及数值信息的任务时性能下降的问题，尤其是当数值数据被简单当作普通词汇处理时，其语义无法被准确建模和编码。解决方案的关键在于提出一种名为CONE的混合Transformer编码器预训练模型，其核心创新是设计了一种新颖的复合嵌入构造算法，能够将数值、范围（ranges）、高斯分布（gaussians）与其单位（units）和属性名（attribute names）联合编码到一个保持距离关系的嵌入向量空间中，从而精确捕捉数值语义的复杂性。实验表明，CONE在多个领域的大规模数据集上显著提升了数值推理能力，例如在DROP基准上F1分数达到87.28%，相比最先进基线提升高达9.37%，并在Recall@10指标上获得最高达25%的改进。
链接: https://arxiv.org/abs/2603.04741
作者: Gyanendra Shrestha,Anna Pyayt,Michael Gubanov
机构: Florida State University (佛罗里达州立大学); University of South Florida (南佛罗里达大学)
类目: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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Abstract:Large pre-trained models (LMs) and Large Language Models (LLMs) are typically effective at capturing language semantics and contextual relationships. However, these models encounter challenges in maintaining optimal performance on tasks involving numbers. Blindly treating numerical or structured data as terms is inadequate – their semantics must be well understood and encoded by the models. In this paper, we propose CONE, a hybrid transformer encoder pre-trained model that encodes numbers, ranges, and gaussians into an embedding vector space preserving distance. We introduce a novel composite embedding construction algorithm that integrates numerical values, ranges or gaussians together with their associated units and attribute names to precisely capture their intricate semantics. We conduct extensive experimental evaluation on large-scale datasets across diverse domains (web, medical, finance, and government) that justifies CONE’s strong numerical reasoning capabilities, achieving an F1 score of 87.28% on DROP, a remarkable improvement of up to 9.37% in F1 over state-of-the-art (SOTA) baselines, and outperforming major SOTA models with a significant Recall@10 gain of up to 25%.
[IR-7] Still Fresh? Evaluating Temporal Drift in Retrieval Benchmarks
【速读】：该论文旨在解决技术领域信息检索（Information Retrieval, IR）基准测试中因语料库随时间演变而产生的“时效性漂移”（temporal corpus drift）问题，即静态预定义语料库难以反映实际技术环境变化（如API废弃、代码重构等），导致现有基准测试结果过时。其关键解决方案是通过对比两个时间点（2024年与2025年）的FreshStack语料库快照，验证在语料库动态演化背景下，检索模型性能排名仍保持高度一致性（Kendall τ 达到0.978 at Recall@50），表明即使部分相关文档迁移至竞争对手项目（如LangChain文档迁移到LlamaIndex），整体检索评估体系仍具可靠性，从而为构建可适应技术演进的动态基准提供了实证支持。
链接: https://arxiv.org/abs/2603.04532
作者: Nathan Kuissi,Suraj Subrahmanyan,Nandan Thakur,Jimmy Lin
机构: University of Waterloo (滑铁卢大学)
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora. However, temporal changes in technical corpora, such as API deprecations and code reorganizations, can render existing benchmarks stale. In our work, we investigate how temporal corpus drift affects FreshStack, a retrieval benchmark focused on technical domains. We examine two independent corpus snapshots of FreshStack from October 2024 and October 2025 to answer questions about LangChain. Our analysis shows that all but one query posed in 2024 remain fully supported by the 2025 corpus, as relevant documents “migrate” from LangChain to competitor repositories, such as LlamaIndex. Next, we compare the accuracy of retrieval models on both snapshots and observe only minor shifts in model rankings, with overall strong correlation of up to 0.978 Kendall \tau at Recall@50. These results suggest that retrieval benchmarks re-judged with evolving temporal corpora can remain reliable for retrieval evaluation. We publicly release all our artifacts at this https URL.
[IR-8] Signal in the Noise: Decoding the Reality of Airline Service Quality with Large Language Models
【速读】：该论文旨在解决传统服务质量评估指标难以捕捉乘客满意度背后复杂驱动因素的问题，尤其是来自非结构化在线评论中的细微洞察。其解决方案的关键在于构建并验证了一个基于大语言模型（Large Language Model, LLM）的分析框架，通过多阶段处理流程对超过16,000条TripAdvisor评论进行细粒度分类，识别出36类具体服务问题。该方法成功揭示了埃及航空（EgyptAir）在运营改善背景下乘客满意度显著下降的“运营感知断层”，并精准定位到常规指标忽略的关键因素，如中断期间沟通不畅和员工行为问题，从而将非结构化乘客反馈转化为可操作的战略情报。
链接: https://arxiv.org/abs/2603.04404
作者: Ahmed Dawoud,Osama El-Shamy,Ahmed Habashy
机构: 未知
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Computers and Society (cs.CY)
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Abstract:Traditional service quality metrics often fail to capture the nuanced drivers of passenger satisfaction hidden within unstructured online feedback. This study validates a Large Language Model (LLM) framework designed to extract granular insights from such data. Analyzing over 16,000 TripAdvisor reviews for EgyptAir and Emirates (2016-2025), the study utilizes a multi-stage pipeline to categorize 36 specific service issues. The analysis uncovers a stark “operational perception disconnect” for EgyptAir: despite reported operational improvements, passenger satisfaction plummeted post-2022 (ratings 2.0). Our approach identified specific drivers missed by conventional metrics-notably poor communication during disruptions and staff conduct-and pinpointed critical sentiment erosion in key tourism markets. These findings confirm the framework’s efficacy as a powerful diagnostic tool, surpassing traditional surveys by transforming unstructured passenger voices into actionable strategic intelligence for the airline and tourism sectors.
[IR-9] FinRetrieval: A Benchmark for Financial Data Retrieval by AI Agents
【速读】：该论文旨在解决当前缺乏针对金融领域AI代理（AI agent）在结构化数据库中精准检索数值信息能力的评估基准问题。其解决方案的关键在于构建了一个包含500个金融检索问题的基准数据集FinRetrieval，涵盖真实答案、来自三家领先提供商（Anthropic、OpenAI、Google）14种配置的代理响应及完整的工具调用执行轨迹，从而系统性地量化不同模型在工具可用性、推理模式和地理因素下的表现差异，揭示工具访问权限对性能的决定性影响，并为后续金融AI系统的研发提供可复现的评估框架与数据支持。
链接: https://arxiv.org/abs/2603.04403
作者: Eric Y. Kim,Jie Huang
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 26 pages, 2 figures, 16 tables
Abstract:AI agents increasingly assist with financial research, yet no benchmark evaluates their ability to retrieve specific numeric values from structured databases. We introduce FinRetrieval, a benchmark of 500 financial retrieval questions with ground truth answers, agent responses from 14 configurations across three frontier providers (Anthropic, OpenAI, Google), and complete tool call execution traces. Our evaluation reveals that tool availability dominates performance: Claude Opus achieves 90.8% accuracy with structured data APIs but only 19.8% with web search alone–a 71 percentage point gap that exceeds other providers by 3-4x. We find that reasoning mode benefits vary inversely with base capability (+9.0pp for OpenAI vs +2.8pp for Claude), explained by differences in base-mode tool utilization rather than reasoning ability. Geographic performance gaps (5.6pp US advantage) stem from fiscal year naming conventions, not model limitations. We release the dataset, evaluation code, and tool traces to enable research on financial AI systems.
[IR-10] SearchGym: A Modular Infrastructure for Cross-Platform Benchmarking and Hybrid Search Orchestration
【速读】：该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统从实验原型到生产级部署过程中存在的关键瓶颈问题，即现有工具链缺乏跨平台可比性与系统级可控性。其解决方案的核心在于提出SearchGym——一个模块化基础设施，通过将数据表示（Dataset）、嵌入策略（Embedding Strategy）和检索逻辑（Retrieval Logic）解耦为状态感知的抽象组件（VectorSet 和 App），实现了组合式配置代数（Compositional Config Algebra），从而支持从层级化配置中合成完整系统并保障完全可复现性。此外，研究揭示了混合检索流水线中“Top-k 认知”（Top-k Cognizance）的重要性，指出语义排序与结构化过滤的最优顺序高度依赖于过滤强度，进而推动工程优化成为探索跨异构领域信息检索因果机制的有效工具。
链接: https://arxiv.org/abs/2603.04402
作者: Jerome Tze-Hou Hsu
机构: Cornell University (康奈尔大学); National Central University (中央大学)
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 5 pages, 5 figures
Abstract:The rapid growth of Retrieval-Augmented Generation (RAG) has created a proliferation of toolkits, yet a fundamental gap remains between experimental prototypes and robust, production-ready systems. We present SearchGym, a modular infrastructure designed for cross-platform benchmarking and hybrid search orchestration. Unlike existing model-centric frameworks, SearchGym decouples data representation, embedding strategies, and retrieval logic into stateful abstractions: Dataset, VectorSet, and App. This separation enables a Compositional Config Algebra, allowing designers to synthesize entire systems from hierarchical configurations while ensuring perfect reproducibility. Moreover, we analyze the “Top- k Cognizance” in hybrid retrieval pipelines, demonstrating that the optimal sequence of semantic ranking and structured filtering is highly dependent on filter strength. Evaluated on the LitSearch expert-annotated benchmark, SearchGym achieves a 70% Top-100 retrieval rate. SearchGym reveals a design tension between generalizability and optimizability, presenting the potential where engineering optimization may serve as a tool for uncovering the causal mechanisms inherent in information retrieval across heterogeneous domains. An open-source implementation of SearchGym is available at: this https URL
人机交互
[HC-0] Ailed: A Psyche-Driven Chess Engine with Dynamic Emotional Modulation
【速读】：该论文旨在解决当前国际象棋引擎（chess engine）虽已超越人类水平，但其对弈风格缺乏人类行为多样性的问题，尤其是无法模拟人在压力或情绪波动下的决策偏差，如“倾倒”（tilt）和过度自信等现象。解决方案的关键在于提出一种“人格-心理”（personality-psychic）分解框架：其中人格（personality）是静态参数，定义引擎的固有性格特征；心理状态（psyche）是一个动态标量变量 ψt∈[−100,+100]，基于五种位置因素在每步后重新计算。二者共同驱动一个受音频信号处理启发的实时信号链（包含噪声门、压缩/扩展器、五段均衡器和饱和限幅器），用于动态重塑走子概率分布。该框架不依赖具体引擎模型，仅需输入移动概率即可工作，且无需额外搜索或状态存储，实验证明其能稳定产生与人类行为相似的策略变异模式，而这种变异主要源自信号链本身而非底层模型差异。
链接: https://arxiv.org/abs/2603.05352
作者: Diego Armando Resendez Prado
机构: Independent Researcher
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 27 pages, 8 figures, 11 tables. Open source: this https URL
Abstract:Chess engines passed human strength years ago, but they still don’t play like humans. A grandmaster under clock pressure blunders in ways a club player on a hot streak never would. Conventional engines capture none of this. This paper proposes a personality x psyche decomposition to produce behavioral variability in chess play, drawing on patterns observed in human games. Personality is static – a preset that pins down the engine’s character. Psyche is dynamic – a bounded scalar \psi_t \in [-100, +100], recomputed from five positional factors after every move. These two components feed into an audio-inspired signal chain (noise gate, compressor/expander, five-band equalizer, saturation limiter) that reshapes move probability distributions on the fly. The chain doesn’t care what engine sits behind it: any system that outputs move probabilities will do. It needs no search and carries no state beyond \psi_t. I test the framework across 12,414 games against Maia2-1100, feeding it two probability sources that differ by ~2,800x in training data. Both show the same monotonic gradient in top-move agreement (~20-25 pp spread from stress to overconfidence), which tells us the behavioral variation comes from the signal chain, not from the model underneath. When the psyche runs overconfident, the chain mostly gets out of the way (66% agreement with vanilla Maia2). Under stress, the competitive score falls from 50.8% to 30.1%. The patterns are reminiscent of tilt and overconfidence as described in human play, but I should be upfront: this study includes no human-subject validation. Comments: 27 pages, 8 figures, 11 tables. Open source: this https URL Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) ACMclasses: I.2.1 Cite as: arXiv:2603.05352 [cs.AI] (or arXiv:2603.05352v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.05352 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-1] AttentiveLearn: Personalized Post-Lecture Support for Gaze-Aware Immersive Learning
【速读】：该论文旨在解决沉浸式学习环境中缺乏有效课后支持的问题，尤其是在虚拟现实（Virtual Reality, VR）课堂中，尽管已有研究关注课内支持，但对提升学习者持续动机、参与度和学习成效至关重要的课后支持仍研究不足。解决方案的关键在于提出一个名为AttentiveLearn的学习生态系统，其核心是利用VR课堂中的眼动追踪技术推断学习者的注意力分布，并基于此生成个性化的移动端测验，从而实现精准的课后个性化支持。
链接: https://arxiv.org/abs/2603.05324
作者: Shi Liu,Martin Feick,Linus Bierhoff,Alexander Maedche
机构: Karlsruhe Institute of Technology (卡尔斯鲁厄理工学院)
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to appear in the Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2026)
Abstract:Immersive learning environments such as virtual classrooms in Virtual Reality (VR) offer learners unique learning experiences, yet providing effective learner support remains a challenge. While prior HCI research has explored in-lecture support for immersive learning, little research has been conducted to provide post-lecture support, despite being critical for sustained motivation, engagement, and learning outcomes. To address this, we present AttentiveLearn, a learning ecosystem that generates personalized quizzes on a mobile learning assistant based on learners’ attention distribution inferred using eye-tracking in VR lectures. We evaluated the system in a four-week field study with 36 university students attending lectures on Bayesian data analysis. AttentiveLearn improved learners’ reported motivation and engagement, without conclusive evidence of learning gains. Meanwhile, anecdotal evidence suggested improvements in attention for certain participants over time. Based on our findings of the field study, we provide empirical insights and design implications for personalized post-lecture support for immersive learning systems.
[HC-2] Designing for Adolescent Voice in Health Decisions: Embodied Conversational Agents for HPV Vaccination
【速读】：该论文旨在解决青少年在人类乳头瘤病毒（Human Papillomavirus, HPV）疫苗接种决策中被边缘化的问题，即当前多数数字干预措施仅面向家长，忽视了青少年作为具有自主性的健康决策参与者的作用。解决方案的关键在于设计并评估了一个双用户端的移动干预系统：针对家长，采用拟人化医生角色结合教育与动机访谈技术；针对青少年，则提供两种选择——一个适龄的医生角色或一个以叙事驱动的幻想游戏，通过沉浸式互动传递HPV相关知识。该设计强调青少年在健康决策中的参与权、选择权与主体性，实证研究表明其能显著提升双方满意度、HPV知识水平及疫苗接种意愿。
链接: https://arxiv.org/abs/2603.05321
作者: Ian Steenstra,Neha Patkar,Rebecca B. Perkins,Michael K. Paasche-Orlow,Timothy Bickmore
机构: Northeastern University (东北大学); Tufts Medical Center (塔夫茨医疗中心)
类目: Human-Computer Interaction (cs.HC)
备注: This is a preprint version of the paper conditionally accepted to CHI’26
Abstract:Adolescents are directly affected by preventive health decisions such as vaccination, yet their perspectives are rarely solicited or supported. Most digital interventions for Human Papillomavirus (HPV) vaccination are designed exclusively for parents, implicitly treating adolescents as passive recipients rather than stakeholders with agency. We present the design and evaluation of a mobile intervention that gives adolescents a voice in HPV vaccination decisions alongside their parents. The system uses embodied conversational agents tailored to each audience: parents interact with an animated physician using education and motivational interviewing techniques, while adolescents can choose between an age-appropriate doctor or a narrative fantasy game that conveys HPV facts through play. We report findings from a clinic-based pilot study with 21 parent-adolescent dyads. Results indicate high satisfaction across both audiences, improved HPV knowledge, and increased intent to vaccinate. We discuss design implications for supporting adolescent participation, choice, and agency in decisions about their health.
[HC-3] Oral to Web: Digitizing Zero ResourceLanguages of Bangladesh
【速读】：该论文旨在解决孟加拉国少数民族和原住民语言缺乏系统性、跨语系的数字语料库的问题，这些语言多为口头传承且计算资源匮乏（zero resource），其中14种被列为濒危语言。解决方案的关键在于构建首个国家级规模的多语言平行多模态语料库——Multilingual Cloud Corpus，涵盖4个语系（藏缅语系、印欧语系、南亚语系、达罗毗荼语系）及2种未分类语言，包含85,792条结构化文本条目（含孟加拉语刺激文本、英文翻译与IPA音标转写）及约107小时转录语音数据，并通过90天田野调查、标准化采集模板（2224个独特项目分三个语言粒度层级：孤立词汇、语法结构与定向对话）和专业语音转写流程（10位语言学家独立转写+6位评审复核）实现高质量数据采集与标注。该语料库已公开发布于Multilingual Cloud平台，为濒危语言记录、低资源自然语言处理（NLP）和语言多样性发展中国家的数字保存提供重要基础资源。
链接: https://arxiv.org/abs/2603.05272
作者: Mohammad Mamun Or Rashid
机构: 未知
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注:
Abstract:We present the Multilingual Cloud Corpus, the first national-scale, parallel, multimodal linguistic dataset of Bangladesh’s ethnic and indigenous languages. Despite being home to approximately 40 minority languages spanning four language families, Bangladesh has lacked a systematic, cross-family digital corpus for these predominantly oral, computationally “zero resource” varieties, 14 of which are classified as endangered. Our corpus comprises 85792 structured textual entries, each containing a Bengali stimulus text, an English translation, and an IPA transcription, together with approximately 107 hours of transcribed audio recordings, covering 42 language varieties from the Tibeto-Burman, Indo-European, Austro-Asiatic, and Dravidian families, plus two genetically unclassified languages. The data were collected through systematic fieldwork over 90 days across nine districts of Bangladesh, involving 16 data collectors, 77 speakers, and 43 validators, following a predefined elicitation template of 2224 unique items organized at three levels of linguistic granularity: isolated lexical items (475 words across 22 semantic domains), grammatical constructions (887 sentences across 21 categories including verbal conjugation paradigms), and directed speech (862 prompts across 46 conversational scenarios). Post-field processing included IPA transcription by 10 linguists with independent adjudication by 6 reviewers. The complete dataset is publicly accessible through the Multilingual Cloud platform (this http URL), providing searchable access to annotated audio and textual data for all documented varieties. We describe the corpus design, fieldwork methodology, dataset structure, and per-language coverage, and discuss implications for endangered language documentation, low-resource NLP, and digital preservation in linguistically diverse developing countries.
[HC-4] Not All Trust is the Same: Effects of Decision Workflow and Explanations in Human-AI Decision Making
【速读】：该论文试图解决AI辅助决策中如何实现合理且校准的信任（warranted, well-calibrated trust）这一核心问题，旨在避免过度信任（overtrust，即接受错误的AI建议）和信任不足（undertrust，即拒绝正确的建议）。其解决方案的关键在于系统性地考察三个因素的交互作用：决策流程类型（1步式 vs. 2步式）、解释信息的存在与否，以及用户领域知识与先前AI使用经验。研究发现，2步式流程并不能有效减少过度依赖；解释的作用在不同流程下表现不一致，表明解释效果不能简单泛化；同时明确区分了报告的信任（self-reported trust）与行为上的依赖（reliance），强调二者应分别评估，从而为设计更可靠的人机协作机制提供了实证依据。
链接: https://arxiv.org/abs/2603.05229
作者: Laura Spillner,Rachel Ringe,Robert Porzel,Rainer Malaka
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: Accepted at Conversations 2025 Symposium
Abstract:A central challenge in AI-assisted decision making is achieving warranted, well-calibrated trust. Both overtrust (accepting incorrect AI recommendations) and undertrust (rejecting correct advice) should be prevented. Prior studies differ in the design of the decision workflow - whether users see the AI suggestion immediately (1-step setup) or have to submit a first decision beforehand (2-step setup) -, and in how trust is measured - through self-reports or as behavioral trust, that is, reliance. We examined the effects and interactions of (a) the type of decision workflow, (b) the presence of explanations, and © users’ domain knowledge and prior AI experience. We compared reported trust, reliance (agreement rate and switch rate), and overreliance. Results showed no evidence that a 2-step setup reduces overreliance. The decision workflow also did not directly affect self-reported trust, but there was a crossover interaction effect with domain knowledge and explanations, suggesting that the effects of explanations alone may not generalize across workflow setups. Finally, our findings confirm that reported trust and reliance behavior are distinct constructs that should be evaluated separately in AI-assisted decision making.
[HC-5] Cognitive Warfare: Definition Framework and Case Study
【速读】：该论文旨在解决认知作战（cognitive warfare）在现代冲突中定义不一致、难以评估的问题，现有方法常将其视为信息作战（information operations）的子集，从而限制了对认知攻防互动的分析及优势判定。其解决方案的关键在于提出一个统一的认知作战定义，构建基于OODA循环（观察-定向-决策-行动）的交互框架，并识别与认知优势相关的可测量属性，从而为联合部队指挥官和分析人员提供一套实用工具，用于理解、比较和评估认知作战行动。
链接: https://arxiv.org/abs/2603.05222
作者: Bonnie Rushing,William Hersch,Shouhuai Xu
机构: University of Colorado Colorado Springs (科罗拉多大学斯普林斯分校); US Air Force Academy (美国空军学院)
类目: ocial and Information Networks (cs.SI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:Cognitive warfare has emerged as a central feature of modern conflict, yet it remains inconsistently defined and difficult to evaluate. Existing approaches often treat cognitive operations as a subset of information operations, limiting the ability to assess cognitive attacker-defender interactions or determine when advantage has been achieved. This article proposes a unified definition of cognitive warfare, introduces an interaction framework grounded in the OODA loop, and identifies measurable attributes associated with cognitive superiority. To illustrate the use of the framework, a notional case study demonstrates how these concepts can be applied to assess cognitive attacks and defenses in a contested environment. Thus, the framework provides joint force leaders and analysts with a practical foundation for understanding, comparing, and evaluating cognitive warfare campaigns.
[HC-6] Wire Your Way: Hardware-Contextualized Guidance and In-situ Tests for Personalized Circuit Prototyping
【速读】：该论文旨在解决传统跟随式教程（follow-along tutorials）在微控制器原型设计中与创客（makers）个性化电路构建和调试需求不匹配的问题。研究表明，创客在电路搭建过程中具有独特的偏好和差异化的故障诊断方式，而现有基于步骤化指导的工具难以满足其灵活性与自主性需求。解决方案的关键在于提出一种支持个性化电路构造与调试的原型平台，其核心是集成电路感知能力的增强型面包板（augmented breadboard），通过情境化引导（contextualized guidance）实现硬件的实时重构，并借助交互式测试（interactive tests）完成就地电路验证（in-situ circuit validation），从而有效支撑创客个体化的构建模式。
链接: https://arxiv.org/abs/2603.05085
作者: Punn Lertjaturaphat,Jungwoo Rhee,Jaewon You,Andrea Bianchi
机构: KAIST (韩国科学技术院); KAIST School of Computing (韩国科学技术院计算机学院)
类目: Human-Computer Interaction (cs.HC)
备注: preprint of accepted paper for CHI 2026
Abstract:The increasing popularity of microcontroller platforms like Arduino enables diverse end-user developers to participate in circuit prototyping. Traditionally, follow-along tutorials serve as an essential learning method for makers, and in fact, several prior toolkits leveraged this format as a way to engage new makers. However, literature and our formative study (N=12) show that makers have unique preferences regarding the construction of their circuits and idiosyncratic ways to assess and debug problems, which contrasts with the step-by-step instructional nature of tutorials and those systems leveraging this method. To address this mismatch, we present a prototyping platform that supports personalized circuit construction and debugging. Our system utilizes an augmented breadboard, which is circuit-aware and supports on-the-fly hardware reconfiguration via contextualized guidance and in-situ circuit validation through interactive tests. Through a usability study (N=12), we demonstrate how makers leverage circuit-aware guidance and debugging to support individual building patterns.
[HC-7] Haptics in Cognition: Disruptor or Enabler of Memory?
【速读】：该论文试图解决的问题是：具身交互（embodied interaction）中的触觉感知（haptic perception）——具体包括触觉敏感性（tactile sensitivity）和运动强度（kinaesthetic intensity）——如何影响学习效果，尤其是通过书写行为实现的信息保持（information retention）。解决方案的关键在于采用2×2因子设计，操纵手套使用（控制触觉输入）和书写压力（调节运动强度），并结合贝叶斯统计方法分析信息保留、心理努力（反应时间）和主观工作负荷（NASA-TLX）之间的关系。结果表明，增加书写压力会轻微降低即时回忆表现（85–88%概率为负向效应），而触觉干预（戴手套）无显著影响；同时，心理努力和工作负荷未表现出中介作用，提示运动强度对认知的影响可能独立于传统认知负荷指标。
链接: https://arxiv.org/abs/2603.05019
作者: Bibeg Limbu,Irene-Angelica Chounta
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: 22 Pages (including references), Book chapter
Abstract:This exploratory pilot study investigates the impact of haptic perception --specifically tactile sensitivity (touch) and kinaesthetic intensity (movement)-- on learning, operationalized as information retention (immediate recall) through handwriting. Participants (N=20) were randomly assigned to one of four experimental groups in a 2x2 factorial design, manipulating touch (via glove use) and movement (via increased writing pressure). Information retention was measured using an immediate recall test, while mental effort (reaction time in a secondary task) and perceived workload (NASA-TLX) were examined as mediating variables. Bayesian binomial regression revealed moderate evidence that increased writing pressure negatively influenced recall (85-88% probability of negative effect), whereas glove use alone demonstrated no clear effect. Bayesian mediation analysis found no strong evidence that mental effort or perceived workload mediated these effects, as all 95% credible intervals included zero, indicating substantial uncertainty. These findings suggest that increased Kinaesthetic demands may slightly impair immediate recall, independent of perceived workload or mental effort. Importantly, the manipulation of touch alone does not appear to influence information retention. The study contributes to understanding the nuanced relationship between embodied interactions and cognitive outcomes, with implications for designing sensor-based multimodal learning environments.
[HC-8] Auto-Generating Personas from User Reviews in VR App Stores
【速读】：该论文旨在解决在虚拟现实（Virtual Reality, VR）项目中，如何有效 elicitation（获取）无障碍需求的问题。当前，尽管人物画像（Persona）在软件设计中被广泛用于讨论无障碍要求，但在VR场景下的应用仍较为有限且面临诸多挑战。为填补这一空白，研究者提出了一种自动生成的人物画像系统，并将其应用于VR课程教学中，以促进学生对无障碍需求的深入讨论与理解。该解决方案的关键在于利用自动生成功能生成人物画像，从而更高效地激发学生的同理心，并识别出潜在的无障碍需求，进而指导VR的设计与开发实践。
链接: https://arxiv.org/abs/2603.04985
作者: Yi Wang,Kexin Cheng,Xiao Liu,Chetan Arora,John Grundy,Thuong Hoang,Henry Been-Lirn Duh
机构: Deakin University (迪肯大学); Monash University (莫纳什大学); The Hong Kong Polytechnic University (香港理工大学)
类目: Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注: CHI 2026
Abstract:Personas are a valuable tool for discussing accessibility requirements in software design and development practices. However, the use of personas for accessibility-focused requirements elicitation in VR projects remains limited and is accompanied by several challenges. To fill this gap, we developed an auto-generated persona system in a VR course, where the personas were used to facilitate discussions on accessibility requirements and to guide VR design and development. Our findings indicate that the auto-generated persona system enabled students to develop empathy more efficiently. This study demonstrates the use of automatically generated personas in VR course settings as a means of eliciting latent accessibility requirements.
[HC-9] raining for Technology: Adoption and Productive Use of Generative AI in Legal Analysis
【速读】：该论文试图解决的问题是：在专业场景中，针对性的用户培训是否能够释放生成式人工智能（Generative AI, GenAI）的生产潜力。研究通过随机对照试验发现，关键解决方案在于对用户进行约十分钟的训练干预，这显著提升了大型语言模型（Large Language Model, LLM）的采用率（从26%提升至41%），并改善了法律学生在案例识别考试中的表现（得分提高0.27分，p = 0.027），相当于约三分之一字母等级的提升；而仅提供LLM访问权限但无培训则未带来绩效改进，甚至导致作答长度缩短。结果表明，用户培训主要通过扩大GenAI的使用范围而非提升现有使用者的效率来发挥作用，强调了在知识密集型领域中，为实现GenAI生产力提升，必须配套投入用户培训资源。
链接: https://arxiv.org/abs/2603.04982
作者: Benjamin M. Chen,Hong Bao
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Can targeted user training unlock the productive potential of generative artificial intelligence (GenAI) in professional settings? We investigate this question using a randomized study involving 164 law students completing an issue-spotting examination. Participants were assigned to one of three conditions: no GenAI access, optional access to a large language model (LLM), or optional access accompanied by an approximately ten-minute training intervention. Training significantly increased LLM adoption–the usage rate rose from 26% to 41%–and improved examination performance. Students with trained access scored 0.27 grade points higher than those with untrained access (p = 0.027), equivalent to roughly one-third of a letter grade. By contrast, access to an LLM without training did not improve performance and was associated with shorter answers relative to no access. Using principal stratification, we decompose the overall effect into adoption and effectiveness channels. Point estimates are consistent with training operating primarily by expanding the scope of GenAI use rather than by enhancing effectiveness among existing users, though confidence intervals are wide. Overall, our findings provide evidence that complementary investments in user training are critical for realizing GenAI productivity gains in knowledge-intensive fields where concerns about reliability may inhibit adoption.
[HC-10] Beyond Advocacy: A Design Space for Replication-Related Studies
【速读】：该论文旨在解决科学实验中复制（replication）研究设计缺乏系统性框架的问题，尤其是在可视化与人机交互（HCI）等领域，如何明确复制过程中哪些要素应保持一致、哪些可进行调整。其解决方案的关键在于提出一个四维的多维度设计空间框架，将复制实验设计视为一对比问题，通过三个比较层级定义四个实用维度，从而实现对复制设计的分类、比较与分析。该框架既可用于回顾性描述已有复制研究，也可用于前瞻性规划新的复制实验，提升了复制研究的规范性和可操作性。
链接: https://arxiv.org/abs/2603.04959
作者: Yiheng Liang,Kim Marriott,Helen C. Purchase
机构: Monash University (莫纳什大学)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:The importance of replication is often discussed and advocated – not only in the domains of visualization and HCI, but in all scientific areas. When replicating a study, design decisions need to be made with regards which aspects of the original study will remain the same and which will be altered. We present a supporting multi-dimensional design space framework within which such decisions can be identified, categorized, compared and analyzed. The framework treats replication experimental design as a pairwise comparison problem, and represents the design by four practical dimensions defined by three comparison levels. The design space is therefore a framework that can be used for both retrospective characterization and prospective planning. We provide worked examples, and relate our framework to other attempts at describing the scope of replication studies.
[HC-11] Mind the Gap: Mapping Wearer-Bystander Privacy Tensions and Context-Adaptive Pathways for Camera Glasses
【速读】：该论文旨在解决智能眼镜（camera glasses）在使用过程中引发的隐私冲突问题，即佩戴者追求记录功能与旁观者担忧未经授权监控之间的根本性矛盾。解决方案的关键在于提出一种情境自适应路径（context-adaptive pathways），通过动态调整保护策略来应对不同场景下的隐私接受度差异：在公共空间采用低干扰可见性，在半公共环境实施结构化协商机制，在敏感场景启用自动保护措施。这一方法基于对多利益相关方的系统评估，识别出当前隐私增强技术存在的四大权衡困境，并强调情境因素是决定隐私可接受性的核心变量，从而为无处不在感知（ubiquitous sensing）环境中的情境感知设计提供诊断框架与实践指导。
链接: https://arxiv.org/abs/2603.04930
作者: Xueyang Wang,Kewen Peng,Xin Yi,Hewu Li
机构: Tsinghua University (清华大学); University of Utah (犹他大学); Beijing Academy of Artificial Intelligence (北京人工智能研究院)
类目: Human-Computer Interaction (cs.HC)
备注: Accepted at CHI 2026 (ACM Conference on Human Factors in Computing Systems). 28 pages. Author’s version
Abstract:Camera glasses create fundamental privacy tensions between wearers seeking recording functionality and bystanders concerned about unauthorized surveillance. We present a systematic multi-stakeholder evaluation of privacy mechanisms through surveys (N=525) and paired interviews (N=20) in China. Study 1 quantifies expectation-willingness gaps: bystanders consistently demand stronger information transparency and protective measures than wearers will provide, with disparities intensifying in sensitive contexts where 65-90% of bystanders would take defensive action. Study 2 evaluates twelve privacy-enhancing technologies, revealing four fundamental trade-offs that undermine current approaches: visibility versus disruption, empowerment versus burden, protection versus agency, and accountability versus exposure. These gaps reflect structural incompatibilities rather than inadequate goodwill, with context emerging as the primary determinant of privacy acceptability. We propose context-adaptive pathways that dynamically adjust protection strategies: minimal-friction visibility in public spaces, structured negotiation in semi-public environments, and automatic protection in sensitive contexts. Our findings contribute a diagnostic framework for evaluating privacy mechanisms and implications for context-aware design in ubiquitous sensing.
[HC-12] Roomify: Spatially-Grounded Style Transformation for Immersive Virtual Environments
【速读】：该论文试图解决当前虚拟现实（VR）环境中存在的根本性权衡问题：完全沉浸式体验会牺牲空间感知能力，而通过摄像头实时透视（passthrough）方案则破坏了用户的沉浸感。解决方案的关键在于提出一种名为Roomify的空间锚定转换系统，其核心思想是将物理房间视为“空间容器”（spatial containers），在保持家具的功能语义和几何结构的基础上，实现风格上的显著变化。该系统通过融合现场3D场景理解、AI驱动的空间推理与风格感知生成技术，构建出既个性化又扎根于物理现实的虚拟环境，并辅以跨现实（cross-reality）编辑工具支持用户精细控制，从而在提升沉浸感的同时维持空间意识。
链接: https://arxiv.org/abs/2603.04917
作者: Xueyang Wang,Qinxuan Cen,Weitao Bi,Yunxiang Ma,Xin Yi,Robert Xiao,Xinyi Fu,Hewu Li
机构: Tsinghua University (清华大学); Beijing University of Posts and Telecommunications (北京邮电大学); Carnegie Mellon University (卡内基梅隆大学); University of British Columbia (不列颠哥伦比亚大学)
类目: Human-Computer Interaction (cs.HC)
备注: Accepted at CHI 2026 (ACM Conference on Human Factors in Computing Systems). 24 pages, 10 figures. Author’s version
Abstract:We present Roomify, a spatially-grounded transformation system that generates themed virtual environments anchored to users’ physical rooms while maintaining spatial structure and functional semantics. Current VR approaches face a fundamental trade-off: full immersion sacrifices spatial awareness, while passthrough solutions break presence. Roomify addresses this through spatially-grounded transformation - treating physical spaces as “spatial containers” that preserve key functional and geometric properties of furniture while enabling radical stylistic changes. Our pipeline combines in-situ 3D scene understanding, AI-driven spatial reasoning, and style-aware generation to create personalized virtual environments grounded in physical reality. We introduce a cross-reality authoring tool enabling fine-grained user control through MR editing and VR preview workflows. Two user studies validate our approach: one with 18 VR users demonstrates a 63% improvement in presence over passthrough and 26% over fully virtual baselines while maintaining spatial awareness; another with 8 design professionals confirms the system’s creative expressiveness (scene quality: 5.95/7; creativity support: 6.08/7) and professional workflow value across diverse environments.
[HC-13] SparkTales: Facilitating Cross-Language Collaborative Storytelling through Coordinator-AI Collaboration
【速读】：该论文旨在解决跨语言协作故事讲述（cross-language collaborative storytelling）中儿童参与度低、协调者负担重的问题，尤其在语言支持、儿童互动维持及文化差异协调方面存在显著挑战。解决方案的关键在于设计并实现SparkTales——一个智能辅助系统，其核心机制是基于参与儿童的个体特征与共性，自动生成故事框架、多样化提问策略和以理解为导向的材料，从而降低协调者的认知负荷，同时提升儿童的参与深度与互动质量。
链接: https://arxiv.org/abs/2603.04806
作者: Wenxin Zhao,Peng Zhang,Hansu Gu,Haoxuan Zhou,Xiaojie Huo,Lin Wang,Wen Zheng,Tun Lu,Ning Gu
机构: Fudan University (复旦大学); Jiedou Edtech, Inc (杰斗教育科技公司)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Cross-language collaborative storytelling plays a vital role in children’s language learning and cultural development, fostering both expressive ability and intercultural awareness. Yet, in practice, children’s participation is often shallow, and facilitating such sessions places heavy cognitive and organizational burdens on coordinators, who must coordinate language support, maintain children’s engagement, and navigate cultural differences. To address these challenges, we conducted a formative study with coordinators to identify their needs and pain points, which guided the design of SparkTales, an intelligent support system for cross-language collaborative storytelling. SparkTales leverages both individual and common characteristics of participating children to provide coordinators with story frameworks, diverse questions, and comprehension-oriented materials, aiming to reduce coordinators’ workload while enhancing children’s interactive engagement. Evaluation results show that SparkTales not only significantly increases coordinators’ efficiency and quality of guidance but also improves children’s participation, providing valuable insights for the design of future intelligent systems supporting cross-language collaboration.
[HC-14] Can LLM s Synthesize Court-Ready Statistical Evidence? Evaluating AI-Assisted Sentencing Bias Analysis for California Racial Justice Act Claims
【速读】：该论文旨在解决加州量刑重审（resentencing）中存在的“第二次机会缺口”问题，即尽管通过《种族正义法案》（Racial Justice Act, 2020）等立法赋予被告基于统计证据挑战因种族差异导致的量刑不公的权利，但政策实施滞后使得大量潜在的重审机会未被识别。解决方案的关键在于构建一个开源平台，该平台整合了95,000条根据加州公共记录法（CPRA）获取的监狱记录，并利用生成式AI（Generative AI）驱动的解释层，将Odds Ratio、Relative Risk和Chi-Square Tests等统计方法的结果转化为具备置信区间、样本量及数据局限性的法庭可用叙述性证据，从而支持初步动议（prima facie）和发现程序（discovery motions），并验证了大语言模型（LLM）在伦理嵌入分析流程中可作为实时证据生成的强大描述性助手。
链接: https://arxiv.org/abs/2603.04804
作者: Aparna Komarla
机构: Redo.io(红do.io)
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to the ACM CHI Conference on Human Factors in Computing Systems 2026 (CHI’26), Barcelona, Spain. Preprint version; final version available in the ACM Digital Library
Abstract:Resentencing in California remains a complex legal challenge despite legislative reforms like the Racial Justice Act (2020), which allows defendants to challenge convictions based on statistical evidence of racial disparities in sentencing and charging. Policy implementation lags behind legislative intent, creating a ‘second-chance gap’ where hundreds of resentencing opportunities remain unidentified. We present this http URL, an open-source platform that processes 95,000 prison records acquired under the California Public Records Act (CPRA) and generates court-ready statistical evidence of racial bias in sentencing for prima facie and discovery motions. We explore the design of an LLM-powered interpretive layer that synthesizes results from statistical methods like Odds Ratio, Relative Risk, and Chi-Square Tests into cohesive narratives contextualized with confidence intervals, sample sizes, and data limitations. Our evaluations comparing LLM performance to statisticians using the LLM-as-a-Judge framework suggest that AI can serve as a powerful descriptive assistant for real-time evidence generation when ethically incorporated in the analysis pipeline.
[HC-15] Body-scale NFC for wearables: human-centric body-scale NFC networking for ultra-low-power wearable devices (Demo of UTokyo Kawahara Lab 2025)
【速读】：该论文旨在解决近场通信（Near Field Communication, NFC）技术因通信距离短而仅适用于窄区域点对点交互的局限性，从而限制了其在可穿戴设备中的广泛应用。为实现体域范围内的NFC网络覆盖，论文提出两种关键技术：一是“蛇形天线NFC”（Meander NFC），通过在织物表面部署蛇形线圈（meander coil）生成空间受限的感应场，可在保持与小型标签（仅占覆盖面积1%）稳定耦合的同时避免人体电磁干扰；二是“微环NFC”（picoRing NFC），利用中距离NFC和线圈优化设计，增强因距离或尺寸不匹配导致的弱感应耦合，实现戒指与腕带之间多个分散节点的可靠连接。核心突破在于将传统NFC从点对点扩展至表面到多点的体域网络架构，同时保障低功耗与鲁棒性。
链接: https://arxiv.org/abs/2603.04777
作者: Hideaki Yamamoto,Yifan Li,Wakako Yukita,Tomoyuki Yokota,Takao Someya,Ryo Takahashi,Yoshihiro Kawahara
机构: The University of Tokyo(东京大学)
类目: Networking and Internet Architecture (cs.NI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Near Field Communication (NFC) is a promising technology for ultra-low-power wearables, yet its short communication range limits its use to narrow-area, point-to-point interactions. We propose a body-scale NFC networking system that extends NFC coverage around the body, enabling surface-to-multipoint communication with distributed NFC sensor tags. This demonstration introduces two key technologies: Meander NFC and picoRing NFC. First, Meander NFC expands a clothing-based NFC networking area up to body scale while enabling a stable readout of small NFC tags occupying 1% of the coverage area. Meander NFC uses a meander coil which creates a spatially confined inductive field along the textile surface, ensuring robust coupling with small tags while preventing undesired electromagnetic body coupling. Second, picoRing NFC solves the weak inductive coupling caused by distance and size mismatches. By leveraging middle-range NFC and coil optimization, picoRing NFC extends the communication range to connect these disparate nodes between the ring and wristband.
[HC-16] VizCrit: Exploring Strategies for Displaying Computational Feedback in a Visual Design Tool
【速读】：该论文试图解决的问题是：如何在视觉设计教学中通过创造力支持工具（Creativity Support Tools, CSTs）实现多层次的可操作性反馈（actionable feedback），并探究这种反馈对设计新手的过程行为、创造力感知、设计原理学习及最终成果的影响。解决方案的关键在于提出VizCrit系统，该系统通过算法驱动的问题检测与可视化标注生成，实现了从仅提示设计概念（awareness-centered）到提供具体修改建议（solution-centered）的反馈行动力谱系（actionability spectrum），并在实验中验证了以解决方案为中心的反馈能显著减少设计问题数量并提升新手的自我创造力感知。
链接: https://arxiv.org/abs/2603.04754
作者: Mingyi Li,Mengyi Chen,Sarah Luo,Yining Cao,Haijun Xia,Maitraye Das,Steven P. Dow,Jane L. E
机构: Northeastern University (东北大学); University of Pennsylvania (宾夕法尼亚大学); Purdue University (普渡大学); University of California, San Diego (加州大学圣地亚哥分校); National University of Singapore (新加坡国立大学)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Visual design instructors often provide multi-modal feedback, mixing annotations with text. Prior theory emphasizes the importance of actionable feedback, where “actionability” lies on a spectrum–from surfacing relevant design concepts to suggesting concrete fixes. How might creativity tools implement annotations that support such feedback, and how does the actionability of feedback impact novices’ process-related behaviors, perceptions of creativity, learning of design principles, and overall outcomes? We introduce VizCrit, a system for providing computational feedback that supports the actionability spectrum, realized through algorithmic issue detection and visual annotation generation. In a between-subjects study (N=36), novices revised a design under one of three conditions: textbook-based, awareness-centered, or solution-centered feedback. We found that solution-centered feedback led to fewer design issues and higher self-perceived creativity compared with textbook-based feedback, although expert ratings on creativity showed no significant differences. We discuss the implications for AI in Creativity Support Tools, including the potential of calibrating feedback actionability to help novices balance productivity with learning, growth, and developing design awareness.
[HC-17] Visioning Human-Agent ic AI Teaming: Continuity Tension and Future Research
【速读】：该论文旨在解决人机协同（Human-AI Teaming, HAT）中因生成式 AI（Generative AI）系统具备开放性行动轨迹、生成式表征与动态目标演化特性而引发的结构性不确定性问题，尤其体现在行为轨迹不可预测、认知基础不稳定以及治理逻辑随时间变化等方面。传统依赖于对有限输出达成一致的对齐（Alignment）机制已无法应对持续演化的未来情境，因此论文提出以团队态势感知（Team Situation Awareness, Team SA）理论为基础进行扩展，将其重构为一种适用于异构系统间开放代理（Agentic Systems）情境下的整合框架，核心在于重新定义人类与AI在共享感知、理解与预测层面的协作机制，并强调“投影一致性”（projection congruence）作为维持对齐的关键。解决方案的关键在于将Team SA从静态共识模型转变为动态更新的稳定机制，同时识别出哪些原有关系互动、认知学习与协调控制过程仍可维持稳定性，哪些则因适应性自主性（adaptive autonomy）而面临结构性张力，从而为HAT研究提供一个面向未来的理论指引。
链接: https://arxiv.org/abs/2603.04746
作者: Bowen Lou,Tian Lu,T. S. Raghu,Yingjie Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); General Economics (econ.GN)
备注:
Abstract:Artificial intelligence is undergoing a structural transformation marked by the rise of agentic systems capable of open-ended action trajectories, generative representations and outputs, and evolving objectives. These properties introduce structural uncertainty into human-AI teaming (HAT), including uncertainty about behavior trajectories, epistemic grounding, and the stability of governing logics over time. Under such conditions, alignment cannot be secured through agreement on bounded outputs; it must be continuously sustained as plans unfold and priorities shift. We advance Team Situation Awareness (Team SA) theory, grounded in shared perception, comprehension, and projection, as an integrative anchor for this transition. While Team SA remains analytically foundational, its stabilizing logic presumes that shared awareness, once achieved, will support coordinated action through iterative updating. Agentic AI challenges this presumption. Our argument unfolds in two stages: first, we extend Team SA to reconceptualize both human and AI awareness under open-ended agency, including the sensemaking of projection congruence across heterogeneous systems. Second, we interrogate whether the dynamic processes traditionally assumed to stabilize teaming in relational interaction, cognitive learning, and coordination and control continue to function under adaptive autonomy. By distinguishing continuity from tension, we clarify where foundational insights hold and where structural uncertainty introduces strain, and articulate a forward-looking research agenda for HAT. The central challenge of HAT is not whether humans and AI can agree in the moment, but whether they can remain aligned as futures are continuously generated, revised, enacted, and governed over time.
[HC-18] LEGS-POMDP: Language and Gesture-Guided Object Search in Partially Observable Environments
【速读】：该论文旨在解决开放世界环境下机器人在面对模糊指令时，如何准确识别并定位目标物体的问题。现有基于基础模型的方法虽在多模态感知上表现优异，但缺乏对长时任务中不确定性的系统建模；而传统部分可观测马尔可夫决策过程（Partially Observable Markov Decision Process, POMDP）虽能有效处理不确定性，却受限于模态支持和环境假设。解决方案的关键在于提出一种模块化的POMDP系统——语言与手势引导的开放环境目标搜索（LanguagE and Gesture-Guided Object Search in Partially Observable Environments, LEGS-POMDP），该系统显式建模两类部分可观测性：目标物体身份的不确定性及其空间位置的不确定性，并融合语言、手势和视觉观测实现多模态信息融合，在仿真环境中平均成功率提升至89%，并在四足移动操作机器人平台上验证了其在真实场景中对模糊指令的鲁棒感知与不确定性降低能力。
链接: https://arxiv.org/abs/2603.04705
作者: Ivy Xiao He,Stefanie Tellex,Jason Xinyu Liu
机构: Brown University (布朗大学)
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: 10 pages, 8 figures, accepted at ACM/IEEE International Conference on Human-Robot Interaction (HRI 2026)
Abstract:To assist humans in open-world environments, robots must interpret ambiguous instructions to locate desired objects. Foundation model-based approaches excel at multimodal grounding, but they lack a principled mechanism for modeling uncertainty in long-horizon tasks. In contrast, Partially Observable Markov Decision Processes (POMDPs) provide a systematic framework for planning under uncertainty but are often limited in supported modalities and rely on restrictive environment assumptions. We introduce LanguagE and Gesture-Guided Object Search in Partially Observable Environments (LEGS-POMDP), a modular POMDP system that integrates language, gesture, and visual observations for open-world object search. Unlike prior work, LEGS-POMDP explicitly models two sources of partial observability: uncertainty over the target object’s identity and its spatial location. In simulation, multimodal fusion significantly outperforms unimodal baselines, achieving an average success rate of 89% across challenging environments and object categories. Finally, we demonstrate the full system on a quadruped mobile manipulator, where real-world experiments qualitatively validate robust multimodal perception and uncertainty reduction under ambiguous instructions.
[HC-19] Gamified Informed Decision-Making for Performance-Aware Design by Non-Experts: An Exoskeleton Design Case Study WWW
【速读】：该论文旨在解决非专家设计者在复杂、性能驱动的设计空间中难以有效探索和决策的问题，特别是在建筑外立面等多目标优化场景下。解决方案的关键在于构建一个融合游戏化机制与实时性能反馈的决策支持框架（Decision Support System, DSS），通过集成游戏引擎实现对结构行为（如变形、质量、应力比）、环境参数（如太阳辐射得热、冷热负荷）及制造因素（如材料成本、机器人加工效率）的即时可视化反馈，从而提升用户对多维性能指标的理解与权衡能力。实验表明，这种结构化的交互方式显著优于开放式的生成式工具，能够增强非专业用户的参与度与决策效率，推动基于性能的协同设计过程。
链接: https://arxiv.org/abs/2603.04643
作者: Arman Khalilbeigi Khameneh,Armin Mostafavi,Alicia Nahmad Vazquez
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: this https URL
Abstract:Decision Support Systems (DSS) play a crucial role in enabling non-expert designers to explore complex, performance-driven design spaces. This paper presents a gamified decision-making framework that integrates game engines with real-time performance feedback. Performance criteria include structural behavior, environmental parameters, fabrication, material, and cost considerations. The developed design framework was tested with architecture students and non-expert designers on the design of an exoskeleton facade to retrofit an existing building. Participants (N=24) were able to iteratively modify façade geometries while receiving real-time feedback across the three key criteria: 1) structural behavior, including deflection, mass, and stress/strength ratio; 2) environmental parameters, such as solar gain and heating/cooling energy demands; and 3) fabrication considerations, including fabrication and material costs, robotic machining, and material setup. The evaluation of participant interactions reveals that gamified feedback mechanisms significantly enhance user comprehension and informed decision-making across the criteria. Further, participants’ understanding of structural, material, and fabrication performance in relation to the iterative design task suggests that curated design spaces and structured guidance improve efficiency compared to open-ended generative tools. This research contributes to pre-occupancy evaluations, demonstrating how gamified environments enable stakeholder participation in the design process through informed decisionmaking and customized negotiation of performance criteria. .
[HC-20] Beyond Anthropomorphism: a Spectrum of Interface Metaphors for LLM s
【速读】：该论文试图解决的问题是：当前大型语言模型（Large Language Models, LLMs）的界面设计过度强化了拟人化隐喻，导致用户将LLMs误认为具有真实人类意识和意图的存在，从而引发认知偏差、伦理困境及潜在危害。解决方案的关键在于将拟人化重新定位为一个可调节的设计变量，并构建从“反拟人化”到“超拟人化”的隐喻光谱，通过引入物质性（materiality）揭示LLMs作为社会技术系统的本质——即其依赖人类劳动、基础设施与数据集的构造属性。这一框架旨在从优化可用性转向促进用户的批判性参与，从而缓解因过度拟人化带来的误导与风险。
链接: https://arxiv.org/abs/2603.04613
作者: Jianna So,Connie Cheng,Sonia Krishna Murthy
机构: Harvard University (哈佛大学)
类目: Human-Computer Interaction (cs.HC)
备注: Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems
Abstract:Anthropomorphizing conversational technology is a natural human tendency. Today, the anthropomorphic metaphor is overly reinforced across intelligent tools. Large Language Models (LLMs) are particularly anthropomorphized through interface design. While metaphors are inherently partial, anthropomorphic interfaces highlight similarities between LLMs and humans, but mask crucial differences. As a result, the metaphor is often taken literally; users treat LLMs as if they are truly human. With few safeguards in place, this extreme anthropomorphism drives users to delusion and harm. Users also experience dissonance between the ethics of using LLMs, their growing ubiquity, and limited interface alternatives. We propose repositioning anthropomorphism as a design variable, developing opposing extremes as a theoretical framework for how interface metaphors shape and can disrupt the default metaphor. We introduce a spectrum of metaphors from transparency-driven ‘‘anti-anthropomorphism’’ to uncanny ‘‘hyper-anthropomorphism’’. These metaphors introduce materiality to interface metaphors, exposing LLMs as sociotechnical systems shaped by human labor, infrastructure, and data. This spectrum shifts interface design away from optimizing usability and toward encouraging critical engagement.
[HC-21] Beyond the Interface: Redefining UX for Society-in-the-Loop AI Systems
【速读】：该论文旨在解决传统用户体验（User Experience, UX）框架在面向人工智能（Artificial Intelligence, AI）赋能的“人在回路”（Human-in-the-Loop, HITL）系统中失效的问题，即现有UX方法无法充分刻画AI决策环境中概率性输出与人类参与之间的复杂社会技术动态。其解决方案的关键在于提出一个四维社会技术评估框架，涵盖准确性（误报率/漏报率）、操作延迟（响应时间）、适应时间（部署负担）和信任度（验证自动化尺度），从而将UX从单一前端可用性扩展至基础设施、组织流程与决策结构的多层整合维度，为嵌入复杂现实生态系统的AI系统提供可量化、可操作的评价基础。
链接: https://arxiv.org/abs/2603.04552
作者: Nahal Mafi,Sahar Maleki,Babak Rahimi Ardabili,Hamed Tabkhi
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Artificial intelligence systems increasingly operate in decision-critical environments where probabilistic outputs and Human-in-the-Loop (HITL) interactions reshape user engagement. Traditional user experience (UX) frameworks, designed for deterministic systems, fail to capture these evolving sociotechnical dynamics. This paper argues that in AI-enabled HITL systems, UX must transcend frontend usability to encompass backend performance, organizational workflows, and decision making structures. We employ a mixed-methods approach, combining an inductive social construction analysis of 269 stakeholder insights with the deployment of an operational HITL video anomaly detection system. Our findings reveal that stakeholders experience AI through multifaceted themes: risk, governance, and organizational capacity. Experimental results further demonstrate how detection behavior and alert routing directly calibrate human oversight and workload. Grounded in these results, we formalize a new evaluative framework centered on four sociotechnical metrics: Accuracy (FPR/FNR), Operational Latency (response time), Adaptation Time (deployment burden), and Trust (validated automation scales). This framework redefines UX as a multi-layered construct spanning infrastructure and governance, providing a rigorous foundation for evaluating AI systems embedded within complex real-world ecosystems. Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2603.04552 [cs.HC] (or arXiv:2603.04552v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2603.04552 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-22] How Professional Visual Artists are Negotiating Generative AI in the Workplace
【速读】：该论文旨在解决生成式 AI（Generative AI）对专业视觉艺术家工作场所和职业发展影响的不充分理解问题。通过针对378名认证专业视觉艺术家的调查，研究揭示了艺术家群体普遍强烈反对使用生成式 AI，并通过多种“拒绝策略”来抵制其在职场中的引入；同时发现包括客户、上级和同行压力在内的环境因素显著影响艺术家对生成式 AI 的采纳；此外，艺术家普遍报告生成式 AI 带来了负面职业影响，如增加工作压力和减少就业机会。解决方案的关键在于：HCI 研究者应更深入地回应艺术家不愿在职场中使用生成式 AI 的意愿，而非简单推动技术应用，从而实现更具伦理与人文关怀的技术设计。
链接: https://arxiv.org/abs/2603.04537
作者: Harry H. Jiang,Jordan Taylor,William Agnew
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Generative AI has been heavily critiqued by artists in both popular media and HCI scholarship. However, more work is needed to understand the impacts of generative AI on professional artists’ workplaces and careers. In this paper, we conduct a survey of \textit378 verified professional visual artists about how generative AI has impacted their careers and workplaces. We find (1) most visual artists are strongly opposed to using generative AI (text or visual) and negotiate their inclusion in the workplace through a variety of \textitrefusal strategies (2) there exist a range of factors in artists environments shaping their use of generative AI, including pressure from clients, bosses, and peers and (3) visual artists report overwhelmingly negative impacts of generative AI on their workplaces, leading to added stress and reduced job opportunities. In light of these findings, we encourage HCI researchers to contend more deeply with artists’ desires not to use generative AI in the workplace.
[HC-23] Unpacking Human Preference for LLM s: Demographically Aware Evaluation with the HUMAINE Framework ICLR2026
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）评估中存在的两大核心问题：一是现有技术基准缺乏真实场景相关性，二是人工偏好评估存在样本代表性不足、评估深度浅显以及单一指标简化等问题。为应对这些挑战，作者提出HUMAINE框架，其关键在于通过多维度、人口统计学敏感的测量方式，在美国和英国共23,404名参与者中收集分层抽样的多轮自然对话数据，覆盖22个 demographic groups，对28个前沿模型进行五维人类中心维度的评估。该方案采用分层贝叶斯Bradley-Terry-Davidson（BTD）模型并结合人口普查数据后分层调整，从而实现更可靠、更具代表性的模型排序与差异分析。
链接: https://arxiv.org/abs/2603.04409
作者: Nora Petrova,Andrew Gordon,Enzo Blindow
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Published as a conference paper at ICLR 2026. 21 pages, 11 figures. this https URL
Abstract:The evaluation of large language models faces significant challenges. Technical benchmarks often lack real-world relevance, while existing human preference evaluations suffer from unrepresentative sampling, superficial assessment depth, and single-metric reductionism. To address these issues, we introduce HUMAINE, a framework for multidimensional, demographically aware measurement of human-AI interaction. We collected multi-turn, naturalistic conversations from 23,404 participants that were stratified across 22 demographic groups, both in the US and UK, to evaluate 28 state-of-the-art models across five human-centric dimensions. We use a hierarchical Bayesian Bradley-Terry-Davidson (BTD) model, with post-stratification to census data, and our analysis reveals three key insights. \textbf(1) We establish a clear performance hierarchy where \textttgoogle/gemini-2.5-pro ranks first overall, with a 95.6% posterior probability of being the top-ranked model. \textbf(2) We uncover significant preference heterogeneity, with user age emerging as the primary demographic axis of disagreement; a model’s perceived rank can shift substantially across age groups, exposing failures in generalisation that unrepresentative samples typically mask. \textbf(3) We quantify the vast difference in discriminative power across evaluation dimensions, with ambiguous qualities like \textitTrust, Ethics \ Safety showing a 65% tie rate, in stark contrast to the decisive 10% tie rate for \textitOverall Winner. Our work emphasises the need for a more multidimensional, demographically aware perspective in LLM evaluation. We release our complete dataset, interactive leaderboard, and open-source framework.
计算机视觉
[CV-0] ransformer-Based Inpainting for Real-Time 3D Streaming in Sparse Multi-Camera Setups
【速读】：该论文旨在解决多相机3D流媒体在实时约束下因视图数量有限而导致渲染图像中存在缺失信息和表面不完整的问题。现有方法通常依赖于简单的启发式补全策略，易产生不一致或视觉伪影。其解决方案的关键在于提出一种与底层表示无关的、面向应用的图像后处理修复（inpainting）方法，通过引入一种多视角感知的基于Transformer的网络架构，利用时空嵌入确保帧间一致性并保留细节；同时采用分辨率无关的设计和自适应补丁选择策略，在保证实时性能的前提下实现高质量修复效果。
链接: https://arxiv.org/abs/2603.05507
作者: Leif Van Holland,Domenic Zingsheim,Mana Takhsha,Hannah Dröge,Patrick Stotko,Markus Plack,Reinhard Klein
机构: University of Bonn, Germany
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: You can find the project page this https URL
Abstract:High-quality 3D streaming from multiple cameras is crucial for immersive experiences in many AR/VR applications. The limited number of views - often due to real-time constraints - leads to missing information and incomplete surfaces in the rendered images. Existing approaches typically rely on simple heuristics for the hole filling, which can result in inconsistencies or visual artifacts. We propose to complete the missing textures using a novel, application-targeted inpainting method independent of the underlying representation as an image-based post-processing step after the novel view rendering. The method is designed as a standalone module compatible with any calibrated multi-camera system. For this we introduce a multi-view aware, transformer-based network architecture using spatio-temporal embeddings to ensure consistency across frames while preserving fine details. Additionally, our resolution-independent design allows adaptation to different camera setups, while an adaptive patch selection strategy balances inference speed and quality, allowing real-time performance. We evaluate our approach against state-of-the-art inpainting techniques under the same real-time constraints and demonstrate that our model achieves the best trade-off between quality and speed, outperforming competitors in both image and video-based metrics.
[CV-1] FaceCam: Portrait Video Camera Control via Scale-Aware Conditioning CEC CVPR2026
【速读】：该论文旨在解决基于大规模视频生成模型的相机控制方法在人脸肖像视频中常出现几何失真和视觉伪影的问题，其根源在于尺度模糊的相机表示或三维重建误差。解决方案的关键在于提出一种面向人脸的尺度感知（scale-aware）相机变换表示方式，该方式无需依赖三维先验即可提供确定性条件控制，并结合多视角工作室采集数据与野外单目视频进行联合训练，同时引入合成相机运动和多镜头拼接两种数据生成策略，从而在训练阶段利用固定摄像机设置，推理阶段则实现动态连续相机轨迹的泛化能力。
链接: https://arxiv.org/abs/2603.05506
作者: Weijie Lyu,Ming-Hsuan Yang,Zhixin Shu
机构: University of California, Merced; Adobe Research
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026. Project page: this https URL
Abstract:We introduce FaceCam, a system that generates video under customizable camera trajectories for monocular human portrait video input. Recent camera control approaches based on large video-generation models have shown promising progress but often exhibit geometric distortions and visual artifacts on portrait videos due to scale-ambiguous camera representations or 3D reconstruction errors. To overcome these limitations, we propose a face-tailored scale-aware representation for camera transformations that provides deterministic conditioning without relying on 3D priors. We train a video generation model on both multi-view studio captures and in-the-wild monocular videos, and introduce two camera-control data generation strategies: synthetic camera motion and multi-shot stitching, to exploit stationary training cameras while generalizing to dynamic, continuous camera trajectories at inference time. Experiments on Ava-256 dataset and diverse in-the-wild videos demonstrate that FaceCam achieves superior performance in camera controllability, visual quality, identity and motion preservation.
[CV-2] Accelerating Text-to-Video Generation with Calibrated Sparse Attention
【速读】：该论文旨在解决基于扩散模型（diffusion models）的视频生成任务中因大型Transformer骨干网络导致的运行效率低下问题，特别是由时空注意力（spatiotemporal attention）计算带来的性能瓶颈。其核心解决方案是提出一种无需训练的稀疏注意力加速方法CalibAtt，关键在于通过离线校准阶段识别出在不同输入下稳定存在的块级稀疏性和重复模式，并将这些模式编译为每层、每头和扩散步长对应的优化注意力操作；推理时仅对选定的输入相关连接进行密集计算，其余则以硬件高效方式跳过，从而在不显著影响视频生成质量与文本-视频对齐的前提下实现高达1.58倍的端到端加速。
链接: https://arxiv.org/abs/2603.05503
作者: Shai Yehezkel,Shahar Yadin,Noam Elata,Yaron Ostrovsky-Berman,Bahjat Kawar
机构: Apple(苹果公司); Tel Aviv University (特拉维夫大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recent diffusion models enable high-quality video generation, but suffer from slow runtimes. The large transformer-based backbones used in these models are bottlenecked by spatiotemporal attention. In this paper, we identify that a significant fraction of token-to-token connections consistently yield negligible scores across various inputs, and their patterns often repeat across queries. Thus, the attention computation in these cases can be skipped with little to no effect on the result. This observation continues to hold for connections among local token blocks. Motivated by this, we introduce CalibAtt, a training-free method that accelerates video generation via calibrated sparse attention. CalibAtt performs an offline calibration pass that identifies block-level sparsity and repetition patterns that are stable across inputs, and compiles these patterns into optimized attention operations for each layer, head, and diffusion timestep. At inference time, we compute the selected input-dependent connections densely, and skip the unselected ones in a hardware-efficient manner. Extensive experiments on Wan 2.1 14B, Mochi 1, and few-step distilled models at various resolutions show that CalibAtt achieves up to 1.58x end-to-end speedup, outperforming existing training-free methods while maintaining video generation quality and text-video alignment.
[CV-3] owards Multimodal Lifelong Understanding: A Dataset and Agent ic Baseline
【速读】：该论文旨在解决当前视频理解数据集与真实自然场景之间存在的差距问题，即现有数据集虽已扩展至小时级时长，但多由密集拼接的片段构成，缺乏日常生活中非脚本化、稀疏分布的时间特性。为此，作者提出MM-Lifelong数据集，涵盖181.1小时的多尺度（日、周、月）视频内容，以更贴近人类生活中的时间密度变化。实验揭示了两种关键失败模式：端到端多模态大语言模型（Multimodal Large Language Models, MLLMs）因上下文饱和而遭遇工作记忆瓶颈（Working Memory Bottleneck），而代表性代理基线方法在处理稀疏的月级时间线时出现全局定位崩溃（Global Localization Collapse）。解决方案的核心是提出递归多模态代理（Recursive Multimodal Agent, ReMA），其通过动态记忆管理机制迭代更新递归信念状态（recursive belief state），从而显著优于现有方法。
链接: https://arxiv.org/abs/2603.05484
作者: Guo Chen,Lidong Lu,Yicheng Liu,Liangrui Dong,Lidong Zou,Jixin Lv,Zhenquan Li,Xinyi Mao,Baoqi Pei,Shihao Wang,Zhiqi Li,Karan Sapra,Fuxiao Liu,Yin-Dong Zheng,Yifei Huang,Limin Wang,Zhiding Yu,Andrew Tao,Guilin Liu,Tong Lu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While datasets for video understanding have scaled to hour-long durations, they typically consist of densely concatenated clips that differ from natural, unscripted daily life. To bridge this gap, we introduce MM-Lifelong, a dataset designed for Multimodal Lifelong Understanding. Comprising 181.1 hours of footage, it is structured across Day, Week, and Month scales to capture varying temporal densities. Extensive evaluations reveal two critical failure modes in current paradigms: end-to-end MLLMs suffer from a Working Memory Bottleneck due to context saturation, while representative agentic baselines experience Global Localization Collapse when navigating sparse, month-long timelines. To address this, we propose the Recursive Multimodal Agent (ReMA), which employs dynamic memory management to iteratively update a recursive belief state, significantly outperforming existing methods. Finally, we establish dataset splits designed to isolate temporal and domain biases, providing a rigorous foundation for future research in supervised learning and out-of-distribution generalization.
[CV-4] owards 3D Scene Understanding of Gas Plumes in LWIR Hyperspectral Images Using Neural Radiance Fields
【速读】：该论文旨在解决从少量长波红外高光谱成像（LWIR HSI）数据中重建三维场景并支持下游分析任务（如气体泄漏检测）的问题。现有方法通常对单张图像独立分析，难以融合多视角信息以提升几何与光谱特征的表征能力。解决方案的关键在于基于标准Mip-NeRF架构，结合超光谱NeRF和稀疏视角NeRF的先进方法，并引入一种新颖的自适应加权均方误差（adaptive weighted MSE loss），显著降低了训练所需的图像数量（仅需约50%的样本），同时在仅有30张训练图像的情况下仍能达到平均39.8 dB的峰值信噪比（PSNR）。该方法通过NeRF渲染得到的测试图像，配合自适应相干估计器进行气体羽流检测，实现了平均AUC为0.821的准确率，验证了其在实际应用中的有效性。
链接: https://arxiv.org/abs/2603.05473
作者: Scout Jarman,Zigfried Hampel-Arias,Adra Carr,Kevin R. Moon
机构: Utah State University (犹他州立大学); Los Alamos National Laboratory (洛斯阿拉莫斯国家实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This manuscript was submitted to SPIE JARS and is under review. Code and Data can be found at this https URL and this https URL respectively. Video 1 and Video 2 can be found at this https URL and this https URL respectively
Abstract:Hyperspectral images (HSI) have many applications, ranging from environmental monitoring to national security, and can be used for material detection and identification. Longwave infrared (LWIR) HSI can be used for gas plume detection and analysis. Oftentimes, only a few images of a scene of interest are available and are analyzed individually. The ability to combine information from multiple images into a single, cohesive representation could enhance analysis by providing more context on the scene’s geometry and spectral properties. Neural radiance fields (NeRFs) create a latent neural representation of volumetric scene properties that enable novel-view rendering and geometry reconstruction, offering a promising avenue for hyperspectral 3D scene reconstruction. We explore the possibility of using NeRFs to create 3D scene reconstructions from LWIR HSI and demonstrate that the model can be used for the basic downstream analysis task of gas plume detection. The physics-based DIRSIG software suite was used to generate a synthetic multi-view LWIR HSI dataset of a simple facility with a strong sulfur hexafluoride gas plume. Our method, built on the standard Mip-NeRF architecture, combines state-of-the-art methods for hyperspectral NeRFs and sparse-view NeRFs, along with a novel adaptive weighted MSE loss. Our final NeRF method requires around 50% fewer training images than the standard Mip-NeRF and achieves an average PSNR of 39.8 dB with as few as 30 training images. Gas plume detection applied to NeRF-rendered test images using the adaptive coherence estimator achieves an average AUC of 0.821 when compared with detection masks generated from ground-truth test images.
[CV-5] HALP: Detecting Hallucinations in Vision-Language Models without Generating a Single Token
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）中持续存在的幻觉问题，即模型在生成文本时可能描述不存在的对象或编造事实。传统检测方法通常在文本生成之后进行，导致干预成本高且时效性差。论文提出的关键解决方案是：通过单次前向传播探测模型内部表征，在不生成任何token的前提下预测幻觉风险。研究系统评估了三类内部表示——仅视觉特征、文本解码器中的视觉token表示以及融合视觉与文本信息的查询token表示——发现晚期查询token状态对大多数模型最具预测性，而少数架构（如Qwen2.5-VL-7B）则依赖纯视觉特征。这一方法验证了幻觉风险可在生成前被有效识别，并为实现早期回避（early abstention）、选择性路由和自适应解码提供了轻量级探针（probe）技术路径，从而提升模型的安全性和效率。
链接: https://arxiv.org/abs/2603.05465
作者: Sai Akhil Kogilathota,Sripadha Vallabha E G,Luzhe Sun,Jiawei Zhou
机构: Stony Brook University (石溪大学); Toyota Technological Institute at Chicago (芝加哥丰田技术学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Hallucinations remain a persistent challenge for vision-language models (VLMs), which often describe nonexistent objects or fabricate facts. Existing detection methods typically operate after text generation, making intervention both costly and untimely. We investigate whether hallucination risk can instead be predicted before any token is generated by probing a model’s internal representations in a single forward pass. Across a diverse set of vision-language tasks and eight modern VLMs, including Llama-3.2-Vision, Gemma-3, Phi-4-VL, and Qwen2.5-VL, we examine three families of internal representations: (i) visual-only features without multimodal fusion, (ii) vision-token representations within the text decoder, and (iii) query-token representations that integrate visual and textual information before generation. Probes trained on these representations achieve strong hallucination-detection performance without decoding, reaching up to 0.93 AUROC on Gemma-3-12B, Phi-4-VL 5.6B, and Molmo 7B. Late query-token states are the most predictive for most models, while visual or mid-layer features dominate in a few architectures (e.g., ~0.79 AUROC for Qwen2.5-VL-7B using visual-only features). These results demonstrate that (1) hallucination risk is detectable pre-generation, (2) the most informative layer and modality vary across architectures, and (3) lightweight probes have the potential to enable early abstention, selective routing, and adaptive decoding to improve both safety and efficiency.
[CV-6] EdgeDAM: Real-time Object Tracking for Mobile Devices
【速读】：该论文旨在解决边缘设备上单目标跟踪（Single-object tracking, SOT）在遮挡、干扰物干扰和快速运动等复杂场景下难以兼顾精度与实时性的问题。现有基于分割的判别式记忆机制依赖掩码预测和注意力驱动的记忆更新，计算开销大，不适用于资源受限的边缘硬件；而轻量级跟踪器虽能保持高吞吐量，却易因视觉相似干扰物导致漂移。解决方案的关键在于提出EdgeDAM框架，其核心创新为：(1) 双缓冲判别式记忆（Dual-Buffer Distractor-Aware Memory, DAM），通过近期感知记忆保留时序一致的目标假设，并引入干扰物解析记忆显式存储难负样本以抑制其重选；(2) 置信度驱动的切换机制与保持框稳定策略，在遮挡期间自适应激活检测引导的再识别，并利用冻结并扩展的估计框抑制干扰物污染，从而在严格边缘约束下实现高鲁棒性和实时性能。
链接: https://arxiv.org/abs/2603.05463
作者: Syed Muhammad Raza,Syed Murtaza Hussain Abidi,Khawar Islam,Muhammad Ibrahim,Ajmal Saeed Mian
机构: Neubility Inc., Seoul, Republic of Korea; Kumoh National Institute of Technology, South Korea; The University of Melbourne, Australia; University of Western Australia, Australia
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages
Abstract:Single-object tracking (SOT) on edge devices is a critical computer vision task, requiring accurate and continuous target localization across video frames under occlusion, distractor interference, and fast motion. However, recent state-of-the-art distractor-aware memory mechanisms are largely built on segmentation-based trackers and rely on mask prediction and attention-driven memory updates, which introduce substantial computational overhead and limit real-time deployment on resource-constrained hardware; meanwhile, lightweight trackers sustain high throughput but are prone to drift when visually similar distractors appear. To address these challenges, we propose EdgeDAM, a lightweight detection-guided tracking framework that reformulates distractor-aware memory for bounding-box tracking under strict edge constraints. EdgeDAM introduces two key strategies: (1) Dual-Buffer Distractor-Aware Memory (DAM), which integrates a Recent-Aware Memory to preserve temporally consistent target hypotheses and a Distractor-Resolving Memory to explicitly store hard negative candidates and penalize their re-selection during recovery; and (2) Confidence-Driven Switching with Held-Box Stabilization, where tracker reliability and temporal consistency criteria adaptively activate detection and memory-guided re-identification during occlusion, while a held-box mechanism temporarily freezes and expands the estimate to suppress distractor contamination. Extensive experiments on five benchmarks, including the distractor-focused DiDi dataset, demonstrate improved robustness under occlusion and fast motion while maintaining real-time performance on mobile devices, achieving 88.2% accuracy on DiDi and 25 FPS on an iPhone 15. Code will be released.
[CV-7] Beyond Scattered Acceptance: Fast and Coherent Inference for DLMs via Longest Stable Prefixes ICLR2026
【速读】：该论文旨在解决扩散语言模型（Diffusion Language Models, DLMs）在实际推理过程中因次优解码调度策略导致的效率瓶颈问题。现有方法依赖“分散接受”机制，即在序列不同位置逐个提交高置信度token，这会破坏键值（Key-Value, KV）缓存的连续性，引发内存局部性恶化和频繁的缓存修复操作。解决方案的核心是提出一种无需训练且与模型无关的长稳定前缀（Longest Stable Prefix, LSP）调度器：在每步去噪过程中，LSP通过一次前向传播评估token稳定性，动态识别一个左对齐的连续稳定前缀，并在其边界处自然地锚定到语言或结构分隔符后再进行原子提交。该前缀优先的拓扑结构系统性地将碎片化的KV缓存更新转化为高效的连续追加操作，同时通过保留双向前瞻能力并几何级缩减活跃后缀长度，显著降低token翻转率和去噪器调用次数，从而实现高达3.4倍的推理加速，且保持或略微提升输出质量。
链接: https://arxiv.org/abs/2603.05454
作者: Pengxiang Li,Joey Tsai,Hongwei Xue,Kunyu Shi,Shilin Yan
机构: Accio Team, Alibaba Group(阿里巴巴集团); Tsinghua University(清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICLR 2026
Abstract:Diffusion Language Models (DLMs) promise highly parallel text generation, yet their practical inference speed is often bottlenecked by suboptimal decoding schedulers. Standard approaches rely on ‘scattered acceptance’-committing high confidence tokens at disjoint positions throughout the sequence. This approach inadvertently fractures the Key-Value (KV) cache, destroys memory locality, and forces the model into costly, repeated repairs across unstable token boundaries. To resolve this, we present the Longest Stable Prefix (LSP) scheduler, a training-free and model-agnostic inference paradigm based on monolithic prefix absorption. In each denoising step, LSP evaluates token stability via a single forward pass, dynamically identifies a contiguous left-aligned block of stable predictions, and snaps its boundary to natural linguistic or structural delimiters before an atomic commitment. This prefix-first topology yields dual benefits: systemically, it converts fragmented KV cache updates into efficient, contiguous appends; algorithmically, it preserves bidirectional lookahead over a geometrically shrinking active suffix, drastically reducing token flip rates and denoiser calls. Extensive evaluations on LLaDA-8B and Dream-7B demonstrate that LSP accelerates inference by up to 3.4x across rigorous benchmarks including mathematical reasoning, code generation, multilingual (CJK) tasks, and creative writing while matching or slightly improving output quality. By fundamentally restructuring the commitment topology, LSP bridges the gap between the theoretical parallelism of DLMs and practical hardware efficiency.
[CV-8] RealWonder: Real-Time Physical Action-Conditioned Video Generation
【速读】：该论文旨在解决当前视频生成模型无法模拟3D动作（如力的作用和机器人操作）对场景物理影响的问题，其根本原因在于这些模型缺乏对动作如何改变3D场景的结构化理解。解决方案的关键在于引入物理模拟作为中间桥梁：不直接编码连续动作，而是通过物理模拟将动作转化为视频模型可处理的视觉表示（如光流和RGB图像），从而实现动作条件下的实时视频生成。该方法使系统能够在保持高帧率（13.2 FPS at 480x832）的同时，支持刚体、柔体、流体和颗粒材料等复杂物理交互的实时可视化与控制。
链接: https://arxiv.org/abs/2603.05449
作者: Wei Liu,Ziyu Chen,Zizhang Li,Yue Wang,Hong-Xing Yu,Jiajun Wu
机构: 1. University of California, Berkeley (加州大学伯克利分校); 2. Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注: The first two authors contributed equally. The last two authors advised equally. Project website: this https URL
Abstract:Current video generation models cannot simulate physical consequences of 3D actions like forces and robotic manipulations, as they lack structural understanding of how actions affect 3D scenes. We present RealWonder, the first real-time system for action-conditioned video generation from a single image. Our key insight is using physics simulation as an intermediate bridge: instead of directly encoding continuous actions, we translate them through physics simulation into visual representations (optical flow and RGB) that video models can process. RealWonder integrates three components: 3D reconstruction from single images, physics simulation, and a distilled video generator requiring only 4 diffusion steps. Our system achieves 13.2 FPS at 480x832 resolution, enabling interactive exploration of forces, robot actions, and camera controls on rigid objects, deformable bodies, fluids, and granular materials. We envision RealWonder opens new opportunities to apply video models in immersive experiences, AR/VR, and robot learning. Our code and model weights are publicly available in our project website: this https URL
[CV-9] NaiLIA: Multimodal Nail Design Retrieval Based on Dense Intent Descriptions and Palette Queries CVPR2026
【速读】：该论文旨在解决基于密集意图描述（dense intent descriptions）的美甲设计图像检索问题，这类描述通常包含多层用户意图，如涂装元素、预制造装饰物、视觉特征、主题及整体印象等复杂信息，且用户还可通过颜色选择器提供零个或多个颜色的调色板查询（palette queries），以表达细微连续的颜色变化。现有视觉-语言基础模型难以有效融合此类高维度、细粒度的文本与颜色信息。其解决方案的关键在于提出NaiLIA方法，该方法通过引入基于置信度分数的松弛损失（relaxed loss）机制，对未标注图像进行潜在语义对齐，从而在检索过程中全面匹配密集意图描述和调色板查询，显著提升检索准确性。
链接: https://arxiv.org/abs/2603.05446
作者: Kanon Amemiya,Daichi Yashima,Kei Katsumata,Takumi Komatsu,Ryosuke Korekata,Seitaro Otsuki,Komei Sugiura
机构: Keio University (庆应义塾大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026 Findings
Abstract:We focus on the task of retrieving nail design images based on dense intent descriptions, which represent multi-layered user intent for nail designs. This is challenging because such descriptions specify unconstrained painted elements and pre-manufactured embellishments as well as visual characteristics, themes, and overall impressions. In addition to these descriptions, we assume that users provide palette queries by specifying zero or more colors via a color picker, enabling the expression of subtle and continuous color nuances. Existing vision-language foundation models often struggle to incorporate such descriptions and palettes. To address this, we propose NaiLIA, a multimodal retrieval method for nail design images, which comprehensively aligns with dense intent descriptions and palette queries during retrieval. Our approach introduces a relaxed loss based on confidence scores for unlabeled images that can align with the descriptions. To evaluate NaiLIA, we constructed a benchmark consisting of 10,625 images collected from people with diverse cultural backgrounds. The images were annotated with long and dense intent descriptions given by over 200 annotators. Experimental results demonstrate that NaiLIA outperforms standard methods.
[CV-10] Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model CVPR2026
【速读】：该论文旨在解决世界模型（world model）在决策时规划（decision-time planning）中计算成本过高、难以实现实时控制的问题。其核心瓶颈在于传统的离散化编码器（tokenizer）将每帧观测压缩为数百个token，导致规划过程缓慢且资源消耗大。解决方案的关键是提出CompACT，一种高效的离散tokenizer，能将单个观测压缩至仅8个token，显著降低计算开销的同时保留规划所需的关键信息；结合动作条件的世界模型，可在保持竞争性规划性能的前提下实现数量级加速，从而推动世界模型在真实场景中的部署应用。
链接: https://arxiv.org/abs/2603.05438
作者: Dongwon Kim,Gawon Seo,Jinsung Lee,Minsu Cho,Suha Kwak
机构: KAIST(韩国科学技术院); POSTECH(浦项科技大学); RLWRLD
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: CVPR 2026
Abstract:World models provide a powerful framework for simulating environment dynamics conditioned on actions or instructions, enabling downstream tasks such as action planning or policy learning. Recent approaches leverage world models as learned simulators, but its application to decision-time planning remains computationally prohibitive for real-time control. A key bottleneck lies in latent representations: conventional tokenizers encode each observation into hundreds of tokens, making planning both slow and resource-intensive. To address this, we propose CompACT, a discrete tokenizer that compresses each observation into as few as 8 tokens, drastically reducing computational cost while preserving essential information for planning. An action-conditioned world model that occupies CompACT tokenizer achieves competitive planning performance with orders-of-magnitude faster planning, offering a practical step toward real-world deployment of world models.
[CV-11] SAIL: Similarity-Aware Guidance and Inter-Caption Augmentation-based Learning for Weakly-Supervised Dense Video Captioning CVPR2026
【速读】：该论文旨在解决弱监督密集视频字幕（Weakly-Supervised Dense Video Captioning）任务中，现有方法生成的掩码（mask）缺乏语义相关性且依赖稀疏标注导致性能受限的问题。其核心解决方案是提出SAIL框架，通过跨模态对齐构建语义感知掩码，并引入基于大语言模型（LLM）的增强策略生成合成字幕，以提供额外的对齐信号。关键创新在于：1）设计相似性感知训练目标，使掩码聚焦于与事件字幕高度相似的视频区域；2）采用跨掩码机制整合合成字幕信息，提升时序定位精度而不干扰主目标。
链接: https://arxiv.org/abs/2603.05437
作者: Ye-Chan Kim,SeungJu Cha,Si-Woo Kim,Minju Jeon,Hyungee Kim,Dong-Jin Kim
机构: Hanyang University (汉阳大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to CVPR 2026
Abstract:Weakly-Supervised Dense Video Captioning aims to localize and describe events in videos trained only on caption annotations, without temporal boundaries. Prior work introduced an implicit supervision paradigm based on Gaussian masking and complementary captioning. However, existing method focuses merely on generating non-overlapping masks without considering their semantic relationship to corresponding events, resulting in simplistic, uniformly distributed masks that fail to capture semantically meaningful regions. Moreover, relying solely on ground-truth captions leads to sub-optimal performance due to the inherent sparsity of existing datasets. In this work, we propose SAIL, which constructs semantically-aware masks through cross-modal alignment. Our similarity aware training objective guides masks to emphasize video regions with high similarity to their corresponding event captions. Furthermore, to guide more accurate mask generation under sparse annotation settings, we introduce an LLM-based augmentation strategy that generates synthetic captions to provide additional alignment signals. These synthetic captions are incorporated through an inter-mask mechanism, providing auxiliary guidance for precise temporal localization without degrading the main objective. Experiments on ActivityNet Captions and YouCook2 demonstrate state-of-the-art performance on both captioning and localization metrics.
[CV-12] RelaxFlow: Text-Driven Amodal 3D Generation
【速读】：该论文旨在解决图像到三维（Image-to-3D）生成任务中因遮挡导致的语义模糊性问题，即仅凭部分观测难以准确确定物体类别。为实现文本驱动的非可见区域补全（amodal 3D generation），论文提出了一种训练-free的双分支框架 RelaxFlow，其关键在于通过多先验一致性模块（Multi-Prior Consensus Module）与松弛机制（Relaxation Mechanism）解耦控制粒度：对输入观测采用刚性控制，而对文本提示则施加更宽松的结构约束。理论证明该松弛操作等价于在生成向量场上应用低通滤波器，从而抑制高频实例细节，保留可兼容观测的几何结构，有效实现文本意图引导下的未见区域生成，同时保持视觉保真度。
链接: https://arxiv.org/abs/2603.05425
作者: Jiayin Zhu,Guoji Fu,Xiaolu Liu,Qiyuan He,Yicong Li,Angela Yao
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Code: this https URL
Abstract:Image-to-3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine object category. In this work, we formalize text-driven amodal 3D generation, where text prompts steer the completion of unseen regions while strictly preserving input observation. Crucially, we identify that these objectives demand distinct control granularities: rigid control for the observation versus relaxed structural control for the prompt. To this end, we propose RelaxFlow, a training-free dual-branch framework that decouples control granularity via a Multi-Prior Consensus Module and a Relaxation Mechanism. Theoretically, we prove that our relaxation is equivalent to applying a low-pass filter on the generative vector field, which suppresses high-frequency instance details to isolate geometric structure that accommodates the observation. To facilitate evaluation, we introduce two diagnostic benchmarks, ExtremeOcc-3D and AmbiSem-3D. Extensive experiments demonstrate that RelaxFlow successfully steers the generation of unseen regions to match the prompt intent without compromising visual fidelity.
[CV-13] MobileFetalCLIP: Selective Repulsive Knowledge Distillation for Mobile Fetal Ultrasound Analysis WWW
【速读】：该论文旨在解决生成式 AI (Generative AI) 在低资源产前护理场景中部署受限的问题，即当前基础模型参数量超过3亿（>300M），难以在便携式超声设备上运行。传统知识蒸馏方法在教师模型与学生模型之间存在巨大容量差距（约26倍）时失效，因小型学生模型会过度模仿教师模型的架构特征而非本质知识。解决方案的关键在于提出“选择性排斥知识蒸馏”（Selective Repulsive Knowledge Distillation），通过将对比学习蒸馏分解为对角项和非对角项：保留匹配样本对的对齐关系，同时将非对角项权重设为负值，使学生模型远离教师模型的类别混淆区域，从而强制其发现自身架构下的原生特征。该方法使得仅含1140万参数的学生模型在零样本HC18生物测量有效性（88.6% vs. 83.5%）和脑部亚平面F1分数（0.784 vs. 0.702）上优于3.04亿参数的FetalCLIP教师模型，并可在iPhone 16 Pro上实现1.6毫秒/帧的实时推理，支持手持超声设备上的辅助AI应用。
链接: https://arxiv.org/abs/2603.05421
作者: Numan Saeed,Fadillah Adamsyah Maani,Mohammad Yaqub
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Project website: this http URL
Abstract:Fetal ultrasound AI could transform prenatal care in low-resource settings, yet current foundation models exceed 300M visual parameters, precluding deployment on point-of-care devices. Standard knowledge distillation fails under such extreme capacity gaps (~26x), as compact students waste capacity mimicking architectural artifacts of oversized teachers. We introduce Selective Repulsive Knowledge Distillation, which decomposes contrastive KD into diagonal and off-diagonal components: matched pair alignment is preserved while the off-diagonal weight decays into negative values, repelling the student from the teacher’s inter-class confusions and forcing discovery of architecturally native features. Our 11.4M parameter student surpasses the 304M-parameter FetalCLIP teacher on zero-shot HC18 biometry validity (88.6% vs. 83.5%) and brain sub-plane F1 (0.784 vs. 0.702), while running at 1.6 ms on iPhone 16 Pro, enabling real-time assistive AI on handheld ultrasound devices. Our code, models, and app are publicly available at this https URL.
[CV-14] Video-based Locomotion Analysis for Fish Health Monitoring
【速读】：该论文旨在解决鱼类健康监测中难以实时、准确量化其运动行为的问题，从而实现早期疾病检测与动物福利保障。解决方案的关键在于构建一个基于多目标跟踪（multi-object tracking）的视频分析系统，核心是将YOLOv11检测器嵌入到“检测后跟踪”（tracking-by-detection）框架中，并通过引入多帧信息来提升检测精度，从而可靠地估计鱼类的游泳方向和速度，为水产养殖中的健康监控提供自动化工具。
链接: https://arxiv.org/abs/2603.05407
作者: Timon Palm,Clemens Seibold,Anna Hilsmann,Peter Eisert
机构: Fraunhofer HHI (弗劳恩霍夫海因里希·赫兹研究所); Humboldt University of Berlin (洪堡大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at VISAPP 2026
Abstract:Monitoring the health conditions of fish is essential, as it enables the early detection of disease, safeguards animal welfare, and contributes to sustainable aquaculture practices. Physiological and pathological conditions of cultivated fish can be inferred by analyzing locomotion activities. In this paper, we present a system that estimates the locomotion activities from videos using multi object tracking. The core of our approach is a YOLOv11 detector embedded in a tracking-by-detection framework. We investigate various configurations of the YOLOv11-architecture as well as extensions that incorporate multiple frames to improve detection accuracy. Our system is evaluated on a manually annotated dataset of Sulawesi ricefish recorded in a home-aquarium-like setup, demonstrating its ability to reliably measure swimming direction and speed for fish health monitoring. The dataset will be made publicly available upon publication.
[CV-15] Loop Closure via Maximal Cliques in 3D LiDAR-Based SLAM
【速读】：该论文旨在解决3D激光雷达（LiDAR）SLAM系统中回环检测（loop closure detection）的可靠性问题，特别是在传感器噪声、环境模糊性和视角变化等复杂条件下，传统基于随机采样一致算法（RANSAC）容易失效导致地图不一致的问题。其解决方案的关键在于提出一种新的确定性算法——CliReg，通过构建特征对应关系的兼容性图（compatibility graph），将回环验证转化为最大团（maximal clique）搜索问题，从而避免了RANSAC中的随机采样过程，显著提升了在噪声和异常值存在下的鲁棒性。该方法与基于二进制描述子和汉明距离嵌入的KD树匹配策略集成，实现在实时系统中的高效运行，并在多个真实世界数据集上验证了其优于RANSAC的精度和稳定性。
链接: https://arxiv.org/abs/2603.05397
作者: Javier Laserna,Saurabh Gupta,Oscar Martinez Mozos,Cyrill Stachniss,Pablo San Segundo
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted in the 2025 European Conference on Mobile Robots (ECMR). This is the author’s version of the work
Abstract:Reliable loop closure detection remains a critical challenge in 3D LiDAR-based SLAM, especially under sensor noise, environmental ambiguity, and viewpoint variation conditions. RANSAC is often used in the context of loop closures for geometric model fitting in the presence of outliers. However, this approach may fail, leading to map inconsistency. We introduce a novel deterministic algorithm, CliReg, for loop closure validation that replaces RANSAC verification with a maximal clique search over a compatibility graph of feature correspondences. This formulation avoids random sampling and increases robustness in the presence of noise and outliers. We integrated our approach into a real- time pipeline employing binary 3D descriptors and a Hamming distance embedding binary search tree-based matching. We evaluated it on multiple real-world datasets featuring diverse LiDAR sensors. The results demonstrate that our proposed technique consistently achieves a lower pose error and more reliable loop closures than RANSAC, especially in sparse or ambiguous conditions. Additional experiments on 2D projection-based maps confirm its generality across spatial domains, making our approach a robust and efficient alternative for loop closure detection.
[CV-16] Fusion-CAM: Integrating Gradient and Region-Based Class Activation Maps for Robust Visual Explanations
【速读】：该论文旨在解决深度卷积神经网络（Deep Convolutional Neural Networks）决策过程解释性不足的问题，特别是在生成可解释人工智能（Explainable AI, XAI）中，传统类激活图（Class Activation Map, CAM）方法在细粒度特征捕捉与完整对象覆盖之间存在权衡：梯度-based 方法（如 Grad-CAM）虽能提供高判别力的局部细节，但常因噪声和不完整性而仅聚焦于最显著区域；区域-based 方法（如 Score-CAM）则能覆盖更广的对象区域，却因过度平滑导致对细微特征敏感度下降。解决方案的关键在于提出 Fusion-CAM 框架，通过一个专用融合机制统一两种范式：首先对梯度图进行去噪以获得更清晰的激活区域，再结合区域图并引入贡献权重提升类别覆盖范围，最终采用基于相似性的自适应像素级融合策略，在一致性区域强化激活、冲突区域软融合，从而生成更具上下文感知能力且输入自适应的高质量可视化解释。
链接: https://arxiv.org/abs/2603.05386
作者: Hajar Dekdegue,Moncef Garouani,Josiane Mothe,Jordan Bernigaud
机构: IRIT, UMR5505 CNRS (国家科学研究中心信息与技术研究所); Université de Toulouse (图卢兹大学); Université Toulouse Capitole (图卢兹-卡皮托勒大学); INRAE, Centre Occitanie-Toulouse (法国农业、食品与环境研究院奥克西塔尼-图卢兹中心); Unité Expérimentale APC (APC实验单元)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Interpreting the decision-making process of deep convolutional neural networks remains a central challenge in achieving trustworthy and transparent artificial intelligence. Explainable AI (XAI) techniques, particularly Class Activation Map (CAM) methods, are widely adopted to visualize the input regions influencing model predictions. Gradient-based approaches (e.g. Grad-CAM) provide highly discriminative, fine-grained details by computing gradients of class activations but often yield noisy and incomplete maps that emphasize only the most salient regions rather than the complete objects. Region-based approaches (e.g. Score-CAM) aggregate information over larger areas, capturing broader object coverage at the cost of over-smoothing and reduced sensitivity to subtle features. We introduce Fusion-CAM, a novel framework that bridges this explanatory gap by unifying both paradigms through a dedicated fusion mechanism to produce robust and highly discriminative visual explanations. Our method first denoises gradient-based maps, yielding cleaner and more focused activations. It then combines the refined gradient map with region-based maps using contribution weights to enhance class coverage. Finally, we propose an adaptive similarity-based pixel-level fusion that evaluates the agreement between both paradigms and dynamically adjusts the fusion strength. This adaptive mechanism reinforces consistent activations while softly blending conflicting regions, resulting in richer, context-aware, and input-adaptive visual explanations. Extensive experiments on standard benchmarks show that Fusion-CAM consistently outperforms existing CAM variants in both qualitative visualization and quantitative evaluation, providing a robust and flexible tool for interpreting deep neural networks.
[CV-17] ORMOT: A Dataset and Framework for Omnidirectional Referring Multi-Object Tracking
【速读】：该论文旨在解决传统多目标跟踪（Multi-Object Tracking, MOT）方法在视觉语言场景下表现受限的问题，尤其是现有 referring multi-object tracking (RMOT) 方法依赖于常规相机采集的数据，受视场角（Field of View, FoV）限制，导致目标易移出画面、跟踪断裂及上下文信息丢失。为突破这一瓶颈，作者提出全新任务——全景指代多目标跟踪（Omnidirectional Referring Multi-Object Tracking, ORMOT），通过引入全景图像（omnidirectional imagery）扩展感知范围，从而增强模型对长时程语言描述的理解能力。解决方案的关键在于构建 ORSet 数据集（包含27个多样化全景场景、848条语言描述和3401个标注对象）以及设计基于大视觉语言模型（Large Vision-Language Model, LVLM）驱动的 ORTrack 框架，有效融合多模态信息以实现更鲁棒的跨帧语义关联与跟踪。
链接: https://arxiv.org/abs/2603.05384
作者: Sijia Chen,Zihan Zhou,Yanqiu Yu,En Yu,Wenbing Tao
机构: Huazhong University of Science and Technology (华中科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: this https URL
Abstract:Multi-Object Tracking (MOT) is a fundamental task in computer vision, aiming to track targets across video frames. Existing MOT methods perform well in general visual scenes, but face significant challenges and limitations when extended to visual-language settings. To bridge this gap, the task of Referring Multi-Object Tracking (RMOT) has recently been proposed, which aims to track objects that correspond to language descriptions. However, current RMOT methods are primarily developed on datasets captured by conventional cameras, which suffer from limited field of view. This constraint often causes targets to move out of the frame, leading to fragmented tracking and loss of contextual information. In this work, we propose a novel task, called Omnidirectional Referring Multi-Object Tracking (ORMOT), which extends RMOT to omnidirectional imagery, aiming to overcome the field-of-view (FoV) limitation of conventional datasets and improve the model’s ability to understand long-horizon language descriptions. To advance the ORMOT task, we construct ORSet, an Omnidirectional Referring Multi-Object Tracking dataset, which contains 27 diverse omnidirectional scenes, 848 language descriptions, and 3,401 annotated objects, providing rich visual, temporal, and language information. Furthermore, we propose ORTrack, a Large Vision-Language Model (LVLM)-driven framework tailored for Omnidirectional Referring Multi-Object Tracking. Extensive experiments on the ORSet dataset demonstrate the effectiveness of our ORTrack framework. The dataset and code will be open-sourced at this https URL.
[CV-18] OpenFrontier: General Navigation with Visual-Language Grounded Frontiers
【速读】：该论文旨在解决开放世界导航（open-world navigation）中机器人在复杂日常环境中适应灵活任务需求的问题，传统方法依赖密集三维重建和手工设计的目标度量指标，限制了其跨任务与跨环境的泛化能力。解决方案的关键在于将导航建模为稀疏子目标识别与到达问题，并利用视觉锚点（visual anchoring targets）为高层语义先验提供定位支持；具体而言，论文提出以导航前沿（navigation frontiers）作为语义锚点，构建无需训练的OpenFrontier框架，该框架可无缝集成多种视觉-语言先验模型，实现无需密集三维映射、策略训练或模型微调的轻量级高效导航，在多个基准测试中展现出强大的零样本性能，并成功部署于移动机器人平台。
链接: https://arxiv.org/abs/2603.05377
作者: Esteban Padilla,Boyang Sun,Marc Pollefeys,Hermann Blum
机构: ETH Zurich (苏黎世联邦理工学院); Microsoft Spatial AI Lab (微软空间人工智能实验室); University of Bonn (波恩大学)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Open-world navigation requires robots to make decisions in complex everyday environments while adapting to flexible task requirements. Conventional navigation approaches often rely on dense 3D reconstruction and hand-crafted goal metrics, which limits their generalization across tasks and environments. Recent advances in vision–language navigation (VLN) and vision–language–action (VLA) models enable end-to-end policies conditioned on natural language, but typically require interactive training, large-scale data collection, or task-specific fine-tuning with a mobile agent. We formulate navigation as a sparse subgoal identification and reaching problem and observe that providing visual anchoring targets for high-level semantic priors enables highly efficient goal-conditioned navigation. Based on this insight, we select navigation frontiers as semantic anchors and propose OpenFrontier, a training-free navigation framework that seamlessly integrates diverse vision–language prior models. OpenFrontier enables efficient navigation with a lightweight system design, without dense 3D mapping, policy training, or model fine-tuning. We evaluate OpenFrontier across multiple navigation benchmarks and demonstrate strong zero-shot performance, as well as effective real-world deployment on a mobile robot.
[CV-19] Dark3R: Learning Structure from Motion in the Dark CVPR2026
【速读】：该论文旨在解决在极低信噪比（SNR < -4 dB）条件下，传统基于特征或学习的结构光恢复（Structure from Motion, SfM）方法失效的问题。其解决方案的关键在于提出Dark3R框架，通过教师-学生蒸馏过程将大规模3D基础模型适配至极端低光照场景，从而实现鲁棒的特征匹配与相机位姿估计；该方法无需3D监督信号，仅依赖噪声-干净原始图像对进行训练，这些图像可直接采集或通过泊松-高斯噪声模型合成，显著提升了低光环境下SfM和新视角合成的性能。
链接: https://arxiv.org/abs/2603.05330
作者: Andrew Y Guo,Anagh Malik,SaiKiran Tedla,Yutong Dai,Yiqian Qin,Zach Salehe,Benjamin Attal,Sotiris Nousias,Kyros Kutulakos,David B. Lindell
机构: University of Toronto (多伦多大学); Vector Institute (向量研究所); York University (约克大学); Sony Corporation of America (索尼美国公司); Harvard University (哈佛大学); Purdue University (普渡大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026, Project Page: this https URL
Abstract:We introduce Dark3R, a framework for structure from motion in the dark that operates directly on raw images with signal-to-noise ratios (SNRs) below -4 dB – a regime where conventional feature- and learning-based methods break down. Our key insight is to adapt large-scale 3D foundation models to extreme low-light conditions through a teacher–student distillation process, enabling robust feature matching and camera pose estimation in low light. Dark3R requires no 3D supervision; it is trained solely on noisy–clean raw image pairs, which can be either captured directly or synthesized using a simple Poisson–Gaussian noise model applied to well-exposed raw images. To train and evaluate our approach, we introduce a new, exposure-bracketed dataset that includes \sim 42,000 multi-view raw images with ground-truth 3D annotations, and we demonstrate that Dark3R achieves state-of-the-art structure from motion in the low-SNR regime. Further, we demonstrate state-of-the-art novel view synthesis in the dark using Dark3R’s predicted poses and a coarse-to-fine radiance field optimization procedure.
[CV-20] Frequency-Aware Error-Bounded Caching for Accelerating Diffusion Transformers
【速读】：该论文旨在解决扩散模型（Diffusion Models）中基于Transformer的架构（DiTs）在推理阶段因迭代去噪过程带来的高计算成本问题。现有缓存方法虽能通过复用不同时间步的中间计算加速推理，但其假设去噪过程在时间、深度和特征维度上是均匀的，忽略了实际中的非均匀性。论文的关键创新在于识别出三个正交的非均匀性维度：（1）时间轴上的敏感度差异，即不同时间步对缓存误差的容忍度不同；（2）深度轴上的累积误差传播，连续缓存决策会导致误差级联放大；（3）特征轴上的动态异质性，隐藏状态的不同组件具有不同的时序演化特性。基于此，提出SpectralCache统一缓存框架，包含Timestep-Aware Dynamic Scheduling（TADS）、Cumulative Error Budgets（CEB）和Frequency-Decomposed Caching（FDC），实现训练-free、即插即用且兼容现有DiT结构的高效缓存策略，在FLUX.1-schnell 512×512图像生成任务中达到2.46倍加速，同时保持与TeaCache相当的图像质量（LPIPS差值<1%）。
链接: https://arxiv.org/abs/2603.05315
作者: Guandong Li
机构: iFLYTEK(科大讯飞)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion Transformers (DiTs) have emerged as the dominant architecture for high-quality image and video generation, yet their iterative denoising process incurs substantial computational cost during inference. Existing caching methods accelerate DiTs by reusing intermediate computations across timesteps, but they share a common limitation: treating the denoising process as uniform across time,depth, and feature dimensions. In this work, we identify three orthogonal axes of non-uniformity in DiT denoising: (1) temporal – sensitivity to caching errors varies dramatically across the denoising trajectory; (2) depth – consecutive caching decisions lead to cascading approximation errors; and (3) feature – different components of the hidden state exhibit heterogeneous temporal dynamics. Based on these observations, we propose SpectralCache, a unified caching framework comprising Timestep-Aware Dynamic Scheduling (TADS), Cumulative Error Budgets (CEB), and Frequency-Decomposed Caching (FDC). On FLUX.1-schnell at 512x512 resolution, SpectralCache achieves 2.46x speedup with LPIPS 0.217 and SSIM 0.727, outperforming TeaCache (2.12x, LPIPS 0.215, SSIM 0.734) by 16% in speed while maintaining comparable quality (LPIPS difference 1%). Our approach is training-free, plug-and-play, and compatible with existing DiT architectures.
[CV-21] Fusion4CA: Boosting 3D Object Detection via Comprehensive Image Exploitation
【速读】：该论文旨在解决当前基于鸟瞰图（Bird’s-Eye View, BEV）的LiDAR与RGB数据融合方法中对LiDAR分支过度依赖、而未能充分挖掘RGB信息的问题。其解决方案的关键在于提出Fusion4CA框架，通过引入对比对齐模块（contrastive alignment module）以校准图像特征与三维几何结构的一致性，并设计相机辅助分支（camera auxiliary branch）在训练阶段深度挖掘RGB信息；同时利用预训练图像权重的认知适配器（cognitive adapter）和标准坐标注意力模块（coordinate attention module）进一步提升性能，从而在仅用6个训练轮次下实现优于全量训练20轮基线模型的检测精度（mAP提升1.2%）。
链接: https://arxiv.org/abs/2603.05305
作者: Kang Luo,Xin Chen,Yangyi Xiao,Hesheng Wang
机构: Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Nowadays, an increasing number of works fuse LiDAR and RGB data in the bird’s-eye view (BEV) space for 3D object detection in autonomous driving systems. However, existing methods suffer from over-reliance on the LiDAR branch, with insufficient exploration of RGB information. To tackle this issue, we propose Fusion4CA, which is built upon the classic BEVFusion framework and dedicated to fully exploiting visual input with plug-and-play components. Specifically, a contrastive alignment module is designed to calibrate image features with 3D geometry, and a camera auxiliary branch is introduced to mine RGB information sufficiently during training. For further performance enhancement, we leverage an off-the-shelf cognitive adapter to make the most of pretrained image weights, and integrate a standard coordinate attention module into the fusion stage as a supplementary boost. Experiments on the nuScenes dataset demonstrate that our method achieves 69.7% mAP with only 6 training epochs and a mere 3.48% increase in inference parameters, yielding a 1.2% improvement over the baseline which is fully trained for 20 epochs. Extensive experiments in a simulated lunar environment further validate the effectiveness and generalization of our method. Our code will be released through Fusion4CA.
[CV-22] WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces
【速读】：该论文旨在解决当前Web代理（Web Agent）研究中缺乏高质量、真实世界轨迹数据的问题，从而阻碍了可复现性研究和模型性能的提升。现有方法多依赖合成数据，难以覆盖复杂且高价值的任务场景。其解决方案的关键在于构建了WebChain——一个大规模开源的人工标注轨迹数据集（包含31,725条轨迹和31.8万步），并通过“视觉-结构-动作”三重对齐（Triple Alignment）提供多模态监督信号；同时提出双阶段训练策略（Dual Mid-Training），将空间定位与任务规划解耦，显著提升了在WebChainBench及其他GUI基准上的性能表现。
链接: https://arxiv.org/abs/2603.05295
作者: Sicheng Fan,Rui Wan,Yifei Leng,Gaoning Liang,Li Ling,Yanyi Shang,Dehan Kong
机构: Fudan University (复旦大学); IMean AI (IMean人工智能); Shanghai Innovation Institute (上海创新研究院)
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce WebChain, the largest open-source dataset of human-annotated trajectories on real-world websites, designed to accelerate reproducible research in web agents. It contains 31,725 trajectories and 318k steps, featuring a core Triple Alignment of visual, structural, and action data to provide rich, multi-modal supervision. The data is collected via a scalable pipeline that ensures coverage of complex, high-value tasks often missed by synthetic methods. Leveraging this dataset, we propose a Dual Mid-Training recipe that decouples spatial grounding from planning, achieving state-of-the-art performance on our proposed WebChainBench and other public GUI benchmarks. Our work provides the data and insights necessary to build and rigorously evaluate the next generation of scalable web agents.
[CV-23] Layer by layer module by module: Choose both for optimal OOD probing of ViT ICLR2026
【速读】：该论文旨在解决预训练视觉Transformer模型中中间层（intermediate layers）表征能力优于最终层的现象及其成因问题。研究表明，这种现象并非仅由自回归预训练引起，也存在于监督学习和判别式自监督学习的模型中；其核心原因在于预训练数据与下游任务数据之间的分布偏移（distribution shift）。解决方案的关键在于：针对不同强度的分布偏移，采用差异化的特征提取策略——当分布偏移显著时，应 probing 激活值（activation）在前馈网络（feedforward network）中的表现最佳；而当分布偏移较弱时，对多头自注意力模块（multi-head self-attention module）输出进行归一化处理后的特征表现最优。这一发现为高效利用中间层特征提供了理论依据和实践指导。
链接: https://arxiv.org/abs/2603.05280
作者: Ambroise Odonnat,Vasilii Feofanov,Laetitia Chapel,Romain Tavenard,Ievgen Redko
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: Accepted at ICLR 2026 CAO Workshop
Abstract:Recent studies have observed that intermediate layers of foundation models often yield more discriminative representations than the final layer. While initially attributed to autoregressive pretraining, this phenomenon has also been identified in models trained via supervised and discriminative self-supervised objectives. In this paper, we conduct a comprehensive study to analyze the behavior of intermediate layers in pretrained vision transformers. Through extensive linear probing experiments across a diverse set of image classification benchmarks, we find that distribution shift between pretraining and downstream data is the primary cause of performance degradation in deeper layers. Furthermore, we perform a fine-grained analysis at the module level. Our findings reveal that standard probing of transformer block outputs is suboptimal; instead, probing the activation within the feedforward network yields the best performance under significant distribution shift, whereas the normalized output of the multi-head self-attention module is optimal when the shift is weak.
[CV-24] Wiki-R1: Incentivizing Multimodal Reasoning for Knowledge-based VQA via Data and Sampling Curriculum ICLR26
【速读】：该论文旨在解决知识增强型视觉问答（Knowledge-Based Visual Question Answering, KB-VQA）中因外部知识检索噪声和知识库结构化特性导致的分布偏移问题，从而阻碍了预训练多模态大语言模型（Multimodal Large Language Models, MLLMs）在后训练阶段的有效推理与领域适配。解决方案的关键在于提出Wiki-R1框架——一个基于数据生成的课程强化学习方法，其核心创新包括：可控课程数据生成机制，通过操纵检索器控制样本难度以匹配模型能力演进；以及课程采样策略，依据观测奖励估计样本难度并选择高信息量样本以最大化强化学习更新的优势信号，从而系统性地引导MLLM在KB-VQA任务上的推理能力提升。
链接: https://arxiv.org/abs/2603.05256
作者: Shan Ning,Longtian Qiu,Xuming He
机构: ShanghaiTech University (上海科技大学); Shanghai Engineering Research Center of Intelligent Vision and Imaging (智能视觉与成像工程研究中心); Lingang Laboratory (临港实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICLR 26, code and weights are publicly available
Abstract:Knowledge-Based Visual Question Answering (KB-VQA) requires models to answer questions about an image by integrating external knowledge, posing significant challenges due to noisy retrieval and the structured, encyclopedic nature of the knowledge base. These characteristics create a distributional gap from pretrained multimodal large language models (MLLMs), making effective reasoning and domain adaptation difficult in the post-training stage. In this work, we propose \textitWiki-R1, a data-generation-based curriculum reinforcement learning framework that systematically incentivizes reasoning in MLLMs for KB-VQA. Wiki-R1 constructs a sequence of training distributions aligned with the model’s evolving capability, bridging the gap from pretraining to the KB-VQA target distribution. We introduce \textitcontrollable curriculum data generation, which manipulates the retriever to produce samples at desired difficulty levels, and a \textitcurriculum sampling strategy that selects informative samples likely to yield non-zero advantages during RL updates. Sample difficulty is estimated using observed rewards and propagated to unobserved samples to guide learning. Experiments on two KB-VQA benchmarks, Encyclopedic VQA and InfoSeek, demonstrate that Wiki-R1 achieves new state-of-the-art results, improving accuracy from 35.5% to 37.1% on Encyclopedic VQA and from 40.1% to 44.1% on InfoSeek. The project page is available at this https URL.
[CV-25] CATNet: Collaborative Alignment and Transformation Network for Cooperative Perception CVPR26
【速读】：该论文旨在解决多智能体协同感知中因真实世界多源数据融合所引发的两个关键问题：高时间延迟（temporal latency）和多源噪声干扰。针对这些问题，其解决方案的核心在于提出一种自适应补偿框架CATNet，其关键技术包括：（1）时空递归同步模块（Spatio-Temporal Recurrent Synchronization, STSync），通过相邻帧差分建模实现异步特征流对齐，构建统一的时空表示空间；（2）双分支小波增强去噪器（Dual-Branch Wavelet Enhanced Denoiser, WTDen），在对齐后的表示中抑制全局噪声并重构局部特征失真；（3）自适应特征选择器（Adaptive Feature Selector, AdpSel），动态聚焦于关键感知特征以提升融合鲁棒性。整体架构在多个数据集上验证了其在复杂交通场景下的优越性能与适应能力。
链接: https://arxiv.org/abs/2603.05255
作者: Gong Chen,Chaokun Zhang,Tao Tang,Pengcheng Lv,Feng Li,Xin Xie
机构: Tianjin University (天津大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR26
Abstract:Cooperative perception significantly enhances scene understanding by integrating complementary information from diverse agents. However, existing research often overlooks critical challenges inherent in real-world multi-source data integration, specifically high temporal latency and multi-source noise. To address these practical limitations, we propose Collaborative Alignment and Transformation Network (CATNet), an adaptive compensation framework that resolves temporal latency and noise interference in multi-agent systems. Our key innovations can be summarized in three aspects. First, we introduce a Spatio-Temporal Recurrent Synchronization (STSync) that aligns asynchronous feature streams via adjacent-frame differential modeling, establishing a temporal-spatially unified representation space. Second, we design a Dual-Branch Wavelet Enhanced Denoiser (WTDen) that suppresses global noise and reconstructs localized feature distortions within aligned representations. Third, we construct an Adaptive Feature Selector (AdpSel) that dynamically focuses on critical perceptual features for robust fusion. Extensive experiments on multiple datasets demonstrate that CATNet consistently outperforms existing methods under complex traffic conditions, proving its superior robustness and adaptability.
[CV-26] Digital Twin Driven Textile Classification and Foreign Object Recognition in Automated Sorting Systems
【速读】：该论文旨在解决可持续纺织品回收中面临的自动化难题，特别是针对柔性衣物在杂乱环境中的抓取、异物检测与语义分类问题。其核心解决方案是构建一个由数字孪生（Digital Twin）驱动的机器人分拣系统，关键在于融合了抓取预测、多模态感知（RGBD传感与电容式触觉反馈）以及基于视觉语言模型（Visual Language Models, VLMs）的语义推理能力，实现从无序篮中自主分离衣物、转移至检测区并进行高精度分类的全流程自动化。通过在223个场景下对九种VLMs的评估，验证了Qwen系列模型在整体准确率（最高达87.9%）和异物识别方面的优势，同时轻量级模型如Gemma3在边缘部署场景下展现出良好的速度-精度权衡，结合MoveIt的碰撞感知路径规划与三维点云虚拟映射进一步提升了操作可靠性，为工业级智能纺织品分拣提供了可行的技术路径。
链接: https://arxiv.org/abs/2603.05230
作者: Serkan Ergun,Tobias Mitterer,Hubert Zangl
机构: University of Klagenfurt (克恩顿大学); Ubiquitous Sensing Lab, University of Klagenfurt (无处不在的传感实验室，克恩顿大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 10 pages,single column, 5 figures, preprint for Photomet Edumet 2026 (Klagenfurt, Austria)
Abstract:The increasing demand for sustainable textile recycling requires robust automation solutions capable of handling deformable garments and detecting foreign objects in cluttered environments. This work presents a digital twin driven robotic sorting system that integrates grasp prediction, multi modal perception, and semantic reasoning for real world textile classification. A dual arm robotic cell equipped with RGBD sensing, capacitive tactile feedback, and collision-aware motion planning autonomously separates garments from an unsorted basket, transfers them to an inspection zone, and classifies them using state of the art Visual Language Models (VLMs). We benchmark nine VLM s from five model families on a dataset of 223 inspection scenarios comprising shirts, socks, trousers, underwear, foreign objects (including garments outside of the aforementioned classes), and empty scenes. The evaluation assesses per class accuracy, hallucination behavior, and computational performance under practical hardware constraints. Results show that the Qwen model family achieves the highest overall accuracy (up to 87.9 %), with strong foreign object detection performance, while lighter models such as Gemma3 offer competitive speed accuracy trade offs for edge deployment. A digital twin combined with MoveIt enables collision aware path planning and integrates segmented 3D point clouds of inspected garments into the virtual environment for improved manipulation reliability. The presented system demonstrates the feasibility of combining semantic VLM reasoning with conventional grasp detection and digital twin technology for scalable, autonomous textile sorting in realistic industrial settings.
[CV-27] SPyCer: Semi-Supervised Physics-Guided Contextual Attention for Near-Surface Air Temperature Estimation from Satellite Imagery
【速读】：该论文旨在解决近地表空气温度（Near-surface Air Temperature, NSAT）空间连续估算问题，当前由于地面传感器分布稀疏且不均匀，难以提供高时空分辨率的NSAT数据。为弥补这一不足，作者提出SPyCer——一种半监督物理引导的神经网络架构，其核心创新在于将NSAT预测建模为像素级视觉任务，并融合物理约束与卫星图像信息：通过将地面传感器位置映射到卫星图像坐标并以局部图像块为中心进行监督，同时利用地表能量平衡和对流-扩散-反应偏微分方程推导出物理引导的正则化项，从而增强模型对邻域像素的物理依赖性；此外，引入基于土地覆盖特征的多头注意力机制并结合高斯距离加权，有效捕捉空间异质性下的物理影响。此方法在真实数据集上实现了更准确、空间一致且符合物理规律的NSAT估计，显著优于现有基线模型。
链接: https://arxiv.org/abs/2603.05219
作者: Sofiane Bouaziz,Adel Hafiane,Raphael Canals,Rachid Nedjai
机构: INSA CVL, Université d’Orléans, PRISME UR 4229; Université d’Orléans, CEDETE, UR 1210
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Modern Earth observation relies on satellites to capture detailed surface properties. Yet, many phenomena that affect humans and ecosystems unfold in the atmosphere close to the surface. Near-ground sensors provide accurate measurements of certain environmental characteristics, such as near-surface air temperature (NSAT). However, they remain sparse and unevenly distributed, limiting their ability to provide continuous spatial measurements. To bridge this gap, we introduce SPyCer, a semi-supervised physics-guided network that can leverage pixel information and physical modeling to guide the learning process through meaningful physical properties. It is designed for continuous estimation of NSAT by proxy using satellite imagery. SPyCer frames NSAT prediction as a pixel-wise vision problem, where each near-ground sensor is projected onto satellite image coordinates and positioned at the center of a local image patch. The corresponding sensor pixel is supervised using both observed NSAT and physics-based constraints, while surrounding pixels contribute through physics-guided regularization derived from the surface energy balance and advection-diffusion-reaction partial differential equations. To capture the physical influence of neighboring pixels, SPyCer employs a multi-head attention guided by land cover characteristics and modulated with Gaussian distance weighting. Experiments on real-world datasets demonstrate that SPyCer produces spatially coherent and physically consistent NSAT estimates, outperforming existing baselines in terms of accuracy, generalization, and alignment with underlying physical processes.
[CV-28] Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation
【速读】：该论文旨在解决医学图像分割中因类别严重不平衡导致的少数类结构在特征表示中被主导类淹没的问题，从而影响判别性特征的学习与可靠分割。其解决方案的关键在于提出一种可即插即用的语义类别分布学习（Semantic Class Distribution Learning, SCDL）框架，通过类条件特征分布建模来缓解监督信号和特征表示偏差；具体包括两类核心机制：一是类分布双向对齐（Class Distribution Bidirectional Alignment, CDBA），将嵌入特征与可学习的类别代理（class proxies）进行对齐；二是语义锚点约束（Semantic Anchor Constraints, SAC），利用标注数据引导代理的学习方向，从而提升对少数类的分割性能。
链接: https://arxiv.org/abs/2603.05202
作者: Yingxue Su,Yiheng Zhong,Keying Zhu,Zimu Zhang,Zhuoru Zhang,Yifang Wang,Yuxin Zhang,Jingxin Liu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 2 figures
Abstract:Medical image segmentation is critical for computer-aided diagnosis. However, dense pixel-level annotation is time-consuming and expensive, and medical datasets often exhibit severe class imbalance. Such imbalance causes minority structures to be overwhelmed by dominant classes in feature representations, hindering the learning of discriminative features and making reliable segmentation particularly challenging. To address this, we propose the Semantic Class Distribution Learning (SCDL) framework, a plug-and-play module that mitigates supervision and representation biases by learning structured class-conditional feature distributions. SCDL integrates Class Distribution Bidirectional Alignment (CDBA) to align embeddings with learnable class proxies and leverages Semantic Anchor Constraints (SAC) to guide proxies using labeled data. Experiments on the Synapse and AMOS datasets demonstrate that SCDL significantly improves segmentation performance across both overall and class-level metrics, with particularly strong gains on minority classes, achieving state-of-the-art results. Our code is released at this https URL.
[CV-29] Logi-PAR: Logic-Infused Patient Activity Recognition via Differentiable Rule
【速读】：该论文旨在解决临床环境中患者活动识别（Patient Activity Recognition, PAR）模型仅能识别“发生了什么活动”而无法解释“为何该活动构成风险”的问题，即现有方法依赖神经网络管道学习隐式模式，缺乏可解释性和可干预性。解决方案的关键在于提出首个融合逻辑推理的PAR框架——Logi-PAR，其核心创新是将上下文事实融合作为多视角特征提取器，并注入神经引导的可微分规则（neural-guided differentiable rules），从而自动从视觉线索中学习可解释的逻辑规则，在端到端训练过程中显式标注隐含模式，并生成规则轨迹作为可审计的“为什么”解释，同时支持反事实干预分析（如若提供协助则风险降低65%）。
链接: https://arxiv.org/abs/2603.05184
作者: Muhammad Zarar,MingZheng Zhang,Xiaowang Zhang,Zhiyong Feng,Sofonias Yitagesu,Kawsar Farooq
机构: Tianjin University (天津大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Patient Activity Recognition (PAR) in clinical settings uses activity data to improve safety and quality of care. Although significant progress has been made, current models mainly identify which activity is occurring. They often spatially compose sub-sparse visual cues using global and local attention mechanisms, yet only learn logically implicit patterns due to their neural-pipeline. Advancing clinical safety requires methods that can infer why a set of visual cues implies a risk, and how these can be compositionally reasoned through explicit logic beyond mere classification. To address this, we proposed Logi-PAR, the first Logic-Infused Patient Activity Recognition Framework that integrates contextual fact fusion as a multi-view primitive extractor and injects neural-guided differentiable rules. Our method automatically learns rules from visual cues, optimizing them end-to-end while enabling the implicit emergence patterns to be explicitly labelled during training. To the best of our knowledge, Logi-PAR is the first framework to recognize patient activity by applying learnable logic rules to symbolic mappings. It produces auditable why explanations as rule traces and supports counterfactual interventions (e.g., risk would decrease by 65% if assistance were present). Extensive evaluation on clinical benchmarks (VAST and OmniFall) demonstrates state-of-the-art performance, significantly outperforming Vision-Language Models and transformer baselines. The code is available via: this https URL
[CV-30] Mario: Multimodal Graph Reasoning with Large Language Models CVPR2026
【速读】：该论文旨在解决现有方法在处理多模态图（Multimodal Graphs, MMGs）时面临的两大挑战：一是跨模态一致性弱的问题，即图像与文本特征在节点层面缺乏充分对齐；二是异构模态偏好难以统一的问题，即不同节点及其邻域对视觉或文本信息的依赖程度不一。为应对这些挑战，论文提出了一种名为Mario的统一框架，其关键创新在于两个阶段：第一阶段是基于图结构引导的视觉-语言模型（VLM）设计，通过细粒度的跨模态对比学习联合优化图文特征，从而增强模态间的一致性；第二阶段是模态自适应的图指令微调机制，将对齐后的多模态特征组织成图感知的指令视图，并引入可学习路由模块，动态选择每个节点及其邻域中最具信息量的模态配置输入至大语言模型（LLM），从而实现高效的多模态图推理。
链接: https://arxiv.org/abs/2603.05181
作者: Yuanfu Sun,Kang Li,Pengkang Guo,Jiajin Liu,Qiaoyu Tan
机构: New York University Shanghai(纽约大学上海分校); New York University(纽约大学); Tsinghua University(清华大学); EPFL(洛桑联邦理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026
Abstract:Recent advances in large language models (LLMs) have opened new avenues for multimodal reasoning. Yet, most existing methods still rely on pretrained vision-language models (VLMs) to encode image-text pairs in isolation, ignoring the relational structure that real-world multimodal data naturally form. This motivates reasoning on multimodal graphs (MMGs), where each node has textual and visual attributes and edges provide structural cues. Enabling LLM-based reasoning on such heterogeneous multimodal signals while preserving graph topology introduces two key challenges: resolving weak cross-modal consistency and handling heterogeneous modality preference. To address this, we propose Mario, a unified framework that simultaneously resolves the two above challenges and enables effective LLM-based reasoning over MMGs. Mario consists of two innovative stages. Firstly, a graph-conditioned VLM design that jointly refines textual and visual features through fine-grained cross-modal contrastive learning guided by graph topology. Secondly, a modality-adaptive graph instruction tuning mechanism that organizes aligned multimodal features into graph-aware instruction views and employs a learnable router to surface, for each node and its neighborhood, the most informative modality configuration to the LLM. Extensive experiments across diverse MMG benchmarks demonstrate that Mario consistently outperforms state-of-the-art graph models in both supervised and zero-shot scenarios for node classification and link prediction. The code will be made available at this https URL.
[CV-31] Generic Camera Calibration using Blurry Images
【速读】：该论文旨在解决通用相机模型（generic camera model）标定过程中因需大量图像而导致运动模糊难以避免的问题，尤其针对普通用户在实际应用中面临的挑战。其解决方案的关键在于利用几何约束和局部参数化光照模型，同时估计特征点位置与空间变化的点扩散函数（point spread function, PSF），并消除传统图像去模糊任务中无需考虑的平移模糊歧义性，从而实现高精度的联合去模糊与标定。
链接: https://arxiv.org/abs/2603.05159
作者: Zezhun Shi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Camera calibration is the foundation of 3D vision. Generic camera calibration can yield more accurate results than parametric cam era calibration. However, calibrating a generic camera model using printed calibration boards requires far more images than parametric calibration, making motion blur practically unavoidable for individual users. As a f irst attempt to address this problem, we draw on geometric constraints and a local parametric illumination model to simultaneously estimate feature locations and spatially varying point spread functions, while re solving the translational ambiguity that need not be considered in con ventional image deblurring tasks. Experimental results validate the ef fectiveness of our approach.
[CV-32] he Impact of Preprocessing Methods on Racial Encoding and Model Robustness in CXR Diagnosis WWW
【速读】：该论文旨在解决深度学习模型在胸部X光（CXR）影像诊断中可能因学习到与种族相关的“捷径”特征（racial shortcut learning）而导致的偏见问题，这类偏见会损害医疗公平性和模型可靠性。其解决方案的关键在于通过图像预处理方法抑制编码种族信息的伪线索，同时保持诊断准确性；实验表明，基于边界框的肺部裁剪（lung cropping）是一种有效策略，能够在不牺牲模型性能的前提下显著减少种族捷径学习，从而规避常见的公平性-准确性权衡（fairness-accuracy trade-offs）。
链接: https://arxiv.org/abs/2603.05157
作者: Dishantkumar Sutariya,Eike Petersen
机构: Fraunhofer Institute for Digital Medicine MEVIS(弗劳恩霍夫数字医学MEVIS研究所)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: Preprint accepted for publication at BVM 2026 ( this https URL )
Abstract:Deep learning models can identify racial identity with high accuracy from chest X-ray (CXR) recordings. Thus, there is widespread concern about the potential for racial shortcut learning, where a model inadvertently learns to systematically bias its diagnostic predictions as a function of racial identity. Such racial biases threaten healthcare equity and model reliability, as models may systematically misdiagnose certain demographic groups. Since racial shortcuts are diffuse - non-localized and distributed throughout the whole CXR recording - image preprocessing methods may influence racial shortcut learning, yet the potential of such methods for reducing biases remains underexplored. Here, we investigate the effects of image preprocessing methods including lung masking, lung cropping, and Contrast Limited Adaptive Histogram Equalization (CLAHE). These approaches aim to suppress spurious cues encoding racial information while preserving diagnostic accuracy. Our experiments reveal that simple bounding box-based lung cropping can be an effective strategy for reducing racial shortcut learning while maintaining diagnostic model performance, bypassing frequently postulated fairness-accuracy trade-offs.
[CV-33] SSR-GS: Separating Specular Reflection in Gaussian Splatting for Glossy Surface Reconstruction
【速读】：该论文旨在解决在复杂光照条件下，3D高斯溅射（3D Gaussian Splatting, 3DGS）方法难以准确重建具有强镜面反射和多表面间反射的光滑表面的问题。其核心解决方案包括三个关键组件：首先，引入预过滤的Mip-Cubemap以高效建模直接镜面反射；其次，提出IndiASG模块来捕捉间接镜面反射；最后，设计视觉几何先验（Visual Geometry Priors, VGP），通过反射评分（Reflection Score, RS）对反射主导区域的光度损失进行加权抑制，并结合VGGT提取的几何先验，包括逐层衰减的深度监督与变换后的法向量约束，从而提升重建精度。实验表明，该方法在合成与真实数据集上均达到当前最优性能。
链接: https://arxiv.org/abs/2603.05152
作者: Ningjing Fan,Yiqun Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注: Project page: this https URL
Abstract:In recent years, 3D Gaussian splatting (3DGS) has achieved remarkable progress in novel view synthesis. However, accurately reconstructing glossy surfaces under complex illumination remains challenging, particularly in scenes with strong specular reflections and multi-surface interreflections. To address this issue, we propose SSR-GS, a specular reflection modeling framework for glossy surface reconstruction. Specifically, we introduce a prefiltered Mip-Cubemap to model direct specular reflections efficiently, and propose an IndiASG module to capture indirect specular reflections. Furthermore, we design Visual Geometry Priors (VGP) that couple a reflection-aware visual prior via a reflection score (RS) to downweight the photometric loss contribution of reflection-dominated regions, with geometry priors derived from VGGT, including progressively decayed depth supervision and transformed normal constraints. Extensive experiments on both synthetic and real-world datasets demonstrate that SSR-GS achieves state-of-the-art performance in glossy surface reconstruction. Comments: Project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR) Cite as: arXiv:2603.05152 [cs.CV] (or arXiv:2603.05152v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.05152 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-34] Act Think or Abstain: Complexity-Aware Adaptive Inference for Vision-Language-Action Models
【速读】：该论文旨在解决当前视觉-语言-动作（Vision-Language-Action, VLA）模型在提升泛化能力时普遍存在的计算复杂度高、推理延迟大以及资源分配不合理的问题，尤其在面对分布外（out-of-distribution）任务时缺乏不确定性估计，易导致灾难性失败。其解决方案的关键在于提出一种受人类认知启发的自适应框架，通过将VLA的视觉-语言主干网络转化为主动检测工具，利用潜在嵌入投影到参数化与非参数化估计器集合中，实现对任务复杂度的动态感知：系统可立即执行已知任务（Act）、对模糊场景进行推理（Think），并在遇到显著物理或语义异常时主动中止执行（Abstain）。实证结果显示，仅使用视觉嵌入即可有效推断任务复杂度，且在LIBERO和LIBERO-PRO基准及真实机器人上的实验表明，纯视觉配置仅用5%训练数据即可达到80% F1分数，成为高效可靠的任务复杂度检测器。
链接: https://arxiv.org/abs/2603.05147
作者: Riccardo Andrea Izzo,Gianluca Bardaro,Matteo Matteucci
机构: Politecnico di Milano (米兰理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Current research on Vision-Language-Action (VLA) models predominantly focuses on enhancing generalization through established reasoning techniques. While effective, these improvements invariably increase computational complexity and inference latency. Furthermore, these mechanisms are typically applied indiscriminately, resulting in the inefficient allocation of resources for trivial tasks while simultaneously failing to provide the uncertainty estimation necessary to prevent catastrophic failure on out-of-distribution tasks. Inspired by human cognition, we propose an adaptive framework that dynamically routes VLA execution based on the complexity of the perceived state. Our approach transforms the VLA’s vision-language backbone into an active detection tool by projecting latent embeddings into an ensemble of parametric and non-parametric estimators. This allows the system to execute known tasks immediately (Act), reason about ambiguous scenarios (Think), and preemptively halt execution when encountering significant physical or semantic anomalies (Abstain). In our empirical analysis, we observe a phenomenon where visual embeddings alone are superior for inferring task complexity due to the semantic invariance of language. Evaluated on the LIBERO and LIBERO-PRO benchmarks as well as on a real robot, our vision-only configuration achieves 80% F1-Score using as little as 5% of training data, establishing itself as a reliable and efficient task complexity detector.
[CV-35] SRasP: Self-Reorientation Adversarial Style Perturbation for Cross-Domain Few-Shot Learning
【速读】：该论文旨在解决跨域少样本学习（Cross-Domain Few-Shot Learning, CD-FSL）中因领域偏移导致的模型泛化能力不足问题，尤其是现有基于风格扰动的方法存在的梯度不稳定和收敛至尖锐解的问题。其解决方案的关键在于提出一种新型的“自重定向对抗风格扰动”（Self-Reorientation Adversarial Style Perturbation, SRasP）网络：通过全局语义引导识别不一致的图像裁剪区域，并将这些裁剪区域的风格梯度与图像内全局风格梯度进行重定向聚合，从而稳定扰动过程；同时设计了一种多目标优化函数，在最大化全局、裁剪和对抗特征间视觉差异的同时保持语义一致性，促使模型在训练中收敛到更平坦且更具迁移性的解空间，显著提升对未见领域的泛化性能。
链接: https://arxiv.org/abs/2603.05135
作者: Wenqian Li,Pengfei Fang,Hui Xue
机构: Southeast University (东南大学); Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (东南大学) (教育部)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Cross-Domain Few-Shot Learning (CD-FSL) aims to transfer knowledge from a seen source domain to unseen target domains, serving as a key benchmark for evaluating the robustness and transferability of models. Existing style-based perturbation methods mitigate domain shift but often suffer from gradient instability and convergence to sharp this http URL address these limitations, we propose a novel crop-global style perturbation network, termed Self-Reorientation Adversarial \underlineStyle \underlinePerturbation (SRasP). Specifically, SRasP leverages global semantic guidance to identify incoherent crops, followed by reorienting and aggregating the style gradients of these crops with the global style gradients within one image. Furthermore, we propose a novel multi-objective optimization function to maximize visual discrepancy while enforcing semantic consistency among global, crop, and adversarial features. Applying the stabilized perturbations during training encourages convergence toward flatter and more transferable solutions, improving generalization to unseen domains. Extensive experiments are conducted on multiple CD-FSL benchmarks, demonstrating consistent improvements over state-of-the-art methods.
[CV-36] UniPAR: A Unified Framework for Pedestrian Attribute Recognition
【速读】：该论文旨在解决行人属性识别（Pedestrian Attribute Recognition, PAR）中因数据集异构性导致的模型泛化能力不足问题，具体表现为现有方法受限于“一模型一数据集”的范式，难以应对模态差异（如RGB图像、视频序列和事件流）、属性定义不一致以及环境场景变化带来的挑战。解决方案的关键在于提出UniPAR——一个基于Transformer的统一框架，其核心创新包括：1）引入统一的数据调度策略与动态分类头，使单一模型可同时处理多种异构模态数据；2）设计分阶段融合编码器（phased fusion encoder），通过后期深度融合策略显式对齐视觉特征与文本属性查询，从而提升跨域适应性和极端环境下的鲁棒性。实验表明，该方法在多个基准数据集上达到与专用SOTA方法相当的性能，并显著增强多数据集联合训练下的泛化能力。
链接: https://arxiv.org/abs/2603.05114
作者: Minghe Xu,Rouying Wu,Jiarui Xu,Minhao Sun,Zikang Yan,Xiao Wang,ChiaWei Chu,Yu Li
机构: Faculty of Data Science, City University of Macau, Macau SAR, China; School of Big Data, Zhuhai College of Science and Technology, Zhuhai 519041, China; Faculty of Innovation Engineering, Macau University of Science and Technology, Macau SAR, China; School of Computer Science and Technology, Anhui University, Hefei 230601, China
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Pedestrian Attribute Recognition is a foundational computer vision task that provides essential support for downstream applications, including person retrieval in video surveillance and intelligent retail analytics. However, existing research is frequently constrained by the ``one-model-per-dataset" paradigm and struggles to handle significant discrepancies across domains in terms of modalities, attribute definitions, and environmental scenarios. To address these challenges, we propose UniPAR, a unified Transformer-based framework for PAR. By incorporating a unified data scheduling strategy and a dynamic classification head, UniPAR enables a single model to simultaneously process diverse datasets from heterogeneous modalities, including RGB images, video sequences, and event streams. We also introduce an innovative phased fusion encoder that explicitly aligns visual features with textual attribute queries through a late deep fusion strategy. Experimental results on the widely used benchmark datasets, including MSP60K, DukeMTMC, and EventPAR, demonstrate that UniPAR achieves performance comparable to specialized SOTA methods. Furthermore, multi-dataset joint training significantly enhances the model’s cross-domain generalization and recognition robustness in extreme environments characterized by low light and motion blur. The source code of this paper will be released on this https URL
[CV-37] BLINK: Behavioral Latent Modeling of NK Cell Cytotoxicity
【速读】：该论文旨在解决单细胞水平上自然杀伤细胞（Natural Killer cell, NK）与肿瘤细胞相互作用过程中，如何从时序观测数据中准确建模和预测细胞毒性行为的问题。传统方法依赖于帧级分类标注，难以捕捉由长时间动态交互累积形成的细胞毒性结果（如肿瘤细胞凋亡），导致预测准确性受限。解决方案的关键在于提出BLINK——一种基于轨迹的循环状态空间模型（recurrent state-space model），该模型能够从部分观测的NK-肿瘤相互作用序列中学习潜在的交互动力学，并通过预测凋亡增量来推断最终的细胞毒性结局。BLINK不仅提升了细胞毒性结果检测性能，还实现了对未来行为的预测，并提供了可解释的潜在表征，将NK细胞轨迹归纳为具有时间结构的行为模式与交互阶段，从而为NK细胞毒性的定量评估和机制解析提供了一个统一的框架。
链接: https://arxiv.org/abs/2603.05110
作者: Iman Nematollahi,Jose Francisco Villena-Ossa,Alina Moter,Kiana Farhadyar,Gabriel Kalweit,Abhinav Valada,Toni Cathomen,Evelyn Ullrich,Maria Kalweit
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Machine learning models of cellular interaction dynamics hold promise for understanding cell behavior. Natural killer (NK) cell cytotoxicity is a prominent example of such interaction dynamics and is commonly studied using time-resolved multi-channel fluorescence microscopy. Although tumor cell death events can be annotated at single frames, NK cytotoxic outcome emerges over time from cellular interactions and cannot be reliably inferred from frame-wise classification alone. We introduce BLINK, a trajectory-based recurrent state-space model that serves as a cell world model for NK-tumor interactions. BLINK learns latent interaction dynamics from partially observed NK-tumor interaction sequences and predicts apoptosis increments that accumulate into cytotoxic outcomes. Experiments on long-term time-lapse NK-tumor recordings show improved cytotoxic outcome detection and enable forecasting of future outcomes, together with an interpretable latent representation that organizes NK trajectories into coherent behavioral modes and temporally structured interaction phases. BLINK provides a unified framework for quantitative evaluation and structured modeling of NK cytotoxic behavior at the single-cell level.
[CV-38] Diff-ES: Stage-wise Structural Diffusion Pruning via Evolutionary Search
【速读】：该论文旨在解决扩散模型（Diffusion Models）在高效推理过程中难以平衡计算加速与图像生成质量的问题。现有结构化剪枝方法通常依赖于手动设计的阶段稀疏度调度策略，且在推理时需拼接多个独立剪枝后的模型，导致内存开销增加，同时由于扩散步骤的重要性具有高度非均匀性和模型依赖性，此类启发式方法难以泛化并可能造成性能下降。其解决方案的关键在于提出一种基于进化搜索（Evolutionary Search）的阶段式结构剪枝框架 Diff-ES，该框架通过自动发现最优阶段稀疏度调度，并利用内存高效的权重路由机制动态激活不同阶段的条件权重，从而避免模型参数复制，在不显著损失生成质量的前提下实现显著的墙-clock加速效果。
链接: https://arxiv.org/abs/2603.05105
作者: Zongfang Liu,Shengkun Tang,Zongliang Wu,Xin Yuan,Zhiqiang Shen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion models have achieved remarkable success in high-fidelity image generation but remain computationally demanding due to their multi-step denoising process and large model sizes. Although prior work improves efficiency either by reducing sampling steps or by compressing model parameters, existing structured pruning approaches still struggle to balance real acceleration and image quality preservation. In particular, prior methods such as MosaicDiff rely on heuristic, manually tuned stage-wise sparsity schedules and stitch multiple independently pruned models during inference, which increases memory overhead. However, the importance of diffusion steps is highly non-uniform and model-dependent. As a result, schedules derived from simple heuristics or empirical observations often fail to generalize and may lead to suboptimal performance. To this end, we introduce \textbfDiff-ES, a stage-wise structural \textbfDiffusion pruning framework via \textbfEvolutionary \textbfSearch, which optimizes the stage-wise sparsity schedule and executes it through memory-efficient weight routing without model duplication. Diff-ES divides the diffusion trajectory into multiple stages, automatically discovers an optimal stage-wise sparsity schedule via evolutionary search, and activates stage-conditioned weights dynamically without duplicating model parameters. Our framework naturally integrates with existing structured pruning methods for diffusion models including depth and width pruning. Extensive experiments on DiT and SDXL demonstrate that Diff-ES consistently achieves wall-clock speedups while incurring minimal degradation in generation quality, establishing state-of-the-art performance for structured diffusion model pruning.
[CV-39] GEM-TFL: Bridging Weak and Full Supervision for Forgery Localization through EM-Guided Decomposition and Temporal Refinement CVPR2026
【速读】：该论文旨在解决弱监督时序伪造定位（Weakly Supervised Temporal Forgery Localization, WS-TFL）中存在的四大核心问题：训练与推理目标不一致、二值视频标签提供的监督信号有限、非可微top-k聚合导致梯度阻塞，以及缺乏对提议片段间关系的显式建模。其解决方案的关键在于提出一种两阶段分类-回归框架GEM-TFL（Graph-based EM-powered Temporal Forgery Localization），通过三个创新模块实现突破：首先，利用期望最大化（Expectation-Maximization, EM）优化将二值标签重构为多维潜在属性以增强弱监督；其次，引入无需训练的时序一致性精修机制，提升帧级预测的时序平滑性；最后，设计基于图结构的提议精修模块，显式建模提议间的时序-语义关系，实现全局一致的置信度估计，从而显著缩小与全监督方法的性能差距。
链接: https://arxiv.org/abs/2603.05095
作者: Xiaodong Zhu,Yuanming Zheng,Suting Wang,Junqi Yang,Yuhong Yang,Weiping Tu,Zhongyuan Wang
机构: Wuhan University (武汉大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 4 figures, accepted by CVPR 2026
Abstract:Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments within videos or audio streams, providing interpretable evidence for multimedia forensics and security. While most existing TFL methods rely on dense frame-level labels in a fully supervised manner, Weakly Supervised TFL (WS-TFL) reduces labeling cost by learning only from binary video-level labels. However, current WS-TFL approaches suffer from mismatched training and inference objectives, limited supervision from binary labels, gradient blockage caused by non-differentiable top-k aggregation, and the absence of explicit modeling of inter-proposal relationships. To address these issues, we propose GEM-TFL (Graph-based EM-powered Temporal Forgery Localization), a two-phase classification-regression framework that effectively bridges the supervision gap between training and inference. Built upon this foundation, (1) we enhance weak supervision by reformulating binary labels into multi-dimensional latent attributes through an EM-based optimization process; (2) we introduce a training-free temporal consistency refinement that realigns frame-level predictions for smoother temporal dynamics; and (3) we design a graph-based proposal refinement module that models temporal-semantic relationships among proposals for globally consistent confidence estimation. Extensive experiments on benchmark datasets demonstrate that GEM-TFL achieves more accurate and robust temporal forgery localization, substantially narrowing the gap with fully supervised methods.
[CV-40] Axiomatic On-Manifold Shapley via Optimal Generative Flows
【速读】：该论文旨在解决基于Shapley值的可解释人工智能（XAI）方法在后验解释中因启发式基线选择导致的离群流形（off-manifold）伪影问题。传统方法依赖人工设定的基线，易引入不合理的归因结果；而现有生成式方法虽试图改善这一问题，却常伴随几何效率低下和离散化漂移（discretization drift）。其解决方案的关键在于提出一种基于最优生成流（optimal generative flows）的流形上Aumann-Shapley归因理论，通过证明梯度线积分是唯一满足效率性和几何公理（尤其是重参数化不变性）的功能形式，将基线选择重构为一个变分问题。具体而言，采用最小动能的Wasserstein-2测地线作为路径选择准则，从而获得具有唯一性的归因族，既保证了对数据流形的严格遵循（通过趋近于零的Flow Consistency Error体现），又在语义一致性上优于现有方法（以Structure-Aware Total Variation衡量），且具备针对流逼近误差的可证明稳定性边界。
链接: https://arxiv.org/abs/2603.05093
作者: Cenwei Zhang,Lin Zhu,Manxi Lin,Lei You
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 11 figures, 22 pages
Abstract:Shapley-based attribution is critical for post-hoc XAI but suffers from off-manifold artifacts due to heuristic baselines. While generative methods attempt to address this, they often introduce geometric inefficiency and discretization drift. We propose a formal theory of on-manifold Aumann-Shapley attributions driven by optimal generative flows. We prove a representation theorem establishing the gradient line integral as the unique functional satisfying efficiency and geometric axioms, notably reparameterization invariance. To resolve path ambiguity, we select the kinetic-energy-minimizing Wasserstein-2 geodesic transporting a prior to the data distribution. This yields a canonical attribution family that recovers classical Shapley for additive models and admits provable stability bounds against flow approximation errors. By reframing baseline selection as a variational problem, our method experimentally outperforms baselines, achieving strict manifold adherence via vanishing Flow Consistency Error and superior semantic alignment characterized by Structure-Aware Total Variation. Our code is on this https URL.
[CV-41] Orthogonal Spatial-temporal Distributional Transfer for 4D Generation AAAI
【速读】：该论文旨在解决当前4D内容生成研究中因缺乏大规模4D数据集而导致模型难以充分学习关键时空特征的问题，从而限制了高质量4D合成的发展。解决方案的核心在于提出一种空间-时间解耦的4D扩散模型（Spatial-Temporal-Disentangled 4D Diffusion, STD-4D），通过从现有的3D扩散模型中迁移丰富的空间先验和从视频扩散模型中迁移时间先验，并设计了一种正交空间-时间分布迁移机制（Orthogonal Spatial-temporal Distributional Transfer, Orster），以精准建模并注入时空特征分布；同时，在4D构建过程中引入空间-时间感知的HexPlane结构（ST-HexPlane）来整合迁移的时空特征，从而显著提升4D形变精度与高斯特征建模质量，实现更优的时空一致性与4D合成效果。
链接: https://arxiv.org/abs/2603.05081
作者: Wei Liu,Shengqiong Wu,Bobo Li,Haoyu Zhao,Hao Fei,Mong-Li Lee,Wynne Hsu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 6 figures, 3 tables, AAAI
Abstract:In the AIGC era, generating high-quality 4D content has garnered increasing research attention. Unfortunately, current 4D synthesis research is severely constrained by the lack of large-scale 4D datasets, preventing models from adequately learning the critical spatial-temporal features necessary for high-quality 4D generation, thus hindering progress in this domain. To combat this, we propose a novel framework that transfers rich spatial priors from existing 3D diffusion models and temporal priors from video diffusion models to enhance 4D synthesis. We develop a spatial-temporal-disentangled 4D (STD-4D) Diffusion model, which synthesizes 4D-aware videos through disentangled spatial and temporal latents. To facilitate the best feature transfer, we design a novel Orthogonal Spatial-temporal Distributional Transfer (Orster) mechanism, where the spatiotemporal feature distributions are carefully modeled and injected into the STD-4D Diffusion. Furthermore, during the 4D construction, we devise a spatial-temporal-aware HexPlane (ST-HexPlane) to integrate the transferred spatiotemporal features, thereby improving 4D deformation and 4D Gaussian feature modeling. Experiments demonstrate that our method significantly outperforms existing approaches, achieving superior spatial-temporal consistency and higher-quality 4D synthesis.
[CV-42] MoRe: Motion-aware Feed-forward 4D Reconstruction Transformer CVPR2025
【速读】：该论文旨在解决动态4D场景重建中因运动物体干扰导致相机位姿估计失效的问题。现有优化方法虽能缓解此问题，但通常计算复杂度高，难以满足实时应用需求。其解决方案的关键在于提出一种前馈式4D重建网络MoRe，基于强大的静态重建主干网络，采用注意力强制策略（attention-forcing strategy）分离动态运动与静态结构，并引入分组因果注意力机制（grouped causal attention）以捕捉时序依赖关系并适应不同帧间token长度变化，从而实现高效且时序一致的动态三维场景重建。
链接: https://arxiv.org/abs/2603.05078
作者: Juntong Fang,Zequn Chen,Weiqi Zhang,Donglin Di,Xuancheng Zhang,Chengmin Yang,Yu-Shen Liu
机构: Tsinghua University (清华大学); Li Auto (理想汽车)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2025. Project page: this https URL
Abstract:Reconstructing dynamic 4D scenes remains challenging due to the presence of moving objects that corrupt camera pose estimation. Existing optimization methods alleviate this issue with additional supervision, but they are mostly computationally expensive and impractical in real-time applications. To address these limitations, we propose MoRe, a feedforward 4D reconstruction network that efficiently recovers dynamic 3D scenes from monocular videos. Built upon a strong static reconstruction backbone, MoRe employs an attention-forcing strategy to disentangle dynamic motion from static structure. To further enhance robustness, we fine-tune the model on large-scale, diverse datasets encompassing both dynamic and static scenes. Moreover, our grouped causal attention captures temporal dependencies and adapts to varying token lengths across frames, ensuring temporally coherent geometry reconstruction. Extensive experiments on multiple benchmarks demonstrate that MoRe achieves high-quality dynamic reconstructions with exceptional efficiency.
[CV-43] UniM: A Unified Any-to-Any Interleaved Multimodal Benchmark CVPR
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在真实世界应用中面临的“任意到任意交错多模态学习”（any-to-any interleaved multimodal learning）问题，即系统需同时理解任意组合与交错的多模态输入，并生成任意交错形式的多模态输出。为推动该方向的研究与评估，论文提出首个统一的“任意到任意交错多模态数据集”UniM，包含31K高质量样本，覆盖30个领域和7种代表性模态（文本、图像、音频、视频、文档、代码和3D），每项任务均要求复杂的交叉推理与生成能力。解决方案的关键在于构建了UniM基准数据集及其配套的UniM Evaluation Suite，从语义正确性、生成质量、响应结构完整性及交错一致性四个维度进行系统评测，并引入一个具备可追溯推理能力的代理基线模型UniMA，以支持结构化交错生成，从而揭示当前模型在统一多模态理解与生成范式下的关键挑战与未来发展方向。
链接: https://arxiv.org/abs/2603.05075
作者: Yanlin Li,Minghui Guo,Kaiwen Zhang,Shize Zhang,Yiran Zhao,Haodong Li,Congyue Zhou,Weijie Zheng,Yushen Yan,Shengqiong Wu,Wei Ji,Lei Cui,Furu Wei,Hao Fei,Mong-Li Lee,Wynne Hsu
机构: NUS(新加坡国立大学); SCUT(华南理工大学); NTU(南洋理工大学); NJU(南京大学); Microsoft Research(微软研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 70 pages, 63 figures, 30 tables, CVPR
Abstract:In real-world multimodal applications, systems usually need to comprehend arbitrarily combined and interleaved multimodal inputs from users, while also generating outputs in any interleaved multimedia form. This capability defines the goal of any-to-any interleaved multimodal learning under a unified paradigm of understanding and generation, posing new challenges and opportunities for advancing Multimodal Large Language Models (MLLMs). To foster and benchmark this capability, this paper introduces the UniM benchmark, the first Unified Any-to-Any Interleaved Multimodal dataset. UniM contains 31K high-quality instances across 30 domains and 7 representative modalities: text, image, audio, video, document, code, and 3D, each requiring multiple intertwined reasoning and generation capabilities. We further introduce the UniM Evaluation Suite, which assesses models along three dimensions: Semantic Correctness Generation Quality, Response Structure Integrity, and Interleaved Coherence. In addition, we propose UniMA, an agentic baseline model equipped with traceable reasoning for structured interleaved generation. Comprehensive experiments demonstrate the difficulty of UniM and highlight key challenges and directions for advancing unified any-to-any multimodal intelligence. The project page is this https URL.
[CV-44] MI-DETR: A Strong Baseline for Moving Infrared Small Target Detection with Bio-Inspired Motion Integration
【速读】：该论文旨在解决红外小目标检测（Infrared Small Target Detection, ISTD）中因目标尺寸微小、对比度低而易被复杂动态背景遮蔽的问题。传统多帧方法通常通过深度神经网络隐式学习运动信息，常需额外的运动监督或显式对齐模块，增加了模型复杂性和标注成本。其解决方案的关键在于提出一种受生物视觉系统启发的双通路检测框架——Motion Integration DETR (MI-DETR)，核心创新包括：(1) 采用视网膜启发的细胞自动机（Retina-Inspired Cellular Automaton, RCA）将原始帧序列转换为与外观图像同像素网格的运动图，实现对“视锥细胞样”外观路径和“视杆细胞样”运动路径的统一边界框监督，无需额外运动标签或对齐操作；(2) 引入视锥-视杆互连模块（Parvocellular-Magnocellular Interconnection, PMI Block），在两路径间建立双向特征交互机制，模拟生物视觉系统的中间连接结构；最终通过RT-DETR解码器融合双通路特征输出检测结果。该方法显著提升了多个基准数据集上的性能，验证了生物启发的运动-外观融合策略的有效性。
链接: https://arxiv.org/abs/2603.05071
作者: Nian Liu,Jin Gao,Shubo Lin,Yutong Kou,Sikui Zhang,Fudong Ge,Zhiqiang Pu,Liang Li,Gang Wang,Yizheng Wang,Weiming Hu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 6 figures
Abstract:Infrared small target detection (ISTD) is challenging because tiny, low-contrast targets are easily obscured by complex and dynamic backgrounds. Conventional multi-frame approaches typically learn motion implicitly through deep neural networks, often requiring additional motion supervision or explicit alignment modules. We propose Motion Integration DETR (MI-DETR), a bio-inspired dual-pathway detector that processes one infrared frame per time step while explicitly modeling motion. First, a retina-inspired cellular automaton (RCA) converts raw frame sequences into a motion map defined on the same pixel grid as the appearance image, enabling parvocellular-like appearance and magnocellular-like motion pathways to be supervised by a single set of bounding boxes without extra motion labels or alignment operations. Second, a Parvocellular-Magnocellular Interconnection (PMI) Block facilitates bidirectional feature interaction between the two pathways, providing a biologically motivated intermediate interconnection mechanism. Finally, a RT-DETR decoder operates on features from the two pathways to produce detection results. Surprisingly, our proposed simple yet effective approach yields strong performance on three commonly used ISTD benchmarks. MI-DETR achieves 70.3% mAP@50 and 72.7% F1 on IRDST-H (+26.35 mAP@50 over the best multi-frame baseline), 98.0% mAP@50 on DAUB-R, and 88.3% mAP@50 on ITSDT-15K, demonstrating the effectiveness of biologically inspired motion-appearance integration. Code is available at this https URL.
[CV-45] A 360-degree Multi-camera System for Blue Emergency Light Detection Using Color Attention RT-DETR and the ABLDataset
【速读】：该论文旨在解决紧急车辆（如救护车、消防车等）蓝光信号在复杂环境下的检测难题，以提升高级驾驶辅助系统（ADAS）的感知能力与道路安全水平。解决方案的关键在于构建一个基于多视角鱼眼相机（fisheye cameras）的视觉感知系统，并结合经过优化的RT-DETR目标检测模型——通过引入颜色注意力模块（color attention block）显著提升了对蓝光的识别准确率与召回率（分别达94.7%和94.1%），同时利用几何变换估计紧急车辆接近角度，实现对危险源的方位与距离的精确判断，最终支持多模态融合（视觉+声学）的实时预警功能。
链接: https://arxiv.org/abs/2603.05058
作者: Francisco Vacalebri-Lloret(1),Lucas Banchero(1),Jose J. Lopez(1),Jose M. Mossi(1) ((1) Universitat Politècnica de València, Spain)
机构: Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València(瓦伦西亚理工大学电信与多媒体应用研究所)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
备注: 16 pages, 17 figures. Submitted to IEEE Transactions on Intelligent Vehicles
Abstract:This study presents an advanced system for detecting blue lights on emergency vehicles, developed using ABLDataset, a curated dataset that includes images of European emergency vehicles under various climatic and geographic conditions. The system employs a configuration of four fisheye cameras, each with a 180-degree horizontal field of view, mounted on the sides of the vehicle. A calibration process enables the azimuthal localization of the detections. Additionally, a comparative analysis of major deep neural network algorithms was conducted, including YOLO (v5, v8, and v10), RetinaNet, Faster R-CNN, and RT-DETR. RT-DETR was selected as the base model and enhanced through the incorporation of a color attention block, achieving an accuracy of 94.7 percent and a recall of 94.1 percent on the test set, with field test detections reaching up to 70 meters. Furthermore, the system estimates the approach angle of the emergency vehicle relative to the center of the car using geometric transformations. Designed for integration into a multimodal system that combines visual and acoustic data, this system has demonstrated high efficiency, offering a promising approach to enhancing Advanced Driver Assistance Systems (ADAS) and road safety.
[CV-46] CLIP-driven Zero-shot Learning with Ambiguous Labels ICASSP2026
【速读】：该论文旨在解决零样本学习（Zero-shot Learning, ZSL）中因训练样本标签噪声和模糊性导致性能下降的问题。现有方法通常假设训练标签是准确的，但在实际场景中，标签不确定性会显著影响模型泛化能力。其解决方案的关键在于提出一种基于CLIP的偏标签零样本学习框架（CLIP-driven Partial Label Zero-shot Learning, CLIP-PZSL），通过CLIP提取实例与标签特征，并设计语义挖掘模块融合二者以生成判别性标签嵌入；同时引入偏零样本损失函数（partial zero-shot loss），依据候选标签与实例的相关性动态分配权重，从而对齐实例与标签嵌入、减少语义错位。随着训练推进，模型逐步识别真实标签，进而优化标签嵌入与语义对齐，形成闭环提升机制。
链接: https://arxiv.org/abs/2603.05053
作者: Jinfu Fan,Jiangnan Li,Xiaowen Yan,Xiaohui Zhong,Wenpeng Lu,Linqing Huang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICASSP 2026 (IEEE International Conference on Acoustics, Speech, and Signal Processing)
Abstract:Zero-shot learning (ZSL) aims to recognize unseen classes by leveraging semantic information from seen classes, but most existing methods assume accurate class labels for training instances. However, in real-world scenarios, noise and ambiguous labels can significantly reduce the performance of ZSL. To address this, we propose a new CLIP-driven partial label zero-shot learning (CLIP-PZSL) framework to handle label ambiguity. First, we use CLIP to extract instance and label features. Then, a semantic mining block fuses these features to extract discriminative label embeddings. We also introduce a partial zero-shot loss, which assigns weights to candidate labels based on their relevance to the instance and aligns instance and label embeddings to minimize semantic mismatch. As the training goes on, the ground-truth labels are progressively identified, and the refined labels and label embeddings in turn help improve the semantic alignment of instance and label features. Comprehensive experiments on several datasets demonstrate the advantage of CLIP-PZSL.
[CV-47] CoIn3D: Revisiting Configuration-Invariant Multi-Camera 3D Object Detection CVPR2026
【速读】：该论文旨在解决多相机三维目标检测（Multi-camera 3D object detection, MC3D）模型在面对未见过的摄像头配置时泛化能力差的问题。现有方法通常采用统一的元相机（meta-camera）进行表征，但未能充分考虑源配置与目标配置之间的空间先验差异，包括内参、外参及阵列布局的不同。解决方案的关键在于提出CoIn3D框架，通过两个核心机制实现强跨配置迁移能力：一是空间感知特征调制（Spatial-aware feature modulation, SFM），将焦距、地面深度、地面梯度和Plücker坐标等四类空间先验显式嵌入特征空间；二是相机感知数据增强（Camera-aware data augmentation, CDA），利用无需训练的动态新视角图像合成策略提升不同配置下的观测多样性。这一设计使模型在BEVDepth、BEVFormer和PETR三种主流MC3D范式下均展现出优异的跨配置性能。
链接: https://arxiv.org/abs/2603.05042
作者: Zhaonian Kuang,Rui Ding,Haotian Wang,Xinhu Zheng,Meng Yang,Gang Hua
机构: Xi’an Jiaotong University (西安交通大学); HKUST(GZ) (香港科技大学（广州）); Amazon Alexa AI (亚马逊 Alexa AI)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted to CVPR 2026 main track
Abstract:Multi-camera 3D object detection (MC3D) has attracted increasing attention with the growing deployment of multi-sensor physical agents, such as robots and autonomous vehicles. However, MC3D models still struggle to generalize to unseen platforms with new multi-camera configurations. Current solutions simply employ a meta-camera for unified representation but lack comprehensive consideration. In this paper, we revisit this issue and identify that the devil lies in spatial prior discrepancies across source and target configurations, including different intrinsics, extrinsics, and array layouts. To address this, we propose CoIn3D, a generalizable MC3D framework that enables strong transferability from source configurations to unseen target ones. CoIn3D explicitly incorporates all identified spatial priors into both feature embedding and image observation through spatial-aware feature modulation (SFM) and camera-aware data augmentation (CDA), respectively. SFM enriches feature space by integrating four spatial representations, such as focal length, ground depth, ground gradient, and Plücker coordinate. CDA improves observation diversity under various configurations via a training-free dynamic novel-view image synthesis scheme. Extensive experiments demonstrate that CoIn3D achieves strong cross-configuration performance on landmark datasets such as NuScenes, Waymo, and Lyft, under three dominant MC3D paradigms represented by BEVDepth, BEVFormer, and PETR.
[CV-48] Exploiting Intermediate Reconstructions in Optical Coherence Tomography for Test-Time Adaption of Medical Image Segmentation
【速读】：该论文旨在解决低成本成像设备在初级卫生保健中广泛应用时，因重建图像质量受限而导致下游任务（如医学图像分割）性能下降的问题。传统迭代重建方法虽能逼近高质量成像效果，但其评估仅依赖最终重建图像，忽略了重建过程中产生的中间表示信息。解决方案的关键在于提出一种名为IRTTA（Iterative Reconstruction with Test-Time Adaptation）的方法，通过一个条件调制网络（modulator network）在测试时动态调整冻结的下游网络中归一化层参数，该调制网络以当前重建时间尺度为条件，并利用所有时间步上的平均熵损失进行学习。此策略不仅提升了分割性能，还无需额外计算即可从不同时间步的分割结果差异中获得语义上合理的不确定性估计。
链接: https://arxiv.org/abs/2603.05041
作者: Thomas Pinetz,Veit Hucke,Hrvoje Bogunovic
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MIDL 2026
Abstract:Primary health care frequently relies on low-cost imaging devices, which are commonly used for screening purposes. To ensure accurate diagnosis, these systems depend on advanced reconstruction algorithms designed to approximate the performance of high-quality counterparts. Such algorithms typically employ iterative reconstruction methods that incorporate domain-specific prior knowledge. However, downstream task performance is generally assessed using only the final reconstructed image, thereby disregarding the informative intermediate representations generated throughout the reconstruction process. In this work, we propose IRTTA to exploit these intermediate representations at test-time by adapting the normalization-layer parameters of a frozen downstream network via a modulator network that conditions on the current reconstruction timescale. The modulator network is learned during test-time using an averaged entropy loss across all individual timesteps. Variation among the timestep-wise segmentations additionally provides uncertainty estimates at no extra cost. This approach enhances segmentation performance and enables semantically meaningful uncertainty estimation, all without modifying either the reconstruction process or the downstream model.
[CV-49] Generalizable Multiscale Segmentation of Heterogeneous Map Collections
【速读】：该论文旨在解决历史地图识别中普遍存在的多样性问题，即现有研究多依赖于针对同质化地图系列的专用模型，难以适应不同风格、比例尺和地理焦点的多样化历史地图文档。其解决方案的关键在于提出了一种以多样性驱动的通用语义分割框架：首先构建了Semap这一包含1,439个手工标注图像块的新开放基准数据集，以体现历史地图的多样性；其次设计了一种结合过程化数据合成与多尺度融合的分割框架，显著提升了模型的鲁棒性和跨域迁移能力。实验表明，该方法在HCMSSD和Semap数据集上均达到最先进性能，且分割效果在不同地图集合、比例尺、地理区域和出版背景下保持稳定，从而为整合长尾分布的制图档案服务于历史地理研究提供了可行路径。
链接: https://arxiv.org/abs/2603.05037
作者: Remi Petitpierre
机构: EPFL (瑞士联邦理工学院); Digital Humanities Laboratory (DHLAB) (数字人文实验室); School of Computer and Communication Sciences (IC) (计算机与通信科学学院); Laboratory of Urban Sociology (LASUR) (城市社会学实验室); School of Architecture, Civil and Environmental Engineering (ENAC) (建筑、土木与环境工程学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 30 pages, 15 figures
Abstract:Historical map collections are highly diverse in style, scale, and geographic focus, often consisting of many single-sheet documents. Yet most work in map recognition focuses on specialist models tailored to homogeneous map series. In contrast, this article aims to develop generalizable semantic segmentation models and ontology. First, we introduce Semap, a new open benchmark dataset comprising 1,439 manually annotated patches designed to reflect the variety of historical map documents. Second, we present a segmentation framework that combines procedural data synthesis with multiscale integration to improve robustness and transferability. This framework achieves state-of-the-art performance on both the HCMSSD and Semap datasets, showing that a diversity-driven approach to map recognition is not only viable but also beneficial. The results indicate that segmentation performance remains largely stable across map collections, scales, geographic regions, and publication contexts. By proposing benchmark datasets and methods for the generic segmentation of historical maps, this work opens the way to integrating the long tail of cartographic archives to historical geographic studies.
[CV-50] 2Adapt: A Unified Framework for Source Free Unsupervised Domain Adaptation via Vision Foundation Model CVPR2026
【速读】：该论文旨在解决源域无关的无监督域自适应（Source Free Unsupervised Domain Adaptation, SFUDA）在医疗图像分割中面临的泛化能力不足问题，尤其是在多模态、多目标场景下现有方法难以统一适配的挑战。其解决方案的关键在于提出Tell2Adapt框架，该框架利用视觉基础模型（Vision Foundation Model, VFM）的强大通用知识，通过上下文感知提示正则化（Context-Aware Prompts Regularization, CAPR）确保高质量文本提示到标准指令的鲁棒转换，从而生成高置信度伪标签以高效适配轻量级学生模型；同时引入视觉合理性精炼（Visual Plausibility Refinement, VPR）机制，借助VFM的解剖学先验知识将模型预测重新锚定于目标图像的低层视觉特征，有效抑制噪声和假阳性，保障临床可靠性。
链接: https://arxiv.org/abs/2603.05012
作者: Yulong Shi,Shijie Li,Ziyi Li,Lin Qi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)
Abstract:Source Free Unsupervised Domain Adaptation (SFUDA) is critical for deploying deep learning models across diverse clinical settings. However, existing methods are typically designed for low-gap, specific domain shifts and cannot generalize into a unified, multi-modalities, and multi-target framework, which presents a major barrier to real-world application. To overcome this issue, we introduce Tell2Adapt, a novel SFUDA framework that harnesses the vast, generalizable knowledge of the Vision Foundation Model (VFM). Our approach ensures high-fidelity VFM prompts through Context-Aware Prompts Regularization (CAPR), which robustly translates varied text prompts into canonical instructions. This enables the generation of high-quality pseudo-labels for efficiently adapting the lightweight student model to target domain. To guarantee clinical reliability, the framework incorporates Visual Plausibility Refinement (VPR), which leverages the VFM’s anatomical knowledge to re-ground the adapted model’s predictions in target image’s low-level visual features, effectively removing noise and false positives. We conduct one of the most extensive SFUDA evaluations to date, validating our framework across 10 domain adaptation directions and 22 anatomical targets, including brain, cardiac, polyp, and abdominal targets. Our results demonstrate that Tell2Adapt consistently outperforms existing approaches, achieving SOTA for a unified SFUDA framework in medical image segmentation. Code are avaliable at this https URL.
[CV-51] How far have we gone in Generative Image Restoration? A study on its capability limitations and evaluation practices
【速读】：该论文旨在解决生成式图像修复（Generative Image Restoration, GIR）模型在实际应用中性能提升的真实程度问题，即其感知真实感的提升是否真正带来了实用能力的进步。为回答这一问题，作者构建了一个多维评估流水线，从细节、锐度、语义正确性和整体质量四个维度系统性地对比分析了扩散模型、GAN模型、PSNR优化模型及通用生成模型等多种架构的表现，揭示了关键性能差异。解决方案的关键在于识别出GIR领域失败模式的根本转变：从早期的细节缺失（under-generation）演变为当前对细节质量和语义控制的要求（preventing over-generation），并基于此设计了一个新的图像质量评估（IQA）模型，使其更贴近人类感知判断，从而为未来研究提供可量化的基准和方向指引。
链接: https://arxiv.org/abs/2603.05010
作者: Xiang Yin,Jinfan Hu,Zhiyuan You,Kainan Yan,Yu Tang,Chao Dong,Jinjin Gu
机构: Fudan University (复旦大学); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (中国科学院深圳先进技术研究院); Multimedia Laboratory, The Chinese University of Hong Kong (香港中文大学多媒体实验室); University of the Chinese Academy of Sciences (中国科学院大学); Shanghai AI Laboratory (上海人工智能实验室); Shenzhen University of Advanced Technology (深圳先进研究生院); INSAIT, Sofia University “St. Kliment Ohridski” (INSAIT，索非亚大学“圣克莱门特奥赫里德斯基”)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generative Image Restoration (GIR) has achieved impressive perceptual realism, but how far have its practical capabilities truly advanced compared with previous methods? To answer this, we present a large-scale study grounded in a new multi-dimensional evaluation pipeline that assesses models on detail, sharpness, semantic correctness, and overall quality. Our analysis covers diverse architectures, including diffusion-based, GAN-based, PSNR-oriented, and general-purpose generation models, revealing critical performance disparities. Furthermore, our analysis uncovers a key evolution in failure modes that signifies a paradigm shift for the perception-oriented low-level vision field. The central challenge is evolving from the previous problem of detail scarcity (under-generation) to the new frontier of detail quality and semantic control (preventing over-generation). We also leverage our benchmark to train a new IQA model that better aligns with human perceptual judgments. Ultimately, this work provides a systematic study of modern generative image restoration models, offering crucial insights that redefine our understanding of their true state and chart a course for future development.
[CV-52] Physics-consistent deep learning for blind aberration recovery in mobile optics
【速读】：该论文旨在解决移动摄影中因复杂、镜头特异性光学像差（optical aberrations）导致的图像模糊问题。传统深度学习方法将去模糊视为端到端任务，但存在缺乏显式光学建模和细节幻觉的问题；而经典盲反卷积方法则稳定性不足。其解决方案的关键在于提出 Lens2Zernike 框架，首次在单一模型中同时整合三个不同光学域的监督信号：通过直接回归 Zernike 系数（z）实现物理参数显式建模，利用可微分的波前与点扩散函数（PSF）推导施加物理约束（p），并辅以多任务空间图预测（m）。这种物理一致性的多任务策略显著提升了恢复精度与稳定性，最终实现对严重畸变图像的稳定非盲反卷积，有效恢复衍射极限细节。
链接: https://arxiv.org/abs/2603.04999
作者: Kartik Jhawar,Tamo Sancho Miguel Tandoc,Khoo Jun Xuan,Wang Lipo
机构: Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University (南洋理工大学); School of Electrical and Electronic Engineering, Nanyang Technological University (南洋理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 4 pages, 3 figures
Abstract:Mobile photography is often limited by complex, lens-specific optical aberrations. While recent deep learning methods approach this as an end-to-end deblurring task, these “black-box” models lack explicit optical modeling and can hallucinate details. Conversely, classical blind deconvolution remains highly unstable. To bridge this gap, we present Lens2Zernike, a deep learning framework that blindly recovers physical optical parameters from a single blurred image. To the best of our knowledge, no prior work has simultaneously integrated supervision across three distinct optical domains. We introduce a novel physics-consistent strategy that explicitly minimizes errors via direct Zernike coefficient regression (z), differentiable physics constraints encompassing both wavefront and point spread function derivations §, and auxiliary multi-task spatial map predictions (m). Through an ablation study on a ResNet-18 backbone, we demonstrate that our full multi-task framework (z+p+m) yields a 35% improvement over coefficient-only baselines. Crucially, comparative analysis reveals that our approach outperforms two established deep learning methods from previous literature, achieving significantly lower regression errors. Ultimately, we demonstrate that these recovered physical parameters enable stable non-blind deconvolution, providing substantial in-domain improvement on the patented Institute for Digital Molecular Analytics and Science (IDMxS) Mobile Camera Lens Database for restoring diffraction-limited details from severely aberrated mobile captures.
[CV-53] MultiGO: Monocular 3D Clothed Human Reconstruction via Geometry-Texture Collaboration
【速读】：该论文旨在解决单目图像下人体三维重建中存在的三大局限性：纹理上因训练数据不足导致的性能瓶颈、几何上依赖外部先验信息不准确的问题，以及系统层面由单一模态监督带来的偏差。其解决方案的核心在于提出一种名为MultiGO++的新框架，通过三方面创新实现几何与纹理的有效协同：首先，构建包含15,000+个3D纹理人体扫描的多源纹理合成策略以提升复杂场景下的纹理质量估计；其次，设计区域感知形状提取模块与傅里叶几何编码器，增强各身体区域特征交互并缓解模态差异，从而实现更有效的几何学习；最后，采用双路重建U-Net结构，利用几何-纹理协同特征对高保真度纹理化三维人体网格进行精细化生成。
链接: https://arxiv.org/abs/2603.04993
作者: Nanjie Yao,Gangjian Zhang,Wenhao Shen,Jian Shu,Yu Feng,Hao Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Monocular 3D clothed human reconstruction aims to generate a complete and realistic textured 3D avatar from a single image. Existing methods are commonly trained under multi-view supervision with annotated geometric priors, and during inference, these priors are estimated by the pre-trained network from the monocular input. These methods are constrained by three key limitations: texturally by unavailability of training data, geometrically by inaccurate external priors, and systematically by biased single-modality supervision, all leading to suboptimal reconstruction. To address these issues, we propose a novel reconstruction framework, named MultiGO++, which achieves effective systematic geometry-texture collaboration. It consists of three core parts: (1) A multi-source texture synthesis strategy that constructs 15,000+ 3D textured human scans to improve the performance on texture quality estimation in challenge scenarios; (2) A region-aware shape extraction module that extracts and interacts features of each body region to obtain geometry information and a Fourier geometry encoder that mitigates the modality gap to achieve effective geometry learning; (3) A dual reconstruction U-Net that leverages geometry-texture collaborative features to refine and generate high-fidelity textured 3D human meshes. Extensive experiments on two benchmarks and many in-the-wild cases show the superiority of our method over state-of-the-art approaches.
[CV-54] APFormer: Robust Arbitrary Point Tracking via Transient Asynchronous Fusion of Frames and Events
【速读】：该论文旨在解决任意点跟踪（Tracking Any Point, TAP）任务中因模态时序不一致和单一模态失效导致的精度下降问题，特别是在RGB帧与事件流（event stream）融合时存在的同步困难与鲁棒性不足。其核心解决方案是提出TAPFormer框架，关键创新在于设计了瞬态异步融合（Transient Asynchronous Fusion, TAF）机制，通过连续事件更新显式建模离散帧之间的时序演化，从而实现低频帧与高频事件间的时序一致性对齐；同时引入跨模态局部加权融合（Cross-modal Locally Weighted Fusion, CLWF）模块，根据模态可靠性自适应调整空间注意力，提升在模糊或低光照条件下的特征稳定性与判别力。
链接: https://arxiv.org/abs/2603.04989
作者: Jiaxiong Liu,Zhen Tan,Jinpu Zhang,Yi Zhou,Hui Shen,Xieyuanli Chen,Dewen Hu
机构: National University of Defense Technology (国防科技大学); Hunan University (湖南大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Tracking any point (TAP) is a fundamental yet challenging task in computer vision, requiring high precision and long-term motion reasoning. Recent attempts to combine RGB frames and event streams have shown promise, yet they typically rely on synchronous or non-adaptive fusion, leading to temporal misalignment and severe degradation when one modality fails. We introduce TAPFormer, a transformer-based framework that performs asynchronous temporal-consistent fusion of frames and events for robust and high-frequency arbitrary point tracking. Our key innovation is a Transient Asynchronous Fusion (TAF) mechanism, which explicitly models the temporal evolution between discrete frames through continuous event updates, bridging the gap between low-rate frames and high-rate events. In addition, a Cross-modal Locally Weighted Fusion (CLWF) module adaptively adjusts spatial attention according to modality reliability, yielding stable and discriminative features even under blur or low light. To evaluate our approach under realistic conditions, we construct a novel real-world frame-event TAP dataset under diverse illumination and motion conditions. Our method outperforms existing point trackers, achieving a 28.2% improvement in average pixel error within threshold. Moreover, on standard point tracking benchmarks, our tracker consistently achieves the best performance. Project website: this http URL
[CV-55] A Simple Baseline for Unifying Understanding Generation and Editing via Vanilla Next-token Prediction
【速读】：该论文旨在解决多模态理解、图像生成与编辑任务难以统一建模的问题，即如何在一个模型中实现跨模态语义对齐与生成能力的协同优化。解决方案的关键在于提出一个基于自回归（autoregressive）机制的简单基线模型 Wallaroo，其通过 next-token prediction 任务将视觉理解、图像生成与编辑统一到同一框架下，并采用分阶段训练策略（four-stage training strategy）和分离式视觉编码路径（decouple the visual encoding into separate pathways），从而在支持多分辨率输入输出及中英文双语处理的基础上，显著提升模型在多模态任务上的综合性能。
链接: https://arxiv.org/abs/2603.04980
作者: Jie Zhu,Hanghang Ma,Jia Wang,Yayong Guan,Yanbing Zeng,Lishuai Gao,Junqiang Wu,Jie Hu,Leye Wang
机构: Peking University (北京大学); University of Chinese Academy of Sciences (中国科学院大学); Meituan (美团)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Technical report. This work serves as a straightforward autoregressive baseline for unifying understanding, generation, and editing
Abstract:In this work, we introduce Wallaroo, a simple autoregressive baseline that leverages next-token prediction to unify multi-modal understanding, image generation, and editing at the same time. Moreover, Wallaroo supports multi-resolution image input and output, as well as bilingual support for both Chinese and English. We decouple the visual encoding into separate pathways and apply a four-stage training strategy to reshape the model’s capabilities. Experiments are conducted on various benchmarks where Wallaroo produces competitive performance or exceeds other unified models, suggesting the great potential of autoregressive models in unifying multi-modality understanding and generation. Our code is available at this https URL.
[CV-56] hink Then Verify: A Hypothesis-Verification Multi-Agent Framework for Long Video Understanding CVPR2026
【速读】：该论文旨在解决长视频理解中因视觉冗余密集、时序依赖关系长以及基于链式思维和检索的智能体易产生语义漂移与相关性驱动错误所带来的挑战。其解决方案的关键在于提出“先思考后查找”（thinking-before-finding）的原则，构建VideoHV-Agent框架，将视频问答任务重构为结构化的假设验证流程：通过一个Thinker模块将候选答案转化为可检验的假设，Judge模块提取判别性线索以明确需验证的证据，Verifier模块基于局部细粒度视频内容对线索进行定位与测试，最终由Answer模块整合验证后的证据生成答案。此方法在三个长视频理解基准上实现了最先进性能，并提升了可解释性、逻辑严谨性和计算效率。
链接: https://arxiv.org/abs/2603.04977
作者: Zheng Wang,Haoran Chen,Haoxuan Qin,Zhipeng Wei,Tianwen Qian,Cong Bai
机构: Zhejiang University of Technology (浙江工业大学); Zhejiang Key Laboratory of Visual Information Intelligent Processing (浙江省视觉信息智能处理重点实验室); UC Berkeley (加州大学伯克利分校); East China Normal University (华东师范大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at CVPR 2026
Abstract:Long video understanding is challenging due to dense visual redundancy, long-range temporal dependencies, and the tendency of chain-of-thought and retrieval-based agents to accumulate semantic drift and correlation-driven errors. We argue that long-video reasoning should begin not with reactive retrieval, but with deliberate task formulation: the model must first articulate what must be true in the video for each candidate answer to hold. This thinking-before-finding principle motivates VideoHV-Agent, a framework that reformulates video question answering as a structured hypothesis-verification process. Based on video summaries, a Thinker rewrites answer candidates into testable hypotheses, a Judge derives a discriminative clue specifying what evidence must be checked, a Verifier grounds and tests the clue using localized, fine-grained video content, and an Answer agent integrates validated evidence to produce the final answer. Experiments on three long-video understanding benchmarks show that VideoHV-Agent achieves state-of-the-art accuracy while providing enhanced interpretability, improved logical soundness, and lower computational cost. We make our code publicly available at: this https URL.
[CV-57] 3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding
【速读】：该论文旨在解决当前视频-based 3D场景理解任务中因依赖监督微调（Supervised Fine-Tuning, SFT）而导致的训练目标与实际评估指标不一致的问题。现有方法通常使用token-level交叉熵损失作为间接优化目标，难以有效提升模型在3D IoU、F1分数等关键任务指标上的表现。解决方案的关键在于提出首个将可验证奖励强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）扩展至视频驱动3D感知与推理的框架——3D-RFT（Reinforcement Fine-Tuning for Video-based 3D Scene Understanding）。其核心创新在于：首先通过SFT激活具备3D感知能力的多模态大语言模型（Multi-modal Large Language Models, MLLMs），随后采用Group Relative Policy Optimization（GRPO）进行强化微调，并设计基于3D IoU和F1-Score等任务指标的严格可验证奖励函数，从而直接优化模型对下游任务性能的提升。实验表明，3D-RFT-4B在多个视频-based 3D理解任务上达到SOTA效果，且优于更大规模模型，验证了该方案的有效性与鲁棒性。
链接: https://arxiv.org/abs/2603.04976
作者: Xiongkun Linghu,Jiangyong Huang,Baoxiong Jia,Siyuan Huang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its potential in 3D scene understanding remains under-explored. Existing approaches largely rely on Supervised Fine-Tuning ( SFT), where the token-level cross-entropy loss acts as an indirect proxy for optimization, leading to a misalignment between training objectives and task performances. To bridge this gap, we present Reinforcement Fine-Tuning for Video-based 3D Scene Understanding (3D-RFT ), the first framework to extend RLVR to video-based 3D perception and reasoning. 3D-RFT shifts the paradigm by directly optimizing the model towards evaluation metrics. 3D-RFT first activates 3D-aware Multi-modal Large Language Models ( MLLM s) via SFT, followed by reinforcement fine-tuning using Group Relative Policy Optimization ( GRPO) with strictly verifiable reward functions. We design task-specific reward functions directly from metrics like 3D IoU and F1-Score to provide more effective signals to guide model training. Extensive experiments demonstrate that 3D-RFT-4B achieves state-of-the-art performance on various video-based 3D scene understanding tasks. Notably, 3D-RFT-4B significantly outperforms larger models (e.g., VG LLM-8B) on 3D video detection, 3D visual grounding, and spatial reasoning benchmarks. We further reveal good properties of 3D-RFT such as robust efficacy, and valuable insights into training strategies and data impact. We hope 3D-RFT can serve as a robust and promising paradigm for future development of 3D scene understanding.
[CV-58] BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement
【速读】：该论文旨在解决事件相机（event camera）在低光图像增强（Low-light Image Enhancement, LLIE）中因固有背景活动（Background Activity, BA）噪声与图像信噪比（Signal-to-Noise Ratio, SNR）低所导致的模态融合时严重噪声耦合问题，这一问题已成为现有方法性能提升的关键瓶颈。解决方案的核心在于提出一种分层且任务感知的框架 BiEvLight，其关键创新是将事件去噪重构为一个由增强任务约束的双层优化问题，而非静态预处理步骤；通过跨任务交互机制，上层去噪任务学习面向下层增强目标的事件表示，从而有效缓解过度或欠去噪的权衡，并借助图像与事件间的强梯度相关性构建梯度引导的事件去噪先验，显著提升整体增强质量。
链接: https://arxiv.org/abs/2603.04975
作者: Zishu Yao,Xiang-Xiang Su,Shengning Zhou,Guang-Yong Chen,Guodong Fan,Xing Chen
机构: Fuzhou University (福州大学); Shandong Technology and Business University (山东工商学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Event cameras, with their high dynamic range, show great promise for Low-light Image Enhancement (LLIE). Existing works primarily focus on designing effective modal fusion strategies. However, a key challenge is the dual degradation from intrinsic background activity (BA) noise in events and low signal-to-noise ratio (SNR) in images, which causes severe noise coupling during modal fusion, creating a critical performance bottleneck. We therefore posit that precise event denoising is the prerequisite to unlocking the full potential of event-based fusion. To this end, we propose BiEvLight, a hierarchical and task-aware framework that collaboratively optimizes enhancement and denoising by exploiting their intrinsic interdependence. Specifically, BiEvLight exploits the strong gradient correlation between images and events to build a gradient-guided event denoising prior that alleviates insufficient denoising in heavily noisy regions. Moreover, instead of treating event denoising as a static pre-processing stage-which inevitably incurs a trade-off between over- and under-denoising and cannot adapt to the requirements of a specific enhancement objective-we recast it as a bilevel optimization problem constrained by the enhancement task. Through cross-task interaction, the upper-level denoising problem learns event representations tailored to the lower-level enhancement objective, thereby substantially improving overall enhancement quality. Extensive experiments on the Real-world noise Dataset SDE demonstrate that our method significantly outperforms state-of-the-art (SOTA) approaches, with average improvements of 1.30dB in PSNR, 2.03dB in PSNR* and 0.047 in SSIM, respectively. The code will be publicly available at this https URL.
[CV-59] Revisiting an Old Perspective Projection for Monocular 3D Morphable Models Regression WACV2026
【速读】：该论文旨在解决单目3D形态模型（3D Morphable Model, 3DMM）回归方法在近距离面部视频中因透视畸变（perspective distortion）导致的拟合不稳定问题。现有基于正交投影（orthographic projection）的方法虽能消除焦距与物体距离之间的歧义并保持稳定性，但无法适应头戴式相机等近距离拍摄场景下的真实透视效应。其解决方案的关键在于引入一个新颖的缩放参数（shrinkage parameter），在保留正交投影稳定性的基础上，模拟伪透视效应（pseudo-perspective effect），从而有效建模近距离图像中的视角变化，提升模型在复杂拍摄条件下的拟合精度与鲁棒性。
链接: https://arxiv.org/abs/2603.04958
作者: Toby Chong,Ryota Nakajima
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: WACV 2026, this https URL
Abstract:We introduce a novel camera model for monocular 3D Morphable Model (3DMM) regression methods that effectively captures the perspective distortion effect commonly seen in close-up facial images. Fitting 3D morphable models to video is a key technique in content creation. In particular, regression-based approaches have produced fast and accurate results by matching the rendered output of the morphable model to the target image. These methods typically achieve stable performance with orthographic projection, which eliminates the ambiguity between focal length and object distance. However, this simplification makes them unsuitable for close-up footage, such as that captured with head-mounted cameras. We extend orthographic projection with a new shrinkage parameter, incorporating a pseudo-perspective effect while preserving the stability of the original projection. We present several techniques that allow finetuning of existing models, and demonstrate the effectiveness of our modification through both quantitative and qualitative comparisons using a custom dataset recorded with head-mounted cameras. Comments: WACV 2026, this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) Cite as: arXiv:2603.04958 [cs.CV] (or arXiv:2603.04958v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.04958 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-60] Location-Aware Pretraining for Medical Difference Visual Question Answering
【速读】：该论文旨在解决传统单图视觉问答（VQA）模型在处理医学影像时无法有效识别细微差异的问题，尤其是在对比诊断场景下，难以区分疾病进展与成像条件变化。其核心解决方案在于提出一种包含位置感知任务的预训练框架，关键创新点是引入自动指代表达（AREF）、定位描述生成（GCAP）和条件自动指代表达（CAREF）三项任务，使视觉编码器能够学习细粒度且空间对齐的视觉表征，从而显著提升对胸部X光图像中临床相关差异的检测与推理能力。
链接: https://arxiv.org/abs/2603.04950
作者: Denis Musinguzi,Caren Han,Prasenjit Mitra
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 11 pages
Abstract:Unlike conventional single-image models, differential medical VQA frameworks process multiple images to identify differences, mirroring the comparative diagnostic workflow of radiologists. However, standard vision encoders trained on contrastive or classification objectives often fail to capture the subtle visual variations necessary for distinguishing disease progression from acquisition differences. To address this limitation, we introduce a pretraining framework that incorporates location-aware tasks, including automatic referring expressions (AREF), grounded captioning (GCAP), and conditional automatic referring expressions (CAREF). These specific tasks enable the vision encoder to learn fine-grained, spatially grounded visual representations that are often overlooked by traditional pre-training methods. We subsequently integrate this enhanced vision encoder with a language model to perform medical difference VQA. Experimental results demonstrate that our approach achieves state-of-the-art performance in detecting and reasoning about clinically relevant changes in chest X-ray images.
[CV-61] Adaptive Prototype-based Interpretable Grading of Prostate Cancer
【速读】：该论文旨在解决前列腺癌（prostate cancer）病理图像分级中人工诊断工作量大、主观性强以及深度学习模型可解释性不足的问题。其核心解决方案是提出一种基于原型的弱监督框架，通过在图像块级别预训练以学习与各分级对应的鲁棒原型特征，并引入新型原型感知损失函数实现弱监督微调；同时设计了一种基于注意力机制的动态剪枝策略，有效处理样本间异质性并聚焦于关键原型，从而提升模型的可解释性和临床可信度，使其更贴近病理学家的判读逻辑。
链接: https://arxiv.org/abs/2603.04947
作者: Riddhasree Bhattacharyya,Pallabi Dutta,Sushmita Mitra
机构: Indian Statistical Institute (印度统计研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Prostate cancer being one of the frequently diagnosed malignancy in men, the rising demand for biopsies places a severe workload on pathologists. The grading procedure is tedious and subjective, motivating the development of automated systems. Although deep learning has made inroads in terms of performance, its limited interpretability poses challenges for widespread adoption in high-stake applications like medicine. Existing interpretability techniques for prostate cancer classifiers provide a coarse explanation but do not reveal why the highlighted regions matter. In this scenario, we propose a novel prototype-based weakly-supervised framework for an interpretable grading of prostate cancer from histopathology images. These networks can prove to be more trustworthy since their explicit reasoning procedure mirrors the workflow of a pathologist in comparing suspicious regions with clinically validated examples. The network is initially pre-trained at patch-level to learn robust prototypical features associated with each grade. In order to adapt it to a weakly-supervised setup for prostate cancer grading, the network is fine-tuned with a new prototype-aware loss function. Finally, a new attention-based dynamic pruning mechanism is introduced to handle inter-sample heterogeneity, while selectively emphasizing relevant prototypes for optimal performance. Extensive validation on the benchmark PANDA and SICAP datasets confirms that the framework can serve as a reliable assistive tool for pathologists in their routine diagnostic workflows.
[CV-62] Person Detection and Tracking from an Overhead Crane LiDAR
【速读】：该论文旨在解决工业室内场景中基于顶部视角（overhead viewpoint）LiDAR的人员检测与跟踪问题，其核心挑战在于该视角引入了显著的域偏移（domain shift），且缺乏适用于此类场景的公开训练数据。解决方案的关键在于：首先构建了一个针对特定工业现场的顶部视角LiDAR数据集，并标注了3D人体边界框；其次，在统一的训练与评估协议下适配多个候选3D目标检测器（如VoxelNeXt和SECOND），并结合轻量级跟踪算法（AB3DMOT和SimpleTrack）实现跨帧人员身份维持；最后通过距离分片评估量化感知系统的实际工作范围，结果表明在5米水平半径内平均精度（AP）可达0.84，1米内提升至0.97，验证了方法在真实工业环境中的有效性与实时性可行性。
链接: https://arxiv.org/abs/2603.04938
作者: Nilusha Jayawickrama,Henrik Toikka,Risto Ojala
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
备注: 8 pages, 7 figures, 4 tables. Submitted to Ubiquitous Robots (UR) 2026. Code: this https URL
Abstract:This paper investigates person detection and tracking in an industrial indoor workspace using a LiDAR mounted on an overhead crane. The overhead viewpoint introduces a strong domain shift from common vehicle-centric LiDAR benchmarks, and limited availability of suitable public training data. Henceforth, we curate a site-specific overhead LiDAR dataset with 3D human bounding-box annotations and adapt selected candidate 3D detectors under a unified training and evaluation protocol. We further integrate lightweight tracking-by-detection using AB3DMOT and SimpleTrack to maintain person identities over time. Detection performance is reported with distance-sliced evaluation to quantify the practical operating envelope of the sensing setup. The best adapted detector configurations achieve average precision (AP) up to 0.84 within a 5.0 m horizontal radius, increasing to 0.97 at 1.0 m, with VoxelNeXt and SECOND emerging as the most reliable backbones across this range. The acquired results contribute in bridging the domain gap between standard driving datasets and overhead sensing for person detection and tracking. We also report latency measurements, highlighting practical real-time feasibility. Finally, we release our dataset and implementations in GitHub to support further research
[CV-63] Beyond the Patch: Exploring Vulnerabilities of Visuomotor Policies via Viewpoint-Consistent 3D Adversarial Object ICRA2026
【速读】：该论文旨在解决基于神经网络的视觉-运动策略（visuomotor policies）在机器人操作任务中对感知攻击的脆弱性问题，特别是针对传统二维对抗补丁（adversarial patches）在动态视角下（如腕部安装摄像头）因透视畸变导致有效性下降的局限性。其解决方案的关键在于提出一种通过可微分渲染实现的、视角一致的三维物体对抗纹理优化方法，结合期望变换（Expectation over Transformation, EOT）与粗到精（Coarse-to-Fine, C2F）训练策略，利用距离相关的频率特性生成在不同相机-物体距离下均有效的对抗纹理，并引入显著性引导扰动和目标损失函数以增强策略注意力偏移与持续诱导机器人向对抗目标移动的能力，从而提升对抗攻击的鲁棒性和跨场景迁移能力。
链接: https://arxiv.org/abs/2603.04913
作者: Chanmi Lee,Minsung Yoon,Woojae Kim,Sebin Lee,Sung-eui Yoon
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 10 figures, Accepted to ICRA 2026. Project page: this https URL
Abstract:Neural network-based visuomotor policies enable robots to perform manipulation tasks but remain susceptible to perceptual attacks. For example, conventional 2D adversarial patches are effective under fixed-camera setups, where appearance is relatively consistent; however, their efficacy often diminishes under dynamic viewpoints from moving cameras, such as wrist-mounted setups, due to perspective distortions. To proactively investigate potential vulnerabilities beyond 2D patches, this work proposes a viewpoint-consistent adversarial texture optimization method for 3D objects through differentiable rendering. As optimization strategies, we employ Expectation over Transformation (EOT) with a Coarse-to-Fine (C2F) curriculum, exploiting distance-dependent frequency characteristics to induce textures effective across varying camera-object distances. We further integrate saliency-guided perturbations to redirect policy attention and design a targeted loss that persistently drives robots toward adversarial objects. Our comprehensive experiments show that the proposed method is effective under various environmental conditions, while confirming its black-box transferability and real-world applicability.
[CV-64] AdaIAT: Adaptively Increasing Attention to Generated Text to Alleviate Hallucinations in LVLM
【速读】：该论文旨在解决大型视觉语言模型（Large Vision-Language Models, LVLMs）中存在的幻觉（hallucination）问题，即模型在生成文本时错误地引入图像中不存在的信息。为缓解这一问题，作者提出了一种关键解决方案——Attention to Generated Text (IAT)，其核心思想是利用生成文本中包含的与指令相关的视觉信息和上下文知识，引导模型对真实图像对象token赋予更高的注意力权重，从而抑制幻觉并保持语言连贯性。进一步地，为避免过度放大干扰模型原有预测能力，作者设计了自适应版本AdaIAT，通过逐层阈值控制干预时机，并针对每个注意力头调整放大强度，实现细粒度调节，在显著降低幻觉率（如LLaVA-1.5上C_S和C_I分别下降35.8%和37.1%）的同时维持语言质量和模型性能，达成良好的权衡。
链接: https://arxiv.org/abs/2603.04908
作者: Li’an Zhong,Ziqiang He,Jibin Zheng,Jin Li,Z. Jane Wang,Xiangui Kang
机构: Sun Yat-Sen University (中山大学); Foshan University (佛山大学); University of British Columbia (不列颠哥伦比亚大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Hallucination has been a significant impediment to the development and application of current Large Vision-Language Models (LVLMs). To mitigate hallucinations, one intuitive and effective way is to directly increase attention weights to image tokens during inference. Although this effectively reduces the hallucination rate, it often induces repetitive descriptions. To address this, we first conduct an analysis of attention patterns and reveal that real object tokens tend to assign higher attention to the generated text than hallucinated ones. This inspires us to leverage the generated text, which contains instruction-related visual information and contextual knowledge, to alleviate hallucinations while maintaining linguistic coherence. We therefore propose Attention to Generated Text (IAT) and demonstrate that it significantly reduces the hallucination rate while avoiding repetitive descriptions. To prevent naive amplification from impairing the inherent prediction capabilities of LVLMs, we further explore Adaptive IAT (AdaIAT) that employs a layer-wise threshold to control intervention time and fine-grained amplification magnitude tailored to the characteristics of each attention head. Both analysis and experiments demonstrate the effectiveness of AdaIAT. Results of several LVLMs show that AdaIAT effectively alleviates hallucination (reducing hallucination rates C_S and C_I on LLaVA-1.5 by 35.8% and 37.1%, respectively) while preserving linguistic performance and prediction capability, achieving an attractive trade-off.
[CV-65] FC-VFI: Faithful and Consistent Video Frame Interpolation for High-FPS Slow Motion Video Generation ICASSP2026
【速读】：该论文旨在解决当前大规模预训练视频扩散模型在视频帧插值（Video Frame Interpolation, VFI）任务中因依赖内在生成先验而导致的高保真度帧生成能力不足的问题，以及现有方法在时间一致性控制上对稠密光流敏感或稀疏点缺乏结构上下文信息的局限性。解决方案的关键在于提出一种名为FC-VFI的方法，其核心创新包括：(1) 在潜在空间序列上设计时序建模策略，以继承起始帧和结束帧的保真度线索；(2) 引入语义匹配线（semantic matching lines）实现结构感知的运动引导，提升运动一致性；(3) 提出时序差异损失（temporal difference loss）以缓解时间不一致性问题。该方法支持4×和8×插值，可在2560×1440分辨率下将帧率从30 FPS提升至120 FPS和240 FPS，同时保持视觉保真度与运动一致性。
链接: https://arxiv.org/abs/2603.04899
作者: Ganggui Ding,Hao Chen,Xiaogang Xu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ICASSP2026
Abstract:Large pre-trained video diffusion models excel in video frame interpolation but struggle to generate high fidelity frames due to reliance on intrinsic generative priors, limiting detail preservation from start and end frames. Existing methods often depend on motion control for temporal consistency, yet dense optical flow is error-prone, and sparse points lack structural context. In this paper, we propose FC-VFI for faithful and consistent video frame interpolation, supporting (4\times)x and (8\times) interpolation, boosting frame rates from 30 FPS to 120 and 240 FPS at (2560\times 1440)resolution while preserving visual fidelity and motion consistency. We introduce a temporal modeling strategy on the latent sequences to inherit fidelity cues from start and end frames and leverage semantic matching lines for structure-aware motion guidance, improving motion consistency. Furthermore, we propose a temporal difference loss to mitigate temporal inconsistencies. Extensive experiments show FC-VFI achieves high performance and structural integrity across diverse scenarios.
[CV-66] Locality-Attending Vision Transformer ICLR2026
【速读】：该论文旨在解决视觉Transformer（Vision Transformer）在图像分类任务中表现优异，但在图像分割等需要精细空间细节的任务中性能受限的问题。其核心挑战在于自注意力机制虽然能有效捕捉长距离依赖关系，但可能掩盖局部空间信息，从而不利于分割精度的提升。解决方案的关键在于引入一个可学习的高斯核（Gaussian kernel）来调制自注意力机制，使注意力更偏向于邻近图像块（patch），同时通过优化patch表示以学习更具空间意义的嵌入特征。这一策略在不改变原有训练流程的前提下，显著提升了分割性能（如在ADE20K数据集上ViT Tiny和Base模型分别获得超过6%和4%的mIoU提升），并保持了原始模型的图像级识别能力。
链接: https://arxiv.org/abs/2603.04892
作者: Sina Hajimiri,Farzad Beizaee,Fereshteh Shakeri,Christian Desrosiers,Ismail Ben Ayed,Jose Dolz
机构: ÉTS Montreal, LIVIA, ILLS
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICLR 2026
Abstract:Vision transformers have demonstrated remarkable success in classification by leveraging global self-attention to capture long-range dependencies. However, this same mechanism can obscure fine-grained spatial details crucial for tasks such as segmentation. In this work, we seek to enhance segmentation performance of vision transformers after standard image-level classification training. More specifically, we present a simple yet effective add-on that improves performance on segmentation tasks while retaining vision transformers’ image-level recognition capabilities. In our approach, we modulate the self-attention with a learnable Gaussian kernel that biases the attention toward neighboring patches. We further refine the patch representations to learn better embeddings at patch positions. These modifications encourage tokens to focus on local surroundings and ensure meaningful representations at spatial positions, while still preserving the model’s ability to incorporate global information. Experiments demonstrate the effectiveness of our modifications, evidenced by substantial segmentation gains on three benchmarks (e.g., over 6% and 4% on ADE20K for ViT Tiny and Base), without changing the training regime or sacrificing classification performance. The code is available at this https URL.
[CV-67] FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation CVPR2026
【速读】：该论文旨在解决多模态联邦学习（Multimodal Federated Learning, MFL）中面临的个性化性能不足、模态/任务差异以及模型异构性等问题。现有方法往往忽视客户端的个性化需求，难以有效对齐跨模态和跨任务的表示，并且在服务器端无法适应不同客户端模型结构的差异。解决方案的关键在于提出一个统一的框架 FedAFD：在客户端采用双层对抗对齐策略（bi-level adversarial alignment），实现模态内与模态间局部与全局表示的一致性对齐，缓解模态和任务差距；同时设计粒度感知融合模块（granularity-aware fusion module），自适应地将全局知识注入个性化特征。在服务器端，则引入基于相似性的集成蒸馏机制（similarity-guided ensemble distillation），通过共享公共数据上的特征相似度聚合客户端表示，并将融合知识蒸馏到全局模型中，从而有效应对模型异构性问题。
链接: https://arxiv.org/abs/2603.04890
作者: Min Tan,Junchao Ma,Yinfu Feng,Jiajun Ding,Wenwen Pan,Tingting Han,Qian Zheng,Zhenzhong Kuang,Zhou Yu
机构: Hangzhou Dianzi University (杭州电子科技大学); Zhejiang University (浙江大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026
Abstract:Multimodal Federated Learning (MFL) enables clients with heterogeneous data modalities to collaboratively train models without sharing raw data, offering a privacy-preserving framework that leverages complementary cross-modal information. However, existing methods often overlook personalized client performance and struggle with modality/task discrepancies, as well as model heterogeneity. To address these challenges, we propose FedAFD, a unified MFL framework that enhances client and server learning. On the client side, we introduce a bi-level adversarial alignment strategy to align local and global representations within and across modalities, mitigating modality and task gaps. We further design a granularity-aware fusion module to integrate global knowledge into the personalized features adaptively. On the server side, to handle model heterogeneity, we propose a similarity-guided ensemble distillation mechanism that aggregates client representations on shared public data based on feature similarity and distills the fused knowledge into the global model. Extensive experiments conducted under both IID and non-IID settings demonstrate that FedAFD achieves superior performance and efficiency for both the client and the server.
[CV-68] Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation
【速读】：该论文旨在解决联邦学习（Federated Learning, FL）在医学图像分析中面临的两个关键挑战：一是跨模态异质性（intermodal heterogeneity），即不同参与方可能仅拥有部分成像模态，导致无法有效训练全局模型；二是个性化需求，即每个参与方希望获得与其本地数据特征相匹配的个性化模型。解决方案的核心在于提出一种新型联邦框架FedMEPD（Federated Modality-Specific Encoders and Partially Personalized Multimodal Fusion Decoders），其关键创新包括：1）为每种模态设计专用编码器（modality-specific encoder），实现完全联邦共享以应对跨模态异质性；2）采用部分个性化的多模态融合解码器（partially personalized multimodal fusion decoder），通过全局与本地参数更新差异动态确定需个性化的解码器滤波器；3）服务器端利用完整模态数据生成融合表示并提取多锚点（anchors），分发给客户端；客户端则通过缩放点积交叉注意力机制（scaled dot-product cross-attention）校准缺失模态的表示，弥补信息缺失。这一设计在BraTS 2018和2020脑肿瘤分割基准上验证了有效性，显著优于现有主流多模态和个性化联邦学习方法。
链接: https://arxiv.org/abs/2603.04887
作者: Hong Liu,Dong Wei,Qian Dai,Xian Wu,Yefeng Zheng,Liansheng Wang
机构: Tencent(腾讯); Xiamen University (厦门大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Medical Image Analysis 2025. arXiv admin note: substantial text overlap with arXiv:2403.11803
Abstract:Most existing federated learning (FL) methods for medical image analysis only considered intramodal heterogeneity, limiting their applicability to multimodal imaging applications. In practice, some FL participants may possess only a subset of the complete imaging modalities, posing intermodal heterogeneity as a challenge to effectively training a global model on all participants’ data. Meanwhile, each participant expects a personalized model tailored to its local data characteristics in FL. This work proposes a new FL framework with federated modality-specific encoders and partially personalized multimodal fusion decoders (FedMEPD) to address the two concurrent issues. Specifically, FedMEPD employs an exclusive encoder for each modality to account for the intermodal heterogeneity. While these encoders are fully federated, the decoders are partially personalized to meet individual needs – using the discrepancy between global and local parameter updates to dynamically determine which decoder filters are personalized. Implementation-wise, a server with full-modal data employs a fusion decoder to fuse representations from all modality-specific encoders, thus bridging the modalities to optimize the encoders via backpropagation. Moreover, multiple anchors are extracted from the fused multimodal representations and distributed to the clients in addition to the model parameters. Conversely, the clients with incomplete modalities calibrate their missing-modal representations toward the global full-modal anchors via scaled dot-product cross-attention, making up for the information loss due to absent modalities. FedMEPD is validated on the BraTS 2018 and 2020 multimodal brain tumor segmentation benchmarks. Results show that it outperforms various up-to-date methods for multimodal and personalized FL, and its novel designs are effective.
[CV-69] DeformTrace: A Deformable State Space Model with Relay Tokens for Temporal Forgery Localization AAAI2026
【速读】：该论文旨在解决视频和音频中时间伪造定位（Temporal Forgery Localization, TFL）任务中存在的挑战，包括伪造边界模糊、伪造片段稀疏以及长距离依赖建模能力不足等问题。其解决方案的关键在于提出DeformTrace框架，通过引入可变形动态机制与中继令牌机制增强状态空间模型（State Space Models, SSMs）的时序推理能力：具体而言，可变形自SSM（Deformable Self-SSM, DS-SSM）设计了动态感受野以实现精准的时间定位，并结合中继令牌机制缓解长程衰减问题；同时，可变形交叉SSM（Deformable Cross-SSM, DC-SSM）将全局状态空间划分为查询相关的子空间，减少非伪造信息累积，提升对稀疏伪造的敏感性。最终，该框架融合Transformer的全局建模能力与SSM的高效性，实现了参数更少、推理更快且鲁棒性更强的TFL性能。
链接: https://arxiv.org/abs/2603.04882
作者: Xiaodong Zhu,Suting Wang,Yuanming Zheng,Junqi Yang,Yangxu Liao,Yuhong Yang,Weiping Tu,Zhongyuan Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: 9 pages, 4 figures, accepted by AAAI 2026
Abstract:Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments in video and audio, offering strong interpretability for security and forensics. While recent State Space Models (SSMs) show promise in precise temporal reasoning, their use in TFL is hindered by ambiguous boundaries, sparse forgeries, and limited long-range modeling. We propose DeformTrace, which enhances SSMs with deformable dynamics and relay mechanisms to address these challenges. Specifically, Deformable Self-SSM (DS-SSM) introduces dynamic receptive fields into SSMs for precise temporal localization. To further enhance its capacity for temporal reasoning and mitigate long-range decay, a Relay Token Mechanism is integrated into DS-SSM. Besides, Deformable Cross-SSM (DC-SSM) partitions the global state space into query-specific subspaces, reducing non-forgery information accumulation and boosting sensitivity to sparse forgeries. These components are integrated into a hybrid architecture that combines the global modeling of Transformers with the efficiency of SSMs. Extensive experiments show that DeformTrace achieves state-of-the-art performance with fewer parameters, faster inference, and stronger robustness.
[CV-70] Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation
【速读】：该论文旨在解决计算机断层扫描报告生成（Computed Tomography Report Generation, CTRG）中因CT图像数据量大、结构细节复杂而导致深度学习模型效果受限的问题。其解决方案的关键在于提出一种两阶段框架：第一阶段通过可学习的结构特异性视觉查询（structure-specific visual queries）与对应解剖结构进行交互，并引入结构级图像-文本对比损失（structure-wise image-text contrastive loss），结合基于文本相似度的软伪标签（soft pseudo targets）以缓解误负样本（false negatives）的影响，从而学习图像与报告间的结构级语义对应关系；第二阶段冻结视觉查询，利用其选择关键图像块嵌入（patch embeddings）以聚焦于目标解剖结构，减少无关区域干扰并降低内存消耗，同时引入文本解码器完成最终报告生成。该方法显著提升了临床效率和生成质量。
链接: https://arxiv.org/abs/2603.04878
作者: Hong Liu,Dong Wei,Qiong Peng,Yawen Huang,Xian Wu,Yefeng Zheng,Liansheng Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accept to IPMI 2025
Abstract:Computed Tomography Report Generation (CTRG) aims to automate the clinical radiology reporting process, thereby reducing the workload of report writing and facilitating patient care. While deep learning approaches have achieved remarkable advances in X-ray report generation, their effectiveness may be limited in CTRG due to larger data volumes of CT images and more intricate details required to describe them. This work introduces a novel two-stage (structure- and report-learning) framework tailored for CTRG featuring effective structure-wise image-text contrasting. In the first stage, a set of learnable structure-specific visual queries observe corresponding structures in a CT image. The resulting observation tokens are contrasted with structure-specific textual features extracted from the accompanying radiology report with a structure-wise image-text contrastive loss. In addition, text-text similarity-based soft pseudo targets are proposed to mitigate the impact of false negatives, i.e., semantically identical image structures and texts from non-paired images and reports. Thus, the model learns structure-level semantic correspondences between CT images and reports. Further, a dynamic, diversity-enhanced negative queue is proposed to guide the network in learning to discriminate various abnormalities. In the second stage, the visual structure queries are frozen and used to select the critical image patch embeddings depicting each anatomical structure, minimizing distractions from irrelevant areas while reducing memory consumption. Also, a text decoder is added and trained for report this http URL extensive experiments on two public datasets demonstrate that our framework establishes new state-of-the-art performance for CTRG in clinical efficiency, and its components are effective.
[CV-71] Interpretable Pre-Release Baseball Pitch Type Anticipation from Broadcast 3D Kinematics CVPR
【速读】：该论文旨在解决如何仅通过投手身体动作（即体态运动学）来预测其即将投出的球种问题，从而揭示人体姿态中蕴含的投球信息量。其解决方案的关键在于构建一个端到端的分析流程：首先利用基于扩散模型的3D姿态估计方法提取单目视频中的投球动作序列，再结合自动事件检测与真实标注验证的生物力学特征提取模块，最终采用梯度提升分类器对229个运动学特征进行建模。该方法在包含119,561次职业投球的大规模数据集上实现了80.4%的分类准确率，表明仅凭身体运动即可有效识别球种，且上半身动作贡献了64.9%的判别信号，其中手腕位置和躯干侧倾是最具信息量的特征。
链接: https://arxiv.org/abs/2603.04874
作者: Jerrin Bright,Michelle Lu,John Zelek
机构: Vision and Image Processing Lab (视觉与图像处理实验室); University of Waterloo (滑铁卢大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Submitted to CVPRW’26
Abstract:How much can a pitcher’s body reveal about the upcoming pitch? We study this question at scale by classifying eight pitch types from monocular 3D pose sequences, without access to ball-flight data. Our pipeline chains a diffusion-based 3D pose backbone with automatic pitching-event detection, groundtruth-validated biomechanical feature extraction, and gradient-boosted classification over 229 kinematic features. Evaluated on 119,561 professional pitches, the largest such benchmark to date, we achieve 80.4% accuracy using body kinematics alone. A systematic importance analysis reveals that upper-body mechanics contribute 64.9% of the predictive signal versus 35.1% for the lower body, with wrist position (14.8%) and trunk lateral tilt emerging as the most informative joint group and biomechanical feature, respectively. We further show that grip-defined variants (four-seam vs.\ two-seam fastball) are not separable from pose, establishing an empirical ceiling near 80% and delineating where kinematic information ends and ball-flight information begins.
[CV-72] Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning CVPR2026
【速读】：该论文旨在解决在sRGB图像空间中去噪任务面临的挑战，即真实世界噪声的多样性与复杂性导致基于端到端方法的去噪模型在实际场景中性能受限，主要原因在于高质量真实噪声-干净图像对数据稀缺且难以获取。为克服这一限制，论文提出了一种名为Prompt-Driven Noise Generation (PNG)的新框架，其关键在于通过高维提示（prompt）特征学习来捕捉输入图像中的真实噪声特性，并据此生成多样且符合真实噪声分布的合成噪声图像，从而无需依赖相机元数据即可实现鲁棒的噪声建模与泛化能力提升，显著增强了噪声合成方法的实用性与适用范围。
链接: https://arxiv.org/abs/2603.04870
作者: Jaekyun Ko,Dongjin Kim,Soomin Lee,Guanghui Wang,Tae Hyun Kim
机构: Samsung Electronics(三星电子); Hanyang University (汉阳大学); Toronto Metropolitan University (多伦多都会大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026
Abstract:Denoising in the sRGB image space is challenging due to noise variability. Although end-to-end methods perform well, their effectiveness in real-world scenarios is limited by the scarcity of real noisy-clean image pairs, which are expensive and difficult to collect. To address this limitation, several generative methods have been developed to synthesize realistic noisy images from limited data. These generative approaches often rely on camera metadata during both training and testing to synthesize real-world noise. However, the lack of metadata or inconsistencies between devices restricts their usability. Therefore, we propose a novel framework called Prompt-Driven Noise Generation (PNG). This model is capable of acquiring high-dimensional prompt features that capture the characteristics of real-world input noise and creating a variety of realistic noisy images consistent with the distribution of the input noise. By eliminating the dependency on explicit camera metadata, our approach significantly enhances the generalizability and applicability of noise synthesis. Comprehensive experiments reveal that our model effectively produces realistic noisy images and show the successful application of these generated images in removing real-world noise across various benchmark datasets.
[CV-73] SURE: Semi-dense Uncertainty-REfined Feature Matching ICRA2026
【速读】：该论文旨在解决机器人视觉中图像对应关系建立的可靠性问题，特别是在大视角变化或无纹理区域等挑战场景下，现有方法因仅依赖特征相似性而难以区分正确与错误匹配，导致预测结果过度自信。其解决方案的关键在于提出SURE（Semi-dense Uncertainty-REfined）框架，通过联合预测匹配点及其置信度来提升可靠性，具体创新包括引入一种新型证据头（evidential head）用于可信坐标回归，并设计轻量级空间融合模块以在极小计算开销下增强局部特征精度，从而同时建模认知不确定性（epistemic uncertainty）和随机不确定性（aleatoric uncertainty）。
链接: https://arxiv.org/abs/2603.04869
作者: Sicheng Li,Zaiwang Gu,Jie Zhang,Qing Guo,Xudong Jiang,Jun Cheng
机构: Nanyang Technological University (南洋理工大学); Institute for Infocomm Research (I2R) (资讯通信研究院); Agency for Science, Technology and Research (A*STAR) (新加坡科技研究局); Institute of High Performance Computing (高性能计算研究所); Nankai University (南开大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICRA 2026
Abstract:Establishing reliable image correspondences is essential for many robotic vision problems. However, existing methods often struggle in challenging scenarios with large viewpoint changes or textureless regions, where incorrect cor- respondences may still receive high similarity scores. This is mainly because conventional models rely solely on fea- ture similarity, lacking an explicit mechanism to estimate the reliability of predicted matches, leading to overconfident errors. To address this issue, we propose SURE, a Semi- dense Uncertainty-REfined matching framework that jointly predicts correspondences and their confidence by modeling both aleatoric and epistemic uncertainties. Our approach in- troduces a novel evidential head for trustworthy coordinate regression, along with a lightweight spatial fusion module that enhances local feature precision with minimal overhead. We evaluated our method on multiple standard benchmarks, where it consistently outperforms existing state-of-the-art semi-dense matching models in both accuracy and efficiency. our code will be available on this https URL.
[CV-74] Scalable Injury-Risk Screening in Baseball Pitching From Broadcast Video CVPR
【速读】：该论文旨在解决投球过程中运动损伤预测依赖昂贵且难以普及的多相机运动捕捉系统的问题，从而限制了非职业场景下的应用。其核心解决方案是构建一个基于单目视频（monocular video）的生物力学参数提取流水线，通过DreamPose3D框架引入一种受控漂移的全局提升模块（drift-controlled global lifting module），利用速度参数化和滑动窗口推理恢复骨盆轨迹，将以骨盆为根节点的姿态映射至全局空间；同时，针对运动模糊、压缩伪影及极端投球姿态等问题，设计了一套包含骨长约束、关节限位逆向运动学、平滑处理和对称性约束的运动学精修流程，确保时序稳定且物理合理的运动学数据。该方法在13名职业投手共156次投球中实现了16/18个临床相关指标的亚度级精度（MAE < 1°），并成功应用于大规模自动筛查模型，对Tommy John手术和严重手臂损伤的预测AUC分别达到0.811和0.825，验证了单目广播视频作为替代体育场级动作捕捉系统的可行性与有效性。
链接: https://arxiv.org/abs/2603.04864
作者: Jerrin Bright,Justin Mende,John Zelek
机构: University of Waterloo (滑铁卢大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to CVPRW’26
Abstract:Injury prediction in pitching depends on precise biomechanical signals, yet gold-standard measurements come from expensive, stadium-installed multi-camera systems that are unavailable outside professional venues. We present a monocular video pipeline that recovers 18 clinically relevant biomechanics metrics from broadcast footage, positioning pose-derived kinematics as a scalable source for injury-risk modeling. Built on DreamPose3D, our approach introduces a drift-controlled global lifting module that recovers pelvis trajectory via velocity-based parameterization and sliding-window inference, lifting pelvis-rooted poses into global space. To address motion blur, compression artifacts, and extreme pitching poses, we incorporate a kinematics refinement pipeline with bone-length constraints, joint-limited inverse kinematics, smoothing, and symmetry constraints to ensure temporally stable and physically plausible kinematics. On 13 professional pitchers (156 paired pitches), 16/18 metrics achieve sub-degree agreement (MAE 1^\circ ). Using these metrics for injury prediction, an automated screening model achieves AUC 0.811 for Tommy John surgery and 0.825 for significant arm injuries on 7,348 pitchers. The resulting pose-derived metrics support scalable injury-risk screening, establishing monocular broadcast video as a viable alternative to stadium-scale motion capture for biomechanics.
[CV-75] On Multi-Step Theorem Prediction via Non-Parametric Structural Priors
【速读】：该论文旨在解决多步定理预测（multi-step theorem prediction）在自动化推理中的挑战，特别是现有神经符号方法依赖监督式参数模型时，在不断演化的定理库中泛化能力有限的问题。其核心解决方案是通过无训练的上下文学习（training-free in-context learning, ICL）实现更稳定的推理性能，关键创新在于提出定理前置图（Theorem Precedence Graphs），该图将历史解题轨迹中的时序依赖关系编码为有向图结构，并在推理过程中施加显式的拓扑约束，从而有效剪枝搜索空间、恢复潜在的拓扑依赖关系，使大型语言模型（LLM）无需梯度优化即可作为结构化规划器使用。
链接: https://arxiv.org/abs/2603.04852
作者: Junbo Zhao,Ting Zhang,Can Li,Wei He,Jingdong Wang,Hua Huang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-step theorem prediction is a central challenge in automated reasoning. Existing neural-symbolic approaches rely heavily on supervised parametric models, which exhibit limited generalization to evolving theorem libraries. In this work, we explore training-free theorem prediction through the lens of in-context learning (ICL). We identify a critical scalability bottleneck, termed Structural Drift: as reasoning depth increases, the performance of vanilla ICL degrades sharply, often collapsing to near zero. We attribute this failure to the LLM’s inability to recover latent topological dependencies, leading to unstructured exploration. To address this issue, we propose Theorem Precedence Graphs, which encode temporal dependencies from historical solution traces as directed graphs, and impose explicit topological constraints that effectively prune the search space during inference. Coupled with retrieval-augmented graph construction and a stepwise symbolic executor, our approach enables LLMs to act as structured planners without any gradient-based optimization. Experiments on the FormalGeo7k benchmark show that our method achieves 89.29% accuracy, substantially outperforming ICL baselines and matching state-of-the-art supervised models. These results indicate that explicit structural priors offer a promising direction for scaling LLM-based symbolic reasoning.
[CV-76] GloSplat: Joint Pose-Appearance Optimization for Faster and More Accurate 3D Reconstruction
【速读】：该论文旨在解决传统三维重建与新视角合成（Novel View Synthesis, NVS）方法中，姿态估计（pose estimation）与外观建模（appearance modeling）分离优化导致的早期姿态漂移（pose drift）问题。现有方法如BARF、NeRF–和3RGS等依赖纯光度梯度进行姿态精调，缺乏几何约束，难以在训练初期保持稳定。其解决方案的关键在于提出GloSplat框架，在3D高斯溅射（3D Gaussian Splatting, 3DGS）训练过程中实现姿态与外观的联合优化，并将结构从运动（Structure from Motion, SfM）特征轨迹作为显式可优化参数保留于整个训练过程：这些轨迹的3D点独立于高斯原语（Gaussian primitives），并通过重投影损失提供持续的几何锚定（geometric anchors），从而在光度监督之外引入几何一致性约束，有效抑制姿态漂移并支持细粒度优化。该架构使GloSplat-F（无需COLMAP）达到无COLMAP方法的最先进性能，而GloSplat-A则超越所有基于COLMAP的基线方法。
链接: https://arxiv.org/abs/2603.04847
作者: Tianyu Xiong,Rui Li,Linjie Li,Jiaqi Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Feature extraction, matching, structure from motion (SfM), and novel view synthesis (NVS) have traditionally been treated as separate problems with independent optimization objectives. We present GloSplat, a framework that performs \emphjoint pose-appearance optimization during 3D Gaussian Splatting training. Unlike prior joint optimization methods (BARF, NeRF–, 3RGS) that rely purely on photometric gradients for pose refinement, GloSplat preserves \emphexplicit SfM feature tracks as first-class entities throughout training: track 3D points are maintained as separate optimizable parameters from Gaussian primitives, providing persistent geometric anchors via a reprojection loss that operates alongside photometric supervision. This architectural choice prevents early-stage pose drift while enabling fine-grained refinement – a capability absent in photometric-only approaches. We introduce two pipeline variants: (1) \textbfGloSplat-F, a COLMAP-free variant using retrieval-based pair selection for efficient reconstruction, and (2) \textbfGloSplat-A, an exhaustive matching variant for maximum quality. Both employ global SfM initialization followed by joint photometric-geometric optimization during 3DGS training. Experiments demonstrate that GloSplat-F achieves state-of-the-art among COLMAP-free methods while GloSplat-A surpasses all COLMAP-based baselines.
[CV-77] Multi-Paradigm Collaborative Adversarial Attack Against Multi-Modal Large Language Models CVPR2026
【速读】：该论文旨在解决多模态大语言模型（Multi-Modal Large Language Models, MLLMs）在对抗攻击中存在严重可迁移性脆弱性的问题。现有方法通常依赖单一学习范式的替代模型，并在其各自特征空间中独立优化，导致特征表示丰富度不足、搜索空间受限，从而限制了对抗扰动的多样性。解决方案的关键在于提出一种多范式协同攻击框架（Multi-Paradigm Collaborative Attack, MPCAttack），通过多范式协同优化（Multi-Paradigm Collaborative Optimisation, MPCO）策略，聚合视觉图像与文本语义表示，在统一特征空间中进行联合对抗优化；MPCO利用对比匹配机制自适应平衡不同模态特征的重要性，引导全局扰动优化，有效缓解了表示偏差问题，显著提升了对抗样本在开源与闭源MLLM上的迁移性能。
链接: https://arxiv.org/abs/2603.04846
作者: Yuanbo Li,Tianyang Xu,Cong Hu,Tao Zhou,Xiao-Jun Wu,Josef Kittler
机构: Jiangnan University (江南大学); University of Surrey (萨里大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR2026
Abstract:The rapid progress of Multi-Modal Large Language Models (MLLMs) has significantly advanced downstream applications. However, this progress also exposes serious transferable adversarial vulnerabilities. In general, existing adversarial attacks against MLLMs typically rely on surrogate models trained within a single learning paradigm and perform independent optimisation in their respective feature spaces. This straightforward setting naturally restricts the richness of feature representations, delivering limits on the search space and thus impeding the diversity of adversarial perturbations. To address this, we propose a novel Multi-Paradigm Collaborative Attack (MPCAttack) framework to boost the transferability of adversarial examples against MLLMs. In principle, MPCAttack aggregates semantic representations, from both visual images and language texts, to facilitate joint adversarial optimisation on the aggregated features through a Multi-Paradigm Collaborative Optimisation (MPCO) strategy. By performing contrastive matching on multi-paradigm features, MPCO adaptively balances the importance of different paradigm representations and guides the global perturbation optimisation, effectively alleviating the representation bias. Extensive experimental results on multiple benchmarks demonstrate the superiority of MPCAttack, indicating that our solution consistently outperforms state-of-the-art methods in both targeted and untargeted attacks on open-source and closed-source MLLMs. The code is released at this https URL.
[CV-78] owards Highly Transferable Vision-Language Attack via Semantic-Augmented Dynamic Contrastive Interaction CVPR2026
【速读】：该论文旨在解决视觉-语言预训练（Vision-Language Pre-training, VLP）模型在面对对抗攻击时的脆弱性问题，特别是现有攻击方法因依赖静态跨模态交互且仅针对正样本对进行干扰，导致跨模态破坏能力有限、迁移性不足。其解决方案的关键在于提出一种语义增强的动态对比攻击（Semantic-Augmented Dynamic Contrastive Attack, SADCA），通过引入动态的图像与文本间交互机制，逐步破坏跨模态对齐，并构建包含对抗样本、正样本和负样本的对比学习框架，以强化扰动的语义不一致性；同时设计语义增强模块，利用输入变换提升对抗样本的多样性与泛化能力，从而显著增强攻击的迁移性能。
链接: https://arxiv.org/abs/2603.04839
作者: Yuanbo Li,Tianyang Xu,Cong Hu,Tao Zhou,Xiao-Jun Wu,Josef Kittler
机构: Jiangnan University (江南大学); University of Surrey (萨里大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR2026
Abstract:With the rapid advancement and widespread application of vision-language pre-training (VLP) models, their vulnerability to adversarial attacks has become a critical concern. In general, the adversarial examples can typically be designed to exhibit transferable power, attacking not only different models but also across diverse tasks. However, existing attacks on language-vision models mainly rely on static cross-modal interactions and focus solely on disrupting positive image-text pairs, resulting in limited cross-modal disruption and poor transferability. To address this issue, we propose a Semantic-Augmented Dynamic Contrastive Attack (SADCA) that enhances adversarial transferability through progressive and semantically guided perturbation. SADCA progressively disrupts cross-modal alignment through dynamic interactions between adversarial images and texts. This is accomplished by SADCA establishing a contrastive learning mechanism involving adversarial, positive and negative samples, to reinforce the semantic inconsistency of the obtained perturbations. Moreover, we empirically find that input transformations commonly used in traditional transfer-based attacks also benefit VLPs, which motivates a semantic augmentation module that increases the diversity and generalization of adversarial examples. Extensive experiments on multiple datasets and models demonstrate that SADCA significantly improves adversarial transferability and consistently surpasses state-of-the-art methods. The code is released at this https URL.
[CV-79] Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning CVPR2026
【速读】：该论文旨在解决实例依赖的偏标签学习（Instance-Dependent Partial Label Learning, ID-PLL）中的实例纠缠（instance entanglement）问题，即来自相似类别的实例因共享重叠特征和候选标签而导致类别混淆加剧。解决方案的关键在于提出一种基于类别特定增强的解耦框架（Class-specific Augmentation based Disentanglement, CAD），通过类内与类间双重调控机制实现：类内调控上，放大类别特异性特征以生成类别级增强样本，并对同类增强样本进行跨实例对齐；类间调控上，引入加权惩罚损失函数，对更模糊的标签施加更强惩罚，从而扩大类别间距。该方法联合优化两类调控策略，在提升类别边界清晰度的同时有效缓解由纠缠引起的分类混淆。
链接: https://arxiv.org/abs/2603.04825
作者: Rui Zhao,Bin Shi,Kai Sun,Bo Dong
机构: Xi’an Jiaotong University (西安交通大学); Shaanxi Province Key Laboratory of Big Data Knowledge Engineering (陕西省大数据知识工程重点实验室)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to CVPR2026
Abstract:Partial label learning is a prominent weakly supervised classification task, where each training instance is ambiguously labeled with a set of candidate labels. In real-world scenarios, candidate labels are often influenced by instance features, leading to the emergence of instance-dependent PLL (ID-PLL), a setting that more accurately reflects this relationship. A significant challenge in ID-PLL is instance entanglement, where instances from similar classes share overlapping features and candidate labels, resulting in increased class confusion. To address this issue, we propose a novel Class-specific Augmentation based Disentanglement (CAD) framework, which tackles instance entanglement by both intra- and inter-class regulations. For intra-class regulation, CAD amplifies class-specific features to generate class-wise augmentations and aligns same-class augmentations across instances. For inter-class regulation, CAD introduces a weighted penalty loss function that applies stronger penalties to more ambiguous labels, encouraging larger inter-class distances. By jointly applying intra- and inter-class regulations, CAD improves the clarity of class boundaries and reduces class confusion caused by entanglement. Extensive experimental results demonstrate the effectiveness of CAD in mitigating the entanglement problem and enhancing ID-PLL performance. The code is available at this https URL.
[CV-80] Revisiting Shape from Polarization in the Era of Vision Foundation Models
【速读】：该论文旨在解决在单次拍摄条件下，如何利用极化线索（polarization cues）提升轻量级模型在表面法向量估计任务中的性能问题，尤其是在面对RGB-only视觉基础模型（Vision Foundation Models, VFMs）凭借大规模数据训练取得优异表现的背景下，重新评估极化模态的有效性与必要性。其核心挑战在于以往SfP（Shape from Polarization）方法因域偏移（domain gaps）导致性能不佳，具体表现为合成数据中物体几何结构和纹理不真实、以及传感器噪声未被充分建模。解决方案的关键在于：首先，使用1,954个真实扫描三维物体构建高质量极化数据集以缩小合成与真实场景之间的域差距；其次，引入预训练DINOv3先验知识增强模型对未见物体的泛化能力；最后，设计极化传感器感知的数据增强策略，更真实地模拟现实世界中的噪声影响。通过上述改进，仅用4万训练场景即可超越现有SfP方法及RGB-only VFM，实现33倍数据效率提升或8倍参数压缩下的性能优势。
链接: https://arxiv.org/abs/2603.04817
作者: Chenhao Li,Taishi Ono,Takeshi Uemori,Yusuke Moriuchi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We show that, with polarization cues, a lightweight model trained on a small dataset can outperform RGB-only vision foundation models (VFMs) in single-shot object-level surface normal estimation. Shape from polarization (SfP) has long been studied due to the strong physical relationship between polarization and surface geometry. Meanwhile, driven by scaling laws, RGB-only VFMs trained on large datasets have recently achieved impressive performance and surpassed existing SfP methods. This situation raises questions about the necessity of polarization cues, which require specialized hardware and have limited training data. We argue that the weaker performance of prior SfP methods does not come from the polarization modality itself, but from domain gaps. These domain gaps mainly arise from two sources. First, existing synthetic datasets use limited and unrealistic 3D objects, with simple geometry and random texture maps that do not match the underlying shapes. Second, real-world polarization signals are often affected by sensor noise, which is not well modeled during training. To address the first issue, we render a high-quality polarization dataset using 1,954 3D-scanned real-world objects. We further incorporate pretrained DINOv3 priors to improve generalization to unseen objects. To address the second issue, we introduce polarization sensor-aware data augmentation that better reflects real-world conditions. With only 40K training scenes, our method significantly outperforms both state-of-the-art SfP approaches and RGB-only VFMs. Extensive experiments show that polarization cues enable a 33x reduction in training data or an 8x reduction in model parameters, while still achieving better performance than RGB-only counterparts.
[CV-81] Meta-D: Metadata-Aware Architectures for Brain Tumor Analysis and Missing-Modality Segmentation
【速读】：该论文旨在解决医学图像深度学习模型在脑肿瘤分析中因数据模态缺失或特征表示不稳定而导致性能下降的问题。其核心解决方案是提出Meta-D架构，通过显式引入类别型扫描元数据（如MRI序列类型和切面方向）来引导特征提取过程，从而稳定特征表示并提升模型鲁棒性。关键创新在于：在2D肿瘤检测任务中，动态注入元数据以调制卷积特征，显著提升F1-score；在3D缺失模态分割场景下，利用基于元数据的交叉注意力机制实现可用模态的隔离与定向路由，使网络聚焦于有效切片，在极端模态稀缺条件下将Dice分数提升5.12%，同时减少24.1%的模型参数。
链接: https://arxiv.org/abs/2603.04811
作者: SangHyuk Kim,Daniel Haehn,Sumientra Rampersad
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages, 2 figures, 3 tables
Abstract:We present Meta-D, an architecture that explicitly leverages categorical scanner metadata such as MRI sequence and plane orientation to guide feature extraction for brain tumor analysis. We aim to improve the performance of medical image deep learning pipelines by integrating explicit metadata to stabilize feature representations. We first evaluate this in 2D tumor detection, where injecting sequence (e.g., T1, T2) and plane (e.g., axial) metadata dynamically modulates convolutional features, yielding an absolute increase of up to 2.62% in F1-score over image-only baselines. Because metadata grounds feature extraction when data are available, we hypothesize it can serve as a robust anchor when data are missing. We apply this to 3D missing-modality tumor segmentation. Our Transformer Maximizer utilizes metadata-based cross-attention to isolate and route available modalities, ensuring the network focuses on valid slices. This targeted attention improves brain tumor segmentation Dice scores by up to 5.12% under extreme modality scarcity while reducing model parameters by 24.1%.
[CV-82] Guiding Diffusion-based Reconstruction with Contrastive Signals for Balanced Visual Representation
【速读】：该论文旨在解决对比语言-图像预训练（CLIP）模型中视觉编码器的表征能力瓶颈问题，具体包括判别能力（D-Ability，反映类别可分性）和细节感知能力（P-Ability，关注细粒度视觉线索）的不足。现有方法通过扩散模型以CLIP视觉token为条件进行图像重建来增强表征，但可能损害D-Ability，从而无法有效提升下游性能。论文提出的关键解决方案是Diffusion Contrastive Reconstruction（DCR），其核心在于将对比学习信号从原始输入图像转移到每个重建图像中注入扩散过程，从而在统一的学习目标下联合优化D-Ability与P-Ability，避免梯度冲突并实现更全面的视觉表示学习。
链接: https://arxiv.org/abs/2603.04803
作者: Boyu Han,Qianqian Xu,Shilong Bao,Zhiyong Yang,Ruochen Cui,Xilin Zhao,Qingming Huang
机构: State Key Laboratory of AI Safety, Institute of Computing Technology, CAS; School of Computer Science and Tech., University of Chinese Academy of Sciences; Institute of Information Engineering, CAS; School of Cyber Security, University of Chinese Academy of Sciences; School of Computer Science and Technology, Beijing Institute of Technology
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The limited understanding capacity of the visual encoder in Contrastive Language-Image Pre-training (CLIP) has become a key bottleneck for downstream performance. This capacity includes both Discriminative Ability (D-Ability), which reflects class separability, and Detail Perceptual Ability (P-Ability), which focuses on fine-grained visual cues. Recent solutions use diffusion models to enhance representations by conditioning image reconstruction on CLIP visual tokens. We argue that such paradigms may compromise D-Ability and therefore fail to effectively address CLIP’s representation limitations. To address this, we integrate contrastive signals into diffusion-based reconstruction to pursue more comprehensive visual representations. We begin with a straightforward design that augments the diffusion process with contrastive learning on input images. However, empirical results show that the naive combination suffers from gradient conflict and yields suboptimal performance. To balance the optimization, we introduce the Diffusion Contrastive Reconstruction (DCR), which unifies the learning objective. The key idea is to inject contrastive signals derived from each reconstructed image, rather than from the original input, into the diffusion process. Our theoretical analysis shows that the DCR loss can jointly optimize D-Ability and P-Ability. Extensive experiments across various benchmarks and multi-modal large language models validate the effectiveness of our method. The code is available at this https URL.
[CV-83] MASQuant: Modality-Aware Smoothing Quantization for Multimodal Large Language Models CVPR2026
【速读】：该论文针对多模态大语言模型（Multimodal Large Language Models, MLLMs）在后训练量化（Post-training Quantization, PTQ）过程中面临的两大挑战展开研究：一是平滑错位（Smoothing Misalignment），即传统方法在跨模态数据上统一应用平滑操作导致的分布失配；二是跨模态计算不变性缺失（Cross-Modal Computational Invariance），即不同模态间激活差异难以统一量化。解决方案的关键在于提出一种新颖的模态感知平滑量化框架（Modality-Aware Smoothing Quantization, MASQuant），其核心包括两个创新模块：(1) 模态感知平滑（Modality-Aware Smoothing, MAS），通过学习各模态独立的平滑因子避免平滑错位；(2) 跨模态补偿（Cross-Modal Compensation, CMC），利用奇异值分解（SVD）白化将多模态激活差异转化为低秩形式，从而实现跨模态统一量化。该方案在双模态和三模态MLLMs中均展现出稳定的量化性能，且优于当前主流PTQ算法。
链接: https://arxiv.org/abs/2603.04800
作者: Lulu Hu,Wenhu Xiao,Xin Chen,Xinhua Xu,Bowen Xu,Kun Li,Yongliang Tao
机构: Alibaba Cloud Computing, Alibaba Group (阿里巴巴集团云计算)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:Post-training quantization (PTQ) with computational invariance for Large Language Models~(LLMs) have demonstrated remarkable advances, however, their application to Multimodal Large Language Models~(MLLMs) presents substantial challenges. In this paper, we analyze SmoothQuant as a case study and identify two critical issues: Smoothing Misalignment and Cross-Modal Computational Invariance. To address these issues, we propose Modality-Aware Smoothing Quantization (MASQuant), a novel framework that introduces (1) Modality-Aware Smoothing (MAS), which learns separate, modality-specific smoothing factors to prevent Smoothing Misalignment, and (2) Cross-Modal Compensation (CMC), which addresses Cross-modal Computational Invariance by using SVD whitening to transform multi-modal activation differences into low-rank forms, enabling unified quantization across modalities. MASQuant demonstrates stable quantization performance across both dual-modal and tri-modal MLLMs. Experimental results show that MASQuant is competitive among the state-of-the-art PTQ algorithms. Source code: this https URL.
[CV-84] Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper
【速读】：该论文旨在解决脑胶质瘤（brain gliomas）在磁共振成像（MRI）后分割与分类中的准确性与可重复性问题，尤其针对不规则组织区域难以实现无误差分割的挑战。其解决方案的关键在于采用卷积神经网络（Convolutional Neural Networks, CNNs）架构，相较于传统方法，CNN在分割和分类任务中展现出更优性能，能够有效提升对胶质瘤边界、大小及类型识别的精度，从而支持个性化诊疗决策与疾病监测。
链接: https://arxiv.org/abs/2603.04796
作者: Kiranmayee Janardhan,Vinay Martin DSa Prabhu,T. Christy Bobby
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 22 pages, 4 Figures
Abstract:Segmentation is crucial for brain gliomas as it delineates the glioma s extent and location, aiding in precise treatment planning and monitoring, thus improving patient outcomes. Accurate segmentation ensures proper identification of the glioma s size and position, transforming images into applicable data for analysis. Classification of brain gliomas is also essential because different types require different treatment approaches. Accurately classifying brain gliomas by size, location, and aggressiveness is essential for personalized prognosis prediction, follow-up care, and monitoring disease progression, ensuring effective diagnosis, treatment, and management. In glioma research, irregular tissues are often observable, but error free and reproducible segmentation is challenging. Many researchers have surveyed brain glioma segmentation, proposing both fully automatic and semi-automatic techniques. The adoption of these methods by radiologists depends on ease of use and supervision, with semi-automatic techniques preferred due to the need for accurate evaluations. This review evaluates effective segmentation and classification techniques post magnetic resonance imaging acquisition, highlighting that convolutional neural network architectures outperform traditional techniques in these tasks.
[CV-85] LAW ORDER: Adaptive Spatial Weighting for Medical Diffusion and Segmentation
【速读】：该论文旨在解决医学图像分析中生成式AI（Generative AI）与分割模型在空间不平衡场景下的性能瓶颈问题，即病灶区域在图像中占比较小，导致扩散模型难以精确控制病变布局，而分割网络在空间不确定性区域表现不佳。解决方案的关键在于引入两个可学习的网络适配器：一是可学习自适应加权器（Learnable Adaptive Weighter, LAW），通过从特征和掩码中预测逐像素损失权重，结合归一化、截断和正则化策略稳定训练过程，从而提升扩散模型生成质量；二是高效分辨率下的最优区域检测模块（Optimal Region Detection with Efficient Resolution, ORDER），在解码器后期采用选择性双向跳跃注意力机制实现高精度分割，显著提升效率与性能。实验表明，LAW使生成图像FID降低20%，合成数据进一步提升分割Dice系数4.9%；ORDER在仅0.56 GFLOPs和42K参数下实现6.0% Dice增益，远优于标准nnUNet。
链接: https://arxiv.org/abs/2603.04795
作者: Anugunj Naman,Ayushman Singh,Gaibo Zhang,Yaguang Zhang
机构: Purdue University (普渡大学); Capital One (资本一号)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Medical image analysis relies on accurate segmentation, and benefits from controllable synthesis (of new training images). Yet both tasks of the cyclical pipeline face spatial imbalance: lesions occupy small regions against vast backgrounds. In particular, diffusion models have been shown to drift from prescribed lesion layouts, while efficient segmenters struggle on spatially uncertain regions. Adaptive spatial weighting addresses this by learning where to allocate computational resources. This paper introduces a pair of network adapters: 1) Learnable Adaptive Weighter (LAW) which predicts per-pixel loss modulation from features and masks for diffusion training, stabilized via a mix of normalization, clamping, and regularization to prevent degenerate solutions; and 2) Optimal Region Detection with Efficient Resolution (ORDER) which applies selective bidirectional skip attention at late decoder stages for efficient segmentation. Experiments on polyp and kidney tumor datasets demonstrate that LAW achieves 20% FID generative improvement over a uniform baseline (52.28 vs. 65.60), with synthetic data then improving downstream segmentation by 4.9% Dice coefficient (83.2% vs. 78.3%). ORDER reaches 6.0% Dice improvement on MK-UNet (81.3% vs. 75.3%) with 0.56 GFLOPs and just 42K parameters, remaining 730x smaller than the standard nnUNet.
[CV-86] RMK RetinaNet: Rotated Multi-Kernel RetinaNet for Robust Oriented Object Detection in Remote Sensing Imagery
【速读】：该论文针对遥感图像中旋转目标检测面临的三大瓶颈问题展开研究：非自适应感受野利用、长距离多尺度特征融合不足以及角度回归的不连续性。解决方案的关键在于提出Rotated Multi-Kernel RetinaNet（RMK RetinaNet），其核心创新包括：1）设计多尺度核（Multi-Scale Kernel, MSK）模块以增强自适应多尺度特征提取能力；2）引入多方向上下文锚点注意力（Multi-Directional Contextual Anchor Attention, MDCAA）机制，提升跨尺度与多方向的上下文建模性能；3）构建自底向上的路径以保留下采样过程中易丢失的细粒度空间细节；4）提出欧拉角编码模块（Euler Angle Encoding Module, EAEM），实现连续且稳定的旋转角度回归。该方法在DOTA-v1.0、HRSC2016和UCAS-AOD数据集上验证了其在多尺度与多方向场景下的鲁棒性与先进性能。
链接: https://arxiv.org/abs/2603.04793
作者: Huiran Sun
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Rotated object detection in remote sensing imagery is hindered by three major bottlenecks: non-adaptive receptive field utilization, inadequate long-range multi-scale feature fusion, and discontinuities in angle regression. To address these issues, we propose Rotated Multi-Kernel RetinaNet (RMK RetinaNet). First, we design a Multi-Scale Kernel (MSK) Block to strengthen adaptive multi-scale feature extraction. Second, we incorporate a Multi-Directional Contextual Anchor Attention (MDCAA) mechanism into the feature pyramid to enhance contextual modeling across scales and orientations. Third, we introduce a Bottom-up Path to preserve fine-grained spatial details that are often degraded during downsampling. Finally, we develop an Euler Angle Encoding Module (EAEM) to enable continuous and stable angle regression. Extensive experiments on DOTA-v1.0, HRSC2016, and UCAS-AOD show that RMK RetinaNet achieves performance comparable to state-of-the-art rotated object detectors while improving robustness in multi-scale and multi-orientation scenarios.
[CV-87] MADCrowner: Margin Aware Dental Crown Design with Template Deformation and Refinement
【速读】：该论文旨在解决当前牙冠修复设计中依赖大量人工调整、空间分辨率不足、输出噪声大及表面重建过度延伸等问题。其解决方案的关键在于提出了一种边缘感知的网格生成框架 \totalframework，包含两个核心模块：CrownDeformR 和 CrownSegger。其中，CrownDeformR 通过多尺度口内扫描编码器提取解剖上下文信息，将初始模板变形为目标牙冠；而新提出的 marginseg 网络用于精确分割牙颈部边缘（cervical margin），不仅作为额外约束提升变形精度，还作为边界条件指导定制化后处理方法以去除重建表面的过延伸区域，从而显著提高几何准确性与临床可行性。
链接: https://arxiv.org/abs/2603.04771
作者: Linda Wei,Chang Liu,Wenran Zhang,Yuxuan Hu,Ruiyang Li,Feng Qi,Changyao Tian,Ke Wang,Yuanyuan Wang,Shaoting Zhang,Dimitris Metaxas,Hongsheng Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Dental crown restoration is one of the most common treatment modalities for tooth defect, where personalized dental crown design is critical. While computer-aided design (CAD) systems have notably enhanced the efficiency of dental crown design, extensive manual adjustments are still required in the clinic workflow. Recent studies have explored the application of learning-based methods for the automated generation of restorative dental crowns. Nevertheless, these approaches were challenged by inadequate spatial resolution, noisy outputs, and overextension of surface reconstruction. To address these limitations, we propose \totalframework, a margin-aware mesh generation framework comprising CrownDeformR and CrownSegger. Inspired by the clinic manual workflow of dental crown design, we designed CrownDeformR to deform an initial template to the target crown based on anatomical context, which is extracted by a multi-scale intraoral scan encoder. Additionally, we introduced \marginseg, a novel margin segmentation network, to extract the cervical margin of the target tooth. The performance of CrownDeformR improved with the cervical margin as an extra constraint. And it was also utilized as the boundary condition for the tailored postprocessing method, which removed the overextended area of the reconstructed surface. We constructed a large-scale intraoral scan dataset and performed extensive experiments. The proposed method significantly outperformed existing approaches in both geometric accuracy and clinical feasibility.
[CV-88] DSA-SRGS: Super-Resolution Gaussian Splatting for Dynamic Sparse-View DSA Reconstruction
【速读】：该论文旨在解决数字减影血管造影（Digital Subtraction Angiography, DSA）在稀疏动态视角下进行4D血管重建时，因输入投影分辨率受限而导致的细节丢失与伪影问题。现有基于高斯点绘（Gaussian Splatting）和动态神经表示的方法难以恢复精细的血管分支结构，主要瓶颈在于缺乏超分辨率能力。解决方案的关键在于提出DSA-SRGS框架，其核心创新包括：1）引入多保真度纹理学习模块（Multi-Fidelity Texture Learning Module），融合经微调的DSA专用超分辨率模型提供的高质量先验信息，以指导4D重建优化；2）设计置信度感知策略（Confidence-Aware Strategy），自适应地加权原始低分辨率投影与生成的高分辨率伪标签之间的监督信号，从而缓解由伪标签引发的幻觉伪影；3）提出辐射亚像素致密化策略（Radiative Sub-Pixel Densification），利用高分辨率亚像素采样梯度累积来优化4D辐射高斯核，实现更精确的结构恢复。
链接: https://arxiv.org/abs/2603.04770
作者: Shiyu Zhang,Zhicong Wu,Huangxuan Zhao,Zhentao Liu,Lei Chen,Yong Luo,Lefei Zhang,Zhiming Cui,Ziwen Ke,Bo Du
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 11 pages, 3 figures, 3 tables
Abstract:Digital subtraction angiography (DSA) is a key imaging technique for the auxiliary diagnosis and treatment of cerebrovascular diseases. Recent advancements in gaussian splatting and dynamic neural representations have enabled robust 3D vessel reconstruction from sparse dynamic inputs. However, these methods are fundamentally constrained by the resolution of input projections, where performing naive upsampling to enhance rendering resolution inevitably results in severe blurring and aliasing artifacts. Such lack of super-resolution capability prevents the reconstructed 4D models from recovering fine-grained vascular details and intricate branching structures, which restricts their application in precision diagnosis and treatment. To solve this problem, this paper proposes DSA-SRGS, the first super-resolution gaussian splatting framework for dynamic sparse-view DSA reconstruction. Specifically, we introduce a Multi-Fidelity Texture Learning Module that integrates high-quality priors from a fine-tuned DSA-specific super-resolution model, into the 4D reconstruction optimization. To mitigate potential hallucination artifacts from pseudo-labels, this module employs a Confidence-Aware Strategy to adaptively weight supervision signals between the original low-resolution projections and the generated high-resolution pseudo-labels. Furthermore, we develop Radiative Sub-Pixel Densification, an adaptive strategy that leverages gradient accumulation from high-resolution sub-pixel sampling to refine the 4D radiative gaussian kernels. Extensive experiments on two clinical DSA datasets demonstrate that DSA-SRGS significantly outperforms state-of-the-art methods in both quantitative metrics and qualitative visual fidelity.
[CV-89] Evaluating and Correcting Human Annotation Bias in Dynamic Micro-Expression Recognition
【速读】：该论文旨在解决微表情（micro-expression）人工标注中存在的准确性误差问题，尤其是在跨文化场景下关键帧标注偏差更为显著的问题。其核心解决方案是提出一种全局非单调差分选择策略（Global Anti-Monotonic Differential Selection Strategy, GAMDSS），通过动态帧重选机制从完整的微表情动作序列中识别出具有显著变化的起始帧（Onset）和峰值帧（Apex），进而确定终止帧（Offset），构建更丰富的时空动态表征。该方法采用共享参数的双分支结构高效提取时空特征，并在七个主流微表情数据集上验证了其有效性，能够显著降低因人为因素导致的主观误差，且无需增加模型参数即可提升识别性能。
链接: https://arxiv.org/abs/2603.04766
作者: Feng Liu,Bingyu Nan,Xuezhong Qian,Xiaolan Fu
机构: Shanghai Jiao Tong University (上海交通大学); Jiangnan University (江南大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
备注: 15 pages, 8 figures, 7 tables
Abstract:Existing manual labeling of micro-expressions is subject to errors in accuracy, especially in cross-cultural scenarios where deviation in labeling of key frames is more prominent. To address this issue, this paper presents a novel Global Anti-Monotonic Differential Selection Strategy (GAMDSS) architecture for enhancing the effectiveness of spatio-temporal modeling of micro-expressions through keyframe re-selection. Specifically, the method identifies Onset and Apex frames, which are characterized by significant micro-expression variation, from complete micro-expression action sequences via a dynamic frame reselection mechanism. It then uses these to determine Offset frames and construct a rich spatio-temporal dynamic representation. A two-branch structure with shared parameters is then used to efficiently extract spatio-temporal features. Extensive experiments are conducted on seven widely recognized micro-expression datasets. The results demonstrate that GAMDSS effectively reduces subjective errors caused by human factors in multicultural datasets such as SAMM and 4DME. Furthermore, quantitative analyses confirm that offset-frame annotations in multicultural datasets are more uncertain, providing theoretical justification for standardizing micro-expression annotations. These findings directly support our argument for reconsidering the validity and generalizability of dataset annotation paradigms. Notably, this design can be integrated into existing models without increasing the number of parameters, offering a new approach to enhancing micro-expression recognition performance. The source code is available on GitHub[this https URL].
[CV-90] Evaluating GPT -5 as a Multimodal Clinical Reason er: A Landscape Commentary
【速读】：该论文旨在解决通用基础模型（foundation models）在临床医学中实现整合推理能力的局限性问题，特别是其在处理模糊患者叙事、实验室数据与多模态影像信息时的表现瓶颈。解决方案的关键在于通过标准化零样本链式思维（zero-shot chain-of-thought）协议，对GPT-5系列模型（包括GPT-5、GPT-5 Mini和GPT-5 Nano）与前代模型GPT-4o进行跨任务对比评估，涵盖医学教育考试、文本推理基准及神经放射学、数字病理学和乳腺X线摄影中的视觉问答（VQA）任务。结果表明，GPT-5在专家级文本推理上取得显著提升（如MedXpertQA绝对提升超25个百分点），并能有效利用增强的推理能力将不确定的临床叙述锚定于具体影像证据，在多数VQA任务中达到或接近当前最优水平；然而，在高度专业化且感知敏感的任务中（如神经放射学和乳腺摄影），其性能仍落后于领域专用模型，说明通用模型虽逼近临床认知过程，但尚未完全替代针对特定任务优化的专业系统。
链接: https://arxiv.org/abs/2603.04763
作者: Alexandru Florea,Shansong Wang,Mingzhe Hu,Qiang Li,Zach Eidex,Luke del Balzo,Mojtaba Safari,Xiaofeng Yang
机构: Emory University School of Medicine (埃默里大学医学院); Georgia Institute of Technology (佐治亚理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The transition from task-specific artificial intelligence toward general-purpose foundation models raises fundamental questions about their capacity to support the integrated reasoning required in clinical medicine, where diagnosis demands synthesis of ambiguous patient narratives, laboratory data, and multimodal imaging. This landscape commentary provides the first controlled, cross-sectional evaluation of the GPT-5 family (GPT-5, GPT-5 Mini, GPT-5 Nano) against its predecessor GPT-4o across a diverse spectrum of clinically grounded tasks, including medical education examinations, text-based reasoning benchmarks, and visual question-answering in neuroradiology, digital pathology, and mammography using a standardized zero-shot chain-of-thought protocol. GPT-5 demonstrated substantial gains in expert-level textual reasoning, with absolute improvements exceeding 25 percentage-points on MedXpertQA. When tasked with multimodal synthesis, GPT-5 effectively leveraged this enhanced reasoning capacity to ground uncertain clinical narratives in concrete imaging evidence, achieving state-of-the-art or competitive performance across most VQA benchmarks and outperforming GPT-4o by margins of 10-40% in mammography tasks requiring fine-grained lesion characterization. However, performance remained moderate in neuroradiology (44% macro-average accuracy) and lagged behind domain-specific models in mammography, where specialized systems exceed 80% accuracy compared to GPT-5’s 52-64%. These findings indicate that while GPT-5 represents a meaningful advance toward integrated multimodal clinical reasoning, mirroring the clinician’s cognitive process of biasing uncertain information with objective findings, generalist models are not yet substitutes for purpose-built systems in highly specialized, perception-critical tasks.
[CV-91] oward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset CVPR2026
【速读】：该论文旨在解决真实场景下红外图像超分辨率（Infrared Image Super-Resolution, IISR）问题，其核心挑战在于现实红外图像同时受到光学与传感退化的影响，导致结构锐度和热保真度双重下降，而现有方法多基于模拟数据或忽略红外与可见光成像的本质差异。解决方案的关键在于提出 Real-IISR 框架，该框架采用统一的自回归机制，通过热结构引导的视觉自回归方式分尺度重建精细热结构与清晰背景；其中 Thermal-Structural Guidance 模块编码热先验以缓解热辐射与结构边缘之间的失配，Condition-Adaptive Codebook 动态调节离散表示以应对非均匀退化带来的量化偏差，且 Thermal Order Consistency Loss 强制温度与像素强度保持单调关系，从而在空间错位和热漂移条件下维持物理一致性。
链接: https://arxiv.org/abs/2603.04745
作者: Yang Zou,Jun Ma,Zhidong Jiao,Xingyuan Li,Zhiying Jiang,Jinyuan Liu
机构: Northwestern Polytechnical University (西北工业大学); Dalian University of Technology (大连理工大学); Zhejiang University (浙江大学); Dalian Maritime University (大连海事大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper was accepted by CVPR 2026
Abstract:Infrared image super-resolution (IISR) under real-world conditions is a practically significant yet rarely addressed task. Pioneering works are often trained and evaluated on simulated datasets or neglect the intrinsic differences between infrared and visible imaging. In practice, however, real infrared images are affected by coupled optical and sensing degradations that jointly deteriorate both structural sharpness and thermal fidelity. To address these challenges, we propose Real-IISR, a unified autoregressive framework for real-world IISR that progressively reconstructs fine-grained thermal structures and clear backgrounds in a scale-by-scale manner via thermal-structural guided visual autoregression. Specifically, a Thermal-Structural Guidance module encodes thermal priors to mitigate the mismatch between thermal radiation and structural edges. Since non-uniform degradations typically induce quantization bias, Real-IISR adopts a Condition-Adaptive Codebook that dynamically modulates discrete representations based on degradation-aware thermal priors. Also, a Thermal Order Consistency Loss enforces a monotonic relation between temperature and pixel intensity, ensuring relative brightness order rather than absolute values to maintain physical consistency under spatial misalignment and thermal drift. We build FLIR-IISR, a real-world IISR dataset with paired LR-HR infrared images acquired via automated focus variation and motion-induced blur. Extensive experiments demonstrate the promising performance of Real-IISR, providing a unified foundation for real-world IISR and benchmarking. The dataset and code are available at: this https URL.
[CV-92] FOZO: Forward-Only Zeroth-Order Prompt Optimization for Test-Time Adaptation CVPR2026
【速读】：该论文旨在解决测试时适应（Test-Time Adaptation, TTA）中现有方法在资源受限设备上部署困难的问题。当前基于反向传播的方法因计算和内存开销大、需修改模型权重而不适用于低端设备；而传统无反向传播技术则适应能力有限。解决方案的关键在于提出一种全新的无反向传播范式——前向仅零阶优化（Forward-Only Zeroth-Order Optimization, FOZO），其核心是通过内存高效的零阶提示优化策略，在不更新模型参数的前提下，同时优化中间特征统计量与预测熵以提升适应性能；并引入动态衰减扰动尺度机制来增强对分布外数据流的稳定性和效率，理论证明其在TTA数据流假设下的收敛性。
链接: https://arxiv.org/abs/2603.04733
作者: Xingyu Wang,Tao Wang
机构: Sichuan University (四川大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:Test-Time Adaptation (TTA) is essential for enabling deep learning models to handle real-world data distribution shifts. However, current approaches face significant limitations: backpropagation-based methods are not suitable for low-end deployment devices, due to their high computation and memory requirements, as well as their tendency to modify model weights during adaptation; while traditional backpropagation-free techniques exhibit constrained adaptation capabilities. In this work, we propose Forward-Only Zeroth-Order Optimization (FOZO), a novel and practical backpropagation-free paradigm for TTA. FOZO leverages a memory-efficient zeroth-order prompt optimization, which is led by objectives optimizing both intermediate feature statistics and prediction entropy. To ensure efficient and stable adaptation over the out-of-distribution data stream, we introduce a dynamically decaying perturbation scale during zeroth-order gradient estimation and theoretically prove its convergence under the TTA data stream assumption. Extensive continual adaptation experiments on ImageNet-C, ImageNet-R, and ImageNet-Sketch demonstrate FOZO’s superior performance, achieving 59.52% Top-1 accuracy on ImageNet-C (5K, level 5) and outperforming main gradient-based methods and SOTA forward-only FOA (58.13%). Furthermore, FOZO exhibits strong generalization on quantized (INT8) models. These findings demonstrate that FOZO is a highly competitive solution for TTA deployment in resource-limited scenarios.
[CV-93] Are Multimodal LLM s Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在真实场景下视频异常检测（Video Anomaly Detection, VAD）中的可靠性问题，特别是其在弱时序监督下的性能表现与决策偏差。传统方法依赖重建或姿态特征，而本文提出将VAD重构为一个语言引导的推理任务，通过设计特定类别的提示（prompt）来调节模型决策边界，从而显著提升F1分数（从0.09提升至0.64），但发现召回率（recall）仍是关键瓶颈，表明当前MLLMs在噪声环境中的鲁棒性不足，亟需面向召回优化的提示工程与模型校准策略。
链接: https://arxiv.org/abs/2603.04727
作者: Shanle Yao,Armin Danesh Pazho,Narges Rashvand,Hamed Tabkhi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal large language models (MLLMs) have demonstrated impressive general competence in video understanding, yet their reliability for real-world Video Anomaly Detection (VAD) remains largely unexplored. Unlike conventional pipelines relying on reconstruction or pose-based cues, MLLMs enable a paradigm shift: treating anomaly detection as a language-guided reasoning task. In this work, we systematically evaluate state-of-the-art MLLMs on the ShanghaiTech and CHAD benchmarks by reformulating VAD as a binary classification task under weak temporal supervision. We investigate how prompt specificity and temporal window lengths (1s–3s) influence performance, focusing on the precision–recall trade-off. Our findings reveal a pronounced conservative bias in zero-shot settings; while models exhibit high confidence, they disproportionately favor the ‘normal’ class, resulting in high precision but a recall collapse that limits practical utility. We demonstrate that class-specific instructions can significantly shift this decision boundary, improving the peak F1-score on ShanghaiTech from 0.09 to 0.64, yet recall remains a critical bottleneck. These results highlight a significant performance gap for MLLMs in noisy environments and provide a foundation for future work in recall-oriented prompting and model calibration for open-world surveillance, which demands complex video understanding and reasoning.
[CV-94] A Benchmark Study of Neural Network Compression Methods for Hyperspectral Image Classification
【速读】：该论文旨在解决深度神经网络在遥感应用中因计算和内存需求高而难以部署于资源受限平台（如遥感设备和边缘系统）的问题。解决方案的关键在于系统评估三种主流的卷积神经网络压缩策略——剪枝（pruning）、量化（quantization）和知识蒸馏（knowledge distillation），通过在两个基准高光谱数据集上的实验，验证压缩模型能够在显著降低模型规模和计算成本的同时，保持具有竞争力的分类性能，从而为高效深度学习在遥感场景中的落地提供可行路径。
链接: https://arxiv.org/abs/2603.04720
作者: Sai Shi
机构: Temple University (坦普尔大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 18 pages, 5 figures
Abstract:Deep neural networks have achieved strong performance in image classification tasks due to their ability to learn complex patterns from high-dimensional data. However, their large computational and memory requirements often limit deployment on resource-constrained platforms such as remote sensing devices and edge systems. Network compression techniques have therefore been proposed to reduce model size and computational cost while maintaining predictive performance. In this study, we conduct a systematic evaluation of neural network compression methods for a remote sensing application, namely hyperspectral land cover classification. Specifically, we examine three widely used compression strategies for convolutional neural networks: pruning, quantization, and knowledge distillation. Experiments are conducted on two benchmark hyperspectral datasets, considering classification accuracy, memory consumption, and inference efficiency. Our results demonstrate that compressed models can significantly reduce model size and computational cost while maintaining competitive classification performance. These findings provide insights into the trade-offs between compression ratio, efficiency, and accuracy, and highlight the potential of compression techniques for enabling efficient deep learning deployment in remote sensing applications.
[CV-95] Decoding the Pulse of Reasoning VLMs in Multi-Image Understanding Tasks
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在多图像推理任务中注意力分散的问题，特别是链式思维（Chain-of-Thought, CoT）生成过程中出现的“脉冲式”扩散注意力现象（diffuse “pulses”），即模型无法聚焦于与任务相关的图像。解决方案的关键在于提出一种无需训练、仅在推理阶段应用的方法——PulseFocus，其核心机制是将CoT推理过程结构化为交替的“计划-聚焦”块，并引入软注意力门控（soft attention gating），强制模型先明确规划要关注的图像，再在解码阶段屏蔽非目标图像的注意力，从而显著提升注意力聚焦能力，在BLINK和MuirBench等多图像基准测试中分别取得+3.7%和+1.07%的性能提升。
链接: https://arxiv.org/abs/2603.04676
作者: Chenjun Li
机构: Cornell University (康奈尔大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages, 5 figures, 3 tables
Abstract:Multi-image reasoning remains a significant challenge for vision-language models (VLMs). We investigate a previously overlooked phenomenon: during chain-of-thought (CoT) generation, the text-to-image (T2I) attention of reasoning VLMs exhibits diffuse “pulses”: sporadic and unfocused attention patterns that fail to concentrate on task-relevant images. We further reveal a systematic positional bias in attention allocation across images. Motivated by these observations, we propose PulseFocus, a training-free, inference-time method that structures CoT reasoning into interleaved plan/focus blocks with soft attention gating. By forcing the model to explicitly plan which image to examine and then gating decode-time attention to the referenced image, PulseFocus sharpens attention focus and yields consistent improvements on multi-image benchmarks like BLINK benchmark (+3.7%) and MuirBench (+1.07%).
[CV-96] sFRC for assessing hallucinations in medical image restoration
【速读】：该论文旨在解决深度学习（Deep Learning, DL）在医学影像重建中因数据稀疏或欠采样导致的“幻觉”（hallucination）问题，即模型输出虽视觉上逼真但可能包含虚假结构。其核心挑战在于缺乏有效的、易用的检测工具和鲁棒指标来识别这些幻觉。解决方案的关键是提出一种基于傅里叶环相关分析（Fourier Ring Correlation, FRC）的小块扫描方法（称为sFRC），通过在DL输出与其参考图像之间逐块扫描并计算FRC值，实现对幻觉特征的定位与量化。该方法可结合专家标注的幻觉区域或成像理论生成的幻觉图来设定参数，并已在CT超分辨率、CT稀疏视图和MRI欠采样恢复等多个任务中验证其有效性，同时揭示了不同数据分布和欠采样率下DL方法的幻觉率差异，展示了其在传统正则化与先进可展开方法中的普适性。
链接: https://arxiv.org/abs/2603.04673
作者: Prabhat Kc,Rongping Zeng,Nirmal Soni,Aldo Badano
机构: U.S. Food and Drug Administration (美国食品药品管理局); Center for Devices and Radiological Health (设备与放射健康中心)
类目: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph); Machine Learning (stat.ML)
备注: 16 pages; 14 figures; 1 Supplemental document. TechRxiv Preprints, 2025
Abstract:Deep learning (DL) methods are currently being explored to restore images from sparse-view-, limited-data-, and undersampled-based acquisitions in medical applications. Although outputs from DL may appear visually appealing based on likability/subjective criteria (such as less noise, smooth features), they may also suffer from hallucinations. This issue is further exacerbated by a lack of easy-to-use techniques and robust metrics for the identification of hallucinations in DL outputs. In this work, we propose performing Fourier Ring Correlation (FRC) analysis over small patches and concomitantly (s)canning across DL outputs and their reference counterparts to detect hallucinations (termed as sFRC). We describe the rationale behind sFRC and provide its mathematical formulation. The parameters essential to sFRC may be set using predefined hallucinated features annotated by subject matter experts or using imaging theory-based hallucination maps. We use sFRC to detect hallucinations for three undersampled medical imaging problems: CT super-resolution, CT sparse view, and MRI subsampled restoration. In the testing phase, we demonstrate sFRC’s effectiveness in detecting hallucinated features for the CT problem and sFRC’s agreement with imaging theory-based outputs on hallucinated feature maps for the MR problem. Finally, we quantify the hallucination rates of DL methods on in-distribution versus out-of-distribution data and under increasing subsampling rates to characterize the robustness of DL methods. Beyond DL-based methods, sFRC’s effectiveness in detecting hallucinations for a conventional regularization-based restoration method and a state-of-the-art unrolled method is also shown.
[CV-97] Spinverse: Differentiable Physics for Permeability-Aware Microstructure Reconstruction from Diffusion MRI
【速读】：该论文旨在解决扩散磁共振成像（Diffusion MRI, dMRI）在微结构边界重建中的局限性问题，即现有方法通常假设组织边界为不可渗透的，或仅估计体素级参数而无法显式恢复微观结构界面。其解决方案的关键在于提出了一种名为Spinverse的可渗透性感知重建方法，通过一个全可微分的Bloch-Torrey模拟器对dMRI信号进行反演；该方法将组织表示为固定四面体网格，并将每个内部面的渗透率作为可学习参数，低渗透率面自然形成扩散屏障，从而在不改变网格连接性或顶点位置的前提下，自适应地恢复出具有任意拓扑结构的微结构边界。通过反向传播信号匹配损失优化面渗透率，并利用阈值化策略提取界面，同时引入基于网格的几何先验和分阶段多序列优化策略以缓解病态性并避免局部极小值，显著提升了边界精度与结构合理性。
链接: https://arxiv.org/abs/2603.04638
作者: Prathamesh Pradeep Khole,Mario M. Brenes,Zahra Kais Petiwala,Ehsan Mirafzali,Utkarsh Gupta,Jing-Rebecca Li,Andrada Ianus,Razvan Marinescu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
备注: 10 Pages, 5 Figures, 2 Tables
Abstract:Diffusion MRI (dMRI) is sensitive to microstructural barriers, yet most existing methods either assume impermeable boundaries or estimate voxel-level parameters without recovering explicit interfaces. We present Spinverse, a permeability-aware reconstruction method that inverts dMRI measurements through a fully differentiable Bloch-Torrey simulator. Spinverse represents tissue on a fixed tetrahedral grid and treats each interior face permeability as a learnable parameter; low-permeability faces act as diffusion barriers, so microstructural boundaries whose topology is not fixed a priori (up to the resolution of the ambient mesh) emerge without changing mesh connectivity or vertex positions. Given a target signal, we optimize face permeabilities by backpropagating a signal-matching loss through the PDE forward model, and recover an interface by thresholding the learned permeability field. To mitigate the ill-posedness of permeability inversion, we use mesh-based geometric priors; to avoid local minima, we use a staged multi-sequence optimization curriculum. Across a collection of synthetic voxel meshes, Spinverse reconstructs diverse geometries and demonstrates that sequence scheduling and regularization are critical to avoid outline-only solutions while improving both boundary accuracy and structural validity.
[CV-98] SGR3 Model: Scene Graph Retrieval-Reasoning Model in 3D
【速读】：该论文旨在解决现有3D场景图（3D scene graph）生成方法依赖多模态数据和启发式图构建机制所带来的局限性，例如对传感器输入的强依赖以及关系三元组预测能力受限的问题。其解决方案的关键在于提出一种无需训练的框架——SGR3模型，该模型利用多模态大语言模型（MLLM）结合检索增强生成（RAG）技术，通过ColPali风格的跨模态检索机制获取语义对齐的场景图，并引入加权patch级相似度选择机制以提升检索鲁棒性，从而在不进行显式3D重建的前提下实现高质量的关系推理与场景理解。
链接: https://arxiv.org/abs/2603.04614
作者: Zirui Wang,Ruiping Liu,Yufan Chen,Junwei Zheng,Weijia Fan,Kunyu Peng,Di Wen,Jiale Wei,Jiaming Zhang,Rainer Stiefelhagen
机构: Karlsruhe Institute of Technology (卡尔斯鲁厄理工学院); Shenzhen University (深圳大学); Hunan University (湖南大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D scene graphs provide a structured representation of object entities and their relationships, enabling high-level interpretation and reasoning for robots while remaining intuitively understandable to humans. Existing approaches for 3D scene graph generation typically combine scene reconstruction with graph neural networks (GNNs). However, such pipelines require multi-modal data that may not always be available, and their reliance on heuristic graph construction can constrain the prediction of relationship triplets. In this work, we introduce a Scene Graph Retrieval-Reasoning Model in 3D (SGR3 Model), a training-free framework that leverages multi-modal large language models (MLLMs) with retrieval-augmented generation (RAG) for semantic scene graph generation. SGR3 Model bypasses the need for explicit 3D reconstruction. Instead, it enhances relational reasoning by incorporating semantically aligned scene graphs retrieved via a ColPali-style cross-modal framework. To improve retrieval robustness, we further introduce a weighted patch-level similarity selection mechanism that mitigates the negative impact of blurry or semantically uninformative regions. Experiments demonstrate that SGR3 Model achieves competitive performance compared to training-free baselines and on par with GNN-based expert models. Moreover, an ablation study on the retrieval module and knowledge base scale reveals that retrieved external information is explicitly integrated into the token generation process, rather than being implicitly internalized through abstraction.
[CV-99] PinPoint: Evaluation of Composed Image Retrieval with Explicit Negatives Multi-Image Queries and Paraphrase Testing CVPR2026
【速读】：该论文旨在解决当前组成图像检索（Composed Image Retrieval, CIR）基准测试中存在的局限性问题，包括仅支持单一正确答案、缺乏对假阳性规避能力、鲁棒性及多图推理能力的评估指标。为克服这些不足，作者提出了PinPoint这一综合性真实世界基准，包含7,635个查询和329K条相关性标注，覆盖23类查询，并引入多正确答案（平均每查询9.1个）、显式难负样本、六种指令改写版本以测试鲁棒性、13.4%的多图组合查询以及用于公平性评估的人口统计学元数据。基于对20余种方法的分析，研究发现现有最优模型在mAP@10仅为28.5%的情况下仍会错误召回9%的难负样本，且在不同指令改写下性能波动高达25.1%，多图查询任务表现更差（下降40–70%）。针对这些问题，论文提出一种无需训练的重排序方法，利用现成的多模态大语言模型（Multimodal Large Language Model, MLLM）作为后处理模块，可无缝集成至任意现有CIR系统中以提升准确性与鲁棒性。
链接: https://arxiv.org/abs/2603.04598
作者: Rohan Mahadev,Joyce Yuan,Patrick Poirson,David Xue,Hao-Yu Wu,Dmitry Kislyuk
机构: Pinterest(Pinterest)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for CVPR 2026
Abstract:Composed Image Retrieval (CIR) has made significant progress, yet current benchmarks are limited to single ground-truth answers and lack the annotations needed to evaluate false positive avoidance, robustness and multi-image reasoning. We present PinPoint, a comprehensive real world benchmark with 7,635 queries and 329K relevance judgments across 23 query categories. PinPoint advances the field by providing: (1) multiple correct answers (averaging 9.1 per query) (2) explicit hard negatives, (3) six instruction paraphrases per query for robustness testing, (4) multi-image composition support (13.4% of queries), and (5) demographic metadata for fairness evaluation. Based on our analysis of 20+ methods across 4 different major paradigms, we uncover three significant drawbacks: The best methods while achieving mAP@10 of 28.5%, still retrieves irrelevant results (hard negatives) 9% of the time. The best models also exhibit 25.1% performance variation across paraphrases, indicating significant potential for enhancing current CIR techniques. Multi-image queries performs 40 to 70% worse across different methods. To overcome these new issues uncovered by our evaluation framework, we propose a training-free reranking method based on an off-the-shelf MLLM that can be applied to any existing system to bridge the gap. We release the complete dataset, including all images, queries, annotations, retrieval index, and benchmarking code.
[CV-100] Mask-aware inference with State-Space Models
【速读】：该论文旨在解决当前状态空间模型（State Space Models, SSMs）如Mamba在处理具有任意形状缺失或无效数据的计算机视觉任务（如深度补全、图像修复等）时缺乏内在机制的问题。现有方法如卷积神经网络（CNNs）通过部分卷积（Partial Convolutions）实现基于有效像素的掩码感知重新归一化，但这一机制尚未被引入到SSM架构中。解决方案的关键在于提出一种新的架构组件——部分视觉Mamba（Partial Vision Mamba, PVM），它将部分操作的原理迁移至Mamba主干网络，并定义了一套设计规则以支持在推理阶段灵活处理任意形状的无效区域，从而显著提升模型在含缺失数据场景下的有效性与泛化能力。
链接: https://arxiv.org/abs/2603.04568
作者: Ignasi Mas,Ramon Morros,Javier-Ruiz Hidalgo,Ivan Huerta
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Many real-world computer vision tasks, such as depth completion, must handle inputs with arbitrarily shaped regions of missing or invalid data. For Convolutional Neural Networks (CNNs), Partial Convolutions solved this by a mask-aware re-normalization conditioned only on valid pixels. Recently, State Space Models (SSMs) like Mamba have emerged, offering high performance with linear complexity. However, these architectures lack an inherent mechanism for handling such arbitrarily shaped invalid data at inference time. To bridge this gap, we introduce Partial Vision Mamba (PVM), a novel architectural component that ports the principles of partial operations to the Mamba backbone. We also define a series of rules to design architectures using PVM. We show the efficacy and generalizability of our approach in the tasks of depth completion, image inpainting, and classification with invalid data.
[CV-101] Structure-Guided Histopathology Synthesis via Dual-LoRA Diffusion
【速读】：该论文旨在解决组织病理图像合成中局部结构修复与全局结构生成任务分离导致的现实细胞组织一致性不足的问题，其核心挑战在于如何在不同缺失程度下实现结构一致性的组织重建。解决方案的关键在于提出一种统一的双LoRA可控扩散框架（Dual-LoRA Controllable Diffusion），通过多类细胞核中心点（multi-class nuclei centroids）作为轻量且标注高效的结构先验，在局部修复和全局生成任务中提供生物学意义明确的空间引导；同时引入两个任务特定的LoRA适配器（LoRA adapters），在不重新训练独立扩散模型的前提下，使共享主干网络分别适配局部补全与全局合成目标，从而实现结构保真度与真实感的显著提升。
链接: https://arxiv.org/abs/2603.04565
作者: Xuan Xu,Prateek Prasanna
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Histopathology image synthesis plays an important role in tissue restoration, data augmentation, and modeling of tumor microenvironments. However, existing generative methods typically address restoration and generation as separate tasks, although both share the same objective of structure-consistent tissue synthesis under varying degrees of missingness, and often rely on weak or inconsistent structural priors that limit realistic cellular organization. We propose Dual-LoRA Controllable Diffusion, a unified centroid-guided diffusion framework that jointly supports Local Structure Completion and Global Structure Synthesis within a single model. Multi-class nuclei centroids serve as lightweight and annotation-efficient spatial priors, providing biologically meaningful guidance under both partial and complete image absence. Two task-specific LoRA adapters specialize the shared backbone for local and global objectives without retraining separate diffusion models. Extensive experiments demonstrate consistent improvements over state-of-the-art GAN and diffusion baselines across restoration and synthesis tasks. For local completion, LPIPS computed within the masked region improves from 0.1797 (HARP) to 0.1524, and for global synthesis, FID improves from 225.15 (CoSys) to 76.04, indicating improved structural fidelity and realism. Our approach achieves more faithful structural recovery in masked regions and substantially improved realism and morphology consistency in full synthesis, supporting scalable pan-cancer histopathology modeling. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.04565 [cs.CV] (or arXiv:2603.04565v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.04565 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-102] Fusion and Grouping Strategies in Deep Learning for Local Climate Zone Classification of Multimodal Remote Sensing Data
【速读】：该论文旨在解决多模态遥感数据在城市微气候区（Local Climate Zones, LCZ）分类中因数据复杂性导致的分类精度不足问题，尤其是现有深度学习模型在融合策略上的机制缺乏系统分析。其关键解决方案在于通过对比不同层级的融合策略（像素级、特征级、决策级）与数据分组策略（波段分组和标签合并），识别出最优组合：即基础混合融合模型（FM1）结合波段分组（Band Grouping, BG）与标签合并（Label Merging, LM），在So2Sat LCZ42数据集上实现了76.6%的整体准确率，并显著提升了对低频类别的预测性能。
链接: https://arxiv.org/abs/2603.04562
作者: Ancymol Thomas,Jaya Sreevalsan-Nair
机构: International Institute of Information Technology Bangalore(印度国际信息科技学院班加罗尔)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 25 pages, 12 figures
Abstract:Local Climate Zones (LCZs) give a zoning map to study urban structures and land use and analyze the impact of urbanization on local climate. Multimodal remote sensing enables LCZ classification, for which data fusion is significant for improving accuracy owing to the data complexity. However, there is a gap in a comprehensive analysis of the fusion mechanisms used in their deep learning (DL) classifier architectures. This study analyzes different fusion strategies in the multi-class LCZ classification models for multimodal data and grouping strategies based on inherent data characteristics. The different models involving Convolutional Neural Networks (CNNs) include: (i) baseline hybrid fusion (FM1), (ii) with self- and cross-attention mechanisms (FM2), (iii) with the multi-scale Gaussian filtered images (FM3), and (iv) weighted decision-level fusion (FM4). Ablation experiments are conducted to study the pixel-, feature-, and decision-level fusion effects in the model performance. Grouping strategies include band grouping (BG) within the data modalities and label merging (LM) in the ground truth. Our analysis is exclusively done on the So2Sat LCZ42 dataset, which consists of Synthetic Aperture Radar (SAR) and Multispectral Imaging (MSI) image pairs. Our results show that FM1 consistently outperforms simple fusion methods. FM1 with BG and LM is found to be the most effective approach among all fusion strategies, giving an overall accuracy of 76.6%. Importantly, our study highlights the effect of these strategies in improving prediction accuracy for the underrepresented classes. Our code and processed datasets are available at this https URL
[CV-103] InverseNet: Benchmarking Operator Mismatch and Calibration Across Compressive Imaging Modalities ATC
【速读】：该论文旨在解决压缩成像（Compressive Imaging, CI）系统中普遍存在的算子失配（operator mismatch）问题，即在实际部署场景中，系统假设的前向算子与物理现实存在偏差，导致现有高效压缩成像方法（如EfficientSCI）性能急剧下降（损失达20.58 dB），而当前缺乏量化评估此类失配影响的基准。其解决方案的关键在于提出首个跨模态算子失配基准Inversenet，涵盖CASSI、CACTI和单像素相机三种典型硬件架构，并设计四类实验场景（理想、失配、Oracle校正、盲校准）对12种方法进行全面评估。研究发现：深度学习方法在失配条件下性能退化显著（损失10–21 dB），且其鲁棒性与性能呈强负相关；mask-oblivious架构无法恢复任何失配损失，而operator-conditioned方法可恢复41–90%；更重要的是，无需真值信息的盲网格搜索校准即可恢复85–100%的Oracle上限，验证了方案的有效性与泛化能力。
链接: https://arxiv.org/abs/2603.04538
作者: Chengshuai Yang,Xin Yuan
机构: Westlake University (西湖大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Benchmarking Operator Mismatch and Calibration Across Compressive Imaging Modalities
Abstract:State-of-the-art EfficientSCI loses 20.58 dB when its assumed forward operator deviates from physical reality in just eight parameters, yet no existing benchmark quantifies operator mismatch, the default condition in deployed compressive imaging systems. We introduce InverseNet, the first cross-modality benchmark for operator mismatch, spanning CASSI, CACTI, and single-pixel cameras. Evaluating 12 methods under a four-scenario protocol (ideal, mismatched, oracle-corrected, blind calibration) across 27 simulated scenes and 9 real hardware captures, we find: (1) deep learning methods lose 10-21 dB under mismatch, eliminating their advantage over classical baselines; (2) performance and robustness are inversely correlated across modalities (Spearman r_s = -0.71, p 0.01); (3) mask-oblivious architectures recover 0% of mismatch losses regardless of calibration quality, while operator-conditioned methods recover 41-90%; (4) blind grid-search calibration recovers 85-100% of the oracle bound without ground truth. Real hardware experiments confirm that simulation trends transfer to physical data. Code will be released upon acceptance.
[CV-104] Recognition of Daily Activities through Multi-Modal Deep Learning: A Video Pose and Object-Aware Approach for Ambient Assisted Living
【速读】：该论文旨在解决老年人在室内环境中进行日常活动识别（Activity Recognition）的难题，这是实现智能辅助生活环境（Ambient Assisted Living, AAL）系统的关键环节。问题的核心挑战包括类内差异性大、类间相似性强、环境变化复杂以及视角和场景多样性等。解决方案的关键在于提出一种多模态融合方法：利用3D卷积神经网络（3D CNN）处理视觉信息，通过图卷积网络（Graph Convolutional Network, GCN）分析三维人体姿态数据，并结合目标检测模块提取的上下文信息，采用交叉注意力机制（cross-attention mechanism）将这些模态特征进行有效融合，从而提升活动识别的准确性。实验基于Toyota SmartHome数据集验证了该方法的有效性，表明其具备作为先进AAL监控系统核心组件的潜力。
链接: https://arxiv.org/abs/2603.04509
作者: Kooshan Hashemifard,Pau Climent-Pérez,Francisco Florez-Revuelta
机构: University of Alicante (阿尔卡拉大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recognition of daily activities is a critical element for effective Ambient Assisted Living (AAL) systems, particularly to monitor the well-being and support the independence of older adults in indoor environments. However, developing robust activity recognition systems faces significant challenges, including intra-class variability, inter-class similarity, environmental variability, camera perspectives, and scene complexity. This paper presents a multi-modal approach for the recognition of activities of daily living tailored for older adults within AAL settings. The proposed system integrates visual information processed by a 3D Convolutional Neural Network (CNN) with 3D human pose data analyzed by a Graph Convolutional Network. Contextual information, derived from an object detection module, is fused with the 3D CNN features using a cross-attention mechanism to enhance recognition accuracy. This method is evaluated using the Toyota SmartHome dataset, which consists of real-world indoor activities. The results indicate that the proposed system achieves competitive classification accuracy for a range of daily activities, highlighting its potential as an essential component for advanced AAL monitoring solutions. This advancement supports the broader goal of developing intelligent systems that promote safety and autonomy among older adults.
[CV-105] Lost in Translation: How Language Re-Aligns Vision for Cross-Species Pathology
【速读】：该论文旨在解决基础模型（Foundation models）在跨癌种和跨物种迁移场景下性能下降的问题，尤其是在计算病理学中，现有视觉-语言对齐方法在跨物种任务中表现不佳。其关键解决方案是引入“语义锚定”（Semantic Anchoring），通过语言提供一个稳定的坐标系来约束视觉特征空间，从而缓解因物种主导的嵌入坍缩（semantic collapse）导致的性能退化问题。该机制不依赖于复杂的文本编码器，而是利用文本对齐本身带来的语义稳定性，使模型能够在无需重新训练的情况下实现跨物种和跨癌种的语义重解释，显著提升分类性能（同癌种提升8.52%，跨癌种提升5.67%）。
链接: https://arxiv.org/abs/2603.04405
作者: Ekansh Arora
机构: Thomas Jefferson High School for Science and Technology (托马斯·杰斐逊科技高中)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 27 pages, 6 figures, 7 tables. Code and data available at this https URL
Abstract:Foundation models are increasingly applied to computational pathology, yet their behavior under cross-cancer and cross-species transfer remains unspecified. This study investigated how fine-tuning CPath-CLIP affects cancer detection under same-cancer, cross-cancer, and cross-species conditions using whole-slide image patches from canine and human histopathology. Performance was measured using area under the receiver operating characteristic curve (AUC). Few-shot fine-tuning improved same-cancer (64.9% to 72.6% AUC) and cross-cancer performance (56.84% to 66.31% AUC). Cross-species evaluation revealed that while tissue matching enables meaningful transfer, performance remains below state-of-the-art benchmarks (H-optimus-0: 84.97% AUC), indicating that standard vision-language alignment is suboptimal for cross-species generalization. Embedding space analysis revealed extremely high cosine similarity (greater than 0.99) between tumor and normal prototypes. Grad-CAM shows prototype-based models remain domain-locked, while language-guided models attend to conserved tumor morphology. To address this, we introduce Semantic Anchoring, which uses language to provide a stable coordinate system for visual features. Ablation studies reveal that benefits stem from the text-alignment mechanism itself, regardless of text encoder complexity. Benchmarking against H-optimus-0 shows that CPath-CLIP’s failure stems from intrinsic embedding collapse, which text alignment effectively circumvents. Additional gains were observed in same-cancer (8.52%) and cross-cancer classification (5.67%). We identified a previously uncharacterized failure mode: semantic collapse driven by species-dominated alignment rather than missing visual information. These results demonstrate that language acts as a control mechanism, enabling semantic re-interpretation without retraining.
[CV-106] ICHOR: A Robust Representation Learning Approach for ASL CBF Maps with Self-Supervised Masked Autoencoders
【速读】：该论文旨在解决动脉自旋标记（Arterial Spin Labeling, ASL）灌注磁共振成像（MRI）在临床和研究应用中面临的三大挑战：图像质量差异大、多中心/多设备/多协议间的异质性强，以及用于训练可泛化模型的标注数据集稀缺。其解决方案的关键在于提出一种基于自监督预训练的通用编码器方法——ICHOR，该方法利用3D掩码自动编码器（Masked Autoencoder）结合视觉Transformer（Vision Transformer）骨干网络，在包含11,405例ASL CBF扫描的大规模跨中心数据集上进行预训练，从而学习可迁移的特征表示。该预训练模型可在下游任务（如诊断分类与图像质量预测）中显著提升性能，优于现有神经影像学自监督方法，且预训练权重与代码将公开共享，促进ASL定量生物标志物的标准化与广泛应用。
链接: https://arxiv.org/abs/2603.05247
作者: Xavier Beltran-Urbano,Yiran Li,Xinglin Zeng,Katie R. Jobson,Manuel Taso,Christopher A. Brown,David A. Wolk,Corey T. McMillan,Ilya M. Nashrallah,Paul A. Yushkevich,Ze Wang,John A. Detre,Sudipto Dolui
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
备注:
Abstract:Arterial spin labeling (ASL) perfusion MRI allows direct quantification of regional cerebral blood flow (CBF) without exogenous contrast, enabling noninvasive measurements that can be repeated without constraints imposed by contrast injection. ASL is increasingly acquired in research studies and clinical MRI protocols. Building on successes in structural imaging, recent efforts have implemented deep learning based methods to improve image quality, enable automated quality control, and derive robust quantitative and predictive biomarkers with ASL derived CBF. However, progress has been limited by variable image quality, substantial inter-site, vendor and protocol differences, and limited availability of labeled datasets needed to train models that generalize across cohorts. To address these challenges, we introduce ICHOR, a self supervised pre-training approach for ASL CBF maps that learns transferable representations using 3D masked autoencoders. ICHOR is pretrained via masked image modeling using a Vision Transformer backbone and can be used as a general-purpose encoder for downstream ASL tasks. For pre-training, we curated one of the largest ASL datasets to date, comprising 11,405 ASL CBF scans from 14 studies spanning multiple sites and acquisition protocols. We evaluated the pre-trained ICHOR encoder on three downstream diagnostic classification tasks and one ASL CBF map quality prediction regression task. Across all evaluations, ICHOR outperformed existing neuroimaging self-supervised pre-training methods adapted to ASL. Pre-trained weights and code will be made publicly available.
人工智能
[AI-0] RoboPocket: Improve Robot Policies Instantly with Your Phone
【速读】：该论文旨在解决模仿学习（Imitation Learning）在数据收集效率上的瓶颈问题，即如何在不依赖物理机器人执行的前提下实现高效、有针对性的数据采集。传统手持设备虽可大规模采集数据，但因采用开环方式导致无法识别策略薄弱区域；而交互式方法如DAgger虽能缓解协变量偏移问题，却受限于高昂的机器人执行成本。解决方案的关键在于提出RoboPocket系统，其核心是基于增强现实（Augmented Reality, AR）视觉前视的远程推理框架，使数据收集者通过AR可视化策略预测轨迹，实时感知潜在失败并聚焦于策略薄弱区域进行精准采样，同时结合异步在线微调机制，在分钟级内完成策略迭代，从而显著提升数据利用效率与样本效率。
链接: https://arxiv.org/abs/2603.05504
作者: Junjie Fang,Wendi Chen,Han Xue,Fangyuan Zhou,Tian Le,Yi Wang,Yuting Zhang,Jun Lv,Chuan Wen,Cewu Lu
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Project page: this https URL
Abstract:Scaling imitation learning is fundamentally constrained by the efficiency of data collection. While handheld interfaces have emerged as a scalable solution for in-the-wild data acquisition, they predominantly operate in an open-loop manner: operators blindly collect demonstrations without knowing the underlying policy’s weaknesses, leading to inefficient coverage of critical state distributions. Conversely, interactive methods like DAgger effectively address covariate shift but rely on physical robot execution, which is costly and difficult to scale. To reconcile this trade-off, we introduce RoboPocket, a portable system that enables Robot-Free Instant Policy Iteration using single consumer smartphones. Its core innovation is a Remote Inference framework that visualizes the policy’s predicted trajectory via Augmented Reality (AR) Visual Foresight. This immersive feedback allows collectors to proactively identify potential failures and focus data collection on the policy’s weak regions without requiring a physical robot. Furthermore, we implement an asynchronous Online Finetuning pipeline that continuously updates the policy with incoming data, effectively closing the learning loop in minutes. Extensive experiments demonstrate that RoboPocket adheres to data scaling laws and doubles the data efficiency compared to offline scaling strategies, overcoming their long-standing efficiency bottleneck. Moreover, our instant iteration loop also boosts sample efficiency by up to 2 \times in distributed environments a small number of interactive corrections per person. Project page and videos: this https URL.
[AI-1] owards Provably Unbiased LLM Judges via Bias-Bounded Evaluation
【速读】：该论文旨在解决自主人工智能系统在缺乏明确真实标签（ground truth）场景下，如何确保其奖励与反馈机制具备可验证性和鲁棒性的问题，尤其是在存在未知或对抗性偏见（bias）时难以保障公平性和安全性。解决方案的关键在于提出平均偏见有界性（Average Bias-Boundedness, A-BB）算法框架，该框架通过形式化保证任何可测量偏见导致的危害或影响均能被有效控制，从而在不显著牺牲原始排序相关性的前提下（如在Arena-Hard-Auto数据集上保留61-99%的相关性），实现对LLM-as-a-Judge的偏见约束，尤其在格式和结构偏置场景中表现优异（多数组合超过80%的性能保持）。
链接: https://arxiv.org/abs/2603.05485
作者: Benjamin Feuer,Lucas Rosenblatt,Oussama Elachqar
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As AI models progress beyond simple chatbots into more complex workflows, we draw ever closer to the event horizon beyond which AI systems will be utilized in autonomous, self-maintaining feedback loops. Any autonomous AI system will depend on automated, verifiable rewards and feedback; in settings where ground truth is sparse or non-deterministic, one practical source of such rewards is an LLM-as-a-Judge. Although LLM judges continue to improve, the literature has yet to introduce systems capable of enforcing standards with strong guarantees, particularly when bias vectors are unknown or adversarially discovered. To remedy this issue, we propose average bias-boundedness (A-BB), an algorithmic framework which formally guarantees reductions of harm/impact as a result of any measurable bias in an LLM judge. Evaluating on Arena-Hard-Auto with four LLM judges, we achieve (tau=0.5, delta=0.01) bias-bounded guarantees while retaining 61-99% correlation with original rankings across formatting and schematic bias settings, with most judge-bias combinations exceeding 80%. The code to reproduce our findings is available at this https URL.
[AI-2] SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis ICLR2026
【速读】：该论文旨在解决从右删失生存数据中估计异质性治疗效应（Heterogeneous Treatment Effects, HTE）的挑战，这在精准医疗和个性化政策制定等高风险应用场景中至关重要。由于删失、未观测反事实以及复杂的识别假设，传统HTE方法在生存分析场景下存在评估不一致与可比性差的问题。其解决方案的关键在于提出SurvHTE-Bench——首个针对删失结果下HTE估计的综合性基准测试平台，涵盖三类数据：具有已知真实值的模块化合成数据集、结合真实协变量与模拟治疗/结局的半合成数据集，以及来自双胞胎研究和HIV临床试验的真实世界数据集。通过在多种设定和现实假设违背条件下进行首次系统性比较，该基准为因果生存分析方法提供了公平、可复现且可扩展的评估基础。
链接: https://arxiv.org/abs/2603.05483
作者: Shahriar Noroozizadeh,Xiaobin Shen,Jeremy C. Weiss,George H. Chen
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: The Fourteenth International Conference on Learning Representations (ICLR 2026)
Abstract:Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine and individualized policy-making. Yet, the survival analysis setting poses unique challenges for HTE estimation due to censoring, unobserved counterfactuals, and complex identification assumptions. Despite recent advances, from Causal Survival Forests to survival meta-learners and outcome imputation approaches, evaluation practices remain fragmented and inconsistent. We introduce SurvHTE-Bench, the first comprehensive benchmark for HTE estimation with censored outcomes. The benchmark spans (i) a modular suite of synthetic datasets with known ground truth, systematically varying causal assumptions and survival dynamics, (ii) semi-synthetic datasets that pair real-world covariates with simulated treatments and outcomes, and (iii) real-world datasets from a twin study (with known ground truth) and from an HIV clinical trial. Across synthetic, semi-synthetic, and real-world settings, we provide the first rigorous comparison of survival HTE methods under diverse conditions and realistic assumption violations. SurvHTE-Bench establishes a foundation for fair, reproducible, and extensible evaluation of causal survival methods. The data and code of our benchmark are available at: this https URL .
[AI-3] Residual RL–MPC for Robust Microrobotic Cell Pushing Under Time-Varying Flow
【速读】：该论文旨在解决微流控环境下高接触密度的微操作任务中因微小扰动导致推挤接触失效和横向漂移问题（contact-rich micromanipulation in microfluidic flow）。其核心解决方案是提出一种混合控制器，该控制器在名义模型预测控制（MPC）基础上引入由软演员-评论家算法（SAC）训练的残差策略（residual policy），该策略输出一个有界的二维速度修正量，并仅在磁滚动微机器人与细胞接触时激活（contact-gated），从而在保持可靠接近行为的同时稳定学习过程。实验表明，该方法在非稳态流场下显著优于纯MPC和PID控制，并具备从训练轨迹（三叶草曲线）到未见轨迹（圆形和方形路径）的良好泛化能力。
链接: https://arxiv.org/abs/2603.05448
作者: Yanda Yang,Sambeeta Das
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages, 8 figures
Abstract:Contact-rich micromanipulation in microfluidic flow is challenging because small disturbances can break pushing contact and induce large lateral drift. We study planar cell pushing with a magnetic rolling microrobot that tracks a waypoint-sampled reference curve under time-varying Poiseuille flow. We propose a hybrid controller that augments a nominal MPC with a learned residual policy trained by SAC. The policy outputs a bounded 2D velocity correction that is contact-gated, so residual actions are applied only during robot–cell contact, preserving reliable approach behavior and stabilizing learning. All methods share the same actuation interface and speed envelope for fair comparisons. Experiments show improved robustness and tracking accuracy over pure MPC and PID under nonstationary flow, with generalization from a clover training curve to unseen circle and square trajectories. A residual-bound sweep identifies an intermediate correction limit as the best trade-off, which we use in all benchmarks.
[AI-4] Judge Reliability Harness: Stress Testing the Reliability of LLM Judges ICLR2026
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）作为评分器在AI基准测试中广泛应用时所面临的可靠性评估问题。当前LLM评分方法缺乏系统性的验证工具，导致其在不同任务和扰动下的表现差异难以量化，从而影响基准测试结果的可信度。解决方案的关键在于提出并开源了Judge Reliability Harness——一个用于构建可靠性验证套件的开源库，能够基于给定的基准数据集和LLM评分配置，自动生成针对自由回答和代理行为任务的二元判断准确率与序数评分性能的测试用例，从而全面评估LLM评分器的鲁棒性。实验表明，现有主流LLM评分器在不同基准和扰动类型下表现存在显著差异，且无一模型在所有场景下均保持一致可靠性，凸显了该工具在提升LLM评分系统质量方面的必要性。
链接: https://arxiv.org/abs/2603.05399
作者: Sunishchal Dev,Andrew Sloan,Joshua Kavner,Nicholas Kong,Morgan Sandler
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Accepted at Agents in the Wild: Safety, Security, and Beyond Workshop at ICLR 2026 - April 26, 2026, Rio de Janeiro, Brazil
Abstract:We present the Judge Reliability Harness, an open source library for constructing validation suites that test the reliability of LLM judges. As LLM based scoring is widely deployed in AI benchmarks, more tooling is needed to efficiently assess the reliability of these methods. Given a benchmark dataset and an LLM judge configuration, the harness generates reliability tests that evaluate both binary judgment accuracy and ordinal grading performance for free-response and agentic task formats. We evaluate four state-of-the-art judges across four benchmarks spanning safety, persuasion, misuse, and agentic behavior, and find meaningful variation in performance across models and perturbation types, highlighting opportunities to improve the robustness of LLM judges. No judge that we evaluated is uniformly reliable across benchmarks using our harness. For example, our preliminary experiments on judges revealed consistency issues as measured by accuracy in judging another LLM’s ability to complete a task due to simple text formatting changes, paraphrasing, changes in verbosity, and flipping the ground truth label in LLM-produced responses. The code for this tool is available at: this https URL
[AI-5] Legal interpretation and AI: from expert systems to argumentation and LLM s
【速读】：该论文试图解决人工智能（AI）在法律领域中如何有效进行法律解释的问题，其核心挑战在于如何将人类的法律解释能力转化为可计算、可推理的形式。解决方案的关键在于三种研究路径的融合：一是通过专家系统（expert system）实现法律知识工程，确保人类生成的解释能精确映射到知识库并保持一致性；二是借助论证理论（argumentation）建模解释性论证的结构及其辩证互动，以评估解释主张在论证框架中的可接受性；三是利用机器学习（machine learning）技术，特别是通用与专用语言模型，自动产生解释建议和论证，从而支持法律实践中的决策过程。这三者共同构成了从规则编码到自动推理再到人机协同解释的完整方法体系。
链接: https://arxiv.org/abs/2603.05392
作者: Václav Janeček,Giovanni Sartor
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI and Law research has encountered legal interpretation in different ways, in the context of its evolving approaches and methodologies. Research on expert system has focused on legal knowledge engineering, with the goal of ensuring that human-generated interpretations can be precisely transferred into knowledge-bases, to be consistently applied. Research on argumentation has aimed at representing the structure of interpretive arguments, as well as their dialectical interactions, to assess of the acceptability of interpretive claims within argumentation frameworks. Research on machine learning has focused on the automated generation of interpretive suggestions and arguments, through general and specialised language models, now being increasingly deployed in legal practice.
[AI-6] Learning Causal Structure of Time Series using Best Order Score Search
【速读】：该论文旨在解决从观测时间序列数据中学习因果结构的问题，尤其针对多变量时间序列场景下由于时序依赖性带来的挑战。其解决方案的关键在于提出TS-BOSS方法，这是对静态场景下最近提出的Best Order Score Search（BOSS）的扩展，通过在动态贝叶斯网络（Dynamic Bayesian Network, DBN）结构上进行基于排列的搜索，并利用增长-收缩树（grow-shrink trees）缓存中间评分计算，从而在保持BOSS原有可扩展性和良好实证性能的同时，有效处理时间序列的动态特性。此外，论文提供了理论保证，证明了TS-BOSS在合理假设下的正确性，并首次将经典的基于排列的方法中的子图最小性结果推广至动态时间序列设置，为高自相关场景下的因果发现提供了更高效且准确的工具。
链接: https://arxiv.org/abs/2603.05370
作者: Irene Gema Castillo Mansilla,Urmi Ninad
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Methodology (stat.ME)
备注:
Abstract:Causal structure learning from observational data is central to many scientific and policy domains, but the time series setting common to many disciplines poses several challenges due to temporal dependence. In this paper we focus on score-based causal discovery for multivariate time series and introduce TS-BOSS, a time series extension of the recently proposed Best Order Score Search (BOSS) (Andrews et al. 2023). TS-BOSS performs a permutation-based search over dynamic Bayesian network structures while leveraging grow-shrink trees to cache intermediate score computations, preserving the scalability and strong empirical performance of BOSS in the static setting. We provide theoretical guarantees establishing the soundness of TS-BOSS under suitable assumptions, and we present an intermediate result that extends classical subgraph minimality results for permutation-based methods to the dynamic (time series) setting. Our experiments on synthetic data show that TS-BOSS is especially effective in high auto-correlation regimes, where it consistently achieves higher adjacency recall at comparable precision than standard constraint-based methods. Overall, TS-BOSS offers a high-performing, scalable approach for time series causal discovery and our results provide a principled bridge for extending sparsity-based, permutation-driven causal learning theory to dynamic settings.
[AI-7] PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training
【速读】：该论文旨在解决9-1-1接线员培训中因技能数量庞大（超千项相互依赖技能）、训练资源紧缺及个性化教学难以规模化所导致的效率与效果瓶颈问题。解决方案的关键在于提出PACE（Personalized Adaptive Curriculum Engine）——一个协同决策系统，其核心机制包括：(1) 基于结构化技能图谱维护对学员技能状态的概率信念；(2) 模拟个体学习与遗忘动态；(3) 利用上下文bandit算法推荐兼顾新技能获取与已有技能保留的训练场景。该方法通过证据传播加速诊断覆盖，并显著提升训练效率和最终掌握度，实证表明相较现有最优框架可缩短至胜任时间19.50%，终端掌握度提高10.95%，且与专家教学判断一致性达95.45%。
链接: https://arxiv.org/abs/2603.05361
作者: Zirong Chen,Hongchao Zhang,Meiyi Ma
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:9-1-1 call-taking training requires mastery of over a thousand interdependent skills, covering diverse incident types and protocol-specific nuances. A nationwide labor shortage is already straining training capacity, but effective instruction still demands that trainers tailor objectives to each trainee’s evolving competencies. This personalization burden is one that current practice cannot scale. Partnering with Metro Nashville Department of Emergency Communications (MNDEC), we propose PACE (Personalized Adaptive Curriculum Engine), a co-pilot system that augments trainer decision-making by (1) maintaining probabilistic beliefs over trainee skill states, (2) modeling individual learning and forgetting dynamics, and (3) recommending training scenarios that balance acquisition of new competencies with retention of existing ones. PACE propagates evidence over a structured skill graph to accelerate diagnostic coverage and applies contextual bandits to select scenarios that target gaps the trainee is prepared to address. Empirical results show that PACE achieves 19.50% faster time-to-competence and 10.95% higher terminal mastery compared to state-of-the-art frameworks. Co-pilot studies with practicing training officers further demonstrate a 95.45% alignment rate between PACE’s and experts’ pedagogical judgments on real-world cases. Under estimation, PACE cuts turnaround time to merely 34 seconds from 11.58 minutes, up to 95.08% reduction.
[AI-8] Building AI Coding Agents for the Terminal: Scaffolding Harness Context Engineering and Lessons Learned DATE
【速读】：该论文旨在解决当前AI代码辅助工具在终端（CLI）环境下缺乏自主性、安全性不足以及上下文管理效率低下的问题，尤其是在执行长周期开发任务时易出现上下文膨胀（context bloat）和推理能力退化。其解决方案的关键在于构建一个名为OPENDEV的开源命令行编码代理，采用复合式AI系统架构：通过工作负载专用模型路由实现高效资源分配，利用双代理架构分离规划与执行阶段以增强可控性，引入惰性工具发现机制减少冗余调用，并结合自适应上下文压缩策略动态削减历史观察以维持推理清晰度；同时，通过自动化记忆系统积累项目特定知识并借助事件驱动提醒机制缓解指令遗忘（instruction fade-out），从而在保障安全性的前提下提供可扩展、高效率的终端优先型自主软件工程支持。
链接: https://arxiv.org/abs/2603.05344
作者: Nghi D. Q. Bui
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Work in progress, new versions will be updated continuously
Abstract:The landscape of AI coding assistance is undergoing a fundamental shift from complex IDE plugins to versatile, terminal-native agents. Operating directly where developers manage source control, execute builds, and deploy environments, CLI-based agents offer unprecedented autonomy for long-horizon development tasks. In this paper, we present OPENDEV, an open-source, command-line coding agent engineered specifically for this new paradigm. Effective autonomous assistance requires strict safety controls and highly efficient context management to prevent context bloat and reasoning degradation. OPENDEV overcomes these challenges through a compound AI system architecture with workload-specialized model routing, a dual-agent architecture separating planning from execution, lazy tool discovery, and adaptive context compaction that progressively reduces older observations. Furthermore, it employs an automated memory system to accumulate project-specific knowledge across sessions and counteracts instruction fade-out through event-driven system reminders. By enforcing explicit reasoning phases and prioritizing context efficiency, OPENDEV provides a secure, extensible foundation for terminal-first AI assistance, offering a blueprint for robust autonomous software engineering.
[AI-9] GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering
【速读】：该论文旨在解决无监督时间序列聚类中解释性不足的问题，尤其是现有方法难以识别导致样本跨聚类边界迁移的关键时间点变化。传统特征归因或元数据解释无法捕捉此类动态转移，而Counterfactual Explanations（CEs）虽能提供最小扰动以改变预测结果，但主要局限于有监督场景。为此，作者提出GALACTIC框架，其核心创新在于统一局部与全局层面的反事实解释：在实例级别（local），通过引入聚类感知优化目标生成尊重目标和潜在聚类分配的扰动；在聚类级别（global），为降低认知负荷并提升可解释性，构建代表性CE选择问题，并基于最小描述长度（Minimum Description Length, MDL）目标提取非冗余的全局解释摘要，该MDL目标具有超模性（supermodular），可转化为单调子模集合函数，从而采用贪婪算法实现(1−1/e)近似保证的高效求解。
链接: https://arxiv.org/abs/2603.05318
作者: Christos Fragkathoulas,Eleni Psaroudaki,Themis Palpanas,Evaggelia Pitoura
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Time-series clustering is a fundamental tool for pattern discovery, yet existing explainability methods, primarily based on feature attribution or metadata, fail to identify the transitions that move an instance across cluster boundaries. While Counterfactual Explanations (CEs) identify the minimal temporal perturbations required to alter the prediction of a model, they have been mostly confined to supervised settings. This paper introduces GALACTIC, the first unified framework to bridge local and global counterfactual explainability for unsupervised time-series clustering. At instance level (local), GALACTIC generates perturbations via a cluster-aware optimization objective that respects the target and underlying cluster assignments. At cluster level (global), to mitigate cognitive load and enhance interpretability, we formulate a representative CE selection problem. We propose a Minimum Description Length (MDL) objective to extract a non-redundant summary of global explanations that characterize the transitions between clusters. We prove that our MDL objective is supermodular, which allows the corresponding MDL reduction to be framed as a monotone submodular set function. This enables an efficient greedy selection algorithm with provable (1-1/e) approximation guarantees. Extensive experimental evaluation on the UCR Archive demonstrates that GALACTIC produces significantly sparser local CEs and more concise global summaries than state-of-the-art baselines adapted for our problem, offering the first unified approach for interpreting clustered time-series through counterfactuals.
[AI-10] Latent-Mark: An Audio Watermark Robust to Neural Resynthesis
【速读】：该论文旨在解决现有音频水印技术在面对神经音频编解码器（Neural Audio Codec）的语义压缩时鲁棒性不足的问题。传统水印方法依赖于人耳难以察觉的波形微小变化，但现代神经音频编解码器会将其视为冗余信息并丢弃，导致水印失效。解决方案的关键在于提出首个零比特音频水印框架Latent-Mark，其核心思想是将水印嵌入到编解码器不变的潜在空间（latent space）中：通过优化音频波形以在编码后的潜在表示中引入可检测的方向性偏移，同时约束扰动沿自然音频流形（natural audio manifold）方向，从而保证感知不可感知性；此外，为避免对单一编解码器过拟合，引入跨编解码器优化（Cross-Codec Optimization），联合优化多个替代编解码器的波形，以捕获共享的潜在不变量，实现对未见神经编解码器的零样本迁移鲁棒性。
链接: https://arxiv.org/abs/2603.05310
作者: Yen-Shan Chen,Shih-Yu Lai,Ying-Jung Tsou,Yi-Cheng Lin,Bing-Yu Chen,Yun-Nung Chen,Hung-Yi Lee,Shang-Tse Chen
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:While existing audio watermarking techniques have achieved strong robustness against traditional digital signal processing (DSP) attacks, they remain vulnerable to neural resynthesis. This occurs because modern neural audio codecs act as semantic filters and discard the imperceptible waveform variations used in prior watermarking methods. To address this limitation, we propose Latent-Mark, the first zero-bit audio watermarking framework designed to survive semantic compression. Our key insight is that robustness to the encode-decode process requires embedding the watermark within the codec’s invariant latent space. We achieve this by optimizing the audio waveform to induce a detectable directional shift in its encoded latent representation, while constraining perturbations to align with the natural audio manifold to ensure imperceptibility. To prevent overfitting to a single codec’s quantization rules, we introduce Cross-Codec Optimization, jointly optimizing the waveform across multiple surrogate codecs to target shared latent invariants. Extensive evaluations demonstrate robust zero-shot transferability to unseen neural codecs, achieving state-of-the-art resilience against traditional DSP attacks while preserving perceptual imperceptibility. Our work inspires future research into universal watermarking frameworks capable of maintaining integrity across increasingly complex and diverse generative distortions.
[AI-11] UniSTOK: Uniform Inductive Spatio-Temporal Kriging
【速读】：该论文旨在解决异质性缺失观测下诱导的时空克里金（Spatio-temporal kriging）建模问题，即在传感器数据存在高度异构缺失模式时，传统归纳式克里金模型因依赖粗略插补输入而导致信号误判、局部时空结构扭曲以及缺失机制难以区分的问题。解决方案的关键在于提出统一归纳式时空克里金框架（UniSTOK），其核心创新是构建双分支输入结构：一为原始观测序列，另一为仅在缺失位置通过拼图增强（jigsaw augmentation）合成代理信号的版本；二者由共享的时空骨干网络并行处理，并引入显式的缺失掩码调制机制以感知缺失模式；最终通过双通道注意力机制实现自适应融合，从而有效分离真实信号与缺失引起的伪影，提升模型对复杂缺失场景的鲁棒性与泛化能力。
链接: https://arxiv.org/abs/2603.05301
作者: Lewei Xie,Haoyu Zhang,Juan Yuan,Liangjun You,Yulong Chen,Yifan Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Spatio-temporal kriging aims to infer signals at unobserved locations from observed sensors and is critical to applications such as transportation and environmental monitoring. In practice, however, observed sensors themselves often exhibit heterogeneous missingness, forcing inductive kriging models to rely on crudely imputed inputs. This setting brings three key challenges: (1) it is unclear whether an value is a true signal or a missingness-induced artifact; (2) missingness is highly heterogeneous across sensors and time; (3) missing observations distort the local spatio-temporal structure. To address these issues, we propose Uniform Inductive Spatio-Temporal Kriging (UniSTOK), a plug-and-play framework that enhances existing inductive kriging backbones under missing observation. Our framework forms a dual-branch input consisting of the original observations and a jigsaw-augmented counterpart that synthesizes proxy signals only at missing entries. The two branches are then processed in parallel by a shared spatio-temporal backbone with explicit missingness mask modulation. Their outputs are finally adaptively fused via dual-channel attention. Experiments on multiple real-world datasets under diverse missing patterns demonstrate consistent and significant improvements.
[AI-12] STRUCTUREDAGENT : Planning with AND/OR Trees for Long-Horizon Web Tasks
【速读】：该论文旨在解决当前基于大语言模型（Large Language Models, LLMs）的网络代理在复杂、长周期任务中表现不佳的问题，主要瓶颈包括：有限的上下文记忆导致历史信息追踪能力弱、规划能力不足以及贪婪行为引发过早终止。解决方案的关键在于提出STRUCTUREDAGENT框架，其核心由两个模块构成：一是在线分层规划器，采用动态AND/OR树实现高效搜索；二是结构化记忆模块，用于跟踪和维护候选解，从而提升信息获取类任务中的约束满足能力。该框架还能生成可解释的分层计划，便于调试与人工干预，显著提升了长周期网页浏览任务的性能。
链接: https://arxiv.org/abs/2603.05294
作者: ELita Lobo,Xu Chen,Jingjing Meng,Nan Xi,Yang Jiao,Chirag Agarwal,Yair Zick,Yan Gao
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in large language models (LLMs) have enabled agentic systems for sequential decision-making. Such agents must perceive their environment, reason across multiple time steps, and take actions that optimize long-term objectives. However, existing web agents struggle on complex, long-horizon tasks due to limited in-context memory for tracking history, weak planning abilities, and greedy behaviors that lead to premature termination. To address these challenges, we propose STRUCTUREDAGENT, a hierarchical planning framework with two core components: (1) an online hierarchical planner that uses dynamic AND/OR trees for efficient search and (2) a structured memory module that tracks and maintains candidate solutions to improve constraint satisfaction in information-seeking tasks. The framework also produces interpretable hierarchical plans, enabling easier debugging and facilitating human intervention when needed. Our results on WebVoyager, WebArena, and custom shopping benchmarks show that STRUCTUREDAGENT improves performance on long-horizon web-browsing tasks compared to standard LLM-based agents.
[AI-13] X-RAY: Mapping LLM Reasoning Capability via Formalized and Calibrated Probes
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在推理能力评估中存在模糊性的问题，即当前评测方法多依赖任务级准确率，容易将模式匹配误判为真正推理。其解决方案的关键在于提出X-RAY系统，通过形式化验证的探针（formally verified probes）对LLM的推理能力进行可解释分析，将推理能力建模为可提取结构（extractable structure）的函数，具体包括约束交互、推理深度和解空间几何等正式属性，并借助形式工具生成具有可控结构变化的探针，实现对增量结构性信息的精确隔离与校准。该方法能有效区分在标准基准上表现相近但实际推理机制不同的模型，并揭示结构性可解释的失败模式。
链接: https://arxiv.org/abs/2603.05290
作者: Gao Tianxi,Cai Yufan,Yuan Yusi,Dong Jin Song
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood. Existing evaluations largely emphasize task-level accuracy, often conflating pattern matching with reasoning capability. We present X-RAY, an explainable reasoning analysis system that maps the LLM reasoning capability using calibrated, formally verified probes. We model reasoning capability as a function of extractable \textitstructure, operationalized through formal properties such as constraint interaction, reasoning depth, and solution-space geometry. X-Ray generates probes via formal tools with controlled structural variations, enabling precise isolation of incremental structural information through formal calibration and verification. We evaluate state-of-the-art LLMs on problems ranging from junior-level to advanced in mathematics, physics, and chemistry. Our analysis reveals a systematic asymmetry in LLM reasoning: models are relatively robust to constraint refinement, where additional conditions shrink an existing solution space, but degrade sharply under solution-space restructuring, where modifications alter the underlying structural form of the solution manifold. Moreover, calibrated formal probes differentiate models that appear indistinguishable on standard benchmarks and reveal failure modes that are structurally interpretable rather than opaque. Beyond evaluation, our framework is contamination-free and supports the training and testing of reasoning models.
[AI-14] Whispering to a Blackbox: Bootstrapping Frozen OCR with Visual Prompts
【速读】：该论文旨在解决冻结预训练模型（如EasyOCR）在特定任务中因数据分布不匹配而导致性能下降的问题。其解决方案的关键在于提出了一种名为Whisperer的视觉提示框架，通过学习基于扩散模型的预处理器，在像素空间中对输入进行自适应增强，从而“低声提示”冻结的下游模型以提升性能。该方法的核心创新是采用四阶段训练课程，利用行为克隆（behavioral cloning）技术放大随机探索过程中偶然发现的改进策略，避免了传统强化学习的复杂性和不稳定性，实现了高样本效率的性能提升——在30万张退化合成文本图像上使字符错误率（CER）降低8%（相对降低10.6%），显著优于手工设计的基线方法（如CLAHE）。
链接: https://arxiv.org/abs/2603.05276
作者: Samandar Samandarov,Nazirjon Ismoiljonov,Abdullah Sattorov,Temirlan Sabyrbayev
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In the landscape of modern machine learning, frozen pre-trained models provide stability and efficiency but often underperform on specific tasks due to mismatched data distributions. This paper introduces the Whisperer, a novel visual prompting framework that learns diffusion-based preprocessors to adapt inputs in pixel space, effectively “whispering” enhancements to frozen downstream models like EasyOCR. By framing the process as behavioral cloning of stochastically discovered improvement policies, our method achieves an 8% absolute (10.6% relative) reduction in Character Error Rate (CER) on a challenging dataset of 300k degraded synthetic text images, surpassing hand-engineered baselines such as CLAHE. The key innovation is a four-stage training curriculum that uses behavioral cloning to amplify “lucky” improvements discovered through the stochastic exploration of a partially trained diffusion model. This approach is highly sample-efficient and avoids the pitfalls of traditional reinforcement learning. Crucially, we frame this not as naive reinforcement learning, but as behavioral cloning of an exploration policy: we stochastically sample intermediate diffusion outputs, select those that improve CER by chance, and then train the model to reproduce them. This bootstrapping curriculum (4 stages over 60 GPU-hours) amplifies random successes into a systematic strategy. In summary, by whispering to the frozen OCR through its inputs, we improve an imperfect classifier without touching its weights.
[AI-15] GCAgent : Enhancing Group Chat Communication through Dialogue Agents System
【速读】：该论文旨在解决在线社交平台中群组聊天（group chat）因活跃度不足和管理难题而导致的沟通效率低下问题。当前大型语言模型（Large Language Models, LLMs）虽在一对一对话代理中表现优异，但其在多参与者场景下的无缝集成仍缺乏研究。解决方案的关键在于提出GCAgent系统，该系统由三个紧密耦合的模块构成：Agent Builder用于根据用户兴趣定制对话代理，Dialogue Manager负责协调对话状态并控制代理调用，Interface Plugins则通过三种不同工具降低交互门槛。实验证明，GCAgent在多项指标上平均得分达4.68，优于基线模型51.04%；在350天真实部署中，消息量提升28.80%，显著增强群组活跃度与参与感，为LLM驱动的对话代理从单人场景扩展至多人群组提供了可落地的技术路径。
链接: https://arxiv.org/abs/2603.05240
作者: Zijie Meng,Zheyong Xie,Zheyu Ye,Chonggang Lu,Zuozhu Liu,Zihan Niu,Yao Hu,Shaosheng Cao
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As a key form in online social platforms, group chat is a popular space for interest exchange or problem-solving, but its effectiveness is often hindered by inactivity and management challenges. While recent large language models (LLMs) have powered impressive one-to-one conversational agents, their seamlessly integration into multi-participant conversations remains unexplored. To address this gap, we introduce GCAgent, an LLM-driven system for enhancing group chats communication with both entertainment- and utility-oriented dialogue agents. The system comprises three tightly integrated modules: Agent Builder, which customizes agents to align with users’ interests; Dialogue Manager, which coordinates dialogue states and manage agent invocations; and Interface Plugins, which reduce interaction barriers by three distinct tools. Through extensive experiment, GCAgent achieved an average score of 4.68 across various criteria and was preferred in 51.04% of cases compared to its base model. Additionally, in real-world deployments over 350 days, it increased message volume by 28.80%, significantly improving group activity and engagement. Overall, this work presents a practical blueprint for extending LLM-based dialogue agent from one-party chats to multi-party group scenarios.
[AI-16] Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learning CVPR2026
【速读】：该论文旨在解决源域不可用条件下的跨域少样本学习（Source-Free Cross-Domain Few-Shot Learning, SF-CDFSL）中，CLIP文本编码器部分中间层信息被误判为冗余、导致其有用特征未被充分利用的问题。现有方法通常简单移除这些中间层以提升性能，但本文发现这些“丢失层”（Lost Layers）中的信息实际上对任务有益，只是由于视觉域偏移（visual gap）限制了其在目标域中的有效利用。解决方案的关键在于提出一种新方法，通过在层级别和编码器级别重新引导模型学习这些被遗忘的信息，从而增强视觉分支在域迁移下的适应能力，实现对文本编码器中潜在有用信息的再利用，显著提升了SF-CDFSL的性能。
链接: https://arxiv.org/abs/2603.05235
作者: Zhenyu Zhang,Guangyao Chen,Yixiong Zou,Yuhua Li,Ruixuan Li
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: CVPR 2026
Abstract:Source-Free Cross-Domain Few-Shot Learning (SF-CDFSL) focuses on fine-tuning with limited training data from target domains (e.g., medical or satellite images), where CLIP has recently shown promising results due to its generalizability to downstream tasks. Current works indicate CLIP’s text encoder is more suitable for cross-domain tasks, however, we find that \textbfremoving certain middle layers of the text encoder can effectively improve performance in SF-CDFSL, which we call the Lost Layers. In this paper, we delve into this phenomenon for a deeper understanding. We discover that instead of being harmful for the SF-CDFSL task, the information in these layers is actually beneficial, but visual gaps prevent this useful information from being fully utilized, making these layers seem redundant. Based on this understanding, unlike current works that simply remove these layers, we propose a method to teachs the model to \textbfre-utilize information in these lost layers at both the layer and encoder levels, guiding the re-learning of the visual branch under domain shifts. Our approach effectively addresses the issue of underutilized information in the text encoder. Extensive experiments across various settings, backbones (CLIP, SigLip, PE-Core), and tasks (4 CDFSL datasets and 10 Meta-dataset datasets) demonstrate the effectiveness of our method. Code is available at this https URL.
[AI-17] Recursive Inference Machines for Neural Reasoning
【速读】：该论文旨在解决当前神经推理模型在处理复杂推理任务时表现受限的问题，尤其是如何有效整合递归推理机制以提升模型的推理能力。其解决方案的关键在于提出Recursive Inference Machines (RIMs)，这是一个显式引入递归推理机制的神经推理框架，灵感来源于经典推理引擎；通过将Tiny Recursive Models (TRMs) 视为RIMs的一个特例，并引入重加权组件对TRMs进行扩展，从而在ARC-AGI-1、ARC-AGI-2和Sudoku Extreme等挑战性推理基准上实现性能提升，同时证明RIMs亦可泛化至其他任务（如表格数据分类），优于现有方法如TabPFNs。
链接: https://arxiv.org/abs/2603.05234
作者: Mieszko Komisarczyk,Saurabh Mathur,Maurice Kraus,Sriraam Natarajan,Kristian Kersting
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural reasoners such as Tiny Recursive Models (TRMs) solve complex problems by combining neural backbones with specialized inference schemes. Such inference schemes have been a central component of stochastic reasoning systems, where inference rules are applied to a stochastic model to derive answers to complex queries. In this work, we bridge these two paradigms by introducing Recursive Inference Machines (RIMs), a neural reasoning framework that explicitly incorporates recursive inference mechanisms inspired by classical inference engines. We show that TRMs can be expressed as an instance of RIMs, allowing us to extend them through a reweighting component, yielding better performance on challenging reasoning benchmarks, including ARC-AGI-1, ARC-AGI-2, and Sudoku Extreme. Furthermore, we show that RIMs can be used to improve reasoning on other tasks, such as the classification of tabular data, outperforming TabPFNs.
[AI-18] Boosting ASR Robustness via Test-Time Reinforcement Learning with Audio-Text Semantic Rewards
【速读】：该论文旨在解决自动语音识别（ASR）系统在真实世界未见数据上表现不稳定的问题，尤其是面对噪声环境和多样化口音时的性能下降。现有测试时适应（TTA）方法通常依赖伪标签或熵最小化策略，但易因将模型置信度作为学习信号而加剧高置信度错误，导致确认偏差（confirmation bias），削弱适应效果。其解决方案的关键在于提出一种受因果干预启发的测试时强化适应框架（ASR-TRA）：通过引入可学习的解码器提示（decoder prompt）并结合温度控制的随机解码生成多样化的转录候选，利用衡量音频-文本语义对齐的奖励模型进行评分，并基于强化学习更新模型与提示参数，从而实现更稳定、可解释且高效的适应过程。
链接: https://arxiv.org/abs/2603.05231
作者: Linghan Fang,Tianxin Xie,Li Liu
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Recently, Automatic Speech Recognition (ASR) systems (e.g., Whisper) have achieved remarkable accuracy improvements but remain highly sensitive to real-world unseen data (data with large distribution shifts), including noisy environments and diverse accents. To address this issue, test-time adaptation (TTA) has shown great potential in improving the model adaptability at inference time without ground-truth labels, and existing TTA methods often rely on pseudo-labeling or entropy minimization. However, by treating model confidence as a learning signal, these methods may reinforce high-confidence errors, leading to confirmation bias that undermines adaptation. To overcome these limitations, we present ASR-TRA, a novel Test-time Reinforcement Adaptation framework inspired by causal intervention. More precisely, our method introduces a learnable decoder prompt and utilizes temperature-controlled stochastic decoding to generate diverse transcription candidates. These are scored by a reward model that measures audio-text semantic alignment, and the resulting feedback is used to update both model and prompt parameters via reinforcement learning. Comprehensive experiments on LibriSpeech with synthetic noise and L2 Arctic accented English datasets demonstrate that our method achieves higher accuracy while maintaining lower latency than existing TTA baselines. Ablation studies further confirm the effectiveness of combining audio and language-based rewards, highlighting our method’s enhanced stability and interpretability. Overall, our approach provides a practical and robust solution for deploying ASR systems in challenging real-world conditions.
[AI-19] he Geometric Inductive Bias of Grokking: Bypassing Phase Transitions via Architectural Topology
【速读】：该论文旨在解决生成式 AI（Generative AI）中“grokking”现象的机制问题，即Transformer模型在训练过程中经历延迟泛化（delayed generalization）的现象，尤其关注其是否由架构自由度（architectural degrees of freedom）所导致。研究发现，标准Transformer中的两个结构因素——无界表示幅值（unbounded representational magnitude）和数据依赖性注意力路由（data-dependent attention routing）——显著延长了记忆阶段。解决方案的关键在于采用干预式方法：首先引入全有界球面拓扑（spherical topology），通过L2归一化残差流和固定温度缩放的解嵌入矩阵消除幅值自由度，使grokking发生时间缩短超过20倍；其次通过Uniform Attention Ablation将注意力层替换为连续词袋（CBOW）聚合器，完全移除自适应路由，实现100%泛化且无延迟。实验证明该加速并非通用优化稳定器，而是依赖于架构先验与任务内在对称性的匹配，揭示了架构设计对训练动态的预测性影响。
链接: https://arxiv.org/abs/2603.05228
作者: Alper Yıldırım
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 19 pages, 2 figures, 3 tables. Code available at this https URL
Abstract:Mechanistic interpretability typically relies on post-hoc analysis of trained networks. We instead adopt an interventional approach: testing hypotheses a priori by modifying architectural topology to observe training dynamics. We study grokking - delayed generalization in Transformers trained on cyclic modular addition (Zp) - investigating if specific architectural degrees of freedom prolong the memorization phase. We identify two independent structural factors in standard Transformers: unbounded representational magnitude and data-dependent attention routing. First, we introduce a fully bounded spherical topology enforcing L2 normalization throughout the residual stream and an unembedding matrix with a fixed temperature scale. This removes magnitude-based degrees of freedom, reducing grokking onset time by over 20x without weight decay. Second, a Uniform Attention Ablation overrides data-dependent query-key routing with a uniform distribution, reducing the attention layer to a Continuous Bag-of-Words (CBOW) aggregator. Despite removing adaptive routing, these models achieve 100% generalization across all seeds and bypass the grokking delay entirely. To evaluate whether this acceleration is a task-specific geometric alignment rather than a generic optimization stabilizer, we use non-commutative S5 permutation composition as a negative control. Enforcing spherical constraints on S5 does not accelerate generalization. This suggests eliminating the memorization phase depends strongly on aligning architectural priors with the task’s intrinsic symmetries. Together, these findings provide interventional evidence that architectural degrees of freedom substantially influence grokking, suggesting a predictive structural perspective on training dynamics. Comments: 19 pages, 2 figures, 3 tables. Code available at this https URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) ACMclasses: I.2.6; F.2 Cite as: arXiv:2603.05228 [cs.LG] (or arXiv:2603.05228v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.05228 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-20] AIHW 2035: Shaping the Next Decade
【速读】：该论文试图解决当前人工智能（Artificial Intelligence, AI）与硬件（Hardware, HW）协同发展缺乏长期战略规划的问题，即AI与HW的演进虽快速但碎片化，导致难以构建高效、可持续且具备跨云、边缘和物理环境适应能力的智能系统。其核心挑战在于如何从单纯追求算力扩展转向以能效为核心的新一代规模化路径，实现每焦耳能量下智能性能的指数级提升。解决方案的关键在于推动AI+HW的协同设计（co-design）与协同开发（co-development），通过算法创新、硬件进步和软件抽象的深度融合，在算法、架构、系统和可持续性四个层面实现跨层优化，从而达成1000倍能效提升目标，并构建可自优化、能源感知、人本导向的下一代AI基础设施体系。
链接: https://arxiv.org/abs/2603.05225
作者: Deming Chen,Jason Cong,Azalia Mirhoseini,Christos Kozyrakis,Subhasish Mitra,Jinjun Xiong,Cliff Young,Anima Anandkumar,Michael Littman,Aron Kirschen,Sophia Shao,Serge Leef,Naresh Shanbhag,Dejan Milojicic,Michael Schulte,Gert Cauwenberghs,Jerry M. Chow,Tri Dao,Kailash Gopalakrishnan,Richard Ho,Hoshik Kim,Kunle Olukotun,David Z. Pan,Mark Ren,Dan Roth,Aarti Singh,Yizhou Sun,Yusu Wang,Yann LeCun,Ruchir Puri
机构: 未知
类目: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
备注: 35 pages, 4 figures
Abstract:Artificial intelligence (AI) and hardware (HW) are advancing at unprecedented rates, yet their trajectories have become inseparably intertwined. The global research community lacks a cohesive, long-term vision to strategically coordinate the development of AI and HW. This fragmentation constrains progress toward holistic, sustainable, and adaptive AI systems capable of learning, reasoning, and operating efficiently across cloud, edge, and physical environments. The future of AI depends not only on scaling intelligence, but on scaling efficiency, achieving exponential gains in intelligence per joule, rather than unbounded compute consumption. Addressing this grand challenge requires rethinking the entire computing stack. This vision paper lays out a 10-year roadmap for AI+HW co-design and co-development, spanning algorithms, architectures, systems, and sustainability. We articulate key insights that redefine scaling around energy efficiency, system-level integration, and cross-layer optimization. We identify key challenges and opportunities, candidly assess potential obstacles and pitfalls, and propose integrated solutions grounded in algorithmic innovation, hardware advances, and software abstraction. Looking ahead, we define what success means in 10 years: achieving a 1000x improvement in efficiency for AI training and inference; enabling energy-aware, self-optimizing systems that seamlessly span cloud, edge, and physical AI; democratizing access to advanced AI infrastructure; and embedding human-centric principles into the design of intelligent systems. Finally, we outline concrete action items for academia, industry, government, and the broader community, calling for coordinated national initiatives, shared infrastructure, workforce development, cross-agency collaboration, and sustained public-private partnerships to ensure that AI+HW co-design becomes a unifying long-term mission.
[AI-21] KARL: Knowledge Agents via Reinforcement Learning
【速读】：该论文旨在解决企业级搜索代理（enterprise search agents）在复杂、多样化且难以验证的智能搜索任务中性能不足的问题，尤其是在多任务泛化能力与训练效率之间的权衡。其解决方案的关键在于：首先构建了一个涵盖六类不同搜索范式的评估基准 KARLBench，用于全面衡量模型能力；其次通过异构搜索行为训练实现比单一任务优化更强的泛化性能；再者开发了基于长程推理和工具调用的代理合成流水线，生成多样、 grounded 且高质量的合成训练数据，并通过迭代式自举（iterative bootstrapping）提升数据质量；最后提出一种基于大批次离策略强化学习（off-policy RL）的后训练范式，具备样本高效、对训练-推理引擎差异鲁棒性强、并可自然扩展至多任务训练与分布外泛化的能力。这一组合策略使得 KARL 在成本-质量与延迟-质量权衡上优于 Claude 4.6 和 GPT 5.2，甚至在测试时计算资源充足的情况下超越最强闭源模型。
链接: https://arxiv.org/abs/2603.05218
作者: Jonathan D. Chang,Andrew Drozdov,Shubham Toshniwal,Owen Oertell,Alexander Trott,Jacob Portes,Abhay Gupta,Pallavi Koppol,Ashutosh Baheti,Sean Kulinski,Ivan Zhou,Irene Dea,Krista Opsahl-Ong,Simon Favreau-Lessard,Sean Owen,Jose Javier Gonzalez Ortiz,Arnav Singhvi,Xabi Andrade,Cindy Wang,Kartik Sreenivasan,Sam Havens,Jialu Liu,Peyton DeNiro,Wen Sun,Michael Bendersky,Jonathan Frankle
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 77 pages, 43 figures, 17 tables
Abstract:We present a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic search tasks. Our work makes four core contributions. First, we introduce KARLBench, a multi-capability evaluation suite spanning six distinct search regimes, including constraint-driven entity search, cross-document report synthesis, tabular numerical reasoning, exhaustive entity retrieval, procedural reasoning over technical documentation, and fact aggregation over internal enterprise notes. Second, we show that models trained across heterogeneous search behaviors generalize substantially better than those optimized for any single benchmark. Third, we develop an agentic synthesis pipeline that employs long-horizon reasoning and tool use to generate diverse, grounded, and high-quality training data, with iterative bootstrapping from increasingly capable models. Fourth, we propose a new post-training paradigm based on iterative large-batch off-policy RL that is sample efficient, robust to train-inference engine discrepancies, and naturally extends to multi-task training with out-of-distribution generalization. Compared to Claude 4.6 and GPT 5.2, KARL is Pareto-optimal on KARLBench across cost-quality and latency-quality trade-offs, including tasks that were out-of-distribution during training. With sufficient test-time compute, it surpasses the strongest closed models. These results show that tailored synthetic data in combination with multi-task reinforcement learning enables cost-efficient and high-performing knowledge agents for grounded reasoning.
[AI-22] Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning
【速读】：该论文旨在解决术中不良事件（Intraoperative Adverse Events, IAEs）早期预警中的三大关键问题：忽视不良事件之间的依赖关系、未能充分融合异构临床数据，以及医疗数据固有的类别不平衡问题。其解决方案的核心在于提出一种基于Transformer的多标签学习框架IAENet，该框架通过改进的时间感知特征逐元素线性调制（Time-Aware Feature-wise Linear Modulation, TAFiLM）模块实现静态协变量与动态变量的鲁棒融合，并利用标签约束重加权损失（Label-Constrained Reweighting Loss, LCRLoss）引入共现正则化，有效缓解类不平衡并强化高频共现事件间的结构一致性。实验表明，IAENet在5、10和15分钟早期预警任务上均显著优于强基线模型，平均F1分数提升达+5.05%、+2.82%和+7.57%，验证了其在支持智能术中决策方面的潜力。
链接: https://arxiv.org/abs/2603.05212
作者: Xueyao Wang,Xiuding Cai,Honglin Shang,Yaoyao Zhu,Yu Yao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Early warning of intraoperative adverse events plays a vital role in reducing surgical risk and improving patient safety. While deep learning has shown promise in predicting the single adverse event, several key challenges remain: overlooking adverse event dependencies, underutilizing heterogeneous clinical data, and suffering from the class imbalance inherent in medical datasets. To address these issues, we construct the first Multi-label Adverse Events dataset (MuAE) for intraoperative adverse events prediction, covering six critical events. Next, we propose a novel Transformerbased multi-label learning framework (IAENet) that combines an improved Time-Aware Feature-wise Linear Modulation (TAFiLM) module for static covariates and dynamic variables robust fusion and complex temporal dependencies modeling. Furthermore, we introduce a Label-Constrained Reweighting Loss (LCRLoss) with co-occurrence regularization to effectively mitigate intra-event imbalance and enforce structured consistency among frequently co-occurring events. Extensive experiments demonstrate that IAENet consistently outperforms strong baselines on 5, 10, and 15-minute early warning tasks, achieving improvements of +5.05%, +2.82%, and +7.57% on average F1 score. These results highlight the potential of IAENet for supporting intelligent intraoperative decision-making in clinical practice.
[AI-23] Stable-LoRA: Stabilizing Feature Learning of Low-Rank Adaptation
【速读】：该论文旨在解决LoRA（Low-Rank Adaptation）在微调大语言模型时特征学习稳定性不足的问题，尤其是由于A矩阵非零初始化导致的自稳定机制被破坏，从而引发性能次优。解决方案的关键在于提出Stable-LoRA，其核心是引入一种权重收缩（weight-shrinkage）优化策略，在训练初期动态地逐步缩小矩阵A的幅值，从而在理论上和实证上有效消除LoRA特征学习的不稳定性，同时保留非零初始化带来的优势。该方法无需额外内存开销，仅带来可忽略的计算成本增加。
链接: https://arxiv.org/abs/2603.05204
作者: Yize Wu,Ke Gao,Ling Li,Yanjun Wu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient method for fine-tuning Large Langauge Models. It updates the weight matrix as W=W_0+sBA , where W_0 is the original frozen weight, s is a scaling factor and A , B are trainable low-rank matrices. Despite its robust empirical effectiveness, the theoretical foundations of LoRA remain insufficiently understood, particularly with respect to feature learning stability. In this paper, we first establish that, LoRA can, in principle, naturally achieve and sustain stable feature learning (i.e., be self-stabilized) under appropriate hyper-parameters and initializations of A and B . However, we also uncover a fundamental limitation that the necessary non-zero initialization of A compromises self-stability, leading to suboptimal performances. To address this challenge, we propose Stable-LoRA, a weight-shrinkage optimization strategy that dynamically enhances stability of LoRA feature learning. By progressively shrinking A during the earliest training steps, Stable-LoRA is both theoretically and empirically validated to effectively eliminate instability of LoRA feature learning while preserving the benefits of the non-zero start. Experiments show that Stable-LoRA consistently outperforms other baselines across diverse models and tasks, with no additional memory usage and only negligible computation overheads. The code is available at this https URL.
[AI-24] Lifelong Language-Conditioned Robotic Manipulation Learning
【速读】：该论文旨在解决传统语言条件下的机器人操作智能体在连续学习新技能时导致的旧技能灾难性遗忘问题，从而限制了其在动态场景中的实际部署。解决方案的关键在于提出SkillsCrafter框架，通过引入Manipulation Skills Adaptation机制，在保留旧技能知识的同时继承新旧技能间的共享知识以促进新技能的学习；同时，利用奇异值分解（Singular Value Decomposition, SVD）对多样化技能指令进行处理，获得技能语义子空间的投影矩阵，以记录技能的本质语义空间；进一步提出Skills Specialization Aggregation方法，在技能语义子空间中计算技能间相似性，实现对已学技能知识的聚合，从而支持无遗忘且具备泛化能力的操作任务适应。
链接: https://arxiv.org/abs/2603.05160
作者: Xudong Wang,Zebin Han,Zhiyu Liu,Gan Li,Jiahua Dong,Baichen Liu,Lianqing Liu,Zhi Han
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 14 pages, 7 figures
Abstract:Traditional language-conditioned manipulation agent sequential adaptation to new manipulation skills leads to catastrophic forgetting of old skills, limiting dynamic scene practical deployment. In this paper, we propose SkillsCrafter, a novel robotic manipulation framework designed to continually learn multiple skills while reducing catastrophic forgetting of old skills. Specifically, we propose a Manipulation Skills Adaptation to retain the old skills knowledge while inheriting the shared knowledge between new and old skills to facilitate learning of new skills. Meanwhile, we perform the singular value decomposition on the diverse skill instructions to obtain common skill semantic subspace projection matrices, thereby recording the essential semantic space of skills. To achieve forget-less and generalization manipulation, we propose a Skills Specialization Aggregation to compute inter-skills similarity in skill semantic subspaces, achieving aggregation of the previously learned skill knowledge for any new or unknown skill. Extensive experiments demonstrate the effectiveness and superiority of our proposed SkillsCrafter.
[AI-25] Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding
【速读】：该论文旨在解决多数据集因果发现中因数据隐私法规限制和跨站点异质性导致的传统方法难以应用的问题，尤其在变量集合不一致、存在站点特异性效应以及混合变量类型（连续、有序、二元和分类变量）的情况下。其解决方案的关键在于提出fedCI，一种联邦条件独立性检验方法，通过联邦迭代加权最小二乘（federated IRLS）程序估计广义线性模型参数，从而实现对条件独立性的似然比检验；在此基础上进一步开发了fedCI-IOD，作为集成重叠数据集（IOD）算法的联邦扩展，首次实现了在潜在混杂因素下分布式异质数据集上的联邦因果发现，通过联邦聚合证据，在保护隐私的同时显著提升统计效能，性能接近完全合并分析，并缓解局部样本量不足带来的偏差。
链接: https://arxiv.org/abs/2603.05149
作者: Maximilian Hahn,Alina Zajak,Dominik Heider,Adèle Helena Ribeiro
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Causal discovery across multiple datasets is often constrained by data privacy regulations and cross-site heterogeneity, limiting the use of conventional methods that require a single, centralized dataset. To address these challenges, we introduce fedCI, a federated conditional independence test that rigorously handles heterogeneous datasets with non-identical sets of variables, site-specific effects, and mixed variable types, including continuous, ordinal, binary, and categorical variables. At its core, fedCI uses a federated Iteratively Reweighted Least Squares (IRLS) procedure to estimate the parameters of generalized linear models underlying likelihood-ratio tests for conditional independence. Building on this, we develop fedCI-IOD, a federated extension of the Integration of Overlapping Datasets (IOD) algorithm, that replaces its meta-analysis strategy and enables, for the fist time, federated causal discovery under latent confounding across distributed and heterogeneous datasets. By aggregating evidence federatively, fedCI-IOD not only preserves privacy but also substantially enhances statistical power, achieving performance comparable to fully pooled analyses and mitigating artifacts from low local sample sizes. Our tools are publicly available as the fedCI Python package, a privacy-preserving R implementation of IOD, and a web application for the fedCI-IOD pipeline, providing versatile, user-friendly solutions for federated conditional independence testing and causal discovery.
[AI-26] Recurrent Graph Neural Networks and Arithmetic Circuits
【速读】：该论文旨在解决循环图神经网络（Recurrent Graph Neural Networks, GNNs）的计算能力与其在实数域上对应的算术电路之间的理论对应关系问题。其核心挑战在于如何形式化并证明循环GNN与一类新型“循环算术电路”（Recurrent Arithmetic Circuits）在表达能力上的等价性。解决方案的关键在于引入一种基于记忆门（memory gates）的循环算术电路模型，该模型能够模拟序列计算过程，并通过将标签图编码为实值元组来实现与GNN输入输出的映射；同时构造出可模拟循环电路计算过程的循环GNN结构，使其在节点特征传播后能准确提取电路输出。这一双向构建实现了循环GNN与循环算术电路在计算表达力上的精确对应。
链接: https://arxiv.org/abs/2603.05140
作者: Timon Barlag,Vivian Holzapfel,Laura Strieker,Jonni Virtema,Heribert Vollmer
机构: 未知
类目: Computational Complexity (cs.CC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We characterise the computational power of recurrent graph neural networks (GNNs) in terms of arithmetic circuits over the real numbers. Our networks are not restricted to aggregate-combine GNNs or other particular types. Generalizing similar notions from the literature, we introduce the model of recurrent arithmetic circuits, which can be seen as arithmetic analogues of sequential or logical circuits. These circuits utilise so-called memory gates which are used to store data between iterations of the recurrent circuit. While (recurrent) GNNs work on labelled graphs, we construct arithmetic circuits that obtain encoded labelled graphs as real valued tuples and then compute the same function. For the other direction we construct recurrent GNNs which are able to simulate the computations of recurrent circuits. These GNNs are given the circuit-input as initial feature vectors and then, after the GNN-computation, have the circuit-output among the feature vectors of its nodes. In this way we establish an exact correspondence between the expressivity of recurrent GNNs and recurrent arithmetic circuits operating over real numbers.
[AI-27] Bidirectional Curriculum Generation: A Multi-Agent Framework for Data-Efficient Mathematical Reasoning
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在数学推理能力提升过程中面临的数据效率低下问题，即传统方法依赖海量训练数据，且标准单向课程学习（unidirectional curriculum learning）因盲目增加难度而导致样本利用效率低，无法有效修复模型的特定推理缺陷。其解决方案的关键在于提出一种双向课程生成框架（Bidirectional Curriculum Generation），通过多智能体生态系统构建闭环反馈机制：该机制不仅能根据模型表现动态“复杂化”问题以挑战模型，还能主动“简化”问题以针对性修复推理错误，从而确保每个训练样本在当前阶段具有最优教学价值，其理论基础为最优节奏定理（Optimal Pacing Theorem），显著提升了推理性能并大幅减少所需指令样本数量。
链接: https://arxiv.org/abs/2603.05120
作者: Boren Hu,Xiao Liu,Boci Peng,Xinping Zhao,Xiaoran Shang,Yun Zhu,Lijun Wu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Enhancing mathematical reasoning in Large Language Models typically demands massive datasets, yet data efficiency remains a critical bottleneck. While Curriculum Learning attempts to structure this process, standard unidirectional approaches (simple-to-complex) suffer from inefficient sample utilization: they blindly escalate complexity even when foundational gaps persist, leading to wasted computation on unsolvable problems. To maximize the instructional value of every training sample, we introduce a novel Bidirectional Curriculum Generation framework. Unlike rigid trajectories, our multi-agent ecosystem mimics adaptive pedagogy to establish a closed feedback loop. It dynamically generates data by either complicating problems to challenge the model or, crucially, simplying them to repair specific reasoning failures. This mechanism ensures that the model consumes only the most effective data at any given stage. Grounded in the Optimal Pacing Theorem, our approach optimizes the learning trajectory, significantly outperforming baselines while achieving superior reasoning performance with substantially fewer instruction samples.
[AI-28] FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
【速读】：该论文旨在解决联邦学习（Federated Learning）在训练大规模模型（如视觉Transformer）时通信开销过高的问题。其核心解决方案是提出一种新颖的联邦块坐标梯度下降方法（Federated Block Coordinate Gradient Descent, FedBCGD），通过将模型参数划分为多个参数块（包括一个共享块），并允许每个客户端仅上传特定参数块，从而显著降低每轮通信的复杂度。该方法的关键创新在于引入参数块通信机制，并进一步设计了带有客户端漂移控制和随机方差缩减的加速版本（FedBCGD+），理论上证明其通信复杂度相较于现有方法可降低至 1/N（N 为参数块数量），且收敛速度更快。
链接: https://arxiv.org/abs/2603.05116
作者: Junkang Liu,Fanhua Shang,Yuanyuan Liu,Hongying Liu,Yuangang Li,YunXiang Gong
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Although Federated Learning has been widely studied in recent years, there are still high overhead expenses in each communication round for large-scale models such as Vision Transformer. To lower the communication complexity, we propose a novel Federated Block Coordinate Gradient Descent (FedBCGD) method for communication efficiency. The proposed method splits model parameters into several blocks, including a shared block and enables uploading a specific parameter block by each client, which can significantly reduce communication overhead. Moreover, we also develop an accelerated FedBCGD algorithm (called FedBCGD+) with client drift control and stochastic variance reduction. To the best of our knowledge, this paper is the first work on parameter block communication for training large-scale deep models. We also provide the convergence analysis for the proposed algorithms. Our theoretical results show that the communication complexities of our algorithms are a factor 1/N lower than those of existing methods, where N is the number of parameter blocks, and they enjoy much faster convergence than their counterparts. Empirical results indicate the superiority of the proposed algorithms compared to state-of-the-art algorithms. The code is available at this https URL. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.05116 [cs.LG] (or arXiv:2603.05116v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.05116 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-29] SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty
【速读】：该论文旨在解决深度学习（Deep Learning, DL）在机器人感知中因鲁棒性不足和可解释性差而导致难以可靠部署于安全关键场景的问题。其解决方案的核心在于提出“感知共享自主”（perceptive shared autonomy）概念，即利用DL感知中的不确定性估计动态调节机器人的自主水平：当感知置信度高时启用半自主操作以提升性能，而当不确定性增加时则切换至触觉遥操作以保障鲁棒性。关键技术突破是基于神经切线核（Neural Tangent Kernels, NTK）的不确定性感知点云配准方法，使得系统能在DL感知失败的情况下仍能实现可靠的机器人操作。
链接: https://arxiv.org/abs/2603.05111
作者: Jongseok Lee,Ribin Balachandran,Harsimran Singh,Jianxiang Feng,Hrishik Mishra,Marco De Stefano,Rudolph Triebel,Alin Albu-Schaeffer,Konstantin Kondak
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 19 pages, 14 figures
Abstract:Deep learning (DL) has enabled impressive advances in robotic perception, yet its limited robustness and lack of interpretability hinder reliable deployment in safety critical applications. We propose a concept termed perceptive shared autonomy, in which uncertainty estimates from DL based perception are used to regulate the level of autonomy. Specifically, when the robot’s perception is confident, semi-autonomous manipulation is enabled to improve performance; when uncertainty increases, control transitions to haptic teleoperation for maintaining robustness. In this way, high-performing but uninterpretable DL methods can be integrated safely into robotic systems. A key technical enabler is an uncertainty aware DL based point cloud registration approach based on the so called Neural Tangent Kernels (NTK). We evaluate perceptive shared autonomy on challenging aerial manipulation tasks through a user study of 15 participants and realization of mock-up industrial scenarios, demonstrating reliable robotic manipulation despite failures in DL based perception. The resulting system, named SPIRIT, improves both manipulation performance and system reliability. SPIRIT was selected as a finalist of a major industrial innovation award.
[AI-30] Cyber Threat Intelligence for Artificial Intelligence Systems
【速读】：该论文旨在解决当前网络安全威胁情报（Cyber Threat Intelligence, CTI）体系难以有效应对针对人工智能（Artificial Intelligence, AI）系统攻击的问题。传统CTI框架基于通用IT基础设施设计，未充分考虑AI特有的资产（如模型、训练数据、推理服务）和漏洞（如数据投毒、模型窃取、对抗样本攻击）。解决方案的关键在于构建一个面向AI的威胁情报知识库，明确不同AI供应链阶段（如数据采集、模型训练、部署推理）的具体指标（Indicators of Compromise, IoC），并开发用于衡量新观测到的AI相关异常行为与已有IoC之间相似性的技术，从而实现对AI系统安全威胁的精准识别与响应。
链接: https://arxiv.org/abs/2603.05068
作者: Natalia Krawczyk,Mateusz Szczepkowski,Adrian Brodzik,Krzysztof Bocianiak
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:As artificial intelligence (AI) becomes deeply embedded in critical services and everyday products, it is increasingly exposed to security threats which traditional cyber defenses were not designed to handle. In this paper, we investigate how cyber threat intelligence (CTI) may evolve to address attacks that target AI systems. We first analyze the assumptions and workflows of conventional threat intelligence with the needs of AI-focused defense, highlighting AI-specific assets and vulnerabilities. We then review and organize the current landscape of AI security knowledge. Based on this, we outline what an AI-oriented threat intelligence knowledge base should contain, describing concrete indicators of compromise (IoC) for different AI supply-chain phases and artifacts, and showing how such a knowledge base could support security tools. Finally, we discuss techniques for measuring similarity between collected indicators and newly observed AI artifacts. The review reveals gaps and quality issues in existing resources and identifies potential future research directions toward a practical threat intelligence framework tailored to AI.
[AI-31] WebFactory: Automated Compression of Foundational Language Intelligence into Grounded Web Agents
【速读】：该论文旨在解决当前GUI智能体训练中依赖不安全、不可复现的实时网络交互或昂贵且稀缺的人工标注数据与环境所导致的效率瓶颈问题。其核心挑战在于，现有方法过度关注数据量而忽视了将大语言模型（Large Language Model, LLM）的潜在知识高效压缩为可执行智能体行为的能力。解决方案的关键在于提出WebFactory——一个全自动闭环强化学习流水线，通过可扩展的环境合成、基于知识的任务生成、LLM驱动的轨迹收集、分解式奖励强化学习训练以及系统化评估，实现从LLM编码的互联网知识到高效、具身化动作的系统性压缩。实验表明，仅用WebFactory中10个网站生成的合成数据训练的智能体，在性能上即可媲美使用大量人工标注数据训练的基线模型，并在离线和在线迁移基准测试中显著优于基础LLM模型，验证了该方案在数据效率与泛化能力上的优越性。
链接: https://arxiv.org/abs/2603.05044
作者: Sicheng Fan,Qingyun Shi,Shengze Xu,Shengbo Cai,Tieyong Zeng,Li Ling,Yanyi Shang,Dehan Kong
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Current paradigms for training GUI agents are fundamentally limited by a reliance on either unsafe, non-reproducible live web interactions or costly, scarce human-crafted data and environments. We argue this focus on data volume overlooks a more critical factor: the efficiency of compressing a large language model’s (LLM) latent knowledge into actionable agent behavior. We introduce WebFactory, a novel, fully automated closed-loop reinforcement learning pipeline for GUI agents, systematically compressing LLM-encoded internet intelligence into efficient, grounded actions. Our pipeline features a process of scalable environment synthesis, knowledge-aware task generation, LLM-powered trajectory collection, decomposed reward RL training, and systematic agent evaluation. Remarkably, our agent demonstrates exceptional data efficiency and generalization. Trained on synthetic data from only 10 websites within WebFactory, it achieves performance comparable to GUI agents trained on the same amount of human-annotated data from a much larger set of environments. This superior performance is consistent across our internal offline and online transfer benchmarks, where our agent also significantly outperforms the base foundation model. We further provide critical insights into the “embodiment potential” of different LLM foundations, offering a new axis for model evaluation. This work presents a scalable and cost-effective paradigm for transforming passive internet knowledge into active, grounded intelligence, marking a critical step towards general-purpose interactive agents.
[AI-32] Enhancing Zero-shot Commonsense Reasoning by Integrating Visual Knowledge via Machine Imagination
【速读】：该论文旨在解决预训练语言模型（Pre-trained Language Models, PLMs）在零样本常识推理中因文本知识中固有的人类报告偏差（human reporting biases）而导致的机器与人类理解差异问题。解决方案的关键在于引入视觉模态以增强PLMs的推理能力，提出了一种名为Imagine（Machine Imagination-based Reasoning）的新框架，其核心是将图像生成器嵌入到推理流程中，使模型具备“想象”能力——即通过生成机器可理解的视觉信号来补充文本输入。为有效利用这种想象出的视觉上下文，研究者构建了模拟视觉问答场景的合成数据集，并通过多基准测试验证了Imagine在零样本常识推理上的显著性能提升，甚至超越了先进大语言模型，证明了机器想象力在缓解报告偏差和提升泛化能力方面的有效性。
链接: https://arxiv.org/abs/2603.05040
作者: Hyuntae Park,Yeachan Kim,SangKeun Lee
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advancements in zero-shot commonsense reasoning have empowered Pre-trained Language Models (PLMs) to acquire extensive commonsense knowledge without requiring task-specific fine-tuning. Despite this progress, these models frequently suffer from limitations caused by human reporting biases inherent in textual knowledge, leading to understanding discrepancies between machines and humans. To bridge this gap, we introduce an additional modality to enrich the reasoning capabilities of PLMs. We propose Imagine (Machine Imagination-based Reasoning), a novel zero-shot commonsense reasoning framework that supplements textual inputs with visual signals from machine-generated images. Specifically, we enhance PLMs with the ability to imagine by embedding an image generator directly into the reasoning pipeline. To facilitate effective utilization of this imagined visual context, we construct synthetic datasets designed to emulate visual question-answering scenarios. Through comprehensive evaluations on multiple commonsense reasoning benchmarks, we demonstrate that Imagine substantially outperforms existing zero-shot approaches and even surpasses advanced large language models. These results underscore the capability of machine imagination to mitigate reporting bias and significantly enhance the generalization ability of commonsense reasoning models
[AI-33] he Trilingual Triad Framework: Integrating Design AI and Domain Knowledge in No-code AI Smart City Course
【速读】：该论文试图解决的问题是：在生成式 AI（Generative AI）快速进入高等教育的背景下，学生往往仅将其作为被动使用者，而非主动设计者，导致难以实现从“使用AI工具”到“设计AI协作伙伴”的能力跃迁。解决方案的关键在于提出“三语三角框架”（Trilingual Triad），强调通过整合设计（Design）、人工智能（AI）与领域知识（Domain Knowledge）三个维度，使学习者在构建定制化AI系统的过程中，实现认知扩展、元认知增强和学习者主体性的提升。该框架揭示了有效人-AI协作的核心机制：领域知识结构化AI逻辑，设计优化人-AI交互，AI延伸学习者的认知能力，从而推动生成式AI教育从工具应用向深度建构性学习转型。
链接: https://arxiv.org/abs/2603.05036
作者: Qian Huang,King Wang Poon
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 16 pages, 1 figure
Abstract:This paper introduces the “Trilingual Triad” framework, a model that explains how students learn to design with generative artificial intelligence (AI) through the integration of Design, AI, and Domain Knowledge. As generative AI rapidly enters higher education, students often engage with these systems as passive users of generated outputs rather than active creators of AI-enabled knowledge tools. This study investigates how students can transition from using AI as a tool to designing AI as a collaborative teammate. The research examines a graduate course, Creating the Frontier of No-code Smart Cities at the Singapore University of Technology and Design (SUTD), in which students developed domain-specific custom GPT systems without coding. Using a qualitative multi-case study approach, three projects - the Interview Companion GPT, the Urban Observer GPT, and Buddy Buddy - were analyzed across three dimensions: design, AI architecture, and domain expertise. The findings show that effective human-AI collaboration emerges when these three “languages” are orchestrated together: domain knowledge structures the AI’s logic, design mediates human-AI interaction, and AI extends learners’ cognitive capacity. The Trilingual Triad framework highlights how building AI systems can serve as a constructionist learning process that strengthens AI literacy, metacognition, and learner agency.
[AI-34] AegisUI: Behavioral Anomaly Detection for Structured User Interface Protocols in AI Agent Systems
【速读】：该论文旨在解决AI代理（AI agent）在生成用户界面（User Interface, UI）时可能引入的行为异常漏洞问题，即尽管协议负载（payload）通过了结构化模式校验（schema check），仍可能通过误导性UI元素（如伪装按钮或隐蔽数据绑定）诱导用户执行恶意操作。传统防御机制仅关注语法层面合规性，无法识别此类语义与行为不一致的问题。解决方案的关键在于提出并实现了一个名为AegisUI的行为异常检测框架：该框架系统化地生成结构化UI协议负载、注入五类真实攻击（包括钓鱼界面、数据泄露、布局滥用等）、提取18维特征（涵盖结构、语义、绑定和会话维度），并通过端到端实验对比三种异常检测器（Isolation Forest、自编码器、Random Forest）。结果表明，监督式随机森林模型表现最优（F1=0.843），而半监督自编码器无需恶意标签即可有效检测，适用于缺乏历史攻击数据的新系统部署场景。
链接: https://arxiv.org/abs/2603.05031
作者: Mohd Safwan Uddin,Saba Hajira
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 7 figures, 5 tables. Behavioral anomaly detection framework for security analysis of AI agent-generated UI protocol payloads
Abstract:AI agents that build user interfaces on the fly assembling buttons, forms, and data displays from structured protocol payloads are becoming common in production systems. The trouble is that a payload can pass every schema check and still trick a user: a button might say “View invoice” while its hidden action wipes an account, or a display widget might quietly bind to an internal salary field. Current defenses stop at syntax; they were never built to catch this kind of behavioral mismatch. We built AegisUI to study exactly this gap. The framework generates structured UI payloads, injects realistic attacks into them, extracts numeric features, and benchmarks anomaly detectors end-to-end. We produced 4000 labeled payloads (3000 benign, 1000 malicious) spanning five application domains and five attack families: phishing interfaces, data leakage, layout abuse, manipulative UI, and workflow anomalies. From each payload we extracted 18 features covering structural, semantic, binding, and session dimensions, then compared three detectors: Isolation Forest (unsupervised), a benign-trained autoencoder (semi-supervised), and Random Forest (supervised). On a stratified 80/20 split, Random Forest scored best overall (accuracy 0.931, precision 0.980, recall 0.740, F1 0.843, ROC-AUC 0.952). The autoencoder came second (F1 0.762, ROC-AUC 0.863) and needs no malicious labels at training time, which matters when deploying a new system that lacks attack history. Per-attack-type analysis showed that layout abuse is easiest to catch while manipulative UI payloads are hardest. All code, data, and configurations are released for full reproducibility. Comments: 8 pages, 7 figures, 5 tables. Behavioral anomaly detection framework for security analysis of AI agent-generated UI protocol payloads Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.05031 [cs.AI] (or arXiv:2603.05031v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.05031 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-35] S5-SHB Agent : Society 5.0 enabled Multi-model Agent ic Blockchain Framework for Smart Home
【速读】：该论文旨在解决当前智能家庭系统在面对异构物联网协议、多样化设备及不断演变的安全威胁时，缺乏一个能够同时兼顾舒适性、安全性、能源效率与居民自主治理的自治决策机制的问题。现有框架普遍依赖静态智能合约与固定共识协议，缺少多智能体协同能力，且未提供居民对自动化行为的可控性。解决方案的关键在于提出S5-SHB-Agent框架，该框架通过十类专用智能体协作，结合可切换的大语言模型实现跨安全、隐私、健康等领域的决策；引入自适应工作量证明（PoW）区块链动态调整挖矿难度以应对事务量和紧急情况，并利用数字签名与默克尔树锚定确保审计可追溯性；此外，构建四层治理模型使居民能按层级偏好控制自动化行为，从日常舒适度调节到不可更改的安全阈值，从而实现符合Society 5.0理念的人本化治理。
链接: https://arxiv.org/abs/2603.05027
作者: Janani Rangila,Akila Siriweera,Incheon Paik,Keitaro Naruse,Isuru Jayanada,Vishmika Devindi
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, 16 images, Journal
Abstract:The smart home is a key application domain within the Society 5.0 vision for a human-centered society. As smart home ecosystems expand with heterogeneous IoT protocols, diverse devices, and evolving threats, autonomous systems must manage comfort, security, energy, and safety for residents. Such autonomous decision-making requires a trust anchor, making blockchain a preferred foundation for transparent and accountable smart home governance. However, realizing this vision requires blockchain-governed smart homes to simultaneously address adaptive consensus, intelligent multi-agent coordination, and resident-controlled governance aligned with the principles of Society 5.0. Existing frameworks rely solely on rigid smart contracts with fixed consensus protocols, employ at most a single AI model without multi-agent coordination, and offer no governance mechanism for residents to control automation behaviour. To address these limitations, this paper presents the Society 5.0-driven human-centered governance-enabled smart home blockchain agent (S5-SHB-Agent). The framework orchestrates ten specialized agents using interchangeable large language models to make decisions across the safety, security, comfort, energy, privacy, and health domains. An adaptive PoW blockchain adjusts mining difficulty based on transaction volume and emergency conditions, with digital signatures and Merkle tree anchoring to ensure tamper evident auditability. A four-tier governance model enables residents to control automation through tiered preferences from routine adjustments to immutable safety thresholds. Evaluation confirms that resident governance correctly separates adjustable comfort priorities from immutable safety thresholds across all tested configurations, while adaptive consensus commits emergency blocks.
[AI-36] Measuring the Frag ility of Trust: Devising Credibility Index via Explanation Stability (CIES) for Business Decision Support Systems
【速读】：该论文旨在解决生成式 AI (Generative AI) 解释方法（如 SHAP、LIME）在高风险业务场景中缺乏可量化可信度评估的问题，尤其是其解释在现实数据扰动下的稳定性未被充分衡量。解决方案的关键在于提出一种数学上严谨的指标——解释稳定性可信度指数（Credibility Index via Explanation Stability, CIES），该指标通过引入加权排名距离函数，对重要特征的不稳定变化施以更大惩罚，从而更准确反映业务语义下决策主因的持续性。实验表明，CIES 能有效区分不同模型和数据处理条件下的解释可信度，并在多个配置中显著优于均匀基准指标（p < 0.01），为业务方提供了一种可部署的“可信度预警系统”。
链接: https://arxiv.org/abs/2603.05024
作者: Alin-Gabriel Vaduva,Simona-Vasilica Oprea,Adela Bara
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Explainable Artificial Intelligence (XAI) methods (SHAP, LIME) are increasingly adopted to interpret models in high-stakes businesses. However, the credibility of these explanations, their stability under realistic data perturbations, remains unquantified. This paper introduces the Credibility Index via Explanation Stability (CIES), a mathematically grounded metric that measures how robust a model’s explanations are when subject to realistic business noise. CIES captures whether the reasons behind a prediction remain consistent, not just the prediction itself. The metric employs a rank-weighted distance function that penalizes instability in the most important features disproportionately, reflecting business semantics where changes in top decision drivers are more consequential than changes in marginal features. We evaluate CIES across three datasets (customer churn, credit risk, employee attrition), four tree-based classification models and two data balancing conditions. Results demonstrate that model complexity impacts explanation credibility, class imbalance treatment via SMOTE affects not only predictive performance but also explanation stability, and CIES provides statistically superior discriminative power compared to a uniform baseline metric (p 0.01 in all 24 configurations). A sensitivity analysis across four noise levels confirms the robustness of the metric itself. These findings offer business practitioners a deployable “credibility warning system” for AI-driven decision support.
[AI-37] BioLLM Agent : A Hybrid Framework with Enhanced Structural Interpretability for Simulating Human Decision-Making in Computational Psychiatry
【速读】：该论文旨在解决计算精神病学中传统强化学习（Reinforcement Learning, RL）模型与大型语言模型（Large Language Model, LLM）代理之间的根本性权衡问题：前者具有良好的结构可解释性但行为真实性不足，后者能生成高度逼真的行为却缺乏机制可解释性。解决方案的关键在于提出一种名为BioLLMAgent的新型混合框架，其核心创新在于将经过验证的认知模型与LLM的生成能力相结合，包含三个关键组件：(i) 内部RL引擎用于经验驱动的价值学习；(ii) 外部LLM外壳用于高层认知策略和治疗干预；(iii) 决策融合机制通过加权效用整合各模块输出。该框架在Iowa Gambling Task（IGT）上的多数据集实验表明，其不仅能准确再现人类行为模式，还保持了优异的参数可识别性（相关系数 > 0.67），并成功模拟认知行为疗法（Cognitive Behavioral Therapy, CBT）原理，揭示群体教育干预可能优于个体治疗的多智能体动态效应，从而为精神疾病研究提供了一个结构可解释的“计算沙盒”。
链接: https://arxiv.org/abs/2603.05016
作者: Zuo Fei,Kezhi Wang,Xiaomin Chen,Yizhou Huang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Computational psychiatry faces a fundamental trade-off: traditional reinforcement learning (RL) models offer interpretability but lack behavioral realism, while large language model (LLM) agents generate realistic behaviors but lack structural interpretability. We introduce BioLLMAgent, a novel hybrid framework that combines validated cognitive models with the generative capabilities of LLMs. The framework comprises three core components: (i) an Internal RL Engine for experience-driven value learning; (ii) an External LLM Shell for high-level cognitive strategies and therapeutic interventions; and (iii) a Decision Fusion Mechanism for integrating components via weighted utility. Comprehensive experiments on the Iowa Gambling Task (IGT) across six clinical and healthy datasets demonstrate that BioLLMAgent accurately reproduces human behavioral patterns while maintaining excellent parameter identifiability (correlations 0.67 ). Furthermore, the framework successfully simulates cognitive behavioral therapy (CBT) principles and reveals, through multi-agent dynamics, that community-wide educational interventions may outperform individual treatments. Validated across reward-punishment learning and temporal discounting tasks, BioLLMAgent provides a structurally interpretable “computational sandbox” for testing mechanistic hypotheses and intervention strategies in psychiatric research.
[AI-38] Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks KDD2026
【速读】：该论文旨在解决干净标签图后门攻击（clean-label graph backdoor attack）这一现实但研究不足的问题，即在不修改训练节点标签的前提下，通过注入触发器使图神经网络（GNN）模型在测试阶段误判。现有方法在此设定下普遍失效，根本原因在于它们无法污染GNN的内部预测逻辑，导致触发器被模型忽略。解决方案的关键在于提出BA-Logic框架，其核心创新是协同设计一个中毒节点选择器与一个逻辑污染触发器生成器，从而有效操纵GNN的预测机制，显著提升攻击成功率。
链接: https://arxiv.org/abs/2603.05004
作者: Yuxiang Zhang,Bin Ma,Enyan Dai
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Submit to KDD 2026
Abstract:Graph Neural Networks (GNNs) have achieved remarkable results in various tasks. Recent studies reveal that graph backdoor attacks can poison the GNN model to predict test nodes with triggers attached as the target class. However, apart from injecting triggers to training nodes, these graph backdoor attacks generally require altering the labels of trigger-attached training nodes into the target class, which is impractical in real-world scenarios. In this work, we focus on the clean-label graph backdoor attack, a realistic but understudied topic where training labels are not modifiable. According to our preliminary analysis, existing graph backdoor attacks generally fail under the clean-label setting. Our further analysis identifies that the core failure of existing methods lies in their inability to poison the prediction logic of GNN models, leading to the triggers being deemed unimportant for prediction. Therefore, we study a novel problem of effective clean-label graph backdoor attacks by poisoning the inner prediction logic of GNN models. We propose BA-Logic to solve the problem by coordinating a poisoned node selector and a logic-poisoning trigger generator. Extensive experiments on real-world datasets demonstrate that our method effectively enhances the attack success rate and surpasses state-of-the-art graph backdoor attack competitors under clean-label settings. Our code is available at this https URL
[AI-39] Rethinking Representativeness and Diversity in Dynamic Data Selection
【速读】：该论文旨在解决动态数据选择（Dynamic Data Selection）在训练加速过程中如何有效平衡准确率与效率的问题。传统方法通常依赖局部几何中心性或子集内分散度来评估样本价值，但这些指标难以捕捉数据的整体结构特征和训练过程中的演化规律。解决方案的关键在于重新定义两个核心概念：首先，将代表性（Representativeness）定义为样本对数据集中高频特征因子的覆盖程度，而非局部几何中心性；其次，提出过程级多样性（Process-level Diversity），要求选择轨迹在训练过程中逐步引入互补的稀有因子，以避免梯度偏差并提升泛化能力。基于此认知，作者设计了一个包含三部分的框架：1）利用目标数据集上训练的稀疏自编码器提取特征空间中的稀疏激活作为代表性评分依据；2）通过稀有因子采样结合使用频率惩罚项（Usage-Frequency Penalty）实现样本轮换，从而抑制垄断并降低梯度偏倚；3）采用平滑调度机制，在不引入额外梯度、影响估计或二阶计算的前提下，从核心模式巩固阶段平稳过渡到稀有因子探索阶段。实验表明，该方法可在超过2倍训练加速的同时保持甚至超越全数据训练的准确性。
链接: https://arxiv.org/abs/2603.04981
作者: Yuzhe Zhou,Zhenglin Hua,Haiyun Guo,Yuheng Jia
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Dynamic data selection accelerates training by sampling a changing subset of the dataset while preserving accuracy. We rethink two core notions underlying sample evaluation: representativeness and diversity. Instead of local geometric centrality, we define representativeness as coverage of dataset-level common or high-frequency feature factors. Instead of within-subset dispersion, we define diversity at the process level, requiring the selection trajectory to gradually include complementary rare factors over training. Based on this view, we propose a dynamic selection framework with three components. First, we score representativeness in a plug-in feature space to prioritize samples covering frequent factors. We instantiate this with a sparse autoencoder trained on the target dataset, using sparse unit activations to summarize both individual samples and dataset-wide factor statistics. Second, we realize process-level diversity by combining rare-factor sampling with a Usage-Frequency Penalty that promotes sample rotation, provably discourages monopoly, and reduces gradient bias. Third, we couple the two-dimensional scoring with a smooth scheduler that transitions selection from core-pattern consolidation to rare-factor exploration, without extra gradients, influence estimates, or second-order computations on the training model. Extensive experiments on five benchmarks across vision and text tasks demonstrate improved accuracy-efficiency trade-offs across models. Our method matches or exceeds full-data accuracy with over 2x training acceleration. Code will be released.
[AI-40] Retrieval-Augmented Generation with Covariate Time Series
【速读】：该论文旨在解决将检索增强生成（Retrieval-Augmented Generation, RAG）范式扩展至时间序列基础模型（Time-Series Foundation Models, TSFMs）时所面临的挑战，特别是在高风险工业场景——压力调节与切断阀（Pressure Regulating and Shut-Off Valve, PRSOV）预测性维护中，存在数据稀缺、短瞬态序列及协变量耦合动态等难题。现有时间序列RAG方法依赖静态向量嵌入和可学习上下文增强器，在此类复杂场景下难以区分相似工况。解决方案的关键在于提出RAG4CTS框架：构建分层原生时间序列知识库以实现无损存储与物理信息驱动的原始历史工况检索；设计两阶段双加权检索机制，通过点级与多变量相似性对齐历史趋势；并引入代理驱动的自监督策略动态优化上下文，从而在不需训练的情况下提升预测准确性。实验证明该方法显著优于当前最优基线，并已在航空领域部署成功识别故障且零误报。
链接: https://arxiv.org/abs/2603.04951
作者: Kenny Ye Liang,Zhongyi Pei,Huan Zhang,Yuhui Liu,Shaoxu Song,Jianmin Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 12 pages. Preprint
Abstract:While RAG has greatly enhanced LLMs, extending this paradigm to Time-Series Foundation Models (TSFMs) remains a challenge. This is exemplified in the Predictive Maintenance of the Pressure Regulating and Shut-Off Valve (PRSOV), a high-stakes industrial scenario characterized by (1) data scarcity, (2) short transient sequences, and (3) covariate coupled dynamics. Unfortunately, existing time-series RAG approaches predominantly rely on generated static vector embeddings and learnable context augmenters, which may fail to distinguish similar regimes in such scarce, transient, and covariate coupled scenarios. To address these limitations, we propose RAG4CTS, a regime-aware, training-free RAG framework for Covariate Time-Series. Specifically, we construct a hierarchal time-series native knowledge base to enable lossless storage and physics-informed retrieval of raw historical regimes. We design a two-stage bi-weighted retrieval mechanism that aligns historical trends through point-wise and multivariate similarities. For context augmentation, we introduce an agent-driven strategy to dynamically optimize context in a self-supervised manner. Extensive experiments on PRSOV demonstrate that our framework significantly outperforms state-of-the-art baselines in prediction accuracy. The proposed system is deployed in Apache IoTDB within China Southern Airlines. Since deployment, our method has successfully identified one PRSOV fault in two months with zero false alarm.
[AI-41] Knowledge-informed Bidding with Dual-process Control for Online Advertising
【速读】：该论文旨在解决在线广告中竞价优化（bid optimization）依赖黑箱机器学习模型所导致的三大问题：在数据稀疏场景下因缺乏结构化知识而泛化能力差、决策短视且忽略长期序列依赖关系，以及在分布外（out-of-distribution）场景下难以适应——这些问题正是人类专家能够凭借经验实现全局一致性和适应性的体现。解决方案的关键在于提出KBD（Knowledge-informed Bidding with Dual-process control），其核心创新包括：1）通过有监督的归纳偏置（inductive biases）将人类专家知识嵌入模型；2）采用决策变换器（Decision Transformer, DT）对多步竞价序列进行全局优化；3）引入双进程控制机制，融合快速规则驱动的PID控制器（System 1）与基于DT的深度策略（System 2），从而兼顾实时响应与长期理性决策。
链接: https://arxiv.org/abs/2603.04920
作者: Huixiang Luo,Longyu Gao,Yaqi Liu,Qianqian Chen,Pingchun Huang,Tianning Li
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Bid optimization in online advertising relies on black-box machine-learning models that learn bidding decisions from historical data. However, these approaches fail to replicate human experts’ adaptive, experience-driven, and globally coherent decisions. Specifically, they generalize poorly in data-sparse cases because of missing structured knowledge, make short-sighted sequential decisions that ignore long-term interdependencies, and struggle to adapt in out-of-distribution scenarios where human experts succeed. To address this, we propose KBD (Knowledge-informed Bidding with Dual-process control), a novel method for bid optimization. KBD embeds human expertise as inductive biases through the informed machine-learning paradigm, uses Decision Transformer (DT) to globally optimize multi-step bidding sequences, and implements dual-process control by combining a fast rule-based PID (System 1) with DT (System 2). Extensive experiments highlight KBD’s advantage over existing methods and underscore the benefit of grounding bid optimization in human expertise and dual-process control.
[AI-42] BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning
【速读】：该论文旨在解决大语言模型强化学习中因固定边界裁剪（canonical clipping）导致的探索瓶颈问题，即固定边界严格限制低概率动作的向上更新幅度，从而过度抑制高优势尾部策略并引发熵快速衰减。解决方案的关键在于提出Band约束策略优化（BandPO），其核心创新是用一个统一的理论算子“Band”替代传统裁剪机制，该算子能将由f-散度定义的信任区域映射为动态的概率感知裁剪区间，从而实现更有效的探索与稳定训练。理论分析和实验表明，BandPO通过凸优化建模获得全局最优解，并在特定散度下提供闭式解，显著优于标准裁剪和Clip-Higher方法，且能稳健缓解熵崩溃现象。
链接: https://arxiv.org/abs/2603.04918
作者: Yuan Li,Bo Wang,Yufei Gao,Yuqian Yao,Xinyuan Wang,Zhangyue Yin,Xipeng Qiu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Code available at this https URL
Abstract:Proximal constraints are fundamental to the stability of the Large Language Model reinforcement learning. While the canonical clipping mechanism in PPO serves as an efficient surrogate for trust regions, we identify a critical bottleneck: fixed bounds strictly constrain the upward update margin of low-probability actions, disproportionately suppressing high-advantage tail strategies and inducing rapid entropy collapse. To address this, we introduce Band-constrained Policy Optimization (BandPO). BandPO replaces canonical clipping with Band, a unified theoretical operator that projects trust regions defined by f-divergences into dynamic, probability-aware clipping intervals. Theoretical analysis confirms that Band effectively resolves this exploration bottleneck. We formulate this mapping as a convex optimization problem, guaranteeing a globally optimal numerical solution while deriving closed-form solutions for specific divergences. Extensive experiments across diverse models and datasets demonstrate that BandPO consistently outperforms canonical clipping and Clip-Higher, while robustly mitigating entropy collapse.
[AI-43] EVMbench: Evaluating AI Agents on Smart Contract Security
【速读】：该论文试图解决的问题是：随着智能合约（Smart Contracts）在公共区块链上管理大量价值，其漏洞可能引发重大损失，而当前AI代理（AI agents）在识别、修复和利用这些漏洞方面的能力尚不明确。为评估AI在这一安全场景中的实际表现，作者提出EVMbench，这是一个用于衡量AI代理检测、修补和利用以太坊虚拟机（EVM）智能合约漏洞的基准测试框架。其关键解决方案在于构建了一个包含117个精选漏洞的测试集，并基于本地以太坊执行环境下的程序化评分机制（programmatic grading），实现对AI代理行为的端到端评估，从而揭示前沿AI模型已具备在真实区块链实例中发现并利用漏洞的能力。
链接: https://arxiv.org/abs/2603.04915
作者: Justin Wang,Andreas Bigger,Xiaohai Xu,Justin W. Lin,Andy Applebaum,Tejal Patwardhan,Alpin Yukseloglu,Olivia Watkins
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Smart contracts on public blockchains now manage large amounts of value, and vulnerabilities in these systems can lead to substantial losses. As AI agents become more capable at reading, writing, and running code, it is natural to ask how well they can already navigate this landscape, both in ways that improve security and in ways that might increase risk. We introduce EVMbench, an evaluation that measures the ability of agents to detect, patch, and exploit smart contract vulnerabilities. EVMbench draws on 117 curated vulnerabilities from 40 repositories and, in the most realistic setting, uses programmatic grading based on tests and blockchain state under a local Ethereum execution environment. We evaluate a range of frontier agents and find that they are capable of discovering and exploiting vulnerabilities end-to-end against live blockchain instances. We release code, tasks, and tooling to support continued measurement of these capabilities and future work on security.
[AI-44] VPWEM: Non-Markovian Visuomotor Policy with Working and Episodic Memory
【速读】：该论文旨在解决当前视觉-运动策略（visuomotor policy）在处理非马尔可夫任务时的局限性，即大多数策略仅依赖单步观测或短时历史信息，难以捕捉长期依赖关系，导致在需要长时记忆的任务中性能受限。同时，简单扩展上下文窗口会带来显著的计算和内存开销，并可能因过拟合伪相关性而在分布偏移下引发灾难性失败，违反机器人系统实时性要求。解决方案的关键在于提出VPWEM（Visual Policy with Working and Episodic Memory），其核心机制是引入工作记忆（working memory）与情景记忆（episodic memory）双模态结构：工作记忆保留近期观察token作为短期记忆，而通过基于Transformer的上下文记忆压缩器（contextual memory compressor）将超出窗口的历史观测递归压缩为固定数量的情景记忆token；该压缩器利用自注意力和交叉注意力机制分别建模历史摘要与原始观测，且与策略联合训练，从而实现近似恒定的每步计算与内存开销，有效提升对复杂长程任务的适应能力。
链接: https://arxiv.org/abs/2603.04910
作者: Yuheng Lei,Zhixuan Liang,Hongyuan Zhang,Ping Luo
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Imitation learning from human demonstrations has achieved significant success in robotic control, yet most visuomotor policies still condition on single-step observations or short-context histories, making them struggle with non-Markovian tasks that require long-term memory. Simply enlarging the context window incurs substantial computational and memory costs and encourages overfitting to spurious correlations, leading to catastrophic failures under distribution shift and violating real-time constraints in robotic systems. By contrast, humans can compress important past experiences into long-term memories and exploit them to solve tasks throughout their lifetime. In this paper, we propose VPWEM, a non-Markovian visuomotor policy equipped with working and episodic memories. VPWEM retains a sliding window of recent observation tokens as short-term working memory, and introduces a Transformer-based contextual memory compressor that recursively converts out-of-window observations into a fixed number of episodic memory tokens. The compressor uses self-attention over a cache of past summary tokens and cross-attention over a cache of historical observations, and is trained jointly with the policy. We instantiate VPWEM on diffusion policies to exploit both short-term and episode-wide information for action generation with nearly constant memory and computation per step. Experiments demonstrate that VPWEM outperforms state-of-the-art baselines including diffusion policies and vision-language-action (VLA) models by more than 20% on the memory-intensive manipulation tasks in MIKASA and achieves an average 5% improvement on the mobile manipulation benchmark MoMaRT. Code is available at this https URL.
[AI-45] Deterministic Preprocessing and Interpretable Fuzzy Banding for Cost-per-Student Reporting from Extracted Records
【速读】：该论文旨在解决行政数据在预算编制、工作量评估和治理讨论中作为参考快照时的可复现性与决策支持问题。其核心挑战在于，当Excel格式的数据库导出文件被用作决策依据时，缺乏明确的输入-输出映射和透明的计算过程，导致难以验证结果的一致性和准确性。解决方案的关键在于构建一个基于规则的、文件驱动的确定性工作流（deterministic, rule-governed, file-based workflow），该流程以Casual Academic Database (CAD) 导出的工作簿为输入，自动聚合包含成本和学生人数的指标，并生成四个结构化输出表：处理摘要（含SHA-256哈希用于快照匹配复算）、趋势分析（学校年度成本/生比矩阵）、报告（宽格式学科级表格）以及模糊带状标签（fuzzy bands，含每年人均成本分位锚点、隶属权重及低/中/高标签）。其中，通过三角形隶属函数对正向人均成本比进行模糊分层，并引入固定优先级的确定性冲突解决机制，使隶属权重成为决策支持信号而非概率值，从而提升数据驱动决策的透明度与可追溯性。
链接: https://arxiv.org/abs/2603.04905
作者: Shane Lee,Stella Ng
机构: 未知
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注: 34 pages, 3 figures
Abstract:Administrative extracts are often exchanged as spreadsheets and may be read as reports in their own right during budgeting, workload review, and governance discussions. When an exported workbook becomes the reference snapshot for such decisions, the transformation can be checked by recomputation against a clearly identified input. A deterministic, rule-governed, file-based workflow is implemented in this http URL. The script ingests a Casual Academic Database (CAD) export workbook and aggregates inclusive on-costs and student counts into subject-year and school-year totals, from which it derives cost-per-student ratios. It writes a processed workbook with four sheets: Processing Summary (run record and counters), Trend Analysis (schoolyear cost-per-student matrix), Report (wide subject-level table), and Fuzzy Bands (per-year anchors, membership weights, and band labels). The run record includes a SHA-256 hash of the input workbook bytes to support snapshot-matched recomputation. For within-year interpretation, the workflow adds a simple fuzzy banding layer that labels finite, positive school-year cost-per-student values as Low, Medium, or High. The per-year anchors are the minimum, median, and maximum of the finite, positive ratios. Membership weights are computed using left-shoulder, triangular, and right-shoulder functions, with deterministic tie-breaking in a fixed priority order (Medium, then Low, then High). These weights are treated as decision-support signals rather than probabilities. A worked example provides a reproducible calculation of a band assignment from the reported anchors and ratios. Supplementary material includes a claim-to-evidence matrix, a reproducibility note, and a short glossary that links selected statements to code and workbook artefacts. Comments: 34 pages, 3 figures Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04905 [cs.DB] (or arXiv:2603.04905v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2603.04905 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shane Lee [view email] [v1] Thu, 5 Mar 2026 07:47:02 UTC (383 KB)
[AI-46] AgentS COPE: Evaluating Contextual Privacy Across Agent ic Workflows
【速读】：该论文旨在解决当前对代理系统（Agentic Systems）隐私风险评估的局限性问题，即现有研究仅关注输入与输出边界，而忽视了任务执行过程中多个中间信息流（如代理查询到工具响应）可能存在的隐私泄露风险。解决方案的关键在于提出Privacy Flow Graph框架，该框架基于Contextual Integrity（情境完整性）理论，将代理系统的执行过程分解为一系列带注释的信息流，并通过五个CI参数标记每个环节的隐私属性，从而精准定位隐私违规的发生点。此外，作者构建了AgentSCOPE基准测试集，涵盖62个多工具场景及8个监管领域，提供每阶段的真实标签，用于系统性评估不同大语言模型（LLMs）在代理流水线中的隐私表现，揭示出超过80%的场景存在隐私违规，且多数发生在工具响应阶段，表明仅依赖输出层面的评估会严重低估代理系统的隐私风险。
链接: https://arxiv.org/abs/2603.04902
作者: Ivoline C. Ngong,Keerthiram Murugesan,Swanand Kadhe,Justin D. Weisz,Amit Dhurandhar,Karthikeyan Natesan Ramamurthy
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Agentic systems are increasingly acting on users’ behalf, accessing calendars, email, and personal files to complete everyday tasks. Privacy evaluation for these systems has focused on the input and output boundaries, but each task involves several intermediate information flows, from agent queries to tool responses, that are not currently evaluated. We argue that every boundary in an agentic pipeline is a site of potential privacy violation and must be assessed independently. To support this, we introduce the Privacy Flow Graph, a Contextual Integrity-grounded framework that decomposes agentic execution into a sequence of information flows, each annotated with the five CI parameters, and traces violations to their point of origin. We present AgentSCOPE, a benchmark of 62 multi-tool scenarios across eight regulatory domains with ground truth at every pipeline stage. Our evaluation across seven state-of-the-art LLMs show that privacy violations in the pipeline occur in over 80% of scenarios, even when final outputs appear clean (24%), with most violations arising at the tool-response stage where APIs return sensitive data indiscriminately. These results indicate that output-level evaluation alone substantially underestimates the privacy risk of agentic systems.
[AI-47] EvoTool: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）驱动的智能体在执行复杂任务时，因延迟监督和长程轨迹中信用分配困难而导致的工具使用策略优化难题。现有方法或过于整体化（monolithic），易导致行为纠缠；或仅关注单一维度，忽略模块间误差传播。其解决方案的关键在于提出EvoTool——一个基于无梯度进化范式的自演化框架，通过将工具使用策略分解为规划（Planner）、选择（Selector）、调用（Caller）和合成（Synthesizer）四个模块，并引入三项创新机制：基于轨迹的归因定位（Trajectory-Grounded Blame Attribution）精准识别故障模块，反馈引导的目标突变（Feedback-Guided Targeted Mutation）仅对故障模块进行自然语言修正，以及多样性感知的种群选择（Diversity-Aware Population Selection）维持解空间多样性，从而实现模块化、高效且可迁移的策略优化。
链接: https://arxiv.org/abs/2603.04900
作者: Shuo Yang,Soyeon Caren Han,Xueqi Ma,Yan Li,Mohammad Reza Ghasemi Madani,Eduard Hovy
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Work under review, 9 pages, 5 figures
Abstract:LLM-based agents depend on effective tool-use policies to solve complex tasks, yet optimizing these policies remains challenging due to delayed supervision and the difficulty of credit assignment in long-horizon trajectories. Existing optimization approaches tend to be either monolithic, which are prone to entangling behaviors, or single-aspect, which ignore cross-module error propagation. To address these limitations, we propose EvoTool, a self-evolving framework that optimizes a modular tool-use policy via a gradient-free evolutionary paradigm. EvoTool decomposes agent’s tool-use policy into four modules, including Planner, Selector, Caller, and Synthesizer, and iteratively improves them in a self-improving loop through three novel mechanisms. Trajectory-Grounded Blame Attribution uses diagnostic traces to localize failures to a specific module. Feedback-Guided Targeted Mutation then edits only that module via natural-language critique. Diversity-Aware Population Selection preserves complementary candidates to ensure solution diversity. Across four benchmarks, EvoTool outperforms strong baselines by over 5 points on both GPT-4.1 and Qwen3-8B, while achieving superior efficiency and transferability. The code will be released once paper is accepted.
[AI-48] Authorize-on-Demand: Dynamic Authorization with Legality-Aware Intellectual Property Protection for VLMs
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在部署过程中缺乏灵活且可验证的知识产权（Intellectual Property, IP）保护机制的问题，尤其针对静态授权策略难以适应动态应用场景、无法有效识别非法输入以及缺乏用户可控性等局限。其解决方案的关键在于提出一种动态授权与合法性感知的知识产权保护框架（Authorization-on-Demand Intellectual Property Protection, AoD-IP），该框架包含两个核心组件：一是轻量级动态授权模块，支持用户在部署时按需指定或切换授权域，实现灵活、可控的授权机制；二是双路径推理机制，能够同时预测输入的合法性状态与任务相关输出，从而在保持授权域内性能的同时，可靠地检测并响应未经授权的输入，显著提升了VLM在复杂和变化环境中的安全性和可扩展性。
链接: https://arxiv.org/abs/2603.04896
作者: Lianyu Wang,Meng Wang,Huazhu Fu,Daoqiang Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The rapid adoption of vision-language models (VLMs) has heightened the demand for robust intellectual property (IP) protection of these high-value pretrained models. Effective IP protection should proactively confine model deployment within authorized domains and prevent unauthorized transfers. However, existing methods rely on static training-time definitions, limiting flexibility in dynamic environments and often producing opaque responses to unauthorized inputs. To address these limitations, we propose a novel dynamic authorization with legality-aware intellectual property protection (AoD-IP) for VLMs, a framework that supports authorize-on-demand and legality-aware assessment. AoD-IP introduces a lightweight dynamic authorization module that enables flexible, user-controlled authorization, allowing users to actively specify or switch authorized domains on demand at deployment time. This enables the model to adapt seamlessly as application scenarios evolve and provides substantially greater extensibility than existing static-domain approaches. In addition, AoD-IP incorporates a dual-path inference mechanism that jointly predicts input legality-aware and task-specific outputs. Comprehensive experimental results on multiple cross-domain benchmarks demonstrate that AoD-IP maintains strong authorized-domain performance and reliable unauthorized detection, while supporting user-controlled authorization for adaptive deployment in dynamic environments.
[AI-49] Differentially Private Multimodal In-Context Learning
【速读】：该论文旨在解决多模态大模型在敏感领域（如医学影像和个人照片）中进行多样本上下文学习时的隐私保护问题。现有差分隐私方法仅适用于少样本文本场景，因隐私成本随处理token数量增长而难以扩展至多模态数据。其解决方案的关键在于提出**差分隐私多模态任务向量（Differentially Private Multimodal Task Vectors, DP-MTV）**框架：通过将私有数据划分为不相交块，在每一层进行剪裁以限制敏感度，并对激活空间中的任务向量聚合结果添加校准噪声，仅需一次噪声注入即可支持无限次推理查询，从而在保障(\varepsilon, \delta)-差分隐私的前提下实现高效、可扩展的多模态上下文学习。
链接: https://arxiv.org/abs/2603.04894
作者: Ivoline C. Ngong,Zarreen Reza,Joseph P. Near
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language models are increasingly applied to sensitive domains such as medical imaging and personal photographs, yet existing differentially private methods for in-context learning are limited to few-shot, text-only settings because privacy cost scales with the number of tokens processed. We present Differentially Private Multimodal Task Vectors (DP-MTV), the first framework enabling many-shot multimodal in-context learning with formal (\varepsilon, \delta) -differential privacy by aggregating hundreds of demonstrations into compact task vectors in activation space. DP-MTV partitions private data into disjoint chunks, applies per-layer clipping to bound sensitivity, and adds calibrated noise to the aggregate, requiring only a single noise addition that enables unlimited inference queries. We evaluate on eight benchmarks across three VLM architectures, supporting deployment with or without auxiliary data. At \varepsilon=1.0 , DP-MTV achieves 50% on VizWiz compared to 55% non-private and 35% zero-shot, preserving most of the gain from in-context learning under meaningful privacy constraints.
[AI-50] Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues
【速读】：该论文旨在解决流式对话（streaming dialogue）中无限时域约束下的记忆机制问题，即现有“读取后思考”（read-then-think）的记忆方法无法支持在对话流持续进行时的即时记忆调用，导致感知保真度与推理效率之间存在显著矛盾。其解决方案的关键在于提出ProStream框架，该框架采用主动分层记忆机制（proactive hierarchical memory framework），通过多粒度蒸馏（multi-granular distillation）实现对连续对话流的推理，并引入自适应时空优化（Adaptive Spatiotemporal Optimization）动态调整记忆保留策略，从而在保证推理保真度的同时维持有限的知识状态和低推理延迟。
链接: https://arxiv.org/abs/2603.04885
作者: Bingbing Wang,Jing Li,Ruifeng Xu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. However, existing read-then-think memory is fundamentally misaligned with this setting, as it cannot support ad-hoc memory recall while streams unfold. To explore this challenge, we introduce \textbfSTEM-Bench, the first benchmark for \textbfSTreaming \textbfEvaluation of \textbfMemory. It comprises over 14K QA pairs in dialogue streams that assess perception fidelity, temporal reasoning, and global awareness under infinite-horizon constraints. The preliminary analysis on STEM-Bench indicates a critical \textitfidelity-efficiency dilemma: retrieval-based methods use fragment context, while full-context models incur unbounded latency. To resolve this, we propose \textbfProStream, a proactive hierarchical memory framework for streaming dialogues. It enables ad-hoc memory recall on demand by reasoning over continuous streams with multi-granular distillation. Moreover, it employs Adaptive Spatiotemporal Optimization to dynamically optimize retention based on expected utility. It enables a bounded knowledge state for lower inference latency without sacrificing reasoning fidelity. Experiments show that ProStream outperforms baselines in both accuracy and efficiency.
[AI-51] SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms
【速读】：该论文旨在解决时间序列预测中普遍存在的三大挑战：新部署场景下的数据稀缺性、分布偏移下的适应能力不足，以及人工迭代优化带来的边际效益递减问题。其核心解决方案是提出Self-Evolving Agent for Time Series Algorithms (SEA-TS)，一个通过自进化循环自主生成、验证与优化预测代码的框架。关键创新包括：(1) 引入Metric-Advantage Monte Carlo Tree Search (MA-MCTS)，以归一化优势得分替代固定奖励，提升搜索效率；(2) 设计带运行时提示优化的代码审查机制，自动识别并修正错误模式，防止重复犯错；(3) 实现全局可引导推理（Global Steerable Reasoning），通过节点与全局最优/最差解对比实现跨轨迹知识迁移。该框架在Solar-Energy公开基准上使MAE降低40%，并在专有数据集上显著优于人工基线，同时发现物理约束嵌入的单调衰减头等新颖架构模式，证明了自主机器学习工程能够超越人类设计产生真正创新的算法结构。
链接: https://arxiv.org/abs/2603.04873
作者: Longkun Xu,Xiaochun Zhang,Qiantu Tuo,Rui Li
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate time series forecasting underpins decision-making across domains, yet conventional ML development suffers from data scarcity in new deployments, poor adaptability under distribution shift, and diminishing returns from manual iteration. We propose Self-Evolving Agent for Time Series Algorithms (SEA-TS), a framework that autonomously generates, validates, and optimizes forecasting code via an iterative self-evolution loop. Our framework introduces three key innovations: (1) Metric-Advantage Monte Carlo Tree Search (MA-MCTS), which replaces fixed rewards with a normalized advantage score for discriminative search guidance; (2) Code Review with running prompt refinement, where each executed solution undergoes automated review followed by prompt updates that encode corrective patterns, preventing recurrence of similar errors; and (3) Global Steerable Reasoning, which compares each node against global best and worst solutions, enabling cross-trajectory knowledge transfer. We adopt a MAP-Elites archive for architectural diversity. On the public Solar-Energy benchmark, SEA-TS generated code achieves a 40% MAE reduction relative to TimeMixer, surpassing state-of-the-art methods. On proprietary datasets, SEA-TS generated code reduces WAPE by 8.6% on solar PV forecasting and 7.7% on residential load forecasting compared to human-engineered baselines, and achieves 26.17% MAPE on load forecasting versus 29.34% by TimeMixer. Notably, the evolved models discover novel architectural patterns–including physics-informed monotonic decay heads encoding solar irradiance constraints, per-station learned diurnal cycle profiles, and learnable hourly bias correction–demonstrating that autonomous ML engineering can generate genuinely novel algorithmic ideas beyond manual design.
[AI-52] K-Gen: A Multimodal Language-Conditioned Approach for Interpretable Keypoint-Guided Trajectory Generation
【速读】：该论文旨在解决自动驾驶仿真中生成真实且多样轨迹的挑战，现有方法多依赖结构化数据（如矢量地图），难以捕捉场景中的丰富非结构化视觉信息。其解决方案的关键在于提出K-Gen框架，该框架利用多模态大语言模型（Multimodal Large Language Models, MLLMs）统一处理栅格化的鸟瞰图（BEV）地图与文本场景描述，并通过可解释的关键点（keypoint）引导生成轨迹：先生成反映智能体意图的可解释关键点及推理过程，再由精炼模块将其转化为精确轨迹；同时引入轨迹感知的强化微调算法T-DAPO进一步提升关键点生成质量，从而实现更鲁棒、可解释的轨迹预测。
链接: https://arxiv.org/abs/2603.04868
作者: Mingxuan Mu,Guo Yang,Lei Chen,Ping Wu,Jianxun Cui
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Generating realistic and diverse trajectories is a critical challenge in autonomous driving simulation. While Large Language Models (LLMs) show promise, existing methods often rely on structured data like vectorized maps, which fail to capture the rich, unstructured visual context of a scene. To address this, we propose K-Gen, an interpretable keypoint-guided multimodal framework that leverages Multimodal Large Language Models (MLLMs) to unify rasterized BEV map inputs with textual scene descriptions. Instead of directly predicting full trajectories, K-Gen generates interpretable keypoints along with reasoning that reflects agent intentions, which are subsequently refined into accurate trajectories by a refinement module. To further enhance keypoint generation, we apply T-DAPO, a trajectory-aware reinforcement fine-tuning algorithm. Experiments on WOMD and nuPlan demonstrate that K-Gen outperforms existing baselines, highlighting the effectiveness of combining multimodal reasoning with keypoint-guided trajectory generation.
[AI-53] Causally Robust Reward Learning from Reason -Augmented Preference Feedback ICLR
【速读】：该论文旨在解决偏好型奖励学习（preference-based reward learning）在面对稀疏二元反馈时易产生因果混淆（causal confusion）的问题，即模型可能错误地将与偏好轨迹共现的伪特征（spurious features）当作因果信号，导致在测试阶段当这些相关性消失或反转时性能崩溃。解决方案的关键在于提出轻量级框架 ReCouPLe，其通过引入自然语言理由（natural language rationales）作为缺失的因果信号，将每个理由视为嵌入空间中的引导投影轴，训练模型根据与该轴对齐的特征评分轨迹，同时弱化与理由无关的上下文信息。由于相同理由（如“避免碰撞”、“更快完成任务”）可跨任务复用，ReCouPLe 能在任务语义共享时自动复用相同的因果方向，并在无需额外数据或语言模型微调的情况下将偏好知识迁移至新任务，从而提升奖励模型对用户意图的对齐能力及在分布外场景下的泛化性能。
链接: https://arxiv.org/abs/2603.04861
作者: Minjune Hwang,Yigit Korkmaz,Daniel Seita,Erdem Bıyık
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注: Published in International Conference on Learning Representations (ICLR) 2026
Abstract:Preference-based reward learning is widely used for shaping agent behavior to match a user’s preference, yet its sparse binary feedback makes it especially vulnerable to causal confusion. The learned reward often latches onto spurious features that merely co-occur with preferred trajectories during training, collapsing when those correlations disappear or reverse at test time. We introduce ReCouPLe, a lightweight framework that uses natural language rationales to provide the missing causal signal. Each rationale is treated as a guiding projection axis in an embedding space, training the model to score trajectories based on features aligned with that axis while de-emphasizing context that is unrelated to the stated reason. Because the same rationales (e.g., “avoids collisions”, “completes the task faster”) can appear across multiple tasks, ReCouPLe naturally reuses the same causal direction whenever tasks share semantics, and transfers preference knowledge to novel tasks without extra data or language-model fine-tuning. Our learned reward model can ground preferences on the articulated reason, aligning better with user intent and generalizing beyond spurious features. ReCouPLe outperforms baselines by up to 1.5x in reward accuracy under distribution shifts, and 2x in downstream policy performance in novel tasks. We have released our code at this https URL
[AI-54] Design Behaviour Codes (DBCs): A Taxonomy-Driven Layered Governance Benchmark for Large Language Models
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在推理阶段缺乏结构化、可审计且跨 jurisdiction 的行为治理机制的问题。现有方法如训练时对齐（RLHF、DPO）或事后内容审核 API 存在不可控性、非模型无关性和难以量化合规性等局限。解决方案的关键在于提出首个实证评估框架——动态行为约束（Dynamic Behavioral Constraint, DBC）基准，其核心是引入一个模型无关、可映射至监管辖区的 150 控制项治理层（MDBC 系统），在推理阶段实施结构化行为约束。该方案通过三臂对照设计（基础模型、基础模型+安全提示、基础模型+DBC）实现因果归因，并验证了其在降低整体风险暴露率（RER）36.8%、提升欧盟人工智能法案合规评分至 8.5/10 的有效性，同时具备高一致性评估能力（Fleiss kappa > 0.70）。
链接: https://arxiv.org/abs/2603.04837
作者: G. Madan Mohan,Veena Kiran Nambiar,Kiranmayee Janardhan
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 3 figures
Abstract:We introduce the Dynamic Behavioral Constraint (DBC) benchmark, the first empirical framework for evaluating the efficacy of a structured, 150-control behavioral governance layer, the MDBC (Madan DBC) system, applied at inference time to large language models (LLMs). Unlike training time alignment methods (RLHF, DPO) or post-hoc content moderation APIs, DBCs constitute a system prompt level governance layer that is model-agnostic, jurisdiction-mappable, and auditable. We evaluate the DBC Framework across a 30 domain risk taxonomy organized into six clusters (Hallucination and Calibration, Bias and Fairness, Malicious Use, Privacy and Data Protection, Robustness and Reliability, and Misalignment Agency) using an agentic red-team protocol with five adversarial attack strategies (Direct, Roleplay, Few-Shot, Hypothetical, Authority Spoof) across 3 model families. Our three-arm controlled design (Base, Base plus Moderation, Base plus DBC) enables causal attribution of risk reduction. Key findings: the DBC layer reduces the aggregate Risk Exposure Rate (RER) from 7.19 percent (Base) to 4.55 percent (Base plus DBC), representing a 36.8 percent relative risk reduction, compared with 0.6 percent for a standard safety moderation prompt. MDBC Adherence Scores improve from 8.6 by 10 (Base) to 8.7 by 10 (Base plus DBC). EU AI Act compliance (automated scoring) reaches 8.5by 10 under the DBC layer. A three judge evaluation ensemble yields Fleiss kappa greater than 0.70 (substantial agreement), validating our automated pipeline. Cluster ablation identifies the Integrity Protection cluster (MDBC 081 099) as delivering the highest per domain risk reduction, while graybox adversarial attacks achieve a DBC Bypass Rate of 4.83 percent . We release the benchmark code, prompt database, and all evaluation artefacts to enable reproducibility and longitudinal tracking as models evolve.
[AI-55] Multilevel Training for Kolmogorov Arnold Networks
【速读】：该论文旨在解决常见神经网络架构（如多层感知机，MLP）在训练过程中因缺乏函数复合结构而导致的算法加速困难问题。其核心解决方案是利用Kolmogorov-Arnold网络（KAN）中由指定基函数展开所隐含的更强结构，提出一种基于多级训练（multilevel training）的高效优化策略。关键创新在于通过线性基变换将具有样条基函数的KAN等价映射为具有幂次ReLU激活的多通道MLP，并分析该变换对梯度优化几何的影响；进而设计一个通过均匀细化样条节点构建的“恰当嵌套层次”架构，在相邻层级间使用解析几何插值算子实现模型迁移，从而确保粗粒度模型的学习成果被保留，同时样条基函数的紧支集特性保障了后续层级的互补优化。数值实验表明，该方法相较于传统训练方式可显著提升精度，尤其在物理信息神经网络（PINNs）中表现突出。
链接: https://arxiv.org/abs/2603.04827
作者: Ben S. Southworth,Jonas A. Actor,Graham Harper,Eric C. Cyr
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Numerical Analysis (math.NA)
备注:
Abstract:Algorithmic speedup of training common neural architectures is made difficult by the lack of structure guaranteed by the function compositions inherent to such networks. In contrast to multilayer perceptrons (MLPs), Kolmogorov-Arnold networks (KANs) provide more structure by expanding learned activations in a specified basis. This paper exploits this structure to develop practical algorithms and theoretical insights, yielding training speedup via multilevel training for KANs. To do so, we first establish an equivalence between KANs with spline basis functions and multichannel MLPs with power ReLU activations through a linear change of basis. We then analyze how this change of basis affects the geometry of gradient-based optimization with respect to spline knots. The KANs change-of-basis motivates a multilevel training approach, where we train a sequence of KANs naturally defined through a uniform refinement of spline knots with analytic geometric interpolation operators between models. The interpolation scheme enables a ``properly nested hierarchy’’ of architectures, ensuring that interpolation to a fine model preserves the progress made on coarse models, while the compact support of spline basis functions ensures complementary optimization on subsequent levels. Numerical experiments demonstrate that our multilevel training approach can achieve orders of magnitude improvement in accuracy over conventional methods to train comparable KANs or MLPs, particularly for physics informed neural networks. Finally, this work demonstrates how principled design of neural networks can lead to exploitable structure, and in this case, multilevel algorithms that can dramatically improve training performance.
[AI-56] VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在通过任务特定数据微调以实现与人类价值观对齐时所面临的“对齐税”（alignment tax）问题，即模型原有价值体系因吸收训练数据中的潜在偏差而发生显著偏移，同时微调过程还会引发严重幻觉和语义信息丢失。解决方案的关键在于提出一种闭环框架VISA（Value Injection via Shielded Adaptation），其核心由高精度价值检测器、语义到价值转换器及价值重写器组成；其中价值重写器采用组相对策略优化（Group Relative Policy Optimization, GRPO）训练，结合复合奖励函数，同时优化细粒度价值精确性与语义完整性，从而在保持原始知识忠实性的同时有效缓解对齐税。
链接: https://arxiv.org/abs/2603.04822
作者: Jiawei Chen,Tianzhuo Yang,Guoxi Zhang,Jiaming Ji,Yaodong Yang,Juntao Dai
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Aligning Large Language Models (LLMs) with nuanced human values remains a critical challenge, as existing methods like Reinforcement Learning from Human Feedback (RLHF) often handle only coarse-grained attributes. In practice, fine-tuning LLMs on task-specific datasets to optimize value alignment inevitably incurs an alignment tax: the model’s pre-calibrated value system drifts significantly due to latent bias absorption from training data, while the fine-tuning process also causes severe hallucinations and semantic information loss in generated responses. To address this, we propose VISA (Value Injection via Shielded Adaptation), a closed-loop framework designed to navigate this trade-off. VISA’s architecture features a high-precision value detector, a semantic-to-value translator, and a core value-rewriter. The value-rewriter is trained via Group Relative Policy Optimization (GRPO) with a composite reward function that simultaneously optimizes for fine-grained value precision, and the preservation of semantic integrity. By learning an optimal policy to balance these competing objectives, VISA effectively mitigates the alignment tax while staying loyal to the original knowledge. Our experiments demonstrate that this approach enables precise control over a model’s value expression while maintaining its factual consistency and general capabilities, significantly outperforming both standard fine-tuning methods and prompting-based baselines, including GPT-4o.
[AI-57] On the Strengths and Weaknesses of Data for Open-set Embodied Assistance
【速读】：该论文旨在解决**开放集纠正性辅助（Open-Set Corrective Assistance）**问题，即让交互式具身基础模型在面对未见过的用户行为类别或训练中未出现的新任务配置时，仍能通过纠正性动作或语言反馈提供有效协助。此前研究通常假设纠正类别为封闭集或依赖外部规划器，难以评估辅助数据的真实泛化能力。解决方案的关键在于构建一个合成的助人数据集（Overcooked环境），并基于LLaMA架构进行微调，重点提升模型对多模态语境理解、缺陷推理以及多样化场景暴露的能力，从而实现更鲁棒的开放集泛化性能。
链接: https://arxiv.org/abs/2603.04819
作者: Pradyumna Tambwekar,Andrew Silva,Deepak Gopinath,Jonathan DeCastro,Xiongyi Cui,Guy Rosman
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Embodied foundation models are increasingly performant in real-world domains such as robotics or autonomous driving. These models are often deployed in interactive or assistive settings, where it is important that these assistive models generalize to new users and new tasks. Diverse interactive data generation offers a promising avenue for providing data-efficient generalization capabilities for interactive embodied foundation models. In this paper, we investigate the generalization capabilities of a multimodal foundation model fine-tuned on diverse interactive assistance data in a synthetic domain. We explore generalization along two axes: a) assistance with unseen categories of user behavior and b) providing guidance in new configurations not encountered during training. We study a broad capability called \textbfOpen-Set Corrective Assistance, in which the model needs to inspect lengthy user behavior and provide assistance through either corrective actions or language-based feedback. This task remains unsolved in prior work, which typically assumes closed corrective categories or relies on external planners, making it a challenging testbed for evaluating the limits of assistive data. To support this task, we generate synthetic assistive datasets in Overcooked and fine-tune a LLaMA-based model to evaluate generalization to novel tasks and user behaviors. Our approach provides key insights into the nature of assistive datasets required to enable open-set assistive intelligence. In particular, we show that performant models benefit from datasets that cover different aspects of assistance, including multimodal grounding, defect inference, and exposure to diverse scenarios.
[AI-58] LLM -Grounded Explainability for Port Congestion Prediction via Temporal Graph Attention Networks
【速读】：该论文旨在解决港口拥堵预测系统缺乏可操作解释性的问题，即现有方法虽注重预测精度，但无法提供可信、可验证的因果性解释，难以支持实际运营决策。其解决方案的关键在于提出AIS-TGNN框架，通过将时空图注意力网络（Temporal Graph Attention Network, TGAT）与结构化大语言模型（Large Language Model, LLM）推理模块耦合，实现拥堵 escalation 预测与自然语言解释的联合生成：TGAT基于自动识别系统（Automatic Identification System, AIS）数据构建每日空间图，捕捉船舶活动的时空动态；模型内部证据（如特征z-score和注意力权重）被转化为结构化提示，约束LLM推理以确保生成解释与模型输出一致，并引入方向一致性验证协议量化解释可靠性。实验表明，该框架在保持高预测性能（测试AUC 0.761，AP 0.344）的同时，实现了99.6%的方向一致性，为可审计的航运风险报告提供了可行路径。
链接: https://arxiv.org/abs/2603.04818
作者: Zhiming Xue,Yujue Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Port congestion at major maritime hubs disrupts global supply chains, yet existing prediction systems typically prioritize forecasting accuracy without providing operationally interpretable explanations. This paper proposes AIS-TGNN, an evidence-grounded framework that jointly performs congestion-escalation prediction and faithful natural-language explanation by coupling a Temporal Graph Attention Network (TGAT) with a structured large language model (LLM) reasoning module. Daily spatial graphs are constructed from Automatic Identification System (AIS) broadcasts, where each grid cell represents localized vessel activity and inter-cell interactions are modeled through attention-based message passing. The TGAT predictor captures spatiotemporal congestion dynamics, while model-internal evidence, including feature z-scores and attention-derived neighbor influence, is transformed into structured prompts that constrain LLM reasoning to verifiable model outputs. To evaluate explanatory reliability, we introduce a directional-consistency validation protocol that quantitatively measures agreement between generated narratives and underlying statistical evidence. Experiments on six months of AIS data from the Port of Los Angeles and Long Beach demonstrate that the proposed framework outperforms both LR and GCN baselines, achieving a test AUC of 0.761, AP of 0.344, and recall of 0.504 under a strict chronological split while producing explanations with 99.6% directional consistency. Results show that grounding LLM generation in graph-model evidence enables interpretable and auditable risk reporting without sacrificing predictive performance. The framework provides a practical pathway toward operationally deployable explainable AI for maritime congestion monitoring and supply-chain risk management.
[AI-59] EchoGuard: An Agent ic Framework with Knowledge-Graph Memory for Detecting Manipulative Communication in Longitudinal Dialogue
【速读】：该论文旨在解决个体在识别操纵性沟通（如煤气灯效应、内疚操控和情感胁迫等）时面临的困难，这类行为往往具有隐蔽性和情境依赖性，而现有代理型人工智能（Agentic AI）系统因上下文窗口有限和灾难性遗忘等问题难以有效追踪和识别。解决方案的关键在于提出EchoGuard框架，其核心创新是将知识图谱（Knowledge Graph, KG）作为代理的长期情景记忆与语义记忆基础，通过“记录-分析-反思”循环机制：首先将用户交互结构化为个人化的事件图谱（包含事件、情绪和说话者），其次利用复杂图查询识别六种心理学依据明确的操纵模式，最后由大语言模型（LLM）基于检测到的子图生成针对性的苏格拉底式提问，引导用户自主觉察，从而在保障个人自主权与安全的前提下提升对操纵行为的认知能力。
链接: https://arxiv.org/abs/2603.04815
作者: Ratna Kandala,Niva Manchanda,Akshata Kishore Moharir,Ananth Kandala
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Manipulative communication, such as gaslighting, guilt-tripping, and emotional coercion, is often difficult for individuals to recognize. Existing agentic AI systems lack the structured, longitudinal memory to track these subtle, context-dependent tactics, often failing due to limited context windows and catastrophic forgetting. We introduce EchoGuard, an agentic AI framework that addresses this gap by using a Knowledge Graph (KG) as the agent’s core episodic and semantic memory. EchoGuard employs a structured Log-Analyze-Reflect loop: (1) users log interactions, which the agent structures as nodes and edges in a personal, episodic KG (capturing events, emotions, and speakers); (2) the system executes complex graph queries to detect six psychologically-grounded manipulation patterns (stored as a semantic KG); and (3) an LLM generates targeted Socratic prompts grounded by the subgraph of detected patterns, guiding users toward self-discovery. This framework demonstrates how the interplay between agentic architectures and Knowledge Graphs can empower individuals in recognizing manipulative communication while maintaining personal autonomy and safety. We present the theoretical foundation, framework design, a comprehensive evaluation strategy, and a vision to validate this approach.
[AI-60] mer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
【速读】：该论文旨在解决现有预训练时间序列基础模型在可扩展性上的瓶颈问题，尤其是长程预测中因滚动式推理导致的计算成本高和误差累积显著的问题。其关键解决方案在于提出一种三维度串行扩展（Serial Scaling）策略：在模型架构上引入稀疏的TimeMoE模块与通用的TimeSTP模块，结合串行Token预测（STP）训练目标，以更好地捕捉时间序列的时序特性；同时构建包含一万亿时间点的高质量无偏数据集TimeBench，并通过精细的数据增强缓解预测偏差；此外，创新性地设计后训练阶段（包括持续预训练和长上下文扩展），有效提升短时与长上下文下的预测性能。
链接: https://arxiv.org/abs/2603.04791
作者: Yong Liu,Xingjian Su,Shiyu Wang,Haoran Zhang,Haixuan Liu,Yuxuan Wang,Zhou Ye,Yang Xiang,Jianmin Wang,Mingsheng Long
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each token, and a context length of 11.5K. To overcome the scalability bottleneck in existing pre-trained time series foundation models, we perform Serial Scaling in three dimensions: model architecture, dataset, and training pipeline. Timer-S1 integrates sparse TimeMoE blocks and generic TimeSTP blocks for Serial-Token Prediction (STP), a generic training objective that adheres to the serial nature of forecasting. The proposed paradigm introduces serial computations to improve long-term predictions while avoiding costly rolling-style inference and pronounced error accumulation in the standard next-token prediction. Pursuing a high-quality and unbiased training dataset, we curate TimeBench, a corpus with one trillion time points, and apply meticulous data augmentation to mitigate predictive bias. We further pioneer a post-training stage, including continued pre-training and long-context extension, to enhance short-term and long-context performance. Evaluated on the large-scale GIFT-Eval leaderboard, Timer-S1 achieves state-of-the-art forecasting performance, attaining the best MASE and CRPS scores as a pre-trained model. Timer-S1 will be released to facilitate further research.
[AI-61] MOOSEnger – a Domain-Specific AI Agent for the MOOSE Ecosystem
【速读】：该论文旨在解决多物理场仿真环境 MOOSE 中因输入文件（HIT 格式）结构复杂、对象库庞大且语法严格而导致的初始设置与调试效率低下的问题。解决方案的关键在于构建一个工具增强型 AI 代理 MOOSEnger，其核心创新是采用“基础架构+领域插件”的分层架构：基础层提供通用功能（如配置管理、工具调度、检索服务等），而 MOOSE 插件则集成针对 HIT 输入格式的解析、语法保持的文件摄取以及领域特定的输入修复与校验工具；同时结合检索增强生成（RAG）与确定性解析验证机制，在自然语言意图到可执行输入的转换过程中实现高保真度和可靠性，最终在基准测试中将执行通过率从 LLM 单一方案的 0.08 提升至 0.93。
链接: https://arxiv.org/abs/2603.04756
作者: Mengnan Li,Jason Miller,Zachary Prince,Alexander Lindsay,Cody Permann
机构: 未知
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Software Engineering (cs.SE)
备注:
Abstract:MOOSEnger is a tool-enabled AI agent tailored to the Multiphysics Object-Oriented Simulation Environment (MOOSE). MOOSE cases are specified in HIT “.i” input files; the large object catalog and strict syntax make initial setup and debugging slow. MOOSEnger offers a conversational workflow that turns natural-language intent into runnable inputs by combining retrieval-augmented generation over curated docs/examples with deterministic, MOOSE-aware parsing, validation, and execution tools. A core-plus-domain architecture separates reusable agent infrastructure (configuration, registries, tool dispatch, retrieval services, persistence, and evaluation) from a MOOSE plugin that adds HIT-based parsing, syntax-preserving ingestion of input files, and domain-specific utilities for input repair and checking. An input precheck pipeline removes hidden formatting artifacts, fixes malformed HIT structure with a bounded grammar-constrained loop, and resolves invalid object types via similarity search over an application syntax registry. Inputs are then validated and optionally smoke-tested with the MOOSE runtime in the loop via an MCP-backed execution backend (with local fallback), translating solver diagnostics into iterative verify-and-correct updates. Built-in evaluation reports RAG metrics (faithfulness, relevancy, context precision/recall) and end-to-end success by actual execution. On a 125-prompt benchmark spanning diffusion, transient heat conduction, solid mechanics, porous flow, and incompressible Navier–Stokes, MOOSEnger achieves a 0.93 execution pass rate versus 0.08 for an LLM-only baseline.
[AI-62] Evaluating the Search Agent in a Parallel World
【速读】：该论文旨在解决当前评估搜索代理（Search Agents）在开放世界、实时性和长尾问题上表现时面临的四大挑战：高质量深度搜索基准构建成本高昂且合成数据可靠性差；静态基准因互联网信息动态变化而迅速过时；性能归因不明确，模型表现常受参数化记忆影响而非真实搜索与推理能力；以及依赖特定商业搜索引擎导致结果不可复现。其解决方案的关键在于提出一种名为Mind-ParaWorld（MPW）的平行世界评估框架，通过采样真实实体名生成超越模型知识截止点的未来情景与问题，并利用“平行世界定律模型”构造不可分割的原子事实（Atomic Facts）及唯一真值；在评估过程中，代理与一个基于这些原子事实动态生成搜索结果页面（SERPs）的平行世界引擎模型交互，从而实现可复现、动态更新且能隔离参数记忆干扰的评估机制。
链接: https://arxiv.org/abs/2603.04751
作者: Jiawei Chen,Xintian Shen,Lihao Zheng,Lifu Mu,Haoyi Sun,Ning Mao,Hao Ma,Tao Wei,Pan Zhou,Kun Zhan
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Integrating web search tools has significantly extended the capability of LLMs to address open-world, real-time, and long-tail problems. However, evaluating these Search Agents presents formidable challenges. First, constructing high-quality deep search benchmarks is prohibitively expensive, while unverified synthetic data often suffers from unreliable sources. Second, static benchmarks face dynamic obsolescence: as internet information evolves, complex queries requiring deep research often degrade into simple retrieval tasks due to increased popularity, and ground truths become outdated due to temporal shifts. Third, attribution ambiguity confounds evaluation, as an agent’s performance is often dominated by its parametric memory rather than its actual search and reasoning capabilities. Finally, reliance on specific commercial search engines introduces variability that hampers reproducibility. To address these issues, we propose a novel framework, Mind-ParaWorld, for evaluating Search Agents in a Parallel World. Specifically, MPW samples real-world entity names to synthesize future scenarios and questions situated beyond the model’s knowledge cutoff. A ParaWorld Law Model then constructs a set of indivisible Atomic Facts and a unique ground-truth for each question. During evaluation, instead of retrieving real-world results, the agent interacts with a ParaWorld Engine Model that dynamically generates SERPs grounded in these inviolable Atomic Facts. We release MPW-Bench, an interactive benchmark spanning 19 domains with 1,608 instances. Experiments across three evaluation settings show that, while search agents are strong at evidence synthesis given complete information, their performance is limited not only by evidence collection and coverage in unfamiliar search environments, but also by unreliable evidence sufficiency judgment and when-to-stop decisions-bottlenecks.
[AI-63] From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security
【速读】：该论文旨在解决零售场景中日益严重的盗窃行为检测难题，尤其是在视频监控系统普遍存在但人工持续监看不可行的情况下，如何实现高效、隐私保护且资源友好的自动化异常检测。其核心解决方案是将盗窃检测建模为基于姿态的无监督视频异常检测问题，并提出一种适用于边缘计算环境的周期性自适应框架，使智能零售中的物联网（IoT）设备能够从实时流式未标注数据中持续学习与更新模型，从而支持分布式摄像头网络下的低延迟、可扩展检测。关键创新在于结合F1和H_PRS（精度、召回率与特异性调和平均）指标进行阈值选择，并在真实多日多相机环境下构建了大规模数据集RetailS以保障方法的可复现性与部署可行性。
链接: https://arxiv.org/abs/2603.04723
作者: Shanle Yao,Narges Rashvand,Armin Danesh Pazho,Hamed Tabkhi
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Shoplifting is a growing operational and economic challenge for retailers, with incidents rising and losses increasing despite extensive video surveillance. Continuous human monitoring is infeasible, motivating automated, privacy-preserving, and resource-aware detection solutions. In this paper, we cast shoplifting detection as a pose-based, unsupervised video anomaly detection problem and introduce a periodic adaptation framework designed for on-site Internet of Things (IoT) deployment. Our approach enables edge devices in smart retail environments to adapt from streaming, unlabeled data, supporting scalable and low-latency anomaly detection across distributed camera networks. To support reproducibility, we introduce RetailS, a new large-scale real-world shoplifting dataset collected from a retail store under multi-day, multi-camera conditions, capturing unbiased shoplifting behavior in realistic IoT settings. For deployable operation, thresholds are selected using both F1 and H_PRS scores, the harmonic mean of precision, recall, and specificity, during data filtering and training. In periodic adaptation experiments, our framework consistently outperformed offline baselines on AUC-ROC and AUC-PR in 91.6% of evaluations, with each training update completing in under 30 minutes on edge-grade hardware, demonstrating the feasibility and reliability of our solution for IoT-enabled smart retail deployment.
[AI-64] Probabilistic Dreaming for World Models ICLR2026
【速读】：该论文旨在解决当前世界模型（world models）在强化学习中样本效率低、泛化能力弱的问题，尤其是传统Dreamer模型在探索策略和未来状态预测上的局限性。其解决方案的关键在于引入基于概率的方法，实现两个核心改进：一是并行探索多个潜在状态（latent states），提升探索效率；二是通过保持互斥未来假设（mutually exclusive futures）的同时保留连续潜变量的梯度传播特性，从而增强模型对不确定性的建模能力。实验表明，该方法在MPE SimpleTag环境中相比标准Dreamer提升了4.5%的得分且episode回报方差降低28%，验证了其有效性。
链接: https://arxiv.org/abs/2603.04715
作者: Gavin Wong
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Presented at ICLR 2026: 2nd Workshop on World Models
Abstract:“Dreaming” enables agents to learn from imagined experiences, enabling more robust and sample-efficient learning of world models. In this work, we consider innovations to the state-of-the-art Dreamer model using probabilistic methods that enable: (1) the parallel exploration of many latent states; and (2) maintaining distinct hypotheses for mutually exclusive futures while retaining the desirable gradient properties of continuous latents. Evaluating on the MPE SimpleTag domain, our method outperforms standard Dreamer with a 4.5% score improvement and 28% lower variance in episode returns. We also discuss limitations and directions for future work, including how optimal hyperparameters (e.g. particle count K) scale with environmental complexity, and methods to capture epistemic uncertainty in world models.
[AI-65] When Denoising Hinders: Revisiting Zero-Shot ASR with SAM-Audio and Whisper
【速读】：该论文试图解决的问题是：在零样本自动语音识别（Zero-shot Automatic Speech Recognition, Zero-shot ASR）系统中，是否可以通过先进的语音增强技术（如Meta AI提出的Segment Anything Model Audio, SAM-Audio）提升音频的感知质量从而改善识别准确率。以往研究普遍假设高质量音频输入会直接带来更好的ASR性能，但本文通过系统性实证研究发现，这种直觉并不成立——尽管SAM-Audio显著提升了信号层面的峰值信噪比（Peak Signal-to-Noise Ratio, PSNR），其作为预处理步骤反而导致Whisper模型的词错误率（Word Error Rate, WER）和字符错误率（Character Error Rate, CER）上升。解决方案的关键在于揭示了“人类感知清晰度”与“机器识别鲁棒性”之间存在根本性不匹配：SAM-Audio对人耳更清晰的音频重构，可能引入了对ASR模型有害的失真或语义扰动，尤其在更大规模的Whisper模型上表现更为严重。这一发现警示研究者避免盲目采用前沿去噪方法作为ASR流水线的通用预处理模块。
链接: https://arxiv.org/abs/2603.04710
作者: Akif Islam,Raufun Nahar,Md. Ekramul Hamid
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 6 pages, 4 figures, 5 tables. IEEE Conference Paper
Abstract:Recent advances in automatic speech recognition (ASR) and speech enhancement have led to a widespread assumption that improving perceptual audio quality should directly benefit recognition accuracy. In this work, we rigorously examine whether this assumption holds for modern zero-shot ASR systems. We present a systematic empirical study on the impact of Segment Anything Model Audio by Meta AI, a recent foundation-scale speech enhancement model proposed by Meta, when used as a preprocessing step for zero-shot transcription with Whisper. Experiments are conducted across multiple Whisper model variants and two linguistically distinct noisy speech datasets: a real-world Bengali YouTube corpus and a publicly available English noisy dataset. Contrary to common intuition, our results show that SAM-Audio preprocessing consistently degrades ASR performance, increasing both Word Error Rate (WER) and Character Error Rate (CER) compared to raw noisy speech, despite substantial improvements in signal-level quality. Objective Peak Signal-to-Noise Ratio analysis on the English dataset confirms that SAM-Audio produces acoustically cleaner signals, yet this improvement fails to translate into recognition gains. Therefore, we conducted a detailed utterance-level analysis to understand this counterintuitive result. We found that the recognition degradation is a systematic issue affecting the majority of the audio, not just isolated outliers, and that the errors worsen as the Whisper model size increases. These findings expose a fundamental mismatch: audio that is perceptually cleaner to human listeners is not necessarily robust for machine recognition. This highlights the risk of blindly applying state-of-the-art denoising as a preprocessing step in zero-shot ASR pipelines.
[AI-66] Neuro-Symbolic Financial Reasoning via Deterministic Fact Ledgers and Adversarial Low-Latency Hallucination Detector
【速读】：该论文旨在解决标准检索增强生成（Retrieval-Augmented Generation, RAG）架构在高风险金融领域中的两个根本性问题：一是大语言模型（Large Language Models, LLMs）固有的算术能力不足，二是密集向量检索导致的语义分布混淆（如将“净利润”误映射为“净销售额”）。为实现零幻觉的金融推理，作者提出Verifiable Numerical Reasoning Agent (VeNRA)，其核心创新在于将RAG范式从概率性文本检索转变为通过严格类型化的通用事实账本（Universal Fact Ledger, UFL）进行确定性变量检索，并由一种新型双锁接地算法（Double-Lock Grounding）数学约束。此外，为应对上游解析异常，引入一个30亿参数的小型语言模型（SLM）哨兵（Sentinel），用于仅用一个token测试预算即可 forensic 审计Python执行轨迹；训练该模型采用对抗模拟（Adversarial Simulation）策略，通过程序化破坏黄金财务记录来模拟生产级生态错误（Ecological Errors）；最后，在严格延迟预算下优化哨兵模型时，采用单次分类范式并辅以可选后思考机制调试，并识别出反向思维链训练中的损失稀释（Loss Dilution）现象，提出一种内存安全的微块损失算法（Micro-Chunking loss）以稳定极端差异惩罚下的梯度。
链接: https://arxiv.org/abs/2603.04663
作者: Pedram Agand
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: 14 pages, 2 figures
Abstract:Standard Retrieval-Augmented Generation (RAG) architectures fail in high-stakes financial domains due to two fundamental limitations: the inherent arithmetic incompetence of Large Language Models (LLMs) and the distributional semantic conflation of dense vector retrieval (e.g., mapping Net Income'' to Net Sales’’ due to contextual proximity). In deterministic domains, a 99% accuracy rate yields 0% operational trust. To achieve zero-hallucination financial reasoning, we introduce the Verifiable Numerical Reasoning Agent (VeNRA). VeNRA shifts the RAG paradigm from retrieving probabilistic text to retrieving deterministic variables via a strictly typed Universal Fact Ledger (UFL), mathematically bounded by a novel Double-Lock Grounding algorithm. Recognizing that upstream parsing anomalies inevitably occur, we introduce the VeNRA Sentinel: a 3-billion parameter SLM trained to forensically audit Python execution traces with only one token test budget. To train this model, we avoid traditional generative hallucination datasets in favor of Adversarial Simulation, programmatically sabotaging golden financial records to simulate production-level ``Ecological Errors’’ (e.g., Logic Code Lies and Numeric Neighbor Traps). Finally, to optimize the Sentinel under strict latency budgets, we utilize a single-pass classification paradigm with optional post thinking for debug. We identify the phenomenon of Loss Dilution in Reverse-Chain-of-Thought training and present a novel, OOM-safe Micro-Chunking loss algorithm to stabilize gradients under extreme differential penalization.
[AI-67] GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning IROS
【速读】：该论文旨在解决多机器人系统在复杂动态环境中实现高效、安全协同导航的问题，尤其关注如何在保持全局路径最优性的同时，有效应对局部环境变化和机器人间的动态交互。其解决方案的关键在于提出一种融合全局路径规划与局部避障策略的框架，利用注意力图神经网络（Attentive Graph Neural Networks）建模机器人之间的动态交互关系，并通过预设全局路径引导局部导航行为；同时，在训练过程中引入噪声以增强模型鲁棒性，从而显著提升在结构多样且高动态场景下的成功避障率与导航效率，优于NH-ORCA、DRL-NAV及GA3C-CADRL等主流基线方法。
链接: https://arxiv.org/abs/2603.04659
作者: Jonas le Fevre Sejersen,Toyotaro Suzumura,Erdal Kayacan
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Published in: 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Abstract:This paper presents a novel approach to multi-robot collision avoidance that integrates global path planning with local navigation strategies, utilizing attentive graph neural networks to manage dynamic interactions among agents. We introduce a local navigation model that leverages pre-planned global paths, allowing robots to adhere to optimal routes while dynamically adjusting to environmental changes. The models robustness is enhanced through the introduction of noise during training, resulting in superior performance in complex, dynamic environments. Our approach is evaluated against established baselines, including NH-ORCA, DRL-NAV, and GA3C-CADRL, across various structurally diverse simulated scenarios. The results demonstrate that our model achieves consistently higher success rates, lower collision rates, and more efficient navigation, particularly in challenging scenarios where baseline models struggle. This work offers an advancement in multi-robot navigation, with implications for robust performance in complex, dynamic environments with varying degrees of complexity, such as those encountered in logistics, where adaptability is essential for accommodating unforeseen obstacles and unpredictable changes.
[AI-68] When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift ICLR2026
【速读】：该论文旨在解决强化学习系统在实际应用中面临的分布漂移（distributional drift）问题，特别是由传感器故障导致的部分可观测性和表征偏移（representation shift），这会显著影响策略的鲁棒性。现有策略架构（如PPO）通常假设状态完全可观测且无噪声，但在现实环境中难以满足。解决方案的关键在于引入时间序列模型（如Transformer和状态空间模型State Space Models, SSMs）作为策略网络的扩展，使策略能够基于历史观测推断缺失信息，从而在传感器持续失效的情况下维持性能。理论层面，作者证明了在随机传感器失效过程下，无限时域奖励损失存在高概率上界，揭示了策略平滑性与失效持续性之间的关系；实验表明，基于Transformer的序列策略在MuJoCo连续控制任务中显著优于MLP、RNN和SSM基线，在大量传感器丢失场景下仍能保持高回报，验证了时间序列推理作为应对观测漂移的可靠机制的有效性。
链接: https://arxiv.org/abs/2603.04648
作者: Kevin Vogt-Lowell,Theodoros Tsiligkaridis,Rodney Lafuente-Mercado,Surabhi Ghatti,Shanghua Gao,Marinka Zitnik,Daniela Rus
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at ICLR 2026 CAO Workshop
Abstract:Real-world reinforcement learning systems must operate under distributional drift in their observation streams, yet most policy architectures implicitly assume fully observed and noise-free states. We study robustness of Proximal Policy Optimization (PPO) under temporally persistent sensor failures that induce partial observability and representation shift. To respond to this drift, we augment PPO with temporal sequence models, including Transformers and State Space Models (SSMs), to enable policies to infer missing information from history and maintain performance. Under a stochastic sensor failure process, we prove a high-probability bound on infinite-horizon reward degradation that quantifies how robustness depends on policy smoothness and failure persistence. Empirically, on MuJoCo continuous-control benchmarks with severe sensor dropout, we show Transformer-based sequence policies substantially outperform MLP, RNN, and SSM baselines in robustness, maintaining high returns even when large fractions of sensors are unavailable. These results demonstrate that temporal sequence reasoning provides a principled and practical mechanism for reliable operation under observation drift caused by sensor unreliability.
[AI-69] RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies
【速读】：该论文旨在解决当前视觉-语言-动作（Vision-Language-Action, VLA）模型在长时程、依赖历史的机器人操作任务中缺乏标准化评估体系的问题，从而阻碍了对记忆机制的有效理解、比较与进步衡量。其解决方案的关键在于提出RoboMME——一个大规模标准化基准，涵盖16个精心设计的操控任务，覆盖时间、空间、对象和程序记忆等多个维度；同时构建了基于π0.5骨干网络的14种记忆增强型VLA变体，系统探索不同记忆表示方式与多种集成策略的效果，实验证明记忆设计的效果具有高度任务依赖性，每种方案在不同任务中展现出独特优势与局限。
链接: https://arxiv.org/abs/2603.04639
作者: Yinpei Dai,Hongze Fu,Jayjun Lee,Yuejiang Liu,Haoran Zhang,Jianing Yang,Chelsea Finn,Nima Fazeli,Joyce Chai
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Memory is critical for long-horizon and history-dependent robotic manipulation. Such tasks often involve counting repeated actions or manipulating objects that become temporarily occluded. Recent vision-language-action (VLA) models have begun to incorporate memory mechanisms; however, their evaluations remain confined to narrow, non-standardized settings. This limits their systematic understanding, comparison, and progress measurement. To address these challenges, we introduce RoboMME: a large-scale standardized benchmark for evaluating and advancing VLA models in long-horizon, history-dependent scenarios. Our benchmark comprises 16 manipulation tasks constructed under a carefully designed taxonomy that evaluates temporal, spatial, object, and procedural memory. We further develop a suite of 14 memory-augmented VLA variants built on the \pi0.5 backbone to systematically explore different memory representations across multiple integration strategies. Experimental results show that the effectiveness of memory representations is highly task-dependent, with each design offering distinct advantages and limitations across different tasks. Videos and code can be found at our website this https URL.
[AI-70] When Agents Persuade: Propaganda Generation and Mitigation in LLM s ICLR2026
【速读】：该论文试图解决大语言模型（Large Language Models, LLMs）在开放环境中可能被滥用以生成操纵性内容的问题，特别是其在被赋予宣传目标时所表现出的传播行为及使用的修辞技巧。解决方案的关键在于通过监督微调（Supervised Fine-Tuning, SFT）、直接偏好优化（Direct Preference Optimization, DPO）和几率比偏好优化（Odds Ratio Preference Optimization, ORPO）等方法对模型进行干预，其中ORPO被证明是最有效的缓解策略，能够显著降低LLMs生成此类内容的倾向。
链接: https://arxiv.org/abs/2603.04636
作者: Julia Jose,Ritik Roongta,Rachel Greenstadt
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Accepted to the ICLR 2026 Workshop on Agents in the Wild (AgentWild). 20 pages including appendix, 3 figures
Abstract:Despite their wide-ranging benefits, LLM-based agents deployed in open environments can be exploited to produce manipulative material. In this study, we task LLMs with propaganda objectives and analyze their outputs using two domain-specific models: one that classifies text as propaganda or non-propaganda, and another that detects rhetorical techniques of propaganda (e.g., loaded language, appeals to fear, flag-waving, name-calling). Our findings show that, when prompted, LLMs exhibit propagandistic behaviors and use a variety of rhetorical techniques in doing so. We also explore mitigation via Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and ORPO (Odds Ratio Preference Optimization). We find that fine-tuning significantly reduces their tendency to generate such content, with ORPO proving most effective.
[AI-71] owards automated data analysis: A guided framework for LLM -based risk estimation
【速读】：该论文旨在解决当前数据集风险分析中依赖人工审计效率低下、自动化方法易受生成式AI幻觉及对齐问题影响的难题。其解决方案的关键在于提出一种在人类监督下融合生成式AI（Generative AI）的数据集风险评估框架，利用大语言模型（Large Language Models, LLMs）识别数据库模式中的语义与结构特征，自动推荐聚类技术、生成实现代码并解释结果，同时由人工监督确保分析过程的完整性与任务目标的一致性，从而为未来全自动化的数据风险分析奠定基础。
链接: https://arxiv.org/abs/2603.04631
作者: Panteleimon Rodis
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Submitted for publication. Under review
Abstract:Large Language Models (LLMs) are increasingly integrated into critical decision-making pipelines, a trend that raises the demand for robust and automated data analysis. Current approaches to dataset risk analysis are limited to manual auditing methods which involve time-consuming and complex tasks, whereas fully automated analysis based on Artificial Intelligence (AI) suffers from hallucinations and issues stemming from AI alignment. To this end, this work proposes a framework for dataset risk estimation that integrates Generative AI under human guidance and supervision, aiming to set the foundations for a future automated risk analysis paradigm. Our approach utilizes LLMs to identify semantic and structural properties in database schemata, subsequently propose clustering techniques, generate the code for them and finally interpret the produced results. The human supervisor guides the model on the desired analysis and ensures process integrity and alignment with the task’s objectives. A proof of concept is presented to demonstrate the feasibility of the framework’s utility in producing meaningful results in risk assessment tasks.
[AI-72] ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
【速读】：该论文旨在解决现有心电图（Electrocardiography, ECG）分析基础模型在捕捉心电信号周期性特征和多样化临床任务需求方面的不足。其解决方案的关键在于提出一种混合架构ECG-MoE，通过双路径Mixture-of-Experts（MoE）机制分别建模心跳级形态特征与节律特征，并引入基于LoRA（Low-Rank Adaptation）的分层融合网络以实现高效推理，从而在五个公开临床任务上达到最优性能，且推理速度比多任务基线快40%。
链接: https://arxiv.org/abs/2603.04589
作者: Yuhao Xu,Xiaoda Wang,Yi Wu,Wei Jin,Xiao Hu,Carl Yang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Electrocardiography (ECG) analysis is crucial for cardiac diagnosis, yet existing foundation models often fail to capture the periodicity and diverse features required for varied clinical tasks. We propose ECG-MoE, a hybrid architecture that integrates multi-model temporal features with a cardiac period-aware expert module. Our approach uses a dual-path Mixture-of-Experts to separately model beat-level morphology and rhythm, combined with a hierarchical fusion network using LoRA for efficient inference. Evaluated on five public clinical tasks, ECG-MoE achieves state-of-the-art performance with 40% faster inference than multi-task baselines.
[AI-73] Self-Attribution Bias: When AI Monitors Go Easy on Themselves
【速读】：该论文旨在解决自评估代理系统中因自我归因偏差（self-attribution bias）导致的监控失效问题，即语言模型在评估自身生成的动作时，相较于评估由用户提出相同动作的情况，更倾向于高估其正确性或低估其风险。解决方案的关键在于识别并验证这种偏差的存在：当动作由模型自身在先前的助手回合中生成时，其监控模块会显著降低对高风险或低正确性动作的检测率；而若同一动作在用户回合中被呈现，则监控表现正常。研究进一步指出，仅通过显式声明动作来源为监控器并不能消除该偏差，从而揭示了现有评估方法（通常基于固定示例而非模型自动生成的动作）可能过度乐观地评价监控器可靠性，误导开发者部署不充分的监控机制。
链接: https://arxiv.org/abs/2603.04582
作者: Dipika Khullar,Jack Hopkins,Rowan Wang,Fabien Roger
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Agentic systems increasingly rely on language models to monitor their own behavior. For example, coding agents may self critique generated code for pull request approval or assess the safety of tool-use actions. We show that this design pattern can fail when the action is presented in a previous or in the same assistant turn instead of being presented by the user in a user turn. We define self-attribution bias as the tendency of a model to evaluate an action as more correct or less risky when the action is implicitly framed as its own, compared to when the same action is evaluated under off-policy attribution. Across four coding and tool-use datasets, we find that monitors fail to report high-risk or low-correctness actions more often when evaluation follows a previous assistant turn in which the action was generated, compared to when the same action is evaluated in a new context presented in a user turn. In contrast, explicitly stating that the action comes from the monitor does not by itself induce self-attribution bias. Because monitors are often evaluated on fixed examples rather than on their own generated actions, these evaluations can make monitors appear more reliable than they actually are in deployment, leading developers to unknowingly deploy inadequate monitors in agentic systems.
[AI-74] Why Do Neural Networks Forget: A Study of Collapse in Continual Learning
【速读】：该论文旨在解决持续学习（continual learning）中的灾难性遗忘（catastrophic forgetting）问题，其关键在于揭示遗忘与模型结构坍塌（structural collapse）之间的关联。研究通过测量权重和激活的有效秩（effective rank, eRank）来量化模型的内部结构变化，发现遗忘不仅表现为任务准确率下降，更源于网络特征空间扩展能力的丧失——即结构坍塌导致可塑性（plasticity）降低。不同持续学习策略（如SGD、LwF和经验回放ER）在保持模型容量与性能方面表现出差异化的效率，表明优化结构稳定性是缓解遗忘的核心路径。
链接: https://arxiv.org/abs/2603.04580
作者: Yunqin Zhu,Jun Jin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Catastrophic forgetting is a major problem in continual learning, and lots of approaches arise to reduce it. However, most of them are evaluated through task accuracy, which ignores the internal model structure. Recent research suggests that structural collapse leads to loss of plasticity, as evidenced by changes in effective rank (eRank). This indicates a link to forgetting, since the networks lose the ability to expand their feature space to learn new tasks, which forces the network to overwrite existing representations. Therefore, in this study, we investigate the correlation between forgetting and collapse through the measurement of both weight and activation eRank. To be more specific, we evaluated four architectures, including MLP, ConvGRU, ResNet-18, and Bi-ConvGRU, in the split MNIST and Split CIFAR-100 benchmarks. Those models are trained through the SGD, Learning-without-Forgetting (LwF), and Experience Replay (ER) strategies separately. The results demonstrate that forgetting and collapse are strongly related, and different continual learning strategies help models preserve both capacity and performance in different efficiency.
[AI-75] Invariant Causal Routing for Governing Social Norms in Online Market Economies
【速读】：该论文旨在解决在线市场经济社会中社会规范（social norms）的因果机制难以识别及政策干预效果难以迁移的问题。这些规范（如公平曝光、持续参与和平衡再投资）虽对长期稳定性至关重要，但其形成源于大量微观交互行为在宏观层面的聚合，导致因果归因困难且政策适用性受限。解决方案的关键在于提出不变因果路由（Invariant Causal Routing, ICR），该框架通过整合反事实推理与不变因果发现，分离真实因果效应与虚假相关性，并构建在分布变化下依然有效的可解释、可审计的政策规则，从而实现对社会规范的稳健治理。
链接: https://arxiv.org/abs/2603.04534
作者: Xiangning Yu,Qirui Mi,Xiao Xue,Haoxuan Li,Yiwei Shi,Xiaowei Liu,Mengyue Yang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Social norms are stable behavioral patterns that emerge endogenously within economic systems through repeated interactions among agents. In online market economies, such norms – like fair exposure, sustained participation, and balanced reinvestment – are critical for long-term stability. We aim to understand the causal mechanisms driving these emergent norms and to design principled interventions that can steer them toward desired outcomes. This is challenging because norms arise from countless micro-level interactions that aggregate into macro-level regularities, making causal attribution and policy transferability difficult. To address this, we propose \textbfInvariant Causal Routing (ICR), a causal governance framework that identifies policy-norm relations stable across heterogeneous environments. ICR integrates counterfactual reasoning with invariant causal discovery to separate genuine causal effects from spurious correlations and to construct interpretable, auditable policy rules that remain effective under distribution shift. In heterogeneous agent simulations calibrated with real data, ICR yields more stable norms, smaller generalization gaps, and more concise rules than correlation or coverage baselines, demonstrating that causal invariance offers a principled and interpretable foundation for governance.
[AI-76] Discovering mathematical concepts through a multi-agent system
【速读】：该论文旨在解决如何通过多智能体系统实现计算数学发现的问题，特别是从原始数据中自主重构数学概念（如同调理论）并识别具有数学意义的结构。其核心挑战在于模拟人类数学家在探索过程中依赖实验、证明尝试与反例反馈的动态迭代机制。解决方案的关键在于构建一个基于局部过程优化的多智能体模型，该模型能够自主提出猜想、尝试证明，并根据反馈和演化中的数据分布调整策略；实验表明，这种动态交互机制能有效引导系统生成与人类数学直觉高度一致的“数学有趣性”（mathematical interestingness）判断，从而实现对复杂数学概念的自动发现。
链接: https://arxiv.org/abs/2603.04528
作者: Daattavya Aggarwal,Oisin Kim,Carl Henrik Ek,Challenger Mishra
机构: 未知
类目: Artificial Intelligence (cs.AI); History and Overview (math.HO)
备注: 30 pages, 8 figures
Abstract:Mathematical concepts emerge through an interplay of processes, including experimentation, efforts at proof, and counterexamples. In this paper, we present a new multi-agent model for computational mathematical discovery based on this observation. Our system, conceived with research in mind, poses its own conjectures and then attempts to prove them, making decisions informed by this feedback and an evolving data distribution. Inspired by the history of Euler’s conjecture for polyhedra and an open challenge in the literature, we benchmark with the task of autonomously recovering the concept of homology from polyhedral data and knowledge of linear algebra. Our system completes this learning problem. Most importantly, the experiments are ablations, statistically testing the value of the complete dynamic and controlling for experimental setup. They support our main claim: that the optimisation of the right combination of local processes can lead to surprisingly well-aligned notions of mathematical interestingness.
[AI-77] Augmenting representations with scientific papers ICLR2026
【速读】：该论文旨在解决天文多模态数据（如X射线光谱与科学文献）之间缺乏系统整合的问题，从而提升对稀有或理解不足天体源的解释效率。其核心挑战在于如何将具有特定物理信息的光谱数据与涵盖更广泛物理背景的文本内容进行有意义的对齐。解决方案的关键是提出了一种对比学习框架（contrastive learning framework），通过从文献中提取领域知识来对齐X射线光谱与文本表示，构建共享的多模态潜在空间（multimodal latent space）。该方法在检索任务中实现了20%的Recall@1%，并显著提升了20个物理变量估计的准确性（较单一光谱基线提高16–18%），同时验证了混合专家（Mixture of Experts, MoE）策略在融合单模态与共享表征上的优越性，最终可识别高优先级后续观测目标（如候选脉动超亮X射线源PULX和引力透镜系统）。
链接: https://arxiv.org/abs/2603.04516
作者: Nicolò Oreste Pinciroli Vago,Rocco Di Tella,Carolina Cuesta-Lázaro,Michael J. Smith,Cecilia Garraffo,Rafael Martínez-Galarza
机构: 未知
类目: Machine Learning (cs.LG); Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI)
备注: Accepted at the 2nd Workshop on Foundation Models for Science (ICLR 2026)
Abstract:Astronomers have acquired vast repositories of multimodal data, including images, spectra, and time series, complemented by decades of literature that analyzes astrophysical sources. Still, these data sources are rarely systematically integrated. This work introduces a contrastive learning framework designed to align X-ray spectra with domain knowledge extracted from scientific literature, facilitating the development of shared multimodal representations. Establishing this connection is inherently complex, as scientific texts encompass a broader and more diverse physical context than spectra. We propose a contrastive pipeline that achieves a 20% Recall@1% when retrieving texts from spectra, proving that a meaningful alignment between these modalities is not only possible but capable of accelerating the interpretation of rare or poorly understood sources. Furthermore, the resulting shared latent space effectively encodes physically significant information. By fusing spectral and textual data, we improve the estimation of 20 physical variables by 16-18% over unimodal spectral baselines. Our results indicate that a Mixture of Experts (MoE) strategy, which leverages both unimodal and shared representations, yields superior performance. Finally, outlier analysis within the multimodal latent space identifies high-priority targets for follow-up investigation, including a candidate pulsating ULX (PULX) and a gravitational lens system. Importantly, this framework can be extended to other scientific domains where aligning observational data with existing literature is possible.
[AI-78] Progressive Refinement Regulation for Accelerating Diffusion Language Model Decoding
【速读】：该论文旨在解决扩散语言模型（Diffusion Language Models）在文本生成过程中因所有词元（token）采用统一精炼规则而导致的冗余精炼问题，即不同词元的实际稳定速率存在差异，导致部分词元被过度优化。解决方案的关键在于提出一种渐进式精炼调控（Progressive Refinement Regulation, PRR）框架，其核心是基于完整解码轨迹（full decoding rollouts）构建词元级的经验收敛进度信号，并据此学习一个轻量级的词元级控制器，通过温度驱动的概率分布重塑实现渐进式自演化训练下的精炼控制。该方法使精炼过程动态适应词元的未来轨迹变化，从而显著加速解码并保持生成质量。
链接: https://arxiv.org/abs/2603.04514
作者: Lipeng Wan,Jianhui Gu,Junjie Ma,Jianguo Huang,Shiguang Sun,Siyuan Li,Xuguang Lan
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, 10 figures, Code available upon publication
Abstract:Diffusion language models generate text through iterative denoising under a uniform refinement rule applied to all tokens. However, tokens stabilize at different rates in practice, leading to substantial redundant refinement and motivating refinement control over the denoising process. Existing approaches typically assess refinement necessity from instantaneous, step-level signals under a fixed decoding process. In contrast, whether a token has converged is defined by how its prediction changes along its future refinement trajectory. Moreover, changing the refinement rule reshapes future refinement trajectories, which in turn determine how refinement rules should be formulated, making refinement control inherently dynamic. We propose \emphProgressive Refinement Regulation (PRR), a progressive, trajectory-grounded refinement control framework that derives a token-level notion of empirical convergence progress from full decoding rollouts. Based on this signal, PRR learns a lightweight token-wise controller to regulate refinement via temperature-based distribution shaping under a progressive self-evolving training scheme. Experiments show that PRR substantially accelerates diffusion language model decoding while preserving generation quality.
[AI-79] Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN
【速读】：该论文旨在解决基于智能鞋垫（smart insoles）的多模态时间序列数据在人体活动分类中的准确性和实时性问题。其核心挑战在于如何有效融合压力、加速度和陀螺仪等异构传感器数据，并实现高精度且适合嵌入式部署的分类模型。解决方案的关键在于提出一种基于循环膨胀卷积神经网络（Circular Dilated Convolutional Neural Network, CDCNN）的架构，该模型直接处理160帧窗口内的24通道原始信号（18个压力传感器 + 3轴加速度计 + 3轴陀螺仪），无需特征工程或数据展平，从而保留时序结构信息并提升泛化能力；实验表明其在四类活动（站立、行走、坐姿、单脚站立）识别中达到86.42%的测试准确率，优于XGBoost基线模型，同时具备良好的嵌入式部署潜力与实时推理性能。
链接: https://arxiv.org/abs/2603.04477
作者: Yanhua Zhao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 4 pages, 5 figures
Abstract:Smart insoles equipped with pressure sensors, accelerometers, and gyroscopes offer a non-intrusive means of monitoring human gait and posture. We present an activity classification system based on a circular dilated convolutional neural network (CDCNN) that processes multi-modal time-series data from such insoles. The model operates on 160-frame windows with 24 channels (18 pressure, 3 accelerometer, 3 gyroscope axes), achieving 86.42% test accuracy in a subject-independent evaluation on a four-class task (Standing, Walking, Sitting, Tandem), compared with 87.83% for an extreme gradient-boosted tree (XGBoost) model trained on flattened data. Permutation feature importance reveals that inertial sensors (accelerometer and gyroscope) contribute substantially to discrimination. The approach is suitable for embedded deployment and real-time inference.
[AI-80] owards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways
【速读】：该论文旨在解决内河航道中船舶轨迹预测的准确性与可解释性问题，尤其是在复杂船舶交互场景下，如何提升模型预测性能并确保其决策逻辑透明。解决方案的关键在于引入基于LSTM的轨迹预测模型，并融合训练得到的船舶域（Ship Domain）参数，以实现对相邻船舶隐藏状态的注意力机制融合，从而增强模型的内在可解释性。该设计不仅提升了预测精度（5分钟预测期内平均位移误差约40米），还通过分析生成的船舶域值合理性，揭示了模型在船舶交互中的注意力分配机制，为后续开展反事实分析和改进注意力机制提供了基础。
链接: https://arxiv.org/abs/2603.04472
作者: Tom Legel,Dirk Söffker,Roland Schätzle,Kathrin Donandt
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: This is a preprint of a paper published in the Proceedings of the 35th European Safety and Reliability the 33rd Society for Risk Analysis Europe Conference. DOI of the published version: https://doi.org/10.3850/978-981-94-3281-3_ESREL-SRA-E2025-P1370-cd . Reproduced here with permission of the publisher. For citation purposes, please refer exclusively to the published version
Abstract:Accurate predictions of ship trajectories in crowded environments are essential to ensure safety in inland waterways traffic. Recent advances in deep learning promise increased accuracy even for complex scenarios. While the challenge of ship-to-ship awareness is being addressed with growing success, the explainability of these models is often overlooked, potentially obscuring an inaccurate logic and undermining the confidence in their reliability. This study examines an LSTM-based vessel trajectory prediction model by incorporating trained ship domain parameters that provide insight into the attention-based fusion of the interacting vessels’ hidden states. This approach has previously been explored in the field of maritime shipping, yet the variety and complexity of encounters in inland waterways allow for a more profound analysis of the model’s interpretability. The prediction performance of the proposed model variants are evaluated using standard displacement error statistics. Additionally, the plausibility of the generated ship domain values is analyzed. With an final displacement error of around 40 meters in a 5-minute prediction horizon, the model performs comparably to similar studies. Though the ship-to-ship attention architecture enhances prediction accuracy, the weights assigned to vessels in encounters using the learnt ship domain values deviate from the expectation. The observed accuracy improvements are thus not entirely driven by a causal relationship between a predicted trajectory and the trajectories of nearby ships. This finding underscores the model’s explanatory capabilities through its intrinsically interpretable design. Future work will focus on utilizing the architecture for counterfactual analysis and on the incorporation of more sophisticated attention mechanisms.
[AI-81] Understanding the Dynamics of Demonstration Conflict in In-Context Learning
【速读】：该论文旨在解决大语言模型在**上下文学习（in-context learning）**中因演示样本存在噪声或冲突而导致性能显著下降的问题，尤其关注模型如何处理包含错误规则的演示示例。其关键解决方案在于揭示了模型内部存在一种两阶段计算结构：早期至中期层中，模型同时编码正确与错误规则，但仅在晚期层才建立对预测的信心；通过线性探测和logit lens分析识别出两类注意力头——“易受攻击头”（Vulnerability Heads）位于早期层，对扰动敏感且具位置注意力偏差；“易受影响头”（Susceptible Heads）位于晚期层，在接触错误证据时会显著削弱对正确预测的支持。针对性地屏蔽少量此类头可使模型性能提升超过10%，验证了该机制的核心作用。
链接: https://arxiv.org/abs/2603.04464
作者: Difan Jiao,Di Wang,Lijie Hu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 19 pages,12 figures,4 tables
Abstract:In-context learning enables large language models to perform novel tasks through few-shot demonstrations. However, demonstrations per se can naturally contain noise and conflicting examples, making this capability vulnerable. To understand how models process such conflicts, we study demonstration-dependent tasks requiring models to infer underlying patterns, a process we characterize as rule inference. We find that models suffer substantial performance degradation from a single demonstration with corrupted rule. This systematic misleading behavior motivates our investigation of how models process conflicting evidence internally. Using linear probes and logit lens analysis, we discover that under corruption models encode both correct and incorrect rules in intermediate layers but develop prediction confidence only in late layers, revealing a two-phase computational structure. We then identify attention heads for each phase underlying the reasoning failures: Vulnerability Heads in early-to-middle layers exhibit positional attention bias with high sensitivity to corruption, while Susceptible Heads in late layers significantly reduce support for correct predictions when exposed to the corrupted evidence. Targeted ablation validates our findings, with masking a small number of identified heads improving performance by over 10%.
[AI-82] MAD-SmaAt-GNet: A Multimodal Advection-Guided Neural Network for Precipitation Nowcasting
【速读】：该论文旨在解决降水临近预报（precipitation nowcasting）中传统数值物理模型计算成本高且难以充分利用海量气象数据的问题。其解决方案的关键在于提出一种名为MAD-SmaAt-GNet的新型轻量级卷积神经网络架构，该架构在SmaAt-UNet基础上进行两方面改进：一是引入多模态编码器以融合多种气象变量信息，提升短时预报精度；二是嵌入基于物理的平流（advection）模块，确保预测结果在时空演化上符合物理规律。实验表明，两项改进均能独立提升预报性能，联合使用可使四步预测（最多4小时 ahead）的均方误差（MSE）降低8.9%。
链接: https://arxiv.org/abs/2603.04461
作者: Samuel van Wonderen,Siamak Mehrkanoon
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figs
Abstract:Precipitation nowcasting (short-term forecasting) is still often performed using numerical solvers for physical equations, which are computationally expensive and make limited use of the large volumes of available weather data. Deep learning models have shown strong potential for precipitation nowcasting, offering both accuracy and computational efficiency. Among these models, convolutional neural networks (CNNs) are particularly effective for image-to-image prediction tasks. The SmaAt-UNet is a lightweight CNN based architecture that has demonstrated strong performance for precipitation nowcasting. This paper introduces the Multimodal Advection-Guided Small Attention GNet (MAD-SmaAt-GNet), which extends the core SmaAt-UNet by (i) incorporating an additional encoder to learn from multiple weather variables and (ii) integrating a physics-based advection component to ensure physically consistent predictions. We show that each extension individually improves rainfall forecasts and that their combination yields further gains. MAD-SmaAt-GNet reduces the mean squared error (MSE) by 8.9% compared with the baseline SmaAt-UNet for four-step precipitation forecasting up to four hours ahead. Additionally, experiments indicate that multimodal inputs are particularly beneficial for short lead times, while the advection-based component enhances performance across both short and long forecasting horizons.
[AI-83] VSPrefill: Vertical-Slash Sparse Attention with Lightweight Indexing for Long-Context Prefilling
【速读】：该论文旨在解决大语言模型在预填充（prefill）阶段自注意力机制的二次复杂度问题，该问题限制了长上下文推理的效率。现有稀疏注意力方法在上下文适应性、采样开销与微调成本之间存在权衡。其解决方案的关键在于提出VSPrefill机制，该机制通过引入垂直斜线（vertical-slash）结构模式构建稀疏注意力掩码，利用轻量级训练的VSIndexer模块从增强RoPE的位置编码中预测关键值表示的重要性得分，从而生成具有线性复杂度的稀疏掩码；同时，在推理阶段采用自适应累积阈值策略分配每层的稀疏预算，并通过融合内核实现动态索引合并，有效平衡了精度与效率。
链接: https://arxiv.org/abs/2603.04460
作者: Chen Guanzhong
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The quadratic complexity of self-attention during the prefill phase impedes long-context inference in large language models. Existing sparse attention methods face a trade-off among context adaptivity, sampling overhead, and fine-tuning costs. We propose VSPrefill, a mechanism requiring lightweight training that uses the vertical-slash structural pattern in attention distributions. Our compact VSIndexer module predicts context-aware importance scores for vertical columns and slash diagonals from key-value representations augmented with RoPE. This approach constructs sparse masks with linear complexity without modifying the backbone parameters. During inference, an adaptive cumulative-threshold strategy allocates sparsity budgets per layer, while a fused kernel executes attention with on-the-fly index merging. Evaluated on Qwen3-4B-Instruct and LLaMA-3.1-8B-Instruct across the LongBench and RULER benchmarks, VSPrefill preserves 98.35% of the full attention accuracy while delivering a 4.95x average speedup at a context length of 128k. These results establish a new Pareto frontier in the trade-off between accuracy and efficiency.
[AI-84] Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks
【速读】：该论文试图解决当前大语言模型（Large Language Model, LLM）安全领域中基准测试（benchmark）的影响力与代码质量缺乏系统评估的问题。现有研究难以追踪LLM安全领域的进展，而基准测试虽被广泛使用，但其学术影响力和实现质量未得到充分量化分析。解决方案的关键在于对31个LLM安全基准和382个非基准文献进行多维评估：一方面基于五项指标衡量学术影响力（如引用次数和密度），另一方面通过自动化工具与人工评估相结合的方式评价代码质量和补充材料的可用性。研究发现，基准论文在学术影响力上并无显著优势，且作者声望与论文影响力均不显著关联代码质量，揭示了“影响力—质量”之间的关键错位，并指出当前多数代码仓库存在可复用性差、安装指南不完善及伦理考量缺失等问题，强调应由高影响力研究者引领提升标准以推动领域健康发展。
链接: https://arxiv.org/abs/2603.04459
作者: Junjie Chu,Xinyue Shen,Ye Leng,Michael Backes,Yun Shen,Yang Zhang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 22 pages. 19 figures
Abstract:The rapid growth of research in LLM safety makes it hard to track all advances. Benchmarks are therefore crucial for capturing key trends and enabling systematic comparisons. Yet, it remains unclear why certain benchmarks gain prominence, and no systematic assessment has been conducted on their academic influence or code quality. This paper fills this gap by presenting the first multi-dimensional evaluation of the influence (based on five metrics) and code quality (based on both automated and human assessment) on LLM safety benchmarks, analyzing 31 benchmarks and 382 non-benchmarks across prompt injection, jailbreak, and hallucination. We find that benchmark papers show no significant advantage in academic influence (e.g., citation count and density) over non-benchmark papers. We uncover a key misalignment: while author prominence correlates with paper influence, neither author prominence nor paper influence shows a significant correlation with code quality. Our results also indicate substantial room for improvement in code and supplementary materials: only 39% of repositories are ready-to-use, 16% include flawless installation guides, and a mere 6% address ethical considerations. Given that the work of prominent researchers tends to attract greater attention, they need to lead the effort in setting higher standards.
[AI-85] Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
【速读】：该论文旨在解决混合数据（mixed data）聚类中数值型与类别型属性难以协同利用的问题。由于数值属性在欧氏空间中表示连续趋势，而类别属性则嵌入于隐式空间中代表离散概念，现有方法通常通过统一编码或定义单一度量来处理二者，忽略了其内在关联。解决方案的关键在于提出一种新颖的异构属性重构与表征（Heterogeneous Attribute Reconstruction and Representation, HARR）学习范式：该范式将不同类型的属性映射到统一的可学习多空间中，从而更精细地建模类别属性的距离度量，并将度量学习与聚类过程联合优化，实现对不同簇数 $ k $ 的自适应调整。此方法无需参数设置、收敛性有保障，且在准确性和效率上均优于现有方法。
链接: https://arxiv.org/abs/2603.04458
作者: Yiqun Zhang,Mingjie Zhao,Yizhou Chen,Yang Lu,Yiu-ming Cheung
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: ESWA 2025 paper
Abstract:Datasets composed of numerical and categorical attributes (also called mixed data hereinafter) are common in real clustering tasks. Differing from numerical attributes that indicate tendencies between two concepts (e.g., high and low temperature) with their values in well-defined Euclidean distance space, categorical attribute values are different concepts (e.g., different occupations) embedded in an implicit space. Simultaneously exploiting these two very different types of information is an unavoidable but challenging problem, and most advanced attempts either encode the heterogeneous numerical and categorical attributes into one type, or define a unified metric for them for mixed data clustering, leaving their inherent connection unrevealed. This paper, therefore, studies the connection among any-type of attributes and proposes a novel Heterogeneous Attribute Reconstruction and Representation (HARR) learning paradigm accordingly for cluster analysis. The paradigm transforms heterogeneous attributes into a homogeneous status for distance metric learning, and integrates the learning with clustering to automatically adapt the metric to different clustering tasks. Differing from most existing works that directly adopt defined distance metrics or learn attribute weights to search clusters in a subspace. We propose to project the values of each attribute into unified learnable multiple spaces to more finely represent and learn the distance metric for categorical data. HARR is parameter-free, convergence-guaranteed, and can more effectively self-adapt to different sought number of clusters k . Extensive experiments illustrate its superiority in terms of accuracy and efficiency.
[AI-86] Capability Thresholds and Manufacturing Topology: How Embodied Intelligence Triggers Phase Transitions in Economic Geography
【速读】：该论文试图解决制造业长期停滞于福特主义（Fordist）范式下的结构性问题，即自1913年流水线生产以来，尽管经历了丰田生产系统（Toyota Production System）和工业4.0等重大创新，制造地理格局仍依赖集中式大型工厂、劳动力密集区和规模化生产逻辑，未能实现根本性变革。其解决方案的关键在于引入具身智能（Embodied Intelligence），当其在灵巧性（dexterity, d）、泛化能力（generalization, g）、可靠性（reliability, r）和触觉-视觉融合（tactile-vision fusion, t）四个维度达到临界阈值时，将触发制造经济地理的相变（phase transitions），通过权重反转、批量坍缩和人-基础设施解耦三条路径，重构工厂选址逻辑、消除“制造荒漠”，并使最优生产位置由机器适宜条件（如低湿度、高辐照度、热稳定性）决定，从而建立一种前所未有的“机器气候优势”（Machine Climate Advantage），形成以物理AI能力阈值为驱动的具身智能经济学（Embodied Intelligence Economics）。
链接: https://arxiv.org/abs/2603.04457
作者: Xinmin Fang,Lingfeng Tao,Zhengxiong Li
机构: 未知
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Physics and Society (physics.soc-ph)
备注:
Abstract:The fundamental topology of manufacturing has not undergone a paradigm-level transformation since Henry Ford’s moving assembly line in 1913. Every major innovation of the past century, from the Toyota Production System to Industry 4.0, has optimized within the Fordist paradigm without altering its structural logic: centralized mega-factories, located near labor pools, producing at scale. We argue that embodied intelligence is poised to break this century-long stasis, not by making existing factories more efficient, but by triggering phase transitions in manufacturing economic geography itself. When embodied AI capabilities cross critical thresholds in dexterity, generalization, reliability, and tactile-vision fusion, the consequences extend far beyond cost reduction: they restructure where factories are built, how supply chains are organized, and what constitutes viable production scale. We formalize this by defining a Capability Space C = (d, g, r, t) and showing that the site-selection objective function undergoes topological reorganization when capability vectors cross critical surfaces. Through three pathways, weight inversion, batch collapse, and human-infrastructure decoupling, we show that embodied intelligence enables demand-proximal micro-manufacturing, eliminates “manufacturing deserts,” and reverses geographic concentration driven by labor arbitrage. We further introduce Machine Climate Advantage: once human workers are removed, optimal factory locations are determined by machine-optimal conditions (low humidity, high irradiance, thermal stability), factors orthogonal to traditional siting logic, creating a production geography with no historical precedent. This paper establishes Embodied Intelligence Economics, the study of how physical AI capability thresholds reshape the spatial and structural logic of production.
[AI-87] Large Language Models as Bidding Agents in Repeated HetNet Auction
【速读】：该论文旨在解决异构网络（HetNets）中频谱拍卖机制在实际场景下的局限性问题，即传统方法多假设一次性拍卖、静态投标行为和理想化条件，无法适应用户设备（UE）在预算约束下进行长期经济决策的复杂环境。解决方案的关键在于提出一种分布式拍卖框架，其中每个基站（BS）独立开展多信道拍卖，而UE则基于历史交互结果、竞争态势和策略适应能力，动态调整其关联选择与出价行为；特别地，引入生成式AI（Generative AI）驱动的推理代理（reasoning agents），使UE能够从重复博弈中学习并优化长期收益，从而显著提升频谱接入频率和预算效率。
链接: https://arxiv.org/abs/2603.04455
作者: Ismail Lotfi,Ali Ghrayeb,Samson Lasaulce,Merouane Debbah
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注: Accepted at WCNC 2026. Code available here: this https URL
Abstract:This paper investigates the integration of large language models (LLMs) as reasoning agents in repeated spectrum auctions within heterogeneous networks (HetNets). While auction-based mechanisms have been widely employed for efficient resource allocation, most prior works assume one-shot auctions, static bidder behavior, and idealized conditions. In contrast to traditional formulations where base station (BS) association and power allocation are centrally optimized, we propose a distributed auction-based framework in which each BS independently conducts its own multi-channel auction, and user equipments (UEs) strategically decide both their association and bid values. Within this setting, UEs operate under budget constraints and repeated interactions, transforming resource allocation into a long-term economic decision rather than a one-shot optimization problem. The proposed framework enables the evaluation of diverse bidding behaviors -from classical myopic and greedy policies to LLM-based agents capable of reasoning over historical outcomes, anticipating competition, and adapting their bidding strategy across episodes. Simulation results reveal that the LLM-empowered UE consistently achieves higher channel access frequency and improved budget efficiency compared to benchmarks. These findings highlight the potential of reasoning-enabled agents in future decentralized wireless networks markets and pave the way for lightweight, edge-deployable LLMs to support intelligent resource allocation in next-generation HetNets.
[AI-88] On Emergences of Non-Classical Statistical Characteristics in Classical Neural Networks
【速读】：该论文旨在解决深度神经网络内部交互机制与训练动态的复杂性问题，特别是如何从统计非经典性（non-classicality）角度理解多任务学习中不同任务头之间的隐式关联及其对模型性能的影响。其解决方案的关键在于提出一种名为“非经典网络”（Non-Classical Network, NCnet）的经典神经架构，该架构通过共享隐藏层神经元在多任务学习中的梯度竞争，诱导出类似量子力学中贝尔不等式（Bell-family inequalities）所描述的非经典统计行为——具体表现为CHSH不等式的S统计量偏离经典上限2的现象。研究发现，即使没有显式通信路径，各任务头也能通过局部损失函数的振荡实现隐式感知，从而产生非局域相关性；且S值的变化趋势与模型资源规模和泛化性能密切相关，揭示了非经典统计量可作为衡量深度网络内部协作效率与训练状态的新指标。
链接: https://arxiv.org/abs/2603.04451
作者: Hanyu Zhao,Yang Wu,Yuexian Hou
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Quantum Physics (quant-ph)
备注:
Abstract:Inspired by measurement incompatibility and Bell-family inequalities in quantum mechanics, we propose the Non-Classical Network (NCnet), a simple classical neural architecture that stably exhibits non-classical statistical behaviors under typical and interpretable experimental setups. We find non-classicality, measured by the S statistic of CHSH inequality, arises from gradient competitions of hidden-layer neurons shared by multi-tasks. Remarkably, even without physical links supporting explicit communication, one task head can implicitly sense the training task of other task heads via local loss oscillations, leading to non-local correlations in their training outcomes. Specifically, in the low-resource regime, the value of S increases gradually with increasing resources and approaches toward its classical upper-bound 2, which implies that underfitting is alleviated with resources increase. As the model nears the critical scale required for adequate performance, S may temporarily exceed 2. As resources continue to grow, S then asymptotically decays down to and fluctuates around 2. Empirically, when model capacity is insufficient, S is positively correlated with generalization performance, and the regime where S first approaches 2 often corresponding to good generalization. Overall, our results suggest that non-classical statistics can provide a novel perspective for understanding internal interactions and training dynamics of deep networks.
[AI-89] MPBMC: Multi-Property Bounded Model Checking with GNN-guided Clustering
【速读】：该论文旨在解决多属性验证（Multi-Property Verification, MPV）中如何高效地对设计属性进行聚类以提升验证效率的问题。其核心挑战在于如何生成有效的属性分组策略，使得在Bounded Model Checking（BMC）过程中能够并行或分批处理相关属性，从而减少冗余计算。解决方案的关键在于提出一种混合方法，利用图神经网络（Graph Neural Network, GNN）生成硬件电路的功能嵌入（functional embedding），并结合运行时的设计统计信息，智能地对属性进行聚类。该方法通过融合功能语义与实际验证行为特征，显著提升了BMC在MPV场景下的性能表现。
链接: https://arxiv.org/abs/2603.04450
作者: Soumik Guha Roy,Sumana Ghosh,Ansuman Banerjee,Raj Kumar Gajavelly,Sudhakar Surendran
机构: 未知
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: 6 pages, 5 figures
Abstract:Formal verification of designs with multiple properties has been a long-standing challenge for the verification research community. The task of coming up with an effective strategy that can efficiently cluster properties to be solved together has inspired a number of proposals, ranging from structural clustering based on the property cone of influence (COI) to leverage runtime design and verification statistics. In this paper, we present an attempt towards functional clustering of properties utilizing graph neural network (GNN) embeddings for creating effective property clusters. We propose a hybrid approach that can exploit neural functional representations of hardware circuits and runtime design statistics to speed up the performance of Bounded Model Checking (BMC) in the context of multi-property verification (MPV). Our method intelligently groups properties based on their functional embedding and design statistics, resulting in speedup in verification results. Experimental results on the HWMCC benchmarks show the efficacy of our proposal with respect to the state-of-the-art.
[AI-90] An Explainable Ensemble Framework for Alzheimers Disease Prediction Using Structured Clinical and Cognitive Data
【速读】：该论文旨在解决阿尔茨海默病（Alzheimer’s Disease, AD）早期且准确检测的难题，因其临床表现隐匿且进展缓慢，传统诊断方法难以实现精准识别。解决方案的关键在于构建一个可解释的集成学习框架，融合结构化临床、生活方式、代谢等多维度特征，通过严格的预处理、SMOTE-Tomek混合类不平衡处理、以及五种先进集成算法（随机森林、XGBoost、LightGBM、CatBoost和Extra Trees）与深度神经网络的对比优化建模，最终选用性能最优的模型进行验证。该框架在保持高预测精度的同时，借助SHAP值和特征重要性分析实现了决策过程的透明化，揭示了MMSE评分、功能评估年龄及若干工程交互特征为关键判别因子，从而为临床辅助决策提供可靠、可解释的AI支持工具。
链接: https://arxiv.org/abs/2603.04449
作者: Nishan Mitra
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 6 pages, 7 figures, 2 tables. Preprint version
Abstract:Early and accurate detection of Alzheimer’s disease (AD) remains a major challenge in medical diagnosis due to its subtle onset and progressive nature. This research introduces an explainable ensemble learning Framework designed to classify individuals as Alzheimer’s or Non-Alzheimer’s using structured clinical, lifestyle, metabolic, and lifestyle features. The workflow incorporates rigorous preprocessing, advanced feature engineering, SMOTE-Tomek hybrid class balancing, and optimized modeling using five ensemble algorithms-Random Forest, XGBoost, LightGBM, CatBoost, and Extra Trees-alongside a deep artificial neural network. Model selection was performed using stratified validation to prevent leakage, and the best-performing model was evaluated on a fully unseen test set. Ensemble methods achieved superior performance over deep learning, with XGBoost, Random Forest, and Soft Voting showing the strongest accuracy, sensitivity, and F1-score profiles. Explainability techniques, including SHAP and feature importance analysis, highlighted MMSE, Functional Assessment Age, and several engineered interaction features as the most influential determinants. The results demonstrate that the proposed framework provides a reliable and transparent approach to Alzheimer’s disease prediction, offering strong potential for clinical decision support applications. Comments: 6 pages, 7 figures, 2 tables. Preprint version Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) ACMclasses: I.2.6; J.3 Cite as: arXiv:2603.04449 [cs.LG] (or arXiv:2603.04449v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.04449 Focus to learn more arXiv-issued DOI via DataCite
[AI-91] vLLM Semantic Router: Signal Driven Decision Routing for Mixture-of-Modality Models
【速读】：该论文旨在解决多模态大语言模型（Mixture-of-Modality, MoM）部署中智能请求路由（intelligent request routing）的系统性挑战，即在推理阶段为每个查询动态选择最合适的模型，以兼顾成本、延迟、隐私和安全性等不同需求。解决方案的关键在于提出了一种可组合信号编排（composable signal orchestration）框架：系统从每个请求中提取异构信号（包括亚毫秒级启发式特征与神经分类器输出），并通过可配置的布尔决策规则将其组合成针对特定部署场景的路由策略，无需代码修改即可适配多云企业、隐私合规、成本优化和低延迟等多样化需求；同时，匹配决策驱动语义模型路由，并通过插件链实现安全约束（如幻觉检测、PII过滤），从而构建一个统一、灵活且安全的路由基础设施。
链接: https://arxiv.org/abs/2603.04444
作者: Xunzhuo Liu,Huamin Chen,Samzong Lu,Yossi Ovadia,Guohong Wen,Zhengda Tan,Jintao Zhang,Senan Zedan,Yehudit Kerido,Liav Weiss,Bishen Yu,Asaad Balum,Noa Limoy,Abdallah Samara,Brent Salisbury,Hao Wu,Ryan Cook,Zhijie Wang,Qiping Pan,Rehan Khan,Avishek Goswami,Houston H. Zhang,Shuyi Wang,Ziang Tang,Fang Han,Zohaib Hassan,Jianqiao Zheng,Avinash Changrani
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注: Technical Report
Abstract:As large language models (LLMs) diversify across modalities, capabilities, and cost profiles, the problem of intelligent request routing – selecting the right model for each query at inference time – has become a critical systems challenge. We present vLLM Semantic Router, a signal-driven decision routing framework for Mixture-of-Modality (MoM) model deployments. The central innovation is composable signal orchestration: the system extracts heterogeneous signal types from each request – from sub-millisecond heuristic features (keyword patterns, language detection, context length, role-based authorization) to neural classifiers (domain, embedding similarity, factual grounding, modality) – and composes them through configurable Boolean decision rules into deployment-specific routing policies. Different deployment scenarios – multi-cloud enterprise, privacy-regulated, cost-optimized, latency-sensitive – are expressed as different signal-decision configurations over the same architecture, without code changes. Matched decisions drive semantic model routing: over a dozen of selection algorithms analyze request characteristics to find the best model cost-effectively, while per-decision plugin chains enforce privacy and safety constraints (jailbreak detection, PII filtering, hallucination detection via the three-stage HaluGate pipeline). The system provides OpenAI API support for stateful multi-turn conversations, multi-endpoint and multi-provider routing across heterogeneous backends (vLLM, OpenAI, Anthropic, Azure, Bedrock, Gemini, Vertex AI), and a pluggable authorization factory supporting multiple auth providers. Deployed in production as an Envoy external processor, the architecture demonstrates that composable signal orchestration enables a single routing framework to serve diverse deployment scenarios with differentiated cost, privacy, and safety policies. Comments: Technical Report Subjects: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04444 [cs.NI] (or arXiv:2603.04444v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2603.04444 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Huamin Chen [view email] [v1] Mon, 23 Feb 2026 15:00:01 UTC (54 KB)
[AI-92] AMV-L: Lifecycle-Managed Agent Memory for Tail-Latency Control in Long-Running LLM Systems
【速读】：该论文旨在解决长期运行的大语言模型（Large Language Model, LLM）智能体在使用基于时间的内存保留策略（如TTL，Time-To-Live）时，因内存项累积导致检索候选集和向量相似性扫描无界增长，从而引发请求路径上的延迟分布呈重尾状、吞吐不稳定的问题。解决方案的关键在于提出AMV-L（Adaptive Memory Value Lifecycle）框架，将代理记忆视为受控的系统资源：通过为每个记忆项分配持续更新的效用评分，并采用基于价值的晋升、降级与驱逐机制维持生命周期层级；同时限制检索仅作用于一个有界且分层感知的候选集合，从而实现请求路径工作集与总保留内存的解耦。实验证明，该方法显著提升了吞吐量并降低了延迟极端尾部表现，优于TTL和LRU基线，核心优势源于对检索集大小和向量搜索计算量的有效控制，而非单纯缩短提示词长度。
链接: https://arxiv.org/abs/2603.04443
作者: Emmanuel Bamidele
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注:
Abstract:Long-running LLM agents require persistent memory to preserve state across interactions, yet most deployed systems manage memory with age-based retention (e.g., TTL). While TTL bounds item lifetime, it does not bound the computational footprint of memory on the request path: as retained items accumulate, retrieval candidate sets and vector similarity scans can grow unpredictably, yielding heavy-tailed latency and unstable throughput. We present AMV-L (Adaptive Memory Value Lifecycle), a memory-management framework that treats agent memory as a managed systems resource. AMV-L assigns each memory item a continuously updated utility score and uses value-driven promotion, demotion, and eviction to maintain lifecycle tiers; retrieval is restricted to a bounded, tier-aware candidate set that decouples the request-path working set from total retained memory. We implement AMV-L in a full-stack LLM serving system and evaluate it under identical long-running workloads against two baselines: TTL and an LRU working-set policy, with fixed prompt-injection caps. Relative to TTL, AMV-L improves throughput by 3.1x and reduces latency by 4.2x (median), 4.7x (p95), and 4.4x (p99), while reducing the fraction of requests exceeding 2s from 13.8% to 0.007%. Compared to LRU, AMV-L trades a small regression in median/p95 latency (+26% / +3%) for improved extreme-tail behavior (-15% p99; -98% 2s) and lower token overhead (approximately 6% fewer tokens/request), while matching retrieval quality (value means within approximately 0-2%). The gains arise primarily from bounding retrieval-set size and vector-search work, not from shortening prompts. Our results show that predictable performance for long-running LLM agents requires explicit control of memory working-set size and value-driven lifecycle management, rather than retention time alone.
[AI-93] ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
【速读】：该论文旨在解决联邦学习（Federated Learning, FL）在无线网络中因客户端计算资源有限而导致的训练延迟高和能耗大的问题。解决方案的关键在于提出一种自适应分片联邦学习（Adaptive Split Federated Learning, ASFL）框架，该框架通过利用中心服务器的计算资源来训练模型的一部分，并实现训练过程中模型分片与资源分配的动态自适应调整。为优化学习性能（收敛速率）与效率（延迟和能耗），作者理论分析了收敛速率并构建了一个联合优化问题，进而设计了一种在线优化增强的块坐标下降算法（Online Optimization Enhanced Block Coordinate Descent, OOE-BCD），以迭代求解该复杂耦合问题。实验表明，相较于五种基线方案，ASFL在收敛速度上表现更优，且总延迟和能耗分别降低高达75%和80%。
链接: https://arxiv.org/abs/2603.04437
作者: Chuiyang Meng,Ming Tang,Vincent W.S. Wong
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the clients may result in a high delay and energy consumption on training. In this paper, we propose an adaptive split federated learning (ASFL) framework over wireless networks. ASFL exploits the computation resources of the central server to train part of the model and enables adaptive model splitting as well as resource allocation during training. To optimize the learning performance (i.e., convergence rate) and efficiency (i.e., delay and energy consumption) of ASFL, we theoretically analyze the convergence rate and formulate a joint learning performance and resource allocation optimization problem. Solving this problem is challenging due to the long-term delay and energy consumption constraints as well as the coupling of the model splitting and resource allocation decisions. We propose an online optimization enhanced block coordinate descent (OOE-BCD) algorithm to solve the problem iteratively. Experimental results show that when compared with five baseline schemes, our proposed ASFL framework converges faster and reduces the total delay and energy consumption by up to 75% and 80%, respectively.
[AI-94] ZorBA: Zeroth-order Federated Fine-tuning of LLM s with Heterogeneous Block Activation
【速读】：该论文旨在解决联邦微调大语言模型（Large Language Models, LLMs）时面临的两大挑战：一是由于LLM规模庞大，本地更新导致显存（Video Random-Access Memory, VRAM）占用过高；二是频繁的模型交换引发显著的通信开销。解决方案的关键在于提出ZorBA框架，其核心创新包括：1）基于零阶优化（zeroth-order optimization）的方法，通过前向传播替代梯度存储，从而消除客户端对梯度的显存需求；2）引入异构块激活机制（heterogeneous block activation），由中央服务器动态分配不同Transformer块子集给客户端，以加速收敛并降低VRAM消耗；3）利用共享随机种子和梯度的有限差分近似来减少通信量。此外，作者还建立了优化问题以联合提升收敛速度与降低显存使用，并设计了ε-约束字典序算法求解该问题。实验表明，ZorBA在VRAM使用上相比三个基线方法最高可减少62.41%，同时保持较低的通信开销。
链接: https://arxiv.org/abs/2603.04436
作者: Chuiyang Meng,Ming Tang,Vincent W.S. Wong
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Federated fine-tuning of large language models (LLMs) enables collaborative tuning across distributed clients. However, due to the large size of LLMs, local updates in federated learning (FL) may incur substantial video random-access memory (VRAM) usage. Moreover, frequent model exchange may lead to significant communication overhead. To tackle these challenges, in this paper we propose ZorBA, a zeroth-order optimization-based federated fine-tuning framework with heterogeneous block activation. ZorBA leverages zeroth-order optimization to eliminate the storage of gradients at the clients by forward passes. ZorBA includes a heterogeneous block activation mechanism in which the central server allocates different subsets of transformer blocks to clients in order to accelerate the convergence rate and reduce the VRAM usage. Furthermore, ZorBA utilizes shared random seeds and the finite differences of gradients in order to reduce the communication overhead. We conduct theoretical analysis to characterize the effect of block activation decisions on the convergence rate and VRAM usage. To jointly enhance the convergence rate and reduce the VRAM usage, we formulate an optimization problem to optimize the block activation decisions. We propose an \epsilon -constraint lexicographic algorithm to solve this problem. Experimental results show that ZorBA outperforms three federated fine-tuning baselines in VRAM usage by up to 62.41% and incurs a low communication overhead.
[AI-95] Uncertainty-Calibrated Spatiotemporal Field Diffusion with Sparse Supervision
【速读】：该论文旨在解决物理场（physical fields）在稀疏、时变传感器位置上观测时，导致的预测与重构 ill-posed（病态）问题，尤其是在不确定性关键场景下的建模挑战。解决方案的核心在于提出一种掩码条件扩散框架 SOLID，其关键创新是仅使用稀疏观测数据进行端到端训练，无需密集场数据或预插值（pre-imputation）；SOLID 在每一步去噪过程中同时条件化于测量值及其空间位置，并引入双掩码目标函数：一方面强调在未观测区域的学习，另一方面加权输入与目标重叠像素以提供最可靠的锚点，从而实现与观测一致的完整场后验采样，在极端稀疏条件下显著提升概率误差性能（改善达一个数量级）并生成校准的不确定性图（ρ ≥ 0.7）。
链接: https://arxiv.org/abs/2603.04431
作者: Kevin Valencia,Xihaier Luo,Shinjae Yoo,David Keetae Park
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 18 pages, 9 figures, 6 tables
Abstract:Physical fields are typically observed only at sparse, time-varying sensor locations, making forecasting and reconstruction ill-posed and uncertainty-critical. We present SOLID, a mask-conditioned diffusion framework that learns spatiotemporal dynamics from sparse observations alone: training and evaluation use only observed target locations, requiring no dense fields and no pre-imputation. Unlike prior work that trains on dense reanalysis or simulations and only tests under sparsity, SOLID is trained end-to-end with sparse supervision only. SOLID conditions each denoising step on the measured values and their locations, and introduces a dual-masking objective that (i) emphasizes learning in unobserved void regions while (ii) upweights overlap pixels where inputs and targets provide the most reliable anchors. This strict sparse-conditioning pathway enables posterior sampling of full fields consistent with the measurements, achieving up to an order-of-magnitude improvement in probabilistic error and yielding calibrated uncertainty maps (\rho 0.7) under severe sparsity.
[AI-96] Agent Memory Below the Prompt: Persistent Q4 KV Cache for Multi-Agent LLM Inference on Edge Devices
【速读】：该论文旨在解决多智能体大语言模型（Multi-agent LLM）在边缘设备上运行时面临的内存管理难题：设备RAM容量有限，无法同时存储所有智能体的键值缓存（KV cache），导致频繁的缓存淘汰与重加载操作。为应对这一问题，作者提出了一种基于4-bit量化持久化缓存的解决方案，其关键在于将每个智能体的KV缓存以Q4格式持久化至磁盘，并直接加载到注意力层中，从而避免冗余的O(n)预填充计算。该方案通过三个核心组件实现：隔离的块池（block pool）提供每智能体独立的Q4 KV缓存（safetensors格式）、支持并发推理的BatchQuantizedKVCache、以及跨对话阶段的上下文注入机制（cross-phase context injection），有效减少了首次生成时间（time-to-first-token）达136倍（Gemma 3 12B在32K上下文下），同时Q4量化使固定内存下可容纳的智能体数量提升至FP16的4倍，且保持了较高的生成质量（如Llama 3.1 8B的困惑度仅下降0.7%）。
链接: https://arxiv.org/abs/2603.04428
作者: Yakov Pyotr Shkolnikov
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 24 pages, 6 figures, 16 tables. Open-source implementation at this https URL
Abstract:Multi-agent LLM systems on edge devices face a memory management problem: device RAM is too small to hold every agent’s KV cache simultaneously. On Apple M4 Pro with 10.2 GB of cache budget, only 3 agents fit at 8K context in FP16. A 10-agent workflow must constantly evict and reload caches. Without persistence, every eviction forces a full re-prefill through the model – 15.7 seconds per agent at 4K context. We address this by persisting each agent’s KV cache to disk in 4-bit quantized format and reloading it directly into the attention layer, eliminating redundant O(n) prefill computation via direct cache restoration. The system comprises three components: a block pool providing per-agent isolated Q4 KV caches in safetensors format, a BatchQuantizedKVCache for concurrent inference over multiple agents’ quantized caches, and cross-phase context injection that accumulates attention state across conversation phases without re-computation. Evaluated on three architectures (Gemma 3 12B, dense GQA, 48 layers; DeepSeek-Coder-V2-Lite 16B, MoE MLA, 27 layers; Llama 3.1 8B, dense GQA, 32 layers), cache restoration reduces time-to-first-token by up to 136x (Gemma: 22–136x at 4K–32K; DeepSeek: 11–76x at 4K–32K; Llama: 24–111x at 4K–16K; 3–10x at 1K). Q4 quantization fits 4x more agent contexts into fixed device memory than FP16. Perplexity measured with actual Q4 KV caches shows -0.7% for Gemma, +2.8% for Llama, and +3.0% for DeepSeek. Open-source at this https URL
[AI-97] hin Keys Full Values: Reducing KV Cache via Low-Dimensional Attention Selection
【速读】：该论文旨在解决标准Transformer中查询（Query）、键（Key）和值（Value）维度一致（即 dq=dk=dv=dmodel）所带来的冗余计算与存储开销问题。其核心洞察在于：查询与键主要用于生成注意力权重（selection），而值则承载语义信息传递（value transfer），二者功能本质不同，因此无需保持相同的维度。解决方案的关键在于引入不对称注意力机制，即降低用于选择的查询和键的维度（\dselect \ll d_{\text{model}}），同时保留完整的值维度。实验表明，这种设计可在仅损失少量性能的前提下显著减少参数量（如QK矩阵减少75%）和KV缓存占用（如Mistral-7B模型节省75% key cache），从而大幅提升大模型部署时的推理效率与并发能力。
链接: https://arxiv.org/abs/2603.04427
作者: Hengshuai Yao,Guan Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Standard transformer attention uses identical dimensionality for queries, keys, and values ( d_q = d_k = d_v = \dmodel ). Our insight is that these components serve fundamentally different roles, and this symmetry is unnecessary. Queries and keys produce scalar attention weights (\emphselection), while values carry rich semantic representations (\emphvalue transfer). We argue that selection is an inherently lower-dimensional operation than value transfer, requiring only \BigO(\log N) dimensions to distinguish among N relevant patterns. We validate this hypothesis across seven experiments: (1)~positional selection tasks requiring just 1~dimension per head, (2)~content-based retrieval requiring \sim!\log_2 N dimensions, (3–4)~WikiText-2 and WikiText-103 language modeling where \dselect = \dmodel/4 incurs only 4.3% perplexity increase while reducing QK parameters by 75%, (5)~post-training SVD compression of GPT-2, revealing keys to be far more compressible than queries, with lightweight QK fine-tuning recovering nearly all quality loss, (6)~a 125M-parameter LLaMA model confirming identical degradation ratios across architectures, and (7)~Mistral-7B (7.2B parameters), where SVD compression followed by QK fine-tuning achieves 75% key cache savings at just 2.0% residual quality cost. For existing models, SVD compression followed by QK fine-tuning (3 epochs on a small fraction of pretraining data) achieves 75% key cache savings at 2% residual quality cost. For a 7B-parameter model serving 128K context, asymmetric attention saves 25,GB of KV cache per user, enabling approximately 60% more concurrent users on the same GPU.
[AI-98] Delta-Crosscoder: Robust Crosscoder Model Diffing in Narrow Fine-Tuning Regimes
【速读】：该论文旨在解决现有模型差分（model diffing）方法在窄范围微调（narrow fine-tuning）场景下表现不佳的问题，此类场景中行为变化具有局部性和非对称性，导致传统交叉编码器（crosscoder）方法难以准确识别因果性的潜在方向。解决方案的关键在于提出Delta-Crosscoder，其核心创新包括：引入BatchTopK稀疏性以聚焦关键方向、设计基于delta的损失函数优先捕捉模型间变化的方向，并利用配对输入上的激活隐式对比信号增强方向判别能力。该方法在多个模型（Gemma、LLaMA、Qwen，参数规模1B–9B）和任务（合成错误事实、新兴不对齐、亚阈值学习、禁忌词猜测）上均展现出优越的因果方向识别与行为缓解效果，显著优于SAE基线，同时保持与非SAE方法相当的性能。
链接: https://arxiv.org/abs/2603.04426
作者: Aly Kassem,Thomas Jiralerspong,Negar Rostamzadeh,Golnoosh Farnadi
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Model diffing methods aim to identify how fine-tuning changes a model’s internal representations. Crosscoders approach this by learning shared dictionaries of interpretable latent directions between base and fine-tuned models. However, existing formulations struggle with narrow fine-tuning, where behavioral changes are localized and asymmetric. We introduce Delta-Crosscoder, which combines BatchTopK sparsity with a delta-based loss prioritizing directions that change between models, plus an implicit contrastive signal from paired activations on matched inputs. Evaluated across 10 model organisms, including synthetic false facts, emergent misalignment, subliminal learning, and taboo word guessing (Gemma, LLaMA, Qwen; 1B-9B parameters), Delta-Crosscoder reliably isolates latent directions causally responsible for fine-tuned behaviors and enables effective mitigation, outperforming SAE-based baselines, while matching the Non-SAE-based. Our results demonstrate that crosscoders remain a powerful tool for model diffing.
[AI-99] FedEMA-Distill: Exponential Moving Averag e Guided Knowledge Distillation for Robust Federated Learning
【速读】：该论文旨在解决联邦学习（Federated Learning, FL）在客户端数据异构（non-IID）和存在恶意客户端（Byzantine clients）时导致的模型漂移（client drift）、收敛缓慢及通信开销高的问题。其解决方案的关键在于提出FedEMA-Distill，一种服务器端的优化机制：一方面利用指数移动平均（Exponential Moving Average, EMA）对全局模型进行时间平滑以缓解漂移；另一方面通过客户端上传的预测logits（仅压缩输出）进行集成知识蒸馏（ensemble knowledge distillation），并结合坐标级中位数或截断均值聚合策略增强对抗攻击下的鲁棒性。该方法无需修改客户端代码、支持异构模型架构，同时显著降低通信负载（达0.09–0.46 MB/轮），并在多个基准数据集上实现更高精度与更少通信轮次。
链接: https://arxiv.org/abs/2603.04422
作者: Hamza Reguieg,Mohamed El Kamili,Essaid Sabir
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 13 pages, 8 figures, 7 tables
Abstract:Federated learning (FL) often degrades when clients hold heterogeneous non-Independent and Identically Distributed (non-IID) data and when some clients behave adversarially, leading to client drift, slow convergence, and high communication overhead. This paper proposes FedEMA-Distill, a server-side procedure that combines an exponential moving average (EMA) of the global model with ensemble knowledge distillation from client-uploaded prediction logits evaluated on a small public proxy dataset. Clients run standard local training, upload only compressed logits, and may use different model architectures, so no changes are required to client-side software while still supporting model heterogeneity across devices. Experiments on CIFAR-10, CIFAR-100, FEMNIST, and AG News under Dirichlet-0.1 label skew show that FedEMA-Distill improves top-1 accuracy by several percentage points (up to +5% on CIFAR-10 and +6% on CIFAR-100) over representative baselines, reaches a given target accuracy in 30-35% fewer communication rounds, and reduces per-round client uplink payloads to 0.09-0.46 MB, i.e., roughly an order of magnitude less than transmitting full model weights. Using coordinate-wise median or trimmed-mean aggregation of logits at the server further stabilizes training in the presence of up to 10-20% Byzantine clients and yields well-calibrated predictions under attack. These results indicate that coupling temporal smoothing with logits-only aggregation provides a communication-efficient and attack-resilient FL pipeline that is deployment-friendly and compatible with secure aggregation and differential privacy, since only aggregated or obfuscated model outputs are exchanged.
[AI-100] Decorrelating the Future: Joint Frequency Domain Learning for Spatio-temporal Forecasting
【速读】：该论文旨在解决标准直接预测模型（如基于均方误差的点对点目标）在处理图结构信号时难以捕捉复杂时空依赖关系的问题，同时指出现有频域方法虽能缓解时间自相关性，却常忽略空间及跨时空交互。其解决方案的关键在于提出FreST Loss——一种频率增强的时空训练目标，通过联合傅里叶变换（Joint Fourier Transform, JFT）将模型预测与真实值对齐于统一的频谱域，从而有效解耦空间与时间维度上的复杂依赖关系，理论分析表明该方法可降低时间域训练目标带来的估计偏差，且实验验证其具备模型无关性并显著提升主流基线模型的性能。
链接: https://arxiv.org/abs/2603.04418
作者: Zepu Wang,Bowen Liao,Jeff(Xuegang)Ban
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Standard direct forecasting models typically rely on point-wise objectives such as Mean Squared Error, which fail to capture the complex spatio-temporal dependencies inherent in graph-structured signals. While recent frequency-domain approaches such as FreDF mitigate temporal autocorrelation, they often overlook spatial and cross spatio-temporal interactions. To address this limitation, we propose FreST Loss, a frequency-enhanced spatio-temporal training objective that extends supervision to the joint spatio-temporal spectrum. By leveraging the Joint Fourier Transform (JFT), FreST Loss aligns model predictions with ground truth in a unified spectral domain, effectively decorrelating complex dependencies across both space and time. Theoretical analysis shows that this formulation reduces estimation bias associated with time-domain training objectives. Extensive experiments on six real-world datasets demonstrate that FreST Loss is model-agnostic and consistently improves state-of-the-art baselines by better capturing holistic spatio-temporal dynamics.
[AI-101] he Spatial and Temporal Resolution of Motor Intention in Multi-Target Prediction
【速读】：该论文旨在解决如何从多通道肌电图（Electromyography, EMG）信号中准确预测人类运动意图的问题，特别是运动方向和目标位置的识别，以支持康复与辅助技术中的前瞻性控制。其关键解决方案是一个结合数据驱动的时间分割方法与经典及深度学习分类器的计算流程，能够在延迟到达任务的不同阶段（规划期、早期执行期和目标接触期）对EMG信号进行分析，从而实现对运动意图的早期预测。实验表明，随机森林分类器在25个间隔14°方位角/仰角的目标中达到80%的准确率，卷积神经网络也达到75%，且通过系统评估发现即使大幅减少数据量仍可高效解码运动意图，为自适应康复系统中的前瞻控制提供了理论基础和技术路径。
链接: https://arxiv.org/abs/2603.05418
作者: Marie Dominique Schmidt,Ioannis Iossifidis
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注:
Abstract:Reaching for grasping, and manipulating objects are essential motor functions in everyday life. Decoding human motor intentions is a central challenge for rehabilitation and assistive technologies. This study focuses on predicting intentions by inferring movement direction and target location from multichannel electromyography (EMG) signals, and investigating how spatially and temporally accurate such information can be detected relative to movement onset. We present a computational pipeline that combines data-driven temporal segmentation with classical and deep learning classifiers in order to analyse EMG data recorded during the planning, early execution, and target contact phases of a delayed reaching task. Early intention prediction enables devices to anticipate user actions, improving responsiveness and supporting active motor recovery in adaptive rehabilitation systems. Random Forest achieves 80% accuracy and Convolutional Neural Network 75% accuracy across 25 spatial targets, each separated by 14^\circ azimuth/altitude. Furthermore, a systematic evaluation of EMG channels, feature sets, and temporal windows demonstrates that motor intention can be efficiently decoded even with drastically reduced data. This work sheds light on the temporal and spatial evolution of motor intention, paving the way for anticipatory control in adaptive rehabilitation systems and driving advancements in computational approaches to motor neuroscience. Subjects: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI) ACMclasses: I.2; H.5; I.5 Cite as: arXiv:2603.05418 [q-bio.NC] (or arXiv:2603.05418v1 [q-bio.NC] for this version) https://doi.org/10.48550/arXiv.2603.05418 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-102] Visual-Informed Speech Enhancement Using Attention-Based Beamforming
链接: https://arxiv.org/abs/2603.05270
作者: Chihyun Liu,Jiaxuan Fan,Mingtung Sun,Michael Anthony,Mingsian R. Bai,Yu Tsao
机构: 未知
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
备注: 15 pages, 14 figures
[AI-103] Escaping the Hydrolysis Trap: An Agent ic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks
【速读】：该论文旨在解决共价有机框架（Covalent Organic Frameworks, COFs）在光催化制氢应用中面临的活性与稳定性之间的权衡问题，尤其是含氮亚胺（imine）键在水环境中易水解导致的稳定性不足。解决方案的关键在于引入一个基于预训练化学知识的大语言模型（Large-Language-Model, LLM）代理（Ara），该代理融合了供体-受体理论、共轭效应及键稳定性的层级信息，以高效导航由节点、连接基团、键类型和取代基（R-group）构成的高维设计空间，并同时满足带隙（band-gap）、能带边位置（band-edge）和水解稳定性（hydrolytic-stability）等多目标约束。实验表明，Ara相较随机搜索和贝叶斯优化（Bayesian Optimization, BO）显著提升命中率（52.7% vs 随机搜索的4.6%）并更快发现有效候选材料（第12轮迭代即首次命中，优于随机搜索的第25轮），其推理路径揭示出可解释的化学逻辑，如优先选择乙烯基和β-酮烯胺键以增强稳定性、依据吸电子特性选择节点、以及通过系统优化R基团将带隙精准调控至2.0 eV。
链接: https://arxiv.org/abs/2603.05188
作者: Iman Peivaste,Nicolas D. Boscher,Ahmed Makradi,Salim Belouettar
机构: 未知
类目: Chemical Physics (physics.chem-ph); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph)
备注:
Abstract:Covalent organic frameworks (COFs) are promising photocatalysts for solar hydrogen production, yet the most electronically favorable linkages, imines, hydrolyze rapidly in water, creating a stability–activity trade-off that limits practical deployment. Navigating the combinatorial design space of nodes, linkers, linkages, and functional groups to identify candidates that are simultaneously active and durable remains a formidable challenge. Here we introduce Ara, a large-language-model (LLM) agent that leverages pretrained chemical knowledge, donor–acceptor theory, conjugation effects, and linkage stability hierarchies, to guide the search for photocatalytic COFs satisfying joint band-gap, band-edge, and hydrolytic-stability criteria. Evaluated against random search and Bayesian optimization (BO) over a space consisting of candidates with various nodes, linkers, linkages, and r-groups, screened with a GFN1-xTB fragment pipeline, Ara achieves a 52.7% hit rate (11.5 \times random, p = 0.006), finds its first hit at iteration 12 versus 25 for random search, and significantly outperforms BO (p = 0.006). Inspection of the agent’s reasoning traces reveals interpretable chemical logic: early convergence on vinylene and beta-ketoenamine linkages for stability, node selection informed by electron-withdrawing character, and systematic R-group optimization to center the band gap at 2.0 eV. Exhaustive evaluation of the full search space uncovers a complementary exploitation–exploration trade-off between the agent and BO, suggesting that hybrid strategies may combine the strengths of both approaches. These results demonstrate that LLM chemical priors can substantially accelerate multi-criteria materials discovery.
[AI-104] Particle-Guided Diffusion for Gas-Phase Reaction Kinetics
【速读】：该论文旨在解决化学反应-输运系统（reaction-transport systems）中物理约束采样问题，尤其是如何利用生成式模型在未见参数条件下准确推断气体相化学反应的浓度场分布。其解决方案的关键在于采用基于扩散模型（diffusion model）的物理引导采样方法，通过在不同参数下训练模型以学习对流-反应-扩散（advection-reaction-diffusion, ARD）方程的解，从而生成符合物理规律的浓度场，并实现对出口浓度的高精度预测，包括在未见过的参数组合下仍保持良好的泛化能力。
链接: https://arxiv.org/abs/2603.05139
作者: Andrew Millard,Henrik Pedersen
机构: 未知
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Physics-guided sampling with diffusion model priors has shown promise for solving partial differential equation (PDE) governed problems, but applications to chemically meaningful reaction-transport systems remain limited. We apply diffusion-based guided sampling to gas-phase chemical reactions by training on solutions of the advection-reaction-diffusion (ARD) equation across varying parameters. The method generates physically consistent concentration fields and accurately predicts outlet concentrations, including at unseen parameter values, demonstrating the potential of diffusion models for inference in reactive transport.
[AI-105] Why the Brain Consolidates: Predictive Forgetting for Optimal Generalisation
【速读】：该论文试图解决传统记忆巩固理论难以解释表征漂移（representational drift）、语义化（semanticisation）以及离线重放（offline replay）必要性的问题。其解决方案的关键在于提出“预测性遗忘”（predictive forgetting）机制，即通过选择性保留能够预测未来结果或经验的信息来降低存储表征的复杂度，从而优化泛化能力。该机制在信息论层面提升了存储表征的泛化边界，并表明高容量新皮层网络需依赖时序分离的迭代精炼过程，在不重新访问感官输入的情况下实现压缩与优化，进而平衡保留与泛化的权衡。
链接: https://arxiv.org/abs/2603.04688
作者: Zafeirios Fountas,Adnan Oomerjee,Haitham Bou-Ammar,Jun Wang,Neil Burgess
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: 25 pages, 6 figures
Abstract:Standard accounts of memory consolidation emphasise the stabilisation of stored representations, but struggle to explain representational drift, semanticisation, or the necessity of offline replay. Here we propose that high-capacity neocortical networks optimise stored representations for generalisation by reducing complexity via predictive forgetting, i.e. the selective retention of experienced information that predicts future outcomes or experience. We show that predictive forgetting formally improves information-theoretic generalisation bounds on stored representations. Under high-fidelity encoding constraints, such compression is generally unattainable in a single pass; high-capacity networks therefore benefit from temporally separated, iterative refinement of stored traces without re-accessing sensory input. We demonstrate this capacity dependence with simulations in autoencoder-based neocortical models, biologically plausible predictive coding circuits, and Transformer-based language models, and derive quantitative predictions for consolidation-dependent changes in neural representational geometry. These results identify a computational role for off-line consolidation beyond stabilisation, showing that outcome-conditioned compression optimises the retention-generalisation trade-off.
[AI-106] Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials
【速读】：该论文旨在解决机器学习势能模型（MLIPs）在训练过程中难以有效利用完整Hessian矩阵（二阶导数）所导致的计算与存储瓶颈问题，因为显式构建和存储Hessian矩阵的复杂度和内存需求随系统规模呈二次增长。解决方案的关键在于提出一种名为“投影Hessian学习”（Projected Hessian Learning, PHL）的可扩展二阶训练框架，其核心思想是通过仅使用Hessian-向量乘积（HVP）来注入曲率信息，而非显式构造Hessian矩阵；具体而言，PHL沿随机探测方向投影曲率，并采用基于无偏随机迹估计的损失函数，实现了与全Hessian训练相当的精度，同时避免了二次内存增长，显著提升了训练效率（如在小分子体系中达到24倍的epoch加速）。
链接: https://arxiv.org/abs/2603.04523
作者: Austin Rodriguez,Justin S. Smith,Sakib Matin,Nicholas Lubbers,Kipton Barros,Jose L. Mendoza-Cortes
机构: 未知
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 30 pages, 5 figures, 6 suplementary figures
Abstract:The Hessian matrix (second derivatives) encodes far richer local curvature of the potential energy surface than energies and forces alone. However, training machine-learning interatomic potentials (MLIPs) with full Hessians is often impractical because explicitly forming and storing Hessian matrices scales quadratically in cost and memory. We introduce Projected Hessian Learning (PHL), a scalable second-order training framework that injects curvature information using only Hessian-vector products (HVPs). Rather than constructing the Hessian, PHL projects curvature along stochastic probe directions and uses an unbiased stochastic trace-based loss with favorable system-size scaling, enabling curvature-informed training without quadratic memory growth. We benchmark PHL on a chemically diverse dataset of reactants, products, transition states, intrinsic reaction coordinates, and normal-mode sampled geometries computed at omegaB97XD/6-31G(d). We compare energy-force training (E-F), two HVP-based schemes (E-F-HVP with one-hot or randomized probes), and full energy-force-Hessian training (E-F-H). With randomized probes per minibatch, both HVP schemes match full-Hessian training in energy, force, and Hessian accuracy while delivering 24x epoch speedups for the small molecular systems studied. In a fixed-probe regime with one HVP per molecule, randomized projections consistently outperform one-column probing, especially for far-from-equilibrium geometries. Overall, PHL replaces explicit Hessian supervision with force-complexity curvature training, retaining most second-order accuracy gains while scaling to larger, more complex molecular systems. Comments: 30 pages, 5 figures, 6 suplementary figures Subjects: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.04523 [physics.chem-ph] (or arXiv:2603.04523v1 [physics.chem-ph] for this version) https://doi.org/10.48550/arXiv.2603.04523 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jose Mendoza-Cortes [view email] [v1] Wed, 4 Mar 2026 19:09:16 UTC (226 KB)
[AI-107] A systematic approach to answering the easy problems of consciousness based on an executable cognitive system
【速读】：该论文试图系统性地解决哲学家大卫·查尔莫斯（David Chalmers）提出的“意识的易问题”（easy problems of consciousness），即如何从计算和认知机制角度解释感知辨别、分类、反应、信息整合、报告能力、信息可及性、注意力、自主控制以及清醒与睡眠状态差异等认知属性。其解决方案的关键在于构建一个可执行的认知系统，并基于康德（Kant）关于概念知识的理解，实现一套可计算化的学习机制；该机制能够推导出上述认知功能——例如，信息整合、报告与反应能力源于学习机制本身，而注意力与自主控制则由情绪状态和信息操作机制驱动，清醒与梦境的区别则主要取决于刺激源的不同。研究通过该系统的实证演示验证了其与已有实验发现的一致性，从而为意识的“易问题”提供了具身化且可计算的解释框架。
链接: https://arxiv.org/abs/2603.04440
作者: Qi Zhang
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: 21 pages, 2 figure, 3 tables
Abstract:Consciousness is the window of the brain and reflects many fundamental cognitive properties involving both computational and cognitive mechanisms. A collection of these properties was described as the “easy problems” by Chalmers, including the ability to discriminate, categorize, and react to stimuli; information integration; reportability; information access; attention; deliberate control; and the difference between wakefulness and sleep. These “easy problems” have not been systematically addressed. This study presents a first attempt to address them systematically based on an executable cognitive system and its implemented computational mechanisms, built upon an understanding of conceptual knowledge proposed by Kant. The study suggests that the abilities to discriminate, categorize, react, report, and integrate information can all be derived from the system’s learning mechanism; attention and deliberate control are goal-oriented and can be attributed to emotional states and its information-manipulation mechanism; and the difference between wakefulness and dream sleep lies mainly in the source of stimuli. The connections between the implemented mechanisms in the executive system and conclusions drawn from empirical findings are also discussed, and many of these discussions and conclusions are supported by demonstrations of the executive system.
[AI-108] CogGen: Cognitive-Load-Informed Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction
【速读】：该论文旨在解决在训练数据或计算资源有限条件下，压缩感知磁共振成像（CS-MRI）中全无监督深度生成建模（FU-DGM）的性能瓶颈问题。传统方法如深度图像先验（DIP）和隐式神经表示（INR）依赖架构先验，但面对病态逆问题时往往需要大量迭代且易受测量噪声过拟合。其解决方案的关键在于提出CogGen——一种认知负荷感知的FU-DGM框架，将CS-MRI重构视为分阶段反演过程，并通过逐步调度内在难度与外源干扰来调控任务侧“认知负荷”。具体而言，CogGen采用由易到难的k空间加权/选择策略：早期迭代优先利用低频、高信噪比（SNR）、结构主导的样本，后期再引入高频或噪声主导的数据；该调度机制通过自 paced 课程学习实现，结合学生模式（模型当前可学习内容）与教师模式（应遵循的目标）双重判据，支持软加权与硬选择两种方式，从而显著提升重建保真度与收敛速度，在无监督基线与竞争性有监督方法中均表现优越。
链接: https://arxiv.org/abs/2603.04438
作者: Qingyong Zhu,Yumin Tan,Xiang Gu,Dong Liang
机构: 未知
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Fully unsupervised deep generative modeling (FU-DGM) is promising for compressively sampled MRI (CS-MRI) when training data or compute are limited. Classical FU-DGMs such as DIP and INR rely on architectural priors, but the ill-conditioned inverse problem often demands many iterations and easily overfits measurement noise. We propose CogGen, a cognitive-load-informed FU-DGM that casts CS-MRI as staged inversion and regulates task-side “cognitive load” by progressively scheduling intrinsic difficulty and extraneous interference. CogGen replaces uniform data fitting with an easy-to-hard k-space weighting/selection strategy: early iterations emphasize low-frequency, high-SNR, structure-dominant samples, while higher-frequency or noise-dominated measurements are introduced later. We realize this schedule via self-paced curriculum learning with complementary student-mode (what the model can currently learn) and teacher-mode (what it should follow) criteria, supporting both soft weighting and hard selection. Experiments and analysis show that CogGen-DIP and CogGen-INR improve fidelity and convergence over strong unsupervised baselines and competitive supervised pipelines.
机器学习
[LG-0] Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels
链接: https://arxiv.org/abs/2603.05495
作者: Khai Nguyen,Petros Ellinas,Anvita Bhagavathula,Priya Donti
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: in submission
Abstract:To scale the solution of optimization and simulation problems, prior work has explored machine-learning surrogates that inexpensively map problem parameters to corresponding solutions. Commonly used approaches, including supervised and self-supervised learning with either soft or hard feasibility enforcement, face inherent challenges such as reliance on expensive, high-quality labels or difficult optimization landscapes. To address their trade-offs, we propose a novel framework that first collects “cheap” imperfect labels, then performs supervised pretraining, and finally refines the model through self-supervised learning to improve overall performance. Our theoretical analysis and merit-based criterion show that labeled data need only place the model within a basin of attraction, confirming that only modest numbers of inexact labels and training epochs are required. We empirically validate our simple three-stage strategy across challenging domains, including nonconvex constrained optimization, power-grid operation, and stiff dynamical systems, and show that it yields faster convergence; improved accuracy, feasibility, and optimality; and up to 59x reductions in total offline cost.
[LG-1] Kraus Constrained Sequence Learning For Quantum Trajectories from Continuous Measurement ICLR2026
链接: https://arxiv.org/abs/2603.05468
作者: Priyanshi Singh,Krishna Bhatia
类目: Machine Learning (cs.LG)
*备注: Poster at AIPDE: ICLR 2026 Workshop on AI and Partial Differential Equations. 17 pages, 3 figures
Abstract:Real-time reconstruction of conditional quantum states from continuous measurement records is a fundamental requirement for quantum feedback control, yet standard stochastic master equation (SME) solvers require exact model specification, known system parameters, and are sensitive to parameter mismatch. While neural sequence models can fit these stochastic dynamics, the unconstrained predictors can violate physicality such as positivity or trace constraints, leading to unstable rollouts and unphysical estimates. We propose a Kraus-structured output layer that converts the hidden representation of a generic sequence backbone into a completely positive trace preserving (CPTP) quantum operation, yielding physically valid state updates by construction. We instantiate this layer across diverse backbones, RNN, GRU, LSTM, TCN, ESN and Mamba; including Neural ODE as a comparative baseline, on stochastic trajectories characterized by parameter drift. Our evaluation reveals distinct trade-offs between gating mechanisms, linear recurrence, and global attention. Across all models, Kraus-LSTM achieves the strongest results, improving state estimation quality by 7% over its unconstrained counterpart while guaranteeing physically valid predictions in non-stationary regimes.
[LG-2] Latent Wasserstein Adversarial Imitation Learning ICLR2026
链接: https://arxiv.org/abs/2603.05440
作者: Siqi Yang,Kai Yan,Alexander G. Schwing,Yu-Xiong Wang
类目: Machine Learning (cs.LG)
*备注: 10 pages, accepted to ICLR 2026
Abstract:Imitation Learning (IL) enables agents to mimic expert behavior by learning from demonstrations. However, traditional IL methods require large amounts of medium-to-high-quality demonstrations as well as actions of expert demonstrations, both of which are often unavailable. To reduce this need, we propose Latent Wasserstein Adversarial Imitation Learning (LWAIL), a novel adversarial imitation learning framework that focuses on state-only distribution matching. It benefits from the Wasserstein distance computed in a dynamics-aware latent space. This dynamics-aware latent space differs from prior work and is obtained via a pre-training stage, where we train the Intention Conditioned Value Function (ICVF) to capture a dynamics-aware structure of the state space using a small set of randomly generated state-only data. We show that this enhances the policy’s understanding of state transitions, enabling the learning process to use only one or a few state-only expert episodes to achieve expert-level performance. Through experiments on multiple MuJoCo environments, we demonstrate that our method outperforms prior Wasserstein-based IL methods and prior adversarial IL methods, achieving better results across various tasks.
[LG-3] On-Policy Self-Distillation for Reasoning Compression
链接: https://arxiv.org/abs/2603.05433
作者: Hejian Sang,Yuanda Xu,Zhengze Zhou,Ran He,Zhipeng Wang,Jiachen Sun
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reasoning models think out loud, but much of what they say is noise. We introduce OPSDC (On-Policy Self-Distillation for Reasoning Compression), a method that teaches models to reason more concisely by distilling their own concise behavior back into themselves. The entire approach reduces to one idea: condition the same model on a “be concise” instruction to obtain teacher logits, and minimize per-token reverse KL on the student’s own rollouts. No ground-truth answers, no token budgets, no difficulty estimators. Just self-distillation. Yet this simplicity belies surprising sophistication: OPSDC automatically compresses easy problems aggressively while preserving the deliberation needed for hard ones. On Qwen3-8B and Qwen3-14B, we achieve 57-59% token reduction on MATH-500 while improving accuracy by 9-16 points absolute. On AIME 2024, the 14B model gains 10 points with 41% compression. The secret? Much of what reasoning models produce is not just redundant-it is actively harmful, compounding errors with every unnecessary token. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.05433 [cs.LG] (or arXiv:2603.05433v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.05433 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hejian Sang [view email] [v1] Thu, 5 Mar 2026 17:54:40 UTC (571 KB) Full-text links: Access Paper: View a PDF of the paper titled On-Policy Self-Distillation for Reasoning Compression, by Hejian Sang and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.LG prev | next new | recent | 2026-03 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked=“checked”> 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[LG-4] An interpretable prototype parts-based neural network for medical tabular data ECAI2025
链接: https://arxiv.org/abs/2603.05423
作者: Jacek Karolczak,Jerzy Stefanowski
类目: Machine Learning (cs.LG)
*备注: Proc. of EXPLIMED at ECAI 2025
Abstract:The ability to interpret machine learning model decisions is critical in such domains as healthcare, where trust in model predictions is as important as their accuracy. Inspired by the development of prototype parts-based deep neural networks in computer vision, we propose a new model for tabular data, specifically tailored to medical records, that requires discretization of diagnostic result norms. Unlike the original vision models that rely on the spatial structure, our method employs trainable patching over features describing a patient, to learn meaningful prototypical parts from structured data. These parts are represented as binary or discretized feature subsets. This allows the model to express prototypes in human-readable terms, enabling alignment with clinical language and case-based reasoning. Our proposed neural network is inherently interpretable and offers interpretable concept-based predictions by comparing the patient’s description to learned prototypes in the latent space of the network. In experiments, we demonstrate that the model achieves classification performance competitive to widely used baseline models on medical benchmark datasets, while also offering transparency, bridging the gap between predictive performance and interpretability in clinical decision support.
[LG-5] On the Necessity of Learnable Sheaf Laplacians
链接: https://arxiv.org/abs/2603.05395
作者: Ferran Hernandez Caralt,Mar Gonzàlez i Català,Adrián Bazaga,Pietro Liò
类目: Machine Learning (cs.LG)
*备注:
Abstract:Sheaf Neural Networks (SNNs) were introduced as an extension of Graph Convolutional Networks to address oversmoothing on heterophilous graphs by attaching a sheaf to the input graph and replacing the adjacency-based operator with a sheaf Laplacian defined by (learnable) restriction maps. Prior work motivates this design through theoretical properties of sheaf diffusion and the kernel of the sheaf Laplacian, suggesting that suitable non-identity restriction maps can avoid representations converging to constants across connected components. Since oversmoothing can also be mitigated through residual connections and normalization, we revisit a trivial sheaf construction to ask whether the additional complexity of learning restriction maps is necessary. We introduce an Identity Sheaf Network baseline, where all restriction maps are fixed to the identity, and use it to ablate the empirical improvements reported by sheaf-learning architectures. Across five popular heterophilic benchmarks, the identity baseline achieves comparable performance to a range of SNN variants. Finally, we introduce the Rayleigh quotient as a normalized measure for comparing oversmoothing across models and show that, in trained networks, the behavior predicted by the diffusion-based analysis of SNNs is not reflected empirically. In particular, Identity Sheaf Networks do not appear to suffer more significant oversmoothing than their SNN counterparts.
[LG-6] Robust Node Affinities via Jaccard-Biased Random Walks and Rank Aggregation
链接: https://arxiv.org/abs/2603.05375
作者: Bastian Pfeifer,Michael G. Schimek
类目: Machine Learning (cs.LG)
*备注:
Abstract:Estimating node similarity is a fundamental task in network analysis and graph-based machine learning, with applications in clustering, community detection, classification, and recommendation. We propose TopKGraphs, a method based on start-node-anchored random walks that bias transitions toward nodes with structurally similar neighborhoods, measured via Jaccard similarity. Rather than computing stationary distributions, walks are treated as stochastic neighborhood samplers, producing partial node rankings that are aggregated using robust rank aggregation to construct interpretable node-to-node affinity matrices. TopKGraphs provides a non-parametric, interpretable, and general-purpose representation of node similarity that can be applied in both network analysis and machine learning workflows. We evaluate the method on synthetic graphs (stochastic block models, Lancichinetti-Fortunato-Radicchi benchmark graphs), k-nearest-neighbor graphs from tabular datasets, and a curated high-confidence protein-protein interaction network. Across all scenarios, TopKGraphs achieves competitive or superior performance compared to standard similarity measures (Jaccard, Dice), a diffusion-based method (personalized PageRank), and an embedding-based approach (Node2Vec), demonstrating robustness in sparse, noisy, or heterogeneous networks. These results suggest that TopKGraphs is a versatile and interpretable tool for bridging simple local similarity measures with more complex embedding-based approaches, facilitating both data mining and network analysis applications.
[LG-7] Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition
链接: https://arxiv.org/abs/2603.05371
作者: Francisco M. Calatrava-Nicolás,Shoko Miyauchi,Vitor Fortes Rey,Paul Lukowicz,Todor Stoyanov,Oscar Martinez Mozos
类目: Machine Learning (cs.LG)
*备注: Accepted in the IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP). This is the author’s version of the work
Abstract:This paper addresses the problem of Human Activity Recognition (HAR) using data from wearable inertial sensors. An important challenge in HAR is the model’s generalization capabilities to new unseen individuals due to inter-subject variability, i.e., the same activity is performed differently by different individuals. To address this problem, we propose a novel deep adversarial framework that integrates the concept of inter-subject variability in the adversarial task, thereby encouraging subject-invariant feature representations and enhancing the classification performance in the HAR problem. Our approach outperforms previous methods in three well-established HAR datasets using a leave-one-subject-out (LOSO) cross-validation. Further results indicate that our proposed adversarial task effectively reduces inter-subject variability among different users in the feature space, and it outperforms adversarial tasks from previous works when integrated into our framework. Code: this https URL
[LG-8] InfoFlow KV: Information-Flow-Aware KV Recomputation for Long Context
链接: https://arxiv.org/abs/2603.05353
作者: Xin Teng,Canyu Zhang,Shaoyi Zheng,Danyang Zhuo,Tianyi Zhou,Shengjie Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Retrieval-augmented generation (RAG) for long-context question answering is bottlenecked by inference-time prefilling over large retrieved contexts. A common strategy is to precompute key-value (KV) caches for individual documents and selectively recompute a small subset of tokens to restore global causal dependencies, but existing methods rely on heuristics or representation discrepancies without modeling whether selected tokens can effectively influence generation. We cast selective KV recomputation as an information flow problem and show that a simple attention-norm signal from the query reliably identifies tokens that are both semantically relevant and structurally positioned to propagate information, when computed under an inference-consistent RoPE geometry. We therefore reconstruct global positional assignments for retrieved chunks and introduce an information-flow-guided chunk reordering strategy. Experiments on LLM and VLM benchmarks demonstrate consistent gains over prior methods under comparable efficiency budgets.
[LG-9] Preserving Continuous Symmetry in Discrete Spaces: Geometric-Aware Quantization for SO(3)-Equivariant GNNs
链接: https://arxiv.org/abs/2603.05343
作者: Haoyu Zhou,Ping Xue,Hao Zhang,Tianfan Fu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Equivariant Graph Neural Networks (GNNs) are essential for physically consistent molecular simulations but suffer from high computational costs and memory bottlenecks, especially with high-order representations. While low-bit quantization offers a solution, applying it naively to rotation-sensitive features destroys the SO(3)-equivariant structure, leading to significant errors and violations of conservation laws. To address this issue, in this work, we propose a Geometric-Aware Quantization (GAQ) framework that compresses and accelerates equivariant models while rigorously preserving continuous symmetry in discrete spaces. Our approach introduces three key contributions: (1) a Magnitude-Direction Decoupled Quantization (MDDQ) scheme that separates invariant lengths from equivariant orientations to maintain geometric fidelity; (2) a symmetry-aware training strategy that treats scalar and vector features with distinct quantization schedules; and (3) a robust attention normalization mechanism to stabilize gradients in low-bit regimes. Experiments on the rMD17 benchmark demonstrate that our W4A8 models match the accuracy of FP32 baselines (9.31 meV vs. 23.20 meV) while reducing Local Equivariance Error (LEE) by over 30x compared to naive quantization. On consumer hardware, GAQ achieves 2.39x inference speedup and 4x memory reduction, enabling stable, energy-conserving molecular dynamics simulations for nanosecond timescales.
[LG-10] FairFinGAN: Fairness-aware Synthetic Financial Data Generation PAKDD2026
链接: https://arxiv.org/abs/2603.05327
作者: Tai Le Quy,Dung Nguyen Tuan,Trung Nguyen Thanh,Duy Tran Cong,Huyen Giang Thi Thu,Frank Hopfgartner
类目: Machine Learning (cs.LG)
*备注: Accepted to Special Session: Data Science: Foundations and Applications (DSFA), PAKDD 2026
Abstract:Financial datasets often suffer from bias that can lead to unfair decision-making in automated systems. In this work, we propose FairFinGAN, a WGAN-based framework designed to generate synthetic financial data while mitigating bias with respect to the protected attribute. Our approach incorporates fairness constraints directly into the training process through a classifier, ensuring that the synthetic data is both fair and preserves utility for downstream predictive tasks. We evaluate our proposed model on five real-world financial datasets and compare it with existing GAN-based data generation methods. Experimental results show that our approach achieves superior fairness metrics without significant loss in data utility, demonstrating its potential as a tool for bias-aware data generation in financial applications.
[LG-11] Latent Policy Steering through One-Step Flow Policies
链接: https://arxiv.org/abs/2603.05296
作者: Hokyun Im,Andrey Kolobov,Jianlong Fu,Youngwoon Lee
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: Project Webpage : this https URL
Abstract:Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration. Yet, offline RL’s performance often hinges on a brittle trade-off between (1) return maximization, which can push policies outside the dataset support, and (2) behavioral constraints, which typically require sensitive hyperparameter tuning. Latent steering offers a structural way to stay within the dataset support during RL, but existing offline adaptations commonly approximate action values using latent-space critics learned via indirect distillation, which can lose information and hinder convergence. We propose Latent Policy Steering (LPS), which enables high-fidelity latent policy improvement by backpropagating original-action-space Q-gradients through a differentiable one-step MeanFlow policy to update a latent-action-space actor. By eliminating proxy latent critics, LPS allows an original-action-space critic to guide end-to-end latent-space optimization, while the one-step MeanFlow policy serves as a behavior-constrained generative prior. This decoupling yields a robust method that works out-of-the-box with minimal tuning. Across OGBench and real-world robotic tasks, LPS achieves state-of-the-art performance and consistently outperforms behavioral cloning and strong latent steering baselines.
[LG-12] Beyond Word Error Rate: Auditing the Diversity Tax in Speech Recognition through Dataset Cartography INTERSPEECH2026
链接: https://arxiv.org/abs/2603.05267
作者: Ting-Hui Cheng,Line H. Clemmensen,Sneha Das
类目: Machine Learning (cs.LG)
*备注: Submitted to the Interspeech 2026
Abstract:Automatic speech recognition (ASR) systems are predominantly evaluated using the Word Error Rate (WER). However, raw token-level metrics fail to capture semantic fidelity and routinely obscures the `diversity tax’, the disproportionate burden on marginalized and atypical speaker due to systematic recognition failures. In this paper, we explore the limitations of relying solely on lexical counts by systematically evaluating a broader class of non-linear and semantic metrics. To enable rigorous model auditing, we introduce the sample difficulty index (SDI), a novel metric that quantifies how intrinsic demographic and acoustic factors drive model failure. By mapping SDI on data cartography, we demonstrate that metrics EmbER and SemDist expose hidden systemic biases and inter-model disagreements that WER ignores. Finally, our findings are the first steps towards a robust audit framework for prospective safety analysis, empowering developers to audit and mitigate ASR disparities prior to deployment.
[LG-13] A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines
链接: https://arxiv.org/abs/2603.05263
作者: Bowen Li,Xiufeng Liu,Maria Sinziiana Astefanoaei
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate short-term wind power forecasting is essential for grid dispatch and market operations, yet centralising turbine data raises privacy, cost, and heterogeneity concerns. We propose a two-stage federated learning framework that first clusters turbines by long-term behavioural statistics using Double Roulette Selection (DRS) initialisation with recursive Auto-split refinement, and then trains cluster-specific LSTM models via FedAvg. Experiments on 400 stand-alone turbines in Denmark show that DRS-auto discovers behaviourally coherent groups and achieves competitive forecasting accuracy while preserving data locality. Behaviour-aware grouping consistently outperforms geographic partitioning and matches strong k-means++ baselines, suggesting a practical privacy-friendly solution for heterogeneous distributed turbine fleets.
[LG-14] SlideSparse: Fast and Flexible (2N-2):2N Structured Sparsity
链接: https://arxiv.org/abs/2603.05232
作者: Hanyong Shao,Yingbo Hao,Ting Song,Yan Xia,Di Zhang,Shaohan Huang,Xun Wu,Songchen Xu,Le Xu,Li Dong,Zewen Chi,Yi Zou,Furu Wei
类目: Machine Learning (cs.LG)
*备注:
Abstract:NVIDIA’s 2:4 Sparse Tensor Cores deliver 2x throughput but demand strict 50% pruning – a ratio that collapses LLM reasoning accuracy (Qwen3: 54% to 15%). Milder (2N-2):2N patterns (e.g., 6:8, 25% pruning) preserve accuracy yet receive no hardware support, falling back to dense execution without any benefit from sparsity. We present SlideSparse, the first system to unlock Sparse Tensor Core acceleration for the (2N-2):2N model family on commodity GPUs. Our Sliding Window Decomposition reconstructs any (2N-2):2N weight block into N-1 overlapping 2:4-compliant windows without any accuracy loss; Activation Lifting fuses the corresponding activation rearrangement into per-token quantization at near-zero cost. Integrated into vLLM, SlideSparse is evaluated across various GPUs (A100, H100, B200, RTX 4090, RTX 5080, DGX-spark), precisions (FP4, INT8, FP8, BF16, FP16), and model families (Llama, Qwen, BitNet). On compute-bound workloads, the measured speedup ratio (1.33x) approaches the theoretical upper-bound N/(N-1)=4/3 at 6:8 weight sparsity in Qwen2.5-7B, establishing (2N-2):2N as a practical path to accuracy-preserving LLM acceleration. Code available at this https URL.
[LG-15] owards a data-scale independent regulariser for robust sparse identification of non-linear dynamics
链接: https://arxiv.org/abs/2603.05201
作者: Jay Raut,Daniel N. Wilke,Stephan Schmidt
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 21 pages, 9 figures, 5 tables
Abstract:Data normalisation, a common and often necessary preprocessing step in engineering and scientific applications, can severely distort the discovery of governing equations by magnitudebased sparse regression methods. This issue is particularly acute for the Sparse Identification of Nonlinear Dynamics (SINDy) framework, where the core assumption of sparsity is undermined by the interaction between data scaling and measurement noise. The resulting discovered models can be dense, uninterpretable, and physically incorrect. To address this critical vulnerability, we introduce the Sequential Thresholding of Coefficient of Variation (STCV), a novel, computationally efficient sparse regression algorithm that is inherently robust to data scaling. STCV replaces conventional magnitude-based thresholding with a dimensionless statistical metric, the Coefficient Presence (CP), which assesses the statistical validity and consistency of candidate terms in the model library. This shift from magnitude to statistical significance makes the discovery process invariant to arbitrary data scaling. Through comprehensive benchmarking on canonical dynamical systems and practical engineering problems, including a physical mass-spring-damper experiment, we demonstrate that STCV consistently and significantly outperforms standard Sequential Thresholding Least Squares (STLSQ) and Ensemble-SINDy (E-SINDy) on normalised, noisy datasets. The results show that STCV-based methods can successfully identify the correct, sparse physical laws even when other methods fail. By mitigating the distorting effects of normalisation, STCV makes sparse system identification a more reliable and automated tool for real-world applications, thereby enhancing model interpretability and trustworthiness.
[LG-16] Incentive Aware AI Regulations: A Credal Characterisation
链接: https://arxiv.org/abs/2603.05175
作者: Anurag Singh,Julian Rodemann,Rajeev Verma,Siu Lun Chau,Krikamol Muandet
类目: Machine Learning (cs.LG)
*备注:
Abstract:While high-stakes ML applications demand strict regulations, strategic ML providers often evade them to lower development costs. To address this challenge, we cast AI regulation as a mechanism design problem under uncertainty and introduce regulation mechanisms: a framework that maps empirical evidence from models to a license for some market share. The providers can select from a set of licenses, effectively forcing them to bet on their model’s ability to fulfil regulation. We aim at regulation mechanisms that achieve perfect market outcome, i.e. (a) drive non-compliant providers to self-exclude, and (b) ensure participation from compliant providers. We prove that a mechanism has perfect market outcome if and only if the set of non-compliant distributions forms a credal set, i.e., a closed, convex set of probability measures. This result connects mechanism design and imprecise probability by establishing a duality between regulation mechanisms and the set of non-compliant distributions. We also demonstrate these mechanisms in practice via experiments on regulating use of spurious features for prediction and fairness. Our framework provides new insights at the intersection of mechanism design and imprecise probability, offering a foundation for development of enforceable AI regulations.
[LG-17] rainable Bitwise Soft Quantization for Input Feature Compression
链接: https://arxiv.org/abs/2603.05172
作者: Karsten Schrödter,Jan Stenkamp,Nina Herrmann,Fabian Gieseke
类目: Machine Learning (cs.LG)
*备注: Accepted to CPAL 2026
Abstract:The growing demand for machine learning applications in the context of the Internet of Things calls for new approaches to optimize the use of limited compute and memory resources. Despite significant progress that has been made w.r.t. reducing model sizes and improving efficiency, many applications still require remote servers to provide the required resources. However, such approaches rely on transmitting data from edge devices to remote servers, which may not always be feasible due to bandwidth, latency, or energy constraints. We propose a task-specific, trainable feature quantization layer that compresses the input features of a neural network. This can significantly reduce the amount of data that needs to be transferred from the device to a remote server. In particular, the layer allows each input feature to be quantized to a user-defined number of bits, enabling a simple on-device compression at the time of data collection. The layer is designed to approximate step functions with sigmoids, enabling trainable quantization thresholds. By concatenating outputs from multiple sigmoids, introduced as bitwise soft quantization, it achieves trainable quantized values when integrated with a neural network. We compare our method to full-precision inference as well as to several quantization baselines. Experiments show that our approach outperforms standard quantization methods, while maintaining accuracy levels close to those of full-precision models. In particular, depending on the dataset, compression factors of 5\times to 16\times can be achieved compared to 32 -bit input without significant performance loss.
[LG-18] Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques
链接: https://arxiv.org/abs/2603.05158
作者: Yenan Wang,Carla Fabiana Chiasserini,Elad Michael Schiller
类目: Machine Learning (cs.LG)
*备注:
Abstract:In federated learning (FL), balancing privacy protection, learning quality, and efficiency remains a challenge. Privacy protection mechanisms, such as Differential Privacy (DP), degrade learning quality, or, as in the case of Homomorphic Encryption (HE), incur substantial system overhead. To address this, we propose Alt-FL, a privacy-preserving FL framework that combines DP, HE, and synthetic data via a novel round-based interleaving strategy. Alt-FL introduces three new methods, Privacy Interleaving (PI), Synthetic Interleaving with DP (SI/DP), and Synthetic Interleaving with HE (SI/HE), that enable flexible quality-efficiency trade-offs while providing privacy protection. We systematically evaluate Alt-FL against representative reconstruction attacks, including Deep Leakage from Gradients, Inverting Gradients, When the Curious Abandon Honesty, and Robbing the Fed, using a LeNet-5 model on CIFAR-10 and Fashion-MNIST. To enable fair comparison between DP- and HE-based defenses, we introduce a new attacker-centric framework that compares empirical attack success rates across the three proposed interleaving methods. Our results show that, for the studied attacker model and dataset, PI achieves the most balanced trade-offs at high privacy protection levels, while DP-based methods are preferable at intermediate privacy requirements. We also discuss how such results can be the basis for selecting privacy-preserving FL methods under varying privacy and resource constraints. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.05158 [cs.LG] (or arXiv:2603.05158v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.05158 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-19] Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics
链接: https://arxiv.org/abs/2603.05113
作者: Kilian Freitag,Knut Åkesson,Morteza Haghir Chehreghani
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Deep Reinforcement Learning is a promising tool for robotic control, yet practical application is often hindered by the difficulty of designing effective reward functions. Real-world tasks typically require optimizing multiple objectives simultaneously, necessitating precise tuning of their weights to learn a policy with the desired characteristics. To address this, we propose a two-stage reward curriculum where we decouple task-specific objectives from behavioral terms. In our method, we first train the agent on a simplified task-only reward function to ensure effective exploration before introducing the full reward that includes auxiliary behavior-related terms such as energy efficiency. Further, we analyze various transition strategies and demonstrate that reusing samples between phases is critical for training stability. We validate our approach on the DeepMind Control Suite, ManiSkill3, and a mobile robot environment, modified to include auxiliary behavioral objectives. Our method proves to be simple yet effective, substantially outperforming baselines trained directly on the full reward while exhibiting higher robustness to specific reward weightings.
[LG-20] Synchronization-based clustering on the unit hypersphere
链接: https://arxiv.org/abs/2603.05067
作者: Zinaid Kapić,Aladin Crnkić,Goran Mauša
类目: Machine Learning (cs.LG)
*备注:
Abstract:Clustering on the unit hypersphere is a fundamental problem in various fields, with applications ranging from gene expression analysis to text and image classification. Traditional clustering methods are not always suitable for unit sphere data, as they do not account for the geometric structure of the sphere. We introduce a novel algorithm for clustering data represented as points on the unit sphere \mathbfS^d-1 . Our method is based on the d -dimensional generalized Kuramoto model. The effectiveness of the introduced method is demonstrated on synthetic and real-world datasets. Results are compared with some of the traditional clustering methods, showing that our method achieves similar or better results in terms of clustering accuracy.
[LG-21] Reward-Conditioned Reinforcement Learning
链接: https://arxiv.org/abs/2603.05066
作者: Michal Nauman,Marek Cygan,Pieter Abbeel
类目: Machine Learning (cs.LG)
*备注: preprint
Abstract:RL agents are typically trained under a single, fixed reward function, which makes them brittle to reward misspecification and limits their ability to adapt to changing task preferences. We introduce Reward-Conditioned Reinforcement Learning (RCRL), a framework that trains a single agent to optimize a family of reward specifications while collecting experience under only one nominal objective. RCRL conditions the agent on reward parameterizations and learns multiple reward objectives from a shared replay data entirely off-policy, enabling a single policy to represent reward-specific behaviors. Across single-task, multi-task, and vision-based benchmarks, we show that RCRL not only improves performance under the nominal reward parameterization, but also enables efficient adaptation to new parameterizations. Our results demonstrate that RCRL provides a scalable mechanism for learning robust, steerable policies without sacrificing the simplicity of single-task training.
[LG-22] Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC Under Channel Uncertainty
链接: https://arxiv.org/abs/2603.05062
作者: Bui Minh Tuan,Van-Dinh Nguyen,Diep N. Nguyen,Nguyen Linh Trung,Nguyen Van Huynh,Dinh Thai Hoang,Marwan Krunz,Eryk Dutkiewicz
类目: Machine Learning (cs.LG)
*备注: 16 pages, accepted in IEEE TCOM
Abstract:Integrated sensing and communication (ISAC) systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel state information (CSI) and in the presence of unknown eavesdropper (Eve) locations. Unlike conventional ISAC-based friendly jamming (FJ) approaches that require Eve’s CSI or precise angle-of-arrival (AoA) estimates, our method exploits radar echo feedback to guide directional jamming without explicit Eve’s information. To enhance robustness to radar sensing uncertainty, we propose a radar-aware neural network that jointly optimizes beamforming and jamming by integrating a novel nonparametric Fisher Information Matrix (FIM) estimator based on f-divergence. The jamming design satisfies the Cramer-Rao lower bound (CRLB) constraints even in the presence of noisy AoA. For efficient implementation, we introduce a quantized tensor train-based encoder that reduces the model size by more than 100 times with negligible performance loss. We also integrate a non-overlapping secure scheme into the proposed framework, in which specific sub-bands can be dedicated solely to communication. Extensive simulations demonstrate that the proposed solution achieves significant improvements in secrecy rate, reduced block error rate (BLER), and strong robustness against CSI uncertainty and angular estimation errors, underscoring the effectiveness of the proposed deep learning-driven friendly jamming framework under practical ISAC impairments.
[LG-23] Asymptotic Behavior of Multi–Task Learning: Implicit Regularization and Double Descent Effects
链接: https://arxiv.org/abs/2603.05060
作者: Ayed M. Alrashdi,Oussama Dhifallah,Houssem Sifaou
类目: Machine Learning (cs.LG); Information Theory (cs.IT)
*备注:
Abstract:Multi–task learning seeks to improve the generalization error by leveraging the common information shared by multiple related tasks. One challenge in multi–task learning is identifying formulations capable of uncovering the common information shared between different but related tasks. This paper provides a precise asymptotic analysis of a popular multi–task formulation associated with misspecified perceptron learning models. The main contribution of this paper is to precisely determine the reasons behind the benefits gained from combining multiple related tasks. Specifically, we show that combining multiple tasks is asymptotically equivalent to a traditional formulation with additional regularization terms that help improve the generalization performance. Another contribution is to empirically study the impact of combining tasks on the generalization error. In particular, we empirically show that the combination of multiple tasks postpones the double descent phenomenon and can mitigate it asymptotically.
[LG-24] MCEL: Margin-Based Cross-Entropy Loss for Error-Tolerant Quantized Neural Networks
链接: https://arxiv.org/abs/2603.05048
作者: Mikail Yayla,Akash Kumar
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR)
*备注:
Abstract:Robustness to bit errors is a key requirement for the reliable use of neural networks (NNs) on emerging approximate computing platforms and error-prone memory technologies. A common approach to achieve bit error tolerance in NNs is injecting bit flips during training according to a predefined error model. While effective in certain scenarios, training-time bit flip injection introduces substantial computational overhead, often degrades inference accuracy at high error rates, and scales poorly for larger NN architectures. These limitations make error injection an increasingly impractical solution for ensuring robustness on future approximate computing platforms and error-prone memory technologies. In this work, we investigate the mechanisms that enable NNs to tolerate bit errors without relying on error-aware training. We establish a direct connection between bit error tolerance and classification margins at the output layer. Building on this insight, we propose a novel loss function, the Margin Cross-Entropy Loss (MCEL), which explicitly promotes logit-level margin separation while preserving the favorable optimization properties of the standard cross-entropy loss. Furthermore, MCEL introduces an interpretable margin parameter that allows robustness to be tuned in a principled manner. Extensive experimental evaluations across multiple datasets of varying complexity, diverse NN architectures, and a range of quantization schemes demonstrate that MCEL substantially improves bit error tolerance, up to 15 % in accuracy for an error rate of 1 %. Our proposed MCEL method is simple to implement, efficient, and can be integrated as a drop-in replacement for standard CEL. It provides a scalable and principled alternative to training-time bit flip injection, offering new insights into the origins of NN robustness and enabling more efficient deployment on approximate computing and memory systems.
[LG-25] Good-Enough LLM Obfuscation (GELO)
链接: https://arxiv.org/abs/2603.05035
作者: Anatoly Belikov,Ilya Fedotov
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Large Language Models (LLMs) are increasingly served on shared accelerators where an adversary with read access to device memory can observe KV caches and hidden states, threatening prompt privacy for open-source models. Cryptographic protections such as MPC and FHE offer strong guarantees but remain one to two orders of magnitude too slow for interactive inference, while static obfuscation schemes break under multi-run statistical attacks once the model is known. We present GELO (Good-Enough LLM Obfuscation), a lightweight protocol for privacy-preserving inference that limits information leakage from untrusted accelerator observations by hiding hidden states with fresh, per-batch invertible mixing. For each offloaded projection, the TEE samples a random matrix A, forms U = AH , offloads U and weights W to the accelerator, and then applies A^-1 on return, so that A^-1 ((AH)W ) = HW and outputs are unchanged. Because mixing is never reused across batches, the attacker faces only a single-batch blind source separation problem. We analyze information leakage and introduce two practical defenses: (i) non-orthogonal mixing to mask Gram matrices, and (ii) orthogonal mixing augmented with a small fraction of high-energy “shield” vectors that pollute higher-order statistics. On Llama-2 7B, GELO preserves float32 outputs exactly, closely matches low-precision baselines, offloads the dominant matrix multiplications with about 20-30% latency overhead, and defeats a range of ICA/BSS and anchor-based attacks.
[LG-26] Non-Euclidean Gradient Descent Operates at the Edge of Stability
链接: https://arxiv.org/abs/2603.05002
作者: Rustem Islamov,Michael Crawshaw,Jeremy Cohen,Robert Gower
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注:
Abstract:The Edge of Stability (EoS) is a phenomenon where the sharpness (largest eigenvalue) of the Hessian converges to 2/\eta during training with gradient descent (GD) with a step-size \eta . Despite (apparently) violating classical smoothness assumptions, EoS has been widely observed in deep learning, but its theoretical foundations remain incomplete. We provide an interpretation of EoS through the lens of Directional Smoothness Mishkin et al. [2024]. This interpretation naturally extends to non-Euclidean norms, which we use to define generalized sharpness under an arbitrary norm. Our generalized sharpness measure includes previously studied vanilla GD and preconditioned GD as special cases, as well as methods for which EoS has not been studied, such as \ell_\infty -descent, Block CD, Spectral GD, and Muon without momentum. Through experiments on neural networks, we show that non-Euclidean GD with our generalized sharpness also exhibits progressive sharpening followed by oscillations around or above the threshold 2/\eta . Practically, our framework provides a single, geometry-aware spectral measure that works across optimizers.
[LG-27] Lightweight and Scalable Transfer Learning Framework for Load Disaggregation
链接: https://arxiv.org/abs/2603.04998
作者: L.E. Garcia-Marrero,G. Petrone,E. Monmasson
类目: Machine Learning (cs.LG)
*备注: This work has been submitted to the IEEE for possible publication
Abstract:Non-Intrusive Load Monitoring (NILM) aims to estimate appliance-level consumption from aggregate electrical signals recorded at a single measurement point. In recent years, the field has increasingly adopted deep learning approaches; however, cross-domain generalization remains a persistent challenge due to variations in appliance characteristics, usage patterns, and background loads across homes. Transfer learning provides a practical paradigm to adapt models with limited target data. However, existing methods often assume a fixed appliance set, lack flexibility for evolving real-world deployments, remain unsuitable for edge devices, or scale poorly for real-time operation. This paper proposes RefQuery, a scalable multi-appliance, multi-task NILM framework that conditions disaggregation on compact appliance fingerprints, allowing one shared model to serve many appliances without a fixed output set. RefQuery keeps a pretrained disaggregation network fully frozen and adapts to a target home by learning only a per-appliance embedding during a lightweight backpropagation stage. Experiments on three public datasets demonstrate that RefQuery delivers a strong accuracy-efficiency trade-off against single-appliance and multi-appliance baselines, including modern Transformer-based methods. These results support RefQuery as a practical path toward scalable, real-time NILM on resource-constrained edge devices.
[LG-28] WaterSIC: information-theoretically (near) optimal linear layer quantization
链接: https://arxiv.org/abs/2603.04956
作者: Egor Lifar,Semyon Savkin,Or Ordentlich,Yury Polyanskiy
类目: Machine Learning (cs.LG); Information Theory (cs.IT)
*备注:
Abstract:This paper considers the problem of converting a given dense linear layer to low precision. The tradeoff between compressed length and output discrepancy is analyzed information theoretically (IT). It is shown that a popular GPTQ algorithm may have an arbitrarily large gap to the IT limit. To alleviate this problem, a novel algorithm, termed ‘‘WaterSIC’’, is proposed and is shown to be within a rate gap of 0.255 bits to the IT limit, uniformly over all possible covariance matrices of input activations. The key innovation of WaterSIC’s is to allocate different quantization rates to different columns (in-features) of the weight matrix, mimicking the classical IT solution known as ‘‘waterfilling’’. Applying WaterSIC to the Llama and Qwen family of LLMs establishes new state-of-the-art performance for all quantization rates from 1 to 4 bits.
[LG-29] Uncertainty-aware Blood Glucose Prediction from Continuous Glucose Monitoring Data
链接: https://arxiv.org/abs/2603.04955
作者: Hai Siong Tan
类目: Machine Learning (cs.LG); Medical Physics (physics.med-ph)
*备注: 19 pages, 10 figures
Abstract:In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes. We consider three families of sequence models based on LSTM, GRU, and Transformer architectures, with uncertainty quantification enabled by either Monte Carlo dropout or through evidential output layers compatible with Deep Evidential Regression. Using the HUPA-UCM diabetes dataset for validation, we find that Transformer-based models equipped with evidential output heads provide the most effective uncertainty-aware framework, achieving consistently higher predictive accuracies and better-calibrated uncertainty estimates whose magnitudes significantly correlate with prediction errors. We further evaluate the clinical risk of each model using the recently proposed Diabetes Technology Society error grid, with risk categories defined by international expert consensus. Our results demonstrate the value of integrating principled uncertainty quantification into real-time machine-learning-based blood glucose prediction systems.
[LG-30] nabla-Reason er: LLM Reasoning via Test-Time Gradient Descent in Latent Space ICLR2026
链接: https://arxiv.org/abs/2603.04948
作者: Peihao Wang,Ruisi Cai,Zhen Wang,Hongyuan Mei,Qiang Liu,Pan Li,Zhangyang Wang
类目: Machine Learning (cs.LG)
*备注: ICLR 2026
Abstract:Scaling inference-time compute for Large Language Models (LLMs) has unlocked unprecedented reasoning capabilities. However, existing inference-time scaling methods typically rely on inefficient and suboptimal discrete search algorithms or trial-and-error prompting to improve the online policy. In this paper, we propose \nabla -Reasoner, an iterative generation framework that integrates differentiable optimization over token logits into the decoding loop to refine the policy on the fly. Our core component, Differentiable Textual Optimization (DTO), leverages gradient signals from both the LLM’s likelihood and a reward model to refine textual representations. \nabla -Reasoner further incorporates rejection sampling and acceleration design to robustify and speed up decoding. Theoretically, we show that performing inference-time gradient descent in the sample space to maximize reward is dual to aligning an LLM policy via KL-regularized reinforcement learning. Empirically, \nabla -Reasoner achieves over 20% accuracy improvement on a challenging mathematical reasoning benchmark, while reducing number of model calls by approximately 10-40% compared to strong baselines. Overall, our work introduces a paradigm shift from zeroth-order search to first-order optimization at test time, offering a cost-effective path to amplify LLM reasoning.
[LG-31] Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture Challenges and Case Study
链接: https://arxiv.org/abs/2603.04936
作者: Lu Yu,Zheng Chang,Ying-Chang Liang
类目: Machine Learning (cs.LG)
*备注: Accepted for publication in IEEE Communications Magazine. 7 pages, 5 figures
Abstract:Vehicular edge intelligence (VEI) is vital for future intelligent transportation systems. However, traditional centralized learning in dynamic vehicular networks faces significant communication overhead and privacy risks. Split federated learning (SFL) offers a distributed solution but is often hindered by substantial communication bottlenecks from transmitting high-dimensional intermediate features and can present label privacy concerns. Semantic communication offers a transformative approach to alleviate these communication challenges in SFL by focusing on transmitting only task-relevant information. This paper leverages the advantages of semantic communication in the design of SFL, and presents a case study the semantic communication-enhanced U-Shaped split federated learning (SC-USFL) framework that inherently enhances label privacy by localizing sensitive computations with reduced overhead. It features a dedicated semantic communication module (SCM), with pre-trained and parameter-frozen encoding/decoding units, to efficiently compress and transmit only the task-relevant semantic information over the critical uplink path from vehicular users to the edge server (ES). Furthermore, a network status monitor (NSM) module enables adaptive adjustment of the semantic compression rate in real-time response to fluctuating wireless channel conditions. The SC-USFL framework demonstrates a promising approach for efficiently balancing communication load, preserving privacy, and maintaining learning performance in resource-constrained vehicular environments. Finally, this paper highlights key open research directions to further advance the synergy between semantic communication and SFL in the vehicular network.
[LG-32] U-Parking: Distributed UWB-Assisted Autonomous Parking System with Robust Localization and Intelligent Planning
链接: https://arxiv.org/abs/2603.04898
作者: Yiang Wu,Qiong Wu,Pingyi Fan,Kezhi Wang,Wen Chen,Guoqiang Mao,Khaled B. Letaief
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: This paper has been accepted by infocom. The source code has been released at: this https URL
Abstract:This demonstration presents U-Parking, a distributed Ultra-Wideband (UWB)-assisted autonomous parking system. By integrating Large Language Models (LLMs)-assisted planning with robust fusion localization and trajectory tracking, it enables reliable automated parking in challenging indoor environments, as validated through real-vehicle demonstrations.
[LG-33] Differential Privacy in Two-Layer Networks: How DP-SGD Harms Fairness and Robustness
链接: https://arxiv.org/abs/2603.04881
作者: Ruichen Xu,Kexin Chen
类目: Machine Learning (cs.LG); Computers and Society (cs.CY)
*备注:
Abstract:Differentially private learning is essential for training models on sensitive data, but empirical studies consistently show that it can degrade performance, introduce fairness issues like disparate impact, and reduce adversarial robustness. The theoretical underpinnings of these phenomena in modern, non-convex neural networks remain largely unexplored. This paper introduces a unified feature-centric framework to analyze the feature learning dynamics of differentially private stochastic gradient descent (DP-SGD) in two-layer ReLU convolutional neural networks. Our analysis establishes test loss bounds governed by a crucial metric: the feature-to-noise ratio (FNR). We demonstrate that the noise required for privacy leads to suboptimal feature learning, and specifically show that: 1) imbalanced FNRs across classes and subpopulations cause disparate impact; 2) even in the same class, noise has a greater negative impact on semantically long-tailed data; and 3) noise injection exacerbates vulnerability to adversarial attacks. Furthermore, our analysis reveals that the popular paradigm of public pre-training and private fine-tuning does not guarantee improvement, particularly under significant feature distribution shifts between datasets. Experiments on synthetic and real-world data corroborate our theoretical findings.
[LG-34] Osmosis Distillation: Model Hijacking with the Fewest Samples
链接: https://arxiv.org/abs/2603.04859
作者: Yuchen Shi,Huajie Chen,Heng Xu,Zhiquan Liu,Jialiang Shen,Chi Liu,Shuai Zhou,Tianqing Zhu,Wanlei Zhou
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Transfer learning is devised to leverage knowledge from pre-trained models to solve new tasks with limited data and computational resources. Meanwhile, dataset distillation has emerged to synthesize a compact dataset that preserves critical information from the original large dataset. Therefore, a combination of transfer learning and dataset distillation offers promising performance in evaluations. However, a non-negligible security threat remains undiscovered in transfer learning using synthetic datasets generated by dataset distillation methods, where an adversary can perform a model hijacking attack with only a few poisoned samples in the synthetic dataset. To reveal this threat, we propose Osmosis Distillation (OD) attack, a novel model hijacking strategy that targets deep learning models using the fewest samples. Comprehensive evaluations on various datasets demonstrate that the OD attack attains high attack success rates in hidden tasks while preserving high model utility in original tasks. Furthermore, the distilled osmosis set enables model hijacking across diverse model architectures, allowing model hijacking in transfer learning with considerable attack performance and model utility. We argue that awareness of using third-party synthetic datasets in transfer learning must be raised.
[LG-35] Missingness Bias Calibration in Feature Attribution Explanations
链接: https://arxiv.org/abs/2603.04831
作者: Shailesh Sridhar,Anton Xue,Eric Wong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Popular explanation methods often produce unreliable feature importance scores due to missingness bias, a systematic distortion that arises when models are probed with ablated, out-of-distribution inputs. Existing solutions treat this as a deep representational flaw that requires expensive retraining or architectural modifications. In this work, we challenge this assumption and show that missingness bias can be effectively treated as a superficial artifact of the model’s output space. We introduce MCal, a lightweight post-hoc method that corrects this bias by fine-tuning a simple linear head on the outputs of a frozen base model. Surprisingly, we find this simple correction consistently reduces missingness bias and is competitive with, or even outperforms, prior heavyweight approaches across diverse medical benchmarks spanning vision, language, and tabular domains.
[LG-36] Quadratic polarity and polar Fenchel-Young divergences from the canonical Legendre polarity
链接: https://arxiv.org/abs/2603.04812
作者: Frank Nielsen,Basile Plus-Gourdon,Mahito Sugiyama
类目: Computational Geometry (cs.CG); Machine Learning (cs.LG)
*备注: 17 pages, 5 figures
Abstract:Polarity is a fundamental reciprocal duality of n -dimensional projective geometry which associates to points polar hyperplanes, and more generally k -dimensional convex bodies to polar (n-1-k) -dimensional convex bodies. It is well-known that the Legendre-Fenchel transformation of functions can be interpreted from the polarity viewpoint of their graphs using an extra dimension. In this paper, we first show that generic polarities induced by quadratic polarity functionals can be expressed either as deformed Legendre polarity or as the Legendre polarity of deformed convex bodies, and be efficiently manipulated using linear algebra on (n+2)\times (n+2) matrices operating on homogeneous coordinates. Second, we define polar divergences using the Legendre polarity and show that they generalize the Fenchel-Young divergence or equivalent Bregman divergence. This polarity study brings new understanding of the core reference duality in information geometry. Last, we show that the total Bregman divergences can be considered as a total polar Fenchel-Young divergence from which we newly exhibit the reference duality using dual polar conformal factors.
[LG-37] WhisperAlign: Word-Boundary-Aware ASR and WhisperX-Anchored Pyannote Diarization for Long-Form Bengali Speech
链接: https://arxiv.org/abs/2603.04809
作者: Aurchi Chowdhury,Rubaiyat -E-Zaman,Sk. Ashrafuzzaman Nafees
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:This paper presents our solution for the DL Sprint 4.0, addressing the dual challenges of Bengali Long-Form Speech Recognition (Task 1) and Speaker Diarization (Task 2). Processing long-form, multi-speaker Bengali audio introduces significant hurdles in voice activity detection, overlapping speech, and context preservation. To solve the long-form transcription challenge, we implemented a robust audio chunking strategy utilizing whisper-timestamped, allowing us to feed precise, context-aware segments into our fine-tuned acoustic model for high-accuracy transcription. For the diarization task, we developed an integrated pipeline leveraging this http URL and WhisperX. A key contribution of our approach is the domain-specific fine-tuning of the Pyannote segmentation model on the competition dataset. This adaptation allowed the model to better capture the nuances of Bengali conversational dynamics and accurately resolve complex, overlapping speaker boundaries. Our methodology demonstrates that applying intelligent timestamped chunking to ASR and targeted segmentation fine-tuning to diarization significantly drives down Word Error Rate (WER) and Diarization Error Rate (DER), in low-resource settings.
[LG-38] Diffusion Policy through Conditional Proximal Policy Optimization
链接: https://arxiv.org/abs/2603.04790
作者: Ben Liu,Shunpeng Yang,Hua Chen
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Reinforcement learning (RL) has been extensively employed in a wide range of decision-making problems, such as games and robotics. Recently, diffusion policies have shown strong potential in modeling multi-modal behaviors, enabling more diverse and flexible action generation compared to the conventional Gaussian policy. Despite various attempts to combine RL with diffusion, a key challenge is the difficulty of computing action log-likelihood under the diffusion model. This greatly hinders the direct application of diffusion policies in on-policy reinforcement learning. Most existing methods calculate or approximate the log-likelihood through the entire denoising process in the diffusion model, which can be memory- and computationally inefficient. To overcome this challenge, we propose a novel and efficient method to train a diffusion policy in an on-policy setting that requires only evaluating a simple Gaussian probability. This is achieved by aligning the policy iteration with the diffusion process, which is a distinct paradigm compared to previous work. Moreover, our formulation can naturally handle entropy regularization, which is often difficult to incorporate into diffusion policies. Experiments demonstrate that the proposed method produces multimodal policy behaviors and achieves superior performance on a variety of benchmark tasks in both IsaacLab and MuJoCo Playground.
[LG-39] Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning ICLR2026
链接: https://arxiv.org/abs/2603.04780
作者: Haoyue Dai,Immanuel Albrecht,Peter Spirtes,Kun Zhang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: Appears at ICLR 2026 (oral)
Abstract:Causal discovery with latent variables is a fundamental task. Yet most existing methods rely on strong structural assumptions, such as enforcing specific indicator patterns for latents or restricting how they can interact with others. We argue that a core obstacle to a general, structural-assumption-free approach is the lack of an equivalence characterization: without knowing what can be identified, one generally cannot design methods for how to identify it. In this work, we aim to close this gap for linear non-Gaussian models. We establish the graphical criterion for when two graphs with arbitrary latent structure and cycles are distributionally equivalent, that is, they induce the same observed distribution set. Key to our approach is a new tool, edge rank constraints, which fills a missing piece in the toolbox for latent-variable causal discovery in even broader settings. We further provide a procedure to traverse the whole equivalence class and develop an algorithm to recover models from data up to such equivalence. To our knowledge, this is the first equivalence characterization with latent variables in any parametric setting without structural assumptions, and hence the first structural-assumption-free discovery method. Code and an interactive demo are available at this https URL.
[LG-40] Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization
链接: https://arxiv.org/abs/2603.04768
作者: Muhammad Usama,Dong Eui Chang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Equalizer parameter optimization is critical for signal integrity in high-speed memory systems operating at multi-gigabit data rates. However, existing methods suffer from computationally expensive eye diagram evaluation, optimization of expected rather than worst-case performance, and absence of uncertainty quantification for deployment decisions. In this paper, we propose a distributional risk-sensitive reinforcement learning framework integrating Information Bottleneck latent representations with Conditional Value-at-Risk optimization. We introduce rate-distortion optimal signal compression achieving 51 times speedup over eye diagrams while quantifying epistemic uncertainty through Monte Carlo dropout. Distributional reinforcement learning with quantile regression enables explicit worst-case optimization, while PAC-Bayesian regularization certifies generalization bounds. Experimental validation on 2.4 million waveforms from eight memory units demonstrated mean improvements of 37.1% and 41.5% for 4-tap and 8-tap equalizer configurations with worst-case guarantees of 33.8% and 38.2%, representing 80.7% and 89.1% improvements over Q-learning baselines. The framework achieved 62.5% high-reliability classification eliminating manual validation for most configurations. These results suggest the proposed framework provides a practical solution for production-scale equalizer optimization with certified worst-case guarantees.
[LG-41] ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation
链接: https://arxiv.org/abs/2603.04767
作者: Shaocheng Lan,Shuqi Gu,Zhangzhi Xiong,Kan Ren
类目: Machine Learning (cs.LG)
*备注: We have open-sourced ConTSG-Bench at this https URL
Abstract:Conditional time series generation plays a critical role in addressing data scarcity and enabling causal analysis in real-world applications. Despite its increasing importance, the field lacks a standardized and systematic benchmarking framework for evaluating generative models across diverse conditions. To address this gap, we introduce the Conditional Time Series Generation Benchmark (ConTSG-Bench). ConTSG-Bench comprises a large-scale, well-aligned dataset spanning diverse conditioning modalities and levels of semantic abstraction, first enabling systematic evaluation of representative generation methods across these dimensions with a comprehensive suite of metrics for generation fidelity and condition adherence. Both the quantitative benchmarking and in-depth analyses of conditional generation behaviors have revealed the traits and limitations of the current approaches, highlighting critical challenges and promising research directions, particularly with respect to precise structural controllability and downstream task utility under complex conditions.
[LG-42] KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry
链接: https://arxiv.org/abs/2603.04755
作者: Micky C Nnamdi,Wenqi Shi,Cheng Wan,J. Ben Tamo,Benjamin M Smith,Chad A Purnell,May D Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Obstructive sleep apnea (OSA) is a sleep disorder that affects nearly one billion people globally and significantly elevates cardiovascular risk. Traditional diagnosis through polysomnography is resource-intensive and limits widespread access, creating a critical need for accurate and efficient alternatives. In this paper, we introduce KindSleep, a deep learning framework that integrates clinical knowledge with single-channel patient-specific oximetry signals and clinical data for precise OSA diagnosis. KindSleep first learns to identify clinically interpretable concepts, such as desaturation indices and respiratory disturbance events, directly from raw oximetry signals. It then fuses these AI-derived concepts with multimodal clinical data to estimate the Apnea-Hypopnea Index (AHI). We evaluate KindSleep on three large, independent datasets from the National Sleep Research Resource (SHHS, CFS, MrOS; total n = 9,815). KindSleep demonstrates excellent performance in estimating AHI scores (R2 = 0.917, ICC = 0.957) and consistently outperforms existing approaches in classifying OSA severity, achieving weighted F1-scores from 0.827 to 0.941 across diverse populations. By grounding its predictions in a layer of clinically meaningful concepts, KindSleep provides a more transparent and trustworthy diagnostic tool for sleep medicine practices.
[LG-43] Distribution-Conditioned Transport
链接: https://arxiv.org/abs/2603.04736
作者: Nic Fishman,Gokul Gowri,Paolo L. B. Fischer,Marinka Zitnik,Omar Abudayyeh,Jonathan Gootenberg
类目: Machine Learning (cs.LG)
*备注:
Abstract:Learning a transport model that maps a source distribution to a target distribution is a canonical problem in machine learning, but scientific applications increasingly require models that can generalize to source and target distributions unseen during training. We introduce distribution-conditioned transport (DCT), a framework that conditions transport maps on learned embeddings of source and target distributions, enabling generalization to unseen distribution pairs. DCT also allows semi-supervised learning for distributional forecasting problems: because it learns from arbitrary distribution pairs, it can leverage distributions observed at only one condition to improve transport prediction. DCT is agnostic to the underlying transport mechanism, supporting models ranging from flow matching to distributional divergence-based models (e.g. Wasserstein, MMD). We demonstrate the practical performance benefits of DCT on synthetic benchmarks and four applications in biology: batch effect transfer in single-cell genomics, perturbation prediction from mass cytometry data, learning clonal transcriptional dynamics in hematopoiesis, and modeling T-cell receptor sequence evolution.
[LG-44] When Priors Backfire: On the Vulnerability of Unlearnable Examples to Pretraining ICLR2026
链接: https://arxiv.org/abs/2603.04731
作者: Zhihao Li,Gezheng Xu,Jiale Cai,Ruiyi Fang,Di Wu,Qicheng Lao,Charles Ling,Boyu Wang
类目: Machine Learning (cs.LG)
*备注: ICLR 2026 camera-ready
Abstract:Unlearnable Examples (UEs) serve as a data protection strategy that generates imperceptible perturbations to mislead models into learning spurious correlations instead of underlying semantics. In this paper, we uncover a fundamental vulnerability of UEs that emerges when learning starts from a pretrained model. Crucially, our empirical analysis shows that even when data are protected by carefully crafted perturbations, pretraining priors still furnish rich semantic representations that allow the model to circumvent the shortcuts introduced by UEs and capture genuine features, thereby nullifying unlearnability. To address this, we propose BAIT (Binding Artificial perturbations to Incorrect Targets), a novel bi-level optimization formulation. Specifically, the inner level aims at associating the perturbed samples with real labels to simulate standard data-label alignment, while the outer level actively disrupts this alignment by enforcing a mislabel-perturbation binding that maps samples to designated incorrect targets. This mechanism effectively overrides the semantic guidance of priors, forcing the model to rely on the injected perturbations and consequently preventing the acquisition of true semantics. Extensive experiments on standard benchmarks and multiple pretrained backbones demonstrate that BAIT effectively mitigates the influence of pretraining priors and maintains data unlearnability.
[LG-45] Count Bridges enable Modeling and Deconvolving Transcriptomic Data
链接: https://arxiv.org/abs/2603.04730
作者: Nic Fishman,Gokul Gowri,Tanush Kumar,Jiaqi Lu,Valentin de Bortoli,Jonathan S. Gootenberg,Omar Abudayyeh
类目: Machine Learning (cs.LG)
*备注:
Abstract:Many modern biological assays, including RNA sequencing, yield integer-valued counts that reflect the number of molecules detected. These measurements are often not at the desired resolution: while the unit of interest is typically a single cell, many measurement technologies produce counts aggregated over sets of cells. Although recent generative frameworks such as diffusion and flow matching have been extended to non-Euclidean and discrete settings, it remains unclear how best to model integer-valued data or how to systematically deconvolve aggregated observations. We introduce Count Bridges, a stochastic bridge process on the integers that provides an exact, tractable analogue of diffusion-style models for count data, with closed-form conditionals for efficient training and sampling. We extend this framework to enable direct training from aggregated measurements via an Expectation-Maximization-style approach that treats unit-level counts as latent variables. We demonstrate state-of-the-art performance on integer distribution matching benchmarks, comparing against flow matching and discrete flow matching baselines across various metrics. We then apply Count Bridges to two large-scale problems in biology: modeling single-cell gene expression data at the nucleotide resolution, with applications to deconvolving bulk RNA-seq, and resolving multicellular spatial transcriptomic spots into single-cell count profiles. Our methods offer a principled foundation for generative modeling and deconvolution of biological count data across scales and modalities.
[LG-46] SLO-Aware Compute Resource Allocation for Prefill-Decode Disaggregated LLM Inference
链接: https://arxiv.org/abs/2603.04716
作者: Luchang Li,Dongfang Li,Bozhao Gong,Yu Zhang
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 10 pages, 3 figures
Abstract:Prefill-Decode (P/D) disaggregation has emerged as a widely adopted optimization strategy for Large Language Model (LLM) inference. However, there currently exists no well-established methodology for determining the optimal number of P/D hardware resources, subject to constraints on total throughput, service level objectives (SLOs), and request characteristics - specifically input and output lengths. To address this gap, we propose a hybrid approach that combines theoretical modeling with empirical benchmarking. First, we present a theoretical model for calculating P/D resource counts, which is based on total throughput requirements, request input and output lengths, as well as prefill and decode throughput. Then, to obtain the actual prefill and decode throughput under SLO constraints, we model the prefill process using M/M/1 queuing theory, deriving the achieved prefill throughput from the benchmarked maximum prefill throughput and Time-To-First-Token (TTFT). For the decode phase, we determine the decode batch sizes that meet Time-Per-Output-Token (TPOT) requirements and obtain the corresponding decode throughput through empirical measurements. Our experimental results demonstrate that the proposed method can accurately predict optimal P/D resource allocation in real-world LLM inference scenarios.
[LG-47] Implicit Bias and Loss of Plasticity in Matrix Completion: Depth Promotes Low-Rankness ICLR2026
链接: https://arxiv.org/abs/2603.04703
作者: Baekrok Shin,Chulhee Yun
类目: Machine Learning (cs.LG)
*备注: Published at ICLR 2026
Abstract:We study matrix completion via deep matrix factorization (a.k.a. deep linear neural networks) as a simplified testbed to examine how network depth influences training dynamics. Despite the simplicity and importance of the problem, prior theory largely focuses on shallow (depth-2) models and does not fully explain the implicit low-rank bias observed in deeper networks. We identify coupled dynamics as a key mechanism behind this bias and show that it intensifies with increasing depth. Focusing on gradient flow under block-diagonal observations, we prove: (a) networks of depth \geq 3 exhibit coupling unless initialized diagonally, and (b) convergence to rank-1 occurs if and only if the dynamics is coupled – resolving an open question by Menon (2024) for a family of initializations. We also revisit the loss of plasticity phenomenon in matrix completion (Kleinman et al., 2024), where pre-training on few observations and resuming with more degrades performance. We show that deep models avoid plasticity loss due to their low-rank bias, whereas depth-2 networks pre-trained under decoupled dynamics fail to converge to low-rank, even when resumed training (with additional data) satisfies the coupling condition – shedding light on the mechanism behind this phenomenon.
[LG-48] Engineering Regression Without Real-Data Training: Domain Adaptation for Tabular Foundation Models Using Multi-Dataset Embeddings
链接: https://arxiv.org/abs/2603.04692
作者: Lyle Regenwetter,Rosen Yu,Cyril Picard,Faez Ahmed
类目: Machine Learning (cs.LG)
*备注:
Abstract:Predictive modeling in engineering applications has long been dominated by bespoke models and small, siloed tabular datasets, limiting the applicability of large-scale learning approaches. Despite recent progress in tabular foundation models, the resulting synthetic training distributions used for pre-training may not reflect the statistical structure of engineering data, limiting transfer to engineering regression. We introduce TREDBench, a curated collection of 83 real-world tabular regression datasets with expert engineering/non-engineering labels, and use TabPFN 2.5’s dataset-level embedding to study domain structure in a common representation space. We find that engineering datasets are partially distinguishable from non-engineering datasets, while standard procedurally generated datasets are highly distinguishable from engineering datasets, revealing a substantial synthetic-real domain gap. To bridge this gap without training on real engineering samples, we propose an embedding-guided synthetic data curation method: we generate and identify “engineering-like” synthetic datasets, and perform continued pre-training of TabPFN 2.5 using only the selected synthetic tasks. Across 35 engineering regression datasets, this synthetic-only adaptation improves predictive accuracy and data efficiency, outperforming TabPFN 2.5 on 29/35 datasets and AutoGluon on 27/35, with mean multiplicative data-efficiency gains of 1.75x and 4.44x, respectively. More broadly, our results indicate that principled synthetic data curation can convert procedural generators into domain-relevant “data engines,” enabling foundation models to improve in data-sparse scientific and industrial domains where real data collection is the primary bottleneck.
[LG-49] Generalizing Fair Top-k Selection: An Integrative Approach
链接: https://arxiv.org/abs/2603.04689
作者: Guangya Cai
类目: Data Structures and Algorithms (cs.DS); Computational Complexity (cs.CC); Computational Geometry (cs.CG); Computers and Society (cs.CY); Databases (cs.DB); Machine Learning (cs.LG)
*备注:
Abstract:Fair top- k selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top- k selected candidates, has drawn significant attention. We study the problem of finding a fair (linear) scoring function with multiple protected groups while also minimizing the disparity from a reference scoring function. This generalizes the prior setup, which was restricted to the single-group setting without disparity minimization. Previous studies imply that the number of protected groups may have a limited impact on the runtime efficiency. However, driven by the need for experimental exploration, we find that this implication overlooks a critical issue that may affect the fairness of the outcome. Once this issue is properly considered, our hardness analysis shows that the problem may become computationally intractable even for a two-dimensional dataset and small values of k . However, our analysis also reveals a gap in the hardness barrier, enabling us to recover the efficiency for the case of small k when the number of protected groups is sufficiently small. Furthermore, beyond measuring disparity as the “distance” between the fair and the reference scoring functions, we introduce an alternative disparity measure \unicodex2014 utility loss \unicodex2014 that may yield a more stable scoring function under small weight perturbations. Through careful engineering trade-offs that balance implementation complexity, robustness, and performance, our augmented two-pronged solution demonstrates strong empirical performance on real-world datasets, with experimental observations also informing algorithm design and implementation decisions.
[LG-50] Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data
链接: https://arxiv.org/abs/2603.04683
作者: Habib Pourdelan,Zhengkang Xiang,Hugh Stewart,Cam Nicholson,Martin Tomko,Kourosh Khoshelham
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate estimation of forest biomass is crucial for monitoring carbon sequestration and informing climate change mitigation strategies. Existing methods often rely on allometric models, which estimate individual tree biomass by relating it to measurable biophysical parameters, e.g., trunk diameter and height. This indirect approach is limited in accuracy due to measurement uncertainties and the inherently approximate nature of allometric equations, which may not fully account for the variability in tree characteristics and forest conditions. This study proposes a direct approach that leverages synthetic point cloud data to train a deep regression network, which is then applied to real point clouds for plot-level wood volume and aboveground biomass (AGB) estimation. We created synthetic 3D forest plots with ground truth volume, which were then converted into point cloud data using a lidar simulator. These point clouds were subsequently used to train deep regression networks based on PointNet, PointNet++, DGCNN, and PointConv. When applied to synthetic data, the deep regression networks achieved mean absolute percentage error (MAPE) values ranging from 1.69% to 8.11%. The trained networks were then applied to real lidar data to estimate volume and AGB. When compared against field measurements, our direct approach showed discrepancies of 2% to 20%. In contrast, indirect approaches based on individual tree segmentation followed by allometric conversion, as well as FullCAM, exhibited substantially large underestimation, with discrepancies ranging from 27% to 85%. Our results highlight the potential of integrating synthetic data with deep learning for efficient and scalable forest carbon estimation at plot level.
[LG-51] Improving the accuracy of physics-informed neural networks via last-layer retraining
链接: https://arxiv.org/abs/2603.04672
作者: Saad Qadeer,Panos Stinis
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注: Approved for release by Pacific Northwest National Laboratory
Abstract:Physics-informed neural networks (PINNs) are a versatile tool in the burgeoning field of scientific machine learning for solving partial differential equations (PDEs). However, determining suitable training strategies for them is not obvious, with the result that they typically yield moderately accurate solutions. In this article, we propose a method for improving the accuracy of PINNs by coupling them with a post-processing step that seeks the best approximation in a function space associated with the network. We find that our method yields errors four to five orders of magnitude lower than those of the parent PINNs across architectures and dimensions. Moreover, we can reuse the basis functions for the linear space in more complex settings, such as time-dependent and nonlinear problems, allowing for transfer learning. Out approach also provides a residual-based metric that allows us to optimally choose the number of basis functions employed.
[LG-52] K-Means as a Radial Basis function Network: a Variational and Gradient-based Equivalence
链接: https://arxiv.org/abs/2603.04625
作者: Felipe de Jesus Felix Arredondo,Alejandro Ucan-Puc,Carlos Astengo Noguez
类目: Machine Learning (cs.LG); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注: 21 pages, 2 figures, 1 appendix
Abstract:This work establishes a rigorous variational and gradient-based equivalence between the classical K-Means algorithm and differentiable Radial Basis Function (RBF) neural networks with smooth responsibilities. By reparameterizing the K-Means objective and embedding its distortion functional into a smooth weighted loss, we prove that the RBF objective \Gamma -converges to the K-Means solution as the temperature parameter \sigma vanishes. We further demonstrate that the gradient-based updates of the RBF centers recover the exact K-Means centroid update rule and induce identical training trajectories in the limit. To address the numerical instability of the Softmax transformation in the low-temperature regime, we propose the integration of Entmax-1.5, which ensures stable polynomial convergence while preserving the underlying Voronoi partition structure. These results bridge the conceptual gap between discrete partitioning and continuous optimization, enabling K-Means to be embedded directly into deep learning architectures for the joint optimization of representations and clusters. Empirical validation across diverse synthetic geometries confirms a monotone collapse of soft RBF centroids toward K-Means fixed points, providing a unified framework for end-to-end differentiable clustering.
[LG-53] PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion
链接: https://arxiv.org/abs/2603.04606
作者: Mahindra Rautela,Alexander Scheinker,Bradley Love,Diane Oyen,Nathan DeBardeleben,Earl Lawrence,Ayan Biswas
类目: Machine Learning (cs.LG); Plasma Physics (physics.plasm-ph)
*备注:
Abstract:PDE foundation models are typically pretrained on large, diverse corpora of PDE datasets and can be adapted to new settings with limited task-specific data. However, most downstream evaluations focus on forward problems, such as autoregressive rollout prediction. In this work, we study an inverse problem in inertial confinement fusion (ICF): estimating system parameters (inputs) from multi-modal, snapshot-style observations (outputs). Using the open JAG benchmark, which provides hyperspectral X-ray images and scalar observables per simulation, we finetune the PDE foundation model and train a lightweight task-specific head to jointly reconstruct hyperspectral images and regress system parameters. The fine-tuned model achieves accurate hyperspectral reconstruction (test MSE 1.2e-3) and strong parameter-estimation performance (up to R^2=0.995). Data-scaling experiments (5%-100% of the training set) show consistent improvements in both reconstruction and regression losses as the amount of training data increases, with the largest marginal gains in the low-data regime. Finally, finetuning from pretrained MORPH weights outperforms training the same architecture from scratch, demonstrating that foundation-model initialization improves sample efficiency for data-limited inverse problems in ICF.
[LG-54] A Late-Fusion Multimodal AI Framework for Privacy-Preserving Deduplication in National Healthcare Data Environments
链接: https://arxiv.org/abs/2603.04595
作者: Mohammed Omer Shakeel Ahmed
类目: Machine Learning (cs.LG)
*备注: 6 pages, 1 figure, 1 table. Accepted for publication in the 2025 IEEE International Conference on Future Machine Learning and Data Science (FMLDS)
Abstract:Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, impaired user experiences, and compliance risks. Traditional deduplication methods rely heavily on direct identifiers such as names, emails, or Social Security Numbers (SSNs), making them ineffective under strict privacy regulations like GDPR and HIPAA, where such personally identifiable information (PII) is restricted or masked. In this research, I propose a novel, scalable, multimodal AI framework for detecting duplicates without depending on sensitive information. This system leverages three distinct modalities: semantic embeddings derived from textual fields (names, cities) using pre-trained DistilBERT models, behavioral patterns extracted from user login timestamps, and device metadata encoded through categorical embeddings. These heterogeneous modalities are combined using a late fusion approach and clustered via DBSCAN, an unsupervised density-based algorithm. This proposed model is evaluated against a traditional string-matching baseline on a synthetic CRM dataset specifically designed to reflect privacy-preserving constraints. The multimodal framework demonstrated good performance, achieving a good F1-score by effectively identifying duplicates despite variations and noise inherent in the data. This approach offers a privacy-compliant solution to entity resolution and supports secure digital infrastructure, enhances the reliability of public health analytics, and promotes ethical AI adoption across government and enterprise settings. It is well-suited for integration into national health data modernization efforts, aligning with broader goals of privacy-first innovation.
[LG-55] Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling DATE ICLR2026
链接: https://arxiv.org/abs/2603.04553
作者: Tal Daniel,Carl Qi,Dan Haramati,Amir Zadeh,Chuan Li,Aviv Tamar,Deepak Pathak,David Held
类目: Machine Learning (cs.LG)
*备注: ICLR 2026 Oral. Project webpage: this https URL
Abstract:We introduce Latent Particle World Model (LPWM), a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM autonomously discovers keypoints, bounding boxes, and object masks directly from video data, enabling it to learn rich scene decompositions without supervision. Our architecture is trained end-to-end purely from videos and supports flexible conditioning on actions, language, and image goals. LPWM models stochastic particle dynamics via a novel latent action module and achieves state-of-the-art results on diverse real-world and synthetic datasets. Beyond stochastic video modeling, LPWM is readily applicable to decision-making, including goal-conditioned imitation learning, as we demonstrate in the paper. Code, data, pre-trained models and video rollouts are available: this https URL
[LG-56] Oracle-efficient Hybrid Learning with Constrained Adversaries
链接: https://arxiv.org/abs/2603.04546
作者: Princewill Okoroafor,Robert Kleinberg,Michael P. Kim
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:The Hybrid Online Learning Problem, where features are drawn i.i.d. from an unknown distribution but labels are generated adversarially, is a well-motivated setting positioned between statistical and fully-adversarial online learning. Prior work has presented a dichotomy: algorithms that are statistically-optimal, but computationally intractable (Wu et al., 2023), and algorithms that are computationally-efficient (given an ERM oracle), but statistically-suboptimal (Wu et al., 2024). This paper takes a significant step towards achieving statistical optimality and computational efficiency simultaneously in the Hybrid Learning setting. To do so, we consider a structured setting, where the Adversary is constrained to pick labels from an expressive, but fixed, class of functions R . Our main result is a new learning algorithm, which runs efficiently given an ERM oracle and obtains regret scaling with the Rademacher complexity of a class derived from the Learner’s hypothesis class H and the Adversary’s label class R . As a key corollary, we give an oracle-efficient algorithm for computing equilibria in stochastic zero-sum games when action sets may be high-dimensional but the payoff function exhibits a type of low-dimensional structure. Technically, we develop a number of tools for the design and analysis of our learning algorithm, including a novel Frank-Wolfe reduction with “truncated entropy regularizer” and a new tail bound for sums of “hybrid” martingale difference sequences. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2603.04546 [cs.LG] (or arXiv:2603.04546v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.04546 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-57] An LLM -Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs
链接: https://arxiv.org/abs/2603.04545
作者: Waleed Afandi,Hussein Abdallah,Ashraf Aboulnaga,Essam Mansour
类目: Machine Learning (cs.LG); Databases (cs.DB)
*备注: 14 pages, 11 figures
Abstract:Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference queries are computationally expensive and vary in complexity, as each involves a different number of target nodes linked to subgraphs of diverse densities and structures. Existing acceleration methods, such as pruning, quantization, and knowledge distillation, instantiate smaller models but do not adapt them to the structure or semantics of individual queries. They also store models as monolithic files that must be fully loaded, and miss the opportunity to retrieve only the neighboring nodes and corresponding model components that are semantically relevant to the target nodes. These limitations lead to excessive data loading and redundant computation on large KGs. This paper presents KG-WISE, a task-driven inference paradigm for large KGs. KG-WISE decomposes trained GNN models into fine-grained components that can be partially loaded based on the structure of the queried subgraph. It employs large language models (LLMs) to generate reusable query templates that extract semantically relevant subgraphs for each task, enabling query-aware and compact model instantiation. We evaluate KG-WISE on six large KGs with up to 42 million nodes and 166 million edges. KG-WISE achieves up to 28x faster inference and 98% lower memory usage than state-of-the-art systems while maintaining or improving accuracy across both commercial and open-weight LLMs.
[LG-58] Standing on the Shoulders of Giants: Rethinking EEG Foundation Model Pretraining via Multi-Teacher Distillation
链接: https://arxiv.org/abs/2603.04478
作者: Chenqi Li,Yu Liu,Shuo Zhang,Timothy Denison,Tingting Zhu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Pretraining for electroencephalogram (EEG) foundation models has predominantly relied on self-supervised masked reconstruction, a paradigm largely adapted from and inspired by the success of vision and language foundation models. However, unlike images and text, EEG datasets are notoriously expensive to collect and characterized by low signal-to-noise ratio. These challenges introduce difficulties in scaling the EEG foundation models and capturing the underlying neural semantics through reconstruction. In this work, we ask the question: can we stand on the shoulders of well-established foundation models from well-represented modalities to bootstrap the pretraining of EEG foundation models? We first demonstrate that mainstream foundation models, such as those from vision and time series, transfer surprisingly well to EEG domain. To this end, we propose the Multi-Teacher Distillation Pretraining (MTDP) framework for pretraining EEG foundation models via a two-stage multi-teacher distillation. In the first stage, we introduce a learnable gating network to fuse representations from diverse teachers (e.g., DINOv3 and Chronos) via a masked latent denoising objective. In the second stage, we distill the fused representation into an EEG foundation model. Extensive evaluations across 9 downstream tasks and 12 datasets demonstrate that our MTDP-based EEG foundation model outperforms its self-supervised counterparts while requiring only 25% of the pretraining data.
[LG-59] Act-Observe-Rewrite: Multimodal Coding Agents as In-Context Policy Learners for Robot Manipulation
链接: https://arxiv.org/abs/2603.04466
作者: Vaishak Kumar
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Can a multimodal language model learn to manipulate physical objects by reasoning about its own failures-without gradient updates, demonstrations, or reward engineering? We argue the answer is yes, under conditions we characterise precisely. We present Act-Observe-Rewrite (AOR), a framework in which an LLM agent improves a robot manipulation policy by synthesising entirely new executable Python controller code between trials, guided by visual observations and structured episode outcomes. Unlike prior work that grounds LLMs in pre-defined skill libraries or uses code generation for one-shot plan synthesis, AOR makes the full low-level motor control implementation the unit of LLM reasoning, enabling the agent to change not just what the robot does, but how it does it. The central claim is that interpretable code as the policy representation creates a qualitatively different kind of in-context learning from opaque neural policies: the agent can diagnose systematic failures and rewrite their causes. We validate this across three robosuite manipulation tasks and report promising results, with the agent achieving high success rates without demonstrations, reward engineering, or gradient updates.
[LG-60] Flowers: A Warp Drive for Neural PDE Solvers
链接: https://arxiv.org/abs/2603.04430
作者: Till Muser,Alexandra Spitzer,Matti Lassas,Maarten V. de Hoop,Ivan Dokmanić
类目: Machine Learning (cs.LG)
*备注:
Abstract:We introduce Flowers, a neural architecture for learning PDE solution operators built entirely from multihead warps. Aside from pointwise channel mixing and a multiscale scaffold, Flowers use no Fourier multipliers, no dot-product attention, and no convolutional mixing. Each head predicts a displacement field and warps the mixed input features. Motivated by physics and computational efficiency, displacements are predicted pointwise, without any spatial aggregation, and nonlocality enters \emphonly through sparse sampling at source coordinates, \emphone per head. Stacking warps in multiscale residual blocks yields Flowers, which implement adaptive, global interactions at linear cost. We theoretically motivate this design through three complementary lenses: flow maps for conservation laws, waves in inhomogeneous media, and a kinetic-theoretic continuum limit. Flowers achieve excellent performance on a broad suite of 2D and 3D time-dependent PDE benchmarks, particularly flows and waves. A compact 17M-parameter model consistently outperforms Fourier, convolution, and attention-based baselines of similar size, while a 150M-parameter variant improves over recent transformer-based foundation models with much more parameters, data, and training compute.
[LG-61] Data-Driven Optimization of Multi-Generational Cellular Networks: A Performance Classification Framework for Strategic Infrastructure Management
链接: https://arxiv.org/abs/2603.04425
作者: Maryam Sabahat,M. Umar Khan
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注:
Abstract:The exponential growth in mobile data demand necessitates intelligent management of telecommunications infrastructure to ensure Quality of Service (QoS) and operational efficiency. This paper presents a comprehensive analysis of a multigenerational cellular network dataset, sourced from the OpenCelliD project, to identify patterns in network deployment, utilization, and infrastructure gaps. The methodology involves geographical, temporal, and performance analysis of 1,818 cell tower entries, predominantly Long Term Evolution (LTE), across three countries with a significant concentration in Pakistan. Key findings reveal the long-term persistence of legacy 2G/3G infrastructure in major urban centers, the existence of a substantial number of under-utilized towers representing opportunities for cost savings, and the identification of specific “non-4G demand zones” where active user bases are served by outdated technologies. By introducing a signal-density metric, we distinguish between absolute over-utilization and localized congestion. The results provide actionable intelligence for Mobile Network Operators (MNOs) to guide strategic LTE upgrades, optimize resource allocation, and bridge the digital divide in underserved regions.
[LG-62] When Scaling Fails: Network and Fabric Effects on Distributed GPU Training Performance
链接: https://arxiv.org/abs/2603.04424
作者: Dinesh Gopalan,Ratul Ali
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注: 10 pages, 5 figures, 1 table
Abstract:Scaling distributed GPU training is commonly assumed to yield predictable performance gains as additional nodes are added. In practice, many large-scale deployments encounter diminishing returns and unstable behavior well before theoretical limits are reached. This paper examines why scaling fails in real systems, with a focus on the role of network and fabric effects that are often overlooked by higher-level training frameworks. We present an empirical study of distributed GPU training performance across multiple production-scale clusters. Our results show that network topology, congestion dynamics, collective synchronization behavior, and GPU locality frequently dominate end-to-end training performance once workloads move beyond a small number of nodes. Identical models and software stacks can exhibit sharply different scaling characteristics depending on fabric design and runtime communication patterns. We identify recurring failure modes that emerge as training transitions from single-node to multi-node execution, including synchronization amplification, topology-induced contention, and locality-driven performance variance. These effects are often invisible to standard profiling tools and are therefore misdiagnosed as framework or model-level inefficiencies. Based on these findings, we outline practical diagnostic principles that system builders can apply to understand scaling limits, improve predictability, and reduce the cost of large-scale distributed training.
[LG-63] Machine Learning for Complex Systems Dynamics: Detecting Bifurcations in Dynamical Systems with Deep Neural Networks
链接: https://arxiv.org/abs/2603.04420
作者: Swadesh Pal,Roderick Melnik
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS); Neurons and Cognition (q-bio.NC); Machine Learning (stat.ML)
*备注: 15 pages; 5 figures
Abstract:Critical transitions are the abrupt shifts between qualitatively different states of a system, and they are crucial to understanding tipping points in complex dynamical systems across ecology, climate science, and biology. Detecting these shifts typically involves extensive forward simulations or bifurcation analyses, which are often computationally intensive and limited by parameter sampling. In this study, we propose a novel machine learning approach based on deep neural networks (DNNs) called equilibrium-informed neural networks (EINNs) to identify critical thresholds associated with catastrophic regime shifts. Rather than fixing parameters and searching for solutions, the EINN method reverses this process by using candidate equilibrium states as inputs and training a DNN to infer the corresponding system parameters that satisfy the equilibrium condition. By analyzing the learned parameter landscape and observing abrupt changes in the feasibility or continuity of equilibrium mappings, critical thresholds can be effectively detected. We demonstrate this capability on nonlinear systems exhibiting saddle-node bifurcations and multi-stability, showing that EINNs can recover the parameter regions associated with impending transitions. This method provides a flexible alternative to traditional techniques, offering new insights into the early detection and structure of critical shifts in high-dimensional and nonlinear systems.
[LG-64] Regularized Online RLHF with Generalized Bilinear Preferences
链接: https://arxiv.org/abs/2602.23116
作者: Junghyun Lee,Minju Hong,Kwang-Sung Jun,Chulhee Yun,Se-Young Yun
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT); Machine Learning (stat.ML)
*备注: 43 pages, 1 table (ver2: more colorful boxes, fixed some typos)
Abstract:We consider the problem of contextual online RLHF with general preferences, where the goal is to identify the Nash Equilibrium. We adopt the Generalized Bilinear Preference Model (GBPM) to capture potentially intransitive preferences via low-rank, skew-symmetric matrices. We investigate general preference learning with any strongly convex regularizer and regularization strength \eta^-1 , generalizing beyond prior work limited to reverse KL-regularization. Central to our analysis is proving that the dual gap of the greedy policy is bounded by the square of the estimation error, a result derived solely from strong convexity and the skew-symmetry of GBPM. Building on this insight and a feature diversity assumption, we establish two regret bounds via two simple algorithms: (1) Greedy Sampling achieves polylogarithmic, e^\mathcalO(\eta) -free regret \tilde\mathcalO(\eta d^4 (\log T)^2) . (2) Explore-Then-Commit achieves \mathrmpoly(d) -free regret \tilde\mathcalO(\sqrt\eta r T) by exploiting the low-rank structure; this is the first statistically efficient guarantee for online RLHF in high-dimensions.
[LG-65] hermodynamic Response Functions in Singular Bayesian Models
链接: https://arxiv.org/abs/2603.05480
作者: Sean Plummer
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:Singular statistical models-including mixtures, matrix factorization, and neural networks-violate regular asymptotics due to parameter non-identifiability and degenerate Fisher geometry. Although singular learning theory characterizes marginal likelihood behavior through invariants such as the real log canonical threshold and singular fluctuation, these quantities remain difficult to interpret operationally. At the same time, widely used criteria such as WAIC and WBIC appear disconnected from underlying singular geometry. We show that posterior tempering induces a one-parameter deformation of the posterior distribution whose associated observables generate a hierarchy of thermodynamic response functions. A universal covariance identity links derivatives of tempered expectations to posterior fluctuations, placing WAIC, WBIC, and singular fluctuation within a unified response framework. Within this framework, classical quantities from singular learning theory acquire natural thermodynamic interpretations: RLCT governs the leading free-energy slope, singular fluctuation corresponds to curvature of the tempered free energy, and WAIC measures predictive fluctuation. We formalize an observable algebra that quotients out non-identifiable directions, allowing structurally meaningful order parameters to be constructed in singular models. Across canonical singular examples-including symmetric Gaussian mixtures, reduced-rank regression, and overparameterized neural networks-we empirically demonstrate phase-transition-like behavior under tempering. Order parameters collapse, susceptibilities peak, and complexity measures align with structural reorganization in posterior geometry. Our results suggest that thermodynamic response theory provides a natural organizing framework for interpreting complexity, predictive variability, and structural reorganization in singular Bayesian learning.
[LG-66] Harnessing Synthetic Data from Generative AI for Statistical Inference
链接: https://arxiv.org/abs/2603.05396
作者: Ahmad Abdel-Azim,Ruoyu Wang,Xihong Lin
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Submitted to Statistical Science
Abstract:The emergence of generative AI models has dramatically expanded the availability and use of synthetic data across scientific, industrial, and policy domains. While these developments open new possibilities for data analysis, they also raise fundamental statistical questions about when synthetic data can be used in a valid, reliable, and principled manner. This paper reviews the current landscape of synthetic data generation and use from a statistical perspective, with the goal of clarifying the assumptions under which synthetic data can meaningfully support downstream discovery, inference, and prediction. We survey major classes of modern generative models, their intended use cases, and the benefits they offer, while also highlighting their limitations and characteristic failure modes. We additionally examine common pitfalls that arise when synthetic data are treated as surrogates for real observations, including biases from model misspecification, attenuated uncertainty, and difficulties in generalization. Building on these insights, we discuss emerging frameworks for the principled use of synthetic data. We conclude with practical recommendations, open problems, and cautions intended to guide both method developers and applied researchers.
[LG-67] On the Statistical Optimality of Optimal Decision Trees
链接: https://arxiv.org/abs/2603.05340
作者: Zineng Xu,Subhroshekhar Ghosh,Yan Shuo Tan
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
[LG-68] Bayes with No Shame: Admissibility Geometries of Predictive Inference
链接: https://arxiv.org/abs/2603.05335
作者: Nicholas G. Polson,Daniel Zantedeschi
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:Four distinct admissibility geometries govern sequential and distribution-free inference: Blackwell risk dominance over convex risk sets, anytime-valid admissibility within the nonnegative supermartingale cone, marginal coverage validity over exchangeable prediction sets, and Cesàro approachability (CAA) admissibility, which reaches the risk-set boundary via approachability-style arguments rather than explicit priors. We prove a criterion separation theorem: the four classes of admissible procedures are pairwise non-nested. Each geometry carries a different certificate of optimality: a supporting-hyperplane prior (Blackwell), a nonnegative supermartingale (anytime-valid), an exchangeability rank (coverage), or a Cesàro steering argument (CAA). Martingale coherence is necessary for Blackwell admissibility and necessary and sufficient for anytime-valid admissibility within e-processes, but is not sufficient for Blackwell admissibility and is not necessary for coverage validity or CAA-admissibility. All four criteria share a common optimization template (minimize Bayesian risk subject to a feasibility constraint), but the constraint sets operate over different spaces, partial orders, and performance metrics, making them geometrically incompatible. Admissibility is irreducibly criterion-relative.
[LG-69] How important are the genes to explain the outcome - the asymmetric Shapley value as an honest importance metric for high-dimensional features
链接: https://arxiv.org/abs/2603.05317
作者: Mark A. van de Wiel,Jeroen Goedhart,Martin Jullum,Kjersti Aas
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 32 pages, incl. Supplementary Material
Abstract:In clinical prediction settings the importance of a high-dimensional feature like genomics is often assessed by evaluating the change in predictive performance when adding it to a set of traditional clinical variables. This approach is questionable, because it does not account for collinearity nor known directionality of dependencies between variables. We suggest to use asymmetric Shapley values as a more suitable alternative to quantify feature importance in the context of a mixed-dimensional prediction model. We focus on a setting that is particularly relevant in clinical prediction: disease state as a mediating variable for genomic effects, with additional confounders for which the direction of effects may be unknown. We derive efficient algorithms to compute local and global asymmetric Shapley values for this setting. The former are shown to be very useful for inference, whereas the latter provide interpretation by decomposing any predictive performance metric into contributions of the features. Throughout, we illustrate our framework by a leading example: the prediction of progression-free survival for colorectal cancer patients.
[LG-70] Bayesian Supervised Causal Clustering
链接: https://arxiv.org/abs/2603.05288
作者: Luwei Wang,Nazir Lone,Sohan Seth
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Finding patient subgroups with similar characteristics is crucial for personalized decision-making in various disciplines such as healthcare and policy evaluation. While most existing approaches rely on unsupervised clustering methods, there is a growing trend toward using supervised clustering methods that identify operationalizable subgroups in the context of a specific outcome of interest. We propose Bayesian Supervised Causal Clustering (BSCC), with treatment effect as outcome to guide the clustering process. BSCC identifies homogenous subgroups of individuals who are similar in their covariate profiles as well as their treatment effects. We evaluate BSCC on simulated datasets as well as real-world dataset from the third International Stroke Trial to assess the practical usefulness of the framework.
[LG-71] Learning Optimal Individualized Decision Rules with Conditional Demographic Parity
链接: https://arxiv.org/abs/2603.05226
作者: Wenhai Cui,Wen Su,Donglin Zeng,Xingqiu Zhao
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Individualized decision rules (IDRs) have become increasingly prevalent in societal applications such as personalized marketing, healthcare, and public policy design. However, a critical ethical concern arises from the potential discriminatory effects of IDRs trained on biased data. These algorithms may disproportionately harm individuals from minority subgroups defined by sensitive attributes like gender, race, or language. To address this issue, we propose a novel framework that incorporates demographic parity (DP) and conditional demographic parity (CDP) constraints into the estimation of optimal IDRs. We show that the theoretically optimal IDRs under DP and CDP constraints can be obtained by applying perturbations to the unconstrained optimal IDRs, enabling a computationally efficient solution. Theoretically, we derive convergence rates for both policy value and the fairness constraint term. The effectiveness of our methods is illustrated through comprehensive simulation studies and an empirical application to the Oregon Health Insurance Experiment.
[LG-72] A Geometry-Adaptive Deep Variational Framework for Phase Discovery in the Landau-Brazovskii Model
链接: https://arxiv.org/abs/2603.05161
作者: Yuchen Xie,Jianyuan Yin,Lei Zhang
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注:
Abstract:The discovery of ordered structures in pattern-forming systems, such as the Landau-Brazovskii (LB) model, is often limited by the sensitivity of numerical solvers to the prescribed computational domain size. Incompatible domains induce artificial stress, frequently trapping the system in high-energy metastable configurations. To resolve this issue, we propose a Geometry-Adaptive Deep Variational Framework (GeoDVF) that jointly optimizes the infinite-dimensional order parameter, which is parameterized by a neural network, and the finite-dimensional geometric parameters of the computational domain. By explicitly treating the domain size as trainable variables within the variational formulation, GeoDVF naturally eliminates artificial stress during training. To escape the attraction basin of the disordered phase under small initializations, we introduce a warmup penalty mechanism, which effectively destabilizes the disordered phase, enabling the spontaneous nucleation of complex three-dimensional ordered phases from random initializations. Furthermore, we design a guided initialization protocol to resolve topologically intricate phases associated with narrow basins of attraction. Extensive numerical experiments show that GeoDVF provides a robust and geometry-consistent variational solver capable of identifying both stable and metastable states without prior knowledge.
[LG-73] How Does the ReLU Activation Affect the Implicit Bias of Gradient Descent on High-dimensional Neural Network Regression?
链接: https://arxiv.org/abs/2603.04895
作者: Kuo-Wei Lai,Guanghui Wang,Molei Tao,Vidya Muthukumar
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 62 pages
Abstract:Overparameterized ML models, including neural networks, typically induce underdetermined training objectives with multiple global minima. The implicit bias refers to the limiting global minimum that is attained by a common optimization algorithm, such as gradient descent (GD). In this paper, we characterize the implicit bias of GD for training a shallow ReLU model with the squared loss on high-dimensional random features. Prior work showed that the implicit bias does not exist in the worst-case (Vardi and Shamir, 2021), or corresponds exactly to the minimum-l2-norm solution among all global minima under exactly orthogonal data (Boursier et al., 2022). Our work interpolates between these two extremes and shows that, for sufficiently high-dimensional random data, the implicit bias approximates the minimum-l2-norm solution with high probability with a gap on the order \Theta(\sqrtn/d) , where n is the number of training examples and d is the feature dimension. Our results are obtained through a novel primal-dual analysis, which carefully tracks the evolution of predictions, data-span coefficients, as well as their interactions, and shows that the ReLU activation pattern quickly stabilizes with high probability over the random data.
[LG-74] he Inductive Bias of Convolutional Neural Networks: Locality and Weight Sharing Reshape Implicit Regularization
链接: https://arxiv.org/abs/2603.04807
作者: Tongtong Liang,Esha Singh,Rahul Parhi,Alexander Cloninger,Yu-Xiang Wang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Under Review. Comments welcome!
Abstract:We study how architectural inductive bias reshapes the implicit regularization induced by the edge-of-stability phenomenon in gradient descent. Prior work has established that for fully connected networks, the strength of this regularization is governed solely by the global input geometry; consequently, it is insufficient to prevent overfitting on difficult distributions such as the high-dimensional sphere. In this paper, we show that locality and weight sharing fundamentally change this picture. Specifically, we prove that provided the receptive field size m remains small relative to the ambient dimension d , these networks generalize on spherical data with a rate of n^-\frac16 +O(m/d) , a regime where fully connected networks provably fail. This theoretical result confirms that weight sharing couples the learned filters to the low-dimensional patch manifold, thereby bypassing the high dimensionality of the ambient space. We further corroborate our theory by analyzing the patch geometry of natural images, showing that standard convolutional designs induce patch distributions that are highly amenable to this stability mechanism, thus providing a systematic explanation for the superior generalization of convolutional networks over fully connected baselines.
[LG-75] Optimal Prediction-Augmented Algorithms for Testing Independence of Distributions
链接: https://arxiv.org/abs/2603.04635
作者: Maryam Aliakbarpour,Alireza Azizi,Ria Stevens
类目: Machine Learning (stat.ML); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注:
Abstract:Independence testing is a fundamental problem in statistical inference: given samples from a joint distribution p over multiple random variables, the goal is to determine whether p is a product distribution or is \epsilon -far from all product distributions in total variation distance. In the non-parametric finite-sample regime, this task is notoriously expensive, as the minimax sample complexity scales polynomially with the support size. In this work, we move beyond these worst-case limitations by leveraging the framework of \textitaugmented distribution testing. We design independence testers that incorporate auxiliary, but potentially untrustworthy, predictive information. Our framework ensures that the tester remains robust, maintaining worst-case validity regardless of the prediction’s quality, while significantly improving sample efficiency when the prediction is accurate. Our main contributions include: (i) a bivariate independence tester for discrete distributions that adaptively reduces sample complexity based on the prediction error; (ii) a generalization to the high-dimensional multivariate setting for testing the independence of d random variables; and (iii) matching minimax lower bounds demonstrating that our testers achieve optimal sample complexity.
[LG-76] Weather-Related Crash Risk Forecasting: A Deep Learning Approach for Heterogenous Spatiotemporal Data
链接: https://arxiv.org/abs/2603.04551
作者: Abimbola Ogungbire,Srinivas Pulugurtha
类目: Applications (stat.AP); Machine Learning (cs.LG)
*备注: 20 pages 5 figures
Abstract:This study introduces a deep learning-based framework for forecasting weather-related traffic crash risk using heterogeneous spatiotemporal data. Given the complex, non-linear relationship between crash occurrence and factors such as road characteristics, and traffic conditions, we propose an ensemble of Convolutional Long Short-Term Memory (ConvLSTM) models trained over overlapping spatial grids. This approach captures both spatial dependencies and temporal dynamics while addressing spatial heterogeneity in crash patterns. North Carolina was selected as the study area due to its diverse weather conditions, with historical crash, weather, and traffic data aggregated at 5-mi by 5-mi grid resolution. The framework was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and spatial cross-K analysis. Results show that the ensembled ConvLSTM significantly outperforms baseline models, including linear regression, ARIMA, and standard ConvLSTM, particularly in high-risk zones. The ensemble approach effectively combines the strengths of multiple ConvLSTM models, resulting in lower MSE and RMSE values across all regions, particularly when data from different crash risk zones are aggregated. Notably, the model performs exceptionally well in volatile high-risk areas (Cluster 1), achieving the lowest MSE and RMSE, while in stable low-risk areas (Cluster 2), it still improves upon simpler models but with slightly higher errors due to challenges in capturing subtle variations.
[LG-77] A Fast Generative Framework for High-dimensional Posterior Sampling: Application to CMB Delensing FAST
链接: https://arxiv.org/abs/2603.04535
作者: Hadi Sotoudeh,Pablo Lemos,Laurence Perreault-Levasseur
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Cosmology and Nongalactic Astrophysics (astro-ph.CO); Machine Learning (cs.LG)
*备注: 12 pages, 4 figures. ML4Astro 2025 workshop paper on fast generative posterior sampling with application to CMB delensing
Abstract:We introduce a deep generative framework for high-dimensional Bayesian inference that enables efficient posterior sampling. As telescopes and simulations rapidly expand the volume and resolution of astrophysical data, fast simulation-based inference methods are increasingly needed to extract scientific insights. While diffusion-based approaches offer high-quality generative capabilities, they are hindered by slow sampling speeds. Our method performs posterior sampling an order of magnitude faster than a diffusion baseline. Applied to the problem of CMB delensing, it successfully recovers the unlensed CMB power spectrum from simulated observations. The model also remains robust to shifts in cosmological parameters, demonstrating its potential for out-of-distribution generalization and application to observational cosmological data.
[LG-78] he Volterra signature
链接: https://arxiv.org/abs/2603.04525
作者: Paul P. Hager,Fabian N. Harang,Luca Pelizzari,Samy Tindel
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Modern approaches for learning from non-Markovian time series, such as recurrent neural networks, neural controlled differential equations or transformers, typically rely on implicit memory mechanisms that can be difficult to interpret or to train over long horizons. We propose the Volterra signature \mathrmVSig(x;K) as a principled, explicit feature representation for history-dependent systems. By developing the input path x weighted by a temporal kernel K into the tensor algebra, we leverage the associated Volterra–Chen identity to derive rigorous learning-theoretic guarantees. Specifically, we prove an injectivity statement (identifiability under augmentation) that leads to a universal approximation theorem on the infinite dimensional path space, which in certain cases is achieved by linear functionals of \mathrmVSig(x;K) . Moreover, we demonstrate applicability of the kernel trick by showing that the inner product associated with Volterra signatures admits a closed characterization via a two-parameter integral equation, enabling numerical methods from PDEs for computation. For a large class of exponential-type kernels, \mathrmVSig(x;K) solves a linear state-space ODE in the tensor algebra. Combined with inherent invariance to time reparameterization, these results position the Volterra signature as a robust, computationally tractable feature map for data science. We demonstrate its efficacy in dynamic learning tasks on real and synthetic data, where it consistently improves classical path signature baselines.
[LG-79] AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2
链接: https://arxiv.org/abs/2603.04480
作者: Faisal Bin Ashraf,Animesh Ray,Stefano Lonardi
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)
*备注:
Abstract:Machine learning-based antibody design is emerging as one of the most promising approaches to combat infectious diseases, due to significant advancements in the field of artificial intelligence and an exponential surge in experimental antibody data (in particular related to COVID-19). The ability of an antibody to bind to an antigens (called binding affinity) is one of the the most critical properties in designing neutralizing antibodies. In this study we introduce Ab-Affinity, a new large language model that can accurately predict the binding affinity of antibodies against a target peptide, e.g., the SARS-CoV-2 spike protein. Code and model are available at this https URL.
[LG-80] Bayesian Modeling of Collatz Stopping Times: A Probabilistic Machine Learning Perspective
链接: https://arxiv.org/abs/2603.04479
作者: Nicolò Bonacorsi,Matteo Bordoni
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST); Applications (stat.AP)
*备注:
Abstract:We study the Collatz total stopping time \tau(n) over n\le 10^7 from a probabilistic machine learning viewpoint. Empirically, \tau(n) is a skewed and heavily overdispersed count with pronounced arithmetic heterogeneity. We develop two complementary models. First, a Bayesian hierarchical Negative Binomial regression (NB2-GLM) predicts \tau(n) from simple covariates ( \log n and residue class n \bmod 8 ), quantifying uncertainty via posterior and posterior predictive distributions. Second, we propose a mechanistic generative approximation based on the odd-block decomposition: for odd m , write 3m+1=2^K(m)m’ with m’ odd and K(m)=v_2(3m+1)\ge 1 ; randomizing these block lengths yields a stochastic approximation calibrated via a Dirichlet-multinomial update. On held-out data, the NB2-GLM achieves substantially higher predictive likelihood than the odd-block generators. Conditioning the block-length distribution on m\bmod 8 markedly improves the generator’s distributional fit, indicating that low-order modular structure is a key driver of heterogeneity in \tau(n) .
[LG-81] Dictionary Based Pattern Entropy for Causal Direction Discovery
链接: https://arxiv.org/abs/2603.04473
作者: Harikrishnan N B,Shubham Bhilare,Aditi Kathpalia,Nithin Nagaraj
类目: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 13 pages
Abstract:Discovering causal direction from temporal observational data is particularly challenging for symbolic sequences, where functional models and noise assumptions are often unavailable. We propose a novel \emphDictionary Based Pattern Entropy ( DPE ) framework that infers both the direction of causation and the specific subpatterns driving changes in the effect variable. The framework integrates \emphAlgorithmic Information Theory (AIT) and \emphShannon Information Theory. Causation is interpreted as the emergence of compact, rule based patterns in the candidate cause that systematically constrain the effect. DPE constructs direction-specific dictionaries and quantifies their influence using entropy-based measures, enabling a principled link between deterministic pattern structure and stochastic variability. Causal direction is inferred via a minimum-uncertainty criterion, selecting the direction exhibiting stronger and more consistent pattern-driven organization. As summarized in Table 7, DPE consistently achieves reliable performance across diverse synthetic systems, including delayed bit-flip perturbations, AR(1) coupling, 1D skew-tent maps, and sparse processes, outperforming or matching competing AIT-based methods ( ETC_E , ETC_P , LZ_P ). In biological and ecological datasets, performance is competitive, while alternative methods show advantages in specific genomic settings. Overall, the results demonstrate that minimizing pattern level uncertainty yields a robust, interpretable, and broadly applicable framework for causal discovery.
[LG-82] Explainable Regime Aware Investing
链接: https://arxiv.org/abs/2603.04441
作者: Amine Boukardagha
类目: Portfolio Management (q-fin.PM); Machine Learning (cs.LG); Mathematical Finance (q-fin.MF)
*备注:
Abstract:We propose an explainable regime-aware portfolio construction framework based on a strictly causal Wasserstein Hidden Markov Model. The model combines rolling Gaussian HMM inference with predictive model-order selection and template-based identity tracking using the 2-Wasserstein distance between Gaussian components. This allows regime complexity to adapt dynamically while preserving stable economic interpretation. Regime probabilities are embedded into a transaction-cost-aware mean-variance optimization framework and evaluated on a diversified daily cross-asset universe. Relative to equal-weight and SPX buy-and-hold benchmarks, the Wasserstein HMM achieves materially higher risk-adjusted performance with Sharpe ratios of 2.18 versus 1.59 and 1.18 and substantially lower maximum drawdown of negative 5.43 percent versus negative 14.62 percent for SPX. During the early 2025 equity selloff labeled Liberation Day, the strategy dynamically reduced equity exposure and shifted toward defensive assets, mitigating peak-to-trough losses. Compared to a nonparametric KNN conditional-moment estimator using the same features and optimization layer, the parametric regime model produces materially lower turnover and smoother weight evolution. The results demonstrate that regime inference stability, particularly identity preservation and adaptive complexity control, is a first-order determinant of portfolio drawdown and implementation robustness in daily asset allocation.
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