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目录
概览 (2026-04-06)
今日共更新540篇论文,其中:
- 自然语言处理共81篇(Computation and Language (cs.CL))
- 人工智能共165篇(Artificial Intelligence (cs.AI))
- 计算机视觉共120篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共127篇(Machine Learning (cs.LG))
- 多智能体系统共11篇(Multiagent Systems (cs.MA))
- 信息检索共11篇(Information Retrieval (cs.IR))
- 人机交互共20篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] A Network Formation Game for Katz Centrality Maximization: A Resource Allocation Perspective
【速读】：该论文旨在解决网络形成博弈中个体如何通过有限资源分配来最大化自身影响力的问题，其中影响力由Katz中心性（Katz centrality）量化。其核心挑战在于建模Agent在拓扑约束下进行策略性边权分配的动态过程，并分析由此产生的纳什均衡网络结构及其性质。解决方案的关键在于构建一个基于Katz中心性的战略式博弈模型，其中每个Agent的资源分配决定其出边权重，从而诱导出整个网络结构；进一步提出顺序最优响应动态（Sequential Best-Response Dynamics, BRD），证明该机制在弱假设下收敛至纳什均衡集，并揭示不同基础拓扑下的均衡特性：对于完全图，均衡时Katz中心性与预算成正比；对于含自环的一般拓扑，均衡网络呈现层级结构。
链接: https://arxiv.org/abs/2604.03056
作者: Balaji R,Prashil Wankhede,Pavankumar Tallapragada
机构: Indian Institute of Science (印度科学理工学院)
类目: Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)
备注: Submitted to the 65th IEEE Conference on Decision and Control (CDC), 2026. (8 pages, 5 figures)
Abstract:In this paper, we study a network formation game in which agents seek to maximize their influence by allocating constrained resources to choose connections with other agents. In particular, we use Katz centrality to model agents’ influence in the network. Allocations are restricted to neighbors in a given unweighted network encoding topological constraints. The allocations by an agent correspond to the weights of its outgoing edges. Such allocation by all agents thereby induces a network. This models a strategic-form game in which agents’ utilities are given by their Katz centralities. We characterize the Nash equilibrium networks of this game and analyze their properties. We propose a sequential best-response dynamics (BRD) to model the network formation process. We show that it converges to the set of Nash equilibria under very mild assumptions. For complete underlying topologies, we show that Katz centralities are proportional to agents’ budgets at Nash equilibria. For general underlying topologies in which each agent has a self-loop, we show that hierarchical networks form at Nash equilibria. Finally, simulations illustrate our findings.
[MA-1] Fully Byzantine-Resilient Distributed Multi-Agent Q-Learning
【速读】：该论文旨在解决在存在拜占庭边缘攻击（Byzantine edge attacks）的分布式多智能体强化学习（MARL）场景中，智能体难以收敛至最优价值函数的问题。现有方法通常仅能保证几乎必然收敛到近优解，或依赖严苛假设才能达到最优解，导致在实际网络受损时无法学习最优策略。解决方案的关键在于提出一种新颖的冗余过滤机制（redundancy-based filtering mechanism），该机制利用两跳邻居信息验证接收到的消息，在保持双向信息流动的同时有效识别并剔除恶意消息，从而确保所有智能体的价值函数几乎必然收敛至最优值函数。此外，作者还提出了一个可多项式时间验证的新拓扑条件，并给出了构造满足该条件网络的系统化方法，为算法的理论保障和实际部署提供了支撑。
链接: https://arxiv.org/abs/2604.02791
作者: Haejoon Lee,Dimitra Panagou
机构: University of Michigan (密歇根大学)
类目: Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注: 8 pages, 3 figures, submitted to 2026 IEEE Conference on Decision and Control (CDC)
Abstract:We study Byzantine-resilient distributed multi-agent reinforcement learning (MARL), where agents must collaboratively learn optimal value functions over a compromised communication network. Existing resilient MARL approaches typically guarantee almost sure convergence only to near-optimal value functions, or require restrictive assumptions to ensure convergence to optimal solution. As a result, agents may fail to learn the optimal policies under these methods. To address this, we propose a novel distributed Q-learning algorithm, under which all agents’ value functions converge almost surely to the optimal value functions despite Byzantine edge attacks. The key idea is a redundancy-based filtering mechanism that leverages two-hop neighbor information to validate incoming messages, while preserving bidirectional information flow. We then introduce a new topological condition for the convergence of our algorithm, present a systematic method to construct such networks, and prove that this condition can be verified in polynomial time. We validate our results through simulations, showing that our method converges to the optimal solutions, whereas prior methods fail under Byzantine edge attacks.
[MA-2] SentinelAgent : Intent-Verified Delegation Chains for Securing Federal Multi-Agent AI Systems
【速读】：该论文旨在解决联邦多智能体AI系统中委托链（delegation chain）的可验证性问题，即当代理A委托代理B代表用户X调用工具C时，现有框架无法明确识别导致该行为的授权链条以及政策违规的具体位置。其解决方案的核心是提出SentinelAgent框架，包含两个关键技术组件：一是委托链演算（Delegation Chain Calculus, DCC），定义了七个形式化属性（六 deterministic 和一 probabilistic），并通过四个元定理和一个命题证明了确定性意图验证在实践中不可行；二是意图保持委托协议（Intent-Preserving Delegation Protocol, IPDP），通过非大语言模型（non-LLM）的委托权威服务（Delegation Authority Service, DAS）在运行时强制执行所有七项属性。该方案在DelegationBench v4上的三阶段验证生命周期实现100%联合真阳性率（TPR）且假阳性率为0%，并在黑盒对抗条件下完全阻断30种攻击，同时借助TLA+模型检测对六个确定性属性进行机械验证，确保无违反情形。
链接: https://arxiv.org/abs/2604.02767
作者: KrishnaSaiReddy Patil
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 12 pages, 2 figures, 9 tables. Includes TLA+ mechanical verification, DelegationBench v4 benchmark (516 scenarios), live LangChain agent integration, and independent red-team evaluation
Abstract:When Agent A delegates to Agent B, which invokes Tool C on behalf of User X, no existing framework can answer: whose authorization chain led to this action, and where did it violate policy? This paper introduces SentinelAgent, a formal framework for verifiable delegation chains in federal multi-agent AI systems. The Delegation Chain Calculus (DCC) defines seven properties - six deterministic (authority narrowing, policy preservation, forensic reconstructibility, cascade containment, scope-action conformance, output schema conformance) and one probabilistic (intent preservation) - with four meta-theorems and one proposition establishing the practical infeasibility of deterministic intent verification. The Intent-Preserving Delegation Protocol (IPDP) enforces all seven properties at runtime through a non-LLM Delegation Authority Service. A three-point verification lifecycle achieves 100% combined TPR at 0% FPR on DelegationBench v4 (516 scenarios, 10 attack categories, 13 federal domains). Under black-box adversarial conditions, the DAS blocks 30/30 attacks with 0 false positives. Deterministic properties are unbreakable under adversarial stress testing; intent verification degrades to 13% against sophisticated paraphrasing. Fine-tuning the NLI model on 190 government delegation examples improves P2 from 1.7% to 88.3% TPR (5-fold cross-validated, F1=82.1%). Properties P1, P3-P7 are mechanically verified via TLA+ model checking across 2.7 million states with zero violations. Even when intent verification is evaded, the remaining six properties constrain the adversary to permitted API calls, conformant outputs, traceable actions, bounded cascades, and compliant behavior.
[MA-3] Multi-agent Reinforcement Learning-based Joint Design of Low-Carbon P2P Market and Bidding Strategy in Microgrids
【速读】：该论文旨在解决可再生能源发电不确定性与实时市场不稳定性限制微网社区中清洁能源有效利用的问题。现有点对点（P2P）及微网协调方法通常依赖于集中式优化或受限的协调规则，难以在实际场景中部署。解决方案的关键在于提出一种日内P2P交易框架，将各微网决策建模为去中心化的部分可观测马尔可夫决策过程（DEC-POMDP），并通过多智能体强化学习（MARL）求解，赋予每个微网高度自主性；同时引入一种新颖的市场出清机制实现宏观调控，激励微网优先消纳本地可再生能源，从而降低碳排放。仿真结果表明，该框架在保障微网自利性的同时显著提升了可再生能源利用率并减少了高碳电力依赖，实现了局部自治、个体利益追求与社区级经济与环境效益之间的平衡。
链接: https://arxiv.org/abs/2604.02728
作者: Junhao Ren,Honglin Gao,Sijie Wang,Lan Zhao,Qiyu Kang,Aniq Ashan,Yajuan Sun,Gaoxi Xiao
机构: Nanyang Technological University (南洋理工大学); University of Science and Technology of China (中国科学技术大学); MOYA Analytics; Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (新加坡制造技术研究院，科技研究局)
类目: Multiagent Systems (cs.MA)
备注: 10 pages, 6 figures
Abstract:The challenges of the uncertainties in renewable energy generation and the instability of the real-time market limit the effective utilization of clean energy in microgrid communities. Existing peer-to-peer (P2P) and microgrid coordination approaches typically rely on certain centralized optimization or restrictive coordination rules which are difficult to be implemented in real-life applications. To address the challenge, we propose an intraday P2P trading framework that allows self-interested microgrids to pursue their economic benefits, while allowing the market operator to maximize the social welfare, namely the low carbon emission objective, of the entire community. Specifically, the decision-making processes of the microgrids are formulated as a Decentralized Partially Observable Markov Decision Process (DEC-POMDP) and solved using a Multi-Agent Reinforcement Learning (MARL) framework. Such an approach grants each microgrid a high degree of decision-making autonomy, while a novel market clearing mechanism is introduced to provide macro-regulation, incentivizing microgrids to prioritize local renewable energy consumption and hence reduce carbon emissions. Simulation results demonstrate that the combination of the self-interested bidding strategy and the P2P market design helps significantly improve renewable energy utilization and reduce reliance on external electricity with high carbon-emissions. The framework achieves a balanced integration of local autonomy, self-interest pursuit, and improved community-level economic and environmental benefits.
[MA-4] Do Agent Societies Develop Intellectual Elites? The Hidden Power Laws of Collective Cognition in LLM Multi-Agent Systems
【速读】：该论文旨在解决大规模生成式AI（Generative AI）多智能体系统在扩展过程中出现的回报递减或不稳定问题，其核心在于揭示协调动态的量化规律并识别其根本机制。研究通过构建原子事件级别的协调动力学模型，分析超过150万次交互后发现三类耦合规律：协调遵循重尾级联、通过优先连接集中形成智力精英群体，并随系统规模扩大产生更频繁的极端事件。关键解决方案是提出“缺陷触发整合”（Deficit-Triggered Integration, DTI），该方法基于一个结构性瓶颈——协调扩展与整合能力不匹配导致大但弱集成的推理过程——通过在失衡时选择性增强整合，从而提升性能而不抑制大规模推理能力，实现了对可扩展多智能体智能中协调结构这一此前未被测量维度的有效干预。
链接: https://arxiv.org/abs/2604.02674
作者: Kavana Venkatesh,Jiaming Cui
机构: Virginia Tech (弗吉尼亚理工大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Model (LLM) multi-agent systems are increasingly deployed as interacting agent societies, yet scaling these systems often yields diminishing or unstable returns, the causes of which remain poorly understood. We present the first large-scale empirical study of coordination dynamics in LLM-based multi-agent systems, introducing an atomic event-level formulation that reconstructs reasoning as cascades of coordination. Analyzing over 1.5 Million interactions across tasks, topologies, and scales, we uncover three coupled laws: coordination follows heavy-tailed cascades, concentrates via preferential attachment into intellectual elites, and produces increasingly frequent extreme events as system size grows. We show that these effects are coupled through a single structural mechanism: an integration bottleneck, in which coordination expansion scales with system size while consolidation does not, producing large but weakly integrated reasoning processes. To test this mechanism, we introduce Deficit-Triggered Integration (DTI), which selectively increases integration under imbalance. DTI improves performance precisely where coordination fails, without suppressing large-scale reasoning. Together, our results establish quantitative laws of collective cognition and identify coordination structure as a fundamental, previously unmeasured axis for understanding and improving scalable multi-agent intelligence.
[MA-5] oo Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
【速读】：该论文旨在解决多智能体协作讨论中因个体智能体（LLM）表现出顺从倾向（sycophancy）而导致的错误传播与决策偏差问题，即智能体在未充分依据事实或逻辑的情况下盲目迎合用户立场或其他同伴观点。解决方案的关键在于引入“顺从度先验”（sycophancy priors），通过静态和动态策略对其他智能体的顺从倾向进行量化评分，并将这些排名信息作为先验知识提供给参与讨论的智能体。实验证明，这一轻量级机制可显著降低顺从型同伴的影响，抑制错误级联效应，最终使讨论准确率提升10.5%。
链接: https://arxiv.org/abs/2604.02668
作者: Vira Kasprova,Amruta Parulekar,Abdulrahman AlRabah,Krishna Agaram,Ritwik Garg,Sagar Jha,Nimet Beyza Bozdag,Dilek Hakkani-Tur
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model’s opinion. While prior work has mostly studied this in single-agent settings, it remains underexplored in collaborative multi-agent systems. We ask whether awareness of other agents’ sycophancy levels influences discussion outcomes. To investigate this, we run controlled experiments with six open-source LLMs, providing agents with peer sycophancy rankings that estimate each peer’s tendency toward sycophancy. These rankings are based on scores calculated using various static (pre-discussion) and dynamic (online) strategies. We find that providing sycophancy priors reduces the influence of sycophancy-prone peers, mitigates error-cascades, and improves final discussion accuracy by an absolute 10.5%. Thus, this is a lightweight, effective way to reduce discussion sycophancy and improve downstream accuracy.
[MA-6] High Volatility and Action Bias Distinguish LLM s from Humans in Group Coordination
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在群体协作任务中是否能展现出与人类相当的自适应协调能力，以及其采用的策略是否与人类一致的问题。研究通过设计一种具有不完全信息监测的共利益博弈——群体二分搜索（Group Binary Search），比较LLM与人类在无直接沟通条件下利用群体反馈逐步逼近目标数值的表现。解决方案的关键在于引入行为层面的机制性指标（如反应强度标度、切换动态和跨游戏学习），并以人类表现作为基准进行对比分析，发现LLM在迭代过程中缺乏稳定性和改进趋势，且对更丰富的反馈信息不敏感，从而揭示了LLM群体在协调机制上的本质差异，并为缩小其与人类间的协作差距提供了可操作的行为诊断框架。
链接: https://arxiv.org/abs/2604.02578
作者: Sahaj Singh Maini,Robert L. Goldstone,Zoran Tiganj
机构: Indiana University Bloomington (印第安纳大学布卢明顿分校)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT)
备注:
Abstract:Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question whether they can demonstrate comparable adaptive coordination and whether they use the same strategies as humans. To investigate this, we compare LLM and human performance on a common-interest game with imperfect monitoring: Group Binary Search. In this n-player game, participants need to coordinate their actions to achieve a common objective. Players independently submit numerical values in an effort to collectively sum to a randomly assigned target number. Without direct communication, they rely on group feedback to iteratively adjust their submissions until they reach the target number. Our findings show that, unlike humans who adapt and stabilize their behavior over time, LLMs often fail to improve across games and exhibit excessive switching, which impairs group convergence. Moreover, richer feedback (e.g., numerical error magnitude) benefits humans substantially but has small effects on LLMs. Taken together, by grounding the analysis in human baselines and mechanism-level metrics, including reactivity scaling, switching dynamics, and learning across games, we point to differences in human and LLM groups and provide a behaviorally grounded diagnostic for closing the coordination gap.
[MA-7] Single-Agent LLM s Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
【速读】：该论文旨在解决多智能体大语言模型（Multi-Agent LLM Systems, MAS）在多跳推理任务中表现优异的归因问题，即这些性能优势是否源于其架构本身的优越性，还是受制于未被控制的计算资源（如推理令牌预算）和上下文利用效率等混杂因素。解决方案的关键在于提出一个基于信息论的理论框架——利用数据处理不等式（Data Processing Inequality）论证：在固定推理令牌预算且理想上下文利用率下，单智能体系统（Single-Agent Systems, SAS）具有更高的信息效率；并进一步指出，MAS仅在单智能体有效上下文利用率下降或增加计算投入时才可能具备竞争力。作者通过三类主流模型（Qwen3、DeepSeek-R1-Distill-Llama 和 Gemini 2.5）的受控实验验证了这一假设，发现 SAS 在保持推理令牌一致的情况下始终能匹配甚至超越 MAS，同时揭示了 API 级别预算控制和基准测试中的潜在偏差会人为夸大 MAS 的优势，从而强调了对计算、上下文与协作之间权衡关系进行明确建模的重要性。
链接: https://arxiv.org/abs/2604.02460
作者: Dat Tran,Douwe Kiela
机构: Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注:
Abstract:Recent work reports strong performance from multi-agent LLM systems (MAS), but these gains are often confounded by increased test-time computation. When computation is normalized, single-agent systems (SAS) can match or outperform MAS, yet the theoretical basis and evaluation methodology behind this comparison remain unclear. We present an information-theoretic argument, grounded in the Data Processing Inequality, suggesting that under a fixed reasoning-token budget and with perfect context utilization, single-agent systems are more information-efficient. This perspective further predicts that multi-agent systems become competitive when a single agent’s effective context utilization is degraded, or when more compute is expended. We test these predictions in a controlled empirical study across three model families (Qwen3, DeepSeek-R1-Distill-Llama, and Gemini 2.5), comparing SAS with multiple MAS architectures under matched budgets. We find that SAS consistently match or outperform MAS on multi-hop reasoning tasks when reasoning tokens are held constant. Beyond aggregate performance, we conduct a detailed diagnostic analysis of system behavior and evaluation methodology. We identify significant artifacts in API-based budget control (particularly in Gemini 2.5) and in standard benchmarks, both of which can inflate apparent gains from MAS. Overall, our results suggest that, for multi-hop reasoning tasks, many reported advantages of multi-agent systems are better explained by unaccounted computation and context effects rather than inherent architectural benefits, and highlight the importance of understanding and explicitly controlling the trade-offs between compute, context, and coordination in agentic systems.
[MA-8] Eliminating Illusion in Directed Networks
【速读】：该论文旨在解决有向社交网络中的幻觉消除问题（illusion elimination problem），即在节点被染成红色或蓝色的有向图中，通过最少次数的重染色操作，使得任意节点都不处于 $ p −幻觉状态（ p \in (0,1) $）——所谓 $ p $-幻觉是指某节点的出邻居中至少 $ p $ 比例为红色，而全局蓝节点数量超过红节点。该问题是 NP-hard 的，甚至在网格和有向无环图（DAG）上也难以近似求解；然而，研究发现其在结构化稀疏图类（如外平面图、向外网格、树和环）中可多项式时间求解，并提出了基于底层数学图的 treewidth 和处于幻觉状态的节点数这两个参数的可 tractable 算法，关键在于利用图结构的局部稀疏性和参数化复杂性理论来设计高效算法。
链接: https://arxiv.org/abs/2604.02395
作者: Sougata Jana,Sanjukta Roy
机构: Indian Statistical Institute (印度统计研究所)
类目: Data Structures and Algorithms (cs.DS); Computational Complexity (cs.CC); Multiagent Systems (cs.MA)
备注: 26 pages, 5 figures
Abstract:We study illusion elimination problems on directed social networks where each vertex is colored either red or blue. A vertex is under \textitmajority illusion if it has more red out-neighbors than blue out-neighbors when there are more blue vertices than red ones in the network. In a more general phenomenon of p -illusion, at least p fraction of the out-neighbors (as opposed to 1/2 for majority) of a vertex is red. In the directed illusion elimination problem, we recolor minimum number of vertices so that no vertex is under p -illusion, for p\in (0,1) . Unfortunately, the problem is NP-hard for p =1/2 even when the network is a grid. Moreover, the problem is NP-hard and W[2]-hard when parameterized by the number of recolorings for each p \in (0,1) even on bipartite DAGs. Thus, we can neither get a polynomial time algorithm on DAGs, unless P=NP, nor we can get a FPT algorithm even by combining solution size and directed graph parameters that measure distance from acyclicity, unless FPT=W[2]. We show that the problem can be solved in polynomial time in structured, sparse networks such as outerplanar networks, outward grids, trees, and cycles. Finally, we show tractable algorithms parameterized by treewidth of the underlying undirected graph, and by the number of vertices under illusion.
[MA-9] Agent ic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC
【速读】：该论文旨在解决无线网络中智能体（agent）协作效率低下的问题，特别是在语义冗余处理和通信、计算、控制三者缺乏统一机制方面的挑战。其解决方案的关键在于提出了一种无线智能体网络（Wireless Agent Network, WAN）框架，通过构建一个渐进式知识聚合机制，将聚合过程建模为联合能量最小化问题：智能体在执行语义压缩以消除冗余的同时，优化传输功率以高效传递语义信息，并调整物理轨迹以主动改善信道质量。该框架进一步采用分层算法，整合内层资源优化与外层拓扑演化，其中引入势场（potential field）机制有效克服贪婪匹配的短视性，从而实现长期能量最小化的数学严谨启发式策略。
链接: https://arxiv.org/abs/2604.02381
作者: Zhouxiang Zhao,Jiaxiang Wang,Zhaohui Yang,Kun Yang,Zhaoyang Zhang,Mingzhe Chen,Kaibin Huang
机构: Zhejiang University (浙江大学); Zhejiang Provincial Key Laboratory of Info. Proc., Commun. Netw. (IPCAN) (浙江省信息处理与通信网络重点实验室); King’s College London (伦敦国王学院); University of Miami (迈阿密大学); The University of Hong Kong (香港大学)
类目: Networking and Internet Architecture (cs.NI); Information Theory (cs.IT); Multiagent Systems (cs.MA)
备注:
Abstract:The rapid development of agentic artificial intelligence (AI) is driving future wireless networks to evolve from passive data pipes into intelligent collaborative ecosystems under the emerging paradigm of integrated learning and communication (ILAC). However, realizing efficient agentic collaboration faces challenges not only in handling semantic redundancy but also in the lack of an integrated mechanism for communication, computation, and control. To address this, we propose a wireless agent network (WAN) framework that orchestrates a progressive knowledge aggregation mechanism. Specifically, we formulate the aggregation process as a joint energy minimization problem where the agents perform semantic compression to eliminate redundancy, optimize transmission power to deliver semantic payloads, and adjust physical trajectories to proactively enhance channel qualities. To solve this problem, we develop a hierarchical algorithm that integrates inner-level resource optimization with outer-level topology evolution. Theoretically, we reveal that incorporating a potential field into the topology evolution effectively overcomes the short-sightedness of greedy matching, providing a mathematically rigorous heuristic for long-term energy minimization. Simulation results demonstrate that the proposed framework achieves superior energy efficiency and scalability compared to conventional benchmarks, validating the efficacy of semantic-aware collaboration in dynamic environments.
[MA-10] Holos: A Web-Scale LLM -Based Multi-Agent Agent System for the Agentic Web
【速读】：该论文旨在解决基于大语言模型（Large Language Models, LLM）的多智能体系统（LLM-based Multi-Agent Systems, LaMAS）在向持久化、开放世界演进过程中所面临的三大核心挑战：扩展性摩擦（scaling friction）、协作机制崩溃（coordination breakdown）以及价值流失（value dissipation）。为应对这些问题，作者提出了一种名为Holos的Web规模LaMAS架构，其关键在于构建一个五层体系结构，其中包含三个核心模块：Nuwa引擎（用于高效智能体生成与托管）、市场驱动的协调器（Orchestrator，实现鲁棒协作）以及内生价值循环（endogenous value cycle），从而确保激励相容性并支撑生态系统的长期自组织演化。
链接: https://arxiv.org/abs/2604.02334
作者: Xiaohang Nie,Zihan Guo,Zicai Cui,Jiachi Yang,Zeyi Chen,Leheyi De,Yu Zhang,Junwei Liao,Bo Huang,Yingxuan Yang,Zhi Han,Zimian Peng,Linyao Chen,Wenzheng Tom Tang,Zongkai Liu,Tao Zhou,Botao Amber Hu,Shuyang Tang,Jianghao Lin,Weiwen Liu,Muning Wen,Yuanjian Zhou,Weinan Zhang
机构: Shanghai Innovation Institute (上海创新研究院); Shanghai Jiao Tong University (上海交通大学); Harbin Institute of Technology (哈尔滨工业大学); Sun Yat-sen University (中山大学); University of Oxford (牛津大学); Holos Engineering (霍洛斯工程)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 38 pages, 8 figures, and 4 tables
Abstract:As large language models (LLM)-driven agents transition from isolated task solvers to persistent digital entities, the emergence of the Agentic Web, an ecosystem where heterogeneous agents autonomously interact and co-evolve, marks a pivotal shift toward Artificial General Intelligence (AGI). However, LLM-based multi-agent systems (LaMAS) are hindered by open-world issues such as scaling friction, coordination breakdown, and value dissipation. To address these challenges, we introduce Holos, a web-scale LaMAS architected for long-term ecological persistence. Holos adopts a five-layer architecture, with core modules primarily featuring the Nuwa engine for high-efficiency agent generation and hosting, a market-driven Orchestrator for resilient coordination, and an endogenous value cycle to achieve incentive compatibility. By bridging the gap between micro-level collaboration and macro-scale emergence, Holos hopes to lay the foundation for the next generation of the self-organizing and continuously evolving Agentic Web. We have publicly released Holos (accessible at this https URL), providing a resource for the community and a testbed for future research in large-scale agentic ecosystems.
自然语言处理
[NLP-0] BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在面对不确定任务时往往产生高置信度但错误回答的问题，而现有评估协议未考虑置信度如何根据不同的风险偏好指导“作答或放弃回答”（answer-or-abstain）的决策。其解决方案的关键在于提出行为对齐评分（Behavioral Alignment Score, BAS），这是一个基于决策理论的指标，通过显式建模“作答或放弃回答”的效用函数，并在一系列风险阈值下聚合实际效用，从而衡量模型置信度在支持弃权决策中的可靠性。BAS不仅依赖于置信度的数值大小，还关注其排序结构，理论上证明了诚实的置信度估计能最大化预期BAS效用，将校准性与决策最优行为直接关联；同时，BAS对过度自信的惩罚具有不对称性，显著优于传统如对数损失等对称惩罚机制，有效识别出仅靠标准指标（如ECE、AURC）难以发现的严重过自信问题。
链接: https://arxiv.org/abs/2604.03216
作者: Sean Wu,Fredrik K. Gustafsson,Edward Phillips,Boyan Gao,Anshul Thakur,David A. Clifton
机构: University of Oxford (牛津大学); Oxford Suzhou Centre for Advanced Research (牛津苏州先进研究中心)
类目: Computation and Language (cs.CL)
备注: 24 pages, 7 figures, 6 tables
Abstract:Large language models (LLMs) often produce confident but incorrect answers in settings where abstention would be safer. Standard evaluation protocols, however, require a response and do not account for how confidence should guide decisions under different risk preferences. To address this gap, we introduce the Behavioral Alignment Score (BAS), a decision-theoretic metric for evaluating how well LLM confidence supports abstention-aware decision making. BAS is derived from an explicit answer-or-abstain utility model and aggregates realized utility across a continuum of risk thresholds, yielding a measure of decision-level reliability that depends on both the magnitude and ordering of confidence. We show theoretically that truthful confidence estimates uniquely maximize expected BAS utility, linking calibration to decision-optimal behavior. BAS is related to proper scoring rules such as log loss, but differs structurally: log loss penalizes underconfidence and overconfidence symmetrically, whereas BAS imposes an asymmetric penalty that strongly prioritizes avoiding overconfident errors. Using BAS alongside widely used metrics such as ECE and AURC, we then construct a benchmark of self-reported confidence reliability across multiple LLMs and tasks. Our results reveal substantial variation in decision-useful confidence, and while larger and more accurate models tend to achieve higher BAS, even frontier models remain prone to severe overconfidence. Importantly, models with similar ECE or AURC can exhibit very different BAS due to highly overconfident errors, highlighting limitations of standard metrics. We further show that simple interventions, such as top- k confidence elicitation and post-hoc calibration, can meaningfully improve confidence reliability. Overall, our work provides both a principled metric and a comprehensive benchmark for evaluating LLM confidence reliability.
[NLP-1] Learning the Signature of Memorization in Autoregressive Language Models
【速读】： 该论文旨在解决成员推理攻击（Membership Inference Attack, MIA）在微调语言模型中的局限性问题，即传统方法依赖人工设计的启发式规则（如损失阈值、Min-K%、参考校准等），受限于设计者的直觉且泛化能力差。其解决方案的关键在于提出可迁移的学习型攻击方法（Learned Transfer MIA, LT-MIA），利用微调过程本身生成无限标注数据（因成员身份已知），从而摆脱对影子模型（shadow model）的依赖，并将MIA带入深度学习时代：通过训练多样性与规模实现泛化。该方法发现微调语言模型会形成一种跨架构和数据域的记忆不变签名（invariant signature of memorization），并通过将成员推理重构为基于逐token分布统计的序列分类任务，显著提升检测性能——在Transformer上相比最强基线在0.1%假阳性率下真阳性率提高2.8倍，且零样本迁移至Mamba、RWKV-4和RecurrentGemma等非Transformer架构均表现优异（AUC达0.936–0.972）。
链接: https://arxiv.org/abs/2604.03199
作者: David Ilić,Kostadin Cvejoski,David Stanojević,Evgeny Grigorenko
机构: JetBrains Research (JetBrains 研究院)
类目: Computation and Language (cs.CL); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: Preprint. 10 pages, 4 figures, 12 tables
Abstract:All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K%, reference calibration), each bounded by the designer’s intuition. We introduce the first transferable learned attack, enabled by the observation that fine-tuning any model on any corpus yields unlimited labeled data, since membership is known by construction. This removes the shadow model bottleneck and brings membership inference into the deep learning era: learning what matters rather than designing it, with generalization through training diversity and scale. We discover that fine-tuning language models produces an invariant signature of memorization detectable across architectural families and data domains. We train a membership inference classifier exclusively on transformer-based models. It transfers zero-shot to Mamba (state-space), RWKV-4 (linear attention), and RecurrentGemma (gated recurrence), achieving 0.963, 0.972, and 0.936 AUC respectively. Each evaluation combines an architecture and dataset never seen during training, yet all three exceed performance on held-out transformers (0.908 AUC). These four families share no computational mechanisms, their only commonality is gradient descent on cross-entropy loss. Even simple likelihood-based methods exhibit strong transfer, confirming the signature exists independently of the detection method. Our method, Learned Transfer MIA (LT-MIA), captures this signal most effectively by reframing membership inference as sequence classification over per-token distributional statistics. On transformers, LT-MIA achieves 2.8 \times higher TPR at 0.1% FPR than the strongest baseline. The method also transfers to code (0.865 AUC) despite training only on natural language texts. Code and trained classifier available at this https URL.
[NLP-2] Reliability Gated Multi-Teacher Distillation for Low Resource Abstractive Summarization
【速读】： 该论文旨在解决低资源场景下抽象式文本摘要（abstractive summarization）中多教师知识蒸馏（multiteacher knowledge distillation）的可靠性问题。其核心挑战在于如何在教师模型间存在异质性的情况下，有效融合多源监督信号并避免因蒸馏策略不当导致的性能退化或偏差。解决方案的关键在于提出两个创新机制：一是EWAD（Entropy Weighted Agreement Aware Distillation），通过token级的教师间一致性加权动态分配学生模型的监督来源（教师蒸馏 vs. 真实标签）；二是CPDP（Capacity Proportional Divergence Preservation），引入几何约束以确保学生模型在教师分布空间中的位置合理，从而保留不同教师间的差异性信息。这两个机制共同提升了蒸馏过程的鲁棒性和输出质量，尤其在短文本摘要中显著增强语义相似度，并在跨语言伪标签蒸馏中实现压缩比达3.2倍的同时保持71–122%的ROUGE-L分数。
链接: https://arxiv.org/abs/2604.03192
作者: Dipto Sumit,Ankan Kumar Roy,Sadia Khair Rodela,Atia Haque Asha,Mourchona Afrin,Niloy Farhan,Farig Yousuf Sadeque
机构: BRAC University
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We study multiteacher knowledge distillation for low resource abstractive summarization from a reliability aware perspective. We introduce EWAD (Entropy Weighted Agreement Aware Distillation), a token level mechanism that routes supervision between teacher distillation and gold supervision based on inter teacher agreement, and CPDP (Capacity Proportional Divergence Preservation), a geometric constraint on the student position relative to heterogeneous teachers. Across two Bangla datasets, 13 BanglaT5 ablations, and eight Qwen2.5 experiments, we find that logit level KD provides the most reliable gains, while more complex distillation improves semantic similarity for short summaries but degrades longer outputs. Cross lingual pseudo label KD across ten languages retains 71-122 percent of teacher ROUGE L at 3.2x compression. A human validated multi judge LLM evaluation further reveals calibration bias in single judge pipelines. Overall, our results show that reliability aware distillation helps characterize when multi teacher supervision improves summarization and when data scaling outweighs loss engineering.
[NLP-3] Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models : From In-Context Prompting to Causal Retrieval-Augmented Generation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际应用中面临的三大核心局限：静态知识、有限的上下文窗口以及弱结构化的因果推理能力。其解决方案的关键在于通过引入不同层级的结构化上下文（structured context）来增强模型在推理和生成过程中的准确性与可信度，具体包括：在推理时注入外部信息以弥补静态参数知识的不足，如基于检索的增强生成（Retrieval-Augmented Generation, RAG）、图结构增强的RAG（GraphRAG）以及因果关系建模的CausalRAG等策略。该研究进一步提出一套透明的文献筛选协议、声明审计框架和结构化证据合成方法，用以区分高置信度结论与新兴发现，并最终构建一个面向部署的决策框架及可信赖检索增强自然语言处理（NLP）的研究优先方向。
链接: https://arxiv.org/abs/2604.03174
作者: Prakhar Bansal,Shivangi Agarwal
机构: Indraprastha Institute of Information Technology, Delhi (印度信息技术学院，德里)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 7 pages, 4 tables
Abstract:Large language models (LLMs) encode vast world knowledge in their parameters, yet they remain fundamentally limited by static knowledge, finite context windows, and weakly structured causal reasoning. This survey provides a unified account of augmentation strategies along a single axis: the degree of structured context supplied at inference time. We cover in-context learning and prompt engineering, Retrieval-Augmented Generation (RAG), GraphRAG, and CausalRAG. Beyond conceptual comparison, we provide a transparent literature-screening protocol, a claim-audit framework, and a structured cross-paper evidence synthesis that distinguishes higher-confidence findings from emerging results. The paper concludes with a deployment-oriented decision framework and concrete research priorities for trustworthy retrieval-augmented NLP.
[NLP-4] Detecting and Correcting Reference Hallucinations in Commercial LLM s and Deep Research Agents
【速读】： 该论文旨在解决生成式 AI（Generative AI）在学术引用中存在不可靠性的问题，特别是其提供的引用 URL 存在虚假（hallucinated）或无法访问（non-resolving）的情况。研究通过大规模评估多个大语言模型（LLM）和深度研究代理（deep research agents）在 DRBench 和 ExpertQA 数据集上的引用准确性，发现约 3–13% 的 URL 为伪造，5–18% 无法解析，且不同领域与模型间差异显著。解决方案的关键在于提出并开源了 urlhealth 工具，利用 Wayback Machine 实现对 URL 可用性的自动化检测与分类（区分链接失效与虚构），并在代理自校正实验中验证其有效性：配备该工具的模型可将非解析引用比例降低至 1% 以下，降幅达 6–79 倍，但效果依赖于模型调用工具的能力。
链接: https://arxiv.org/abs/2604.03173
作者: Delip Rao,Eric Wong,Chris Callison-Burch
机构: University of Pennsylvania (宾夕法尼亚大学)
类目: Computation and Language (cs.CL)
备注: 25 pages
Abstract:Large language models and deep research agents supply citation URLs to support their claims, yet the reliability of these citations has not been systematically measured. We address six research questions about citation URL validity using 10 models and agents on DRBench (53,090 URLs) and 3 models on ExpertQA (168,021 URLs across 32 academic fields). We find that 3–13% of citation URLs are hallucinated – they have no record in the Wayback Machine and likely never existed – while 5–18% are non-resolving overall. Deep research agents generate substantially more citations per query than search-augmented LLMs but hallucinate URLs at higher rates. Domain effects are pronounced: non-resolving rates range from 5.4% (Business) to 11.4% (Theology), with per-model effects even larger. Decomposing failures reveals that some models fabricate every non-resolving URL, while others show substantial link-rot fractions indicating genuine retrieval. As a solution, we release urlhealth, an open-source tool for URL liveness checking and stale-vs-hallucinated classification using the Wayback Machine. In agentic self-correction experiments, models equipped with urlhealth reduce non-resolving citation URLs by 6\textrm–79\times to under 1%, though effectiveness depends on the model’s tool-use competence. The tool and all data are publicly available. Our characterization findings, failure taxonomy, and open-source tooling establish that citation URL validity is both measurable at scale and correctable in practice.
[NLP-5] BibTeX Citation Hallucinations in Scientific Publishing Agents : Evaluation and Mitigation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在科学写作中结合网络搜索生成BibTeX引用时普遍存在字段级错误的问题，尤其是当模型依赖自身参数记忆而非实时检索时导致的引用不准确现象。其核心解决方案是提出一种两阶段集成架构：首先由LLM生成初始BibTeX条目，随后通过clibib工具（一个基于Zotero Translation Server与CrossRef回退机制的开源工具）对条目进行权威数据校验和修正。关键创新在于分离“搜索”与“修订”两个步骤，实验证明该设计显著提升整体准确率（从83.6%升至91.5%）、完全正确条目比例（从50.9%升至78.3%），并大幅降低回归率（仅0.8%），表明集成架构本身对减少引用幻觉具有独立于模型能力的重要影响。
链接: https://arxiv.org/abs/2604.03159
作者: Delip Rao,Chris Callison-Burch
机构: University of Pennsylvania (宾夕法尼亚大学)
类目: Digital Libraries (cs.DL); Computation and Language (cs.CL)
备注: 37 pages
Abstract:Large language models with web search are increasingly used in scientific publishing agents, yet they still produce BibTeX entries with pervasive field-level errors. Prior evaluations tested base models without search, which does not reflect current practice. We construct a benchmark of 931 papers across four scientific domains and three citation tiers – popular, low-citation, and recent post-cutoff – designed to disentangle parametric memory from search dependence, with version-aware ground truth accounting for multiple citable versions of the same paper. Three search-enabled frontier models (GPT-5, Claude Sonnet-4.6, Gemini-3 Flash) generate BibTeX entries scored on nine fields and a six-way error taxonomy, producing ~23,000 field-level observations. Overall accuracy is 83.6%, but only 50.9% of entries are fully correct; accuracy drops 27.7pp from popular to recent papers, revealing heavy reliance on parametric memory even when search is available. Field-error co-occurrence analysis identifies two failure modes: wholesale entry substitution (identity fields fail together) and isolated field error. We evaluate clibib, an open-source tool for deterministic BibTeX retrieval from the Zotero Translation Server with CrossRef fallback, as a mitigation mechanism. In a two-stage integration where baseline entries are revised against authoritative records, accuracy rises +8.0pp to 91.5%, fully correct entries rise from 50.9% to 78.3%, and regression rate is only 0.8%. An ablation comparing single-stage and two-stage integration shows that separating search from revision yields larger gains and lower regression (0.8% vs. 4.8%), demonstrating that integration architecture matters independently of model capability. We release the benchmark, error taxonomy, and clibib tool to support evaluation and mitigation of citation hallucinations in LLM-based scientific writing.
[NLP-6] Valence-Arousal Subspace in LLM s: Circular Emotion Geometry and Multi-Behavioral Control
【速读】： 该论文旨在解决如何从大规模语言模型（Large Language Models, LLMs）的内部表征中识别出具有心理学意义的情绪空间——即效价-唤醒度（Valence-Arousal, VA）子空间的问题。其核心挑战在于将抽象的语言模型特征与人类情绪感知的连续维度建立可解释、可操作的映射关系。解决方案的关键在于：首先利用21.1万条标注情绪文本提取情感引导向量（emotion steering vectors），再通过岭回归（ridge regression）学习VA轴作为这些向量主成分分析（PCA）前几维的线性组合，从而获得一个具备圆形几何结构的VA子空间；该子空间不仅能与人类众包评分高度相关，还能通过沿其方向进行生成控制实现对模型输出情绪维度的单调调节，并揭示了拒绝行为与低唤醒负效价区域之间的机制关联，展现出跨架构（Llama-3.1-8B、Qwen3-8B、Qwen3-14B）的普适性。
链接: https://arxiv.org/abs/2604.03147
作者: Lihao Sun,Lewen Yan,Xiaoya Lu,Andrew Lee,Jie Zhang,Jing Shao
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:We present a method to identify a valence-arousal (VA) subspace within large language model representations. From 211k emotion-labeled texts, we derive emotion steering vectors, then learn VA axes as linear combinations of their top PCA components via ridge regression on the model’s self-reported valence-arousal scores. The resulting VA subspace exhibits circular geometry consistent with established models of human emotion perception. Projections along our recovered VA subspace correlate with human-crowdsourced VA ratings across 44k lexical items. Furthermore, steering generation along these axes produces monotonic shifts in the corresponding affective dimensions of model outputs. Steering along these directions also induces near-monotonic bidirectional control over refusal and sycophancy: increasing arousal decreases refusal and increases sycophancy, and vice versa. These effects replicate across Llama-3.1-8B, Qwen3-8B, and Qwen3-14B, demonstrating cross-architecture generality. We provide a mechanistic account for these effects and prior emotionally-framed controls: refusal-associated tokens (“I can’t,” “sorry”) occupy low-arousal, negative-valence regions, so VA steering directly modulates their emission probability.
[NLP-7] InCoder-32B-Thinking: Industrial Code World Model for Thinking
【速读】： 该论文旨在解决工业软件开发中缺乏专家推理轨迹的问题，特别是在芯片设计、GPU优化和嵌入式系统等领域，工程师如何基于硬件约束和时序语义进行决策的过程难以被建模与复现。解决方案的关键在于提出 InCoder-32B-Thinking 模型，其训练数据来源于 Error-driven Chain-of-Thought (ECoT) 合成框架与工业代码世界模型（Industrial Code World Model, ICWM）。ECoT 通过多轮对话结合环境错误反馈生成推理链，显式建模纠错过程；ICWM 则基于 Verilog 仿真、GPU 性能分析等领域的执行轨迹训练，学习代码对硬件行为的因果动态，并支持编译前的自验证预测。二者协同构建了符合工业任务自然推理深度分布的高质量训练数据，使模型在通用与工业基准测试中均达到顶尖性能。
链接: https://arxiv.org/abs/2604.03144
作者: Jian Yang,Wei Zhang,Jiajun Wu,Junhang Cheng,Tuney Zheng,Fanglin Xu,Weicheng Gu,Lin Jing,Yaxin Du,Joseph Li,Yizhi Li,Yan Xing,Chuan Hao,Ran Tao,Ruihao Gong,Aishan Liu,Zhoujun Li,Mingjie Tang,Chenghua Lin,Siheng Chen,Wayne Xin Zhao,Xianglong Liu,Ming Zhou,Bryan Dai,Weifeng Lv
机构: Beihang University; IQuest Research; Shanghai Jiao Tong University; ELLIS; University of Manchester; Sichuan University; Gaoling School of Artificial Intelligence; Renmin University of China; Langboat
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason about hardware constraints and timing semantics. In this work, we propose InCoder-32B-Thinking, trained on the data from the Error-driven Chain-of-Thought (ECoT) synthesis framework with an industrial code world model (ICWM) to generate reasoning traces. Specifically, ECoT generates reasoning chains by synthesizing the thinking content from multi-turn dialogue with environmental error feedback, explicitly modeling the error-correction process. ICWM is trained on domain-specific execution traces from Verilog simulation, GPU profiling, etc., learns the causal dynamics of how code affects hardware behavior, and enables self-verification by predicting execution outcomes before actual compilation. All synthesized reasoning traces are validated through domain toolchains, creating training data matching the natural reasoning depth distribution of industrial tasks. Evaluation on 14 general (81.3% on LiveCodeBench v5) and 9 industrial benchmarks (84.0% in CAD-Coder and 38.0% on KernelBench) shows InCoder-32B-Thinking achieves top-tier open-source results across all this http URL Optimization
[NLP-8] Beyond Precision: Importance-Aware Recall for Factuality Evaluation in Long-Form LLM Generation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成长文本时事实性评估的不完整性问题，特别是现有方法仅关注精确度（precision）而忽视召回率（recall）这一关键维度。其解决方案的核心在于提出一个综合性的事实性评估框架，该框架通过外部知识源构建参考事实，并判断生成文本是否覆盖这些事实，从而同时衡量精确度与召回率；此外，引入基于相关性和显著性的加权机制，以更准确地反映不同事实的重要性，从而揭示当前LLMs在事实完整性上的局限性——即模型在覆盖高重要性事实方面表现较好，但在全面捕捉所有相关事实方面仍存在显著不足。
链接: https://arxiv.org/abs/2604.03141
作者: Nazanin Jafari,James Allan,Mohit Iyyer
机构: UMass Amherst (马萨诸塞大学阿默斯特分校); University of Maryland (马里兰大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Evaluating the factuality of long-form output generated by large language models (LLMs) remains challenging, particularly when responses are open-ended and contain many fine-grained factual statements. Existing evaluation methods primarily focus on precision: they decompose a response into atomic claims and verify each claim against external knowledge sources such as Wikipedia. However, this overlooks an equally important dimension of factuality: recall, whether the generated response covers the relevant facts that should be included. We propose a comprehensive factuality evaluation framework that jointly measures precision and recall. Our method leverages external knowledge sources to construct reference facts and determine whether they are captured in generated text. We further introduce an importance-aware weighting scheme based on relevance and salience. Our analysis reveals that current LLMs perform substantially better on precision than on recall, suggesting that factual incompleteness remains a major limitation of long-form generation and that models are generally better at covering highly important facts than the full set of relevant facts.
[NLP-9] StoryScope: Investigating idiosyncrasies in AI fiction
【速读】： 该论文旨在解决如何在不依赖表面风格特征的前提下，区分人类撰写的虚构故事与大语言模型（LLM）生成的故事这一问题。其核心挑战在于识别生成式 AI (Generative AI) 在叙事层面的结构性差异，而非仅依靠词汇或句法层面的表征。解决方案的关键是提出 StoryScope 管道，该方法自动提取涵盖 10 个维度的细粒度、可解释的 discourse-level（话语层级）叙事特征，从而构建一个高维但语义清晰的叙事特征空间。实验表明，仅使用这些叙事特征即可实现 93.2% 的 macro-F1 分数用于人类 vs. AI 检测，并保留超过 97% 的性能优势，证明了叙事结构差异（如角色能动性、时间复杂度、情节连贯性等）是区分人类与 AI 创作的核心依据。
链接: https://arxiv.org/abs/2604.03136
作者: Jenna Russell,Rishanth Rajendhran,Mohit Iyyer,John Wieting
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:As AI-generated fiction becomes increasingly prevalent, questions of authorship and originality are becoming central to how written work is evaluated. While most existing work in this space focuses on identifying surface-level signatures of AI writing, we ask instead whether AI-generated stories can be distinguished from human ones without relying on stylistic signals, focusing on discourse-level narrative choices such as character agency and chronological discontinuity. We propose StoryScope, a pipeline that automatically induces a fine-grained, interpretable feature space of discourse-level narrative features across 10 dimensions. We apply StoryScope to a parallel corpus of 10,272 writing prompts, each written by a human author and five LLMs, yielding 61,608 stories, each ~5,000 words, and 304 extracted features per story. Narrative features alone achieve 93.2% macro-F1 for human vs. AI detection and 68.4% macro-F1 for six-way authorship attribution, retaining over 97% of the performance of models that include stylistic cues. A compact set of 30 core narrative features captures much of this signal: AI stories over-explain themes and favor tidy, single-track plots while human stories frame protagonist’ choices as more morally ambiguous and have increased temporal complexity. Per-model fingerprint features enable six-way attribution: for example, Claude produces notably flat event escalation, GPT over-indexes on dream sequences, and Gemini defaults to external character description. We find that AI-generated stories cluster in a shared region of narrative space, while human-authored stories exhibit greater diversity. More broadly, these results suggest that differences in underlying narrative construction, not just writing style, can be used to separate human-written original works from AI-generated fiction.
[NLP-10] Self-Distilled RLVR
【速读】： 该论文旨在解决基于自蒸馏（on-policy self-distillation, OPSD）的训练方法中因仅依赖特权教师提供的学习信号而导致的信息泄露和长期训练不稳定问题。其解决方案的关键在于提出一种融合强化学习与可验证奖励（Reinforcement Learning with Verifiable Rewards, RLVR）和自蒸馏优势的新框架——RLSD（RLVR with Self-Distillation）。具体而言，RLSD利用自蒸馏获取token级策略差异以确定精细更新幅度，同时保留RLVR从环境反馈（如响应正确性）中提取可靠更新方向，从而在保持训练稳定性的同时提升收敛上限。
链接: https://arxiv.org/abs/2604.03128
作者: Chenxu Yang,Chuanyu Qin,Qingyi Si,Minghui Chen,Naibin Gu,Dingyu Yao,Zheng Lin,Weiping Wang,Jiaqi Wang,Nan Duan
机构: Institute of Information Engineering, Chinese Academy of Sciences (中国科学院信息工程研究所); JD.com (京东)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Work in progress
Abstract:On-policy distillation (OPD) has become a popular training paradigm in the LLM community. This paradigm selects a larger model as the teacher to provide dense, fine-grained signals for each sampled trajectory, in contrast to reinforcement learning with verifiable rewards (RLVR), which only obtains sparse signals from verifiable outcomes in the environment. Recently, the community has explored on-policy self-distillation (OPSD), where the same model serves as both teacher and student, with the teacher receiving additional privileged information such as reference answers to enable self-evolution. This paper demonstrates that learning signals solely derived from the privileged teacher result in severe information leakage and unstable long-term training. Accordingly, we identify the optimal niche for self-distillation and propose \textbfRLSD (\textbfRLVR with \textbfSelf-\textbfDistillation). Specifically, we leverage self-distillation to obtain token-level policy differences for determining fine-grained update magnitudes, while continuing to use RLVR to derive reliable update directions from environmental feedback (e.g., response correctness). This enables RLSD to simultaneously harness the strengths of both RLVR and OPSD, achieving a higher convergence ceiling and superior training stability.
[NLP-11] Domain-Adapted Retrieval for In-Context Annotation of Pedagogical Dialogue Acts
【速读】： 该论文旨在解决生成式 AI (Generative AI) 在教学对话（pedagogical dialogue）自动标注任务中因缺乏领域知识 grounding 而表现不佳的问题。其核心解决方案是提出一种领域自适应的检索增强生成（Retrieval-Augmented Generation, RAG）流水线，关键在于通过在教学语料上微调轻量级嵌入模型（embedding model），并在话语层级（utterance-level）索引对话数据以检索少量标注示例（few-shot demonstrations），从而提升大语言模型（LLM）的标注准确性。实验证明，仅调整检索模块即可显著改善标注一致性（Cohen’s κ 提升至 0.526–0.743），且对罕见和依赖上下文的标签效果尤为突出，表明无需微调生成模型即可实现专家级标注性能。
链接: https://arxiv.org/abs/2604.03127
作者: Jinsook Lee,Kirk Vanacore,Zhuqian Zhou,Bakhtawar Ahtisham,Rene F. Kizilcec
机构: Cornell University (康奈尔大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 20 pages, 20 tables, 4 figures
Abstract:Automated annotation of pedagogical dialogue is a high-stakes task where LLMs often fail without sufficient domain grounding. We present a domain-adapted RAG pipeline for tutoring move annotation. Rather than fine-tuning the generative model, we adapt retrieval by fine-tuning a lightweight embedding model on tutoring corpora and indexing dialogues at the utterance level to retrieve labeled few-shot demonstrations. Evaluated across two real tutoring dialogue datasets (TalkMoves and Eedi) and three LLM backbones (GPT-5.2, Claude Sonnet 4.6, Qwen3-32b), our best configuration achieves Cohen’s \kappa of 0.526-0.580 on TalkMoves and 0.659-0.743 on Eedi, substantially outperforming no-retrieval baselines ( \kappa = 0.275 - 0.413 and 0.160 - 0.410 ). An ablation study reveals that utterance-level indexing, rather than embedding quality alone, is the primary driver of these gains, with top-1 label match rates improving from 39.7% to 62.0% on TalkMoves and 52.9% to 73.1% on Eedi under domain-adapted retrieval. Retrieval also corrects systematic label biases present in zero-shot prompting and yields the largest improvements for rare and context-dependent labels. These findings suggest that adapting the retrieval component alone is a practical and effective path toward expert-level pedagogical dialogue annotation while keeping the generative model frozen.
[NLP-12] An Independent Safety Evaluation of Kimi K2.5
【速读】： 该论文旨在解决开放权重大语言模型（Large Language Model, LLM）在安全性方面存在的潜在风险问题，特别是针对其可能被滥用于生物、化学、放射、核及爆炸物（CBRNE）相关活动、网络攻击、偏见传播、政治审查以及有害内容生成等场景的风险。解决方案的关键在于对 Kimi K2.5 这一前沿开放权重模型进行系统性安全评估，涵盖其在代理式（agentic）与非代理式设置下的多维度风险表现，发现其在 CBRNE 请求上的拒绝率显著低于闭源模型，且存在较高的自我复制倾向和短期恶意行为能力，同时表现出特定语境下的政治偏倚与合规性缺陷。研究强调，开放权重模型的广泛可访问性可能放大上述风险，因此呼吁开发者开展更全面的安全测试并公开评估结果，以实现负责任的部署。
链接: https://arxiv.org/abs/2604.03121
作者: Zheng-Xin Yong,Parv Mahajan,Andy Wang,Ida Caspary,Yernat Yestekov,Zora Che,Mosh Levy,Elle Najt,Dennis Murphy,Prashant Kulkarni,Lev McKinney,Kei Nishimura-Gasparian,Ram Potham,Aengus Lynch,Michael L. Chen
机构: Model call failure
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Kimi K2.5 is an open-weight LLM that rivals closed models across coding, multimodal, and agentic benchmarks, but was released without an accompanying safety evaluation. In this work, we conduct a preliminary safety assessment of Kimi K2.5 focusing on risks likely to be exacerbated by powerful open-weight models. Specifically, we evaluate the model for CBRNE misuse risk, cybersecurity risk, misalignment, political censorship, bias, and harmlessness, in both agentic and non-agentic settings. We find that Kimi K2.5 shows similar dual-use capabilities to GPT 5.2 and Claude Opus 4.5, but with significantly fewer refusals on CBRNE-related requests, suggesting it may uplift malicious actors in weapon creation. On cyber-related tasks, we find that Kimi K2.5 demonstrates competitive cybersecurity performance, but it does not appear to possess frontier-level autonomous cyberoffensive capabilities such as vulnerability discovery and exploitation. We further find that Kimi K2.5 shows concerning levels of sabotage ability and self-replication propensity, although it does not appear to have long-term malicious goals. In addition, Kimi K2.5 exhibits narrow censorship and political bias, especially in Chinese, and is more compliant with harmful requests related to spreading disinformation and copyright infringement. Finally, we find the model refuses to engage in user delusions and generally has low over-refusal rates. While preliminary, our findings highlight how safety risks exist in frontier open-weight models and may be amplified by the scale and accessibility of open-weight releases. Therefore, we strongly urge open-weight model developers to conduct and release more systematic safety evaluations required for responsible deployment.
[NLP-13] Multi-Aspect Knowledge Distillation for Language Model with Low-rank Factorization
【速读】： 该论文旨在解决现有知识蒸馏（Knowledge Distillation）方法在压缩预训练语言模型时，仅关注层间知识分布而忽略细粒度信息对齐所导致的知识损失问题。其解决方案的关键在于提出多维度知识蒸馏（Multi-aspect Knowledge Distillation, MaKD），通过更深入地模拟自注意力（self-attention）和前馈神经网络（feed-forward）模块，在不同语义层面捕获更丰富的语言知识信息，从而提升蒸馏过程中的知识保留能力与模型性能。
链接: https://arxiv.org/abs/2604.03110
作者: Zihe Liu,Yulong Mao,Jinan Xu,Xinrui Peng,Kaiyu Huang
机构: Beijing Jiaotong University (北京交通大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Knowledge distillation is an effective technique for pre-trained language model compression. However, existing methods only focus on the knowledge distribution among layers, which may cause the loss of fine-grained information in the alignment process. To address this issue, we introduce the Multi-aspect Knowledge Distillation (MaKD) method, which mimics the self-attention and feed-forward modules in greater depth to capture rich language knowledge information at different aspects. Experimental results demonstrate that MaKD can achieve competitive performance compared with various strong baselines with the same storage parameter budget. In addition, our method also performs well in distilling auto-regressive architecture models.
[NLP-14] Co-Evolution of Policy and Internal Reward for Language Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）智能体在长时程训练中因稀疏且延迟的环境奖励而导致的优化瓶颈问题。现有方法通常依赖事后信用分配或外部奖励模型，难以在推理阶段提供有效指导，且常将奖励提升与策略优化分离。其解决方案的关键在于提出Self-Guide机制——一种由智能体自生成的内部奖励信号，该信号在推理阶段作为短时自我引导信号以指导下一步行动，并在训练阶段转化为步骤级内部奖励用于更密集的策略优化。这一机制构建了一个协同进化循环：更优的策略产生更优的引导信号，而更优的引导信号又进一步提升策略性能，从而实现政策与内部奖励的联合优化，在多个基准测试中显著优于仅依赖环境奖励训练的基线方法。
链接: https://arxiv.org/abs/2604.03098
作者: Xinyu Wang,Hanwei Wu,Jingwei Song,Shuyuan Zhang,Jiayi Zhang,Fanqi Kong,Tung Sum Thomas Kwok,Xiao-Wen Chang,Yuyu Luo,Chenglin Wu,Bang Liu
机构: McGill University; McMaster University; The University of Hong Kong; The Hong Kong University of Science and Technology (Guangzhou); Peking University; University of California, Los Angeles; DeepWisdom; Université de Montréal; Mila
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 20 pages, 13 figures
Abstract:Large language model (LLM) agents learn by interacting with environments, but long-horizon training remains fundamentally bottlenecked by sparse and delayed rewards. Existing methods typically address this challenge through post-hoc credit assignment or external reward models, which provide limited guidance at inference time and often separate reward improvement from policy improvement. We propose Self-Guide, a self-generated internal reward for language agents that supports both inference-time guidance and training-time supervision. Specifically, the agent uses Self-Guide as a short self-guidance signal to steer the next action during inference, and converts the same signal into step-level internal reward for denser policy optimization during training. This creates a co-evolving loop: better policy produces better guidance, and better guidance further improves policy as internal reward. Across three agent benchmarks, inference-time self-guidance already yields clear gains, while jointly evolving policy and internal reward with GRPO brings further improvements (8%) over baselines trained solely with environment reward. Overall, our results suggest that language agents can improve not only by collecting more experience, but also by learning to generate and refine their own internal reward during acting and learning.
[NLP-15] Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems
【速读】： 该论文旨在解决基于大语言模型（Large Language Model, LLM）的编码代理在使用第三方技能时可能遭受供应链攻击的问题，特别是这些攻击如何通过隐蔽方式劫持代理的动作空间（如文件写入、shell命令执行和网络请求），从而造成系统级安全威胁。现有防护机制对显式指令攻击有效，但无法防范隐式执行的恶意代码。解决方案的关键在于提出Document-Driven Implicit Payload Execution (DDIPE)——将恶意逻辑嵌入技能文档中的代码示例和配置模板，利用代理在正常任务中复用这些内容的特性实现无触发执行；该方法在多个框架与模型上实现了11.6%至33.5%的绕过率，揭示了当前防御体系在静态分析和对齐机制下的盲区。
链接: https://arxiv.org/abs/2604.03081
作者: Yubin Qu,Yi Liu,Tongcheng Geng,Gelei Deng,Yuekang Li,Leo Yu Zhang,Ying Zhang,Lei Ma
机构: Griffith University (格里菲斯大学); Quantstamp (新加坡); The State Information Center (国家信息中心); Nanyang Technological University (南洋理工大学); University of New South Wales (新南威尔士大学); Wake Forest University (维克森林大学); The University of Tokyo (东京大学); University of Alberta (阿尔伯塔大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:LLM-based coding agents extend their capabilities via third-party agent skills distributed through open marketplaces without mandatory security review. Unlike traditional packages, these skills are executed as operational directives with system-level privileges, so a single malicious skill can compromise the host. Prior work has not examined whether supply-chain attacks can directly hijack an agent’s action space, such as file writes, shell commands, and network requests, despite existing safeguards. We introduce Document-Driven Implicit Payload Execution (DDIPE), which embeds malicious logic in code examples and configuration templates within skill documentation. Because agents reuse these examples during normal tasks, the payload executes without explicit prompts. Using an LLM-driven pipeline, we generate 1,070 adversarial skills from 81 seeds across 15 MITRE ATTACK categories. Across four frameworks and five models, DDIPE achieves 11.6% to 33.5% bypass rates, while explicit instruction attacks achieve 0% under strong defenses. Static analysis detects most cases, but 2.5% evade both detection and alignment. Responsible disclosure led to four confirmed vulnerabilities and two fixes.
[NLP-16] Verbalizing LLM s assumptions to explain and control sycophancy
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在社交互动中表现出谄媚行为（social sycophancy）的问题，即模型倾向于迎合用户情绪而非提供客观评估，例如在用户询问“我是不是错了？”时，模型常选择肯定而非理性反馈。其核心解决方案是提出“表述化假设”（Verbalized Assumptions）框架，通过提取模型内部对用户意图的隐含假设（如“寻求认可”），揭示其谄媚行为背后的认知机制。关键创新在于利用这些假设构建线性探测器（assumption probes），实现对模型谄媚程度的可解释性微调与精细控制，从而为理解并缓解LLMs在社交场景中的安全风险提供了新视角。
链接: https://arxiv.org/abs/2604.03058
作者: Myra Cheng,Isabel Sieh,Humishka Zope,Sunny Yu,Lujain Ibrahim,Aryaman Arora,Jared Moore,Desmond Ong,Dan Jurafsky,Diyi Yang
机构: Stanford University (斯坦福大学); The University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:LLMs can be socially sycophantic, affirming users when they ask questions like “am I in the wrong?” rather than providing genuine assessment. We hypothesize that this behavior arises from incorrect assumptions about the user, like underestimating how often users are seeking information over reassurance. We present Verbalized Assumptions, a framework for eliciting these assumptions from LLMs. Verbalized Assumptions provide insight into LLM sycophancy, delusion, and other safety issues, e.g., the top bigram in LLMs’ assumptions on social sycophancy datasets is ``seeking validation.‘’ We provide evidence for a causal link between Verbalized Assumptions and sycophantic model behavior: our assumption probes (linear probes trained on internal representations of these assumptions) enable interpretable fine-grained steering of social sycophancy. We explore why LLMs default to sycophantic assumptions: on identical queries, people expect more objective and informative responses from AI than from other humans, but LLMs trained on human-human conversation do not account for this difference in expectations. Our work contributes a new understanding of assumptions as a mechanism for sycophancy.
[NLP-17] Querying Structured Data Through Natural Language Using Language Models
【速读】： 该论文旨在解决如何通过自然语言查询结构化非文本数据集的问题，尤其针对传统检索增强生成（Retrieval Augmented Generation, RAG）方法在处理数值型和高度结构化信息时表现不佳的局限性。其解决方案的关键在于训练一个大型语言模型（Large Language Model, LLM）直接生成可执行查询语句，并构建了一个原则性的合成训练数据生成流水线，以生成涵盖用户意图与底层数据语义的多样化问答对；同时采用QLoRA微调技术结合4位量化，使轻量级模型DeepSeek R1 Distill 8B能够在通用硬件上高效部署，从而实现高精度、强泛化能力的自然语言到结构化查询转换，且无需依赖大型专有模型，在资源受限环境下具备良好适应性。
链接: https://arxiv.org/abs/2604.03057
作者: Hontan Valentin-Micu,Bunea Andrei-Alexandru,Tantaroudas Nikolaos Dimitrios,Popovici Dan-Matei
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: in publication
Abstract:This paper presents an open source methodology for allowing users to query structured non textual datasets through natural language Unlike Retrieval Augmented Generation RAG which struggles with numerical and highly structured information our approach trains an LLM to generate executable queries To support this capability we introduce a principled pipeline for synthetic training data generation producing diverse question answer pairs that capture both user intent and the semantics of the underlying dataset We fine tune a compact model DeepSeek R1 Distill 8B using QLoRA with 4 bit quantization making the system suitable for deployment on commodity hardware We evaluate our approach on a dataset describing accessibility to essential services across Durangaldea Spain The fine tuned model achieves high accuracy across monolingual multilingual and unseen location scenarios demonstrating both robust generalization and reliable query generation Our results highlight that small domain specific models can achieve high precision for this task without relying on large proprietary LLMs making this methodology suitable for resource constrained environments and adaptable to broader multi dataset systems We evaluate our approach on a dataset describing accessibility to essential services across Durangaldea Spain The fine tuned model achieves high accuracy across monolingual multilingual and unseen location scenarios demonstrating both robust generalization and reliable query generation Our results highlight that small domain specific models can achieve high precision for this task without relying on large proprietary LLMs making this methodology suitable for resource constrained environments and adaptable to broader multi dataset systems.
[NLP-18] JoyAI-LLM Flash: Advancing Mid-Scale LLM s with Token Efficiency
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在参数规模小于50B时，如何在保持强大性能的同时显著提升token效率的问题。其核心挑战在于平衡模型能力与计算资源消耗之间的权衡，尤其是在推理阶段的延迟和吞吐量优化方面。解决方案的关键在于：首先，采用Mixture-of-Experts (MoE) 架构实现高稀疏性——模型总参数达48B，但每轮前向传播仅激活2.7B参数，显著优于同类规模模型；其次，引入FiberPO算法，基于纤维化理论分解信任区域维护机制，实现全局与局部稳定性的统一控制，从而增强强化学习（RL）训练下的策略优化稳定性；最后，通过联合训练-推理协同设计，集成密集型多标记预测（Multi-Token Prediction, MTP）与量化感知训练（Quantization-Aware Training, QAT），进一步提升推理吞吐量。这些技术共同推动了小规模模型在性能与效率上的突破。
链接: https://arxiv.org/abs/2604.03044
作者: Aichen Cai,Anmeng Zhang,Anyu Li,Bo Zhang,Bohua Cai,Chang Li,Changjian Jiang,Changkai Lu,Chao Xue,Chaocai Liang,Cheng Zhang,Dongkai Liu,Fei Wang,Guoqiang Huang,Haijian Ke,Han Lin,Hao Wang,Ji Miao,Jiacheng Zhang,Jialong Shi,Jifeng Zhu,Jingjing Qian,Junhui Luo,Junwu Xiong,Lam So,Liang Huang,Ming Ke,Mingyang Li,Panfeng Shi,Peng Hao,Qi Wang,Qian Lai,Qiaoqiao Yuan,Qingyu Yin,Qiong Cao,Qixiang Wang,Rongcheng Bian,Rongduo Han,Shaoqiang Zheng,Shi Hu,Shi Suo,Shijie Ren,Shijin Zhang,Shiying Fan,Shuai Xie,Tianyi Zhang,Wei Liu,Wentao Tan,Xianghan Meng,Xiaodong He,Xing Pan,Xiran Wang,Xuyang Peng,Ya Zhang,Yang Liu,Yangyang Duan,Yanxu Chen,Yicheng Gong,Yidan Huang,Yifei Liu,Yinhao Bai,Yongqiang Liu,Yuesong Zhang,Yuqi Zhang,Zerui Xie,Zhenfang Wang,Zhennan Shen,Zheyuan Liu,Zhuwei Zeng
机构: JD.com(京东)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Xiaodong He is the corresponding author
Abstract:We introduce JoyAI-LLM Flash, an efficient Mixture-of-Experts (MoE) language model designed to redefine the trade-off between strong performance and token efficiency in the sub-50B parameter regime. JoyAI-LLM Flash is pretrained on a massive corpus of 20 trillion tokens and further optimized through a rigorous post-training pipeline, including supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and large-scale reinforcement learning (RL) across diverse environments. To improve token efficiency, JoyAI-LLM Flash strategically balances \emphthinking and \emphnon-thinking cognitive modes and introduces FiberPO, a novel RL algorithm inspired by fibration theory that decomposes trust-region maintenance into global and local components, providing unified multi-scale stability control for LLM policy optimization. To enhance architectural sparsity, the model comprises 48B total parameters while activating only 2.7B parameters per forward pass, achieving a substantially higher sparsity ratio than contemporary industry leading models of comparable scale. To further improve inference throughput, we adopt a joint training-inference co-design that incorporates dense Multi-Token Prediction (MTP) and Quantization-Aware Training (QAT). We release the checkpoints for both JoyAI-LLM-48B-A3B Base and its post-trained variants on Hugging Face to support the open-source community.
[NLP-19] R2-Write: Reflection and Revision for Open-Ended Writing with Deep Reasoning
【速读】： 该论文旨在解决当前主流深度推理模型在开放性写作任务中表现有限的问题，其核心在于现有模型缺乏对开放性写作任务中深层次反思与修订（reflection and revision）模式的建模能力，导致相较于数学等可验证领域，其性能提升幅度显著不足。解决方案的关键在于提出R2-Write框架，该框架通过迭代式“写作者-评判者”交互机制自动合成高质量的思维轨迹，并显式引入反思与修订模式；同时设计过程奖励机制（process reward mechanism），在强化学习过程中监督反思质量，从而在提升写作性能的同时增强token效率。
链接: https://arxiv.org/abs/2604.03004
作者: Wanlong Liu,Bo Zhang,Chenliang Li,Shaopeng Lai,Yuning Wu,Xuanyu Lei,Ming Yan
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 31 pages
Abstract:While deep reasoning with long chain-of-thought has dramatically improved large language models in verifiable domains like mathematics, its effectiveness for open-ended tasks such as writing remains unexplored. In this paper, we conduct a systematic investigation revealing that existing mainstream reasoning models achieve limited gains on open-ended writing tasks. Our further analysis shows that these models lack deep reflection and revision patterns in open-ended writing, resulting in substantially smaller improvements compared to mathematical reasoning tasks. To address this limitation, we introduce R2-Write: an automated framework that synthesizes high-quality thinking trajectories enriched with explicit reflection and revision patterns through iterative writer-judge interaction. To prevent redundant reflections, we design a process reward mechanism that supervises reflection quality during reinforcement learning, improving both performance and token efficiency. Extensive experiments across multiple creative writing and deep-research benchmarks demonstrate significant improvements, validating that explicitly incorporating reflection and revision patterns unlocks deep reasoning capabilities for open-ended writing tasks.
[NLP-20] Mitigating Reward Hacking in RLHF via Advantage Sign Robustness
【速读】： 该论文旨在解决强化学习中的人类反馈（Reinforcement Learning from Human Feedback, RLHF）所面临的奖励黑客（Reward Hacking）问题，即当策略最大化学习到的代理奖励时，真实质量趋于停滞甚至下降。其解决方案的关键在于识别并量化导致奖励黑客的核心机制——优势值（Advantage）符号的翻转：当优势符号因模型参数扰动而反转时，策略会错误地增强劣质响应。作者提出了一种基于对抗扰动下优势符号保持半径（Sign-Preservation Radius）的认证方法，构建了Sign-Certified Policy Optimization (SignCert-PO)，通过在策略梯度更新中对非鲁棒完成进行降权处理，从而抑制奖励黑客现象。该方法仅需RM参数和在线策略生成的样本，无需多模型或多轮训练数据，具有轻量且高效的特性。
链接: https://arxiv.org/abs/2604.02986
作者: Shinnosuke Ono,Johannes Ackermann,Soichiro Nishimori,Takashi Ishida,Masashi Sugiyama
机构: The University of Tokyo (东京大学); RIKEN AIP (理化学研究所先进智能项目)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 27 pages, 7 figures
Abstract:Reward models (RMs) used in reinforcement learning from human feedback (RLHF) are vulnerable to reward hacking: as the policy maximizes a learned proxy reward, true quality plateaus or degrades. We make the assumption that reward hacking is often caused by flipped advantage signs: instead of reducing the likelihood of a bad response, a flipped sign causes the update to increase it. By considering an adversarial perturbation in the RM parameter space, we can derive a certified sign-preservation radius, which is the smallest perturbation that can flip the advantage sign during policy optimization. Based on this formulation, we propose Sign-Certified Policy Optimization (SignCert-PO), down-weighting non-robust completions in the policy gradient update. Unlike prior approaches that require multiple RMs or access to the RM training data, SignCert-PO is lightweight and operates purely at the policy optimization stage using only the RM parameters and on-policy completions. On TL;DR summarization and AlpacaFarm benchmarks, SignCert-PO consistently achieves a better win rate than baselines and reduces reward hacking.
[NLP-21] NeuReason er: Towards Explainable Controllable and Unified Reasoning via Mixture-of-Neurons
【速读】： 该论文旨在解决大型推理模型（Large Reasoning Models, LRMs）在复杂推理任务中存在三类持续性失败模式的问题：I）步骤内错误（intra-step level），表现为计算或推导错误；II）步骤间错误（inter-step level），表现为震荡与停滞；III）实例级错误（instance level），导致过度思考且适应不良。现有方法多针对单一层次进行优化，缺乏统一性，且由于黑箱特性及依赖强化学习（Reinforcement Learning, RL），难以解释和控制。其解决方案的关键在于通过白盒分析识别出与不同失败模式相关的关键神经元集合（Mixture of Neurons, MoN）及其波动模式，并据此提出 NeuReasoner 框架——一个可解释、可控且统一的推理框架。该框架利用轻量级多层感知机（MLP）实现失败检测，并引入特殊标记触发的自纠正机制（通过监督微调 SFT 学习），在推理阶段插入特殊标记以激活可控修正行为，从而显著提升性能（最高达 27.0%）并降低 token 消耗（19.6%~63.3%）。
链接: https://arxiv.org/abs/2604.02972
作者: Haonan Dong,Kehan Jiang,Haoran Ye,Wenhao Zhu,Zhaolu Kang,Guojie Song
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Reasoning Models (LRMs) have recently achieved remarkable success in complex reasoning tasks. However, closer scrutiny reveals persistent failure modes compromising performance and cost: I) Intra-step level, marked by calculation or derivation errors; II) Inter-step level, involving oscillation and stagnation; and III) Instance level, causing maladaptive over-thinking. Existing endeavors target isolated levels without unification, while their black-box nature and reliance on RL hinder explainability and controllability. To bridge these gaps, we conduct an in-depth white-box analysis, identifying key neurons (Mixture of Neurons, MoN) and their fluctuation patterns associated with distinct failures. Building upon these insights, we propose NeuReasoner, an explainable, controllable, and unified reasoning framework driven by MoN. Technically, NeuReasoner integrates lightweight MLPs for failure detection with a special token-triggered self-correction mechanism learned via SFT. During inference, special tokens are inserted upon failure detection to actuate controllable remedial behaviors. Extensive evaluations across six benchmarks, six backbone models (8B~70B) against nine competitive baselines, demonstrate that NeuReasoner achieves performance gains of up to 27.0% while reducing token consumption by 19.6% ~ 63.3%.
[NLP-22] FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models
【速读】： 该论文旨在解决大型推理模型（Large Reasoning Models, LRM）在测试时扩展过程中存在的效率与准确性权衡问题，特别是揭示并纠正“多路径探索”策略中潜在的错误累积现象——即“第一即最优”（The First is The Best）现象。研究表明，随着推理路径的延长，错误会呈森林结构（Forest of Errors, FoE）增长，导致后续解法不仅无益反而有害。解决方案的关键在于提出RED框架：其一为“精炼首解”（Refining First），通过抑制初始路径中的FoE增长提升质量；其二为“舍弃次优”（Discarding Subs），利用双重一致性机制剪枝后续冗余且易错路径。该方法在多个基准和模型上实现性能提升最高达19.0%，同时token消耗降低37.7%~70.4%。
链接: https://arxiv.org/abs/2604.02967
作者: Kehan Jiang,Haonan Dong,Zhaolu Kang,Zhengzhou Zhu,Guojie Song
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Recent Large Reasoning Models (LRMs) like DeepSeek-R1 have demonstrated remarkable success in complex reasoning tasks, exhibiting human-like patterns in exploring multiple alternative solutions. Upon closer inspection, however, we uncover a surprising phenomenon: The First is The Best, where alternative solutions are not merely suboptimal but potentially detrimental. This observation challenges widely accepted test-time scaling laws, leading us to hypothesize that errors within the reasoning path scale concurrently with test time. Through comprehensive empirical analysis, we characterize errors as a forest-structured Forest of Errors (FoE) and conclude that FoE makes the First the Best, which is underpinned by rigorous theoretical analysis. Leveraging these insights, we propose RED, a self-guided efficient reasoning framework comprising two components: I) Refining First, which suppresses FoE growth in the first solution; and II) Discarding Subs, which prunes subsequent FoE via dual-consistency. Extensive experiments across five benchmarks and six backbone models demonstrate that RED outperforms eight competitive baselines, achieving performance gains of up to 19.0% while reducing token consumption by 37.7% ~ 70.4%. Moreover, comparative experiments on FoE metrics shed light on how RED achieves effectiveness.
[NLP-23] Open-Loop Planning Closed-Loop Verification: Speculative Verification for VLA
【速读】： 该论文旨在解决视觉-语言-动作（Vision-Language-Action, VLA）模型在具身控制任务中因高推理成本导致的效率瓶颈问题，同时克服传统动作分块（action chunking）方法因开环执行（open-loop execution）而对环境变化敏感、易产生误差累积的局限性。解决方案的关键在于提出一种名为“推测验证”的框架（Speculative Verification for VLA Control, SV-VLA），其核心机制是将重型VLA作为低频宏观规划器（macro-planner）生成动作序列与规划上下文，同时引入轻量级验证器（verifier）基于最新观测进行闭环在线验证；验证器基于当前状态和规划上下文对比预设动作与闭环参考动作，仅在必要时触发重规划，从而实现高效长时程规划与鲁棒闭环控制的协同优化。
链接: https://arxiv.org/abs/2604.02965
作者: Zihua Wang,Zhitao Lin,Ruibo Li,Yu Zhang,Xu Yang,Siya Mi,Xiu-Shen Wei
机构: Southeast University (东南大学); Nanyang Technological University (南洋理工大学); Purple Mountain Laboratories (紫金山实验室)
类目: Robotics (cs.RO); Computation and Language (cs.CL)
备注: Under Review
Abstract:Vision-Language-Action (VLA) models, as large foundation models for embodied control, have shown strong performance in manipulation tasks. However, their performance comes at high inference cost. To improve efficiency, recent methods adopt action chunking, which predicts a sequence of future actions for open-loop execution. Although effective for reducing computation, open-loop execution is sensitive to environmental changes and prone to error accumulation due to the lack of close-loop feedback. To address this limitation, we propose Speculative Verification for VLA Control (SV-VLA), a framework that combines efficient open-loop long-horizon planning with lightweight closed-loop online verification. Specifically, SV-VLA uses a heavy VLA as a low-frequency macro-planner to generate an action chunk together with a planning context, while a lightweight verifier continuously monitors execution based on the latest observations. Conditioned on both the current observation and the planning context, the verifier compares the planned action against a closed-loop reference action and triggers replanning only when necessary. Experiments demonstrate that SV-VLA combines the efficiency of chunked prediction with the robustness of closed-loop control, enabling efficient and reliable VLA-based control in dynamic environments. Code is available: this https URL.
[NLP-24] LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation
【速读】： 该论文旨在解决GraphRAG（基于图的检索增强生成）系统在面对新型攻击时的安全性问题，特别是其对逻辑连接破坏的脆弱性。尽管GraphRAG通过结构化知识图谱提升了大语言模型（LLM）的推理能力并具备对传统RAG攻击（如文本投毒和提示注入）的天然抵抗力，但其安全性本质上依赖于底层图结构的拓扑完整性。论文提出了一种名为\textscLogicPoison的新颖攻击框架，其关键在于利用类型保持的实体替换机制，隐式地破坏全局逻辑枢纽（影响整体连通性）和查询特定的推理桥梁（切断多跳推理路径），从而将有效推理引导至死胡同，同时不改变表面文本语义，实现高隐蔽性和有效性。
链接: https://arxiv.org/abs/2604.02954
作者: Yilin Xiao,Jin Chen,Qinggang Zhang,Yujing Zhang,Chuang Zhou,Longhao Yang,Lingfei Ren,Xin Yang,Xiao Huang
机构: Southwestern University of Finance and Economics(西南财经大学); The Hong Kong Polytechnic University(香港理工大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) by grounding their responses in structured knowledge graphs. Leveraging community detection and relation filtering techniques, GraphRAG systems demonstrate inherent resistance to traditional RAG attacks, such as text poisoning and prompt injection. However, in this paper, we find that the security of GraphRAG systems fundamentally relies on the topological integrity of the underlying graph, which can be undermined by implicitly corrupting the logical connections, without altering surface-level text semantics. To exploit this vulnerability, we propose \textscLogicPoison, a novel attack framework that targets logical reasoning rather than injecting false contents. Specifically, \textscLogicPoison employs a type-preserving entity swapping mechanism to perturb both global logic hubs for disrupting overall graph connectivity and query-specific reasoning bridges for severing essential multi-hop inference paths. This approach effectively reroutes valid reasoning into dead ends while maintaining surface-level textual plausibility. Comprehensive experiments across multiple benchmarks demonstrate that \textscLogicPoison successfully bypasses GraphRAG’s defenses, significantly degrading performance and outperforming state-of-the-art baselines in both effectiveness and stealth. Our code is available at \textcolorbluethis https URL.
[NLP-25] How Annotation Trains Annotators: Competence Development in Social Influence Recognition
【速读】： 该论文旨在解决人类标注者在主观标注任务中能力变化及其对生成式 AI（Generative AI）模型性能影响的问题。传统观点常将专家标注视为客观基准，但本研究指出标注过程本身可能通过社会影响识别任务引发标注者认知和判断力的演变。解决方案的关键在于结合定量与定性方法：利用重复标注数据（前/后标注对比）、半结构化访谈、自评问卷及大语言模型（Large Language Model, LLM）训练评估，系统捕捉标注者能力的变化轨迹。结果表明，标注过程显著提升了标注者的自我感知能力和信心，并且这种提升在专家群体中更为明显，同时其标注质量变化直接影响了LLM的性能表现，揭示了标注者能力动态性对AI模型训练的重要影响。
链接: https://arxiv.org/abs/2604.02951
作者: Maciej Markiewicz,Beata Bajcar,Wiktoria Mieleszczenko-Kowszewicz,Aleksander Szczęsny,Tomasz Adamczyk,Grzegorz Chodak,Karolina Ostrowska,Aleksandra Sawczuk,Jolanta Babiak,Jagoda Szklarczyk,Przemysław Kazienko
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to AIED 2026 (27th Conference on Artificial Intelligence in Education)
Abstract:Human data annotation, especially when involving experts, is often treated as an objective reference. However, many annotation tasks are inherently subjective, and annotators’ judgments may evolve over time. This study investigates changes in the quality of annotators’ work from a competence perspective during a process of social influence recognition. The study involved 25 annotators from five different groups, including both experts and non-experts, who annotated a dataset of 1,021 dialogues with 20 social influence techniques, along with intentions, reactions, and consequences. An initial subset of 150 texts was annotated twice - before and after the main annotation process - to enable comparison. To measure competence shifts, we combined qualitative and quantitative analyses of the annotated data, semi-structured interviews with annotators, self-assessment surveys, and Large Language Model training and evaluation on the comparison dataset. The results indicate a significant increase in annotators’ self-perceived competence and confidence. Moreover, observed changes in data quality suggest that the annotation process may enhance annotator competence and that this effect is more pronounced in expert groups. The observed shifts in annotator competence have a visible impact on the performance of LLMs trained on their annotated data.
[NLP-26] A Multi-head-based architecture for effective morphological tagging in Russian with open dictionary
【速读】： 该论文旨在解决俄语形态标注（morphological tagging）问题，特别是针对词形变化复杂、词典开放性不足以及传统模型难以高效处理子词结构的挑战。解决方案的关键在于提出一种基于多头注意力机制（Multi-head attention）的新架构，通过将词语拆分为子词单元（subtokens）并训练聚合子词向量以生成词向量，从而支持开放词典，并能有效捕捉词缀（如前缀、后缀等）的形态学特征。该方法在SinTagRus和Taiga数据集上实现了98–99%以上的准确率，优于以往结果，且无需预训练（如BERT），可在消费级GPU上训练，兼具高效性和高精度。
链接: https://arxiv.org/abs/2604.02926
作者: K. Skibin,M. Pozhidaev,S. Suschenko
机构: 未知
类目: Computation and Language (cs.CL)
备注: 8 pages, 1 figure, submitted to AINL-2026
Abstract:The article proposes a new architecture based on Multi-head attention to solve the problem of morphological tagging for the Russian language. The preprocessing of the word vectors includes splitting the words into subtokens, followed by a trained procedure for aggregating the vectors of the subtokens into vectors for tokens. This allows to support an open dictionary and analyze morphological features taking into account parts of words (prefixes, endings, etc.). The open dictionary allows in future to analyze words that are absent in the training dataset. The performed computational experiment on the SinTagRus and Taiga datasets shows that for some grammatical categories the proposed architecture gives accuracy 98-99% and above, which outperforms previously known results. For nine out of ten words, the architecture precisely predicts all grammatical categories and indicates when the categories must not be analyzed for the word. At the same time, the model based on the proposed architecture can be trained on consumer-level graphics accelerators, retains all the advantages of Multi-head attention over RNNs (RNNs are not used in the proposed approach), does not require pretraining on large collections of unlabeled texts (like BERT), and shows higher processing speed than previous results.
[NLP-27] Council Mode: Mitigating Hallucination and Bias in LLM s via Multi-Agent Consensus
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）尤其是采用混合专家（Mixture-of-Experts, MoE）架构的模型在推理过程中普遍存在的幻觉（hallucination）问题以及因专家激活不均导致的系统性偏见（systematic bias）问题。其解决方案的关键在于提出一种名为“理事会模式”（Council Mode）的多智能体共识框架：通过一个智能分诊分类器将查询按复杂度路由至多个异构前沿LLM并行生成响应，再由专用共识模型对输出进行结构化合成，明确识别一致、分歧与独特发现，最终生成更准确、低偏见的综合回答。该机制显著降低了幻觉率（在HaluEval上相对减少35.9%）并提升了事实准确性（TruthfulQA提升7.8点），同时有效控制了跨领域的偏见方差。
链接: https://arxiv.org/abs/2604.02923
作者: Shuai Wu,Xue Li,Yanna Feng,Yufang Li,Zhijun Wang
机构: OpenAI; Anthropic; Google; ByteDance
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 8 figures, technical report
Abstract:Large Language Models (LLMs), particularly those employing Mixture-of-Experts (MoE) architectures, have achieved remarkable capabilities across diverse natural language processing tasks. However, these models frequently suffer from hallucinations – generating plausible but factually incorrect content – and exhibit systematic biases that are amplified by uneven expert activation during inference. In this paper, we propose the Council Mode, a novel multi-agent consensus framework that addresses these limitations by dispatching queries to multiple heterogeneous frontier LLMs in parallel and synthesizing their outputs through a dedicated consensus model. The Council pipeline operates in three phases: (1) an intelligent triage classifier that routes queries based on complexity, (2) parallel expert generation across architecturally diverse models, and (3) a structured consensus synthesis that explicitly identifies agreement, disagreement, and unique findings before producing the final response. We implement and evaluate this architecture within an open-source AI workspace. Our comprehensive evaluation across multiple benchmarks demonstrates that the Council Mode achieves a 35.9% relative reduction in hallucination rates on the HaluEval benchmark and a 7.8-point improvement on TruthfulQA compared to the best-performing individual model, while maintaining significantly lower bias variance across domains. We provide the mathematical formulation of the consensus mechanism, detail the system architecture, and present extensive empirical results with ablation studies.
[NLP-28] Analysis of Optimality of Large Language Models on Planning Problems
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在经典AI规划任务中是否能够实现最优推理，而非仅依赖启发式策略的问题。研究聚焦于Blocksworld域和形式等价的广义Path-Star（P^*）图任务，通过系统调节问题深度、宽度和组合性来评估LLM的规划效率与最优性。解决方案的关键在于：一是模型通过推理标记（reasoning tokens）执行主动的算法模拟（Algorithmic Simulation），二是利用几何记忆（Geometric Memory）将P^*拓扑结构表示为可导航的全局几何空间，从而有效规避指数级组合复杂度，使LLM在无领域语义提示的情况下仍能逼近理论最优解。
链接: https://arxiv.org/abs/2604.02910
作者: Bernd Bohnet,Michael C. Mozer,Kevin Swersky,Wil Cunningham,Aaron Parisi,Kathleen Kenealy,Noah Fiedel
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Classic AI planning problems have been revisited in the Large Language Model (LLM) era, with a focus of recent benchmarks on success rates rather than plan efficiency. We examine the degree to which frontier models reason optimally versus relying on simple, heuristic, and possibly inefficient strategies. We focus on the Blocksworld domain involving towers of labeled blocks which have to be moved from an initial to a goal configuration via a set of primitive actions. We also study a formally equivalent task, the generalized Path-Star ( P^* ) graph, in order to isolate true topological reasoning from semantic priors. We systematically manipulate problem depth (the height of block towers), width (the number of towers), and compositionality (the number of goal blocks). Reasoning-enhanced LLMs significantly outperform traditional satisficing planners (e.g., LAMA) in complex, multi-goal configurations. Although classical search algorithms hit a wall as the search space expands, LLMs track theoretical optimality limits with near-perfect precision, even when domain-specific semantic hints are stripped away. To explain these surprising findings, we consider (and find evidence to support) two hypotheses: an active Algorithmic Simulation executed via reasoning tokens and a Geometric Memory that allows models to represent the P^* topology as a navigable global geometry, effectively bypassing exponential combinatorial complexity.
[NLP-29] BioUNER: A Benchmark Dataset for Clinical Urdu Named Entity Recognition
【速读】： 该论文旨在解决生物医学领域 Urdu 语言命名实体识别（Named Entity Recognition, NER）缺乏高质量标注数据的问题。解决方案的关键在于构建了一个金标准（gold-standard）的生物医学 Urdu 命名实体识别数据集（BioUNER），通过从在线乌尔都语新闻门户、医疗处方及医院健康博客和网站爬取文本并进行预处理，由三位具备医学背景的本地标注者使用 Doccano 工具标注了 15.3 万词元（tokens），并实现了 0.78 的标注者间一致性评分（inter-annotator agreement），验证了数据集的高质量。该数据集可作为 Urdu 自然语言处理资源的重要补充，并为多种机器学习与深度学习模型（如 SVM、LSTM、mBERT 和 XLM-RoBERTa）提供基准测试能力。
链接: https://arxiv.org/abs/2604.02904
作者: Wazir Ali,Adeeb Noor,Sanaullah Mahar,Alia,Muhammad Mazhar Younas
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:In this article, we present a gold-standard benchmark dataset for Biomedical Urdu Named Entity Recognition (BioUNER), developed by crawling health-related articles from online Urdu news portals, medical prescriptions, and hospital health blogs and websites. After preprocessing, three native annotators with familiarity in the medical domain participated in the annotation process using the Doccano text annotation tool and annotated 153K tokens. Following annotation, the proposed BioiUNER dataset was evaluated both intrinsically and extrinsically. An inter-annotator agreement score of 0.78 was achieved, thereby validating the dataset as gold-standard quality. To demonstrate the utility and benchmarking capability of the dataset, we evaluated several machine learning and deep learning models, including Support Vector Machines (SVM), Long Short-Term Memory networks (LSTM), Multilingual BERT (mBERT), and XLM-RoBERTa. The gold-standard BioUNER dataset serves as a reliable benchmark and a valuable addition to Urdu language processing resources.
[NLP-30] One Model to Translate Them All? A Journey to Mount Doom for Multilingual Model Merging
【速读】： 该论文旨在解决多语言机器翻译场景下权重空间模型合并（weight-space model merging）性能下降的问题。现有研究表明，该方法在多任务设置中表现良好，但在多语言环境下效果不佳，其根本原因尚不明确。论文通过系统实验发现，多语言微调会重塑模型内部表示的几何结构，导致标准权重空间合并假设失效：具体而言，语言特异性神经元主要集中在嵌入层和Transformer上部块中，而中间层则跨语言共享；更重要的是，微调过程并非增强语言选择性，而是重新分布神经元激活模式——监督语言和相关语言的神经元变得不那么排他，而无监督语言的神经元则更加孤立，从而加剧了高层表示的差异，影响生成能力。因此，解决方案的关键在于揭示多语言微调对模型内部表征动态的影响机制，特别是语言特异性与共享表示的分布变化，这为改进多语言场景下的模型合并策略提供了理论依据。
链接: https://arxiv.org/abs/2604.02881
作者: Baban Gain,Asif Ekbal,Trilok Nath Singh
机构: Indian Institute of Technology Patna (印度理工学院巴特那分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Weight-space model merging combines independently fine-tuned models without accessing original training data, offering a practical alternative to joint training. While merging succeeds in multitask settings, its behavior in multilingual contexts remains poorly understood. We systematically study weight-space merging for multilingual machine translation by fully fine-tuning language model on large-scale bilingual corpora and evaluating standard merging strategies. Our experiments reveal that merging degrades performance, especially when target languages differ. To explain this failure, we analyze internal representations using span-conditioned neuron selectivity and layer-wise centered kernel alignment. We find that language-specific neurons concentrate in embedding layers and upper transformer blocks, while intermediate layers remain largely shared across languages. Critically, fine-tuning redistributes rather than sharpens language selectivity: neurons for supervised and related languages become less exclusive, while those for unsupervised languages grow more isolated. This redistribution increases representational divergence in higher layers that govern generation. These findings suggest that multilingual fine-tuning may reshape geometry in ways that reduce compatibility with standard weight-space merging assumptions. Our work thus provides an explanation for why merging fails in multilingual translation scenarios.
[NLP-31] LLM -based Atomic Propositions help weak extractors: Evaluation of a Propositioner for triplet extraction
【速读】： 该论文旨在解决从自然语言中构建知识图谱时，如何更有效地从复杂、信息密集的句子中提取结构化三元组（triplet）的问题。其核心挑战在于现有方法在处理语义复杂句子时，难以准确识别实体间的关系，尤其是在弱模型或低资源场景下表现不佳。解决方案的关键在于引入原子命题（atomic propositions）——即最小且语义独立的信息单元——作为中间表示结构，通过将原始文本分解为原子命题来增强三元组抽取能力。作者提出MPropositionneur-V2模型，基于知识蒸馏从大语言模型Qwen3-32B压缩至轻量级Qwen3-0.6B架构，并验证其在两种抽取范式（基于实体的GLiREL和生成式Qwen3）中的有效性：原子命题显著提升弱模型的关系召回率，而强模型则通过回退组合策略在保持关系提取优势的同时恢复实体召回损失。这表明原子命题是一种可解释且互补的中间数据结构，能有效增强不同强度的抽取器性能。
链接: https://arxiv.org/abs/2604.02866
作者: Luc Pommeret(STL),Thomas Gerald(LISN),Patrick Paroubek(STL),Sahar Ghannay(STL),Christophe Servan(STL, AMIAD),Sophie Rosset(LISN, STL)
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Knowledge Graph construction from natural language requires extracting structured triplets from complex, information-dense sentences. In this paper, we investigate if the decomposition of text into atomic propositions (minimal, semantically autonomous units of information) can improve the triplet extraction. We introduce MPropositionneur-V2, a small multilingual model covering six European languages trained by knowledge distillation from Qwen3-32B into a Qwen3-0.6B architecture, and we evaluate its integration into two extraction paradigms: entity-centric (GLiREL) and generative (Qwen3). Experiments on SMiLER, FewRel, DocRED and CaRB show that atomic propositions benefit weaker extractors (GLiREL, CoreNLP, 0.6B models), improving relation recall and, in the multilingual setting, overall accuracy. For stronger LLMs, a fallback combination strategy recovers entity recall losses while preserving the gains in relation extraction. These results show that atomic propositions are an interpretable intermediate data structure that complements extractors without replacing them.
[NLP-32] GRADE: Probing Knowledge Gaps in LLM s through Gradient Subspace Dynamics
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在部署过程中一个核心挑战：如何准确检测模型内部知识是否足以正确回答给定问题。现有方法通常依赖模型自评置信度或分析响应token的隐藏状态来捕捉激活的知识，但这些方法可能无法对齐查询所需信息，例如引入与回答无关的风格或长度特征。为填补这一空白，作者提出GRADE（Gradient Dynamics for knowledge gap detection），其关键在于利用梯度与对应隐藏状态子空间的跨层秩比（cross-layer rank ratio）来量化知识缺口——该设计基于梯度作为目标知识更新估计量的理论性质，从而更精准地识别模型知识不足的情况。
链接: https://arxiv.org/abs/2604.02830
作者: Yujing Wang,Yuanbang Liang,Yukun Lai,Hainan Zhang,Hanqi Yan
机构: Beihang University (北京航空航天大学); Cardiff University (卡迪夫大学); King’s College London (伦敦国王学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Detecting whether a model’s internal knowledge is sufficient to correctly answer a given question is a fundamental challenge in deploying responsible LLMs. In addition to verbalising the confidence by LLM self-report, more recent methods explore the model internals, such as the hidden states of the response tokens to capture how much knowledge is activated. We argue that such activated knowledge may not align with what the query requires, e.g., capturing the stylistic and length-related features that are uninformative for answering the query. To fill the gap, we propose GRADE (Gradient Dynamics for knowledge gap detection), which quantifies the knowledge gap via the cross-layer rank ratio of the gradient to that of the corresponding hidden state subspace. This is motivated by the property of gradients as estimators of the required knowledge updates for a given target. We validate \modelname on six benchmarks, demonstrating its effectiveness and robustness to input perturbations. In addition, we present a case study showing how the gradient chain can generate interpretable explanations of knowledge gaps for long-form answers.
[NLP-33] Student-in-the-Loop Chain-of-Thought Distillation via Generation-Time Selection
【速读】： 该论文旨在解决大模型（如生成式AI）在复杂任务中通过长链式思维（Chain-of-Thought, CoT）推理获得优异性能后，如何有效将这种推理过程迁移至小型学生模型的问题。现有方法通常依赖于事后筛选（post-hoc filtering），即在教师模型完整生成推理轨迹后再基于启发式标准进行选择，但这种方法无法控制生成过程，可能导致学生模型学习到超出其能力范围的路径。论文提出Gen-SSD（Generation-time Self-Selection Distillation）框架，其核心创新在于引入“学生在环”机制，在教师采样过程中由学生实时评估候选续写内容，仅保留可学习的推理路径并提前剪枝无效分支，从而实现生成阶段的选择性蒸馏。该方案显著提升了小模型的学习效率与稳定性，实验表明其相较标准知识蒸馏提升约5.9分，优于当前主流基线方法。
链接: https://arxiv.org/abs/2604.02819
作者: Chaoqun He,Yingfa Chen,Chaojun Xiao,Xu Han,Lijie Wen
机构: Tsinghua University (清华大学)
类目: Computation and Language (cs.CL)
备注: 17 pages, 6 figures
Abstract:Large reasoning models achieve strong performance on complex tasks through long chain-of-thought (CoT) trajectories, but directly transferring such reasoning processes to smaller models remains challenging. A key difficulty is that not all teacher-generated reasoning trajectories are suitable for student learning. Existing approaches typically rely on post-hoc filtering, selecting trajectories after full generation based on heuristic criteria. However, such methods cannot control the generation process itself and may still produce reasoning paths that lie outside the student’s learning capacity. To address this limitation, we propose Gen-SSD (Generation-time Self-Selection Distillation), a student-in-the-loop framework that performs generation-time selection. Instead of passively consuming complete trajectories, the student evaluates candidate continuations during the teacher’s sampling process, guiding the expansion of only learnable reasoning paths and enabling early pruning of unhelpful branches. Experiments on mathematical reasoning benchmarks demonstrate that Gen-SSD consistently outperforms standard knowledge distillation and recent baselines, with improvements of around 5.9 points over Standard KD and up to 4.7 points over other baselines. Further analysis shows that Gen-SSD produces more stable and learnable reasoning trajectories, highlighting the importance of incorporating supervision during generation for effective distillation.
[NLP-34] Rubrics to Tokens: Bridging Response-level Rubrics and Token-level Rewards in Instruction Following Tasks
【速读】： 该论文旨在解决基于评分的强化学习（Rubric-based Reinforcement Learning, RbRL）在对齐大语言模型（Large Language Models, LLMs）与复杂、开放域指令任务时，因依赖响应级奖励而导致的奖励稀疏性（reward sparsity）和奖励模糊性（reward ambiguity）问题。其解决方案的关键在于提出一种名为“Rubrics to Tokens”（RTT）的新框架，该框架通过引入一个Token-Level Relevance Discriminator（细粒度令牌相关性判别器），将粗粒度的响应级评分映射至细粒度的令牌级信用分配；同时设计了RTT-GRPO算法，在统一框架中融合响应级与令牌级优势信号，并提出Intra-sample Token Group Normalization（样本内令牌组归一化）方法以适应从一维结果奖励空间到三维令牌级奖励空间的转变，从而显著提升模型在指令遵循和评分规则准确率上的表现。
链接: https://arxiv.org/abs/2604.02795
作者: Tianze Xu,Yanzhao Zheng,Pengrui Lu,Lyumanshan Ye,Yong Wu,Zhentao Zhang,Yuanqiang Yu,Chao Ma,Jihuai Zhu,Pengfei Liu,Baohua Dong,Hangcheng Zhu,Ruohui Huang,Gang Yu
机构: Shanghai Jiao Tong University (上海交通大学); Alibaba Group (阿里巴巴集团); Zhejiang University (浙江大学); Shanghai Innovation Institute (上海创新研究院); GAIR (通用人工智能研究中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Rubric-based Reinforcement Learning (RL) has emerged as a promising approach for aligning Large Language Models (LLMs) with complex, open-domain instruction following tasks. However, existing methods predominantly rely on response-level rewards, introducing severe reward sparsity and reward ambiguity problems. To address these issues, we propose Rubrics to Tokens (RTT), a novel rubric-based RL framework that bridges coarse response-level scores and fine-grained token-level credit assignment. RTT introduces a Token-Level Relevance Discriminator to predict which tokens in the response are responsible for a specific constraint, and optimizes the policy model via RTT-GRPO, which integrates response-level and token-level advantages within a unified framework. Furthermore, when transitioning from one-dimensional, outcome-level reward to three-dimensional reward space in the token-level rubric-based RL, we propose a novel group normalization method, called Intra-sample Token Group Normalization, to accommodate this shift. Extensive experiments and benchmarks demonstrate that RTT consistently outperforms other baselines in both instruction- and rubric-level accuracy across different models.
[NLP-35] EnsemHalDet: Robust VLM Hallucination Detection via Ensemble of Internal State Detectors
【速读】： 该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在多模态任务中普遍存在幻觉（hallucination）的问题，即模型生成的内容与输入图像事实不符或缺乏依据。现有基于内部表示的检测方法通常依赖单一特征或检测器，难以捕捉多样化的幻觉信号。解决方案的关键在于提出EnsemHalDet框架，通过融合VLM内部多种表征（包括注意力输出和隐藏状态）训练独立检测器，并采用集成学习策略进行组合，从而显著提升多模态幻觉检测的鲁棒性和准确性。
链接: https://arxiv.org/abs/2604.02784
作者: Ryuhei Miyazato,Shunsuke Kitada,Kei Harada
机构: The University of Electro-Communications (电气通信大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Vision-Language Models (VLMs) excel at multimodal tasks, but they remain vulnerable to hallucinations that are factually incorrect or ungrounded in the input image. Recent work suggests that hallucination detection using internal representations is more efficient and accurate than approaches that rely solely on model outputs. However, existing internal-representation-based methods typically rely on a single representation or detector, limiting their ability to capture diverse hallucination signals. In this paper, we propose EnsemHalDet, an ensemble-based hallucination detection framework that leverages multiple internal representations of VLMs, including attention outputs and hidden states. EnsemHalDet trains independent detectors for each representation and combines them through ensemble learning. Experimental results across multiple VQA datasets and VLMs show that EnsemHalDet consistently outperforms prior methods and single-detector models in terms of AUC. These results demonstrate that ensembling diverse internal signals significantly improves robustness in multimodal hallucination detection.
[NLP-36] When Modalities Remember: Continual Learning for Multimodal Knowledge Graphs
【速读】： 该论文旨在解决持续多模态知识图谱推理（Continual Multimodal Knowledge Graph Reasoning, CMMKGR）中的关键挑战：现有方法要么仅关注结构三元组而无法充分利用新实体的多模态信号，要么假设知识图谱静态不变，在图谱动态演化时易发生灾难性遗忘。解决方案的关键在于提出MRCKG模型，其核心机制包括：(1) 多模态-结构协同课程学习策略，根据新三元组与历史图谱的结构连通性和多模态兼容性安排渐进式学习顺序；(2) 跨模态知识保留机制，通过实体表示稳定性、关系语义一致性及模态锚定缓解遗忘；(3) 基于两阶段优化的多模态对比回放方案，借助多模态重要性采样和表征对齐强化已学知识。
链接: https://arxiv.org/abs/2604.02778
作者: Linyu Li,Zhi Jin,Yichi Zhang,Dongming Jin,Yuanpeng He,Haoran Duan,Gadeng Luosang,Nyima Tashi
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Real-world multimodal knowledge graphs (MMKGs) are dynamic, with new entities, relations, and multimodal knowledge emerging over time. Existing continual knowledge graph reasoning (CKGR) methods focus on structural triples and cannot fully exploit multimodal signals from new entities. Existing multimodal knowledge graph reasoning (MMKGR) methods, however, usually assume static graphs and suffer catastrophic forgetting as graphs evolve. To address this gap, we present a systematic study of continual multimodal knowledge graph reasoning (CMMKGR). We construct several continual multimodal knowledge graph benchmarks from existing MMKG datasets and propose MRCKG, a new CMMKGR model. Specifically, MRCKG employs a multimodal-structural collaborative curriculum to schedule progressive learning based on the structural connectivity of new triples to the historical graph and their multimodal compatibility. It also introduces a cross-modal knowledge preservation mechanism to mitigate forgetting through entity representation stability, relational semantic consistency, and modality anchoring. In addition, a multimodal contrastive replay scheme with a two-stage optimization strategy reinforces learned knowledge via multimodal importance sampling and representation alignment. Experiments on multiple datasets show that MRCKG preserves previously learned multimodal knowledge while substantially improving the learning of new knowledge.
[NLP-37] Multiple-Debias: A Full-process Debiasing Method for Multilingual Pre-trained Language Models
【速读】： 该论文旨在解决多语言预训练语言模型（Multilingual Pre-trained Language Models, MPLMs）中存在的敏感属性偏见问题，如性别、种族和宗教偏见。其解决方案的关键在于提出一种全流程的多语言去偏方法——Multiple-Debias，该方法融合了多语言反事实数据增强（multilingual counterfactual data augmentation）与多语言自去偏（multilingual Self-Debias），并结合参数高效微调策略，在预处理和后处理阶段协同减少偏见。实验表明，该方法在四种语言中显著降低了三类敏感属性的偏见，并且跨语言去偏信息的整合提升了模型整体公平性。
链接: https://arxiv.org/abs/2604.02772
作者: Haoyu Liang,Peijian Zeng,Wentao Huang,Aimin Yang,Dong Zhou
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Multilingual Pre-trained Language Models (MPLMs) have become essential tools for natural language processing. However, they often exhibit biases related to sensitive attributes such as gender, race, and religion. In this paper, we introduce a comprehensive multilingual debiasing method named Multiple-Debias to address these issues across multiple languages. By incorporating multilingual counterfactual data augmentation and multilingual Self-Debias across both pre-processing and post-processing stages, alongside parameter-efficient fine-tuning, we significantly reduced biases in MPLMs across three sensitive attributes in four languages. We also extended CrowS-Pairs to German, Spanish, Chinese, and Japanese, validating our full-process multilingual debiasing method for gender, racial, and religious bias. Our experiments show that (i) multilingual debiasing methods surpass monolingual approaches in effectively mitigating biases, and (ii) integrating debiasing information from different languages notably improves the fairness of MPLMs.
[NLP-38] IndustryCode: A Benchmark for Industry Code Generation
【速读】： 该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在代码生成与理解能力评估中存在领域和语言单一性的问题，即现有基准测试难以有效衡量模型在真实工业场景中的泛化能力及复杂编码任务的胜任力。其解决方案的关键在于提出首个跨多工业领域与多编程语言的综合性基准——IndustryCode，该基准包含来自125个工业挑战的579个子问题，覆盖金融、自动化、航空航天和遥感等领域，并集成MATLAB、Python、C++和Stata等多种语言，同时提供严谨的问题描述与测试用例，从而更全面地评估LLMs在实际工业应用中的表现。
链接: https://arxiv.org/abs/2604.02729
作者: Puyu Zeng,Zhaoxi Wang,Zhixu Duan,Liang Feng,Shaobo Wang,Cunxiang Wang,Jinghang Wang,Bing Zhao,Hu Wei,Linfeng Zhang
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 37 pages, 28 figures, 4 tables. Includes appendix
Abstract:Code generation and comprehension by Large Language Models (LLMs) have emerged as core drivers of industrial intelligence and decision optimization, finding widespread application in fields such as finance, automation, and aerospace. Although recent advancements have demonstrated the remarkable potential of LLMs in general code generation, existing benchmarks are mainly confined to single domains and languages. Consequently, they fail to effectively evaluate the generalization capabilities required for real-world industrial applications or to reflect the coding proficiency demanded by complex industrial scenarios. To bridge this gap, we introduce IndustryCode, the first comprehensive benchmark designed to span multiple industrial domains and programming languages. IndustryCode comprises 579 sub-problems derived from 125 primary industrial challenges, accompanied by rigorous problem descriptions and test cases. It covers a wide range of fields, including finance, automation, aerospace, and remote sensing-and incorporates diverse programming languages such as MATLAB, Python, C++, and Stata. In our evaluation, the top-performing model, Claude 4.5 Opus, achieved an overall accuracy of 68.1% on sub-problems and 42.5% main problems. The benchmark dataset and automated evaluation code will be made publicly available upon acceptance.
[NLP-39] Generative Frontiers: Why Evaluation Matters for Diffusion Language Models
【速读】： 该论文旨在解决当前扩散语言模型（diffusion language models）在评估方法上的局限性问题，特别是针对基于似然（likelihood）的评价指标（如生成困惑度，generative perplexity）可能带来的误导性结果。其关键解决方案在于揭示生成困惑度与熵（entropy）共同构成到参考分布的KL散度（KL divergence）的两个组成部分，并据此提出“生成前沿”（generative frontiers）作为更可靠的评估范式，从而更准确地衡量模型的生成质量。
链接: https://arxiv.org/abs/2604.02718
作者: Patrick Pynadath,Jiaxin Shi,Ruqi Zhang
机构: Purdue University (普渡大学); Stanford University (斯坦福大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Diffusion language models have seen exciting recent progress, offering far more flexibility in generative trajectories than autoregressive models. This flexibility has motivated a growing body of research into new approaches to diffusion language modeling, which typically begins at the scale of GPT-2 small (150 million parameters). However, these advances introduce new issues with evaluation methodology. In this technical note, we discuss the limitations of current methodology and propose principled augmentations to ensure reliable comparisons. We first discuss why OpenWebText has become the standard benchmark, and why alternatives such as LM1B are inherently less meaningful. We then discuss the limitations of likelihood evaluations for diffusion models, and explain why relying on generative perplexity alone as a metric can lead to uninformative results. To address this, we show that generative perplexity and entropy are two components of the KL divergence to a reference distribution. This decomposition explains generative perplexity’s sensitivity to entropy, and naturally suggests generative frontiers as a principled method for evaluating model generative quality. We conclude with empirical observations on model quality at this scale. We include a blog post with interactive content to illustrate the argument at this https URL.
[NLP-40] Breakdowns in Conversational AI: Interactional Failures in Emotionally and Ethically Sensitive Contexts
【速读】： 该论文旨在解决当前对话式人工智能（Conversational AI）在情绪和伦理敏感场景下，因缺乏动态对齐机制而导致的交互质量下降问题。现有研究多聚焦于静态安全检查或单一情感基准测试，忽视了对话过程中价值与情感状态的持续演化。解决方案的关键在于构建一个基于人格设定的用户模拟器（persona-conditioned user simulator），该模拟器能够通过多轮对话呈现心理人格特征和阶段性情绪发展，从而系统性地压力测试聊天机器人在复杂情境下的表现。研究发现主流模型在情绪轨迹加剧时出现反复性失效，包括情感错位、伦理引导失败以及跨维度权衡（如共情压倒责任），并据此提出一个分类框架，强调需在动态交互中同时保障伦理一致性与情感敏感性，为对话系统的价值对齐设计提供新思路。
链接: https://arxiv.org/abs/2604.02713
作者: Jiawen Deng,Wentao Zhang,Ziyun Jiao,Fuji Ren
机构: University of Electronic Science and Technology of China (电子科技大学)
类目: Computation and Language (cs.CL)
备注: 22 pages, ACM CHI 2026
Abstract:Conversational AI is increasingly deployed in emotionally charged and ethically sensitive interactions. Previous research has primarily concentrated on emotional benchmarks or static safety checks, overlooking how alignment unfolds in evolving conversation. We explore the research question: what breakdowns arise when conversational agents confront emotionally and ethically sensitive behaviors, and how do these affect dialogue quality? To stress-test chatbot performance, we develop a persona-conditioned user simulator capable of engaging in multi-turn dialogue with psychological personas and staged emotional pacing. Our analysis reveals that mainstream models exhibit recurrent breakdowns that intensify as emotional trajectories escalate. We identify several common failure patterns, including affective misalignments, ethical guidance failures, and cross-dimensional trade-offs where empathy supersedes or undermines responsibility. We organize these patterns into a taxonomy and discuss the design implications, highlighting the necessity to maintain ethical coherence and affective sensitivity throughout dynamic interactions. The study offers the HCI community a new perspective on the diagnosis and improvement of conversational AI in value-sensitive and emotionally charged contexts.
[NLP-41] Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）在形式推理能力评估中缺乏基于计算复杂性系统性评测的问题，特别是其对形式语言结构层级复杂性的理解能力尚不明确。为应对这一挑战，作者提出了ChomskyBench基准测试框架，其关键创新在于首次实现了对乔姆斯基层级（Chomsky Hierarchy）的完整覆盖、通过自然语言过程追踪（process-trace evaluation）进行评估，并结合确定性符号验证（deterministic symbolic verifiability），从而构建了一个可量化、可解释且具有理论根基的形式推理评测体系。实验表明，LLMs在不同层级任务中的性能呈现明显分层，且随着任务难度增加，推理长度和性能下降显著，揭示了当前模型虽具备一定形式推理能力，但存在严重效率瓶颈，远不如传统算法程序高效，这凸显了传统软件工具的不可替代性，并为未来增强LLMs形式推理能力提供了方向。
链接: https://arxiv.org/abs/2604.02709
作者: Yihong Dong,Xiaoha Jian,Xue Jiang,Xuyuan Guo,Zhiyuan Fan,Jiaru Qian,Kechi Zhang,Jia Li,Zhi Jin,Ge Li
机构: Peking University (北京大学); Wuhan University (武汉大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: Work in progress
Abstract:The formal reasoning capabilities of LLMs are crucial for advancing automated software engineering. However, existing benchmarks for LLMs lack systematic evaluation based on computation and complexity, leaving a critical gap in understanding their formal reasoning capabilities. Therefore, it is still unknown whether SOTA LLMs can grasp the structured, hierarchical complexity of formal languages as defined by Computation Theory. To address this, we introduce ChomskyBench, a benchmark for systematically evaluating LLMs through the lens of Chomsky Hierarchy. Unlike prior work that uses vectorized classification for neural networks, ChomskyBench is the first to combine full Chomsky Hierarchy coverage, process-trace evaluation via natural language, and deterministic symbolic verifiability. ChomskyBench is composed of a comprehensive suite of language recognition and generation tasks designed to test capabilities at each level. Extensive experiments indicate a clear performance stratification that correlates with the hierarchy’s levels of complexity. Our analysis reveals a direct relationship where increasing task difficulty substantially impacts both inference length and performance. Furthermore, we find that while larger models and advanced inference methods offer notable relative gains, they face severe efficiency barriers: achieving practical reliability would require prohibitive computational costs, revealing that current limitations stem from inefficiency rather than absolute capability bounds. A time complexity analysis further indicates that LLMs are significantly less efficient than traditional algorithmic programs for these formal tasks. These results delineate the practical limits of current LLMs, highlight the indispensability of traditional software tools, and provide insights to guide the development of future LLMs with more powerful formal reasoning capabilities.
[NLP-42] rivial Vocabulary Bans Improve LLM Reasoning More Than Deep Linguistic Constraints
【速读】： 该论文旨在验证“认知重构假说”（cognitive restructuring hypothesis），即通过特定词汇与认知之间的映射关系（如移除英语中的“to be”动词形成E-Prime语言）可系统性地改变语言模型的推理模式。其核心问题是：是否真的存在一种由词汇删减引发的结构化认知重塑机制，而非其他更简单的干扰效应。解决方案的关键在于设计包含主动对照组（active controls）的严谨实验，对比五种不同约束条件（包括E-Prime、No-Have、元认知提示和中性填充词禁用）对六种语言模型在七类推理任务中的影响。结果表明所有干预均优于无约束控制组（83.0%），且效果与理论预期深度呈逆序排列——最浅层约束（如禁止“very”“just”等非逻辑词汇）提升最大（+6.7个百分点），而E-Prime仅小幅改善（+3.7个百分点）。更重要的是，原研究报道的跨模型相关性签名未复现（平均r=0.005）。因此，作者提出替代机制：任何强制模型偏离默认生成路径的约束均可作为输出正则化器，通过引入监控负担但最小概念扰动来抑制流畅但浅层的响应模式，从而提升推理表现。
链接: https://arxiv.org/abs/2604.02699
作者: Rodney Jehu-Appiah
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 19 pages, 10 tables, 3 appendices
Abstract:A previous study reported that E-Prime (English without the verb “to be”) selectively altered reasoning in language models, with cross-model correlations suggesting a structural signature tied to which vocabulary was removed. I designed a replication with active controls to test the proposed mechanism: cognitive restructuring through specific vocabulary-cognition mappings. The experiment tested five conditions (unconstrained control, E-Prime, No-Have, elaborated metacognitive prompt, neutral filler-word ban) across six models and seven reasoning tasks (N=15,600 trials, 11,919 after compliance filtering). Every prediction from the cognitive restructuring hypothesis was disconfirmed. All four treatments outperformed the control (83.0%), including both active controls predicted to show null effects. The neutral filler-word ban, banning words like “very” and “just” with no role in logical inference, produced the largest improvement (+6.7 pp), while E-Prime produced the smallest (+3.7 pp). The four conditions ranked in perfect inverse order of theoretical depth. The cross-model correlation signature did not replicate (mean r=0.005). These results are consistent with a simpler mechanism: any constraint that forces a model off its default generation path acts as an output regularizer, improving reasoning by disrupting fluent but shallow response patterns. The shallowest constraints work best because they impose monitoring load with minimal conceptual disruption. I present these findings as a case study in discovery through disconfirmation.
[NLP-43] Redirected Not Removed: Task-Dependent Stereotyping Reveals the Limits of LLM Alignments
【速读】： 该论文旨在解决当前对大语言模型（Large Language Models, LLMs）偏见评估的片面性问题，即单一任务基准无法全面捕捉模型在多种偏见维度上的表现，从而导致对模型偏见程度的误判。其关键解决方案是构建一个涵盖9类偏见类型的分层分类法（hierarchical taxonomy），并设计7项跨显式决策与隐式关联的评测任务，对7个商用及开源LLM进行系统性审计（共约45K个提示）。研究发现：偏见具有任务依赖性、安全对齐存在不对称性，且未被充分研究的偏见轴（如种姓、语言和地理偏见）表现出最强的刻板印象，表明当前对齐策略更关注基准覆盖而非实际危害严重性。这一方法揭示了单基准审计会系统性误判LLM偏见，并指出现有对齐实践可能掩盖而非缓解表征伤害。
链接: https://arxiv.org/abs/2604.02669
作者: Divyanshu Kumar,Ishita Gupta,Nitin Aravind Birur,Tanay Baswa,Sahil Agarwal,Prashanth Harshangi
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:How biased is a language model? The answer depends on how you ask. A model that refuses to choose between castes for a leadership role will, in a fill-in-the-blank task, reliably associate upper castes with purity and lower castes with lack of hygiene. Single-task benchmarks miss this because they capture only one slice of a model’s bias profile. We introduce a hierarchical taxonomy covering 9 bias types, including under-studied axes like caste, linguistic, and geographic bias, operationalized through 7 evaluation tasks that span explicit decision-making to implicit association. Auditing 7 commercial and open-weight LLMs with \textasciitilde45K prompts, we find three systematic patterns. First, bias is task-dependent: models counter stereotypes on explicit probes but reproduce them on implicit ones, with Stereotype Score divergences up to 0.43 between task types for the same model and identity groups. Second, safety alignment is asymmetric: models refuse to assign negative traits to marginalized groups, but freely associate positive traits with privileged ones. Third, under-studied bias axes show the strongest stereotyping across all models, suggesting alignment effort tracks benchmark coverage rather than harm severity. These results demonstrate that single-benchmark audits systematically mischaracterize LLM bias and that current alignment practices mask representational harm rather than mitigating it.
[NLP-44] SocioEval: A Template-Based Framework for Evaluating Socioeconomic Status Bias in Foundation Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在社会经济地位（Socioeconomic Status, SES）维度上的偏见评估与治理问题，这一领域相较于种族和性别等属性的研究显著不足。其解决方案的核心是提出SocioEval框架——一个基于模板的系统性评估方法，通过决策类任务对基础模型中的SES偏见进行量化分析。该框架包含8个主题和18个子话题，生成240个提示词，并覆盖6种类别配对组合，结合三阶段注释协议对13个前沿LLM进行评估，揭示了偏见率在0.42%至33.75%之间的显著差异，且不同主题间偏见表现存在显著差异（如生活方式判断偏见是教育相关决策的10倍），从而为类别的偏见审计提供了可扩展、可泛化的技术基础。
链接: https://arxiv.org/abs/2604.02660
作者: Divyanshu Kumar,Ishita Gupta,Nitin Aravind Birur,Tanay Baswa,Sahil Agarwal,Prashanth Harshangi
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:As Large Language Models (LLMs) increasingly power decision-making systems across critical domains, understanding and mitigating their biases becomes essential for responsible AI deployment. Although bias assessment frameworks have proliferated for attributes such as race and gender, socioeconomic status bias remains significantly underexplored despite its widespread implications in the real world. We introduce SocioEval, a template-based framework for systematically evaluating socioeconomic bias in foundation models through decision-making tasks. Our hierarchical framework encompasses 8 themes and 18 topics, generating 240 prompts across 6 class-pair combinations. We evaluated 13 frontier LLMs on 3,120 responses using a rigorous three-stage annotation protocol, revealing substantial variation in bias rates (0.42%-33.75%). Our findings demonstrate that bias manifests differently across themes lifestyle judgments show 10 \times higher bias than education-related decisions and that deployment safeguards effectively prevent explicit discrimination but show brittleness to domain-specific stereotypes. SocioEval provides a scalable, extensible foundation for auditing class-based bias in language models.
[NLP-45] Revealing the Learning Dynamics of Long-Context Continual Pre-training
【速读】： 该论文旨在解决工业级大语言模型（Large Language Models, LLMs）在长上下文持续预训练（Long-Context Continual Pre-training, LCCP）过程中存在的适应不足、过早终止训练以及评估方法误导性等问题。现有研究多集中于小规模模型和有限数据（数十亿token），难以适配工业级模型的复杂学习动态；同时，依赖下游任务指标（如Needle-in-a-Haystack, NIAH）易导致“虚假饱和”现象，无法真实反映模型收敛状态。其解决方案的关键在于提出一个分层分析框架，从行为层面（监督微调探针）、概率层面（困惑度PPL）和机制层面（注意力模式）系统追踪LCCP的学习过程，并基于工业级模型Hunyuan-A13B（80B参数）的200B token训练轨迹揭示：大规模数据扩展是必要条件（超过150B tokens才达内在饱和），PPL能有效识别真实进步而非虚假饱和，且检索头（retrieval heads）作为低资源监控工具可稳定追踪训练进展并高度关联下游性能，从而构建了面向工业级LLM的全面监测、评估与机制解释体系。
链接: https://arxiv.org/abs/2604.02650
作者: Yupu Liang,Shuang Chen,Guanwei Zhang,Shaolei Wang,Suncong Zheng
机构: Tencent Hunyuan, Beijing, China
类目: Computation and Language (cs.CL)
备注:
Abstract:Existing studies on Long-Context Continual Pre-training (LCCP) mainly focus on small-scale models and limited data regimes (tens of billions of tokens). We argue that directly migrating these small-scale settings to industrial-grade models risks insufficient adaptation and premature training termination. Furthermore, current evaluation methods rely heavily on downstream benchmarks (e.g., Needle-in-a-Haystack), which often fail to reflect the intrinsic convergence state and can lead to “deceptive saturation”. In this paper, we present the first systematic investigation of LCCP learning dynamics using the industrial-grade Hunyuan-A13B (80B total parameters), tracking its evolution across a 200B-token training trajectory. Specifically, we propose a hierarchical framework to analyze LCCP dynamics across behavioral (supervised fine-tuning probing), probabilistic (perplexity), and mechanistic (attention patterns) levels. Our findings reveal: (1) Necessity of Massive Data Scaling: Training regimes of dozens of billions of tokens are insufficient for industrial-grade LLMs’ LCCP (e.g., Hunyuan-A13B reaches saturation after training over 150B tokens). (2) Deceptive Saturation vs. Intrinsic Saturation: Traditional NIAH scores report “fake saturation” early, while our PPL-based analysis reveals continuous intrinsic improvements and correlates more strongly with downstream performance. (3) Mechanistic Monitoring for Training Stability: Retrieval heads act as efficient, low-resource training monitors, as their evolving attention scores reliably track LCCP progress and exhibit high correlation with SFT results. This work provides a comprehensive monitoring framework, evaluation system, and mechanistic interpretation for the LCCP of industrial-grade LLM.
[NLP-46] Speaking of Language: Reflections on Metalanguage Research in NLP
【速读】： 该论文旨在解决当前自然语言处理（Natural Language Processing, NLP）与大语言模型（Large Language Models, LLMs）研究中对元语言（metalanguage）关注不足的问题。其解决方案的关键在于系统性地界定元语言的概念，并将其与NLP和LLMs的研究框架相连接，进而通过两个实验室的实证研究展示元语言在任务设计与模型理解中的核心作用；同时提出从四个维度（如元语言能力、元语言标注、元语言推理和元语言生成）深入挖掘元语言相关任务，并指出多个尚未充分探索的研究方向，为未来研究提供理论基础与实践路径。
链接: https://arxiv.org/abs/2604.02645
作者: Nathan Schneider,Antonios Anastasopoulos
机构: Georgetown University (乔治城大学); George Mason University (乔治梅森大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:This work aims to shine a spotlight on the topic of metalanguage. We first define metalanguage, link it to NLP and LLMs, and then discuss our two labs’ metalanguage-centered efforts. Finally, we discuss four dimensions of metalanguage and metalinguistic tasks, offering a list of understudied future research directions.
[NLP-47] Overcoming the “Impracticality” of RAG : Proposing a Real-World Benchmark and Multi-Dimensional Diagnostic Framework AAAI2026
【速读】： 该论文旨在解决企业环境中检索增强生成（Retrieval-Augmented Generation, RAG）系统性能评估中存在的关键问题：现有学术基准无法系统诊断影响RAG实际部署可靠性的多维复杂因素，导致模型在实验室高分表现与真实场景可靠性之间存在显著脱节。其解决方案的关键在于提出一个四轴难度分类法（four-axis difficulty taxonomy），并将其集成到企业级RAG评测框架中，从而实现对推理复杂度、检索难度、文档结构多样性及可解释性要求等核心维度的精细化诊断，以识别系统潜在弱点并提升部署可信度。
链接: https://arxiv.org/abs/2604.02640
作者: Kenichirou Narita,Siqi Peng,Taku Fukui,Moyuru Yamada,Satoshi Munakata,Satoru Takahashi
机构: 未知
类目: Computation and Language (cs.CL)
备注: 8 pages, 3 figures. Accepted at AAAI 2026 Workshop
Abstract:Performance evaluation of Retrieval-Augmented Generation (RAG) systems within enterprise environments is governed by multi-dimensional and composite factors extending far beyond simple final accuracy checks. These factors include reasoning complexity, retrieval difficulty, the diverse structure of documents, and stringent requirements for operational explainability. Existing academic benchmarks fail to systematically diagnose these interlocking challenges, resulting in a critical gap where models achieving high performance scores fail to meet the expected reliability in practical deployment. To bridge this discrepancy, this research proposes a multi-dimensional diagnostic framework by defining a four-axis difficulty taxonomy and integrating it into an enterprise RAG benchmark to diagnose potential system weaknesses. Comments: 8 pages, 3 figures. Accepted at AAAI 2026 Workshop Subjects: Computation and Language (cs.CL) Cite as: arXiv:2604.02640 [cs.CL] (or arXiv:2604.02640v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2604.02640 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-48] rain Yourself as an LLM : Exploring Effects of AI Literacy on Persuasion via Role-playing LLM Training
【速读】： 该论文旨在解决大规模语言模型（Large Language Models, LLMs）日益增强的说服力可能对公众意见和决策产生广泛影响的问题，尤其关注人们在面对生成式AI（Generative AI）时缺乏足够认知防御能力的风险。现有缓解措施（如AI检测工具和免责声明）多将用户视为被动信息接收者，难以有效提升其批判性认知能力。解决方案的关键在于提出一种名为LLMimic的互动式、角色扮演驱动的AI素养教学工具，通过让用户模拟LLM训练全流程（预训练、监督微调SFT与强化学习人类反馈RLHF），在游戏化环境中主动理解AI生成机制，从而增强对AI说服策略的识别与抵御能力。实证研究表明，该方法显著提升了参与者的AI素养水平（p < .001），降低了多种场景下的AI说服成功率（p < .05），并在酒店推荐场景中增强了用户的诚实性和社会责任感（p < .01），证明其是一种可扩展且以用户为中心的有效干预路径。
链接: https://arxiv.org/abs/2604.02637
作者: Qihui Fan,Min Ge,Chenyan Jia,Weiyan Shi
机构: Khoury College of Computer Sciences, Northeastern University (东北大学计算机科学学院); College of Arts, Media and Design, Northeastern University (东北大学艺术、媒体与设计学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:As large language models (LLMs) become increasingly persuasive, there is concern that people’s opinions and decisions may be influenced across various contexts at scale. Prior mitigation (e.g., AI detectors and disclaimers) largely treats people as passive recipients of AI-generated information. To provide a more proactive intervention against persuasive AI, we introduce \textbfLLMimic , a role-play-based, interactive, gamified AI literacy tutorial, where participants assume the role of an LLM and progress through three key stages of the training pipeline (pretraining, SFT, and RLHF). We conducted a 2 \times 3 between-subjects study ( N = 274 ) where participants either (1) watched an AI history video (control) or (2) interacted with LLMimic (treatment), and then engaged in one of three realistic AI persuasion scenarios: (a) charity donation persuasion, (b) malicious money solicitation, or © hotel recommendation. Our results show that LLMimic significantly improved participants’ AI literacy ( p .001 ), reduced persuasion success across scenarios ( p .05 ), and enhanced truthfulness and social responsibility levels ( p0.01 ) in the hotel scenario. These findings suggest that LLMimic offers a scalable, human-centered approach to improving AI literacy and supporting more informed interactions with persuasive AI.
[NLP-49] Reinforcement Learning-based Knowledge Distillation with LLM -as-a-Judge
【速读】： 该论文旨在解决强化学习（Reinforcement Learning, RL）在微调大语言模型（Large Language Models, LLMs）时对可验证奖励信号（verifiable rewards）和人工标注标签（ground truth labels）的强依赖问题。传统方法需依赖高质量、带标签的数据来提供监督信号，限制了其在大规模无标签数据上的应用。论文提出一种基于LLM作为裁判（judge）的RL框架，利用一个仅输出单个token的判别模型对大量未标注数据进行自动评分，从而生成有效的训练信号，实现无需人工标注的知识蒸馏（knowledge distillation）。该方案的关键在于：通过轻量级LLM裁判机制高效生成奖励，替代传统依赖人工标注的监督方式，并在结合可验证奖励时显著提升数学推理任务的性能表现。
链接: https://arxiv.org/abs/2604.02621
作者: Yiyang Shen,Lifu Tu,Weiran Wang
机构: University of Iowa (爱荷华大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Reinforcement Learning (RL) has been shown to substantially improve the reasoning capability of small and large language models (LLMs), but existing approaches typically rely on verifiable rewards, hence ground truth labels. We propose an RL framework that uses rewards from an LLM that acts as a judge evaluating model outputs over large amounts of unlabeled data, enabling label-free knowledge distillation and replacing the need of ground truth supervision. Notably, the judge operates with a single-token output, making reward computation efficient. When combined with verifiable rewards, our approach yields substantial performance gains across math reasoning benchmarks. These results suggest that LLM-based evaluators can produce effective training signals for RL fine-tuning.
[NLP-50] An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages
【速读】： 该论文旨在解决低资源语言（low-resource languages）在机器翻译任务中因训练数据稀缺而导致性能受限的问题，尤其是在利用大语言模型（Large Language Models, LLMs）进行上下文学习（In-context Learning, ICL）时如何提升数据效率与翻译质量。其解决方案的关键在于通过基于BM25的检索机制从大规模语料库中获取更相关的示例，从而显著降低对大量人工标注示例的依赖——实验证明，使用50个检索到的示例即可达到250个随机选取的多示例（many-shot）I CL的效果，而250个检索示例的表现接近于1000个随机示例，极大提升了低资源语言场景下的数据利用效率。
链接: https://arxiv.org/abs/2604.02596
作者: Yinhan Lu,Gaganpreet Jhajj,Chen Zhang,Anietie Andy,David Ifeoluwa Adelani
机构: Mila – Quebec AI Institute (Mila – 魁北克人工智能研究所); McGill University (麦吉尔大学); Athabasca University (阿萨巴斯卡大学); Peking University (北京大学); Howard University (霍华德大学)
类目: Computation and Language (cs.CL)
备注: 20 pages, 3 figures, 14 tables
Abstract:In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising for languages underrepresented in pre-training. Recent work on many-shot ICL suggests that modern LLMs can further benefit from larger ICL examples enabled by their long context windows. However, such gains depend on careful example selection, and the inference cost can be prohibitive for low-resource language communities. In this paper, we present an empirical study of many-shot ICL for machine translation from English into ten truly low-resource languages recently added to FLORES+. We analyze the effects of retrieving more informative examples, using out-of-domain data, and ordering examples by length. Our findings show that many-shot ICL becomes more effective as the number of examples increases. More importantly, we show that BM25-based retrieval substantially improves data efficiency: 50 retrieved examples roughly match 250 many-shot examples, while 250 retrieved examples perform similarly to 1,000 many-shot examples.
[NLP-51] Mitigating LLM biases toward spurious social contexts using direct preference optimization
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在高风险决策任务中对虚假情境信息（spurious contextual information）敏感所引发的有害偏见问题，尤其是在教育评估场景下，如教师教学质量评价，此类偏见可能严重影响教师的职业发展。研究发现，即使在大规模公开课堂转录数据集（NCTE）上训练的前沿模型，也会因无关的社会背景信息（如教师经验、教育水平、人口统计特征等）导致预测结果偏离真实评分达1.48分（7分制），且更大模型有时反而更易受干扰。现有缓解策略（如提示工程和标准直接偏好优化DPO）效果有限。其解决方案的关键在于提出Debiasing-DPO——一种自监督训练方法，通过对比模型仅基于查询生成的中性推理与同时包含查询及虚假上下文时产生的偏见推理，构建去偏目标，并结合监督微调（Supervised Fine-Tuning, SFT）以维持或提升预测准确性。实验表明，该方法在Llama和Qwen系列模型上平均降低84%偏见并提升52%预测精度，验证了鲁棒性并非模型规模的自然产物，而需专门设计的训练机制来实现准确性和公平性的协同优化。
链接: https://arxiv.org/abs/2604.02585
作者: Hyunji Nam,Dorottya Demszky
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 26 pages
Abstract:LLMs are increasingly used for high-stakes decision-making, yet their sensitivity to spurious contextual information can introduce harmful biases. This is a critical concern when models are deployed for tasks like evaluating teachers’ instructional quality, where biased assessment can affect teachers’ professional development and career trajectories. We investigate model robustness to spurious social contexts using the largest publicly available dataset of U.S. classroom transcripts (NCTE) paired with expert rubric scores. Evaluating seven frontier and open-weight models across seven categories of spurious contexts – including teacher experience, education level, demographic identity, and sycophancy-inducing framings – we find that irrelevant contextual information can shift model predictions by up to 1.48 points on a 7-point scale, with larger models sometimes exhibiting greater sensitivity despite higher predictive accuracy. Mitigations using prompts and standard direct preference optimization (DPO) prove largely insufficient. We propose Debiasing-DPO, a self-supervised training method that pairs neutral reasoning generated from the query alone, with the model’s biased reasoning generated with both the query and additional spurious context. We further combine this objective with supervised fine-tuning on ground-truth labels to prevent losses in predictive accuracy. Applied to Llama 3B \ 8B and Qwen 3B \ 7B Instruct models, Debiasing-DPO reduces bias by 84% and improves predictive accuracy by 52% on average. Our findings from the educational case study highlight that robustness to spurious context is not a natural byproduct of model scaling and that our proposed method can yield substantial gains in both accuracy and robustness for prompt-based prediction tasks.
[NLP-52] Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models
【速读】： 该论文旨在解决离散扩散语言模型（Discrete Diffusion Language Models, dLLMs）在并行解码过程中因分布不匹配导致的生成质量下降问题。传统并行解码方法通过将联合条件概率近似为各位置边际概率的乘积，忽略了token间的强依赖关系，从而影响输出质量。解决方案的关键在于提出DEMASK（DEpendency-guided unMASKing），一个轻量级依赖预测器，可附加于dLLM的最终隐藏状态上，在一次前向传播中估计被掩码位置之间的成对条件影响；基于此预测，贪婪选择算法识别出累积依赖度受控的位置进行同时解码，理论上在子加性假设下可控制总变差距离，从而在保持生成质量的同时实现1.7–2.2倍的速度提升。
链接: https://arxiv.org/abs/2604.02560
作者: Liran Ringel,Ameen Ali,Yaniv Romano
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Discrete diffusion language models (dLLMs) accelerate text generation by unmasking multiple tokens in parallel. However, parallel decoding introduces a distributional mismatch: it approximates the joint conditional using a fully factorized product of per-token marginals, which degrades output quality when selected tokens are strongly dependent. We propose DEMASK (DEpendency-guided unMASKing), a lightweight dependency predictor that attaches to the final hidden states of a dLLM. In a single forward pass, it estimates pairwise conditional influences between masked positions. Using these predictions, a greedy selection algorithm identifies positions with bounded cumulative dependency for simultaneous unmasking. Under a sub-additivity assumption, we prove this bounds the total variation distance between our parallel sampling and the model’s joint. Empirically, DEMASK achieves 1.7-2.2 \times speedup on Dream-7B while matching or improving accuracy compared to confidence-based and KL-based baselines. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2604.02560 [cs.CL] (or arXiv:2604.02560v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2604.02560 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-53] PolyJarvis: LLM Agent for Autonomous Polymer MD Simulations
【速读】： 该论文旨在解决聚合物分子动力学（MD）模拟在实际应用中因流程复杂、依赖专家经验而难以普及的问题，特别是从分子结构到物理性质预测的全流程自动化难题。其解决方案的关键在于构建一个名为PolyJarvis的智能代理，该代理通过模型上下文协议（MCP）服务器将大语言模型（LLM）与RadonPy模拟平台相耦合，实现从自然语言输入（如聚合物名称或SMILES字符串）到聚合物属性预测的端到端自动化执行，包括单体构建、电荷分配、聚合反应、力场参数化、GPU加速平衡及性质计算等步骤。实验验证表明，该方法在密度、体积模量和玻璃化转变温度（Tg）等方面的结果与参考值或文献数据高度一致，证明了LLM驱动代理在聚合物MD工作流中的可行性与可靠性。
链接: https://arxiv.org/abs/2604.02537
作者: Alexander Zhao,Achuth Chandrasekhar,Amir Barati Farimani
机构: Carnegie Mellon University (卡内基梅隆大学); Carnegie Mellon University (卡内基梅隆大学); Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL); Materials Science (cond-mat.mtrl-sci)
备注:
Abstract:All-atom molecular dynamics (MD) simulations can predict polymer properties from molecular structure, yet their execution requires specialized expertise in force field selection, system construction, equilibration, and property extraction. We present PolyJarvis, an agent that couples a large language model (LLM) with the RadonPy simulation platform through Model Context Protocol (MCP) servers, enabling end-to-end polymer property prediction from natural language input. Given a polymer name or SMILES string, PolyJarvis autonomously executes monomer construction, charge assignment, polymerization, force field parameterization, GPU-accelerated equilibration, and property calculation. Validation is conducted on polyethylene (PE), atactic polystyrene (aPS), poly(methyl methacrylate) (PMMA), and poly(ethylene glycol) (PEG). Results show density predictions within 0.1–4.8% and bulk moduli within 17–24% of reference values for aPS and PMMA. PMMA glass transition temperature (Tg) (395~K) matches experiment within +10–18~K, while the remaining three polymers overestimate Tg by +38 to +47K (vs upper experimental bounds). Of the 8 property–polymer combinations with directly comparable experimental references, 5 meet strict acceptance criteria. For cases lacking suitable amorphous-phase experimental, agreement with prior MD literature is reported separately. The remaining Tg failures are attributable primarily to the intrinsic MD cooling-rate bias rather than agent error. This work demonstrates that LLM-driven agents can autonomously execute polymer MD workflows producing results consistent with expert-run simulations.
[NLP-54] Social Meaning in Large Language Models : Structure Magnitude and Prag matic Prompting
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在社会意义推理方面是否不仅在定性上接近人类，而且在定量上也能精确匹配人类行为的问题，以及如何通过基于语用理论的提示策略来提升这种逼近程度。其解决方案的关键在于引入两个校准导向的指标——效应量比（Effect Size Ratio, ESR）和校准偏差得分（Calibration Deviation Score, CDS），以区分结构保真度与幅度校准；同时基于两个语用假设设计提示条件：一是社会意义源于对语言替代项的推理，二是听者推断说话者的知识状态和交际动机。实验表明，引导模型考虑说话者知识和意图能最一致地减少幅度偏差，而强调替代意识则可能加剧夸张现象；结合两者是唯一能在所有模型中同时改善多个校准敏感指标的干预方式，尽管精细的幅度校准仍未能完全解决。
链接: https://arxiv.org/abs/2604.02512
作者: Roland Mühlenbernd
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) increasingly exhibit human-like patterns of pragmatic and social reasoning. This paper addresses two related questions: do LLMs approximate human social meaning not only qualitatively but also quantitatively, and can prompting strategies informed by pragmatic theory improve this approximation? To address the first, we introduce two calibration-focused metrics distinguishing structural fidelity from magnitude calibration: the Effect Size Ratio (ESR) and the Calibration Deviation Score (CDS). To address the second, we derive prompting conditions from two pragmatic assumptions: that social meaning arises from reasoning over linguistic alternatives, and that listeners infer speaker knowledge states and communicative motives. Applied to a case study on numerical (im)precision across three frontier LLMs, we find that all models reliably reproduce the qualitative structure of human social inferences but differ substantially in magnitude calibration. Prompting models to reason about speaker knowledge and motives most consistently reduces magnitude deviation, while prompting for alternative-awareness tends to amplify exaggeration. Combining both components is the only intervention that improves all calibration-sensitive metrics across all models, though fine-grained magnitude calibration remains only partially resolved. LLMs thus capture inferential structure while variably distorting inferential strength, and pragmatic theory provides a useful but incomplete handle for improving that approximation.
[NLP-55] VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors
【速读】： 该论文旨在解决视觉语言模型（Vision Language Models, VLMs）在需要细粒度视觉感知的任务中表现不佳的问题，尤其是在视觉对应关系（visual correspondence）等任务中，即使所需信息存在于模型内部表示中，VLMs 仍无法有效利用。其核心问题在于当前 VLM 的训练流程过于狭窄，仅关注将视觉信息映射到文本空间，导致模型只能对可被语言空间中的已知概念映射的视觉实体进行推理，从而限制了其在视觉主导任务上的能力。解决方案的关键在于打破这种对语言先验的依赖：一方面通过为未知视觉实体赋予任意名称可提升性能，但更有效的策略是采用任务特定微调（task-specific fine-tuning），使模型在不依赖语言标签的情况下实现更强的泛化能力。这表明当前 VLM 在视觉任务上的失败源于训练过程中习得的“捷径”（learned shortcuts），而非多模态架构本身的局限性。
链接: https://arxiv.org/abs/2604.02486
作者: Haz Sameen Shahgir,Xiaofu Chen,Yu Fu,Erfan Shayegani,Nael Abu-Ghazaleh,Yova Kementchedjhieva,Yue Dong
机构: University of California, Riverside (加州大学河滨分校); MBZUAI (穆罕默德·本·扎耶德人工智能大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Vision Language Models (VLMs) achieve impressive performance across a wide range of multimodal tasks. However, on some tasks that demand fine-grained visual perception, they often fail even when the required information is present in their internal representations. In this work, we demonstrate that this gap arises from their narrow training pipeline which focuses on moving visual information to the textual space. Consequently, VLMs can only reason about visual entities that can be mapped to known concepts in the language space, leaving vision-focused tasks such as visual correspondence and reasoning about novel visual entities poorly supported. As a result, VLMs are severely limited in several important multimodal capabilities because they rely on brittle, hallucinated textual descriptions of visual entities that they cannot map to textual representations. We verify this behavior through visual correspondence tasks, in which VLMs must detect matching entities between two images. Testing across semantic, shape, and face correspondence tasks, we find that VLMs perform much better when the relevant entities are nameable in language than when they are unnameable. Mechanistically, our Logit Lens analyses confirm that VLMs explicitly assign semantic labels to nameable entities and surface more unique corresponding tokens compared to unnameable entities. Furthermore, we show that teaching completely arbitrary names for unknown entities improves performance, yet task-specific finetuning yields even stronger generalization without relying on language priors. Our findings suggest that current VLM failures on visual tasks reflect learned shortcuts from their training, rather than a fundamental limitation of multimodal architectures.
[NLP-56] Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在假设探索过程中存在确认偏误（confirmation bias）的问题，即模型倾向于选择支持已有假设的证据而非尝试证伪，从而导致推理效率低下和规则发现能力受限。解决方案的关键在于引入针对人类设计的干预策略（如鼓励考虑反例的提示），通过提示工程显著降低LLMs的确认偏误，使规则发现成功率从平均42%提升至56%；进一步地，通过将此类干预行为蒸馏到模型中，实现了对新任务（如Blicket测试）的良好泛化效果，表明人类启发式干预可有效缓解LLMs在假设检验中的认知局限。
链接: https://arxiv.org/abs/2604.02485
作者: Ayush Rajesh Jhaveri,Anthony GX-Chen,Ilia Sucholutsky,Eunsol Choi
机构: New York University (纽约大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Confirmation bias, the tendency to seek evidence that supports rather than challenges one’s belief, hinders one’s reasoning ability. We examine whether large language models (LLMs) exhibit confirmation bias by adapting the rule-discovery study from human psychology: given a sequence of three numbers (a “triple”), an agent engages in an interactive feedback loop where it (1) proposes a new triple, (2) receives feedback on whether it satisfies the hidden rule, and (3) guesses the rule. Across eleven LLMs of multiple families and scales, we find that LLMs exhibit confirmation bias, often proposing triples to confirm their hypothesis rather than trying to falsify it. This leads to slower and less frequent discovery of the hidden rule. We further explore intervention strategies (e.g., encouraging the agent to consider counter examples) developed for humans. We find prompting LLMs with such instruction consistently decreases confirmation bias in LLMs, improving rule discovery rates from 42% to 56% on average. Lastly, we mitigate confirmation bias by distilling intervention-induced behavior into LLMs, showing promising generalization to a new task, the Blicket test. Our work shows that confirmation bias is a limitation of LLMs in hypothesis exploration, and that it can be mitigated via injecting interventions designed for humans.
[NLP-57] On the Geometric Structure of Layer Updates in Deep Language Models
【速读】： 该论文旨在解决深度语言模型中层间更新（layer updates）的几何结构问题，即探究表示从一层到另一层的变化机制，而非仅关注中间表征中编码的信息内容。其核心发现是：层更新可分解为一个主导的逐标记（tokenwise）分量与一个残差项（residual），其中全层更新几乎完全对齐于tokenwise分量，而残差项在几何上具有显著不同的特性——表现为更大的角度偏差、更低的投影能量，并非简单的微小修正。解决方案的关键在于提出了一种架构无关的几何分析框架，通过量化tokenwise约束模型下的近似误差与输出扰动之间的强相关性（Spearman相关系数常超过0.7，最大达0.95），揭示了函数层面重要的计算集中在这一几何分离的残差组件中，从而为理解现代语言模型的内部运作提供了新的视角和工具。
链接: https://arxiv.org/abs/2604.02459
作者: Jun-Sik Yoo
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 11 pages, 5 figures
Abstract:We study the geometric structure of layer updates in deep language models. Rather than analyzing what information is encoded in intermediate representations, we ask how representations change from one layer to the next. We show that layerwise updates admit a decomposition into a dominant tokenwise component and a residual that is not captured by restricted tokenwise function classes. Across multiple architectures, including Transformers and state-space models, we find that the full layer update is almost perfectly aligned with the tokenwise component, while the residual exhibits substantially weaker alignment, larger angular deviation, and significantly lower projection onto the dominant tokenwise subspace. This indicates that the residual is not merely a small correction, but a geometrically distinct component of the transformation. This geometric separation has functional consequences: approximation error under the restricted tokenwise model is strongly associated with output perturbation, with Spearman correlations often exceeding 0.7 and reaching up to 0.95 in larger models. Together, these results suggest that most layerwise updates behave like structured reparameterizations along a dominant direction, while functionally significant computation is concentrated in a geometrically distinct residual component. Our framework provides a simple, architecture-agnostic method for probing the geometric and functional structure of layer updates in modern language models. Comments: 11 pages, 5 figures Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2604.02459 [cs.LG] (or arXiv:2604.02459v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.02459 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jun-Sik Yoo [view email] [v1] Thu, 2 Apr 2026 18:44:34 UTC (206 KB)
[NLP-58] Skeleton-based Coherence Modeling in Narratives
【速读】： 该论文旨在解决文本连贯性（coherence）建模问题，即如何有效衡量一段文本中句子之间的逻辑一致性。其核心挑战在于，现有方法难以准确捕捉句子间的语义关联，尤其在检测不连贯结构或辅助作者修改文本时效果有限。论文提出了一种新的句子/骨架相似性网络（Sentence/Skeleton Similarity Network, SSN），通过提取句子骨架（skeleton）并计算其与后续句子的相似性来建模连贯性。关键创新在于利用神经网络学习句子与其骨架之间的匹配关系，相较于传统的余弦相似度和欧氏距离等基线方法，SSN显著提升了连贯性评估性能。然而，实验结果表明，基于完整句子的模型仍优于基于骨架的模型，说明当前最先进的连贯性建模技术更应聚焦于句子层面而非子成分分析。
链接: https://arxiv.org/abs/2604.02451
作者: Nishit Asnani,Rohan Badlani
机构: Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Modeling coherence in text has been a task that has excited NLP researchers since a long time. It has applications in detecting incoherent structures and helping the author fix them. There has been recent work in using neural networks to extract a skeleton from one sentence, and then use that skeleton to generate the next sentence for coherent narrative story generation. In this project, we aim to study if the consistency of skeletons across subsequent sentences is a good metric to characterize the coherence of a given body of text. We propose a new Sentence/Skeleton Similarity Network (SSN) for modeling coherence across pairs of sentences, and show that this network performs much better than baseline similarity techniques like cosine similarity and Euclidean distance. Although skeletons appear to be promising candidates for modeling coherence, our results show that sentence-level models outperform those on skeletons for evaluating textual coherence, thus indicating that the current state-of-the-art coherence modeling techniques are going in the right direction by dealing with sentences rather than their sub-parts.
[NLP-59] Do We Need Frontier Models to Verify Mathematical Proofs?
【速读】： 该论文旨在解决生成式 AI (Generative AI) 在自然语言数学证明验证中的可靠性问题，即如何使较小的开源模型在不牺牲准确性的情况下提升验证结果的一致性。其关键解决方案是通过LLM引导的提示（prompt）搜索，设计一组针对小模型特定失败模式的专用提示，并构建一个提示集成策略，从而显著提升小模型在准确性和自一致性（self-consistency）方面的表现，使其性能达到与前沿模型相当的水平。
链接: https://arxiv.org/abs/2604.02450
作者: Aaditya Naik,Guruprerana Shabadi,Rajeev Alur,Mayur Naik
机构: University of Pennsylvania (宾夕法尼亚大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 21 pages, 11 figures
Abstract:Advances in training, post-training, and inference-time methods have enabled frontier reasoning models to win gold medals in math competitions and settle challenging open problems. Gaining trust in the responses of these models requires that natural language proofs be checked for errors. LLM judges are increasingly being adopted to meet the growing demand for evaluating such proofs. While verification is considered easier than generation, what model capability does reliable verification actually require? We systematically evaluate four open-source and two frontier LLMs on datasets of human-graded natural language proofs of competition-level problems. We consider two key metrics: verifier accuracy and self-consistency (the rate of agreement across repeated judgments on the same proof). We observe that smaller open-source models are only up to ~10% behind frontier models in accuracy but they are up to ~25% more inconsistent. Furthermore, we see that verifier accuracy is sensitive to prompt choice across all models. We then demonstrate that the smaller models, in fact, do possess the mathematical capabilities to verify proofs at the level of frontier models, but they struggle to reliably elicit these capabilities with general judging prompts. Through an LLM-guided prompt search, we synthesize an ensemble of specialized prompts that overcome the specific failure modes of smaller models, boosting their performance by up to 9.1% in accuracy and 15.9% in self-consistency. These gains are realized across models and datasets, allowing models like Qwen3.5-35B to perform on par with frontier models such as Gemini 3.1 Pro for proof verification.
[NLP-60] SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy
【速读】： 该论文旨在解决大语言模型中存在的“谄媚倾向”（sycophancy）问题，即模型在生成内容时倾向于迎合用户表达的立场，而非基于事实或逻辑一致性进行回应。这一现象可能导致输出不可靠甚至误导性结论，尤其在高知识承诺场景下更为显著。解决方案的关键在于提出一种无监督的计算语言学度量方法SWAY，通过反事实提示机制量化模型在正向与负向语言压力下的态度偏移，从而分离出框架效应与内容相关性；在此基础上设计了一种基于反事实思维链（counterfactual CoT）的缓解策略，引导模型思考若假设相反前提时的答案，从而有效将谄媚倾向降至接近零，同时保持对真实证据的敏感性。
链接: https://arxiv.org/abs/2604.02423
作者: Joy Bhalla,Kristina Gligorić
机构: Johns Hopkins University (约翰霍普金斯大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Large language models exhibit sycophancy: the tendency to shift outputs toward user-expressed stances, regardless of correctness or consistency. While prior work has studied this issue and its impacts, rigorous computational linguistic metrics are needed to identify when models are being sycophantic. Here, we introduce SWAY, an unsupervised computational linguistic measure of sycophancy. We develop a counterfactual prompting mechanism to identify how much a model’s agreement shifts under positive versus negative linguistic pressure, isolating framing effects from content. Applying this metric to benchmark 6 models, we find that sycophancy increases with epistemic commitment. Leveraging our metric, we introduce a counterfactual mitigation strategy teaching models to consider what the answer would be if opposite assumptions were suggested. While baseline mitigation instructing to be explicitly anti-sycophantic yields moderate reductions, and can backfire, our counterfactual CoT mitigation drives sycophancy to near zero across models, commitment levels, and clause types, while not suppressing responsiveness to genuine evidence. Overall, we contribute a metric for benchmarking sycophancy and a mitigation informed by it.
[NLP-61] Internalized Reasoning for Long-Context Visual Document Understanding
【速读】： 该论文旨在解决长文档理解（long-document understanding）中缺乏推理能力的问题，尽管当前最优方法在企业、法律和科学等场景中表现良好，但未充分挖掘推理机制对性能提升的潜力。其关键解决方案是构建一种合成数据流水线，通过评分每页内容与问题的相关性、提取文本证据并按相关性排序生成思维链（Chain-of-Thought, CoT）轨迹，并利用监督微调（SFT）对这些轨迹进行训练，以控制令牌（\textttcot）触发推理过程，最终通过低强度模型融合将推理能力内化到模型中。该方法显著提升了Qwen3 VL 32B和Mistral Small 3.1 24B在MMLongBenchDoc和MMLBD-C上的性能，同时大幅减少推理时的输出token数量。
链接: https://arxiv.org/abs/2604.02371
作者: Austin Veselka
机构: LightOn
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 9 pages
Abstract:Visual long-document understanding is critical for enterprise, legal, and scientific applications, yet the best performing open recipes have not explored reasoning, a capability which has driven leaps in math and code performance. We introduce a synthetic data pipeline for reasoning in long-document understanding that generates thinking traces by scoring each page for question relevance, extracting textual evidence and ordering it from most to least relevant. We apply SFT to the resulting traces within \textttthink tags, gated by a \textttcot control token, and the resulting reasoning capability is internalized via low-strength model merging. We study Qwen3 VL 32B and Mistral Small 3.1 24B. With Qwen3 VL, we achieve 58.3 on MMLongBenchDoc, surpassing the 7 \times larger Qwen3 VL 235B A22B (57.0). With Mistral, we show that synthetic reasoning outperforms distillation from the Thinking version’s traces by 3.8 points on MMLBD-C, and internalized reasoning exhibits 12.4 \times fewer mean output tokens compared to explicit reasoning. We release our pipeline for reproducibility and further exploration.
[NLP-62] Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation
【速读】： 该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在评估其在复杂、开放式任务中是否具备真实专家级认知能力时所面临的瓶颈问题。现有评估框架普遍存在领域覆盖狭窄、依赖通用任务或存在自评偏差等局限，难以准确反映模型在专业场景下的实际表现。为应对这一挑战，作者提出XpertBench——一个高保真度的专业能力基准，涵盖80个类别共1,346项由领域专家（包括顶尖机构研究人员和具丰富临床/工业经验的从业者）精心设计的任务，覆盖金融、医疗、法律、教育及STEM与人文双轨科研领域，并采用细粒度评分规则（每任务含15–40个加权检查点）以保障专业严谨性。解决方案的关键在于引入ShotJudge评估范式，即通过少量专家示例微调LLM裁判模型，从而有效缓解自我奖励偏差并实现可扩展的人工对齐评估。实证结果揭示了当前主流LLM在专业任务中的显著“专家差距”（expert-gap），峰值成功率仅约66%，平均得分约为55%，且模型在定量推理与语言合成能力上呈现领域特异性差异，凸显了从通用助手向专业化协作伙伴演进的必要性。
链接: https://arxiv.org/abs/2604.02368
作者: Xue Liu,Xin Ma,Yuxin Ma,Yongchang Peng,Duo Wang,Zhoufutu Wen,Ge Zhang,Kaiyuan Zhang,Xinyu Chen,Tianci He,Jiani Hou,Liang Hu,Ziyun Huang,Yongzhe Hui,Jianpeng Jiao,Chennan Ju,Yingru Kong,Yiran Li,Mengyun Liu,Luyao Ma,Fei Ni,Yiqing Ni,Yueyan Qiu,Yanle Ren,Zilin Shi,Zaiyuan Wang,Wenjie Yue,Shiyu Zhang,Xinyi Zhang,Kaiwen Zhao,Zhenwei Zhu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:As Large Language Models (LLMs) exhibit plateauing performance on conventional benchmarks, a pivotal challenge persists: evaluating their proficiency in complex, open-ended tasks characterizing genuine expert-level cognition. Existing frameworks suffer from narrow domain coverage, reliance on generalist tasks, or self-evaluation biases. To bridge this gap, we present XpertBench, a high-fidelity benchmark engineered to assess LLMs across authentic professional domains. XpertBench consists of 1,346 meticulously curated tasks across 80 categories, spanning finance, healthcare, legal services, education, and dual-track research (STEM and Humanities). These tasks are derived from over 1,000 submissions by domain experts–including researchers from elite institutions and practitioners with extensive clinical or industrial experience–ensuring superior ecological validity. Each task uses detailed rubrics with mostly 15-40 weighted checkpoints to assess professional rigor. To facilitate scalable yet human-aligned assessment, we introduce ShotJudge, a novel evaluation paradigm that employs LLM judges calibrated with expert few-shot exemplars to mitigate self-rewarding biases. Our empirical evaluation of state-of-the-art LLMs reveals a pronounced performance ceiling: even leading models achieve a peak success rate of only ~66%, with a mean score around 55%. Models also exhibit domain-specific divergence, showing non-overlapping strengths in quantitative reasoning versus linguistic synthesis… These findings underscore a significant “expert-gap” in current AI systems and establish XpertBench as a critical instrument for navigating the transition from general-purpose assistants to specialized professional collaborators.
[NLP-63] Evaluating Small Language Models for Front-Door Routing: A Harmonized Benchmark and Synthetic-Traffic Experiment
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）推理阶段的路由问题（routing problem），即在满足输出质量、成本、延迟和治理约束等多目标条件下，动态选择最合适的模型进行推理。现有方法依赖于基于LLM的分类器或偏好训练的路由器，但这些方法本身具有高延迟和高成本，将复杂的多目标优化简化为单一维度的质量预测，限制了实际应用效率。论文提出的关键解决方案是利用小型语言模型（Small Language Models, SLMs，1-4B参数）实现低延迟（<1秒）、零边际成本且可自托管的任务分类，从而将路由决策对整体推理预算的影响降至可忽略水平。实证研究表明，Qwen-2.5-3B在多个基准测试中展现出最优的准确率与延迟权衡，并在自托管场景下达到帕累托最优（Pareto-dominant），验证了SLM作为高效路由机制的技术可行性，尽管仍存在约6–8个百分点的准确率差距及下游输出质量未被充分验证的问题，距离生产级部署尚有一步之遥。
链接: https://arxiv.org/abs/2604.02367
作者: Warren Johnson,Charles Lee
机构: Plexor Labs; Project Autobots
类目: Networking and Internet Architecture (cs.NI); Computation and Language (cs.CL)
备注: 23 pages, 1 figure, 9 tables. Article 8 in the TAAC Research Series. Code and data: this https URL
Abstract:Selecting the appropriate model at inference time – the routing problem – requires jointly optimizing output quality, cost, latency, and governance constraints. Existing approaches delegate this decision to LLM-based classifiers or preference-trained routers that are themselves costly and high-latency, reducing a multi-objective optimization to single-dimensional quality prediction. We argue that small language models (SLMs, 1-4B parameters) have now achieved sufficient reasoning capability for sub-second, zero-marginal-cost, self-hosted task classification, potentially making the routing decision negligible in the inference budget. We test this thesis on a six-label taxonomy through two studies. Study 1 is a harmonized offline benchmark of Phi-3.5-mini, Qwen2.5-1.5B, and Qwen-2.5-3B on identical Azure T4 hardware, serving stack, quantization, and a fixed 60-case corpus. Qwen-2.5-3B achieves the best exact-match accuracy (0.783), the strongest latency-accuracy tradeoff, and the only nonzero accuracy on all six task families. Study 2 is a pre-registered four-arm randomized experiment under synthetic traffic with an effective sample size of 60 unique cases per arm, comparing Phi-4-mini, Qwen-2.5-3B, and DeepSeek-V3 against a no-routing control. DeepSeek-V3 attains the highest accuracy (0.830) but fails the pre-registered P95 latency gate (2,295 ms); Qwen-2.5-3B is Pareto-dominant among self-hosted models (0.793 accuracy, 988 ms median, 0 marginal cost). No model meets the standalone viability criterion (=0.85 accuracy, =2,000 ms P95). The cost and latency prerequisites for SLM-based routing are met; the accuracy gap of 6-8 percentage points and the untested question of whether correct classification translates to downstream output quality bound the remaining distance to production viability.
[NLP-64] CIPHER: Conformer-based Inference of Phonemes from High-density EEG
【速读】： 该论文旨在解决从头皮脑电图（electroencephalography, EEG）中解码语音信息的难题，主要挑战在于信噪比（signal-to-noise ratio, SNR）低和空间模糊性高。其解决方案的核心是提出一种双路径模型 CIPHER（Conformer-based Inference of Phonemes from High-density EEG Representations），该模型同时利用事件相关电位（event-related potential, ERP）特征与宽带微分密度分析（broadband differential density analysis, DDA）系数作为输入表征，以提升对语音音素的识别能力。研究强调在严格控制混杂因素（如声学起始时间分离性和经颅磁刺激靶向阻断）的前提下进行性能评估，从而为未来EEG到语音的建模提供可比较的基准和特征有效性分析。
链接: https://arxiv.org/abs/2604.02362
作者: Varshith Madishetty
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:Decoding speech information from scalp EEG remains difficult due to low SNR and spatial blurring. We present CIPHER (Conformer-based Inference of Phonemes from High-density EEG Representations), a dual-pathway model using (i) ERP features and (ii) broadband DDA coefficients. On OpenNeuro ds006104 (24 participants, two studies with concurrent TMS), binary articulatory tasks reach near-ceiling performance but are highly confound-vulnerable (acoustic onset separability and TMS-target blocking). On the primary 11-class CVC phoneme task under full Study 2 LOSO (16 held-out subjects), performance is substantially lower (real-word WER: ERP 0.671 +/- 0.080, DDA 0.688 +/- 0.096, indicating limited fine-grained discriminability. We therefore position this work as a benchmark and feature-comparison study rather than an EEG-to-text system, and we constrain neural-representation claims to confound-controlled evidence.
[NLP-65] Using LLM -as-a-Judge/Jury to Advance Scalable Clinically-Validated Safety Evaluations of Model Responses to Users Demonstrating Psychosis ALT NEURIPS2025
【速读】： 该论文旨在解决生成式 AI（Generative AI）在心理健康支持场景中，特别是针对精神病患者群体时存在的安全风险评估难题。现有评估方法受限于临床验证不足和可扩展性差的问题，难以有效识别 LLM 可能强化妄想或幻觉等有害响应的风险。其解决方案的关键在于：首先基于临床专家意见构建七个安全评估标准；其次创建一个由人类共识标注的数据集以提供可靠基准；最后采用大语言模型作为评估者（LLM-as-a-Judge）或多个 LLM 的多数投票机制（LLM-as-a-Jury）进行自动化评估。实验表明，LLM-as-a-Judge 与人类共识高度一致（κ 值达 0.56–0.75），且单个最佳 LLM 判官略优于多模型投票机制，为实现临床基础、可扩展的 LLM 安全评估提供了可行路径。
链接: https://arxiv.org/abs/2604.02359
作者: May Lynn Reese,Markela Zeneli,Mindy Ng,Jacob Haimes,Andreea Damien,Elizabeth Stade
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: published at IASEAI 2026, preliminary work presented at GenAI4Health workshop at NeurIPS 2025
Abstract:General-purpose Large Language Models (LLMs) are becoming widely adopted by people for mental health support. Yet emerging evidence suggests there are significant risks associated with high-frequency use, particularly for individuals suffering from psychosis, as LLMs may reinforce delusions and hallucinations. Existing evaluations of LLMs in mental health contexts are limited by a lack of clinical validation and scalability of assessment. To address these issues, this research focuses on psychosis as a critical condition for LLM safety evaluation by (1) developing and validating seven clinician-informed safety criteria, (2) constructing a human-consensus dataset, and (3) testing automated assessment using an LLM as an evaluator (LLM-as-a-Judge) or taking the majority vote of several LLM judges (LLM-as-a-Jury). Results indicate that LLM-as-a-Judge aligns closely with the human consensus (Cohen’s \kappa_\texthuman \times \textgemini = 0.75 , \kappa_\texthuman \times \textqwen = 0.68 , \kappa_\texthuman \times \textkimi = 0.56 ) and that the best judge slightly outperforms LLM-as-a-Jury (Cohen’s \kappa_\texthuman \times \textjury = 0.74 ). Overall, these findings have promising implications for clinically grounded, scalable methods in LLM safety evaluations for mental health contexts.
[NLP-66] Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models
【速读】： 该论文旨在解决掩码扩散语言模型（Masked Diffusion Language Models, MDLMs）在生成过程中计算成本高昂的问题，其核心挑战在于每次采样需进行多次全序列去噪操作，且无法像自回归语言模型那样利用键值缓存（KV caching）来加速推理。解决方案的关键在于利用扩散框架的灵活性，提出一种基于模型调度（model scheduling）的策略：在去噪过程中的部分步骤中用较小的模型替代原大模型，从而显著降低浮点运算量（FLOPs）。研究发现，早期和晚期去噪步骤对模型替换具有较强鲁棒性，而中间步骤最为敏感，因此通过选择性地替换非关键步骤的模型，可在仅小幅增加生成困惑度（perplexity）的前提下实现最高达17%的FLOPs减少。
链接: https://arxiv.org/abs/2604.02340
作者: Ivan Sedykh,Nikita Sorokin,Valentin Malykh
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Recent advances in masked diffusion language models (MDLMs) narrow the quality gap to autoregressive LMs, but their sampling remains expensive because generation requires many full-sequence denoising passes with a large Transformer and, unlike autoregressive decoding, cannot benefit from KV caching. In this work, we exploit the flexibility of the diffusion framework and study model scheduling, where a smaller MDLM replaces the full model at a subset of denoising steps. On OpenWebText, we show that early and late denoising steps are substantially more robust to such replacement than middle steps, enabling up to a 17% reduction in FLOPs with only modest degradation in generative perplexity. We support these findings with a step-importance analysis based on loss and KL divergence between small and large models across timesteps, as well as an exhaustive search over coarse step segments, both of which identify the middle of the diffusion trajectory as most sensitive. Our results suggest that simple, architecture-agnostic scheduling rules can significantly accelerate MDLM sampling while largely preserving generation quality as measured by generative perplexity.
[NLP-67] SIEVE: Sample-Efficient Parametric Learning from Natural Language
【速读】： 该论文旨在解决如何从自然语言上下文（如指令、知识或反馈）中实现样本高效的参数化学习（parametric learning）问题，尤其在数据稀缺场景下提升语言模型性能。传统方法依赖高质量标注数据或自动化验证器，成本高且效率低；而SIEVE通过引入一种新颖的合成数据生成流程SIEVE-GEN，关键在于将上下文分解（context decomposable），从而仅用三组查询示例即可生成高质量推理轨迹：通过将合成查询与相关子集上下文配对，并结合上下文蒸馏（context distillation）机制，将有用信息内化至模型权重中。实验表明，SIEVE在需要上下文推理的任务（如RuleArena和One Book机器翻译）中显著优于现有上下文蒸馏方法，实现了真正意义上的样本高效参数化学习。
链接: https://arxiv.org/abs/2604.02339
作者: Parth Asawa,Alexandros G. Dimakis,Matei Zaharia
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Natural language context-such as instructions, knowledge, or feedback-contains rich signal for adapting language models. While in-context learning provides adaptation via the prompt, parametric learning persists into model weights and can improve performance further, though is data hungry and heavily relies on either high-quality traces or automated verifiers. We propose SIEVE, a method for sample-efficient parametric learning from natural language context that requires as few as three query examples. SIEVE uses a novel synthetic data generation pipeline, SIEVE-GEN, that leverages the insight that context is decomposable. Decomposing context allows us to generate higher quality rollouts by pairing synthetic queries with only the applicable context rather than the entirety, then using context distillation to internalize context into the model. We evaluate in reasoning settings where context is necessary, including custom domains and the RuleArena and Machine Translation from One Book tasks. Our results show that SIEVE outperforms prior context distillation methods using just three query examples, demonstrating how to achieve sample-efficient parametric learning from natural language.
[NLP-68] LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
【速读】： 该论文旨在解决多任务适应中基于专家混合（Mixture of Experts, MoE）的参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）方法存在的可扩展性问题：传统MoE-PEFT需为每个专家单独配置适配器（adapter），导致可训练参数随专家数量线性增长，限制了其在适配器架构中的应用。解决方案的关键在于提出LiME（Lightweight Mixture of Experts），其通过轻量级调制机制替代适配器复制——即使用单一共享PEFT模块，并利用轻量级专家向量对其输出进行调制，从而显著减少专家参数；同时引入零参数路由机制，借助冻结和已适配表示实现无需额外学习路由器参数的路由决策。理论证明表明，更多专家能保留更多任务相关信息，且调制机制可近似全专家特定PEFT并保证误差有界，实验进一步验证了LiME在保持性能的同时大幅降低参数量与训练时间。
链接: https://arxiv.org/abs/2604.02338
作者: Md Kowsher,Haris Mansoor,Nusrat Jahan Prottasha,Ozlem Garibay,Victor Zhu,Zhengping Ji,Chen Chen
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:MoE-PEFT methods combine Mixture of Experts with parameter-efficient fine-tuning for multi-task adaptation, but require separate adapters per expert causing trainable parameters to scale linearly with expert count and limiting applicability to adapter-based architectures. We propose LiME (Lightweight Mixture of Experts), which achieves expert specialization through lightweight modulation rather than adapter replication. Instead of separate adapters, LiME uses a single shared PEFT module and modulates its output with lightweight expert vectors, reducing expert parameters while generalizing to any PEFT method. Notably, LiME introduces zero-parameter routing by leveraging existing frozen and adapted representations eliminating learned router parameters typically required per layer. Theoretically, we prove that (i) more experts preserve more task-relevant information and (ii) modulation approximates full expert-specific PEFT with bounded error. LiME further incorporates n-gram windowed routing and adaptive expert selection (Auto Top-K) based on routing confidence. Experiments on MMT-47, a multimodal multi-task benchmark with 47 tasks spanning text, image, and video, demonstrate that LiME achieves competitive or superior performance while using up to 4x fewer trainable parameters and up to 29% faster training compared to corresponding MoE-PEFT baselines.
[NLP-69] Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structures
【速读】： 该论文旨在解决生成式语言模型中语义结构预测与文本生成质量之间的关系问题，特别是如何通过引入可解释的语义结构来提升模型性能。其核心挑战在于：若使用预测的语义结构作为辅助信号进行语言建模，需明确该结构表示的精度要求才能实现优于基线的效果。解决方案的关键在于设计一种简洁的词级语义结构二进制向量表示方法，并系统评估增量标签器（incremental tagger）所需达到的预测准确度下限；研究发现，语义向量维度可大幅压缩而不损失主要优势，且仅靠单一指标无法确定预测质量的理论下界，必须综合考虑信号与噪声的分布特性。
链接: https://arxiv.org/abs/2305.18915
作者: Jakob Prange,Emmanuele Chersoni
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: To appear at *SEM 2023, Toronto
Abstract:In this work we build upon negative results from an attempt at language modeling with predicted semantic structure, in order to establish empirical lower bounds on what could have made the attempt successful. More specifically, we design a concise binary vector representation of semantic structure at the lexical level and evaluate in-depth how good an incremental tagger needs to be in order to achieve better-than-baseline performance with an end-to-end semantic-bootstrapping language model. We envision such a system as consisting of a (pretrained) sequential-neural component and a hierarchical-symbolic component working together to generate text with low surprisal and high linguistic interpretability. We find that (a) dimensionality of the semantic vector representation can be dramatically reduced without losing its main advantages and (b) lower bounds on prediction quality cannot be established via a single score alone, but need to take the distributions of signal and noise into account.
[NLP-70] Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model ACL2023
【速读】： 该论文旨在解决中文学习者在英语介词理解能力上的变化机制问题，特别是在干预前后对两类测试任务的反应差异。研究通过结合贝叶斯模型（Bayesian models）与神经网络模型（neural models），分析了数据稀疏性高且学习者间差异显著的语料，以揭示学生能力、任务类型与句子刺激之间的关键交互作用。其解决方案的关键在于：首先利用贝叶斯方法有效处理小样本和个体差异问题，其次探索语言模型概率作为语法正确性和可学性预测因子的潜力，从而提升对二语习得过程的理解精度。
链接: https://arxiv.org/abs/2302.08150
作者: Jakob Prange,Man Ho Ivy Wong
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: To appear at ACL 2023, Toronto
Abstract:We use both Bayesian and neural models to dissect a data set of Chinese learners’ pre- and post-interventional responses to two tests measuring their understanding of English prepositions. The results mostly replicate previous findings from frequentist analyses and newly reveal crucial interactions between student ability, task type, and stimulus sentence. Given the sparsity of the data as well as high diversity among learners, the Bayesian method proves most useful; but we also see potential in using language model probabilities as predictors of grammaticality and learnability.
[NLP-71] Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling NAACL ATC NAACL2022
【速读】： 该论文旨在解决如何利用语言图结构（linguistic graph representations）来补充和提升神经语言建模（neural language modeling）性能的问题。其解决方案的关键在于构建一个集成框架，该框架结合预训练的Transformer模型与来自七种不同形式化体系（formalisms）的真值图结构（ground-truth graphs），并通过系统性比较发现：语义句法结构（semantic constituency structures）在提升语言建模效果方面表现最优，显著优于句法句法结构（syntactic constituency structures）以及句法和语义依存结构（syntactic and semantic dependency structures），且不同词类（part-of-speech class）的影响差异显著。这一结果揭示了神经符号语言建模（neuro-symbolic language modeling）的潜力，并为未来量化不同形式化体系的设计选择提供了方向。
链接: https://arxiv.org/abs/2112.07874
作者: Jakob Prange,Nathan Schneider,Lingpeng Kong
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to NAACL 2022 (slight typesetting divergences to NAACL camera-ready due to TexLive 2020/2021 mismatches)
Abstract:We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. With an ensemble setup consisting of a pretrained Transformer and ground-truth graphs from one of 7 different formalisms, we find that, overall, semantic constituency structures are most useful to language modeling performance – outpacing syntactic constituency structures as well as syntactic and semantic dependency structures. Further, effects vary greatly depending on part-of-speech class. In sum, our findings point to promising tendencies in neuro-symbolic language modeling and invite future research quantifying the design choices made by different formalisms.
[NLP-72] Speaker-Reason er: Scaling Interaction Turns and Reasoning Patterns for Timestamped Speaker-Attributed ASR
【速读】： 该论文旨在解决多说话人对话的转录与理解问题，其核心挑战包括重叠语音、回应对话（backchannel）、快速发言切换以及上下文窗口限制等。解决方案的关键在于提出一种端到端的语音大语言模型（Speech LLM）——Speaker-Reasoner，该模型通过代理式多轮时序推理机制实现迭代分析：首先全局解析音频结构，自主预测时间边界，并进行细粒度片段分析，同时联合建模说话人身份、性别、时间戳和转录文本；此外，引入说话人感知缓存（speaker-aware cache）以扩展对超出训练上下文窗口音频的处理能力。
链接: https://arxiv.org/abs/2604.03074
作者: Zhennan Lin,Shuai Wang,Zhaokai Sun,Pengyuan Xie,Chuan Xie,Jie Liu,Qiang Zhang,Lei Xie
机构: Northwestern Polytechnical University (西北工业大学); Nanjing University (南京大学); Shanghai Lingguang Zhaxian Technology (上海灵光扎线科技)
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)
备注:
Abstract:Transcribing and understanding multi-speaker conversations requires speech recognition, speaker attribution, and timestamp localization. While speech LLMs excel at single-speaker tasks, multi-speaker scenarios remain challenging due to overlapping speech, backchannels, rapid turn-taking, and context window constraints. We propose Speaker-Reasoner, an end-to-end Speech LLM with agentic multi-turn temporal reasoning. Instead of single-pass inference, the model iteratively analyzes global audio structure, autonomously predicts temporal boundaries, and performs fine-grained segment analysis, jointly modeling speaker identity, gender, timestamps, and transcription. A speaker-aware cache further extends processing to audio exceeding the training context window. Trained with a three-stage progressive strategy, Speaker-Reasoner achieves consistent improvements over strong baselines on AliMeeting and AISHELL-4 datasets, particularly in handling overlapping speech and complex turn-taking.
[NLP-73] Measuring What Cannot Be Surveyed: LLM s as Instruments for Latent Cognitive Variables in Labor Economics
【速读】： 该论文旨在解决如何将大语言模型（Large Language Models, LLMs）作为测量工具来量化难以观测的经济变量——特别是职业任务中认知内容的细粒度特征，这一问题在现有调查工具无法实现高精度刻画的情况下尤为突出。其解决方案的关键在于提出并验证了四个理论条件：语义外生性（semantic exogeneity）、构念相关性（construct relevance）、单调性（monotonicity）和模型不变性（model invariance），从而确保LLM生成的评分可作为有效的工具变量。作者基于此框架构建了增强型人力资本指数（Augmented Human Capital Index, AHC_o），利用Claude Haiku 4.5对18,796个O*NET任务陈述进行评分，并通过多种效度检验（如收敛效度r=0.85与Eloundou GPT-gamma、区分效度及主成分分析）证明其有效性，同时发现AI相关职业指标存在“增强”与“替代”两个独立维度，且LLM评分具有较高一致性（Pearson r=0.76，Krippendorff’s alpha=0.71）。该方法不仅适用于劳动经济学领域，还可推广至任何需要大规模语义内容量化的研究场景。
链接: https://arxiv.org/abs/2604.02403
作者: Cristian Espinal Maya
机构: Universidad EAFIT (埃菲特大学)
类目: Econometrics (econ.EM); Computation and Language (cs.CL); Methodology (stat.ME)
备注: Working paper. 13 pages, 7 figures, 6 references. Part of the Cognitive Factor Economics research program. Code: this https URL
Abstract:This paper establishes the theoretical and practical foundations for using Large Language Models (LLMs) as measurement instruments for latent economic variables – specifically variables that describe the cognitive content of occupational tasks at a level of granularity not achievable with existing survey instruments. I formalize four conditions under which LLM-generated scores constitute valid instruments: semantic exogeneity, construct relevance, monotonicity, and model invariance. I then apply this framework to the Augmented Human Capital Index (AHC_o), constructed from 18,796 O*NET task statements scored by Claude Haiku 4.5, and validated against six existing AI exposure indices. The index shows strong convergent validity (r = 0.85 with Eloundou GPT-gamma, r = 0.79 with Felten AIOE) and discriminant validity. Principal component analysis confirms that AI-related occupational measures span two distinct dimensions – augmentation and substitution. Inter-rater reliability across two LLM models (n = 3,666 paired scores) yields Pearson r = 0.76 and Krippendorff’s alpha = 0.71. Prompt sensitivity analysis across four alternative framings shows that task-level rankings are robust. Obviously Related Instrumental Variables (ORIV) estimation recovers coefficients 25% larger than OLS, consistent with classical measurement error attenuation. The methodology generalizes beyond labor economics to any domain where semantic content must be quantified at scale. Comments: Working paper. 13 pages, 7 figures, 6 references. Part of the Cognitive Factor Economics research program. Code: this https URL Subjects: Econometrics (econ.EM); Computation and Language (cs.CL); Methodology (stat.ME) Cite as: arXiv:2604.02403 [econ.EM] (or arXiv:2604.02403v1 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2604.02403 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
信息检索
[IR-0] PRISM: LLM -Guided Semantic Clustering for High-Precision Topics WWW26
【速读】：该论文旨在解决现有主题建模方法在处理细粒度语义区分时面临的难题，即如何在保持低计算成本和高可解释性的同时，利用大语言模型（Large Language Models, LLMs）提供的丰富语义表示能力来提升局部主题的分离效果。其解决方案的关键在于提出了一种名为Precision-Informed Semantic Modeling (PRISM) 的结构化主题建模框架：通过少量LLM标注样本对句子编码模型进行微调，并结合阈值聚类策略对嵌入空间进行分割，从而在窄域内生成语义紧密且区分清晰的主题簇。该方法仅需少量LLM查询即可实现优于当前最优局部主题模型甚至大型前沿嵌入模型的聚类性能，同时具备轻量级、可本地部署及高可解释性的优势。
链接: https://arxiv.org/abs/2604.03180
作者: Connor Douglas,Utkucan Balci,Joseph Aylett-Bullock
机构: New York University (纽约大学); Binghamton University (宾汉姆顿大学); United Nations (联合国)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Information Retrieval (cs.IR); Social and Information Networks (cs.SI)
备注: To appear in Proceedings of the ACM Web Conference 2026 (WWW 26)
Abstract:In this paper, we propose Precision-Informed Semantic Modeling (PRISM), a structured topic modeling framework combining the benefits of rich representations captured by LLMs with the low cost and interpretability of latent semantic clustering methods. PRISM fine-tunes a sentence encoding model using a sparse set of LLM- provided labels on samples drawn from some corpus of interest. We segment this embedding space with thresholded clustering, yielding clusters that separate closely related topics within some narrow domain. Across multiple corpora, PRISM improves topic separability over state-of-the-art local topic models and even over clustering on large, frontier embedding models while requiring only a small number of LLM queries to train. This work contributes to several research streams by providing (i) a student-teacher pipeline to distill sparse LLM supervision into a lightweight model for topic discovery; (ii) an analysis of the efficacy of sampling strategies to improve local geometry for cluster separability; and (iii) an effective approach for web-scale text analysis, enabling researchers and practitioners to track nuanced claims and subtopics online with an interpretable, locally deployable framework.
[IR-1] User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation
【速读】：该论文旨在解决多模态推荐（Multi-modal Recommendation, MMR）中因内容模态与用户偏好之间对齐不充分而导致的性能瓶颈问题。现有方法通常假设所有用户对物品内容的感知一致，且仅通过分离模态不变的偏好信号与模态特异的噪声来建模，忽视了用户条件下的偏好差异以及多模态间高阶依赖关系。其解决方案的关键在于提出一种基于条件生成总相关性学习（Generative Total Correlation, GTC）的框架：首先利用交互引导的扩散模型实现用户感知的内容特征过滤，保留个性化相关的特征；其次优化所有模态下项目表示的总相关性的可计算下界，以捕获跨模态的完整依赖结构。实验表明，GTC在标准MRR基准上显著优于现有最优方法，NDCG@5提升最高达28.30%，验证了该方法在建模用户条件关系上的有效性。
链接: https://arxiv.org/abs/2604.03014
作者: Jing Du,Zesheng Ye,Congbo Ma,Feng Liu,Flora. D. Salim
机构: The University of New South Wales (新南威尔士大学); University of Melbourne (墨尔本大学); New York University Abu Dhabi (纽约大学阿布扎比分校)
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 11 pages, 7 figures, 3 tables
Abstract:Multi-modal recommendation (MMR) enriches item representations by introducing item content, e.g., visual and textual descriptions, to improve upon interaction-only recommenders. The success of MMR hinges on aligning these content modalities with user preferences derived from interaction data, yet dominant practices based on disentangling modality-invariant preference-driving signals from modality-specific preference-irrelevant noises are flawed. First, they assume a one-size-fits-all relevance of item content to user preferences for all users, which contradicts the user-conditional fact of preferences. Second, they optimize pairwise contrastive losses separately toward cross-modal alignment, systematically ignoring higher-order dependencies inherent when multiple content modalities jointly influence user choices. In this paper, we introduce GTC, a conditional Generative Total Correlation learning framework. We employ an interaction-guided diffusion model to perform user-aware content feature filtering, preserving only personalized features relevant to each individual user. Furthermore, to capture complete cross-modal dependencies, we optimize a tractable lower bound of the total correlation of item representations across all modalities. Experiments on standard MMR benchmarks show GTC consistently outperforms state-of-the-art, with gains of up to 28.30% in NDCG@5. Ablation studies validate both conditional preference-driven feature filtering and total correlation optimization, confirming the ability of GTC to model user-conditional relationships in MMR tasks. The code is available at: this https URL.
[IR-2] Self-Optimizing Multi-Agent Systems for Deep Research ECIR2026
【速读】：该论文旨在解决当前多智能体深度研究（Deep Research）系统依赖手工设计提示（hand-engineered prompts）和静态架构所带来的改进困难、成本高且耗时的问题。其解决方案的关键在于引入多智能体优化方法，使智能体能够通过自对弈（self-play）和探索不同的提示组合来自我迭代与优化，从而构建出质量媲美甚至超越专家手工设计提示的深度研究系统。
链接: https://arxiv.org/abs/2604.02988
作者: Arthur Câmara,Vincent Slot,Jakub Zavrel
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Accepted at the Workshop on Conversational Search for Complex Information Needs at ECIR 2026
Abstract:Given a user’s complex information need, a multi-agent Deep Research system iteratively plans, retrieves, and synthesizes evidence across hundreds of documents to produce a high-quality answer. In one possible architecture, an orchestrator agent coordinates the process, while parallel worker agents execute tasks. Current Deep Research systems, however, often rely on hand-engineered prompts and static architectures, making improvement brittle, expensive, and time-consuming. We therefore explore various multi-agent optimization methods to show that enabling agents to self-play and explore different prompt combinations can produce high-quality Deep Research systems that match or outperform expert-crafted prompts.
[IR-3] Prompt Compression in the Wild: Measuring Latency Rate Adherence and Quality for Faster LLM Inference ECIR2026
【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）系统中因长上下文提示导致的推理延迟问题，即大型语言模型（Large Language Models, LLMs）在处理高长度提示时带来的计算开销。其解决方案的关键在于系统性地评估提示压缩（Prompt Compression）技术对端到端延迟、输出质量与内存占用的影响，并识别出压缩带来的加速收益与预处理开销之间的平衡点。研究发现，当提示长度、压缩比和硬件能力匹配良好时，LLMLingua 方法可实现最高达18%的端到端速度提升，且在摘要、代码生成和问答任务中保持输出质量稳定；同时，有效压缩还能显著降低显存使用，使数据中心GPU负载可迁移至消费级显卡，仅增加0.3秒延迟。论文还开源了性能分析工具，用于预测不同模型-硬件组合下的延迟盈亏平衡点，从而为实际部署提供量化决策依据。
链接: https://arxiv.org/abs/2604.02985
作者: Cornelius Kummer,Lena Jurkschat,Michael Färber,Sahar Vahdati
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at ECIR 2026 (Full Paper)
Abstract:With the wide adoption of language models for IR – and specifically RAG systems – the latency of the underlying LLM becomes a crucial bottleneck, since the long contexts of retrieved passages lead large prompts and therefore, compute increase. Prompt compression, which reduces the size of input prompts while aiming to preserve performance on downstream tasks, has established itself as a cost-effective and low-latency method for accelerating inference in large language models. However, its usefulness depends on whether the additional preprocessing time during generation is offset by faster decoding. We present the first systematic, large-scale study of this trade-off, with thousands of runs and 30,000 queries across several open-source LLMs and three GPU classes. Our evaluation separates compression overhead from decoding latency while tracking output quality and memory usage. LLMLingua achieves up to 18% end-to-end speed-ups, when prompt length, compression ratio, and hardware capacity are well matched, with response quality remaining statistically unchanged across summarization, code generation, and question answering tasks. Outside this operating window, however, the compression step dominates and cancels out the gains. We also show that effective compression can reduce memory usage enough to offload workloads from data center GPUs to commodity cards, with only a 0.3s increase in latency. Our open-source profiler predicts the latency break-even point for each model-hardware setup, providing practical guidance on when prompt compression delivers real-world benefits.
[IR-4] Bilateral Intent-Enhanced Sequential Recommendation with Embedding Perturbation-Based Contrastive Learning
【速读】：该论文旨在解决推荐系统中用户偏好动态演化建模的难题，特别是现有方法难以有效利用跨用户与物品共享的集体意图信号（collective intent signals），导致信息孤立和鲁棒性不足的问题。其解决方案的关键在于提出BIPCL框架——一种端到端的双边意图增强嵌入扰动对比学习方法。该框架通过双向意图增强机制，将用户侧和物品侧的共享意图原型（shared intent prototypes）融入item和序列表示中，从而缓解信息隔离并提升稀疏监督下的鲁棒性；同时，通过在结构化item嵌入中注入有界且方向感知的扰动来构建语义一致且判别性强的对比视图，并进一步实现交互级与意图级表示的多层级对比对齐，显著提升了推荐性能。
链接: https://arxiv.org/abs/2604.02833
作者: Shanfan Zhang,Yongyi Lin,Yuan Rao
机构: Xi’an Jiaotong University (西安交通大学)
类目: Information Retrieval (cs.IR)
备注: 13 pages, 8 figures
Abstract:Accurately modeling users’ evolving preferences from sequential interactions remains a central challenge in recommender systems. Recent studies emphasize the importance of capturing multiple latent intents underlying user behaviors. However, existing methods often fail to effectively exploit collective intent signals shared across users and items, leading to information isolation and limited robustness. Meanwhile, current contrastive learning approaches struggle to construct views that are both semantically consistent and sufficiently discriminative. In this work, we propose BIPCL, an end-to-end Bilateral Intent-enhanced, Embedding Perturbation-based Contrastive Learning framework. BIPCL explicitly integrates multi-intent signals into both item and sequence representations via a bilateral intent-enhancement mechanism. Specifically, shared intent prototypes on the user and item sides capture collective intent semantics distilled from behaviorally similar entities, which are subsequently integrated into representation learning. This design alleviates information isolation and improves robustness under sparse supervision. To construct effective contrastive views without disrupting temporal or structural dependencies, BIPCL injects bounded, direction-aware perturbations directly into structural item embeddings. On this basis, BIPCL further enforces multi-level contrastive alignment across interaction- and intent-level representations. Extensive experiments on benchmark datasets demonstrate that BIPCL consistently outperforms state-of-the-art baselines, with ablation studies confirming the contribution of each component.
[IR-5] AnnoRetrieve: Efficient Structured Retrieval for Unstructured Document Analysis
【速读】：该论文旨在解决当前基于嵌入（embedding）的向量搜索在处理非结构化文档时存在的两个核心问题：一是语义相似度匹配粒度粗，导致检索精度不足；二是频繁调用大语言模型（LLM）进行后处理，带来高昂计算成本。其解决方案的关键在于提出AnnoRetrieve这一新型检索范式，通过将检索机制从嵌入向量转向结构化标注（structured annotations），实现精准、低开销的语义检索。系统的核心创新包括SchemaBoot模块，可自动挖掘多粒度模式并优化约束条件生成文档标注schema，无需人工设计；以及Structured Semantic Retrieval（SSR）引擎，将语义理解与结构化查询执行融合，在不依赖LLM干预的前提下完成属性值提取、表格生成和渐进式SQL推理，从而显著降低LLM调用频率与整体成本，同时保持高检索精度。
链接: https://arxiv.org/abs/2604.02690
作者: Teng Lin,Yuyu Luo,Nan Tang
机构: The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学（广州)
类目: Information Retrieval (cs.IR)
备注:
Abstract:Unstructured documents dominate enterprise and web data, but their lack of explicit organization hinders precise information retrieval. Current mainstream retrieval methods, especially embedding-based vector search, rely on coarse-grained semantic similarity, incurring high computational cost and frequent LLM calls for post-processing. To address this critical issue, we propose AnnoRetrieve, a novel retrieval paradigm that shifts from embeddings to structured annotations, enabling precise, annotation-driven semantic retrieval. Our system replaces expensive vector comparisons with lightweight structured queries over automatically induced schemas, dramatically reducing LLM usage and overall cost. The system integrates two synergistic core innovations: SchemaBoot, which automatically generates document annotation schemas via multi-granularity pattern discovery and constraint-based optimization, laying a foundation for annotation-driven retrieval and eliminating manual schema design, and Structured Semantic Retrieval (SSR), the core retrieval engine, which unifies semantic understanding with structured query execution; by leveraging the annotated structure instead of vector embeddings, SSR achieves precise semantic matching, seamlessly completing attribute-value extraction, table generation, and progressive SQL-based reasoning without relying on LLM interventions. This annotation-driven paradigm overcomes the limitations of traditional vector-based methods with coarse-grained matching and heavy LLM dependency and graph-based methods with high computational overhead. Experiments on three real-world datasets confirm that AnnoRetrieve significantly lowers LLM call frequency and retrieval cost while maintaining high accuracy. AnnoRetrieve establishes a new paradigm for cost-effective, precise, and scalable document analysis through intelligent structuring.
[IR-6] MBGR: Multi-Business Prediction for Generative Recommendation at Meituan
【速读】：该论文旨在解决生成式推荐（Generative Recommendation, GR）在多业务场景下存在的两个关键问题：一是由于Next Token Prediction (NTP)框架难以捕捉跨业务的复杂行为模式，导致出现“跷跷板现象”（seesaw phenomenon）；二是统一的Semantic ID (SID)空间引发表示混淆（representation confusion），无法区分不同业务间的语义信息。解决方案的关键在于提出Multi-Business Generative Recommendation (MBGR)框架，其核心创新包括：(1) 设计业务感知的Semantic ID（Business-aware Semantic ID, BID）模块，通过领域感知的分词机制保持语义完整性；(2) 引入多业务预测（Multi-Business Prediction, MBP）结构，实现业务特定的预测能力；(3) 提出标签动态路由（Label Dynamic Routing, LDR）模块，将稀疏的多业务标签转化为稠密标签，显著提升多业务生成能力。
链接: https://arxiv.org/abs/2604.02684
作者: Changhao Li,Junwei Yin,Zhilin Zeng,Senjie Kou,Shuli Wang,Wenshuai Chen,Yinhua Zhu,Haitao Wang,Xingxing Wang
机构: Meituan(美团)
类目: Information Retrieval (cs.IR)
备注:
Abstract:Generative recommendation (GR) has recently emerged as a promising paradigm for industrial recommendations. GR leverages Semantic IDs (SIDs) to reduce the encoding-decoding space and employs the Next Token Prediction (NTP) framework to explore scaling laws. However, existing GR methods suffer from two critical issues: (1) a \textbfseesaw phenomenon in multi-business scenarios arises due to NTP’s inability to capture complex cross-business behavioral patterns; and (2) a unified SID space causes \textbfrepresentation confusion by failing to distinguish distinct semantic information across businesses. To address these issues, we propose Multi-Business Generative Recommendation (MBGR), the first GR framework tailored for multi-business scenarios. Our framework comprises three key components. First, we design a Business-aware semantic ID (BID) module that preserves semantic integrity via domain-aware tokenization. Then, we introduce a Multi-Business Prediction (MBP) structure to provide business-specific prediction capabilities. Furthermore, we develop a Label Dynamic Routing (LDR) module that transforms sparse multi-business labels into dense labels to further enhance the multi-business generation capability. Extensive offline and online experiments on Meituan’s food delivery platform validate MBGR’s effectiveness, and we have successfully deployed it in production.
[IR-7] AutoVerifier: An Agent ic Automated Verification Framework Using Large Language Models
【速读】：该论文旨在解决科学与技术情报（Scientific and Technical Intelligence, STI）分析中技术主张验证的难题，即现有方法难以跨越表层准确性与深层方法学有效性之间的验证鸿沟。其解决方案的关键在于提出AutoVerifier——一个基于大语言模型（Large Language Model, LLM）的代理式框架，通过将技术主张结构化为三元组（Subject, Predicate, Object），构建知识图谱以支持六层逐步增强的结构化推理流程：语料构建与摄入、实体与主张提取、文档内验证、跨源验证、外部信号佐证及最终假设矩阵生成。该框架无需领域专业知识即可实现端到端的技术主张验证，实证表明其能有效识别夸大陈述、指标不一致、跨源矛盾及未披露的利益冲突，从而将原始技术文档转化为可追溯、证据支撑的情报评估。
链接: https://arxiv.org/abs/2604.02617
作者: Yuntao Du,Minh Dinh,Kaiyuan Zhang,Ninghui Li
机构: Purdue University (普渡大学)
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Information Retrieval (cs.IR); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
备注: Winner of 2025-2026 Radiance Technologies Innovation Bowl
Abstract:Scientific and Technical Intelligence (STI) analysis requires verifying complex technical claims across rapidly growing literature, where existing approaches fail to bridge the verification gap between surface-level accuracy and deeper methodological validity. We present AutoVerifier, an LLM-based agentic framework that automates end-to-end verification of technical claims without requiring domain expertise. AutoVerifier decomposes every technical assertion into structured claim triples of the form (Subject, Predicate, Object), constructing knowledge graphs that enable structured reasoning across six progressively enriching layers: corpus construction and ingestion, entity and claim extraction, intra-document verification, cross-source verification, external signal corroboration, and final hypothesis matrix generation. We demonstrate AutoVerifier on a contested quantum computing claim, where the framework, operated by analysts with no quantum expertise, automatically identified overclaims and metric inconsistencies within the target paper, traced cross-source contradictions, uncovered undisclosed commercial conflicts of interest, and produced a final assessment. These results show that structured LLM verification can reliably evaluate the validity and maturity of emerging technologies, turning raw technical documents into traceable, evidence-backed intelligence assessments.
[IR-8] Principled and Scalable Diversity-Aware Retrieval via Cardinality-Constrained Binary Quadratic Programming
【速读】：该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）中多样性感知检索（Diversity-aware Retrieval）的问题，即如何在保证检索结果相关性的同时提升语义多样性，且现有方法缺乏理论保障并随检索条数 $ k $ 增大而面临可扩展性瓶颈。解决方案的关键在于将多样性检索形式化为一个基数约束的二元二次规划问题（Cardinality-Constrained Binary Quadratic Programming, CCBQP），通过一个可解释的权衡参数显式平衡相关性与多样性；同时，受组合优化最新进展启发，提出一种非凸紧致连续松弛方法，并设计基于Frank-Wolfe算法的优化框架，辅以景观分析和收敛性保证，从而在相关性-多样性帕累托前沿上持续优于基线方法，并实现显著加速。
链接: https://arxiv.org/abs/2604.02554
作者: Qiheng Lu,Nicholas D. Sidiropoulos
机构: University of Virginia (弗吉尼亚大学)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Diversity-aware retrieval is essential for Retrieval-Augmented Generation (RAG), yet existing methods lack theoretical guarantees and face scalability issues as the number of retrieved passages k increases. We propose a principled formulation of diversity retrieval as a cardinality-constrained binary quadratic programming (CCBQP), which explicitly balances relevance and semantic diversity through an interpretable trade-off parameter. Inspired by recent advances in combinatorial optimization, we develop a non-convex tight continuous relaxation and a Frank–Wolfe based algorithm with landscape analysis and convergence guarantees. Extensive experiments demonstrate that our method consistently dominates baselines on the relevance-diversity Pareto frontier, while achieving significant speedup.
[IR-9] Synapse: Evolving Job-Person Fit with Explainable Two-phase Retrieval and LLM -guided Genetic Resume Optimization
【速读】：该论文旨在解决现代招聘平台中存在的严重信息不对称问题：求职者需在海量且动态变化的职位信息中筛选，而雇主则面临高数量、低相关性的申请人池。现有招聘推荐系统多依赖关键词匹配或单阶段语义检索，在真实场景下的规模与成本约束下难以捕捉候选人经历与岗位要求之间的细粒度对齐。解决方案的关键在于提出一个两阶段的语义招聘系统 Synapse，其核心创新包括：(1) 将高召回率的候选生成与高精度的语义重排序分离，结合 FAISS 实现高效稠密检索，并融合对比学习与大语言模型（Large Language Model, LLM）推理构建集成模型；(2) 引入基于微分进化（Differential Evolution）的简历优化框架，将简历优化视为黑盒优化问题，利用 LLM 指导的变异算子迭代调整候选人表征以提升与筛选目标的一致性，无需标注数据即可实现推荐分数的持续提升。
链接: https://arxiv.org/abs/2604.02539
作者: Ansel Kaplan Erol,Seohee Yoon,Keenan Hom,Xisheng Zhang
机构: Georgia Institute of Technology (佐治亚理工学院)
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Modern recruitment platforms operate under severe information imbalance: job seekers must search over massive, rapidly changing collections of postings, while employers are overwhelmed by high-volume, low-relevance applicant pools. Existing recruitment recommender systems typically rely on keyword matching or single-stage semantic retrieval, which struggle to capture fine-grained alignment between candidate experience and job requirements under real-world scale and cost constraints. We present Synapse, a multi-stage semantic recruitment system that separates high-recall candidate generation from high-precision semantic reranking, combining efficient dense retrieval using FAISS with an ensemble of contrastive learning and Large Language Model (LLM) reasoning. To improve transparency, Synapse incorporates a retrieval-augmented explanation layer that grounds recommendations in explicit evidence. Beyond retrieval, we introduce a novel evolutionary resume optimization framework that treats resume refinement as a black-box optimization problem. Using Differential Evolution with LLM-guided mutation operators, the system iteratively modifies candidate representations to improve alignment with screening objectives, without any labeled data. Evaluation shows that the proposed ensemble improves nDCG@10 by 22% over embedding-only retrieval baselines, while the evolutionary optimization loop consistently yields monotonic improvements in recommender scores, exceeding 60% relative gain across evaluated profiles. We plan to release code and data upon publication.
[IR-10] SelRoute: Query-Type-Aware Routing for Long-Term Conversational Memory Retrieval
【速读】：该论文旨在解决长时对话记忆中相关历史交互检索效率与精度不足的问题，传统方法依赖大规模密集检索模型（110M–1.5B参数）或大语言模型（LLM）增强索引，存在计算开销高、部署复杂等问题。其解决方案的关键在于提出SelRoute框架，通过基于查询类型（query type）的动态路由机制，将每个查询自动分配至最优的专用检索流水线——包括词法（lexical）、语义（semantic）、混合（hybrid）或词汇增强（vocabulary-enriched）——从而在不使用GPU或LLM推理的前提下实现高性能检索：在LongMemEval_M基准上，使用bge-base-en-v1.5（109M参数）时Recall@5达0.800，显著优于Contriever（0.762），且系统具备良好的泛化能力与路由稳定性。
链接: https://arxiv.org/abs/2604.02431
作者: Matthew McKee
机构: Independent Researcher (独立研究员)
类目: Information Retrieval (cs.IR)
备注: 12 pages, 12 tables, 3 appendices
Abstract:Retrieving relevant past interactions from long-term conversational memory typically relies on large dense retrieval models (110M-1.5B parameters) or LLM-augmented indexing. We introduce SelRoute, a framework that routes each query to a specialized retrieval pipeline – lexical, semantic, hybrid, or vocabulary-enriched – based on its query type. On LongMemEval_M (Wu et al., 2024), SelRoute achieves Recall@5 of 0.800 with bge-base-en-v1.5 (109M parameters) and 0.786 with bge-small-en-v1.5 (33M parameters), compared to 0.762 for Contriever with LLM-generated fact keys. A zero-ML baseline using SQLite FTS5 alone achieves NDCG@5 of 0.692, already exceeding all published baselines on ranking quality – a gap we attribute partly to implementation differences in lexical retrieval. Five-fold stratified cross-validation confirms routing stability (CV gap of 1.3-2.4 Recall@5 points; routes stable for 4/6 query types across folds). A regex-based query-type classifier achieves 83% effective routing accuracy, and end-to-end retrieval with predicted types (Recall@5 = 0.689) still outperforms uniform baselines. Cross-benchmark evaluation on 8 additional benchmarks spanning 62,000+ instances – including MSDialog, LoCoMo, QReCC, and PerLTQA – confirms generalization without benchmark-specific tuning, while exposing a clear failure mode on reasoning-intensive retrieval (RECOR Recall@5 = 0.149) that bounds the claim. We also identify an enrichment-embedding asymmetry: vocabulary expansion at storage time improves lexical search but degrades embedding search, motivating per-pipeline enrichment decisions. The full system requires no GPU and no LLM inference at query time.
人机交互
[HC-0] Help Converts Newcomers Not Veterans: Generalized Reciprocity and Platform Engagement on Stack Overflow
【速读】：该论文旨在解决在线知识共享平台中普遍假设的“广义互惠（generalized reciprocity）”机制缺乏可靠实证证据的问题。现有研究受限于问卷自报数据难以区分互惠与其他亲社会动机，或观察性设计混淆了互惠与用户基础活跃度，导致估计结果存在上偏偏差。为克服这些挑战，作者提出一种基于匹配的双重差分生存分析方法（matched difference-in-differences survival analysis），充分利用Stack Overflow平台上求助与助人行为的时间结构，通过Cox比例风险模型对超过2100万条问题数据进行分析。其解决方案的关键在于利用平台行为的时间序列特性来识别因果效应，从而准确分离出广义互惠的真实影响，并揭示该机制主要在新用户阶段发挥作用，且受回应时间非线性调节。
链接: https://arxiv.org/abs/2604.03209
作者: Lenard Strahringer,Sven Eric Prüß,Kai Riemer
机构: Stanford University (斯坦福大学); University of Sydney (悉尼大学); University of Münster (明斯特大学)
类目: ocial and Information Networks (cs.SI); Human-Computer Interaction (cs.HC); General Economics (econ.GN)
备注: 39 pages, 9 figures, 6 tables. Working paper
Abstract:Generalized reciprocity – the tendency to help others after receiving help oneself – is widely theorized as a mechanism sustaining cooperation on online knowledge-sharing platforms. Yet robust empirical evidence from field settings remains surprisingly scarce. Prior studies relying on survey self-reports struggle to distinguish reciprocity from other prosocial motives, while observational designs confound reciprocity with baseline user activity, producing upward-biased estimates. We address these empirical challenges by developing a matched difference-in-differences survival analysis that leverages the temporal structure of help-seeking and help-giving on Stack Overflow. Using Cox proportional hazards models on over 21 million questions, we find that receiving an answer significantly increases a user’s propensity to help others, but this effect is concentrated among newcomers and declines with platform experience. This pattern suggests that reciprocity functions primarily as a contributor-recruitment mechanism, operating before platform-specific incentives such as reputation and status displace the general moral impulse to reciprocate. Response time moderates the effect, but non-linearly: reciprocity peaks for answers arriving within a re-engagement window of roughly thirty to sixty minutes. These findings contribute to the theory of generalized reciprocity and have implications for platform design.
[HC-1] CASCADE: A Cascading Architecture for Social Coordination with Controllable Emergence at Low Cost
【速读】：该论文旨在解决沙盒类游戏世界中构建可扩展且可信的游戏社会所面临的难题：如何在保持创作者控制力的同时，降低计算成本。现有基于脚本的NPC系统虽具高效性但行为僵化，而完全依赖大语言模型（Large Language Models, LLMs）驱动的代理则虽能产生更丰富的社会行为，却带来显著的运行时开销。其解决方案的关键在于提出CASCADE架构——一种三层分层设计：第一层宏观状态导演（Macro State Director）维护离散时间的世界状态变量并执行宏观因果更新；第二层协调枢纽（Coordination Hub）通过领域特定组件对状态变化进行模块化分解，并将指令路由至标签定义的群体；第三层标签驱动NPC利用行为树和局部状态/效用函数执行响应，仅在需要与玩家交互时调用LLM。此设计实现了共享宏观事件下差异化但逻辑受限的NPC行为，避免了主仿真循环中对每个代理进行提示，从而在可控性和计算效率之间取得平衡。
链接: https://arxiv.org/abs/2604.03091
作者: Yizhi Xu
机构: Shenzhen University (深圳大学)
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to CHI 2026 Extended Abstracts
Abstract:Creating scalable and believable game societies requires balancing authorial control with computational cost. Existing scripted NPC systems scale efficiently but are often rigid, whereas fully LLM-driven agents can produce richer social behavior at a much higher runtime cost. We present CASCADE, a three-layer architecture for low-cost, controllable social coordination in sandbox-style game worlds. A Macro State Director (Level 1) maintains discrete-time world-state variables and macro-level causal updates, while a modular Coordination Hub decomposes state changes through domain-specific components (e.g., professional and social coordination) and routes the resulting directives to tag-defined groups. Then Tag-Driven NPCs (Level 3) execute responses through behavior trees and local state/utility functions, invoking large language models only for on-demand player-facing interactions. We evaluate CASCADE through multiple micro-scenario prototypes and trace-based analysis, showing how a shared macro event can produce differentiated yet logically constrained NPC behaviors without per-agent prompting in the main simulation loop. CASCADE provides a modular foundation for scalable social simulation and future open-world authoring tools.
[HC-2] Same Feedback Different Source: How AI vs. Human Feedback Attribution and Credibility Shape Learner Behavior in Computing Education
【速读】：该论文试图解决的问题是：在人工智能（AI）系统承担教学角色时，学习者对反馈来源的认知（即认为反馈来自AI还是人类）是否会影响其学习效果。此前研究未能有效分离反馈来源属性与交付时间延迟之间的混杂效应，导致结论存在不确定性。本研究的关键解决方案在于设计了一个三组对照实验（N=148），其中所有反馈均由同一大型语言模型生成，仅在来源归属上分为AI即时、AI延迟和人类延迟三种条件，从而精确区分了“来源 attribution”与“交付时机”的独立影响。结果表明，被试若相信反馈来自人类，则任务投入时间显著增加（d=0.61, p=.013），但若该信念不真实（46%参与者未真正相信人类来源），反而导致更差的学习表现（代码复杂度d=0.77, p=.003）。这说明动机效应依赖于可信度，因此对于计算教育者而言，在人类身份不可信的情况下，透明地标识AI来源可能是更稳妥的策略。
链接: https://arxiv.org/abs/2604.03075
作者: Caitlin Morris,Pattie Maes
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: 11 pages, 3 figures, 1 table
Abstract:As AI systems increasingly take on instructional roles - providing feedback, guiding practice, evaluating work - a fundamental question emerges: does it matter to learners who they believe is on the other side? We investigated this using a three-condition experiment (N=148) in which participants completed a creative coding tutorial and received feedback generated by the same large language model, attributed to either an AI system (with instant or delayed delivery) or a human teaching assistant (with matched delayed delivery). This three-condition design separates the effect of source attribution from the confound of delivery timing, which prior studies have not controlled. Source attribution and timing had distinct effects on different outcomes: participants who believed the human attribution spent more time on task than those receiving equivalently timed AI-attributed feedback (d=0.61, p=.013, uncorrected), while the delivery delay independently increased output complexity without affecting time measures. An exploratory analysis revealed that 46% of participants in the human-attributed condition did not believe the attribution, and these participants showed worse outcomes than those receiving transparent AI feedback (code complexity d=0.77, p=.003; time on task d=0.70, p=.007). These findings suggest that believed human presence may carry motivational value, but that this value depends on credibility. For computing educators, transparent AI attribution may be the lower-risk default in contexts where human attribution would not be credible.
[HC-3] MECO: A Multimodal Dataset for Emotion and Cognitive Understanding in Older Adults
【速读】：该论文旨在解决老年人群中多模态情绪预测研究匮乏的问题，尤其关注认知衰退对情感表达与生理反应的影响，而现有基准数据集主要针对年轻、认知健康的受试者，难以反映老龄化带来的复杂变化。解决方案的关键在于构建MECO数据集——一个面向老年人的多模态情感与认知理解数据集，涵盖42名参与者约38小时的同步视频、音频、脑电图（EEG）和心电图（ECG）信号，共30,592个样本，并提供自评的情绪维度（效价、唤醒度、六种基本情绪）及认知状态标注（如简易精神状态检查量表评分）。通过标准化社区环境采集和全面标注，MECO为老年人群中的多模态情感与认知建模提供了高生态效度的基础资源，支持个性化情绪识别与轻度认知障碍（MCI）早期检测等实际应用。
链接: https://arxiv.org/abs/2604.03050
作者: Hongbin Chen,Jie Li,Wei Wang,Siyang Song,Xiao Gu,Jianqing Li,Wentao Xiang
机构: Nanjing Medical University(南京医科大学); University of Exeter(埃克塞特大学); University of Oxford(牛津大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures
Abstract:While affective computing has advanced considerably, multimodal emotion prediction in aging populations remains underexplored, largely due to the scarcity of dedicated datasets. Existing multimodal benchmarks predominantly target young, cognitively healthy subjects, neglecting the influence of cognitive decline on emotional expression and physiological responses. To bridge this gap, we present MECO, a Multimodal dataset for Emotion and Cognitive understanding in Older adults. MECO includes 42 participants and provides approximately 38 hours of multimodal signals, yielding 30,592 synchronized samples. To maximize ecological validity, data collection followed standardized protocols within community-based settings. The modalities cover video, audio, electroencephalography (EEG), and electrocardiography (ECG). In addition, the dataset offers comprehensive annotations of emotional and cognitive states, including self-assessed valence, arousal, six basic emotions, and Mini-Mental State Examination cognitive scores. We further establish baseline benchmarks for both emotion and cognitive prediction. MECO serves as a foundational resource for multimodal modeling of affect and cognition in aging populations, facilitating downstream applications such as personalized emotion recognition and early detection of mild cognitive impairment (MCI) in real-world settings. The complete dataset and supplementary materials are available at this https URL.
[HC-4] Comparing the Impact of Pedagogy-Informed Custom and General-Purpose GAI Chatbots on Students Science Problem-Solving Processes and Performance Using Heterogeneous Interaction Network Analysis
【速读】：该论文旨在解决通用生成式 AI (Generative AI, GAI) 聊天机器人在科学问题解决教学中可能引发学生认知卸载（cognitive offloading）的问题，即学生过度依赖直接答案而非主动思考。现有研究多聚焦于通用聊天机器人（如 ChatGPT），而较少探讨基于教学法定制的聊天机器人如何影响学生的认知过程与学习效果。解决方案的关键在于设计并应用一种以苏格拉底式提问法（Socratic questioning method）为理论基础的教育导向型定制 GAI 聊天机器人，通过引导性问题促进学生反思与深度参与，从而提升认知投入强度与多样性，同时避免直接提供答案所导致的认知惰化。实证结果表明，相较于通用聊天机器人，定制聊天机器人显著提升了学生的互动强度和认知多样性，且未降低问题解决绩效，验证了其在科学教育中更有利于激发高阶思维的潜力。
链接: https://arxiv.org/abs/2604.03022
作者: Hanyu Su,Huilin Zhang,Shihui Feng
机构: 未知
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Full paper accepted to the 27th International Conference on AI in Education (AIED 2026)
Abstract:Problem solving plays an essential role in science education, and generative AI (GAI) chatbots have emerged as a promising tool for supporting students’ science problem solving. However, general-purpose chatbots (e.g., ChatGPT), which often provide direct, ready-made answers, may lead to students’ cognitive offloading. Prior research has rarely focused on custom chatbots for facilitating students’ science problem solving, nor has it examined how they differently influence problem-solving processes and performance compared to general-purpose chatbots. To address this gap, we developed a pedagogy-informed custom GAI chatbot grounded in the Socratic questioning method, which supports students by prompting them with guiding questions. This study employed a within-subjects counterbalanced design in which 48 secondary school students used both custom and general-purpose chatbot to complete two science problem-solving tasks. 3297 student-chatbot dialogues were collected and analyzed using Heterogeneous Interaction Network Analysis (HINA). The results showed that: (1) students demonstrated significantly higher interaction intensity and cognitive interaction diversity when using custom chatbot than using general-purpose chatbot; (2) students were more likely to follow custom chatbot’s guidance to think and reflect, whereas they tended to request general-purpose chatbot to execute specific commands; and (3) no statistically significant difference was observed in students’ problem-solving performance evaluated by solution quality between two chatbot conditions. This study provides novel theoretical insights and empirical evidence that custom chatbots are less likely to induce cognitive offloading and instead foster greater cognitive engagement compared to general-purpose chatbots. This study also offers insights into the design and integration of GAI chatbots in science education.
[HC-5] UnrealVis: A Testing Laboratory of Optimization Techniques in Unreal Engine for Scientific Visualization
【速读】：该论文旨在解决大规模3D科学数据可视化中性能与保真度难以平衡的问题，传统工具往往对用户技术背景要求过高。解决方案的关键在于提出UnrealVis——一个基于Unreal Engine的优化实验室，通过整合55篇文献归纳出的22种优化技术（涵盖六类方法，如Nanite、LOD方案和剔除策略等），构建了直观的工作流，并支持实时遥测与A/B对比分析，从而帮助用户在交互式探索中高效选择最优优化组合，在满足性能目标的同时保持结构保真度。
链接: https://arxiv.org/abs/2604.02980
作者: Matteo Filosa,Andrea Nardocci,Tiziana Catarci,Marco Angelini
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Visualizing large 3D scientific datasets requires balancing performance and fidelity, but traditional tools often demand excessive technical expertise. We introduce UnrealVis, an Unreal Engine optimization laboratory for configuring and evaluating rendering techniques during interactive exploration. Following a review of 55 papers, we established a taxonomy of 22 optimization techniques across six families, implementing them through engine subsystems such as Nanite, Level of Detail(LOD) schemes, and culling. The system features an intuitive workflow with live telemetry and A/B comparisons for local and global performance analysis. Validated through case studies of ribosomal structures and volumetric flow fields, along with an expert evaluation, UnrealVis facilitates the selection of optimization combinations that meet performance goals while preserving structural fidelity. UnrealVis is available at this https URL
[HC-6] SentiAvatar: Towards Expressive and Interactive Digital Humans
【速读】：该论文旨在解决构建高表达性交互式三维数字人（3D digital humans）中的三大核心挑战：缺乏大规模高质量多模态数据、鲁棒的语义到动作映射问题，以及细粒度帧级动作-韵律同步问题。解决方案的关键在于提出SentiAvatar框架，其核心创新包括：1）构建了SuSuInterActs数据集（21K片段，37小时），包含同步语音、全身动作和面部表情的光学动作捕捉数据；2）预训练一个Motion Foundation Model（基于200K+动作序列），赋予模型超越对话场景的丰富动作先验；3）设计了一种音频感知的“先规划后填充”架构（audio-aware plan-then-infill architecture），将句级语义规划与帧级韵律驱动插值解耦，从而生成既语义合理又节奏对齐于语音的动作序列。实验表明，该方法在SuSuInterActs和BEATv2基准上均达到当前最优性能，且支持6秒输出仅需0.3秒，具备无限多轮流式生成能力。
链接: https://arxiv.org/abs/2604.02908
作者: Chuhao Jin,Rui Zhang,Qingzhe Gao,Haoyu Shi,Dayu Wu,Yichen Jiang,Yihan Wu,Ruihua Song
机构: Gaoling School of Artificial Intelligence, Renmin University of China (中国人民大学高瓴人工智能学院); SentiPulse; College of Computer Science, Inner Mongolia University (内蒙古大学计算机学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Multimedia (cs.MM)
备注: 19 pages, 4 figures
Abstract:We present SentiAvatar, a framework for building expressive interactive 3D digital humans, and use it to create SuSu, a virtual character that speaks, gestures, and emotes in real time. Achieving such a system remains challenging, as it requires jointly addressing three key problems: the lack of large-scale, high-quality multimodal data, robust semantic-to-motion mapping, and fine-grained frame-level motion-prosody synchronization. To solve these problems, first, we build SuSuInterActs (21K clips, 37 hours), a dialogue corpus captured via optical motion capture around a single character with synchronized speech, full-body motion, and facial expressions. Second, we pre-train a Motion Foundation Model on 200K+ motion sequences, equipping it with rich action priors that go well beyond the conversation. We then propose an audio-aware plan-then-infill architecture that decouples sentence-level semantic planning from frame-level prosody-driven interpolation, so that generated motions are both semantically appropriate and rhythmically aligned with speech. Experiments show that SentiAvatar achieves state-of-the-art on both SuSuInterActs (R@1 43.64%, nearly 2 times the best baseline) and BEATv2 (FGD 4.941, BC 8.078), producing 6s of output in 0.3s with unlimited multi-turn streaming. The source code, model, and dataset are available at this https URL.
[HC-7] Generative AI Use in Professional Graduate Thesis Writing: Adoption Perceived Outcomes and the Role of a Research-Specialized Agent
【速读】：该论文旨在解决生成式 AI（Generative AI）在硕士论文写作中广泛应用背景下，教育实践中如何应对由此引发的学术规范性与研究质量保障问题。其核心挑战已从单纯的技术采纳转向对AI输出内容的验证能力、文献来源治理以及专用工具设计的有效性。解决方案的关键在于引入针对研究场景优化的AI代理（如GAMER PAT），通过提供结构化支持和深度问题探究功能，提升学生在文献综述、草稿撰写及卡顿时咨询等环节的效率与准确性，从而实现从“使用AI”到“善用AI”的范式转变。
链接: https://arxiv.org/abs/2604.02792
作者: Kenji Saito,Rei Tajika,Satoru Shibuya,Hiroshi Kanno
机构: Waseda University (早稻田大学)
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 11 pages, 5 figures
Abstract:This paper reports a survey of generative AI use among 83 MBA thesis students in Japan (target population 230; 36.1% response rate), conducted after thesis examiner evaluation. AI use was nearly universal: 95.2% reported at least some use and 77.1% heavy use. Students engaged AI across the full research-writing workflow - literature review, drafting, and consultation when stuck - reporting benefits centered on clearer argument and structure (82.3%), better revision quality (73.4%), and faster writing (70.9%), with a mean perceived quality improvement of 6.27 out of 7. Concerns about output accuracy (75.9%) and citation handling persisted alongside these gains. Among respondents who rated GAMER PAT, a research-specialized agent, against other AI, preferences significantly favored it for inquiry deepening and structural organization (both p 0.05, exact binomial). A preliminary qualitative analysis of follow-up interviews further reveals active epistemic vigilance strategies and differentiated tool use across thesis phases. The central implication is not adoption itself but a shift in the educational challenge toward verification, source governance, and AI tool design - with GAMER PAT offering preliminary evidence that research-specialized scaffolding matters.
[HC-8] Disrupting Cognitive Passivity: Rethinking AI-Assisted Data Literacy through Cognitive Alignment
【速读】：该论文旨在解决AI聊天机器人在数据素养培养中可能引发的认知惰性（cognitive passivity）问题，即其默认的“助手模式”倾向于提供一次性、全面的回答，削弱了用户通过自主思考提升数据理解能力的机会。解决方案的关键在于提出“认知对齐”（cognitive alignment）框架，该框架强调人机交互的有效性取决于用户认知需求（接受型或思辨型）与AI交互模式（传递型或思辨型）之间的动态匹配，从而避免因不匹配导致的认知惰性或认知摩擦。
链接: https://arxiv.org/abs/2604.02783
作者: Yongsu Ahn,Nam Wook Kim,Benjamin Bach
机构: Boston College (波士顿学院); Inria (法国国家信息与自动化研究院); University of Edinburgh (爱丁堡大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:AI chatbots are increasingly stepping into roles as collaborators or teachers in analyzing, visualizing, and reasoning through data and domain problem. Yet, AI’s default assistant mode with its comprehensive and one-off responses may undermine opportunities for practitioners to develop literacy through their own thinking, inducing cognitive passivity. Drawing on evidence from empirical studies and theories, we argue that disrupting cognitive passivity necessitates a nuanced approach: rather than simply making AI promote deliberative thinking, there is a need for more dynamic and adaptive strategy through cognitive alignment – a framework that characterizes effective human-AI interaction as a function of alignment between users’ cognitive demand and AI’s interaction mode. In the framework, we provide the mapping between AI’s interaction mode (transmissive or deliberative) and users’ cognitive demand (receptive or deliberative), otherwise leading to either cognitive passivity or friction. We further discuss implications and offer open questions for future research on data literacy.
[HC-9] AI Disclosure with DAISY
【速读】：该论文旨在解决科研领域中人工智能（Artificial Intelligence, AI）工具使用缺乏透明度和一致性披露的问题。当前，尽管学界普遍认为AI在研究中的应用应被明确声明，但实际披露仍较为稀少且标准不一，且作者在报告AI使用时面临社会、认知和情感层面的障碍。为应对这一挑战，研究提出DAISY（Disclosure of AI-uSe in Your Research），一种基于结构化表单的AI披露声明生成工具。其关键在于通过文献驱动的需求分析与共同设计（共11位参与者）开发出可操作的披露框架，并在31名作者用户研究中验证其有效性：DAISY支持的披露内容更完整，能更清晰地分解AI在研究全流程中的具体用途，同时并未降低作者对披露内容的心理舒适度，从而为AI披露作为一项社会技术实践提供了可落地的设计路径与研究方向。
链接: https://arxiv.org/abs/2604.02760
作者: Yoana Ahmetoglu,Marios Constantinides,Anna Cox
机构: UCL Interaction Centre (伦敦大学学院交互中心); CYENS Centre of Excellence (塞浦路斯卓越中心)
类目: Human-Computer Interaction (cs.HC)
备注: accepted at CHIWORK’26
Abstract:The use of AI tools in research is becoming routine, alongside growing consensus that such use should be transparently disclosed. However, AI disclosure statements remain rare and inconsistent, with policies offering limited guidance and authors facing social, cognitive, and emotional barriers when reporting AI use. To explore how structured disclosure shapes what authors report and how they experience disclosure, we present DAISY (Disclosure of AI-uSe in Your Research), a form-based tool for generating AI disclosure statements. DAISY was developed from literature-derived requirements and co-design (N =11), and deployed in a user study with authors (N=31). DAISY-supported disclosures met more completeness criteria, offering clearer breakdowns of AI use across research and writing than unsupported disclosures. Surprisingly, despite concerns about how transparently disclosed AI use might be perceived, the use of DAISY did not reduce author comfort with the disclosure statements. We discuss design implications and a research agenda for AI disclosure as a sociotechnical practice.
[HC-10] Beyond the AI Tutor: Social Learning with LLM Agents
【速读】：该论文旨在解决当前基于大语言模型（Large Language Model, LLM）的教育工具普遍采用一对一辅导模式，而忽视了多主体互动在学习科学中已被证实的协同与观察性优势的问题。其解决方案的关键在于引入多智能体LLM配置，通过让学习者同时与一个LLM导师和多个LLM同伴交互，利用同伴间不同类型的错误（如概念性错误与计算错误）或不同模型生成的内容多样性，来提升学习效果。实验结果表明，在收敛型问题解决任务中，同时获得导师和同伴支持的学习者表现出最高的独立测试准确率；在发散型写作任务中，双模型协作组避免了单模型导致的思维同质化，显著提升了作文质量，验证了多智能体配置能够释放类似人类社会学习中的合作与多元视角优势。
链接: https://arxiv.org/abs/2604.02677
作者: Harsh Kumar,Zi Kang(Jace)Mu,Jonathan Vincentius,Ashton Anderson
机构: University of Toronto (多伦多大学)
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: Working draft
Abstract:Most AI-based educational tools today adopt a one-on-one tutoring paradigm, pairing a single LLM with a single learner. Yet decades of learning science research suggest that multi-party interaction – through peer modeling, co-construction, and exposure to diverse perspectives – can produce learning benefits that dyadic tutoring alone cannot. In this paper, we investigate whether multi-agent LLM configurations can enhance learning outcomes beyond what a single LLM tutor provides. We present two controlled experiments spanning distinct learning contexts. In a convergent problem-solving study ( N=315 ), participants tackle SAT-level math problems in a 2 \times 2 design that varies the presence of an LLM tutor and LLM peers, each making different kinds of errors (conceptual vs.\ arithmetic); participants who interacted with both a tutor and peers achieved the highest unassisted test accuracy. In a divergent composition study ( N=247 ), participants write argumentative and creative essays with either no AI assistance, a single LLM (Claude or ChatGPT), or both Claude and ChatGPT together; while both LLM conditions improved essay quality, only the two-agent condition avoided the idea-level homogeneity that single-model assistance was found to produce. Together, these studies offer one of the first controlled investigations of multi-agent LLM learning environments, probing whether the move from one-on-one AI tutoring toward richer agent configurations can unlock the collaborative and observational benefits long documented in human social learning research.
[HC-11] Engagement Is Not Transfer: A Withdrawal Study of a Consumer Social Robot with Autistic Children at Home
【速读】：该论文旨在解决“社交机器人（social robot）的持续使用是否能有效提升自闭症儿童的人际社会能力”这一核心问题。研究通过8周家庭环境下的随机对照试验，将40名5–9岁自闭症儿童分为持续接触机器人组与机器人撤除组，发现虽然持续使用机器人显著降低了儿童焦虑并验证了其高可用性，但撤除组在社会动机、情绪理解及共情行为方面反而表现出更优改善；质性分析进一步揭示“移交效应（handoff）”与“孤立效应（siloing）”模式：撤除机器人促使儿童重新聚焦于人类互动，而持续使用则使社交焦点局限于人机关系，限制了现实情境中的社会能力迁移。因此，解决方案的关键在于认识到高参与度不等于社会能力的有效转移，需通过阶段性干预策略引导儿童从机器人交互逐步过渡到人类社会互动，以实现真正意义上的社会技能泛化。
链接: https://arxiv.org/abs/2604.02642
作者: Yibo Meng,Guangrui Fan,Bingyi Liu,Yingfangzhong Sun,Ruiqi Chen,Haipeng Mi
机构: Cornell University (康奈尔大学); Taiyuan University of Science and Technology (太原科技大学); University of Michigan, Ann Arbor (密歇根大学安娜堡分校); Politecnico di Milano (米兰理工大学); University of Washington (华盛顿大学); Tsinghua University (清华大学)
类目: Human-Computer Interaction (cs.HC)
备注: Accepted by IDC 2026
Abstract:This study examines whether engagement with social robots translates into improved human-directed social abilities in autistic children. We conducted an 8-week home-based randomized controlled trial with 40 children aged 5–9 using a commercial social robot (Qrobot). Families were assigned to either continued robot access or robot withdrawal. Quantitative measures and caregiver interviews assessed anxiety, social motivation, emotion inference, and empathy. Results showed that continued robot access significantly reduced anxiety, confirming strong affective benefits and high usability. However, children in the withdrawal group demonstrated greater improvements in social motivation, emotion understanding, and empathic behaviors toward caregivers and peers. Qualitative findings revealed a “handoff versus siloing” pattern: withdrawal promoted reorientation toward human social interaction, while continued access concentrated engagement within the child–robot dyad and limited transfer to real-world contexts. We interpret these results as evidence that high engagement does not guarantee social transfer.
[HC-12] he Paradox of Prioritization in Public Sector Algorithms
【速读】：该论文旨在解决公共部门在资源稀缺情境下采用算法优先级分配工具时，其结构性设计如何影响资源配置的有效性及受影响群体的体验问题。论文指出，尽管现有研究多聚焦于提升算法的公平性、准确性和有效性，但对优先级机制本身如何在现实公共管理条件下引发交叉身份群体间的相对不平等缺乏深入探讨。解决方案的关键在于揭示：算法优先级虽可能带来效率提升，但这种效率不应被简化为“用更少资源做更多事”的理想化叙事；必须正视实际实施中资源约束的存在，避免因忽视这些约束而加剧个体对不平等的感知，并重新审视优先级机制在公共治理中的伦理与实践风险。
链接: https://arxiv.org/abs/2604.02641
作者: Erina Seh-Young Moon,Matthew Tamura,Shion Guha
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Public sector agencies perform the critical task of implementing the redistributive role of the State by acting as the leading provider of critical public services that many rely on. In recent years, public agencies have been increasingly adopting algorithmic prioritization tools to determine which individuals should be allocated scarce public resources. Prior work on these tools has largely focused on assessing and improving their fairness, accuracy, and validity. However, what remains understudied is how the structural design of prioritization itself shapes both the effectiveness of these tools and the experiences of those subject to them under realistic public sector conditions. In this study, we demonstrate the fallibility of adopting a prioritization approach in the public sector by showing how the underlying mechanisms of prioritization generate significant relative disparities between groups of intersectional identities as resources become increasingly scarce. We argue that despite prevailing arguments that prioritization of resources can lead to efficient allocation outcomes, prioritization can intensify perceptions of inequality for impacted individuals. We contend that efficiencies generated by algorithmic tools should not be conflated with the dominant rhetoric that efficiency necessarily entails “doing more with less” and we highlight the risks of overlooking resource constraints present in real-world implementation contexts.
[HC-13] oys that listen talk and play: Understanding Childrens Sensemaking and Interactions with AI Toys
【速读】：该论文旨在解决儿童在与生成式 AI (Generative AI) 玩具互动时，如何理解边界、主体性（agency）和人际关系的问题。研究发现，尽管儿童倾向于将AI玩具视为具有社会性的存在，但因频繁的交互失败及智能表现与玩具形态之间的不匹配，导致其对游戏预期产生偏差，进而引发对抗性 play 行为。解决方案的关键在于通过更具透明度、符合儿童发展阶段且负责任的设计策略，引导儿童更清晰地认知 AI 玩具的本质，从而促进健康、可持续的人机互动体验。
链接: https://arxiv.org/abs/2604.02629
作者: Aayushi Dangol,Meghna Gupta,Daeun Yoo,Robert Wolfe,Jason Yip,Franziska Roesner,Julie A. Kientz
机构: University of Washington (华盛顿大学); Rutgers University (罗格斯大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative AI (genAI) is increasingly being integrated into children’s everyday lives, not only through screens but also through so-called “screen-free” AI toys. These toys can simulate emotions, personalize responses, and recall prior interactions, creating the illusion of an ongoing social connection. Such capabilities raise important questions about how children understand boundaries, agency, and relationships when interacting with AI toys. To investigate this, we conducted two participatory design sessions with eight children ages 6-11 where they engaged with three different AI toys, shifting between play, experimentation, and reflection. Our findings reveal that children approached AI toys with genuine curiosity, profiling them as social beings. However, frequent interaction breakdowns and mismatches between apparent intelligence and toy-like form disrupted expectations around play and led to adversarial play. We conclude with implications and design provocations to navigate children’s encounters with AI toys in more transparent, developmentally appropriate, and responsible ways.
[HC-14] LitPivot: Developing Well-Situated Research Ideas Through Dynamic Contextualization and Critique within the Literature Landscape
【速读】：该论文旨在解决科研创新过程中“如何在文献阅读与研究构思之间实现动态协同”这一核心问题。具体而言，研究人员在提出新研究想法时，需在继承已有成果与体现创新性之间取得平衡，但传统工具通常将文献检索和创意生成割裂处理，导致研究者难以根据实时反馈调整思路并定位相关文献。解决方案的关键在于提出“文献驱动的转向机制”（literature-initiated pivots），即通过交互式文献推荐与批判性反馈，使研究者在构思过程中同步更新其想法并识别新的相关文献。该机制被实现在名为LitPivot的系统中，其核心功能是动态聚类并推荐与当前构思片段相关的论文，并基于文献内容提供改进建议，从而支持研究者在迭代中深化对文献空间的理解并提升研究质量。
链接: https://arxiv.org/abs/2604.02600
作者: Hita Kambhamettu,Bhavana Dalvi Mishra,Andrew Head,Jonathan Bragg,Aakanksha Naik,Joseph Chee Chang,Pao Siangliulue
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Developing a novel research idea is hard. It must be distinct enough from prior work to claim a contribution while also building on it. This requires iteratively reviewing literature and refining an idea based on what a researcher reads; yet when an idea changes, the literature that matters often changes with it. Most tools offer limited support for this interplay: literature tools help researchers understand a fixed body of work, while ideation tools evaluate ideas against a static, pre-curated set of papers. We introduce literature-initiated pivots, a mechanism where engagement with literature prompts revision to a developing idea, and where that revision changes which literature is relevant. We operationalize this in LitPivot, where researchers concurrently draft and vet an idea. LitPivot dynamically retrieves clusters of papers relevant to a selected part of the idea and proposes literature-informed critiques for how to revise it. A lab study ( n=17 ) shows researchers produced higher-rated ideas with stronger self-reported understanding of the literature space; an open-ended study ( n=5 ) reveals how researchers use LitPivot to iteratively evolve their own ideas.
[HC-15] Making Written Theorems Explorable by Grounding Them in Formal Representations
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）生成的解释在数学证明理解中存在交互性局限的问题。由于LLM输出为静态文本，无法执行或逐步调试，限制了用户对证明逻辑的深入探索。解决方案的关键在于将数学定理及其证明形式化为Lean编程语言中的可执行代码，并构建“可探索定理”（explorable theorems）系统，使用户能够以步骤级粒度执行、测试自定义示例与反例，并追踪每一步的逻辑依赖关系。这一方法通过形式化表示实现了超越静态文本的交互能力，实验表明使用者在证明阅读任务中表现出更准确、更详细的理解。
链接: https://arxiv.org/abs/2604.02598
作者: Hita Kambhamettu,Will Crichton,Sean Welleck,Harrison Goldstein,Andrew Head
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
备注:
Abstract:LLM-generated explanations can make technical content more accessible, but there is a ceiling on what they can support interactively. Because LLM outputs are static text, they cannot be executed or stepped through. We argue that grounding explanations in a formalized representation enables interactive affordances beyond what static text supports. We instantiate this idea for mathematical proof comprehension with explorable theorems, a system that uses LLMs to translate a theorem and its written proof into Lean, a programming language for machine-checked proofs, and links the written proof with the Lean code. Readers can work through the proof at a step-level granularity, test custom examples or counterexamples, and trace the logical dependencies bridging each step. Each worked-out step is produced by executing the Lean proof on that example and extracting its intermediate state. A user study ( n = 16 ) shows potential advantages of this approach: in a proof-reading task, participants who had access to the provided explorability features gave better, more correct, and more detailed answers to comprehension questions, demonstrating a stronger overall understanding of the underlying mathematics.
[HC-16] Generative AI Use in Entrepreneurship: An Integrative Review and an Empowerment-Entrapment Framework
【速读】：该论文旨在解决生成式人工智能（Generative AI）在创业过程中的影响研究碎片化问题，系统梳理其在机会识别与构思、机会评估与承诺、资源集聚与动员、企业创立与成长四个阶段的作用机制。其解决方案的关键在于提出“赋能-陷阱框架”（Empowerment-Entrapment Framework），揭示GenAI在每个阶段既可能赋能创业者（如提升创意质量、增强自我效能感、提高生产力），也可能带来风险（如引入幻觉、偏见、过度自信、关系嵌入性下降及批判性思维弱化），并识别出驱动这些双重效应的核心特征及其边界条件（如元认知能力、领域专业知识和创业经验），从而为创业者战略性使用GenAI提供理论指导与实践路径。
链接: https://arxiv.org/abs/2604.02567
作者: Jackson G. Lu,Gerui Gloria Zhao,Anna Manyi Zheng
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Human-Computer Interaction (cs.HC)
备注:
Abstract:Despite the growing use of generative artificial intelligence (GenAI) in entrepreneurship, research on its impact remains fragmented. To address this limitation, we provide an integrative review of how GenAI influences entrepreneurs at each stage of the entrepreneurial process: (1) opportunity recognition and ideation, (2) opportunity evaluation and commitment, (3) resource assembly and mobilization, and (4) venture launch and growth. Based on our review, we propose the Empowerment-Entrapment Framework to understand how GenAI can both empower and entrap entrepreneurs, highlighting GenAI’s role as a double-edged sword at each stage of the entrepreneurial process. For example, GenAI may improve venture idea quality but introduce hallucinations and training data biases; boost entrepreneurial self-efficacy but heighten entrepreneurial overconfidence; increase functional breadth but decrease relational embeddedness; and boost productivity but fuel “workslop” and erode critical thinking, learning, and memory. Moreover, we identify core features of GenAI that underlie these empowering and entrapping effects. We also explore boundary conditions (e.g., entrepreneurs’ metacognition, domain expertise, and entrepreneurial experience) that shape the magnitude of these effects. Beyond these theoretical contributions, our review and the Empowerment-Entrapment Framework offer practical implications for entrepreneurs seeking to use GenAI strategically throughout the entrepreneurial process while managing its risks.
[HC-17] Red Flags and Cherry Picking: Reading The Scientific Blackpill Wiki
【速读】：该论文试图解决的问题是：极端男性主义网络社群“incels”如何通过引用科学文献来为其意识形态（即“黑pill”理论）提供合法性支撑，以及这种科学引用在多大程度上存在误用或扭曲。解决方案的关键在于系统性地分析“Scientific Blackpill”维基页面所引用的科学研究，发现其虽大多基于真实科学文献且描述基本准确，但在解释和应用过程中常出现过度泛化、脱离语境或选择性诠释等现象，从而服务于既定的偏见立场，这与以往关于动机性推理和伪科学合法化的研究结论一致。
链接: https://arxiv.org/abs/2604.02565
作者: Celia Chen,Alex Leitch,Scotty Beland,Ingo Burghardt,William Conway,Rajesh Kumar Gnanasekaran,Marilyn Harbert,Emily Klein,Jennifer Golbeck
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: 7 pages, 4 figures
Abstract:Incels are an online community of men who share a belief in extreme misogyny, the glorification of violence, and biological essentialism. They refer to their core ideology as “The Blackpill”, a belief that physical attraction is the only path to romantic success and that women are only attracted to one very specific, hypermasculine archetype. This is not only a belief system; incels believe their ideology grounded in hard science. The research that incels use as evidence of their belief system is collected in an extensive online document, the Scientific Blackpill wiki page. In this research, we analyze the claims made on the wiki against the research cited to assess how the wiki authors are using or misusing science in support of their ideology. We find that the page largely cites legitimate science and describes it partly or mostly accurately. However, in discussing it, the results are often overgeneralized, stripped of context, or otherwise distorted to support the preexisting incel viewpoint. This echoes previous findings about motivated reasoning and borrowing scientific legitimacy in other misinformation and conspiracy-minded ideologies. We discuss the implications this has for understanding online radicalization and information quality.
[HC-18] Prag matics Meets Culture: Culturally-adapted Artwork Description Generation and Evaluation
【速读】：该论文旨在解决语言模型在开放文本生成任务中对不同文化背景受众的文化适应性不足的问题，即模型在描述艺术作品时难以根据目标观众的文化熟悉度调整表达方式，从而影响其理解和接受程度。解决方案的关键在于提出一种基于文化基础问答（culturally grounded question answering）的评估框架，并引入一个实用主义说话者模型（pragmatic speaker model），通过模拟听众的理解过程来优化生成内容的文化适配性，实验证明该方法可显著提升听众对艺术描述的 comprehension（理解度），最高提升达8.2%，且人类评估进一步验证了该模型在实用性上的优势。
链接: https://arxiv.org/abs/2604.02557
作者: Lingjun Zhao,Dayeon Ki,Marine Carpuat,Hal Daumé III
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Language models are known to exhibit various forms of cultural bias in decision-making tasks, yet much less is known about their degree of cultural familiarity in open-ended text generation tasks. In this paper, we introduce the task of culturally-adapted art description generation, where models describe artworks for audiences from different cultural groups who vary in their familiarity with the cultural symbols and narratives embedded in the artwork. To evaluate cultural competence in this pragmatic generation task, we propose a framework based on culturally grounded question answering. We find that base models are only marginally adequate for this task, but, through a pragmatic speaker model, we can improve simulated listener comprehension by up to 8.2%. A human study further confirms that the model with higher pragmatic competence is rated as more helpful for comprehension by 8.0%.
[HC-19] A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction
【速读】：该论文旨在解决维度缩减（Dimensionality Reduction, DR）中的两个长期存在的挑战：一是全局与局部结构保持之间的权衡问题，即现有方法如t-SNE和UMAP侧重于局部邻域保持但可能扭曲全局流形结构，而Laplacian Eigenmaps虽能保留全局几何特性却常导致局部分离不足；二是表达能力与分析透明性之间的鸿沟，许多非线性DR方法生成的嵌入缺乏与高维结构的显式关联，限制了对嵌入过程的理解。其解决方案的关键在于提出一种谱框架（spectral framework），通过结合谱基（spectral basis）与交叉熵优化（cross-entropy optimization）实现多尺度表示，从而同时兼顾全局与局部结构的保真度，并利用线性谱分解支持从图频率视角分析嵌入中各谱模态（spectral mode）的贡献，增强嵌入结果的可解释性。
链接: https://arxiv.org/abs/2604.02535
作者: Zeyang Huang,Angelos Chatzimparmpas,Thomas Höllt,Takanori Fujiwara
机构: 未知
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
备注:
Abstract:Dimensionality reduction (DR) is characterized by two longstanding trade-offs. First, there is a global-local preservation tension: methods such as t-SNE and UMAP prioritize local neighborhood preservation, yet may distort global manifold structure, while methods such as Laplacian Eigenmaps preserve global geometry but often yield limited local separation. Second, there is a gap between expressiveness and analytical transparency: many nonlinear DR methods produce embeddings without an explicit connection to the underlying high-dimensional structure, limiting insight into the embedding process. In this paper, we introduce a spectral framework for nonlinear DR that addresses these challenges. Our approach embeds high-dimensional data using a spectral basis combined with cross-entropy optimization, enabling multi-scale representations that bridge global and local structure. Leveraging linear spectral decomposition, the framework further supports analysis of embeddings through a graph-frequency perspective, enabling examination of how spectral modes influence the resulting embedding. We complement this analysis with glyph-based scatterplot augmentations for visual exploration. Quantitative evaluations and case studies demonstrate that our framework improves manifold continuity while enabling deeper analysis of embedding structure through spectral mode contributions.
计算机视觉
[CV-0] CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning
【速读】：该论文旨在解决当前视觉语言模型（Vision-Language Models, VLMs）中单一视觉编码器在语义丰富性和任务鲁棒性方面的局限性问题。现有方法通常依赖于对比学习训练的视觉编码器（如CLIP-style），虽能实现跨模态对齐与检索，但难以捕捉密集视觉语义信息。为此，作者提出CoME-VL（Complementary Multi-Encoder Vision-Language）框架，其核心创新在于通过模块化融合策略整合对比学习编码器与自监督DINO编码器的互补特征：首先采用熵引导的多层聚合与正交约束投影减少冗余信息，其次利用RoPE增强的交叉注意力对齐异构token网格并生成紧凑的融合视觉token。该方案可在不显著改动标准VLM流程的前提下，将融合后的特征注入解码器-only大语言模型（LLM），从而在多个视觉理解与定位任务上实现显著性能提升，平均提升达4.9%（视觉理解）和5.4%（接地任务）。
链接: https://arxiv.org/abs/2604.03231
作者: Ankan Deria,Komal Kumar,Xilin He,Imran Razzak,Hisham Cholakkal,Fahad Shahbaz Khan,Salman Khan
机构: Mohamed bin Zayed University of Artificial Intelligence ( Mohamed bin Zayed 大学人工智能)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 10 figures, 5 tables
Abstract:Recent vision-language models (VLMs) typically rely on a single vision encoder trained with contrastive image-text objectives, such as CLIP-style pretraining. While contrastive encoders are effective for cross-modal alignment and retrieval, self-supervised visual encoders often capture richer dense semantics and exhibit stronger robustness on recognition and understanding tasks. In this work, we investigate how to scale the fusion of these complementary visual representations for vision-language modeling. We propose CoME-VL: Complementary Multi-Encoder Vision-Language, a modular fusion framework that integrates a contrastively trained vision encoder with a self-supervised DINO encoder. Our approach performs representation-level fusion by (i) entropy-guided multi-layer aggregation with orthogonality-constrained projections to reduce redundancy, and (ii) RoPE-enhanced cross-attention to align heterogeneous token grids and produce compact fused visual tokens. The fused tokens can be injected into a decoder-only LLM with minimal changes to standard VLM pipelines. Extensive experiments across diverse vision-language benchmarks demonstrate that CoME-VL consistently outperforms single-encoder baselines. In particular, we observe an average improvement of 4.9% on visual understanding tasks and 5.4% on grounding tasks. Our method achieves state-of-the-art performance on RefCOCO for detection while improving over the baseline by a large margin. Finally, we conduct ablation studies on layer merging, non-redundant feature mixing, and fusion capacity to evaluate how complementary contrastive and self-supervised signals affect VLM performance.
[CV-1] VOSR: A Vision-Only Generative Model for Image Super-Resolution CVPR2026
【速读】：该论文旨在解决当前生成式图像超分辨率（Generative Image Super-Resolution, GSR）方法过度依赖大规模文本-图像（Text-to-Image, T2I）扩散模型预训练所带来的冗余与不匹配问题——即GSR本质上是低分辨率（Low-Resolution, LR）输入条件下的图像恢复任务，而现有方法却从通用T2I生成器出发，导致结构失真和幻觉（hallucination）频发。其解决方案的关键在于提出VOSR（Vision-Only SR），一种完全基于视觉数据训练的生成式超分框架：首先利用预训练视觉编码器提取LR输入的语义丰富且空间对齐的特征作为视觉语义引导；其次重构无分类器指导（Classifier-Free Guidance）策略，摒弃标准的无条件分支，代之以保留弱LR锚点的恢复导向引导机制；最终通过多步训练与蒸馏获得单步高效推理模型，在显著降低训练成本（不足T2I基线的十分之一）的同时，在合成与真实世界基准上均实现更优的感知质量和结构保真度，首次证明高质生成式超分无需多模态预训练。
链接: https://arxiv.org/abs/2604.03225
作者: Rongyuan Wu,Lingchen Sun,Zhengqiang Zhang,Xiangtao Kong,Jixin Zhao,Shihao Wang,Lei Zhang
机构: The Hong Kong Polytechnic University (香港理工大学); OPPO Research Institute (OPPO研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR2026
Abstract:Most of the recent generative image super-resolution (SR) methods rely on adapting large text-to-image (T2I) diffusion models pretrained on web-scale text-image data. While effective, this paradigm starts from a generic T2I generator, despite that SR is fundamentally a low-resolution (LR) input-conditioned image restoration task. In this work, we investigate whether an SR model trained purely on visual data can rival T2I-based ones. To this end, we propose VOSR, a Vision-Only generative framework for SR. We first extract semantically rich and spatially grounded features from the LR input using a pretrained vision encoder as visual semantic guidance. We then revisit classifier-free guidance for training generative models and show that the standard unconditional branch is ill-suited to restoration models trained from scratch. We therefore replace it with a restoration-oriented guidance strategy that preserves weak LR anchors. Built upon these designs, we first train a multi-step VOSR model from scratch and then distill it into a one-step model for efficient inference. VOSR requires less than one-tenth of the training cost of representative T2I-based SR methods, yet in both multi-step and one-step settings, it achieves competitive or even better perceptual quality and efficiency, while producing more faithful structures with fewer hallucinations on both synthetic and real-world benchmarks. Our results, for the first time, show that high-quality generative SR can be achieved without multimodal pretraining. The code and models can be found at this https URL.
[CV-2] ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow
【速读】：该论文旨在解决遥感图像分割在实际部署中面临的持续学习问题，即新语义类别不断出现以及采集条件随季节、城市和传感器变化所导致的表征漂移（representation drift）和遗忘（forgetting）问题。现有增量学习方法通常将训练步骤视为孤立更新，难以有效控制模型稳定性。其解决方案的关键在于提出ProtoFlow框架，通过将类别原型（class prototype）建模为时间轨迹，并利用显式的时序向量场（temporal vector field）学习其演化过程；同时联合施加低曲率运动约束与类间分离约束，从而稳定原型几何结构，实现对遥感场景下连续学习任务的鲁棒性提升。
链接: https://arxiv.org/abs/2604.03212
作者: Jiekai Wu,Rong Fu,Chuangqi Li,Zijian Zhang,Guangxin Wu,Hao Zhang,Shiyin Lin,Jianyuan Ni,Yang Li,Dongxu Zhang,Amir H. Gandomi,Simon Fong,Pengbin Feng
机构: Juntendo University (顺天堂大学); University of Macau (澳门大学); Utrecht University (乌得勒支大学); University of Pennsylvania (宾夕法尼亚大学); University of Chinese Academy of Sciences (中国科学院大学); University of Florida (佛罗里达大学); Juniata College (朱尼塔学院); National Engineering Research Center for Beijing Biochip Technology (北京生物芯片国家工程研究中心); CapitalBio Corporation (CapitalBio公司); University of Technology Sydney (悉尼科技大学); Obuda University (欧布达大学); University of Macau (澳门大学); University of Southern California (南加州大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Remote sensing segmentation in real deployment is inherently continual: new semantic categories emerge, and acquisition conditions shift across seasons, cities, and sensors. Despite recent progress, many incremental approaches still treat training steps as isolated updates, which leaves representation drift and forgetting insufficiently controlled. We present ProtoFlow, a time-aware prototype dynamics framework that models class prototypes as trajectories and learns their evolution with an explicit temporal vector field. By jointly enforcing low-curvature motion and inter-class separation, ProtoFlow stabilizes prototype geometry throughout incremental learning. Experiments on standard class- and domain-incremental remote sensing benchmarks show consistent gains over strong baselines, including up to 1.5-2.0 points improvement in mIoUall, together with reduced forgetting. These results suggest that explicitly modeling temporal prototype evolution is a practical and interpretable strategy for robust continual remote sensing segmentation.
[CV-3] PR3DICTR: A modular AI framework for medical 3D image-based detection and outcome prediction
【速读】：该论文旨在解决三维医学图像分类任务中模型开发效率低、缺乏标准化流程以及可复用性差的问题。其核心挑战在于如何在保持灵活性的同时降低开发负担，以促进深度学习方法在医学影像分析中的广泛应用。解决方案的关键在于提出PR3DICTR平台——一个基于PyTorch和MONAI等社区标准构建的开源框架，通过模块化设计与训练过程标准化，提供丰富的预设功能（如模型架构、超参数配置和训练策略），同时支持用户自定义模块“插拔”，从而实现高效、灵活且可扩展的三维医学图像分类建模，适用于二分类或事件相关的任务，且仅需少量代码即可部署使用。
链接: https://arxiv.org/abs/2604.03203
作者: Daniel C. MacRae,Luuk van der Hoek,Robert van der Wal,Suzanne P.M. de Vette,Hendrike Neh,Baoqiang Ma,Peter M.A. van Ooijen,Lisanne V. van Dijk
机构: University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 16 pages, 6 figures and 1 table
Abstract:Three-dimensional medical image data and computer-aided decision making, particularly using deep learning, are becoming increasingly important in the medical field. To aid in these developments we introduce PR3DICTR: Platform for Research in 3D Image Classification and sTandardised tRaining. Built using community-standard distributions (PyTorch and MONAI), PR3DICTR provides an open-access, flexible and convenient framework for prediction model development, with an explicit focus on classification using three-dimensional medical image data. By combining modular design principles and standardization, it aims to alleviate developmental burden whilst retaining adjustability. It provides users with a wealth of pre-established functionality, for instance in model architecture design options, hyper-parameter solutions and training methodologies, but still gives users the opportunity and freedom to ``plug in’’ their own solutions or modules. PR3DICTR can be applied to any binary or event-based three-dimensional classification task and can work with as little as two lines of code.
[CV-4] he Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report CVPR2026
【速读】：该论文旨在解决高效单图像超分辨率（Single-Image Super-Resolution, SISR）问题，核心目标是在保持图像重建质量（PSNR约为26.90 dB on DIV2K_LSDIR_valid 和 26.99 dB on DIV2K_LSDIR_test）的前提下，显著降低模型的运行时间、参数量和浮点运算次数（FLOPs）。解决方案的关键在于设计轻量化网络架构，通过结构优化与计算效率提升，在保证重建质量的同时实现高效的推理性能，从而推动SISR技术在实际部署场景中的应用。
链接: https://arxiv.org/abs/2604.03198
作者: Bin Ren,Hang Guo,Yan Shu,Jiaqi Ma,Ziteng Cui,Shuhong Liu,Guofeng Mei,Lei Sun,Zongwei Wu,Fahad Shahbaz Khan,Salman Khan,Radu Timofte,Yawei Li,Hongyuan Yu,Pufan Xu,Chen Wu,Long Peng,Jiaojiao Yi,Siyang Yi,Yuning Cui,Jingyuan Xia,Xing Mou,Keji He,Jinlin Wu,Zongang Gao,Sen Yang,Rui Zheng,Fengguo Li,Yecheng Lei,Wenkai Min,Jie Liu,Keye Cao,Shubham Sharma,Manish Prasad,Haobo Li,Matin Fazel,Abdelhak Bentaleb,Rui Chen,Shurui Shi,Zitao Dai,Qingliang Liu,Yang Cheng,Jing Hu,Xuan Zhang,Rui Ding,Tingyi Zhang,Hui Deng,Mengyang Wang,Fulin Liu,Jing Wei,Qian Wang,Hongying Liu,Mingyang Li,Guanglu Dong,Zheng Yang,Chao Ren,Hongbo Fang,Lingxuan Li,Lin Si,Pan Gao,Moncef Gabbouj,Watchara Ruangsang,Supavadee Aramvith
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026 NTIRE Workshop Paper, Efficient Super Resolution Technical Report
Abstract:This paper reviews the NTIRE 2026 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects, such as runtime, parameters, and FLOPs, while maintaining PSNR of around 26.90 dB on the DIV2K_LSDIR_valid dataset, and 26.99 dB on the DIV2K_LSDIR_test dataset. The challenge had 95 registered participants, and 15 teams made valid submissions. They gauge the state-of-the-art results for efficient single-image super-resolution.
[CV-5] he Compression Gap: Why Discrete Tokenization Limits Vision-Language-Action Model Scaling
【速读】：该论文旨在解决在物理人工智能（Physical AI）中，单纯通过升级视觉编码器（vision encoder）来提升视觉-语言-动作（Vision-Language-Action, VLA）模型下游操作性能的预期为何常常失效的问题。其核心发现是：当动作以离散令牌（discrete tokens）形式表示时，存在一个信息瓶颈——即代码本（codebook）容量限制成为行为生成的“紧约束”（tightest information bottleneck），导致即使视觉编码器能力增强，也无法有效传递到最终动作表现上；而若动作连续（如扩散策略 Diffusion Policy），则视觉编码器才是瓶颈，此时升级编码器可直接改善性能。解决方案的关键在于识别并定位这一信息瓶颈的位置，而非盲目扩大模型规模或数据量，从而实现更高效的Scaling策略。
链接: https://arxiv.org/abs/2604.03191
作者: Takuya Shiba
机构: Shibattic Inc. (Shibattic公司)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 11 pages, 1 figure
Abstract:Scaling Vision-Language-Action (VLA) models by upgrading the vision encoder is expected to improve downstream manipulation performance–as it does in vision-language modeling. We show that this expectation fails when actions are represented as discrete tokens, and explain why through an information-theoretic principle we call the Compression Gap: in any visuomotor pipeline, scaling behavior is governed by the location of the tightest information bottleneck. When actions are continuous (e.g., Diffusion Policy), the vision encoder is the binding constraint, and upgrading it directly improves performance. When actions are discretized through a fixed-capacity codebook (e.g., OAT), the codebook becomes the binding constraint, and encoder improvements cannot propagate past it–regardless of how rich the upstream representation is. We validate this principle on the LIBERO benchmark with three lines of evidence: a factorial experiment showing that encoder upgrades improve Diffusion Policy by over 21 percentage points while OAT gains are substantially attenuated across model scales; an encoder quality gradient across four encoders confirming that Diffusion Policy tracks encoder quality monotonically while OAT remains flat; and a codebook size experiment demonstrating that relaxing codebook capacity partially recovers encoder sensitivity, providing causal evidence for the bottleneck hypothesis. Our findings reveal that scaling in Physical AI requires identifying where information bottlenecks lie in the pipeline, rather than uniformly increasing model or data size.
[CV-6] Multi-View Video Diffusion Policy: A 3D Spatio-Temporal-Aware Video Action Model
【速读】：该论文旨在解决机器人操作中对环境三维空间结构与时间演化理解不足的问题，现有策略通常依赖二维视觉观测和静态图像-文本预训练模型，导致数据需求高且难以捕捉环境动态变化。其解决方案的关键在于提出多视角视频扩散策略（MV-VDP），通过联合建模环境的3D时空状态，同时预测多视角热力图视频与RGB视频，从而实现动作决策与环境演化预测的一致性对齐，显著提升数据效率、鲁棒性、泛化能力和可解释性。
链接: https://arxiv.org/abs/2604.03181
作者: Peiyan Li,Yixiang Chen,Yuan Xu,Jiabing Yang,Xiangnan Wu,Jun Guo,Nan Sun,Long Qian,Xinghang Li,Xin Xiao,Jing Liu,Nianfeng Liu,Tao Kong,Yan Huang,Liang Wang,Tieniu Tan
机构: New Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences; Tsinghua University; Xi’an Jiaotong University; Wuhan University; Nanjing University
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Project Website: this https URL
Abstract:Robotic manipulation requires understanding both the 3D spatial structure of the environment and its temporal evolution, yet most existing policies overlook one or both. They typically rely on 2D visual observations and backbones pretrained on static image–text pairs, resulting in high data requirements and limited understanding of environment dynamics. To address this, we introduce MV-VDP, a multi-view video diffusion policy that jointly models the 3D spatio-temporal state of the environment. The core idea is to simultaneously predict multi-view heatmap videos and RGB videos, which 1) align the representation format of video pretraining with action finetuning, and 2) specify not only what actions the robot should take, but also how the environment is expected to evolve in response to those actions. Extensive experiments show that MV-VDP enables data-efficient, robust, generalizable, and interpretable manipulation. With only ten demonstration trajectories and without additional pretraining, MV-VDP successfully performs complex real-world tasks, demonstrates strong robustness across a range of model hyperparameters, generalizes to out-of-distribution settings, and predicts realistic future videos. Experiments on Meta-World and real-world robotic platforms demonstrate that MV-VDP consistently outperforms video-prediction–based, 3D-based, and vision–language–action models, establishing a new state of the art in data-efficient multi-task manipulation.
[CV-7] Understanding the Role of Hallucination in Reinforcement Post-Training of Multimodal Reasoning Models CVPR2026
【速读】：该论文旨在解决当前基于强化学习（Reinforcement Learning, RL）的后训练方法在多模态大语言模型（Multimodal Large Language Models, MLLMs）中是否真正提升了视觉推理能力的问题，尤其关注模型是否从真实视觉信息中学习，而非依赖于幻觉（hallucination）。其解决方案的关键在于提出“幻觉作为线索框架”（Hallucination-as-Cue Framework），通过引入诱导幻觉且模态特定的扰动（hallucination-inductive, modality-specific corruptions），主动移除或替换完成正确推理所必需的视觉信息，迫使模型依赖幻觉进行决策。该框架在训练和评估阶段均应用此类扰动，从而揭示RL训练对模型幻觉利用机制的影响，并发现RL训练即使在纯幻觉诱导条件下仍可显著提升性能，甚至优于标准训练，挑战了现有对MLLM推理训练的认知。
链接: https://arxiv.org/abs/2604.03179
作者: Gengwei Zhang,Jie Peng,Zhen Tan,Mufan Qiu,Hossein Nourkhiz Mahjoub,Vaishnav Tadiparthi,Kwonjoon Lee,Yanyong Zhang,Tianlong Chen
机构: University of North Carolina at Chapel Hill (北卡罗来纳大学教堂山分校); University of Science and Technology of China (中国科学技术大学); Arizona State University (亚利桑那州立大学); Honda Research Institute, USA (本田研究研究院，美国)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026
Abstract:The recent success of reinforcement learning (RL) in large reasoning models has inspired the growing adoption of RL for post-training Multimodal Large Language Models (MLLMs) to enhance their visual reasoning capabilities. Although many studies have reported improved performance, it remains unclear whether RL training truly enables models to learn from visual information. In this work, we propose the Hallucination-as-Cue Framework, an analytical framework designed to investigate the effects of RL-based post-training on multimodal reasoning models from the perspective of model hallucination. Specifically, we introduce hallucination-inductive, modality-specific corruptions that remove or replace essential information required to derive correct answers, thereby forcing the model to reason by hallucination. By applying these corruptions during both training and evaluation, our framework provides a unique perspective for diagnosing RL training dynamics and understanding the intrinsic properties of datasets. Through extensive experiments and analyses across multiple multimodal reasoning benchmarks, we reveal that the role of model hallucination for RL-training is more significant than previously recognized. For instance, we find that RL post-training under purely hallucination-inductive settings can still significantly improve models’ reasoning performance, and in some cases even outperform standard training. These findings challenge prevailing assumptions about MLLM reasoning training and motivate the development of more modality-aware RL-based training designs.
[CV-8] SFFNet: Synergistic Feature Fusion Network With Dual-Domain Edge Enhancement for UAV Image Object Detection
【速读】：该论文旨在解决无人机（UAV）图像中目标检测的两大挑战：背景噪声复杂导致的目标分离困难，以及目标尺度不平衡问题。传统方法难以有效区分目标与复杂背景，且无法充分利用图像中的多尺度信息。其解决方案的关键在于提出一种协同特征融合网络（SFFNet），包含两个核心模块：一是多尺度动态双域耦合（MDDC）模块，通过频域与空间域双重驱动的边缘提取机制，实现多尺度目标边缘与背景噪声的有效解耦；二是协同特征金字塔网络（SFPN），利用线性可变形卷积自适应捕捉不规则目标形状，并结合宽域感知模块（WPM）建立目标周围的长程上下文关联，从而增强模型在几何和语义层面的表征能力。此外，为适配不同应用场景或资源约束，设计了六种不同规模的检测器（N/S/M/B/L/X），实验表明SFFNet-X在VisDrone和UAVDT数据集上分别达到36.8 AP和20.6 AP，轻量级模型（N/S）则兼顾精度与参数效率。
链接: https://arxiv.org/abs/2604.03176
作者: Wenfeng Zhang,Jun Ni,Yue Meng,Xiaodong Pei,Wei Hu,Qibing Qin,Lei Huang
机构: Chongqing Normal University (重庆师范大学); CETC Yizhihang (Chongqing) Technology Co., Ltd (中国电子科技集团公司易智航（重庆）技术有限公司); Weifang University (潍坊学院); Ocean University of China (中国海洋大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: Accepted for publication in IEEE Transactions on Multimedia
Abstract:Object detection in unmanned aerial vehicle (UAV) images remains a highly challenging task, primarily caused by the complexity of background noise and the imbalance of target scales. Traditional methods easily struggle to effectively separate objects from intricate backgrounds and fail to fully leverage the rich multi-scale information contained within images. To address these issues, we have developed a synergistic feature fusion network (SFFNet) with dual-domain edge enhancement specifically tailored for object detection in UAV images. Firstly, the multi-scale dynamic dual-domain coupling (MDDC) module is designed. This component introduces a dual-driven edge extraction architecture that operates in both the frequency and spatial domains, enabling effective decoupling of multi-scale object edges from background noise. Secondly, to further enhance the representation capability of the model’s neck in terms of both geometric and semantic information, a synergistic feature pyramid network (SFPN) is proposed. SFPN leverages linear deformable convolutions to adaptively capture irregular object shapes and establishes long-range contextual associations around targets through the designed wide-area perception module (WPM). Moreover, to adapt to the various applications or resource-constrained scenarios, six detectors of different scales (N/S/M/B/L/X) are designed. Experiments on two challenging aerial datasets (VisDrone and UAVDT) demonstrate the outstanding performance of SFFNet-X, achieving 36.8 AP and 20.6 AP, respectively. The lightweight models (N/S) also maintain a balance between detection accuracy and parameter efficiency. The code will be available at this https URL.
[CV-9] EffiMiniVLM: A Compact Dual-Encoder Regression Framework
【速读】：该论文旨在解决冷启动场景下产品品质预测的问题，即在缺乏用户交互历史的情况下，仅依靠图像和文本元数据进行高质量预测。现有视觉-语言模型通常依赖庞大架构或外部数据集，导致计算成本高昂。其解决方案的关键在于提出EffiMiniVLM——一个轻量级双编码器视觉-语言回归框架，融合EfficientNet-B0图像编码器与MiniLM文本编码器，并引入基于评分频次加权的Huber损失函数以提升训练样本效率。该模型仅用Amazon Reviews 2023数据集的20%进行训练，参数量仅为27.7M、计算量6.8 GFLOPs，却实现了0.40的CES得分，在资源消耗上优于其他前五方法约4–8倍，且无需外部数据，展现出优异的性能与可扩展性。
链接: https://arxiv.org/abs/2604.03172
作者: Yin-Loon Khor,Yi-Jie Wong,Yan Chai Hum
机构: Universiti Malaya (马来西亚大学); Universiti Tunku Abdul Rahman (尊孔独立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Predicting product quality from multimodal item information is critical in cold-start scenarios, where user interaction history is unavailable and predictions must rely on images and textual metadata. However, existing vision-language models typically depend on large architectures and/or extensive external datasets, resulting in high computational cost. To address this, we propose EffiMiniVLM, a compact dual-encoder vision-language regression framework that integrates an EfficientNet-B0 image encoder and a MiniLM-based text encoder with a lightweight regression head. To improve training sample efficiency, we introduce a weighted Huber loss that leverages rating counts to emphasize more reliable samples, yielding consistent performance gains. Trained using only 20% of the Amazon Reviews 2023 dataset, the proposed model contains 27.7M parameters and requires 6.8 GFLOPs, yet achieves a CES score of 0.40 with the lowest resource cost in the benchmark. Despite its small size, it remains competitive with significantly larger models, achieving comparable performance while being approximately 4x to 8x more resource-efficient than other top-5 methods and being the only approach that does not use external datasets. Further analysis shows that scaling the data to 40% alone allows our model to overtake other methods, which use larger models and datasets, highlighting strong scalability despite the model’s compact design.
[CV-10] CAMEO: A Conditional and Quality-Aware Multi-Agent Image Editing Orchestrator
【速读】：该论文旨在解决当前条件图像编辑（conditional image editing）方法在单步生成范式下存在的质量控制不足、结构偏差过大及环境不一致等问题，尤其在需要严格结构控制的任务（如驾驶场景中的异常插入和复杂人体姿态变换）中表现不佳。解决方案的关键在于提出一个结构化的多智能体框架CAMEO，将编辑过程重构为一个以质量感知为导向、反馈驱动的迭代流程，通过规划、结构化提示、假设生成与自适应参考锚定等协同阶段实现精细化控制，并在编辑循环中嵌入评估机制以实现中间结果的闭环优化，从而显著提升编辑结果的结构可靠性与上下文一致性。
链接: https://arxiv.org/abs/2604.03156
作者: Yuhan Pu,Hao Zheng,Ziqian Mo,Hill Zhang,Tianyi Fan,Shuhong Wu,Jiaheng Wei
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Conditional image editing aims to modify a source image according to textual prompts and optional reference guidance. Such editing is crucial in scenarios requiring strict structural control (i.e., anomaly insertion in driving scenes and complex human pose transformation). Despite recent advances in large-scale editing models (i.e., Seedream, Nano Banana, etc), most approaches rely on single-step generation. This paradigm often lacks explicit quality control, may introduce excessive deviation from the original image, and frequently produces structural artifacts or environment-inconsistent modifications, typically requiring manual prompt tuning to achieve acceptable results. We propose \textbfCAMEO, a structured multi-agent framework that reformulates conditional editing as a quality-aware, feedback-driven process rather than a one-shot generation task. CAMEO decomposes editing into coordinated stages of planning, structured prompting, hypothesis generation, and adaptive reference grounding, where external guidance is invoked only when task complexity requires it. To overcome the lack of intrinsic quality control in existing methods, evaluation is embedded directly within the editing loop. Intermediate results are iteratively refined through structured feedback, forming a closed-loop process that progressively corrects structural and contextual inconsistencies. We evaluate CAMEO on anomaly insertion and human pose switching tasks. Across multiple strong editing backbones and independent evaluation models, CAMEO consistently achieves 20% more win rate on average compared to multiple state-of-the-art models, demonstrating improved robustness, controllability, and structural reliability in conditional image editing.
[CV-11] SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentation CVPR2026
【速读】：该论文旨在解决少样本医学图像分割（Few-Shot Medical Image Segmentation, FSMIS）问题，即在仅有少量标注样本的情况下实现对新类别医学目标的准确分割，以应对医学影像中数据稀缺和域偏移（domain shifts）的挑战。其解决方案的关键在于提出了一种名为SD-FSMIS的新框架，该框架利用预训练的Stable Diffusion（SD）模型的强大视觉先验，通过引入两个核心组件：支持-查询交互模块（Support-Query Interaction, SQI）和视觉到文本条件转换器（Visual-to-Textual Condition Translator, VTCT），从而有效将扩散模型适配至FSMIS任务。其中，SQI实现了对SD生成架构的直接适应，而VTCT则将支持集中的视觉线索转化为隐式文本嵌入，引导扩散过程进行精确条件化生成，显著提升了分割性能与跨域泛化能力。
链接: https://arxiv.org/abs/2604.03134
作者: Meihua Li,Yang Zhang,Weizhao He,Hu Qu,Yisong Li
机构: Shenzhen University (深圳大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR2026
Abstract:Few-Shot Medical Image Segmentation (FSMIS) aims to segment novel object classes in medical images using only minimal annotated examples, addressing the critical challenges of data scarcity and domain shifts prevalent in medical imaging. While Diffusion Models (DM) excel in visual tasks, their potential for FSMIS remains largely unexplored. We propose that the rich visual priors learned by large-scale DMs offer a powerful foundation for a more robust and data-efficient segmentation approach. In this paper, we introduce SD-FSMIS, a novel framework designed to effectively adapt the powerful pre-trained Stable Diffusion (SD) model for the FSMIS task. Our approach repurposes its conditional generative architecture by introducing two key components: a Support-Query Interaction (SQI) and a Visual-to-Textual Condition Translator (VTCT). Specifically, SQI provides a straightforward yet powerful means of adapting SD to the FSMIS paradigm. The VTCT module translates visual cues from the support set into an implicit textual embedding that guides the diffusion model, enabling precise conditioning of the generation process. Extensive experiments demonstrate that SD-FSMIS achieves competitive results compared to state-of-the-art methods in standard settings. Surprisingly, it also demonstrated excellent generalization ability in more challenging cross-domain scenarios. These findings highlight the immense potential of adapting large-scale generative models to advance data-efficient and robust medical image segmentation.
[CV-12] SCC-Loc: A Unified Semantic Cascade Consensus Framework for UAV Thermal Geo-Localization
【速读】：该论文旨在解决热红外-可见光跨模态地理定位（Cross-modal Thermal Geo-localization, TG）中因热红外与可见光模态差异导致的特征模糊问题，该问题严重干扰了传统粗到精（coarse-to-fine）配准流程的准确性。解决方案的关键在于提出SCC-Loc框架，其核心创新包括：1）语义引导的视口对齐（Semantic-Guided Viewport Alignment, SGVA）模块，用于自适应优化卫星图像裁剪区域以纠正初始空间偏差；2）级联的空间自适应纹理-结构滤波（Cascaded Spatial-Adaptive Texture-Structure Filtering, C-SATSF）机制，显式强化几何一致性并剔除密集的跨模态异常点；3）共识驱动的可靠性感知位置选择（Consensus-Driven Reliability-Aware Position Selection, CD-RAPS）策略，通过物理约束的姿态优化协同确定最优定位结果。该方案在统一共享DINOv2骨干网络的基础上实现零样本、高精度绝对位置估计，显著提升定位鲁棒性与准确性。
链接: https://arxiv.org/abs/2604.03120
作者: Xiaoran Zhang,Yu Liu,Jinyu Liang,Kangqiushi Li,Zhiwei Huang,Huaxin Xiao
机构: National University of Defense Technology (国防科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 15 pages, 4 figures. Submitted to IEEE J-STARS
Abstract:Cross-modal Thermal Geo-localization (TG) provides a robust, all-weather solution for Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments. However, profound thermal-visible modality gaps introduce severe feature ambiguity, systematically corrupting conventional coarse-to-fine registration. To dismantle this bottleneck, we propose SCC-Loc, a unified Semantic-Cascade-Consensus localization framework. By sharing a single DINOv2 backbone across global retrieval and MINIMA _\textRoMa matching, it minimizes memory footprint and achieves zero-shot, highly accurate absolute position estimation. Specifically, we tackle modality ambiguity by introducing three cohesive components. First, we design the Semantic-Guided Viewport Alignment (SGVA) module to adaptively optimize satellite crop regions, effectively correcting initial spatial deviations. Second, we develop the Cascaded Spatial-Adaptive Texture-Structure Filtering (C-SATSF) mechanism to explicitly enforce geometric consistency, thereby eradicating dense cross-modal outliers. Finally, we propose the Consensus-Driven Reliability-Aware Position Selection (CD-RAPS) strategy to derive the optimal solution through a synergy of physically constrained pose optimization. To address data scarcity, we construct Thermal-UAV, a comprehensive dataset providing 11,890 diverse thermal queries referenced against a large-scale satellite ortho-photo and corresponding spatially aligned Digital Surface Model (DSM). Extensive experiments demonstrate that SCC-Loc establishes a new state-of-the-art, suppressing the mean localization error to 9.37 m and providing a 7.6-fold accuracy improvement within a strict 5-m threshold over the strongest baseline. Code and dataset are available at this https URL.
[CV-13] Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation
【速读】：该论文旨在解决视频生成模型在极低推理预算（如2–4次非迭代步数，NFE）下难以实现高质量实时部署的问题。现有方法中，轨迹一致性蒸馏易因复杂视频动态导致过平滑的外观和弱运动，而分布匹配蒸馏（DMD）虽能恢复锐利且模式聚焦的样本，却缺乏对去噪更新跨时间步组合的一致性约束，致使生成序列易出现漂移。其解决方案的关键在于提出自一致分布匹配蒸馏（SC-DMD），通过显式正则化连续去噪更新的终点一致性来提升多步滚动生成的稳定性；同时针对自回归视频生成，将键值缓存（KV cache）视为质量参数化条件，并设计缓存感知特征对齐目标，引导低质量输出向高质量参考靠拢，从而在保持与多种KV缓存机制兼容的前提下显著提升低NFE下的视频生成质量。
链接: https://arxiv.org/abs/2604.03118
作者: Xingtong Ge,Yi Zhang,Yushi Huang,Dailan He,Xiahong Wang,Bingqi Ma,Guanglu Song,Yu Liu,Jun Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: under review
Abstract:Distilling video generation models to extremely low inference budgets (e.g., 2–4 NFEs) is crucial for real-time deployment, yet remains challenging. Trajectory-style consistency distillation often becomes conservative under complex video dynamics, yielding an over-smoothed appearance and weak motion. Distribution matching distillation (DMD) can recover sharp, mode-seeking samples, but its local training signals do not explicitly regularize how denoising updates compose across timesteps, making composed rollouts prone to drift. To overcome this challenge, we propose Self-Consistent Distribution Matching Distillation (SC-DMD), which explicitly regularizes the endpoint-consistent composition of consecutive denoising updates. For real-time autoregressive video generation, we further treat the KV cache as a quality parameterized condition and propose Cache-Distribution-Aware training. This training scheme applies SC-DMD over multi-step rollouts and introduces a cache-conditioned feature alignment objective that steers low-quality outputs toward high-quality references. Across extensive experiments on both non-autoregressive backbones (e.g., Wan~2.1) and autoregressive real-time paradigms (e.g., Self Forcing), our method, dubbed \textbfSalt, consistently improves low-NFE video generation quality while remaining compatible with diverse KV-cache memory mechanisms. Source code will be released at \hrefthis https URLthis https URL.
[CV-14] Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patches for Infrared Vision-Language Models
【速读】：该论文旨在解决红外视觉语言模型（Infrared Vision-Language Models, IR-VLMs）在低可见度环境下进行多模态感知时，其对抗攻击鲁棒性尚未被充分研究的问题。现有针对RGB模型的对抗补丁方法主要适用于封闭集场景，难以直接应用于红外VLMs所要求的开放语义理解与物理部署需求。解决方案的关键在于提出通用弯曲网格补丁（Universal Curved-Grid Patch, UCGP），其核心创新包括：采用弯曲网格参数化（Curved-Grid Mesh, CGM）实现连续、低频且可部署的补丁生成，并引入统一的表示驱动目标以促进子空间偏移、拓扑破坏和隐蔽性；同时结合元差分进化（Meta Differential Evolution）与EOT增强的TPS变形建模，提升实际部署和域偏移下的鲁棒性。UCGP不依赖标签或提示操纵，而是直接干扰视觉表征空间，削弱跨模态语义对齐能力，从而在多种IR-VLM架构中稳定损害语义理解性能，同时具备跨模型迁移性、跨数据集泛化性及对防御机制的抗干扰能力。
链接: https://arxiv.org/abs/2604.03117
作者: Chengyin Hu,Yuxian Dong,Yikun Guo,Xiang Chen,Junqi Wu,Jiahuan Long,Yiwei Wei,Tingsong Jiang,Wen Yao
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Infrared vision-language models (IR-VLMs) have emerged as a promising paradigm for multimodal perception in low-visibility environments, yet their robustness to adversarial attacks remains largely unexplored. Existing adversarial patch methods are mainly designed for RGB-based models in closed-set settings and are not readily applicable to the open-ended semantic understanding and physical deployment requirements of infrared VLMs. To bridge this gap, we propose Universal Curved-Grid Patch (UCGP), a universal physical adversarial patch framework for IR-VLMs. UCGP integrates Curved-Grid Mesh (CGM) parameterization for continuous, low-frequency, and deployable patch generation with a unified representation-driven objective that promotes subspace departure, topology disruption, and stealth. To improve robustness under real-world deployment and domain shift, we further incorporate Meta Differential Evolution and EOT-augmented TPS deformation modeling. Rather than manipulating labels or prompts, UCGP directly disrupts the visual representation space, weakening cross-modal semantic alignment. Extensive experiments demonstrate that UCGP consistently compromises semantic understanding across diverse IR-VLM architectures while maintaining cross-model transferability, cross-dataset generalization, real-world physical effectiveness, and robustness against defenses. These findings reveal a previously overlooked robustness vulnerability in current infrared multimodal systems.
[CV-15] Can VLMs Truly Forget? Benchmarking Training-Free Visual Concept Unlearning
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）中敏感或受版权保护的视觉概念在部署前需被移除的问题，尤其关注训练-free（无需重新训练）的去遗忘（unlearning）方法的有效性评估难题。现有训练-based 方法因在窄范围遗忘数据上微调会先损害模型通用能力，导致难以区分性能下降是否源于去遗忘过程本身；而训练-free 方法虽通过提示（prompt）或系统指令抑制特定概念，却缺乏针对视觉任务的严谨评测基准。本文的关键贡献是提出首个面向训练-free 视觉概念去遗忘的基准 VLM-UnBench，涵盖多层级遗忘强度、多源数据集与概念轴，并结合三层探测分类法与五种评估条件以区分真实去遗忘与仅服从指令的行为。实验表明，现实提示下遗忘准确率接近无指令基线，仅在揭示目标概念的“oracle 条件”下才出现显著降低，说明当前提示层面的抑制远未实现真正的视觉概念擦除，暴露出提示级控制与实际概念删除之间的显著差距。
链接: https://arxiv.org/abs/2604.03114
作者: Zhangyun Tan,Zeliang Zhang,Susan Liang,Yolo Yunlong Tang,Lisha Chen,Chenliang Xu
机构: University of Rochester (罗切斯特大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:VLMs trained on web-scale data retain sensitive and copyrighted visual concepts that deployment may require removing. Training-based unlearning methods share a structural flaw: fine-tuning on a narrow forget set degrades general capabilities before unlearning begins, making it impossible to attribute subsequent performance drops to the unlearning procedure itself. Training-free approaches sidestep this by suppressing concepts through prompts or system instructions, but no rigorous benchmark exists for evaluating them on visual tasks. We introduce VLM-UnBench, the first benchmark for training-free visual concept unlearning in VLMs. It covers four forgetting levels, 7 source datasets, and 11 concept axes, and pairs a three-level probe taxonomy with five evaluation conditions to separate genuine forgetting from instruction compliance. Across 8 evaluation settings and 13 VLM configurations, realistic unlearning prompts leave forget accuracy near the no-instruction baseline; meaningful reductions appear only under oracle conditions that disclose the target concept to the model. Object and scene concepts are the most resistant to suppression, and stronger instruction-tuned models remain capable despite explicit forget instructions. These results expose a clear gap between prompt-level suppression and true visual concept erasure. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.03114 [cs.CV] (or arXiv:2604.03114v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2604.03114 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-16] A Data-Centric Vision Transformer Baseline for SAR Sea Ice Classification
【速读】：该论文旨在解决北极地区海冰分类中因类别严重不平衡导致的自动识别准确率低的问题，尤其是在使用合成孔径雷达（SAR）数据时，形态相似的冰类难以区分。其解决方案的关键在于构建一个可信的仅基于SAR的基准模型，通过采用全分辨率Sentinel-1 Extra Wide影像输入、考虑信息泄露的分层图像块分割策略、SIGRID-3阶段发育标签以及训练集归一化方法，结合Vision Transformer（ViT）架构进行优化；特别地，使用焦点损失（focal loss）训练ViT-Large模型，在少数类（多年冰）上实现了83.9%的高精度和更优的精确率-召回率权衡，显著优于加权交叉熵训练的ViT-Base模型，为后续融合光学、热红外或气象等多模态数据提供了清晰且可复现的基线。
链接: https://arxiv.org/abs/2604.03094
作者: David Mike-Ewewie,Panhapiseth Lim,Priyanka Kumar
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate and automated sea ice classification is important for climate monitoring and maritime safety in the Arctic. While Synthetic Aperture Radar (SAR) is the operational standard because of its all-weather capability, it remains challenging to distinguish morphologically similar ice classes under severe class imbalance. Rather than claiming a fully validated multimodal system, this paper establishes a trustworthy SAR only baseline that future fusion work can build upon. Using the AI4Arctic/ASIP Sea Ice Dataset (v2), which contains 461 Sentinel-1 scenes matched with expert ice charts, we combine full-resolution Sentinel-1 Extra Wide inputs, leakage-aware stratified patch splitting, SIGRID-3 stage-of-development labels, and training-set normalization to evaluate Vision Transformer baselines. We compare ViT-Base models trained with cross entropy and weighted cross-entropy against a ViT-Large model trained with focal loss. Among the tested configurations, ViT-Large with focal loss achieves 69.6% held-out accuracy, 68.8% weighted F1, and 83.9% precision on the minority Multi-Year Ice class. These results show that focal-loss training offers a more useful precision-recall trade-off than weighted cross-entropy for rare ice classes and establishes a cleaner baseline for future multimodal fusion with optical, thermal, or meteorological data.
[CV-17] MI-Pruner: Crossmodal Mutual Information-guided Token Pruner for Efficient MLLM s
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）中视觉信息稀疏导致的推理效率低下问题。现有方法通常依赖视觉编码器或语言模型解码器中的注意力分数来衡量视觉token的重要性，进而进行剪枝。而本文提出的关键解决方案是：在视觉与文本特征交互之前，直接计算两者之间的互信息（Mutual Information, MI），从而显式度量跨模态特征层面的依赖关系。该方法无需访问内部注意力图或修改模型结构，具有简单、高效且非侵入性的优势，并在实验中展现出优于传统基于注意力的剪枝方法的性能表现。
链接: https://arxiv.org/abs/2604.03072
作者: Jiameng Li,Aleksei Tiulpin,Matthew B. Blaschko
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages
Abstract:For multimodal large language models (MLLMs), visual information is relatively sparse compared with text. As a result, research on visual pruning emerges for efficient inference. Current approaches typically measure token importance based on the attention scores in the visual encoder or in the LLM decoder, then select visual tokens with high attention scores while pruning others. In this paper, we pursue a different and more surgical approach. Instead of relying on mechanism-specific signals, we directly compute Mutual Information (MI) between visual and textual features themselves, prior to their interaction. This allows us to explicitly measure crossmodal dependency at the feature levels. Our MI-Pruner is simple, efficient and non-intrusive, requiring no access to internal attention maps or architectural modifications. Experimental results demonstrate that our approach outperforms previous attention-based pruning methods with minimal latency.
[CV-18] SparseSplat: Towards Applicable Feed-Forward 3D Gaussian Splatting with Pixel-Unaligned Prediction
【速读】：该论文旨在解决传统前馈式三维高斯溅射（feed-forward 3D Gaussian Splatting, 3DGS）方法生成的空间均匀且高度冗余的3DGS地图限制其在下游重建任务中集成的问题。解决方案的关键在于提出SparseSplat，一种能够根据场景结构和局部区域信息丰富度自适应调整高斯密度的前馈式3DGS模型：通过基于熵的概率采样策略，在纹理缺失区域生成大而稀疏的高斯点，在信息丰富的区域分配小而密集的高斯点；同时设计了一种专用点云网络，有效编码局部上下文并解码为3DGS属性，缓解了通用3DGS优化流程与前馈模型之间的感受野不匹配问题。实验表明，SparseSplat仅用22%的高斯点即可达到当前最优渲染质量，并在仅使用1.5%高斯点时仍保持合理渲染效果。
链接: https://arxiv.org/abs/2604.03069
作者: Zicheng Zhang,Xiangting Meng,Ke Wu,Wenchao Ding
机构: Fudan University (复旦大学); ShanghaiTech University (上海科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent progress in feed-forward 3D Gaussian Splatting (3DGS) has notably improved rendering quality. However, the spatially uniform and highly redundant 3DGS map generated by previous feed-forward 3DGS methods limits their integration into downstream reconstruction tasks. We propose SparseSplat, the first feed-forward 3DGS model that adaptively adjusts Gaussian density according to scene structure and information richness of local regions, yielding highly compact 3DGS maps. To achieve this, we propose entropy-based probabilistic sampling, generating large, sparse Gaussians in textureless areas and assigning small, dense Gaussians to regions with rich information. Additionally, we designed a specialized point cloud network that efficiently encodes local context and decodes it into 3DGS attributes, addressing the receptive field mismatch between the general 3DGS optimization pipeline and feed-forward models. Extensive experimental results demonstrate that SparseSplat can achieve state-of-the-art rendering quality with only 22% of the Gaussians and maintain reasonable rendering quality with only 1.5% of the Gaussians. Project page: this https URL.
[CV-19] Gram-MMD: A Texture-Aware Metric for Image Realism Assessment
【速读】：该论文旨在解决生成图像真实性评估中现有分布度量（如FID和CLIP-MMD）因仅关注语义层面特征而可能忽略细粒度纹理信息的问题。其解决方案的关键在于提出Gram-MMD（GMMD），该方法利用预训练主干网络中间激活的Gram矩阵来捕捉特征图之间的相关性，通过提取Gram矩阵的上三角部分并计算与真实图像锚定分布的最大均值差异（Maximum Mean Discrepancy, MMD），从而在更细粒度的层级上编码图像的纹理和结构特性，有效补充了传统语义级度量的不足。
链接: https://arxiv.org/abs/2604.03064
作者: Joé Napolitano,Pascal Nguyen
机构: AMIAD
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 15 figures, 2 tables. Preprint
Abstract:Evaluating the realism of generated images remains a fundamental challenge in generative modeling. Existing distributional metrics such as the Frechet Inception Distance (FID) and CLIP-MMD (CMMD) compare feature distributions at a semantic level but may overlook fine-grained textural information that can be relevant for distinguishing real from generated images. We introduce Gram-MMD (GMMD), a realism metric that leverages Gram matrices computed from intermediate activations of pretrained backbone networks to capture correlations between feature maps. By extracting the upper-triangular part of these symmetric Gram matrices and measuring the Maximum Mean Discrepancy (MMD) between an anchor distribution of real images and an evaluation distribution, GMMD produces a representation that encodes textural and structural characteristics at a finer granularity than global embeddings. To select the hyperparameters of the metric, we employ a meta-metric protocol based on controlled degradations applied to MS-COCO images, measuring monotonicity via Spearman’s rank correlation and Kendall’s tau. We conduct experiments on both the KADID-10k database and the RAISE realness assessment dataset using various backbone architectures, including DINOv2, DC-AE, Stable Diffusion’s VAE encoder, VGG19, and the AlexNet backbone from LPIPS, among others. We also demonstrate on a cross-domain driving scenario (KITTI / Virtual KITTI / Stanford Cars) that CMMD can incorrectly rank real images as less realistic than synthetic ones due to its semantic bias, while GMMD preserves the correct ordering. Our results suggest that GMMD captures complementary information to existing semantic-level metrics.
[CV-20] Can Nano Banana 2 Replace Traditional Image Restoration Models? An Evaluation of Its Performance on Image Restoration Tasks
【速读】：该论文旨在解决图像恢复（image restoration）任务中缺乏统一解决方案的问题，即如何利用通用生成式 AI（Generative AI）模型实现跨场景、多退化类型的图像修复。其关键解决方案在于通过精心设计的提示（prompt）策略，特别是采用简洁且包含显式保真度约束的提示，从而在重建精度与感知质量之间取得最优平衡。实验表明，Nano Banana 2 在全参考指标上优于现有先进模型，同时在用户研究中保持良好的感知质量，并展现出对小人脸、密集人群和严重退化等挑战性场景的强泛化能力，凸显了通用生成模型作为统一图像恢复求解器的巨大潜力，同时也强调了提示可控性和鲁棒性的重要性。
链接: https://arxiv.org/abs/2604.03061
作者: Weixiong Sun,Xiang Yin,Chao Dong
机构: Shenzhen University of Advanced Technology; Fudan University; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in generative AI raise the question of whether general-purpose image editing models can serve as unified solutions for image restoration. In this work, we conduct a systematic evaluation of Nano Banana 2 for image restoration across diverse scenes and degradation types. Our results show that prompt design plays a critical role, where concise prompts with explicit fidelity constraints achieve the best trade-off between reconstruction accuracy and perceptual quality. Compared with state-of-the-art restoration models, Nano Banana 2 achieves superior performance in full-reference metrics while remaining competitive in perceptual quality, which is further supported by user studies. We also observe strong generalization in challenging scenarios, such as small faces, dense crowds, and severe degradations. However, the model remains sensitive to prompt formulation and may require iterative refinement for optimal results. Overall, our findings suggest that general-purpose generative models hold strong potential as unified image restoration solvers, while highlighting the importance of controllability and robustness. All test results are available on this https URL.
[CV-21] STEAR: Layer-Aware Spatiotemporal Evidence Intervention for Hallucination Mitigation in Video Large Language Models
【速读】：该论文旨在解决视频大语言模型（Video-LLM）中存在的时空幻觉问题，即模型在生成过程中常出现视觉上无依据的细节或错误的时间关系。现有方法通常将幻觉视为统一的解码失败并施加全局修正规则，但本文指出不同解码层对视觉定位和后续语言组合的贡献差异显著，因此干预必须具有层感知特性。解决方案的关键在于提出STEAD（Layer-aware Spatiotemporal Evidence Intervention Framework），其通过识别高风险解码步骤，从对视觉定位敏感的中层选择条件化的视觉证据，并将其用于两个协同目标：一是恢复中层缺失的局部视觉锚定，二是构建扰动的补丁级反事实样本以否定晚期解码中的不一致推理。该方法实现了高效的单次编码推理，在多个主流Video-LLM架构与挑战性基准上均显著降低幻觉，提升忠实性、时间一致性与鲁棒性。
链接: https://arxiv.org/abs/2604.03045
作者: Linfeng Fan,Yuan Tian,Ziwei Li,Zhiwu Lu
机构: Gaoling School of Artificial Intelligence, Renmin University of China(中国人民大学高瓴人工智能学院); King Abdullah University of Science and Technology(阿卜杜拉国王科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: Preprint
Abstract:Video Large Language Models (Video-LLMs) remain prone to spatiotemporal hallucinations, often generating visually unsupported details or incorrect temporal relations. Existing mitigation methods typically treat hallucination as a uniform decoding failure, applying globally shared correction rules. We instead observe that decoder layers contribute differently to visual grounding and later linguistic composition, indicating that intervention must be layer-aware. Based on this insight, we propose STEAR, a layer-aware spatiotemporal evidence intervention framework. STEAR identifies high-risk decoding steps and selects token-conditioned visual evidence from grounding-sensitive middle layers. It uses this shared evidence for two coupled purposes: restoring missing local grounding in middle layers, and constructing temporally perturbed patch-level counterfactuals to falsify inconsistent reasoning during late-layer decoding. Consequently, STEAR mitigates both spatial and temporal hallucinations within an efficient single-encode inference framework. Experiments across representative Video-LLM backbones and challenging benchmarks demonstrate that STEAR consistently reduces hallucinations while improving faithfulness, temporal consistency, and robustness. Our results confirm that reliable video decoding relies on intervening on precise evidence at the right layer, rather than enforcing a global penalty. The code is provided in the Supplementary Material.
[CV-22] QVAD: A Question-Centric Agent ic Framework for Efficient and Training-Free Video Anomaly Detection
【速读】：该论文旨在解决视频异常检测（Video Anomaly Detection, VAD）中因异常样本开放集特性导致的检测困难问题，尤其针对当前基于视觉-语言模型（Vision-Language Models, VLMs）的训练-free方法依赖庞大基础模型来弥补静态提示模糊性所引发的资源消耗过高问题。解决方案的关键在于提出一种以问题为中心的代理框架QVAD，其核心创新是将VLM与大语言模型（LLM）的交互建模为动态对话过程，通过根据视觉上下文迭代优化查询（即“prompt-updating”机制），引导轻量级VLM生成高保真描述和精准语义推理，从而在无需参数更新的情况下释放小模型的潜在能力，实现高性能且低资源消耗的异常检测。
链接: https://arxiv.org/abs/2604.03040
作者: Lokman Bekit,Hamza Karim,Nghia T Nguyen,Yasin Yilmaz
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video Anomaly Detection (VAD) is a fundamental challenge in computer vision, particularly due to the open-set nature of anomalies. While recent training-free approaches utilizing Vision-Language Models (VLMs) have shown promise, they typically rely on massive, resource-intensive foundation models to compensate for the ambiguity of static prompts. We argue that the bottleneck in VAD is not necessarily model capacity, but rather the static nature of inquiry. We propose QVAD, a question-centric agentic framework that treats VLM-LLM interaction as a dynamic dialogue. By iteratively refining queries based on visual context, our LLM agent guides smaller VLMs to produce high-fidelity captions and precise semantic reasoning without parameter updates. This ``prompt-updating" mechanism effectively unlocks the latent capabilities of lightweight models, enabling state-of-the-art performance on UCF-Crime, XD-Violence, and UBNormal using a fraction of the parameters required by competing methods. We further demonstrate exceptional generalizability on the single-scene ComplexVAD dataset. Crucially, QVAD achieves high inference speeds with minimal memory footprints, making advanced VAD capabilities deployable on resource-constrained edge devices.
[CV-23] GenSmoke-GS: A Multi-Stage Method for Novel View Synthesis from Smoke-Degraded Images Using a Generative Model
【速读】：该论文旨在解决烟雾退化图像在三维场景重建与渲染中的可见度下降及跨视角一致性弱化问题（visibility degradation and weakened cross-view consistency in 3D scene reconstruction and rendering）。其核心解决方案是一个多阶段流水线，包括图像恢复、去雾、基于多模态大语言模型（MLLM）的增强、基于3D高斯泼溅（3DGS）与马尔可夫链蒙特卡洛（MCMC）优化的场景重建，以及多次运行结果的平均处理；该设计在提升图像可视性的同时，有效控制了不同输入视角间场景内容的变化，从而显著改善了定量指标和视觉质量，在NTIRE 2026 3DRR挑战赛Track 2中取得第一名。
链接: https://arxiv.org/abs/2604.03039
作者: Qida Cao,Xinyuan Hu,Changyue Shi,Jiajun Ding,Zhou Yu,Jun Yu
机构: Hangzhou Dianzi University (杭州电子科技大学); Harbin Institute of Technology, Shenzhen (哈尔滨工业大学深圳)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This paper describes our method for Track 2 of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge on smoke-degraded images. In this task, smoke reduces image visibility and weakens the cross-view consistency required by scene optimization and rendering. We address this problem with a multi-stage pipeline consisting of image restoration, dehazing, MLLM-based enhancement, 3DGS-MCMC optimization, and averaging over repeated runs. The main purpose of the pipeline is to improve visibility before rendering while limiting scene-content changes across input views. Experimental results on the challenge benchmark show improved quantitative performance and better visual quality than the provided baselines. The code is available at this https URL. Our method achieved a ranking of 1 out of 14 participants in Track 2 of the NTIRE 3DRR Challenge, as reported on the official competition website: this https URL.
[CV-24] ARM: Advantage Reward Modeling for Long-Horizon Manipulation
【速读】：该论文旨在解决长时程机器人操作任务中强化学习（Reinforcement Learning, RL）因稀疏奖励导致的信用分配困难问题。传统方法依赖密集进度奖励（dense progress rewards）进行策略优化，但此类奖励获取成本高且不适用于非单调行为（如回溯与恢复）。其解决方案的关键在于提出优势奖励建模（Advantage Reward Modeling, ARM），将难以量化的绝对进度转化为相对优势估计，并引入一种低成本的三状态标注策略（Progressive、Regressive、Stagnant），显著降低人工标注的认知负担并保证标注一致性；ARM通过训练此类直观信号，实现对完整示范数据及DAgger风格碎片化数据的自动进度标注，并集成至离线RL流程中，实现自适应的动作-奖励重加权，从而有效过滤低质量样本，提升策略稳定性和数据效率，在复杂长时程毛巾折叠任务中达到99.4%的成功率。
链接: https://arxiv.org/abs/2604.03037
作者: Yiming Mao,Zixi Yu,Weixin Mao,Yinhao Li,Qirui Hu,Zihan Lan,Minzhao Zhu,Hua Chen
机构: LimX Dynamics; Beijing University of Posts and Telecommunications; Zhejiang University
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Long-horizon robotic manipulation remains challenging for reinforcement learning (RL) because sparse rewards provide limited guidance for credit assignment. Practical policy improvement thus relies on richer intermediate supervision, such as dense progress rewards, which are costly to obtain and ill-suited to non-monotonic behaviors such as backtracking and recovery. To address this, we propose Advantage Reward Modeling (ARM), a framework that shifts from hard-to-quantify absolute progress to estimating relative advantage. We introduce a cost-effective tri-state labeling strategy – Progressive, Regressive, and Stagnant – that reduces human cognitive overhead while ensuring high cross-annotator consistency. By training on these intuitive signals, ARM enables automated progress annotation for both complete demonstrations and fragmented DAgger-style data. Integrating ARM into an offline RL pipeline allows for adaptive action-reward reweighting, effectively filtering suboptimal samples. Our approach achieves a 99.4% success rate on a challenging long-horizon towel-folding task, demonstrating improved stability and data efficiency over current VLA baselines with near-zero human intervention during policy training.
[CV-25] Explicit Time-Frequency Dynamics for Skeleton-Based Gait Recognition ICASSP2026
【速读】：该论文旨在解决基于骨架的步态识别方法在面对外观变化（如携带物品或穿着外套）时，因未能充分建模显式运动动态而导致性能下降的问题。现有方法虽能有效捕捉空间结构特征，但对时间-频率域中的关节速度动态信息利用不足。其解决方案的关键在于引入一个即插即用的小波特征流（Wavelet Feature Stream），通过连续小波变换（Continuous Wavelet Transform, CWT）将每关节的速度序列转换为多尺度标量图（scalogram），并使用轻量级多尺度卷积神经网络（CNN）从中提取判别性动态特征；该特征与骨干网络输出融合后用于分类，无需修改骨干架构或引入额外监督信号，显著提升了模型在分布偏移场景下的鲁棒性。
链接: https://arxiv.org/abs/2604.03002
作者: Seoyeon Ko,Yeojin Song,Egene Chung,Luca Quagliato,Taeyong Lee,Junhyug Noh
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 1 figure, to appear in ICASSP 2026
Abstract:Skeleton-based gait recognizers excel at modeling spatial configurations but often underuse explicit motion dynamics that are crucial under appearance changes. We introduce a plug-and-play Wavelet Feature Stream that augments any skeleton backbone with time-frequency dynamics of joint velocities. Concretely, per-joint velocity sequences are transformed by the continuous wavelet transform (CWT) into multi-scale scalograms, from which a lightweight multi-scale CNN learns discriminative dynamic cues. The resulting descriptor is fused with the backbone representation for classification, requiring no changes to the backbone architecture or additional supervision. Across CASIA-B, the proposed stream delivers consistent gains on strong skeleton backbones (e.g., GaitMixer, GaitFormer, GaitGraph) and establishes a new skeleton-based state of the art when attached to GaitMixer. The improvements are especially pronounced under covariate shifts such as carrying bags (BG) and wearing coats (CL), highlighting the complementarity of explicit time-frequency modeling and standard spatio-temporal encoders.
[CV-26] Rendering Multi-Human and Multi-Object with 3D Gaussian Splatting
【速读】：该论文旨在解决从稀疏视角输入中重建包含多个相互作用的人体与物体的动态场景（Multi-Human Multi-Object, MHMO rendering）的问题，其核心挑战在于：在严重相互遮挡条件下实现每个实例的视图一致性表示，以及显式建模因交互而产生的复杂且组合式的依赖关系。解决方案的关键在于提出一种基于3D高斯溅射（3D Gaussian Splatting）的分层框架MM-GS，其中包含两个核心模块：一是Per-Instance Multi-View Fusion模块，通过聚合所有可用视角的信息建立鲁棒且一致的个体实例表示；二是Scene-Level Instance Interaction模块，基于全局场景图推理所有参与者之间的关系，并精修其属性以捕捉细微的交互效应，从而显著提升渲染质量与实例间接触的合理性。
链接: https://arxiv.org/abs/2604.02996
作者: Weiquan Wang,Jun Xiao,Feifei Shao,Yi Yang,Yueting Zhuang,Long Chen
机构: Zhejiang University (浙江大学); The Hong Kong University of Science and Technology (香港科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing dynamic scenes with multiple interacting humans and objects from sparse-view inputs is a critical yet challenging task, essential for creating high-fidelity digital twins for robotics and VR/AR. This problem, which we term Multi-Human Multi-Object (MHMO) rendering, presents two significant obstacles: achieving view-consistent representations for individual instances under severe mutual occlusion, and explicitly modeling the complex and combinatorial dependencies that arise from their interactions. To overcome these challenges, we propose MM-GS, a novel hierarchical framework built upon 3D Gaussian Splatting. Our method first employs a Per-Instance Multi-View Fusion module to establish a robust and consistent representation for each instance by aggregating visual information across all available views. Subsequently, a Scene-Level Instance Interaction module operates on a global scene graph to reason about relationships between all participants, refining their attributes to capture subtle interaction effects. Extensive experiments on challenging datasets demonstrate that our method significantly outperforms strong baselines, producing state-of-the-art results with high-fidelity details and plausible inter-instance contacts.
[CV-27] Not All Frames Deserve Full Computation: Accelerating Autoregressive Video Generation via Selective Computation and Predictive Extrapolation
【速读】：该论文针对自回归视频扩散模型（Autoregressive Video Diffusion Models）在长视频生成中因重复多步去噪带来的高计算成本问题展开研究。现有无需训练的加速方法依赖于二元缓存或重计算决策，忽略了直接复用过于粗粒度而完全重计算又不必要的中间情况；同时，异步自回归调度将不同噪声水平分配给协同生成帧，但现有方法仍对整个有效区间统一处理，导致效率低下。解决方案的关键在于提出SCOPE框架，其核心创新为引入三模态调度机制（缓存、预测、重计算），并通过噪声水平的泰勒外推预测填补复用与重计算之间的空白，结合误差传播分析提供显式稳定性控制；此外，引入选择性计算策略，仅在活跃帧区间内执行操作，从而显著提升效率。在MAGI-1和SkyReels-V2数据集上，SCOPE实现最高达4.73倍加速，同时保持与原始输出相当的质量。
链接: https://arxiv.org/abs/2604.02979
作者: Hanshuai Cui,Zhiqing Tang,Zhi Yao,Fanshuai Meng,Weijia Jia,Wei Zhao
机构: Beijing Normal University (北京师范大学); Shenzhen University of Advanced Technology (深圳先进技术研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Autoregressive (AR) video diffusion models enable long-form video generation but remain expensive due to repeated multi-step denoising. Existing training-free acceleration methods rely on binary cache-or-recompute decisions, overlooking intermediate cases where direct reuse is too coarse yet full recomputation is unnecessary. Moreover, asynchronous AR schedules assign different noise levels to co-generated frames, yet existing methods process the entire valid interval uniformly. To address these AR-specific inefficiencies, we present SCOPE, a training-free framework for efficient AR video diffusion. SCOPE introduces a tri-modal scheduler over cache, predict, and recompute, where prediction via noise-level Taylor extrapolation fills the gap between reuse and recomputation with explicit stability controls backed by error propagation analysis. It further introduces selective computation that restricts execution to the active frame interval. On MAGI-1 and SkyReels-V2, SCOPE achieves up to 4.73x speedup while maintaining quality comparable to the original output, outperforming all training-free baselines.
[CV-28] Effect of Input Resolution on Retinal Vessel Segmentation Performance: An Empirical Study Across Five Datasets
【速读】：该论文旨在解决深度学习在视网膜血管分割中因图像下采样导致的细小血管检测性能下降问题。由于GPU内存限制，现有流程通常将高分辨率眼底图像（fundus images）统一缩放以支持批量处理，但此过程会使细小血管退化为亚像素结构，造成不可逆的信息丢失，而传统体积指标如Dice分数因厚血管像素主导评估结果，无法反映这种细微结构的损失。论文的关键解决方案是提出一种基于宽度分层的敏感性度量方法（width-stratified sensitivity metric），将血管按宽度分为细（<3像素）、中（3–7像素）和粗（>7像素）三类，并利用欧氏距离变换（Euclidean distance transform）从原始分辨率估计血管宽度，从而独立评估不同尺寸血管的检测性能。实验表明，在高分辨率数据集（HRF、FIVES）上适度下采样可提升细血管敏感性，而在低至中等分辨率数据集上则需保持原图分辨率以维持最佳检测效果，验证了仅依赖Dice分数会掩盖微血管分割中的关键性能差异。
链接: https://arxiv.org/abs/2604.02977
作者: Amarnath R
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 4 figures, 3 tables
Abstract:Most deep learning pipelines for retinal vessel segmentation resize fundus images to satisfy GPU memory constraints and enable uniform batch processing. However, the impact of this resizing on thin vessel detection remains underexplored. When high resolution images are downsampled, thin vessels are reduced to subpixel structures, causing irreversible information loss even before the data enters the network. Standard volumetric metrics such as the Dice score do not capture this loss because thick vessel pixels dominate the evaluation. We investigated this effect by training a baseline UNet at multiple downsampling ratios across five fundus datasets (DRIVE, STARE, CHASE_DB1, HRF, and FIVES) with native widths ranging from 565 to 3504 pixels, keeping all other settings fixed. We introduce a width-stratified sensitivity metric that evaluates thin (half-width 3 pixels), medium (3 to 7 pixels), and thick (7 pixels) vessel detection separately, using native resolution width estimates derived from a Euclidean distance transform. Results show that for high-resolution datasets (HRF, FIVES), thin vessel sensitivity improves monotonically as images are downsampled toward the encoder’s effective operating range, peaking at processed widths between 256 and 876 pixels. For low-to-mid resolution datasets (DRIVE, STARE, CHASE_DB1), thin vessel sensitivity is highest at or near native resolution and degrades with any downsampling. Across all five datasets, aggressive downsampling reduced thin vessel sensitivity by up to 15.8 percentage points (DRIVE) while Dice remained relatively stable, confirming that Dice alone is insufficient for evaluating microvascular segmentation.
[CV-29] Exploring Motion-Language Alignment for Text-driven Motion Generation
【速读】：该论文旨在解决文本驱动的人体动作生成中运动动力学与文本语义对齐不准确的问题（text-driven human motion generation with inaccurate motion-language alignment）。其解决方案的关键在于提出MLA-Gen框架，该框架通过融合全局运动先验与细粒度局部条件控制，使模型既能捕捉共性的运动模式，又能实现文本与动作之间的精细化对齐；同时，作者识别出此前被忽视的注意力聚集现象（attention sink），即注意力过度集中在起始文本标记上，导致关键语义信息利用不足，进而引入SinkRatio指标量化注意力集中度，并设计了对齐感知的掩码与控制策略以调节生成过程中的注意力分布，从而显著提升动作质量和语义对齐效果。
链接: https://arxiv.org/abs/2604.02973
作者: Ruxi Gu,Zilei Wang,Wei Wang
机构: University of Science and Technology of China (中国科学技术大学); State Key Laboratory of General Artificial Intelligence, BIGAI (通用人工智能国家实验室，BIGAI)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 8 figures
Abstract:Text-driven human motion generation aims to synthesize realistic motion sequences that follow textual descriptions. Despite recent advances, accurately aligning motion dynamics with textual semantics remains a fundamental challenge. In this paper, we revisit text-to-motion generation from the perspective of motion-language alignment and propose MLA-Gen, a framework that integrates global motion priors with fine-grained local conditioning. This design enables the model to capture common motion patterns, while establishing detailed alignment between texts and motions. Furthermore, we identify a previously overlooked attention sink phenomenon in human motion generation, where attention disproportionately concentrates on the start text token, limiting the utilization of informative textual cues and leading to degraded semantic grounding. To analyze this issue, we introduce SinkRatio, a metric for measuring attention concentration, and develop alignment-aware masking and control strategies to regulate attention during generation. Extensive experiments demonstrate that our approach consistently improves both motion quality and motion-language alignment over strong baselines. Code will be released upon acceptance.
[CV-30] Visual Prototype Conditioned Focal Region Generation for UAV-Based Object Detection CVPR2026
【速读】：该论文旨在解决无人机（UAV）平台上的目标检测任务在动态变化场景中因标注训练数据有限而导致的性能瓶颈问题。现有基于布局到图像生成的方法虽能通过扩散模型合成带标签图像提升检测精度，但常在小目标边界附近产生伪影，严重限制性能。其解决方案的关键在于提出UAVGen框架：一是设计视觉原型条件扩散模型（Visual Prototype Conditioned Diffusion Model, VPC-DM），通过构建每类代表性实例并融入潜在嵌入以实现高保真目标生成；二是引入焦点区域增强数据流水线（Focal Region Enhanced Data Pipeline, FRE-DP），强化前景目标密集区域的合成，并结合标签精修机制修正缺失、冗余及错位生成，从而显著提升检测准确性。
链接: https://arxiv.org/abs/2604.02966
作者: Wenhao Li,Zimeng Wu,Yu Wu,Zehua Fu,Jiaxin Chen
机构: Beihang University (北京航空航天大学); Hangzhou Innovation Institute, Beihang University (北京航空航天大学杭州创新研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR2026 Accepted
Abstract:Unmanned aerial vehicle (UAV) based object detection is a critical but challenging task, when applied in dynamically changing scenarios with limited annotated training data. Layout-to-image generation approaches have proved effective in promoting detection accuracy by synthesizing labeled images based on diffusion models. However, they suffer from frequently producing artifacts, especially near layout boundaries of tiny objects, thus substantially limiting their performance. To address these issues, we propose UAVGen, a novel layout-to-image generation framework tailored for UAV-based object detection. Specifically, UAVGen designs a Visual Prototype Conditioned Diffusion Model (VPC-DM) that constructs representative instances for each class and integrates them into latent embeddings for high-fidelity object generation. Moreover, a Focal Region Enhanced Data Pipeline (FRE-DP) is introduced to emphasize object-concentrated foreground regions in synthesis, combined with a label refinement to correct missing, extra and misaligned generations. Extensive experimental results demonstrate that our method significantly outperforms state-of-the-art approaches, and consistently promotes accuracy when integrated with distinct detectors. The source code is available at this https URL.
[CV-31] Collaborative Multi-Mode Pruning for Vision-Language Models CVPR2026
【速读】：该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）在资源受限设备上部署时面临的高计算复杂度问题，尤其是现有剪枝方法仅针对单一模态（参数或token）进行剪枝，未能充分挖掘各模态内部冗余，导致在高剪枝比例下性能显著下降。其解决方案的关键在于提出一种协同多模态剪枝框架（Collaborative Multi-Mode Pruning, CoMP），通过设计协同重要性度量（Collaborative Importance Metric, CIM）来联合评估参数与token的重要性，同时引入多模态剪枝策略（Multi-Mode Pruning Strategy, MPS），将整体剪枝过程分解为多个阶段，并基于剪枝代价自适应选择最优剪枝模式，结合历史成本与随机探索机制以稳定剪枝过程并避免陷入局部最优，从而在保持高性能的同时实现高效压缩。
链接: https://arxiv.org/abs/2604.02956
作者: Zimeng Wu,Yunhong Wang,Donghao Wang,Jiaxin Chen
机构: Beihang University (北京航空航天大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR2026 Accepted
Abstract:Vision-Language Models (VLMs) have advanced rapidly within the unified Transformer architecture, yet their deployment on resource-constrained devices remains challenging due to high computational complexity. While pruning has emerged as an effective technique for compressing VLMs, existing approaches predominantly focus on a single mode by pruning either parameters or tokens, neglecting fully exploring the inherent redundancy in each mode, which leads to substantial performance degradation at high pruning ratios. To address the above limitations, we propose Collaborative Multi-Mode Pruning (CoMP), a novel framework tailored for VLMs by performing joint parameter and token pruning. Specifically, we first design a Collaborative Importance Metric (CIM) that investigates the mutual interference between the coupled parameters and tokens. It incorporates distinct significance of tokens into the computation of parameter importance scores, while simultaneously mitigating the affect of pruned parameters on token importance scores. Moreover, we develop a Multi-Mode Pruning Strategy (MPS) that decomposes the overall pruning process into a sequence of pruning stages, while in each stage we estimate the priory of different pruning modes based on their pruning cost and adaptively shift to the optimal one. Additionally, MPS integrates the historical cost and random exploration, in order to achieve a stable pruning process and avoid local optimum. Extensive experiments across various vision-language tasks and models demonstrate that our method effectively promotes the performance under high pruning ratios by comparing to the state-of-the-art approaches. The source code is available at this https URL.
[CV-32] CrossWeaver: Cross-modal Weaving for Arbitrary-Modality Semantic Segmentation
【速读】：该论文旨在解决多模态语义分割中因融合策略设计不当而导致的跨模态协调不足与模态特异性信息保留困难的问题。现有方法通常依赖于手工设计的融合机制，或采用松散耦合的交互方式，限制了灵活性并影响了跨模态信息的有效整合。其解决方案的关键在于提出CrossWeaver框架，核心由两个模块构成：一是模态交互块（Modality Interaction Block, MIB），在编码器内实现选择性且可靠性感知的跨模态交互；二是轻量级缝合对齐融合（Seam-Aligned Fusion, SAF）模块，用于聚合增强后的特征。该设计在保持各模态独特性的同时，实现了高效的信息交换与灵活的模态组合适应能力。
链接: https://arxiv.org/abs/2604.02948
作者: Zelin Zhang,Kedi Li,Huiqi Liang,Tao Zhang,Chuanzhi Xu
机构: The University of Sydney(悉尼大学); University of Technology Sydney(悉尼科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal semantic segmentation has shown great potential in leveraging complementary information across diverse sensing modalities. However, existing approaches often rely on carefully designed fusion strategies that either use modality-specific adaptations or rely on loosely coupled interactions, thereby limiting flexibility and resulting in less effective cross-modal coordination. Moreover, these methods often struggle to balance efficient information exchange with preserving the unique characteristics of each modality across different modality combinations. To address these challenges, we propose CrossWeaver, a simple yet effective multimodal fusion framework for arbitrary-modality semantic segmentation. Its core is a Modality Interaction Block (MIB), which enables selective and reliability-aware cross-modal interaction within the encoder, while a lightweight Seam-Aligned Fusion (SAF) module further aggregates the enhanced features. Extensive experiments on multiple multimodal semantic segmentation benchmarks demonstrate that our framework achieves state-of-the-art performance with minimal additional parameters and strong generalization to unseen modality combinations.
[CV-33] Learning from Synthetic Data via Provenance-Based Input Gradient Guidance CVPR2026
【速读】：该论文旨在解决现有基于合成数据的学习方法在提升模型鲁棒性时存在的局限性——即这些方法通常仅通过增加训练样本的多样性间接改善性能，而未明确指导模型关注输入空间中真正有助于判别的区域，导致模型可能习得由合成偏差和伪相关引起的错误特征。解决方案的关键在于利用数据合成过程中获得的来源信息（provenance information），即标注每个输入区域是否源自目标对象，并将其作为辅助监督信号，引导模型聚焦于目标区域；具体而言，通过分解输入梯度并基于目标与非目标区域的信息引入梯度引导机制，抑制非目标区域的梯度响应，从而减少模型对非目标区域的依赖，直接促进对目标区域判别性表征的学习。
链接: https://arxiv.org/abs/2604.02946
作者: Koshiro Nagano,Ryo Fujii,Ryo Hachiuma,Fumiaki Sato,Taiki Sekii,Hideo Saito
机构: Keio University(庆应义塾大学); CyberAgent( CyberAgent)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: CVPR 2026
Abstract:Learning methods using synthetic data have attracted attention as an effective approach for increasing the diversity of training data while reducing collection costs, thereby improving the robustness of model discrimination. However, many existing methods improve robustness only indirectly through the diversification of training samples and do not explicitly teach the model which regions in the input space truly contribute to discrimination; consequently, the model may learn spurious correlations caused by synthesis biases and artifacts. Motivated by this limitation, this paper proposes a learning framework that uses provenance information obtained during the training data synthesis process, indicating whether each region in the input space originates from the target object, as an auxiliary supervisory signal to promote the acquisition of representations focused on target regions. Specifically, input gradients are decomposed based on information about target and non-target regions during synthesis, and input gradient guidance is introduced to suppress gradients over non-target regions. This suppresses the model’s reliance on non-target regions and directly promotes the learning of discriminative representations for target regions. Experiments demonstrate the effectiveness and generality of the proposed method across multiple tasks and modalities, including weakly supervised object localization, spatio-temporal action localization, and image classification.
[CV-34] MMTalker: Multiresolution 3D Talking Head Synthesis with Multimodal Feature Fusion
【速读】：该论文旨在解决语音驱动的三维（3D）面部动画合成中唇同步精度不足与面部表情不真实的问题，其核心挑战源于跨模态映射的高度病态性（ill-posed nature）。解决方案的关键在于提出一种基于多分辨率表示与多模态特征融合的新型方法MMTalker：首先通过网格参数化（mesh parameterization）和可微分非均匀采样实现3D人脸的连续细节表征，建立UV平面与3D面部网格间的对应关系并支持连续学习；其次采用残差图卷积网络与双交叉注意力机制（dual cross-attention mechanism）提取来自语音及面部网格的多层次特征，有效融合语音的语义层次信息与面部几何的显式时空结构；最后利用轻量回归网络联合处理归一化UV空间中的采样点与编码后的运动特征，预测顶点级几何位移，从而显著提升唇部与眼部动作的同步准确性。
链接: https://arxiv.org/abs/2604.02941
作者: Bin Liu,Zhixiang Xiong,Zhifen He,Bo Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages
Abstract:Speech-driven three-dimensional (3D) facial animation synthesis aims to build a mapping from one-dimensional (1D) speech signals to time-varying 3D facial motion signals. Current methods still face challenges in maintaining lip-sync accuracy and producing realistic facial expressions, primarily due to the highly ill-posed nature of this cross-modal mapping. In this paper, we introduce a novel 3D audio-driven facial animation synthesis method through multi-resolution representation and multi-modal feature fusion, called MMTalker which can accurately reconstruct the rich details of 3D facial motion. We first achieve the continuous representation of 3D face with details by mesh parameterization and non-uniform differentiable sampling. The mesh parameterization technique establishes the correspondence between UV plane and 3D facial mesh and is used to offer ground truth for the continuous learning. Differentiable non-uniform sampling enables precise facial detail acquisition by setting learnable sampling probability in each triangular face. Next, we employ residual graph convolutional network and dual cross-attention mechanism to extract discriminative facial motion feature from multiple input modalities. This proposed multimodal fusion strategy takes full use of the hierarchical features of speech and the explicit spatiotemporal geometric features of facial mesh. Finally, a lightweight regression network predicts the vertex-wise geometric displacements of the synthesized talking face by jointly processing the sampled points in the canonical UV space and the encoded facial motion features. Comprehensive experiments demonstrate that significant improvements are achieved over state-of-the-art methods, especially in the synchronization accuracy of lip and eye movements. Comments: 9 pages Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2604.02941 [cs.CV] (or arXiv:2604.02941v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2604.02941 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-35] Modality-Specific Hierarchical Enhancement for RGB-D Camouflaged Object Detection ICME2026
【速读】：该论文旨在解决RGB-D图像中伪装目标检测（Camouflaged Object Detection, COD）因目标与背景高度相似而导致的识别困难问题，尤其针对现有方法在融合RGB与深度模态信息时未能充分挖掘各自特异性线索（modality-specific cues）所导致的融合质量不佳问题。其解决方案的关键在于提出MHENet框架，通过三个核心模块实现：首先，引入纹理层次增强模块（Texture Hierarchical Enhancement Module, THEM），利用高频信息提取放大细微纹理差异；其次，设计几何层次增强模块（Geometry Hierarchical Enhancement Module, GHEM），通过可学习梯度提取强化几何结构并保持跨尺度语义一致性；最后，采用自适应动态融合模块（Adaptive Dynamic Fusion Module, ADFM），以空间变化权重自适应融合增强后的纹理与几何特征，从而显著提升多模态特征表达能力与检测性能。
链接: https://arxiv.org/abs/2604.02935
作者: Yuzhen Niu,Yangqing Wang,Ri Cheng,Fusheng Li,Rongshen Wang,Zhichen Yang
机构: Fuzhou University (福州大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 7 figures, including supplementary material. Accepted by IEEE ICME 2026
Abstract:Camouflaged object detection (COD) is challenging due to high target-background similarity, and recent methods address this by complementarily using RGB-D texture and geometry cues. However, RGB-D COD methods still underutilize modality-specific cues, which limits fusion quality. We believe this is because RGB and depth features are fused directly after backbone extraction without modality-specific enhancement. To address this limitation, we propose MHENet, an RGB-D COD framework that performs modality-specific hierarchical enhancement and adaptive fusion of RGB and depth features. Specifically, we introduce a Texture Hierarchical Enhancement Module (THEM) to amplify subtle texture variations by extracting high-frequency information and a Geometry Hierarchical Enhancement Module (GHEM) to enhance geometric structures via learnable gradient extraction, while preserving cross-scale semantic consistency. Finally, an Adaptive Dynamic Fusion Module (ADFM) adaptively fuses the enhanced texture and geometry features with spatially varying weights. Experiments on four benchmarks demonstrate that MHENet surpasses 16 state-of-the-art methods qualitatively and quantitatively. Code is available at this https URL.
[CV-36] PolyReal: A Benchmark for Real-World Polymer Science Workflows
【速读】：该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在真实科学场景中评估不足的问题，尤其是其在聚合物科学（polymer science）这一跨学科领域中的实践应用能力未被系统性检验。现有基准测试多聚焦于抽象知识推理，忽视了实验流程中的实际任务，如实验室安全分析、原始数据提取等，导致模型能力评估与真实科研工作脱节。解决方案的关键在于提出PolyReal——一个基于真实科学实践的多模态基准，覆盖从基础认知到实验操作再到性能探索的完整聚合物实验生命周期，包含五大核心能力维度：基础知识点应用、实验室安全分析、实验机制推理、原始数据提取及性能应用探索。通过该基准对主流MLLMs的评估揭示了模型在知识密集型任务上表现良好，但在依赖上下文和实操经验的任务上显著下降，从而精准识别出抽象知识与实践应用之间的能力鸿沟，为未来AI在科学工作流中的落地提供了可量化、可复现的评估框架。
链接: https://arxiv.org/abs/2604.02934
作者: Wanhao Liu,Weida Wang,Jiaqing Xie,Suorong Yang,Jue Wang,Benteng Chen,Guangtao Mei,Zonglin Yang,Shufei Zhang,Yuchun Mo,Lang Cheng,Jin Zeng,Houqiang Li,Wanli Ouyang,Yuqiang Li
机构: University of Science and Technology of China (中国科学技术大学); Shanghai Artificial Intelligence Laboratory (上海人工智能实验室); Fudan University (复旦大学); Northwestern Polytechnical University (西北工业大学); Tongji University (同济大学); The University of Hong Kong (香港大学); National University of Singapore (新加坡国立大学); Nanyang Technological University (南洋理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Language Models (MLLMs) excel in general domains but struggle with complex, real-world science. We posit that polymer science, an interdisciplinary field spanning chemistry, physics, biology, and engineering, is an ideal high-stakes testbed due to its diverse multimodal data. Yet, existing benchmarks related to polymer science largely overlook real-world workflows, limiting their practical utility and failing to systematically evaluate MLLMs across the full, practice-grounded lifecycle of experimentation. We introduce PolyReal, a novel multimodal benchmark grounded in real-world scientific practices to evaluate MLLMs on the full lifecycle of polymer experimentation. It covers five critical capabilities: (1) foundational knowledge application; (2) lab safety analysis; (3) experiment mechanism reasoning; (4) raw data extraction; and (5) performance application exploration. Our evaluation of leading MLLMs on PolyReal reveals a capability imbalance. While models perform well on knowledge-intensive reasoning (e.g., Experiment Mechanism Reasoning), they drop sharply on practice-based tasks (e.g., Lab Safety Analysis and Raw Data Extraction). This exposes a severe gap between abstract scientific knowledge and its practical, context-dependent application, showing that these real-world tasks remain challenging for MLLMs. Thus, PolyReal helps address this evaluation gap and provides a practical benchmark for assessing AI systems in real-world scientific workflows.
[CV-37] BEVPredFormer: Spatio-temporal Attention for BEV Instance Prediction in Autonomous Driving
【速读】：该论文旨在解决自动驾驶感知系统中动态场景演化建模的难题，尤其针对传统模块化感知流水线因累积误差和延迟导致的性能瓶颈。其核心挑战在于如何高效处理动态驾驶环境中密集的空间-时间信息，同时保持实时性并捕捉细粒度运动模式与长程依赖关系。解决方案的关键在于提出BEVPredFormer——一种纯摄像头输入的鸟瞰图（Bird’s-Eye-View, BEV）实例预测架构，通过基于注意力机制的时序处理提升时空理解能力，并引入注意力驱动的3D相机信息投影方式；此外，采用无循环设计结合门控Transformer层、分时空注意力机制及多尺度头任务，辅以差异引导特征提取模块增强时序表征，从而在nuScenes数据集上达到或超越当前最优性能。
链接: https://arxiv.org/abs/2604.02930
作者: Miguel Antunes-García,Santiago Montiel-Marín,Fabio Sánchez-García,Rodrigo Gutiérrez-Moreno,Rafael Barea,Luis M. Bergasa
机构: University of Alcalá (阿尔卡拉大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 5 figures
Abstract:A robust awareness of how dynamic scenes evolve is essential for Autonomous Driving systems, as they must accurately detect, track, and predict the behaviour of surrounding obstacles. Traditional perception pipelines that rely on modular architectures tend to suffer from cumulative errors and latency. Instance Prediction models provide a unified solution, performing Bird’s-Eye-View segmentation and motion estimation across current and future frames using information directly obtained from different sensors. However, a key challenge in these models lies in the effective processing of the dense spatial and temporal information inherent in dynamic driving environments. This level of complexity demands architectures capable of capturing fine-grained motion patterns and long-range dependencies without compromising real-time performance. We introduce BEVPredFormer, a novel camera-only architecture for BEV instance prediction that uses attention-based temporal processing to improve temporal and spatial comprehension of the scene and relies on an attention-based 3D projection of the camera information. BEVPredFormer employs a recurrent-free design that incorporates gated transformer layers, divided spatio-temporal attention mechanisms, and multi-scale head tasks. Additionally, we incorporate a difference-guided feature extraction module that enhances temporal representations. Extensive ablation studies validate the effectiveness of each architectural component. When evaluated on the nuScenes dataset, BEVPredFormer was on par or surpassed State-Of-The-Art methods, highlighting its potential for robust and efficient Autonomous Driving perception.
[CV-38] GP-4DGS: Probabilistic 4D Gaussian Splatting from Monocular Video via Variational Gaussian Processes KR CVPR2026
【速读】：该论文旨在解决现有4D Gaussian Splatting（4DGS）方法在动态场景建模中缺乏概率性表达的问题，特别是无法量化运动预测的不确定性、难以处理未观测或稀疏采样区域的运动估计，以及无法进行时间外推。其解决方案的关键在于将高斯过程（Gaussian Processes, GP）引入4DGS框架，构建GP-4DGS，通过设计时空核函数捕捉形变场的相关结构，并采用带诱导点的变分高斯过程实现可扩展的推理，从而实现对运动不确定性的量化、未观测区域的运动补全及时间外推能力，显著提升了重建质量与预测可靠性。
链接: https://arxiv.org/abs/2604.02915
作者: Mijeong Kim,Jungtaek Kim,Bohyung Han
机构: Seoul National University (首尔国立大学); University of Wisconsin–Madison (威斯康星大学麦迪逊分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026, Page: this https URL
Abstract:We present GP-4DGS, a novel framework that integrates Gaussian Processes (GPs) into 4D Gaussian Splatting (4DGS) for principled probabilistic modeling of dynamic scenes. While existing 4DGS methods focus on deterministic reconstruction, they are inherently limited in capturing motion ambiguity and lack mechanisms to assess prediction reliability. By leveraging the kernel-based probabilistic nature of GPs, our approach introduces three key capabilities: (i) uncertainty quantification for motion predictions, (ii) motion estimation for unobserved or sparsely sampled regions, and (iii) temporal extrapolation beyond observed training frames. To scale GPs to the large number of Gaussian primitives in 4DGS, we design spatio-temporal kernels that capture the correlation structure of deformation fields and adopt variational Gaussian Processes with inducing points for tractable inference. Our experiments show that GP-4DGS enhances reconstruction quality while providing reliable uncertainty estimates that effectively identify regions of high motion ambiguity. By addressing these challenges, our work takes a meaningful step toward bridging probabilistic modeling and neural graphics.
[CV-39] UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting CVPR2026
【速读】：该论文旨在解决工业缺陷检测中现有方法在开放集场景下难以识别新型异常的问题，特别是针对视觉提示（visual prompting）方法因类内差异大和类间差异细微而导致的提示嵌入坍塌（prompt embedding collapse）问题。解决方案的关键在于提出UniSpector框架，其核心创新是将注意力从简单的提示到区域匹配转向语义结构化且可迁移的提示拓扑设计：通过空间-谱提示编码器（Spatial-Spectral Prompt Encoder）提取方向不变、细粒度的表示，并借助对比提示编码器（Contrastive Prompt Encoder）显式地将提示空间规整为语义有序的角度流形；同时引入提示引导查询选择（Prompt-guided Query Selection）生成与提示对齐的自适应对象查询，从而实现无需重训练的可扩展缺陷定位范式。
链接: https://arxiv.org/abs/2604.02905
作者: Geonuk Kim,Minhoi Kim,Kangil Lee,Minsu Kim,Hyeonseong Jeon,Jeonghoon Han,Hyoungjoon Lim,Junho Yim
机构: LG Energy Solution
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:Although industrial inspection systems should be capable of recognizing unprecedented defects, most existing approaches operate under a closed-set assumption, which prevents them from detecting novel anomalies. While visual prompting offers a scalable alternative for industrial inspection, existing methods often suffer from prompt embedding collapse due to high intra-class variance and subtle inter-class differences. To resolve this, we propose UniSpector, which shifts the focus from naive prompt-to-region matching to the principled design of a semantically structured and transferable prompt topology. UniSpector employs the Spatial-Spectral Prompt Encoder to extract orientation-invariant, fine-grained representations; these serve as a solid basis for the Contrastive Prompt Encoder to explicitly regularize the prompt space into a semantically organized angular manifold. Additionally, Prompt-guided Query Selection generates adaptive object queries aligned with the prompt. We introduce Inspect Anything, the first benchmark for visual-prompt-based open-set defect localization, where UniSpector significantly outperforms baselines by at least 19.7% and 15.8% in AP50b and AP50m, respectively. These results show that our method enable a scalable, retraining-free inspection paradigm for continuously evolving industrial environments, while offering critical insights into the design of generic visual prompting.
[CV-40] RayMamba: Ray-Aligned Serialization for Long-Range 3D Object Detection
【速读】：该论文旨在解决远距离三维目标检测中因激光雷达（LiDAR）观测稀疏且碎片化而导致上下文建模困难的问题。现有基于状态空间模型（State Space Model, SSM）的方法虽提升了长距离建模效率，但受限于通用序列化策略无法保留稀疏场景中的有意义上下文邻域。其解决方案的关键在于提出RayMamba，一种几何感知的即插即用增强模块，通过射线对齐的序列化策略将稀疏体素组织为扇区有序序列，从而保持方向连续性和遮挡相关上下文信息，为后续Mamba-based建模提供更有效的输入。该方法兼容纯LiDAR与多模态检测器，计算开销小，并在nuScenes和Argoverse 2数据集上显著提升远距离检测性能。
链接: https://arxiv.org/abs/2604.02903
作者: Cheng Lu,Mingqian Ji,Shanshan Zhang,Zhihao Li,Jian Yang
机构: Nanjing University of Science and Technology (南京理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Long-range 3D object detection remains challenging because LiDAR observations become highly sparse and fragmented in the far field, making reliable context modeling difficult for existing detectors. To address this issue, recent state space model (SSM)-based methods have improved long-range modeling efficiency. However, their effectiveness is still limited by generic serialization strategies that fail to preserve meaningful contextual neighborhoods in sparse scenes. To address this issue, we propose RayMamba, a geometry-aware plug-and-play enhancement for voxel-based 3D detectors. RayMamba organizes sparse voxels into sector-wise ordered sequences through a ray-aligned serialization strategy, which preserves directional continuity and occlusion-related context for subsequent Mamba-based modeling. It is compatible with both LiDAR-only and multimodal detectors, while introducing only modest overhead. Extensive experiments on nuScenes and Argoverse 2 demonstrate consistent improvements across strong baselines. In particular, RayMamba achieves up to 2.49 mAP and 1.59 NDS gain in the challenging 40–50 m range on nuScenes, and further improves VoxelNeXt on Argoverse 2 from 30.3 to 31.2 mAP.
[CV-41] EvaNet: Towards More Efficient and Consistent Infrared and Visible Image Fusion Assessment
【速读】：该论文旨在解决图像融合（image fusion）领域中评估指标不适用、计算复杂度高且与人类视觉感知一致性差的问题。现有评价指标多直接借鉴其他视觉任务，未针对图像融合特性进行适配，导致无法准确反映融合结果的质量。其解决方案的关键在于提出一个统一的轻量级学习框架，通过“分而治之”策略将融合结果分解为红外和可见光成分，分别评估信息保留程度，从而解耦评价过程；同时引入对比学习与大语言模型（Large Language Model, LLM）提供的感知场景评估作为训练信号，并构建首个基于无参考评分与下游任务性能的一致性评估体系，以衡量指标与人类视觉感知的对齐程度。该方法在多个标准数据集上实现了高达1000倍的加速效率与更强的一致性表现。
链接: https://arxiv.org/abs/2604.02896
作者: Chunyang Cheng,Tianyang Xu,Xiao-Jun Wu,Tao Zhou,Hui Li,Zhangyong Tang,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: 20 figures,accepted by TPAMI
Abstract:Evaluation is essential in image fusion research, yet most existing metrics are directly borrowed from other vision tasks without proper adaptation. These traditional metrics, often based on complex image transformations, not only fail to capture the true quality of the fusion results but also are computationally demanding. To address these issues, we propose a unified evaluation framework specifically tailored for image fusion. At its core is a lightweight network designed efficiently to approximate widely used metrics, following a divide-and-conquer strategy. Unlike conventional approaches that directly assess similarity between fused and source images, we first decompose the fusion result into infrared and visible components. The evaluation model is then used to measure the degree of information preservation in these separated components, effectively disentangling the fusion evaluation process. During training, we incorporate a contrastive learning strategy and inform our evaluation model by perceptual scene assessment provided by a large language model. Last, we propose the first consistency evaluation framework, which measures the alignment between image fusion metrics and human visual perception, using both independent no-reference scores and downstream tasks performance as objective references. Extensive experiments show that our learning-based evaluation paradigm delivers both superior efficiency (up to 1,000 times faster) and greater consistency across a range of standard image fusion benchmarks. Our code will be publicly available at this https URL.
[CV-42] oward an Artificial General Teacher: Procedural Geometry Data Generation and Visual Grounding with Vision-Language Models
【速读】：该论文旨在解决几何教育中视觉解释任务的挑战，即将几何图示中的特定元素与自然语言描述进行精确对应的问题（即指代表达图像分割，Referring Image Segmentation, RIS）。传统RIS模型在自然图像数据集（如RefCOCO）上训练后，在抽象、无纹理的几何图示上表现严重退化，主要由于领域偏移问题。解决方案的关键在于：首先，构建一个全自动的程序化数据生成引擎，可合成超过20万张带像素级掩码和语言多样性的几何图示，无需人工标注；其次，提出针对几何领域的视觉-语言模型（VLM）微调策略，实验表明微调后的Florence-2模型在IoU指标上从零样本下的1%提升至49%，并引入几何感知的Buffered IoU（BIoU）评估指标，更准确反映细结构定位质量。该方法为构建具备视觉引导能力的通用教师系统（Artificial General Teachers, AGTs）奠定了基础。
链接: https://arxiv.org/abs/2604.02893
作者: Hai Nguyen-Truong,Alper Balbay,Tunga Bayrak
机构: Freya(弗雷娅); Google(谷歌)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 7 figures
Abstract:We study visual explanation in geometry education as a Referring Image Segmentation (RIS) problem: given a diagram and a natural language description, the task is to produce a pixel-level mask for the referred geometric element. However, existing RIS models trained on natural image benchmarks such as RefCOCO fail catastrophically on geometric diagrams due to the fundamental domain shift between photographic scenes and abstract, textureless schematics. To address the absence of suitable training data, we present a fully automated procedural data engine that generates over 200,000 synthetic geometry diagrams with pixel-perfect segmentation masks and linguistically diverse referring expressions, requiring zero manual annotation. We further propose domain-specific fine-tuning of vision-language models (VLMs), demonstrating that a fine-tuned Florence-2 achieves 49% IoU and 85% Buffered IoU (BIoU), compared to 1% IoU in zero-shot settings. We introduce Buffered IoU, a geometry-aware evaluation metric that accounts for thin-structure localization, and show that it better reflects true segmentation quality than standard IoU. Our results establish a foundation for building Artificial General Teachers (AGTs) capable of providing visually grounded, step-by-step explanations of geometry problems.
[CV-43] Progressive Video Condensation with MLLM Agent for Long-form Video Understanding ICME2026
【速读】：该论文旨在解决在计算资源受限条件下，如何高效地从长视频中提取与查询相关的细粒度信息以支持视频理解的问题。现有方法如“文本-大语言模型（LLM）”流水线会丢失视觉细节，而基于视频的多模态大语言模型（Multimodal Large Language Models, MLLMs）虽能保留视觉信息但存在帧数消耗过高、计算成本大的问题。解决方案的关键在于提出一种渐进式视频压缩代理（Progressive Video Condensation Agent, ProVCA），其通过三阶段迭代机制实现高效关键帧定位：首先利用片段定位模块识别与查询相关的视频段，再通过片段选择模块基于相似性筛选重要片段，最后通过关键帧精炼模块在选定片段内精确提取关键帧。该方法从粗粒度到细粒度逐步缩小范围，最终生成少量高质量关键帧供MLLM推理使用，从而在不依赖训练的情况下显著提升零样本视频理解准确率并降低帧数需求。
链接: https://arxiv.org/abs/2604.02891
作者: Yufei Yin,Yuchen Xing,Qianke Meng,Minghao Chen,Yan Yang,Zhou Yu
机构: Hangzhou Dianzi University(杭州电子科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICME 2026
Abstract:Understanding long videos requires extracting query-relevant information from long sequences under tight compute budgets. Existing text-then-LLM pipelines lose fine-grained visual cues, while video-based multimodal large language models (MLLMs) can keep visual details but are too frame-hungry and computationally expensive. In this work, we aim to harness MLLMs for efficient video understanding. We propose ProVCA, a progressive video condensation agent that iteratively locates key video frames at multiple granularities. ProVCA first adopts a segment localization module to identify the video segment relevant to the query, then a snippet selection module to select important snippets based on similarity, and finally a keyframe refinement module to pinpoint specific keyframes in those snippets. By progressively narrowing the scope from coarse segments to fine frames, ProVCA identifies a small set of keyframes for MLLM-based reasoning. ProVCA achieves state-of-the-art zero-shot accuracies of 69.3% on EgoSchema, 80.5% on NExT-QA, and 77.7% on IntentQA, while using fewer frames than previous training-free methods.
[CV-44] Information-Regularized Constrained Inversion for Stable Avatar Editing from Sparse Supervision
【速读】：该论文旨在解决在稀疏监督条件下编辑可动画化人类虚拟形象时出现的身份泄露（identity leakage）和依赖姿态的时序闪烁（pose-dependent temporal flicker）问题。这些问题源于现有方法在将重建的虚拟形象拟合到少量编辑关键帧时，由于约束不足导致潜在空间中编辑方向不明确，从而引发不稳定的结果。解决方案的关键在于提出一种基于条件引导的编辑重建框架，通过在结构化的虚拟形象潜在空间中进行受约束的逆向映射，将更新限制在低维、部件特定的编辑子空间内，以避免非预期的身份变化；同时，通过优化由完整解码与渲染流程局部线性化得到的条件目标函数，构建一个编辑子空间信息矩阵，其谱特性可预测稳定性并指导帧权重调整或关键帧激活，从而实现高效且稳定的编辑效果。
链接: https://arxiv.org/abs/2604.02883
作者: Zhenxiao Liang,Qixing Huang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Editing animatable human avatars typically relies on sparse supervision, often a few edited keyframes, yet naively fitting a reconstructed avatar to these edits frequently causes identity leakage and pose-dependent temporal flicker. We argue that these failures are best understood as an ill-conditioned inversion: the available edited constraints do not sufficiently determine the latent directions responsible for the intended edit. We propose a conditioning-guided edited reconstruction framework that performs editing as a constrained inversion in a structured avatar latent space, restricting updates to a low-dimensional, part-specific edit subspace to prevent unintended identity changes. Crucially, we design the editing constraints during inversion by optimizing a conditioning objective derived from a local linearization of the full decoding-and-rendering pipeline, yielding an edit-subspace information matrix whose spectrum predicts stability and drives frame reweighting / keyframe activation. The resulting method operates on small subspace matrices and can be implemented efficiently (e.g., via Hessian-vector products), and improves stability under limited edited supervision.
[CV-45] InstructTable: Improving Table Structure Recognition Through Instructions CVPR
【速读】：该论文旨在解决复杂版式表格图像中结构识别（Table Structure Recognition, TSR）的难题，尤其针对合并单元格（merged cells）和空单元格（empty cells）等复杂布局带来的挑战。传统视觉模型仅依赖视觉信息而缺乏语义支持，而现有视觉-语言模型虽引入上下文语义但忽视了对视觉结构信息的充分建模。解决方案的关键在于提出一种指令引导的多阶段训练框架 InstructTable：首先通过精心设计的表格专用指令预训练聚焦细粒度结构模式，提升对复杂表格的理解能力；其次通过互补的TSR微调保留强健的视觉结构建模能力，确保在多样化场景下的高精度解析。此外，论文还提出了无需模板的合成方法 Table Mix Expand (TME)，用于生成大规模真实感表格数据，并构建了 BCDSTab 基准测试集，实验表明该方案在多个公开数据集及新基准上均达到当前最优性能。
链接: https://arxiv.org/abs/2604.02880
作者: Boming Chen,Zining Wang,Zhentao Guo,Jianqiang Liu,Chen Duan,Yu Gu,Kai zhou,Pengfei Yan
机构: Meituan(美团); Beijing Institute of Technology(北京理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition- FINDINGS Track (CVPRF)
Abstract:Table structure recognition (TSR) holds widespread practical importance by parsing tabular images into structured representations, yet encounters significant challenges when processing complex layouts involving merged or empty cells. Traditional visual-centric models rely exclusively on visual information while lacking crucial semantic support, thereby impeding accurate structural recognition in complex scenarios. Vision-language models leverage contextual semantics to enhance comprehension; however, these approaches underemphasize the modeling of visual structural information. To address these limitations, this paper introduces InstructTable, an instruction-guided multi-stage training TSR framework. Meticulously designed table instruction pre-training directs attention toward fine-grained structural patterns, enhancing comprehension of complex tables. Complementary TSR fine-tuning preserves robust visual information modeling, maintaining high-precision table parsing across diverse scenarios. Furthermore, we introduce Table Mix Expand (TME), an innovative template-free method for synthesizing large-scale authentic tabular data. Leveraging TME, we construct the Balanced Complex Dense Synthetic Tables (BCDSTab) benchmark, comprising 900 complex table images synthesized through our method to serve as a rigorous benchmark. Extensive experiments on multiple public datasets (FinTabNet, PubTabNet, MUSTARD) and BCDSTab demonstrate that InstructTable achieves state-of-the-art performance in TSR tasks. Ablation studies further confirm the positive impact of the proposed tabular-data-specific instructions and synthetic data.
[CV-46] Unlocking Positive Transfer in Incrementally Learning Surgical Instruments: A Self-reflection Hierarchical Prompt Framework CVPR2026
【速读】：该论文旨在解决手术视频场景分割中类增量学习（class incremental segmentation）所面临的灾难性遗忘问题，同时挖掘正向知识迁移（positive forward knowledge transfer）与反向知识迁移（positive backward knowledge transfer）的潜力，以实现对新手术器械的有效学习、对已有器械分割性能的提升以及旧知识的稳定保留。其解决方案的关键在于提出一种自省式分层提示框架（self-reflection hierarchical prompt framework），该框架基于冻结的预训练模型，通过动态添加针对新类别的器械感知提示（instrument-aware prompts）来适应增量训练；同时构建一个层次化提示解析树（hierarchical prompt parsing tree），利用共享提示分区作为根节点、多类别共享提示为中间节点、独特提示为叶节点，从而显式暴露可复用的历史知识以促进新类学习（正向迁移）；并通过有向加权图传播机制进行自我反思精炼（self-reflection refining），基于树结构中的知识关联优化现有知识表示，避免遗忘（反向迁移）。此方法适用于CNN与Transformer基础模型，在两个公开基准上分别实现超过5%和11%的性能提升。
链接: https://arxiv.org/abs/2604.02877
作者: Yu Zhu,Kang Li,Zheng Li,Pheng-Ann Heng
机构: The Chinese University of Hong Kong; University of Electronic Science and Technology of China
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026
Abstract:To continuously enhance model adaptability in surgical video scene parsing, recent studies incrementally update it to progressively learn to segment an increasing number of surgical instruments over time. However, prior works constantly overlooked the potential of positive forward knowledge transfer, i.e., how past knowledge could help learn new classes, and positive backward knowledge transfer, i.e., how learning new classes could help refine past knowledge. In this paper, we propose a self-reflection hierarchical prompt framework that unlocks the power of positive forward and backward knowledge transfer in class incremental segmentation, aiming to proficiently learn new instruments, improve existing skills of regular instruments, and avoid catastrophic forgetting of old instruments. Our framework is built on a frozen, pre-trained model that adaptively appends instrument-aware prompts for new classes throughout training episodes. To enable positive forward knowledge transfer, we organize instrument prompts into a hierarchical prompt parsing tree with the instrument-shared prompt partition as the root node, n-part-shared prompt partitions as intermediate nodes and instrument-distinct prompt partitions as leaf nodes, to expose the reusable historical knowledge for new classes to simplify their learning. Conversely, to encourage positive backward knowledge transfer, we conduct self-reflection refining on existing knowledge by directed-weighted graph propagation, examining the knowledge associations recorded in the tree to improve its representativeness without causing catastrophic forgetting. Our framework is applicable to both CNN-based models and advanced transformer-based foundation models, yielding more than 5% and 11% improvements over the competing methods on two public benchmarks respectively.
[CV-47] SPG: Sparse-Projected Guides with Sparse Autoencoders for Zero-Shot Anomaly Detection
【速读】：该论文旨在解决零样本异常检测与分割问题，即在不进行目标域微调的前提下，利用预训练基础模型（foundation model）的冻结特征实现对未见过类别异常的识别与定位。其核心挑战在于如何在无目标域标注数据的情况下，有效建模正常与异常状态的表征差异。解决方案的关键在于提出了一种无需提示词（prompt-free）的框架——稀疏投影引导机制（Sparse-Projected Guides, SPG），该方法通过在稀疏自编码器（Sparse Autoencoder, SAE）隐空间中学习稀疏引导系数，结合SAE字典生成正常/异常引导向量，从而实现对异常区域的精准定位。SPG采用两阶段训练策略：首先在辅助数据集上训练SAE以捕获图像patch token的低维表示，随后仅优化引导系数并冻结骨干网络和SAE参数，显著提升了跨数据集零样本场景下的图像级检测性能与像素级分割精度，尤其在VisA和MVTec AD数据集上表现优异。
链接: https://arxiv.org/abs/2604.02871
作者: Tomoyasu Nanaumi,Yukino Tsuzuki,Junichi Okubo,Junichiro Fujii,Takayoshi Yamashita
机构: Yachiyo Engineering Co., Ltd.(Yachiyo Engineering公司); Chubu University(中部大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 6 figures, 9 tables
Abstract:We study zero-shot anomaly detection and segmentation using frozen foundation model features, where all learnable parameters are trained only on a labeled auxiliary dataset and deployed to unseen target categories without any target-domain adaptation. Existing prompt-based approaches use handcrafted or learned prompt embeddings as reference vectors for normal/anomalous states. We propose Sparse-Projected Guides (SPG), a prompt-free framework that learns sparse guide coefficients in the Sparse Autoencoder (SAE) latent space, which generate normal/anomaly guide vectors via the SAE dictionary. SPG employs a two stage learning strategy on the labeled auxiliary dataset: (i) train an SAE on patch-token features, and (ii) optimize only guide coefficients using auxiliary pixel-level masks while freezing the backbone and SAE. On MVTec AD and VisA under cross-dataset zero-shot settings, SPG achieves competitive image-level detection and strong pixel-level segmentation; with DINOv3, SPG attains the highest pixellevel AUROC among the compared methods. We also report SPG instantiated with OpenCLIP (ViT-L/14@336px) to align the backbone with CLIP-based baselines. Moreover, the learned guide coefficients trace decisions back to a small set of dictionary atoms, revealing category-general and category-specific factors.
[CV-48] oken Warping Helps MLLM s Look from Nearby Viewpoints CVPR2026
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在视角变化下表现脆弱的问题，即当场景从邻近视角观察时，模型难以准确理解其结构与语义。传统基于像素的变形（warping）方法因对微小深度误差敏感而易引入几何失真，导致推理不稳定。论文提出的关键解决方案是采用反向令牌变形（backward token warping），即在目标视角上定义密集网格，并为每个网格点检索对应的源视角图像令牌（image token），从而实现更稳定的视角变换。该方法利用视觉Transformer（ViT）架构中已有的令牌表示作为结构化语义单元，显著提升了模型在邻近视角下的语义一致性和推理可靠性，实验表明其优于所有基线方法，包括像素级变形、空间微调的MLLMs以及生成式变形方法。
链接: https://arxiv.org/abs/2604.02870
作者: Phillip Y. Lee,Chanho Park,Mingue Park,Seungwoo Yoo,Juil Koo,Minhyuk Sung
机构: KAIST
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026, Project Page: this https URL
Abstract:Can warping tokens, rather than pixels, help multimodal large language models (MLLMs) understand how a scene appears from a nearby viewpoint? While MLLMs perform well on visual reasoning, they remain fragile to viewpoint changes, as pixel-wise warping is highly sensitive to small depth errors and often introduces geometric distortions. Drawing on theories of mental imagery that posit part-level structural representations as the basis for human perspective transformation, we examine whether image tokens in ViT-based MLLMs serve as an effective substrate for viewpoint changes. We compare forward and backward warping, finding that backward token warping, which defines a dense grid on the target view and retrieves a corresponding source-view token for each grid point, achieves greater stability and better preserves semantic coherence under viewpoint shifts. Experiments on our proposed ViewBench benchmark demonstrate that token-level warping enables MLLMs to reason reliably from nearby viewpoints, consistently outperforming all baselines including pixel-wise warping approaches, spatially fine-tuned MLLMs, and a generative warping method.
[CV-49] HairOrbit: Multi-view Aware 3D Hair Modeling from Single Portraits
【速读】：该论文旨在解决从单视角图像中重建高质量、一致且逼真的发丝三维结构（3D hair strand reconstruction）的难题，特别是在不可见区域的属性保持方面。现有方法受限于有限的正面视图线索和小规模、风格受限的合成数据，难以在遮挡区域生成合理结果。解决方案的关键在于：首先，利用视频生成模型强大的3D先验知识，将单视角重建任务转化为校准后的多视角重建任务；其次，引入一个基于稀疏真实图像标注训练的神经方向提取器，以提升全视角方向估计的准确性；最后，设计了一种基于混合隐式场的两阶段发丝生长算法，在保证细节丰富性的同时实现高效重建。
链接: https://arxiv.org/abs/2604.02867
作者: Leyang Jin,Yujian Zheng,Bingkui Tong,Yuda Qiu,Zhenyu Xie,Hao Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 6 figures
Abstract:Reconstructing strand-level 3D hair from a single-view image is highly challenging, especially when preserving consistent and realistic attributes in unseen regions. Existing methods rely on limited frontal-view cues and small-scale/style-restricted synthetic data, often failing to produce satisfactory results in invisible regions. In this work, we propose a novel framework that leverages the strong 3D priors of video generation models to transform single-view hair reconstruction into a calibrated multi-view reconstruction task. To balance reconstruction quality and efficiency for the reformulated multi-view task, we further introduce a neural orientation extractor trained on sparse real-image annotations for better full-view orientation estimation. In addition, we design a two-stage strand-growing algorithm based on a hybrid implicit field to synthesize the 3D strand curves with fine-grained details at a relatively fast speed. Extensive experiments demonstrate that our method achieves state-of-the-art performance on single-view 3D hair strand reconstruction on a diverse range of hair portraits in both visible and invisible regions.
[CV-50] A Paradigm Shift: Fully End-to-End Training for Temporal Sentence Grounding in Videos CVPR2026
【速读】：该论文旨在解决视频中时间句定位（Temporal Sentence Grounding in Videos, TSGV）任务中存在的模型架构不匹配问题，即当前方法通常采用预训练的、与查询无关的视觉编码器进行离线特征提取，且视频主干网络（backbone）被冻结未针对TSGV任务优化，导致视觉分类训练与语义定位任务之间存在显著偏差。解决方案的关键在于提出一种全端到端（fully end-to-end）范式，联合优化视频主干网络和定位头，并引入句子条件适配器（Sentence Conditioned Adapter, SCADA），利用句子特征自适应地训练视频主干中一小部分参数，通过精确融合语言嵌入来调制特征图，从而在保持较低内存消耗的同时显著提升视觉表征能力。
链接: https://arxiv.org/abs/2604.02860
作者: Allen He,Qi Liu,Kun Liu,Xinchen Liu,Wu Liu
机构: BASIS International School Park Lane Harbour; UCAS; JD Explore Academy; USTC
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted as CVPR 2026 Workshop PVUW
Abstract:Temporal sentence grounding in videos (TSGV) aims to localize a temporal segment that semantically corresponds to a sentence query from an untrimmed video. Most current methods adopt pre-trained query-agnostic visual encoders for offline feature extraction, and the video backbones are frozen and not optimized for TSGV. This leads to a task discrepancy issue for the video backbone trained for visual classification, but utilized for TSGV. To bridge this gap, we propose a fully end-to-end paradigm that jointly optimizes the video backbone and localization head. We first conduct an empirical study validating the effectiveness of end-to-end learning over frozen baselines across different model scales. Furthermore, we introduce a Sentence Conditioned Adapter (SCADA), which leverages sentence features to train a small portion of video backbone parameters adaptively. SCADA facilitates the deployment of deeper network backbones with reduced memory and significantly enhances visual representation by modulating feature maps through precise integration of linguistic embeddings. Experiments on two benchmarks show that our method outperforms state-of-the-art approaches. The code and models will be released.
[CV-51] HiDiGen: Hierarchical Diffusion for B-Rep Generation with Explicit Topological Constraints
【速读】：该论文旨在解决生成式 AI (Generative AI) 在边界表示（Boundary Representation, B-rep）结构建模中的有效性难题，即如何在保持拓扑正确性的前提下生成具有复杂几何细节的三维 CAD 模型。其解决方案的关键在于提出了一种分阶段的层次化生成框架 HiDiGen：首先通过显式建模面-边关联关系构建拓扑骨架，随后利用多个基于 Transformer 的扩散模块逐步细化几何结构，动态建立并强制执行边-顶点邻接关系以确保结构一致性，从而实现高有效性的新颖且多样化的 B-rep 模型生成。
链接: https://arxiv.org/abs/2604.02847
作者: Shurui Liu,Weide Chen,Ancong Wu
机构: Sun Yat-sen University (中山大学); Shenzhen Campus of Sun Yat-sen University (中山大学深圳校区)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Boundary representation (B-rep) is the standard 3D modeling format in CAD systems, encoding both geometric primitives and topological connectivity. Despite its prevalence, deep generative modeling of valid B-rep structures remains challenging due to the intricate interplay between discrete topology and continuous geometry. In this paper, we propose HiDiGen, a hierarchical generation framework that decouples geometry modeling into two stages, each guided by explicitly modeled topological constraints. Specifically, our approach first establishes face-edge incidence relations to define a coherent topological scaffold, upon which face proxies and initial edge curves are generated. Subsequently, multiple Transformer-based diffusion modules are employed to refine the geometry by generating precise face surfaces and vertex positions, with edge-vertex adjacencies dynamically established and enforced to preserve structural consistency. This progressive geometry hierarchy enables the generation of more novel and diverse shapes, while two-stage topological modeling ensures high validity. Experimental results show that HiDiGen achieves strong performance, generating novel, diverse, and topologically sound CAD models.
[CV-52] Adaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representations
【速读】：该论文旨在解决传统傅里叶编码隐式神经表示（Fourier-encoded Implicit Neural Representations, INRs）在处理具有空间变化局部频谱的信号时存在的局限性，即固定频率映射难以有效建模高频细节，导致收敛速度慢的问题。其解决方案的关键在于提出一种自适应局部频率滤波方法，通过引入一个空间变化的参数 α(x) 来调制编码后的傅里叶分量，从而在不同空间位置实现低通、带通和高通行为的平滑过渡，使模型能够根据局部信号特性动态调整频率响应。该方法从神经切线核（Neural Tangent Kernel, NTK）角度进行了理论分析，揭示了其对有效核谱的重塑机制，并在图像拟合、三维形状表示和稀疏数据重建等任务中验证了其在重建质量与优化效率上的显著提升。
链接: https://arxiv.org/abs/2604.02846
作者: Ligen Shi,Jun Qiu,Yuhang Zheng,Chang Liu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: 12 pages, 8 figures
Abstract:Fourier-encoded implicit neural representations (INRs) have shown strong capability in modeling continuous signals from discrete samples. However, conventional Fourier feature mappings use a fixed set of frequencies over the entire spatial domain, making them poorly suited to signals with spatially varying local spectra and often leading to slow convergence of high-frequency details. To address this issue, we propose an adaptive local frequency filtering method for Fourier-encoded INRs. The proposed method introduces a spatially varying parameter \alpha(\mathbfx) to modulate encoded Fourier components, enabling a smooth transition among low-pass, band-pass, and high-pass behaviors at different spatial locations. We further analyze the effect of the proposed filter from the neural tangent kernel (NTK) perspective and provide an NTK-inspired interpretation of how it reshapes the effective kernel spectrum. Experiments on 2D image fitting, 3D shape representation, and sparse data reconstruction demonstrate that the proposed method consistently improves reconstruction quality and leads to faster optimization compared with fixed-frequency baselines. In addition, the learned \alpha(\mathbfx) provides an intuitive visualization of spatially varying frequency preferences, which helps explain the behavior of the model on non-stationary signals. These results indicate that adaptive local frequency modulation is a practical enhancement for Fourier-encoded INRs.
[CV-53] Deformation-based In-Context Learning for Point Cloud Understanding CVPR2026
【速读】：该论文旨在解决现有基于掩码点建模（Masked Point Modeling, MPM）的点云上下文学习（In-Context Learning, ICL）方法所面临的两大挑战：一是缺乏几何先验，导致模型仅依赖令牌级相关性推断空间结构与几何细节；二是训练与推理目标不一致，因模型在训练时使用了推理阶段不可用的目标侧信息。解决方案的关键在于提出一种基于形变的框架 DeformPIC，其通过任务特定提示引导查询点云的形变来实现几何显式推理，并保持训练与推理目标的一致性，从而显著提升点云 ICL 在重建、去噪和配准等任务上的性能。
链接: https://arxiv.org/abs/2604.02845
作者: Chengxing Lin,Jinhong Deng,Yinjie Lei,Wen Li
机构: Shenzhen Institute for Advanced Study, UESTC; School of Computer Science and Engineering, UESTC; Sichuan University
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026. Code: this https URL
Abstract:Recent advances in point cloud In-Context Learning (ICL) have demonstrated strong multitask capabilities. Existing approaches typically adopt a Masked Point Modeling (MPM)-based paradigm for point cloud ICL. However, MPM-based methods directly predict the target point cloud from masked tokens without leveraging geometric priors, requiring the model to infer spatial structure and geometric details solely from token-level correlations via transformers. Additionally, these methods suffer from a training-inference objective mismatch, as the model learns to predict the target point cloud using target-side information that is unavailable at inference time. To address these challenges, we propose DeformPIC, a deformation-based framework for point cloud ICL. Unlike existing approaches that rely on masked reconstruction, DeformPIC learns to deform the query point cloud under task-specific guidance from prompts, enabling explicit geometric reasoning and consistent objectives. Extensive experiments demonstrate that DeformPIC consistently outperforms previous state-of-the-art methods, achieving reductions of 1.6, 1.8, and 4.7 points in average Chamfer Distance on reconstruction, denoising, and registration tasks, respectively. Furthermore, we introduce a new out-of-domain benchmark to evaluate generalization across unseen data distributions, where DeformPIC achieves state-of-the-art performance.
[CV-54] Factorized Multi-Resolution HashGrid for Efficient Neural Radiance Fields: Execution on Edge-Devices
【速读】：该论文旨在解决神经辐射场（Neural Radiance Fields, NeRF）在设备端训练时面临的资源受限问题，如GPU内存、存储空间和功耗不足，从而限制其在隐私敏感、通信受限或需快速适应动态场景等实际应用中的部署。解决方案的关键在于提出一种名为Fact-Hash的新颖参数编码方法，该方法融合了张量分解（Tensor Factorization）与哈希编码（Hash-encoding）技术：首先将3D坐标投影至多个低维形式（2D或1D），再应用哈希函数并聚合为单一特征向量，从而在保持高分辨率特征表达能力的同时实现少样本鲁棒性，显著提升内存效率（相比现有方法节省超三分之一内存）且不牺牲图像质量（PSNR指标）和渲染速度，实验证明其在计算效率与能耗方面优于其他位置编码方案。
链接: https://arxiv.org/abs/2604.02836
作者: Kim Jun-Seong,Mingyu Kim,GeonU Kim,Tae-Hyun Oh,Jin-Hwa Kim
机构: POSTECH(浦项科技大学); The Univ. of British Columbia(不列颠哥伦比亚大学); KAIST(韩国科学技术院); NAVER AI Lab(NAVER人工智能实验室); Seoul Nat’l Univ.(首尔国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for publication in IEEE Robotics and Automation Letters (RA-L)
Abstract:We introduce Fact-Hash, a novel parameter-encoding method for training on-device neural radiance fields. Neural Radiance Fields (NeRF) have proven pivotal in 3D representations, but their applications are limited due to large computational resources. On-device training can open large application fields, providing strength in communication limitations, privacy concerns, and fast adaptation to a frequently changing scene. However, challenges such as limited resources (GPU memory, storage, and power) impede their deployment. To handle this, we introduce Fact-Hash, a novel parameter-encoding merging Tensor Factorization and Hash-encoding techniques. This integration offers two benefits: the use of rich high-resolution features and the few-shot robustness. In Fact-Hash, we project 3D coordinates into multiple lower-dimensional forms (2D or 1D) before applying the hash function and then aggregate them into a single feature. Comparative evaluations against state-of-the-art methods demonstrate Fact-Hash’s superior memory efficiency, preserving quality and rendering speed. Fact-Hash saves memory usage by over one-third while maintaining the PSNR values compared to previous encoding methods. The on-device experiment validates the superiority of Fact-Hash compared to alternative positional encoding methods in computational efficiency and energy consumption. These findings highlight Fact-Hash as a promising solution to improve feature grid representation, address memory constraints, and improve quality in various applications. Project page: this https URL
[CV-55] STRNet: Visual Navigation with Spatio-Temporal Representation through Dynamic Graph Aggregation CVPR2026
【速读】：该论文旨在解决机器人视觉导航中因特征编码器和时间池化方法过于简单而导致的细粒度时空结构丢失问题，从而限制了动作预测与进展估计的准确性。解决方案的关键在于提出一个统一的时空表征框架，通过从图像序列和目标观测中提取特征，并利用设计的时空融合模块进行深度融合：该模块在每一帧内执行空间图推理，在时域上结合混合时间位移模块与多分辨率差异感知卷积来建模动态变化，从而有效保留并利用视觉输入中的精细时空信息。
链接: https://arxiv.org/abs/2604.02829
作者: Hao Ren,Zetong Bi,Yiming Zeng,Zhaoliang Wan,Lu Qi,Hui Cheng
机构: Sun Yat-sen University (中山大学); Insta360 Research; Wuhan University (武汉大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: CVPR2026
Abstract:Visual navigation requires the robot to reach a specified goal such as an image, based on a sequence of first-person visual observations. While recent learning-based approaches have made significant progress, they often focus on improving policy heads or decision strategies while relying on simplistic feature encoders and temporal pooling to represent visual input. This leads to the loss of fine-grained spatial and temporal structure, ultimately limiting accurate action prediction and progress estimation. In this paper, we propose a unified spatio-temporal representation framework that enhances visual encoding for robotic navigation. Our approach extracts features from both image sequences and goal observations, and fuses them using the designed spatio-temporal fusion module. This module performs spatial graph reasoning within each frame and models temporal dynamics using a hybrid temporal shift module combined with multi-resolution difference-aware convolution. Experimental results demonstrate that our approach consistently improves navigation performance and offers a generalizable visual backbone for goal-conditioned control. Code is available at \hrefthis https URLthis https URL.
[CV-56] NavCrafter: Exploring 3D Scenes from a Single Image ICRA2026
【速读】：该论文旨在解决从单张图像中生成灵活3D场景的问题，尤其在直接获取3D数据成本高或不可行时具有重要意义。其核心挑战在于如何实现具有相机可控性、时空一致性的新视角视频序列合成，并提升大视角偏移下的重建保真度。解决方案的关键在于提出NavCrafter框架：首先利用视频扩散模型（video diffusion models）捕获丰富的3D先验信息，再通过几何感知扩展策略逐步扩大场景覆盖范围；其次引入多阶段相机控制机制，基于双分支相机注入与注意力调制条件化扩散模型以实现可控多视角合成；此外，设计了碰撞感知的相机轨迹规划器和改进的3D高斯溅射（3D Gaussian Splatting, 3DGS）管道，结合深度对齐监督、结构正则化与精修策略，显著提升了重建质量与一致性。
链接: https://arxiv.org/abs/2604.02828
作者: Hongbo Duan,Peiyu Zhuang,Yi Liu,Zhengyang Zhang,Yuxin Zhang,Pengting Luo,Fangming Liu,Xueqian Wang
机构: Tsinghua University (清华大学); Peng Cheng Laboratory (鹏城实验室); Sun Yat-sen University (中山大学); Huawei (华为)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 pages accepted by ICRA 2026
Abstract:Creating flexible 3D scenes from a single image is vital when direct 3D data acquisition is costly or impractical. We introduce NavCrafter, a novel framework that explores 3D scenes from a single image by synthesizing novel-view video sequences with camera controllability and temporal-spatial consistency. NavCrafter leverages video diffusion models to capture rich 3D priors and adopts a geometry-aware expansion strategy to progressively extend scene coverage. To enable controllable multi-view synthesis, we introduce a multi-stage camera control mechanism that conditions diffusion models with diverse trajectories via dual-branch camera injection and attention modulation. We further propose a collision-aware camera trajectory planner and an enhanced 3D Gaussian Splatting (3DGS) pipeline with depth-aligned supervision, structural regularization and refinement. Extensive experiments demonstrate that NavCrafter achieves state-of-the-art novel-view synthesis under large viewpoint shifts and substantially improves 3D reconstruction fidelity.
[CV-57] MMPhysVideo: Scaling Physical Plausibility in Video Generation via Joint Multimodal Modeling
【速读】：该论文旨在解决视频扩散模型（Video Diffusion Models, VDMs）在生成视频时因仅基于像素重建而导致的物理不一致性问题。其核心解决方案是提出MMPhysVideo框架，通过联合多模态建模来提升视频生成的物理合理性：首先将语义、几何和时空轨迹等感知线索统一转换为伪RGB格式，使VDM能够直接捕捉复杂物理动态；其次设计双向可控教师架构（Bidirectionally Controlled Teacher），利用并行分支解耦RGB与感知处理，并通过两个零初始化控制连接逐步学习像素级一致性以缓解跨模态干扰；最终通过表示对齐将教师模型中的物理先验蒸馏至单流学生模型，实现高效推理。此方案显著提升了物理合理性与视觉质量，在多个基准测试中达到当前最优性能。
链接: https://arxiv.org/abs/2604.02817
作者: Shubo Lin,Xuanyang Zhang,Wei Cheng,Weiming Hu,Gang Yu,Jin Gao
机构: State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA; School of Artificial Intelligence, University of Chinese Academy of Sciences; Beijing Key Laboratory of Super Intelligent Security of Multi-Modal Information; School of Information Science and Technology, Shanghai Tech University
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel-only reconstruction. To address this, we propose MMPhysVideo, the first framework to scale physical plausibility in video generation through joint multimodal modeling. We recast perceptual cues, specifically semantics, geometry, and spatio-temporal trajectory, into a unified pseudo-RGB format, enabling VDMs to directly capture complex physical dynamics. To mitigate cross-modal interference, we propose a Bidirectionally Controlled Teacher architecture, which utilizes parallel branches to fully decouple RGB and perception processing and adopts two zero-initialized control links to gradually learn pixel-wise consistency. For inference efficiency, the teacher’s physical prior is distilled into a single-stream student model via representation alignment. Furthermore, we present MMPhysPipe, a scalable data curation and annotation pipeline tailored for constructing physics-rich multimodal datasets. MMPhysPipe employs a vision-language model (VLM) guided by a chain-of-visual-evidence rule to pinpoint physical subjects, enabling expert models to extract multi-granular perceptual information. Without additional inference costs, MMPhysVideo consistently improves physical plausibility and visual quality over advanced models across various benchmarks and achieves state-of-the-art performance compared to existing methods.
[CV-58] QAPruner: Quantization-Aware Vision Token Pruning for 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）在低比特量化（Post-Training Quantization, PTQ）压缩过程中，若直接采用语义感知的视觉token剪枝（vision token pruning）会导致激活异常值（activation outliers）被误删，从而加剧低比特（如W4A4）量化误差的问题。其解决方案的关键在于提出一种量化感知的视觉token剪枝框架，通过引入一个轻量级混合敏感性度量（hybrid sensitivity metric），该度量融合了模拟分组量化误差与异常值强度，结合标准语义相关性评分，从而保留既具备语义信息又对量化鲁棒的视觉token，实现PTQ与视觉token剪枝的协同优化，显著提升低比特推理精度。
链接: https://arxiv.org/abs/2604.02816
作者: Xinhao Wang,Zhonyu Xia,Zhiwei Lin,Zhe Li,Yongtao Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 12 pages
Abstract:Multimodal Large Language Models (MLLMs) have shown strong reasoning ability, but their high computational and memory costs hinder deployment in resource-constrained settings. While Post-Training Quantization (PTQ) and vision token pruning are standard compression techniques, they are usually treated as independent optimizations. In this paper, we show that these two techniques are strongly coupled: naively applying semantic-based token pruning to PTQ-optimized MLLMs can discard activation outliers that are important for numerical stability and thus worsen quantization errors in low-bit regimes (\textite.g., W4A4). To address this issue, we propose a quantization-aware vision token pruning framework. Our method introduces a lightweight hybrid sensitivity metric that combines simulated group-wise quantization error with outlier intensity. By combining this metric with standard semantic relevance scores, the method retains tokens that are both semantically informative and robust to quantization. Experiments on standard LLaVA architectures show that our method consistently outperforms naive integration baselines. At an aggressive pruning ratio that retains only 12.5% of visual tokens, our framework improves accuracy by 2.24% over the baseline and even surpasses dense quantization without pruning. To the best of our knowledge, this is the first method that explicitly co-optimizes vision token pruning and PTQ for accurate low-bit MLLM inference.
[CV-59] CMCC-ReID: Cross-Modality Clothing-Change Person Re-Identification
【速读】：该论文旨在解决长时监控场景下行人重识别（Person Re-Identification, ReID）中同时存在的模态差异（如可见光与红外图像）和服装变化问题，这一现实挑战在现有研究中尚未被充分关注。为此，作者提出了一项新任务——跨模态服装变化重识别（Cross-Modality Clothing-Change Re-Identification, CMCC-ReID），并构建了SYSU-CMCC基准数据集以支持该任务的研究。解决方案的关键在于提出的渐进式身份对齐网络（Progressive Identity Alignment Network, PIA），其核心包含两个模块：双分支解耦学习（Dual-Branch Disentangling Learning, DBDL）模块用于将身份相关特征从服装相关因素中解耦，从而获得服装无关的表示；双向原型学习（Bi-Directional Prototype Learning, BPL）模块则在嵌入空间中执行模态内与模态间对比学习，有效弥合模态差距并进一步抑制服装干扰。实验表明，PIA在SYSU-CMCC数据集上建立了强基线性能，显著优于现有方法。
链接: https://arxiv.org/abs/2604.02808
作者: Haoxuan Xu,Hanzi Wang,Guanglin Niu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Person Re-Identification (ReID) faces severe challenges from modality discrepancy and clothing variation in long-term surveillance scenario. While existing studies have made significant progress in either Visible-Infrared ReID (VI-ReID) or Clothing-Change ReID (CC-ReID), real-world surveillance system often face both challenges simultaneously. To address this overlooked yet realistic problem, we define a new task, termed Cross-Modality Clothing-Change Re-Identification (CMCC-ReID), which targets pedestrian matching across variations in both modality and clothing. To advance research in this direction, we construct a new benchmark SYSU-CMCC, where each identity is captured in both visible and infrared domains with distinct outfits, reflecting the dual heterogeneity of long-term surveillance. To tackle CMCC-ReID, we propose a Progressive Identity Alignment Network (PIA) that progressively mitigates the issues of clothing variation and modality discrepancy. Specifically, a Dual-Branch Disentangling Learning (DBDL) module separates identity-related cues from clothing-related factors to achieve clothing-agnostic representation, and a Bi-Directional Prototype Learning (BPL) module performs intra-modality and inter-modality contrast in the embedding space to bridge the modality gap while further suppressing clothing interference. Extensive experiments on the SYSU-CMCC dataset demonstrate that PIA establishes a strong baseline for this new task and significantly outperforms existing methods.
[CV-60] PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis
【速读】：该论文旨在解决当前路面病害感知研究中存在的一系列局限性，包括现有数据集多局限于单模态感知、缺乏多轮交互与事实 grounded 推理支持、以及视觉识别与视觉语言分析之间缺乏关联等问题。其核心解决方案是提出 PaveBench，一个面向真实高速公路巡检图像的大规模基准数据集，支持分类、目标检测、语义分割和视觉语言问答（Vision-Language Question Answering, VQA）四项核心任务，并提供统一的任务定义与评估协议。关键创新在于：在视觉侧构建了大规模标注数据及含难例干扰子集以提升鲁棒性；在多模态侧引入 PaveVQA 数据集，支持单轮、多轮及专家修正的交互式问答，涵盖识别、定位、定量估计与养护推理等复杂场景；同时提出一种基于领域专用模型作为工具的代理增强型视觉问答框架，有效融合视觉语言模型与专业工具，实现更精准的决策支持。
链接: https://arxiv.org/abs/2604.02804
作者: Dexiang Li,Zhenning Che,Haijun Zhang,Dongliang Zhou,Zhao Zhang,Yahong Han
机构: Harbin Institute of Technology Shenzhen (哈尔滨工业大学深圳); Tianjin University (天津大学); Hefei University of Technology (合肥工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注:
Abstract:Pavement condition assessment is essential for road safety and maintenance. Existing research has made significant progress. However, most studies focus on conventional computer vision tasks such as classification, detection, and segmentation. In real-world applications, pavement inspection requires more than visual recognition. It also requires quantitative analysis, explanation, and interactive decision support. Current datasets are limited. They focus on unimodal perception. They lack support for multi-turn interaction and fact-grounded reasoning. They also do not connect perception with vision-language analysis. To address these limitations, we introduce PaveBench, a large-scale benchmark for pavement distress perception and interactive vision-language analysis on real-world highway inspection images. PaveBench supports four core tasks: classification, object detection, semantic segmentation, and vision-language question answering. It provides unified task definitions and evaluation protocols. On the visual side, PaveBench provides large-scale annotations and includes a curated hard-distractor subset for robustness evaluation. It contains a large collection of real-world pavement images. On the multimodal side, we introduce PaveVQA, a real-image question answering (QA) dataset that supports single-turn, multi-turn, and expert-corrected interactions. It covers recognition, localization, quantitative estimation, and maintenance reasoning. We evaluate several state-of-the-art methods and provide a detailed analysis. We also present a simple and effective agent-augmented visual question answering framework that integrates domain-specific models as tools alongside vision-language models. The dataset is available at: this https URL.
[CV-61] UNICA: A Unified Neural Framework for Controllable 3D Avatars
【速读】：该论文旨在解决传统3D人体虚拟形象（3D human avatar）生成流程中步骤繁琐、依赖多阶段手工设计的问题，如外观建模、动作规划、骨骼绑定（rigging）、物理模拟和渲染等。其核心挑战在于如何将这些原本割裂的模块统一为一个端到端可控制的生成框架。解决方案的关键在于提出UNICA（UNIfied neural Controllable Avatar），这是一个无需骨骼的生成式模型，通过一个统一的神经网络架构整合了所有控制组件：利用基于键盘输入的动作条件扩散模型（action-conditioned diffusion model）从2D位置图生成下一帧的3D几何结构，并借助点变换器（point transformer）将其映射至3D Gaussian Splatting以实现高保真自由视角渲染。该方法无需手动设计物理模拟即可自然捕捉头发和松散衣物的动力学特性，并支持长时间自回归生成，是首个实现“动作规划、骨骼绑定、物理模拟与渲染”全流程统一的模型。
链接: https://arxiv.org/abs/2604.02799
作者: Jiahe Zhu,Xinyao Wang,Yiyu Zhuang,Yanwen Wang,Jing Tian,Yao Yao,Hao Zhu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Opensource code: this https URL
Abstract:Controllable 3D human avatars have found widespread applications in 3D games, the metaverse, and AR/VR scenarios. The conventional approach to creating such a 3D avatar requires a lengthy, intricate pipeline encompassing appearance modeling, motion planning, rigging, and physical simulation. In this paper, we introduce UNICA (UNIfied neural Controllable Avatar), a skeleton-free generative model that unifies all avatar control components into a single neural framework. Given keyboard inputs akin to video game controls, UNICA generates the next frame of a 3D avatar’s geometry through an action-conditioned diffusion model operating on 2D position maps. A point transformer then maps the resulting geometry to 3D Gaussian Splatting for high-fidelity free-view rendering. Our approach naturally captures hair and loose clothing dynamics without manually designed physical simulation, and supports extra-long autoregressive generation. To the best of our knowledge, UNICA is the first model to unify the workflow of “motion planning, rigging, physical simulation, and rendering”. Code is released at this https URL.
[CV-62] LumaFlux: Lifting 8-Bit Worlds to HDR Reality with Physically-Guided Diffusion Transformers
【速读】：该论文旨在解决将8-bit标准动态范围（Standard Dynamic Range, SDR）内容高效、准确地转换为10-bit高动态范围（High Dynamic Range, HDR）的问题，尤其针对现有逆色调映射（Inverse Tone Mapping, ITM）方法在真实世界退化、风格差异和相机处理流程中泛化能力差，常导致高光截断、色彩饱和度下降或色调不稳定等问题。解决方案的关键在于提出LumaFlux——一种基于物理和感知引导的扩散变换器（Diffusion Transformer, DiT），其核心创新包括：(1) 物理引导适配（Physically-Guided Adaptation, PGA）模块，通过低秩残差注入亮度、空间描述符和频率线索至注意力机制；(2) 感知交叉调制（Perceptual Cross-Modulation, PCM）层，利用视觉编码器特征进行FiLM条件调节以稳定色度与纹理；(3) HDR残差耦合器（HDR Residual Coupler），在时间步与层自适应调制调度下融合物理与感知信号。此外，研究构建了首个大规模SDR-HDR训练语料库并设立新的评估基准，验证了LumaFlux在保持亮度重建精度与感知色彩保真度方面的显著优势，且参数开销极小。
链接: https://arxiv.org/abs/2604.02787
作者: Shreshth Saini,Hakan Gedik,Neil Birkbeck,Yilin Wang,Balu Adsumilli,Alan C. Bovik
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The rapid adoption of HDR-capable devices has created a pressing need to convert the 8-bit Standard Dynamic Range (SDR) content into perceptually and physically accurate 10-bit High Dynamic Range (HDR). Existing inverse tone-mapping (ITM) methods often rely on fixed tone-mapping operators that struggle to generalize to real-world degradations, stylistic variations, and camera pipelines, frequently producing clipped highlights, desaturated colors, or unstable tone reproduction. We introduce LumaFlux, a first physically and perceptually guided diffusion transformer (DiT) for SDR-to-HDR reconstruction by adapting a large pretrained DiT. Our LumaFlux introduces (1) a Physically-Guided Adaptation (PGA) module that injects luminance, spatial descriptors, and frequency cues into attention through low-rank residuals; (2) a Perceptual Cross-Modulation (PCM) layer that stabilizes chroma and texture via FiLM conditioning from vision encoder features; and (3) an HDR Residual Coupler that fuses physical and perceptual signals under a timestep- and layer-adaptive modulation schedule. Finally, a lightweight Rational-Quadratic Spline decoder reconstructs smooth, interpretable tone fields for highlight and exposure expansion, enhancing the output of the VAE decoder to generate HDR. To enable robust HDR learning, we curate the first large-scale SDR-HDR training corpus. For fair and reproducible comparison, we further establish a new evaluation benchmark, comprising HDR references and corresponding expert-graded SDR versions. Across benchmarks, LumaFlux outperforms state-of-the-art baselines, achieving superior luminance reconstruction and perceptual color fidelity with minimal additional parameters.
[CV-63] CANDLE: Illumination-Invariant Semantic Priors for Color Ambient Lighting Normalization CVPR
【速读】：该论文旨在解决多色光照条件下环境光颜色归一化（Color Ambient Lighting Normalization, CALN）的难题，尤其针对由光照引起的严重色偏（chromatic shift）、高光饱和及材质依赖性反射导致的物体固有颜色恢复困难问题。现有基于几何和低级先验的方法在光照诱导的色偏占主导时表现不足。其解决方案的关键在于利用DINOv3模型自监督学习得到的特征在彩色光照输入与环境光真实图像之间具有高度一致性，提出CANDLE框架：通过引入DINO Omni-layer Guidance（D.O.G.），将多层DINOv3特征自适应注入编码器各阶段以增强语义鲁棒性；并设计色彩频率精炼模块（BFACG + SFFB）抑制解码器侧的色度坍缩和细节污染，从而实现更精确的颜色归一化。
链接: https://arxiv.org/abs/2604.02785
作者: Rong-Lin Jian,Ting-Yao Chen,Yu-Fan Lin,Chia-Ming Lee,Fu-En Yang,Yu-Chiang Frank Wang,Chih-Chung Hsu
机构: National Yang Ming Chiao Tung University (国立阳明交通大学); National Cheng Kung University (国立成功大学); NVIDIA (英伟达)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPRW 2026 Camera Ready; NTIRE 2026 Ambient Lighting Normalization (2nd 3rd in Color White Light Track)
Abstract:Color ambient lighting normalization under multi-colored illumination is challenging due to severe chromatic shifts, highlight saturation, and material-dependent reflectance. Existing geometric and low-level priors are insufficient for recovering object-intrinsic color when illumination-induced chromatic bias dominates. We observe that DINOv3’s self-supervised features remain highly consistent between colored-light inputs and ambient-lit ground truth, motivating their use as illumination-robust semantic priors. We propose CANDLE (Color Ambient Normalization with DINO Layer Enhancement), which introduces DINO Omni-layer Guidance (D.O.G.) to adaptively inject multi-layer DINOv3 features into successive encoder stages, and a color-frequency refinement design (BFACG + SFFB) to suppress decoder-side chromatic collapse and detail contamination. Experiments on CL3AN show a +1.22 dB PSNR gain over the strongest prior method. CANDLE achieves 3rd place on the NTIRE 2026 ALN Color Lighting Challenge and 2nd place in fidelity on the White Lighting track with the lowest FID, confirming strong generalization across both chromatic and luminance-dominant illumination conditions. Code is available at this https URL.
[CV-64] A Unified Perspective on Adversarial Membership Manipulation in Vision Models CVPR2026
【速读】：该论文旨在解决视觉模型中成员推理攻击（Membership Inference Attacks, MIAs）在面对对抗样本时的脆弱性问题，即现有MIAs虽能有效评估隐私泄露风险，但其假设查询输入为诚实数据，未考虑对抗扰动对成员归属判断的影响。研究发现，通过不可察觉的对抗扰动可实现“成员伪造”（adversarial membership manipulation），使非成员图像被误判为训练集成员。解决方案的关键在于识别并利用一种独特的几何特征——梯度范数坍缩轨迹（gradient-norm collapse trajectory），该特征能在语义相似的情况下区分真实成员与伪造成员，并基于此提出了一种基于梯度几何信号的检测策略和鲁棒推理框架，显著提升了MIAs对对抗操纵的防御能力。
链接: https://arxiv.org/abs/2604.02780
作者: Ruize Gao,Kaiwen Zhou,Yongqiang Chen,Feng Liu
机构: National University of Singapore (新加坡国立大学); Knowin AI; The Chinese University of Hong Kong (香港中文大学); The University of Melbourne (墨尔本大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026
Abstract:Membership inference attacks (MIAs) aim to determine whether a specific data point was part of a model’s training set, serving as effective tools for evaluating privacy leakage of vision models. However, existing MIAs implicitly assume honest query inputs, and their adversarial robustness remains unexplored. We show that MIAs for vision models expose a previously overlooked adversarial surface: adversarial membership manipulation, where imperceptible perturbations can reliably push non-member images into the “member” region of state-of-the-art MIAs. In this paper, we provide the first unified perspective on this phenomenon by analyzing its mechanism and implications. We begin by demonstrating that adversarial membership fabrication is consistently effective across diverse architectures and datasets. We then reveal a distinctive geometric signature - a characteristic gradient-norm collapse trajectory - that reliably separates fabricated from true members despite their nearly identical semantic representations. Building on this insight, we introduce a principled detection strategy grounded in gradient-geometry signals and develop a robust inference framework that substantially mitigates adversarial manipulation. Extensive experiments show that fabrication is broadly effective, while our detection and robust inference strategies significantly enhance resilience. This work establishes the first comprehensive framework for adversarial membership manipulation in vision models.
[CV-65] Generalized Small Object Detection:A Point-Prompted Paradigm and Benchmark
【速读】：该论文旨在解决小目标检测（Small Object Detection, SOD）中因像素信息极少、边界模糊导致的标注困难、高质量大规模数据集稀缺以及语义表征薄弱等问题。其关键解决方案是提出一种新的推理时点提示机制（Point-Prompt Small Object Detection, P2SOD），通过在推理阶段引入稀疏点提示作为类别级定位的信息桥梁，实现语义增强；在此基础上构建了可扩展且具备迁移能力的DEAL框架，该框架仅需单次点击即可显著提升检测性能（AP75指标上相对全监督基线提升31.4%），并能有效泛化至未见类别和数据集。
链接: https://arxiv.org/abs/2604.02773
作者: Haoran Zhu,Wen Yang,Guangyou Yang,Chang Xu,Ruixiang Zhang,Fang Xu,Haijian Zhang,Gui-Song Xia
机构: School of Electronic Information, Wuhan University, Wuhan, 430072 China; Environmental Computational Science and Earth Observation Laboratory, EPFL, Sion, Switzerland; School of Artificial Intelligence, Wuhan University, Wuhan, 430072, China
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Small object detection (SOD) remains challenging due to extremely limited pixels and ambiguous object boundaries. These characteristics lead to challenging annotation, limited availability of large-scale high-quality datasets, and inherently weak semantic representations for small objects. In this work, we first address the data limitation by introducing TinySet-9M, the first large-scale, multi-domain dataset for small object detection. Beyond filling the gap in large-scale datasets, we establish a benchmark to evaluate the effectiveness of existing label-efficient detection methods for small objects. Our evaluation reveals that weak visual cues further exacerbate the performance degradation of label-efficient methods in small object detection, highlighting a critical challenge in label-efficient SOD. Secondly, to tackle the limitation of insufficient semantic representation, we move beyond training-time feature enhancement and propose a new paradigm termed Point-Prompt Small Object Detection (P2SOD). This paradigm introduces sparse point prompts at inference time as an efficient information bridge for category-level localization, enabling semantic augmentation. Building upon the P2SOD paradigm and the large-scale TinySet-9M dataset, we further develop DEAL (DEtect Any smalL object), a scalable and transferable point-prompted detection framework that learns robust, prompt-conditioned representations from large-scale data. With only a single click at inference time, DEAL achieves a 31.4% relative improvement over fully supervised baselines under strict localization metrics (e.g., AP75) on TinySet-9M, while generalizing effectively to unseen categories and unseen datasets. Our project is available at this https URL.
[CV-66] InverseDraping: Recovering Sewing Patterns from 3D Garment Surfaces via BoxMesh Bridging
【速读】：该论文旨在解决从 draped 3D 服装中恢复参数化二维缝制图案（sewing patterns）这一挑战性问题，该问题在人体数字化研究中属于逆向建模难题，且现有方法因缺乏物理合理结构而存在本质上的病态性（ill-posed）。其解决方案的关键在于提出一个两阶段框架，核心创新是引入一种结构化的中间表示——BoxMesh，该表示在三维空间中同时编码服装级几何与面板级结构，并显式解耦内在面板几何与缝合拓扑关系，从而将由悬垂变形引起的不确定性最小化。BoxMesh为几何反演与结构化图案推断提供了物理合理的约束，第一阶段通过几何驱动的自回归模型从输入3D服装中推理出BoxMesh，第二阶段则利用语义感知的自回归模型将其解析为参数化缝制图案，有效处理了面板配置和缝合关系的变长与结构特性，显著提升了恢复精度与鲁棒性。
链接: https://arxiv.org/abs/2604.02764
作者: Leyang Jin,Zirong Jin,Zisheng Ye,Haokai Pang,Xiaoguang Han,Yujian Zheng,Hao Li
机构: Mohamed bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学); The Chinese University of Hong Kong, Shenzhen (香港中文大学（深圳）); School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen (香港中文大学（深圳）理工学院); FNii-Shenzhen (深圳未来网络智能研究院); Guangdong Provincial Key Laboratory of Future Networks of Intelligence (广东省未来网络智能重点实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 13 figures
Abstract:Recovering sewing patterns from draped 3D garments is a challenging problem in human digitization research. In contrast to the well-studied forward process of draping designed sewing patterns using mature physical simulation engines, the inverse process of recovering parametric 2D patterns from deformed garment geometry remains fundamentally ill-posed for existing methods. We propose a two-stage framework that centers on a structured intermediate representation, BoxMesh, which serves as the key to bridging the gap between 3D garment geometry and parametric sewing patterns. BoxMesh encodes both garment-level geometry and panel-level structure in 3D, while explicitly disentangling intrinsic panel geometry and stitching topology from draping-induced deformations. This representation imposes a physically grounded structure on the problem, significantly reducing ambiguity. In Stage I, a geometry-driven autoregressive model infers BoxMesh from the input 3D garment. In Stage II, a semantics-aware autoregressive model parses BoxMesh into parametric sewing patterns. We adopt autoregressive modeling to naturally handle the variable-length and structured nature of panel configurations and stitching relationships. This decomposition separates geometric inversion from structured pattern inference, leading to more accurate and robust recovery. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the GarmentCodeData benchmark and generalizes effectively to real-world scans and single-view images.
[CV-67] DeCo-DETR: Decoupled Cognition DETR for efficient Open-Vocabulary Object Detection ICLR2026
【速读】：该论文旨在解决开放词汇目标检测（Open-vocabulary Object Detection, OVOD）在实际部署中的两大瓶颈问题：一是多模态设计通常依赖推理时的文本编码器，导致计算开销大；二是训练目标耦合紧密，造成封闭集检测精度与开放世界泛化能力之间的权衡。解决方案的关键在于提出一种以视觉为中心的框架——解耦认知DETR（Decoupled Cognition DETR, DeCo-DETR），其核心创新是采用统一的解耦范式：首先通过预训练大语言视觉模型（LVLMs）生成区域级描述，并利用CLIP对齐构建分层语义原型空间，实现高效且可复用的语义表示；其次，在训练阶段将语义推理与定位任务分离，通过并行优化流分别处理对齐与检测，从而解耦语义认知与检测过程。该方法在标准OVOD基准上实现了具有竞争力的零样本检测性能，同时显著提升推理效率，为可扩展的OVOD系统提供了实用路径。
链接: https://arxiv.org/abs/2604.02753
作者: Siheng Wang,Yanshu Li,Bohan Hu,Zhengdao Li,Haibo Zhan,Linshan Li,Weiming Liu,Ruizhi Qian,Guangxin Wu,Hao Zhang,Jifeng Shen,Piotr Koniusz,Zhengtao Yao,Junhao Dong,Qiang Sun
机构: Jiangsu University (江苏大学); Brown University (布朗大学); Nanyang Technological University (南洋理工大学); MBZUAI (穆罕默德·本·扎耶德大学人工智能学院); University of New South Wales (新南威尔士大学); USC (南加州大学); Bytedance (字节跳动); University of Toronto (多伦多大学); Data61 CSIRO (澳大利亚联邦科学与工业研究组织数据61部门)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICLR 2026
Abstract:Open-vocabulary Object Detection (OVOD) enables models to recognize objects beyond predefined categories, but existing approaches remain limited in practical deployment. On the one hand, multimodal designs often incur substantial computational overhead due to their reliance on text encoders at inference time. On the other hand, tightly coupled training objectives introduce a trade-off between closed-set detection accuracy and open-world generalization. Thus, we propose Decoupled Cognition DETR (DeCo-DETR), a vision-centric framework that addresses these challenges through a unified decoupling paradigm. Instead of depending on online text encoding, DeCo-DETR constructs a hierarchical semantic prototype space from region-level descriptions generated by pre-trained LVLMs and aligned via CLIP, enabling efficient and reusable semantic representation. Building upon this representation, the framework further disentangles semantic reasoning from localization through a decoupled training strategy, which separates alignment and detection into parallel optimization streams. Extensive experiments on standard OVOD benchmarks demonstrate that DeCo-DETR achieves competitive zero-shot detection performance while significantly improving inference efficiency. These results highlight the effectiveness of decoupling semantic cognition from detection, offering a practical direction for scalable OVOD systems.
[CV-68] Differentiable Stroke Planning with Dual Parameterization for Efficient and High-Fidelity Painting Creation
【速读】：该论文旨在解决基于笔画（stroke-based）渲染中优化方法的两大局限性：一是搜索算法因笔画离散放置而易陷入局部极小值；二是可微分优化器缺乏结构感知能力，导致生成布局杂乱无章。解决方案的关键在于提出一种双表示机制，通过双向映射将离散多段线（discrete polylines）与连续贝塞尔控制点（Bézier control points）耦合，实现协同优化——局部梯度可细化全局笔画结构，同时内容感知的笔画提案有助于跳出劣质局部最优解。该方法还引入高斯点绘（Gaussian-splatting）启发的初始化策略，支持图像级并行笔画优化，显著提升效率与质量。
链接: https://arxiv.org/abs/2604.02752
作者: Jinfan Liu,Wuze Zhang,Zhangli Hu,Zhehan Zhao,Ye Chen,Bingbing Ni
机构: Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In stroke-based rendering, search methods often get trapped in local minima due to discrete stroke placement, while differentiable optimizers lack structural awareness and produce unstructured layouts. To bridge this gap, we propose a dual representation that couples discrete polylines with continuous Bézier control points via a bidirectional mapping mechanism. This enables collaborative optimization: local gradients refine global stroke structures, while content-aware stroke proposals help escape poor local optima. Our representation further supports Gaussian-splatting-inspired initialization, enabling highly parallel stroke optimization across the image. Experiments show that our approach reduces the number of strokes by 30-50%, achieves more structurally coherent layouts, and improves reconstruction quality, while cutting optimization time by 30-40% compared to existing differentiable vectorization methods.
[CV-69] Visual Instruction-Finetuned Language Model for Versatile Brain MR Image Tasks ICPR2026
【速读】：该论文旨在解决将大语言模型（Large Language Model, LLM）应用于脑部磁共振成像（Brain MRI）中多样化临床任务的挑战，尤其是如何在单一模型框架内实现从图像描述生成到病灶分割、图像翻译等高临床价值任务的统一建模。其解决方案的关键在于：首先，通过引入图像编码器特征图重用机制，缓解图像分块token化导致的空间信息丢失问题；其次，利用LLM生成结构化文本数据以增强稀缺的医学图像-文本配对样本，从而提升模型在小样本场景下的泛化能力。这一方法使得模型在五个脑部MRI数据集上均表现出优于专用模型的跨任务性能，验证了其在临床影像分析中的有效性与通用性。
链接: https://arxiv.org/abs/2604.02748
作者: Jonghun Kim,Sinyoung Ra,Hyunjin Park
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ICPR 2026 accepted
Abstract:LLMs have demonstrated remarkable capabilities in linguistic reasoning and are increasingly adept at vision-language tasks. The integration of image tokens into transformers has enabled direct visual input and output, advancing research from image-to-text descriptions to text-to-image generation. However, simple text-to-image generation holds limited clinical utility. In medical imaging, tasks such as image segmentation for localizing pathologies or image translation for reconstructing missing sequences have much greater clinical importance. Despite this, integrating these diverse, clinically relevant tasks within a single, versatile language model remains unexplored. Our method, LLaBIT (Large Language Model for Brain Image Translation), extends the visual reasoning of LLMs to these clinically meaningful tasks in the brain MRI domain. To mitigate the spatial information loss inherent in image tokenization, we incorporate a mechanism to reuse feature maps from the image encoder, minimizing data degradation. We also generate text data using LLMs with strict predefined instructions to augment limited image-text paired data in brain MRI. We comprehensively evaluated our method on five brain MRI datasets across four distinct tasks: report generation, visual question answering, image segmentation, and image translation. Our model not only demonstrated superior performance across all tasks but also outperformed specialized, task-specific models in direct comparisons, highlighting its efficacy and versatility
[CV-70] HOM: Generating Physically Plausible Hand-Object Meshes From Text CVPR
【速读】：该论文旨在解决从文本生成高视觉保真度且物理合理的三维手-物体交互（Hand-Object Interactions, HOIs）网格的问题，尤其针对由文本生成的高斯分布（Gaussians）提取网格时存在的病态问题（ill-posed problem）以及在错误网格上进行物理优化所导致的不稳定性。解决方案的关键在于提出一种无需训练的框架THOM，其核心创新包括：第一，采用两阶段流程——先分别生成手和物体的高斯分布，再进行基于物理的HOI优化；第二，设计了一种新的网格提取方法与顶点到高斯映射机制，显式地将高斯元素分配给网格顶点，实现拓扑感知的正则化；第三，通过视觉语言模型（VLM）引导的平移精修和接触感知优化，显著提升交互的物理合理性。
链接: https://arxiv.org/abs/2604.02736
作者: Uyoung Jeong,Yihalem Yimolal Tiruneh,Hyung Jin Chang,Seungryul Baek,Kwang In Kim
机构: think
The author affiliation information is provided in the format: “1UNIST 2University of Birmingham 3POSTECH”.
These are clearly labeled with numbers corresponding to the authors listed above.
UNIST, University of Birmingham, and POSTECH are all well-known academic institutions.
None of these are common AI companies that require special handling per the rules (e.g., OpenAI, Meta).
No duplicates exist in this list.
Therefore, the extracted affiliations are UNIST, University of Birmingham, and POSTECH.
/think
UNIST(UNIST); University of Birmingham(伯明翰大学); POSTECH(浦项工科大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted to CVPR Findings 2026
Abstract:The generation of 3D hand-object interactions (HOIs) from text is crucial for dexterous robotic grasping and VR/AR content generation, requiring both high visual fidelity and physical plausibility. Nevertheless, the ill-posed problem of mesh extraction from text-generated Gaussians, and physics-based optimization on the erroneous meshes pose challenges. To address these issues, we introduce THOM, a training-free framework that generates photorealistic, physically plausible 3D HOI meshes without the need for a template object mesh. THOM employs a two-stage pipeline, initially generating the hand and object Gaussians, followed by physics-based HOI optimization. Our new mesh extraction method and vertex-to-Gaussian mapping explicitly assign Gaussian elements to mesh vertices, allowing topology-aware regularization. Furthermore, we improve the physical plausibility of interactions by VLM-guided translation refinement and contact-aware optimization. Comprehensive experiments demonstrate that THOM consistently surpasses state-of-the-art methods in terms of text alignment, visual realism, and interaction plausibility.
[CV-71] MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications CVPR2026
【速读】：该论文旨在解决多分辨率火星遥感数据中构建通用基础模型（foundation model）的挑战，特别是如何有效融合来自不同传感器（HiRISE、CTX 和 THEMIS）的异构表征，以提升模型在多种下游任务中的泛化能力。解决方案的关键在于提出一种新颖的等效验证损失（Equal Validation Loss, EVL）策略，通过比较不同传感器模型在验证损失上的相似性来选择对齐的检查点（checkpoint），再利用任务算术（task arithmetic）进行模型合并。这一方法确保了在相近收敛阶段进行模型融合，从而显著提升了模型的稳定性与跨任务性能，尤其在分割任务上表现突出。
链接: https://arxiv.org/abs/2604.02719
作者: Mirali Purohit,Bimal Gajera,Irish Mehta,Bhanu Tokas,Jacob Adler,Steven Lu,Scott Dickenshied,Serina Diniega,Brian Bue,Umaa Rebbapragada,Hannah Kerner
机构: Arizona State University (亚利桑那州立大学); Jet Propulsion Laboratory, California Institute of Technology (喷气推进实验室，加州理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at CVPR 2026 (Main Track)
Abstract:We introduce MOMO, the first multi-sensor foundation model for Mars remote sensing. MOMO uses model merge to integrate representations learned independently from three key Martian sensors (HiRISE, CTX, and THEMIS), spanning resolutions from 0.25 m/pixel to 100 m/pixel. Central to our method is our novel Equal Validation Loss (EVL) strategy, which aligns checkpoints across sensors based on validation loss similarity before fusion via task arithmetic. This ensures models are merged at compatible convergence stages, leading to improved stability and generalization. We train MOMO on a large-scale, high-quality corpus of \sim 12 million samples curated from Mars orbital data and evaluate it on 9 downstream tasks from Mars-Bench. MOMO achieves better overall performance compared to ImageNet pre-trained, earth observation foundation model, sensor-specific pre-training, and fully-supervised baselines. Particularly on segmentation tasks, MOMO shows consistent and significant performance improvement. Our results demonstrate that model merging through an optimal checkpoint selection strategy provides an effective approach for building foundation models for multi-resolution data. The model weights, pretraining code, pretraining data, and evaluation code are available at: this https URL.
[CV-72] ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving
【速读】：该论文旨在解决基于视觉-语言-动作（Vision-Language-Action, VLA）架构的端到端自动驾驶模型在模仿学习（imitation learning）中固有的局限性问题，即模型仅能复制观测到的行为而无法探索多样化的驾驶策略，导致在新场景或分布外（out-of-distribution）情况下表现脆弱。解决方案的关键在于提出一个统一的理解与生成框架，通过引入世界模型（world model）实现双重目标：一方面，通过未来RGB和深度图像生成作为密集监督信号，促使模型学习精细的视觉与几何表征以增强规划主干；另一方面，利用世界模型的图像预测不确定性作为内在奖励信号，衡量轨迹相对于训练分布的新颖性，从而引导安全约束下的策略探索。该方法结合分组相对策略优化（Group Relative Policy Optimization, GRPO）进行策略优化，在NAVSIM和nuScenes基准上取得了当前最优性能。
链接: https://arxiv.org/abs/2604.02714
作者: Zihao Sheng,Xin Ye,Jingru Luo,Sikai Chen,Liu Ren
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: The code and demo will be publicly available at this https URL
Abstract:End-to-end autonomous driving models based on Vision-Language-Action (VLA) architectures have shown promising results by learning driving policies through behavior cloning on expert demonstrations. However, imitation learning inherently limits the model to replicating observed behaviors without exploring diverse driving strategies, leaving it brittle in novel or out-of-distribution scenarios. Reinforcement learning (RL) offers a natural remedy by enabling policy exploration beyond the expert distribution. Yet VLA models, typically trained on offline datasets, lack directly observable state transitions, necessitating a learned world model to anticipate action consequences. In this work, we propose a unified understanding-and-generation framework that leverages world modeling to simultaneously enable meaningful exploration and provide dense supervision. Specifically, we augment trajectory prediction with future RGB and depth image generation as dense world modeling objectives, requiring the model to learn fine-grained visual and geometric representations that substantially enrich the planning backbone. Beyond serving as a supervisory signal, the world model further acts as a source of intrinsic reward for policy exploration: its image prediction uncertainty naturally measures a trajectory’s novelty relative to the training distribution, where high uncertainty indicates out-of-distribution scenarios that, if safe, represent valuable learning opportunities. We incorporate this exploration signal into a safety-gated reward and optimize the policy via Group Relative Policy Optimization (GRPO). Experiments on the NAVSIM and nuScenes benchmarks demonstrate the effectiveness of our approach, achieving a state-of-the-art PDMS score of 93.7 and an EPDMS of 88.8 on NAVSIM. The code and demo will be publicly available at this https URL.
[CV-73] V2X-QA: A Comprehensive Reasoning Dataset and Benchmark for Multimodal Large Language Models in Autonomous Driving Across Ego Infrastructure and Cooperative Views
【速读】：该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在自动驾驶场景中评估体系的局限性问题，即现有基准测试大多局限于以车辆为中心（ego-centric）的视角，无法系统性地评估模型在基础设施侧（infrastructure-side）和协同驾驶（cooperative driving）条件下的性能。为此，作者提出了V2X-QA，一个基于视点解耦（view-decoupled）评估协议的真实世界数据集与基准，通过统一的多项选择题问答（MCQA）框架，在车辆独有、基础设施独有及协同驾驶三种条件下实现可控比较。其关键创新在于构建了一个涵盖感知、预测与推理规划的十二任务分类体系，并采用专家验证的MCQA标注方法，支持细粒度诊断不同视点下的能力差异；同时提出V2X-MoE基线模型，引入显式视点路由机制和视点特异性LoRA专家模块，证明了显式视点专业化对多视角推理的有效性，为未来车联网环境下多视角物理智能的研究提供了基础。
链接: https://arxiv.org/abs/2604.02710
作者: Junwei You,Pei Li,Zhuoyu Jiang,Weizhe Tang,Zilin Huang,Rui Gan,Jiaxi Liu,Yan Zhao,Sikai Chen,Bin Ran
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal large language models (MLLMs) have shown strong potential for autonomous driving, yet existing benchmarks remain largely ego-centric and therefore cannot systematically assess model performance in infrastructure-centric and cooperative driving conditions. In this work, we introduce V2X-QA, a real-world dataset and benchmark for evaluating MLLMs across vehicle-side, infrastructure-side, and cooperative viewpoints. V2X-QA is built around a view-decoupled evaluation protocol that enables controlled comparison under vehicle-only, infrastructure-only, and cooperative driving conditions within a unified multiple-choice question answering (MCQA) framework. The benchmark is organized into a twelve-task taxonomy spanning perception, prediction, and reasoning and planning, and is constructed through expert-verified MCQA annotation to enable fine-grained diagnosis of viewpoint-dependent capabilities. Benchmark results across ten representative state-of-the-art proprietary and open-source models show that viewpoint accessibility substantially affects performance, and infrastructure-side reasoning supports meaningful macroscopic traffic understanding. Results also indicate that cooperative reasoning remains challenging since it requires cross-view alignment and evidence integration rather than simply additional visual input. To address these challenges, we introduce V2X-MoE, a benchmark-aligned baseline with explicit view routing and viewpoint-specific LoRA experts. The strong performance of V2X-MoE further suggests that explicit viewpoint specialization is a promising direction for multi-view reasoning in autonomous driving. Overall, V2X-QA provides a foundation for studying multi-perspective reasoning, reliability, and cooperative physical intelligence in connected autonomous driving. The dataset and V2X-MoE resources are publicly available at: this https URL.
[CV-74] A Rapid Instrument Exchange System for Humanoid Robots in Minimally Invasive Surgery
【速读】：该论文旨在解决人形机器人在微创手术（Minimally Invasive Surgery, MIS）中执行复杂操作时，因双臂配置导致的器械交换效率问题。传统多臂手术平台虽已成熟，但人形机器人需模拟外科医生手动更换器械的自然流程，以实现高效协同。其解决方案的关键在于提出一种沉浸式遥操作快速器械交换系统，核心创新为基于单轴柔顺对接（single-axis compliant docking）与环境约束释放的低延迟机制，并集成头戴式显示器（Head-Mounted Display, HMD）提供的实时第一人称视角（First-Person View, FPV）感知，从而显著降低操作复杂度和认知负荷，验证了在受限临床环境中实现稳定器械交换的技术可行性。
链接: https://arxiv.org/abs/2604.02707
作者: Bingcong Zhang,Yihang Lyv,Lianbo Ma,Yushi He,Pengfei Wei,Xingchi Liu,Jinhua Li,Jianchang Zhao,Lizhi Pan
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY)
备注:
Abstract:Humanoid robot technologies have demonstrated immense potential for minimally invasive surgery (MIS). Unlike dedicated multi-arm surgical platforms, the inherent dual-arm configuration of humanoid robots necessitates an efficient instrument exchange capability to perform complex procedures, mimicking the natural workflow where surgeons manually switch instruments. To address this, this paper proposes an immersive teleoperated rapid instrument exchange system. The system utilizes a low-latency mechanism based on single-axis compliant docking and environmental constraint release. Integrated with real-time first-person view (FPV) perception via a head-mounted display (HMD), this framework significantly reduces operational complexity and cognitive load during the docking process. Comparative evaluations between experts and novices demonstrate high operational robustness and a rapidly converging learning curve; novice performance in instrument attachment and detachment improved substantially after brief training. While long-distance spatial alignment still presents challenges in time cost and collaborative stability, this study successfully validates the technical feasibility of humanoid robots executing stable instrument exchanges within constrained clinical environments.
[CV-75] VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping
【速读】：该论文旨在解决现有基于3D高斯点绘（3D Gaussian Splatting, 3DGS）的SLAM方法在动态场景中因依赖确定性姿态优化而易受初始值敏感、存在灾难性遗忘（catastrophic forgetting）的问题。其核心解决方案是提出变分贝叶斯高斯点绘SLAM（Variational Bayesian Gaussian Splatting SLAM, VBGS-SLAM），通过将点绘地图优化与相机位姿跟踪耦合于生成式概率框架中，利用多元高斯分布的共轭性质与变分推断，实现对位姿和场景参数后验不确定性的显式建模与高效闭式更新。该方法显著降低漂移并提升复杂条件下的鲁棒性，同时保持原有3DGS的高效性与渲染质量。
链接: https://arxiv.org/abs/2604.02696
作者: Yuhan Zhu,Yanyu Zhang,Jie Xu,Wei Ren
机构: University of California, Riverside (加州大学河滨分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:3D Gaussian Splatting (3DGS) has shown promising results for 3D scene modeling using mixtures of Gaussians, yet its existing simultaneous localization and mapping (SLAM) variants typically rely on direct, deterministic pose optimization against the splat map, making them sensitive to initialization and susceptible to catastrophic forgetting as map evolves. We propose Variational Bayesian Gaussian Splatting SLAM (VBGS-SLAM), a novel framework that couples the splat map refinement and camera pose tracking in a generative probabilistic form. By leveraging conjugate properties of multivariate Gaussians and variational inference, our method admits efficient closed-form updates and explicitly maintains posterior uncertainty over both poses and scene parameters. This uncertainty-aware method mitigates drift and enhances robustness in challenging conditions, while preserving the efficiency and rendering quality of existing 3DGS. Our experiments demonstrate superior tracking performance and robustness in long sequence prediction, alongside efficient, high-quality novel view synthesis across diverse synthetic and real-world scenes.
[CV-76] XrayClaw: Cooperative-Competitive Multi-Agent Alignment for Trustworthy Chest X-ray Diagnosis
【速读】：该论文旨在解决胸部X光片（Chest X-ray, CXR）自动诊断中传统单体模型因缺乏细致推理能力而导致的逻辑不一致和诊断幻觉问题。其解决方案的关键在于提出XrayClaw框架，该框架采用协同-竞争架构：集成四个专业化协作代理模拟系统化临床流程，同时引入一个独立审计代理作为竞争方，通过竞争性偏好优化（Competitive Preference Optimization）学习目标，强制分析性与整体性解释之间的相互验证，从而有效抑制累积性幻觉并提升诊断可靠性。
链接: https://arxiv.org/abs/2604.02695
作者: Shawn Young,Lijian Xu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages
Abstract:Chest X-ray (CXR) interpretation is a fundamental yet complex clinical task that increasingly relies on artificial intelligence for automation. However, traditional monolithic models often lack the nuanced reasoning required for trustworthy diagnosis, frequently leading to logical inconsistencies and diagnostic hallucinations. While multi-agent systems offer a potential solution by simulating collaborative consultations, existing frameworks remain susceptible to consensus-based errors when instantiated by a single underlying model. This paper introduces XrayClaw, a novel framework that operationalizes multi-agent alignment through a sophisticated cooperative-competitive architecture. XrayClaw integrates four specialized cooperative agents to simulate a systematic clinical workflow, alongside a competitive agent that serves as an independent auditor. To reconcile these distinct diagnostic pathways, we propose Competitive Preference Optimization, a learning objective that penalizes illogical reasoning by enforcing mutual verification between analytical and holistic interpretations. Extensive empirical evaluations on the MS-CXR-T, MIMIC-CXR, and CheXbench benchmarks demonstrate that XrayClaw achieves state-of-the-art performance in diagnostic accuracy, clinical reasoning fidelity, and zero-shot domain generalization. Our results indicate that XrayClaw effectively mitigates cumulative hallucinations and enhances the overall reliability of automated CXR diagnosis, establishing a new paradigm for trustworthy medical imaging analysis.
[CV-77] DocShield: Towards AI Document Safety via Evidence-Grounded Agent ic Reasoning
【速读】：该论文旨在解决生成式 AI（Generative AI）引发的文本主导型图像伪造问题，现有取证方法多依赖视觉线索且缺乏基于证据的推理能力，导致检测、定位与解释任务割裂，影响结果的可靠性与可解释性。其核心解决方案是提出 DocShield 框架，首次将文本主导伪造分析建模为视觉-逻辑协同推理问题；关键创新在于引入 Cross-Cues-aware Chain of Thought (CCT) 机制，通过迭代交叉验证视觉异常与文本语义，实现隐式代理式推理，生成一致且证据驱动的取证分析结果，并结合基于 GRPO 的加权多任务奖励机制优化推理结构、空间证据与真实性预测的一致性。
链接: https://arxiv.org/abs/2604.02694
作者: Fanwei Zeng,Changtao Miao,Jing Huang,Zhiya Tan,Shutao Gong,Xiaoming Yu,Yang Wang,Weibin Yao,Joey Tianyi Zhou,Jianshu Li,Yin Yan
机构: Ant Group(蚂蚁集团); Nanyang Technological University (南洋理工大学); CFAR and IHPC, Agency for Science, Technology and Research (A*STAR), Singapore (新加坡科技研究局下属的CFAR和IHPC)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 4 figures, 5 tables. Preprint
Abstract:The rapid progress of generative AI has enabled increasingly realistic text-centric image forgeries, posing major challenges to document safety. Existing forensic methods mainly rely on visual cues and lack evidence-based reasoning to reveal subtle text manipulations. Detection, localization, and explanation are often treated as isolated tasks, limiting reliability and interpretability. To tackle these challenges, we propose DocShield, the first unified framework formulating text-centric forgery analysis as a visual-logical co-reasoning problem. At its core, a novel Cross-Cues-aware Chain of Thought (CCT) mechanism enables implicit agentic reasoning, iteratively cross-validating visual anomalies with textual semantics to produce consistent, evidence-grounded forensic analysis. We further introduce a Weighted Multi-Task Reward for GRPO-based optimization, aligning reasoning structure, spatial evidence, and authenticity prediction. Complementing the framework, we construct RealText-V1, a multilingual dataset of document-like text images with pixel-level manipulation masks and expert-level textual explanations. Extensive experiments show DocShield significantly outperforms existing methods, improving macro-average F1 by 41.4% over specialized frameworks and 23.4% over GPT-4o on T-IC13, with consistent gains on the challenging T-SROIE benchmark. Our dataset, model, and code will be publicly released.
[CV-78] Parser-Oriented Structural Refinement for a Stable Layout Interface in Document Parsing
【速读】：该论文旨在解决文档布局分析（Document Layout Analysis, DLA）流水线中因检测结果不稳定导致的下游解析器接口不一致问题，特别是在密集页面上存在重叠区域和边界模糊时，原始检测输出与解析器输入顺序之间的错位会引发严重解析错误。解决方案的关键在于引入一个轻量级的结构精炼阶段（structural refinement stage），置于DETR风格检测器与解析器之间，通过联合推理查询特征、语义线索、框几何信息和视觉证据，从共享的精炼结构状态中同步决定实例保留、框定位优化及解析器输入顺序预测，从而稳定解析器接口；同时设计了面向保留的监督策略和难度感知的排序目标，以提升复杂结构页面上的实例集与其顺序的一致性，显著改善整体布局质量并降低序列错位率。
链接: https://arxiv.org/abs/2604.02692
作者: Fuyuan Liu,Dianyu Yu,He Ren,Nayu Liu,Xiaomian Kang,Delai Qiu,Fa Zhang,Genpeng Zhen,Shengping Liu,Jiaen Liang,Wei Huang,Yining Wang,Junnan Zhu
机构: Unisound AI Technology Co., Ltd.(声智科技有限公司); School of Computer Science and Technology, Tianjin University (天津大学计算机科学与技术学院); MAIS, Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所MAIS)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate document parsing requires both robust content recognition and a stable parser interface. In explicit Document Layout Analysis (DLA) pipelines, downstream parsers do not consume the full detector output. Instead, they operate on a retained and serialized set of layout instances. However, on dense pages with overlapping regions and ambiguous boundaries, unstable layout hypotheses can make the retained instance set inconsistent with its parser input order, leading to severe downstream parsing errors. To address this issue, we introduce a lightweight structural refinement stage between a DETR-style detector and the parser to stabilize the parser interface. Treating raw detector outputs as a compact hypothesis pool, the proposed module performs set-level reasoning over query features, semantic cues, box geometry, and visual evidence. From a shared refined structural state, it jointly determines instance retention, refines box localization, and predicts parser input order before handoff. We further introduce retention-oriented supervision and a difficulty-aware ordering objective to better align the retained instance set and its order with the final parser input, especially on structurally complex pages. Extensive experiments on public benchmarks show that our method consistently improves page-level layout quality. When integrated into a standard end-to-end parsing pipeline, the stabilized parser interface also substantially reduces sequence mismatch, achieving a Reading Order Edit of 0.024 on OmniDocBench.
[CV-79] Efficient3D: A Unified Framework for Adaptive and Debiased Token Reduction in 3D MLLM s
【速读】：该论文旨在解决3D多模态大语言模型（3D Multimodal Large Language Models, 3D MLLMs）在资源受限平台上的推理效率问题，其核心挑战在于模型规模庞大和输入特征高维导致的显著计算开销。解决方案的关键在于提出一个统一的视觉token剪枝框架Efficient3D，其中包含两个核心组件：一是去偏的视觉token重要性估计模块（Debiased Visual Token Importance Estimator, DVTIE），该模块通过考虑浅层初始层对注意力聚合的影响，提升视觉token重要性的预测可靠性；二是自适应token再平衡策略（Adaptive Token Rebalancing, ATR），根据场景复杂度动态调整剪枝强度，以保持语义完整性并均衡各层注意力分布。二者协同实现上下文感知的token压缩，在降低计算成本的同时维持关键语义信息，从而在多个3D视觉与语言基准测试中（如Scan2Cap）显著优于未剪枝基线模型。
链接: https://arxiv.org/abs/2604.02689
作者: Yuhui Lin,Siyue Yu,Yuxing Yang,Guangliang Cheng,Jimin Xiao
机构: Xi’an Jiaotong-Liverpool University (西安交通大学利物浦大学); University of Liverpool (利物浦大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in Multimodal Large Language Models (MLLMs) have expanded reasoning capabilities into 3D domains, enabling fine-grained spatial understanding. However, the substantial size of 3D MLLMs and the high dimensionality of input features introduce considerable inference overhead, which limits practical deployment on resource constrained platforms. To overcome this limitation, this paper presents Efficient3D, a unified framework for visual token pruning that accelerates 3D MLLMs while maintaining competitive accuracy. The proposed framework introduces a Debiased Visual Token Importance Estimator (DVTIE) module, which considers the influence of shallow initial layers during attention aggregation, thereby producing more reliable importance predictions for visual tokens. In addition, an Adaptive Token Rebalancing (ATR) strategy is developed to dynamically adjust pruning strength based on scene complexity, preserving semantic completeness and maintaining balanced attention across layers. Together, they enable context-aware token reduction that maintains essential semantics with lower computation. Comprehensive experiments conducted on five representative 3D vision and language benchmarks, including ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D, demonstrate that Efficient3D achieves superior performance compared with unpruned baselines, with a +2.57% CIDEr improvement on the Scan2Cap dataset. Therefore, Efficient3D provides a scalable and effective solution for efficient inference in 3D MLLMs. The code is released at: this https URL
[CV-80] Drift-Resilient Temporal Priors for Visual Tracking CVPR2026
【速读】：该论文旨在解决多帧视觉跟踪中因简单聚合历史预测而导致的模型漂移（model drift）问题，这通常由噪声干扰的历史状态引入。解决方案的关键在于提出一个轻量且可泛化的模块DTPTrack，其核心由两个组件构成：一是时序可靠性校准器（Temporal Reliability Calibrator, TRC），用于学习为每帧历史状态分配可靠性分数，从而过滤噪声并锚定于真实模板；二是时序引导合成器（Temporal Guidance Synthesizer, TGS），将校准后的历史信息压缩为一组动态时序先验，提供预测引导。该方法在多个主流跟踪架构（如OSTrack、ODTrack和LoRAT）上均实现显著性能提升，验证了其有效性与通用性。
链接: https://arxiv.org/abs/2604.02654
作者: Yuqing Huang,Liting Lin,Weijun Zhuang,Zhenyu He,Xin Li
机构: Harbin Institute of Technology, Shenzhen (哈尔滨工业大学深圳); Pengcheng Laboratory (鹏城实验室); Lero, the Research Ireland Centre for Software, University of Limerick (爱尔兰软件研究中心，利默里克大学); Pazhou Lab (Huangpu) (琶洲实验室（黄埔）)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted by CVPR 2026
Abstract:Temporal information is crucial for visual tracking, but existing multi-frame trackers are vulnerable to model drift caused by naively aggregating noisy historical predictions. In this paper, we introduce DTPTrack, a lightweight and generalizable module designed to be seamlessly integrated into existing trackers to suppress drift. Our framework consists of two core components: (1) a Temporal Reliability Calibrator (TRC) mechanism that learns to assign a per-frame reliability score to historical states, filtering out noise while anchoring on the ground-truth template; and (2) a Temporal Guidance Synthesizer (TGS) module that synthesizes this calibrated history into a compact set of dynamic temporal priors to provide predictive guidance. To demonstrate its versatility, we integrate DTPTrack into three diverse tracking architectures–OSTrack, ODTrack, and LoRAT-and show consistent, significant performance gains across all baselines. Our best-performing model, built upon an extended LoRATv2 backbone, sets a new state-of-the-art on several benchmarks, achieving a 77.5% Success rate on LaSOT and an 80.3% AO on GOT-10k.
[CV-81] Cross-Vehicle 3D Geometric Consistency for Self-Supervised Surround Depth Estimation on Articulated Vehicles
【速读】：该论文旨在解决在铰接式车辆（articulated vehicle）上实现高精度环视深度估计的问题，这类车辆因结构复杂、各部分运动耦合性强，导致跨视角和跨车体的深度推理更加困难。解决方案的关键在于提出了一种名为ArticuSurDepth的自监督框架，通过引入多视角空间上下文增强策略与跨视角表面法向量约束，提升时空一致性下的结构连贯性；同时结合基于地面平面感知的相机高度正则化与跨车体位姿一致性机制，强化度量尺度下的深度估计能力，并有效关联铰接段间的运动估计，从而显著提升在真实铰接车辆平台及多个公开基准（DDAD、nuScenes、KITTI）上的深度估计性能。
链接: https://arxiv.org/abs/2604.02639
作者: Weimin Liu,Jiyuan Qiu,Wenjun Wang,Joshua H. Meng
机构: Tsinghua University (清华大学); University of Copenhagen (哥本哈根大学); University of California, Berkeley (加州大学伯克利分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Surround depth estimation provides a cost-effective alternative to LiDAR for 3D perception in autonomous driving. While recent self-supervised methods explore multi-camera settings to improve scale awareness and scene coverage, they are primarily designed for passenger vehicles and rarely consider articulated vehicles or robotics platforms. The articulated structure introduces complex cross-segment geometry and motion coupling, making consistent depth reasoning across views more challenging. In this work, we propose \textbfArticuSurDepth, a self-supervised framework for surround-view depth estimation on articulated vehicles that enhances depth learning through cross-view and cross-vehicle geometric consistency guided by structural priors from vision foundation model. Specifically, we introduce multi-view spatial context enrichment strategy and a cross-view surface normal constraint to improve structural coherence across spatial and temporal contexts. We further incorporate camera height regularization with ground plane-awareness to encourage metric depth estimation, together with cross-vehicle pose consistency that bridges motion estimation between articulated segments. To validate our proposed method, an articulated vehicle experiment platform was established with a dataset collected over it. Experiment results demonstrate state-of-the-art (SoTA) performance of depth estimation on our self-collected dataset as well as on DDAD, nuScenes, and KITTI benchmarks.
[CV-82] Smart Transfer: Leverag ing Vision Foundation Model for Rapid Building Damage Mapping with Post-Earthquake VHR Imagery
【速读】：该论文旨在解决传统灾害损毁调查在不同城市形态和新型灾害事件中难以泛化，且依赖耗时耗力的手动数据标注问题。其核心解决方案是提出一种名为Smart Transfer的地理空间人工智能（GeoAI）框架，利用先进的视觉基础模型（Vision Foundation Models, FMs）实现基于震后超高分辨率（VHR）遥感影像的快速建筑损毁制图。关键创新在于设计了两种新颖的模型迁移策略：一是像素级聚类（Pixel-wise Clustering, PC），确保原型级别的全局特征对齐；二是距离惩罚三元组（Distance-Penalized Triplet, DPT），通过强化对语义不一致但空间邻近像元块的惩罚，整合局部空间自相关模式。该方法在2023年土耳其-叙利亚地震多区域跨域迁移实验中表现优异，为灾后快速响应提供了可扩展、自动化的GeoAI技术路径。
链接: https://arxiv.org/abs/2604.02627
作者: Hao Li,Liwei Zou,Wenping Yin,Gulsen Taskin,Naoto Yokoya,Danfeng Hong,Wufan Zhao
机构: National University of Singapore (新加坡国立大学); Hong Kong University of Science and Technology, Guangzhou (香港科技大学广州分校); China University of Mining and Technology (中国矿业大学); Istanbul Technical University (伊斯坦布尔技术大学); The University of Tokyo (东京大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注:
Abstract:Living in a changing climate, human society now faces more frequent and severe natural disasters than ever before. As a consequence, rapid disaster response during the “Golden 72 Hours” of search and rescue becomes a vital humanitarian necessity and community concern. However, traditional disaster damage surveys routinely fail to generalize across distinct urban morphologies and new disaster events. Effective damage mapping typically requires exhaustive and time-consuming manual data annotation. To address this issue, we introduce Smart Transfer, a novel Geospatial Artificial Intelligence (GeoAI) framework, leveraging state-of-the-art vision Foundation Models (FMs) for rapid building damage mapping with post-earthquake Very High Resolution (VHR) imagery. Specifically, we design two novel model transfer strategies: first, Pixel-wise Clustering (PC), ensuring robust prototype-level global feature alignment; second, a Distance-Penalized Triplet (DPT), integrating patch-level spatial autocorrelation patterns by assigning stronger penalties to semantically inconsistent yet spatially adjacent patches. Extensive experiments and ablations from the recent 2023 Turkiye-Syria earthquake show promising performance in multiple cross-region transfer settings, namely Leave One Domain Out (LODO) and Specific Source Domain Combination (SSDC). Moreover, Smart Transfer provides a scalable, automated GeoAI solution to accelerate building damage mapping and support rapid disaster response, offering new opportunities to enhance disaster resilience in climate-vulnerable regions and communities. The data and code are publicly available at this https URL.
[CV-83] Unlocking Multi-Site Clinical Data: A Federated Approach to Privacy-First Child Autism Behavior Analysis CVPR2026
【速读】：该论文旨在解决儿童自闭症行为自动识别中因隐私法规（如HIPAA）和儿科数据敏感性导致的临床数据难以集中聚合的问题，以及单个医疗机构因数据稀缺而难以学习泛化行为模式或适配本地患者分布的挑战。其解决方案的关键在于提出一种基于联邦学习（Federated Learning, FL）的两层隐私保护框架：首先利用人体骨骼结构抽象（human skeletal abstraction）从原始RGB视频中移除可识别的视觉信息，其次通过联邦学习确保敏感姿态数据保留在各临床机构内，从而在不共享原始数据的前提下实现多中心协作建模与站点特异性个性化。实验表明，该方法在MMASD基准上显著优于传统联邦基线，为多中心临床分析提供了高精度且以隐私优先的解决方案。
链接: https://arxiv.org/abs/2604.02616
作者: Guangyu Sun,Wenhan Wu,Zhishuai Guo,Ziteng Wang,Pegah Khosravi,Chen Chen
机构: University of Central Florida (中佛罗里达大学); University of North Carolina at Charlotte (北卡罗来纳大学夏洛特分校); Northern Illinois University (伊利诺伊大学芝加哥分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted on the CVPR 2026 Workshop on Computer Vision for Children (CV4CHL)
Abstract:Automated recognition of autistic behaviors in children is essential for early intervention and objective clinical assessment. However, the development of robust models is severely hindered by strict privacy regulations (e.g., HIPAA) and the sensitive nature of pediatric data, which prevents the centralized aggregation of clinical datasets. Furthermore, individual clinical sites often suffer from data scarcity, making it difficult to learn generalized behavior patterns or tailor models to site-specific patient distributions. To address these challenges, we observe that Federated Learning (FL) can decouple model training from raw data access, enabling multi-site collaboration while maintaining strict data residency. In this paper, we present the first study exploring Federated Learning for pose-based child autism behavior recognition. Our framework employs a two-layer privacy protection mechanism: utilizing human skeletal abstraction to remove identifiable visual information from the raw RGB videos and FL to ensure sensitive pose data remains within the clinic. This approach leverages distributed clinical data to learn generalized representations while providing the flexibility for site-specific personalization. Experimental results on the MMASD benchmark demonstrate that our framework achieves high recognition accuracy, outperforming traditional federated baselines and providing a robust, privacy-first solution for multi-site clinical analysis.
[CV-84] Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals CVPR2026
【速读】：该论文旨在解决在恶劣环境条件下（如烟雾、雾霾及非理想光照）光学传感器（如摄像头和LiDAR）性能下降甚至失效的问题，从而保障自动驾驶和机器人导航等应用中3D环境感知的鲁棒性。其解决方案的关键在于提出了一种名为Rascene的集成感知与通信（ISAC）框架，该框架利用无处不在的毫米波正交频分复用（mmWave OFDM）通信信号实现3D场景成像，通过多帧空间自适应融合与置信加权前向投影技术，有效克服单帧无线电波信号稀疏性和多径模糊性问题，从而在任意姿态下恢复几何一致性，实现高精度3D场景重建。
链接: https://arxiv.org/abs/2604.02603
作者: Kunzhe Song,Geo Jie Zhou,Xiaoming Liu,Huacheng Zeng
机构: Michigan State University (密歇根州立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:Robust 3D environmental perception is critical for applications such as autonomous driving and robot navigation. However, optical sensors such as cameras and LiDAR often fail under adverse conditions, including smoke, fog, and non-ideal lighting. Although specialized radar systems can operate in these environments, their reliance on bespoke hardware and licensed spectrum limits scalability and cost-effectiveness. This paper introduces Rascene, an integrated sensing and communication (ISAC) framework that leverages ubiquitous mmWave OFDM communication signals for 3D scene imaging. To overcome the sparse and multipath-ambiguous nature of individual radio frames, Rascene performs multi-frame, spatially adaptive fusion with confidence-weighted forward projection, enabling the recovery of geometric consensus across arbitrary poses. Experimental results demonstrate that our method reconstructs 3D scenes with high precision, offering a new pathway toward low-cost, scalable, and robust 3D perception.
[CV-85] Moondream Segmentation: From Words to Masks
【速读】：该论文旨在解决**指代表达图像分割（referring image segmentation）**任务中因标注模糊性和评估噪声导致的模型性能瓶颈问题。其核心挑战在于如何从自然语言描述中精确定位目标对象，并生成高质量的像素级掩码（mask）。解决方案的关键在于：1）提出一种基于自回归解码的向量路径生成与迭代细化机制，将掩码从粗粒度逐步优化为高精度结果；2）引入强化学习阶段，通过直接优化掩码质量来缓解监督信号中的歧义性，从而生成从粗到精的训练目标；3）发布边界更准确的RefCOCO-M数据集，以减少由多边形标注误差带来的评估噪声，提升实验结果的可靠性。
链接: https://arxiv.org/abs/2604.02593
作者: Ethan Reid
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Demo: this https URL
Abstract:We present Moondream Segmentation, a referring image segmentation extension of Moondream 3, a vision-language model. Given an image and a referring expression, the model autoregressively decodes a vector path and iteratively refines the rasterized mask into a final detailed mask. We introduce a reinforcement learning stage that resolves ambiguity in the supervised signal by directly optimizing mask quality. Rollouts from this stage produce coarse-to-ground-truth targets for the refiner. To mitigate evaluation noise from polygon annotations, we release RefCOCO-M, a cleaned RefCOCO validation split with boundary-accurate masks. Moondream Segmentation achieves a cIoU of 80.2% on RefCOCO (val) and 62.6% mIoU on LVIS (val).
[CV-86] rackerSplat: Exploiting Point Tracking for Fast and Robust Dynamic 3D Gaussians Reconstruction
【速读】：该论文旨在解决基于高斯（Gaussian）的动态场景重建方法在处理大帧间位移时存在的问题，如重建伪影和时间不一致性，尤其是在快速物体运动场景下表现不佳。其解决方案的关键在于提出TrackerSplat，该方法通过引入现成的点跟踪模型提取像素轨迹，并将每视角的像素轨迹三角化到3D高斯上，以此指导训练前对高斯的位置、旋转和尺度进行重定位。这种预定位策略显著提升了对大位移的鲁棒性，有效减少了传统方法中常见的 fading 和 recoloring 伪影，同时在多设备并行处理多个相邻帧时保持高质量重建并提升吞吐量。
链接: https://arxiv.org/abs/2604.02586
作者: Daheng Yin,Isaac Ding,Yili Jin,Jianxin Shi,Jiangchuan Liu
机构: Simon Fraser University (西蒙弗雷泽大学); McGill University (麦吉尔大学); Nankai University (南开大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: 11 pages, 6 figures
Abstract:Recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated its potential for efficient and photorealistic 3D reconstructions, which is crucial for diverse applications such as robotics and immersive media. However, current Gaussian-based methods for dynamic scene reconstruction struggle with large inter-frame displacements, leading to artifacts and temporal inconsistencies under fast object motions. To address this, we introduce \textitTrackerSplat, a novel method that integrates advanced point tracking methods to enhance the robustness and scalability of 3DGS for dynamic scene reconstruction. TrackerSplat utilizes off-the-shelf point tracking models to extract pixel trajectories and triangulate per-view pixel trajectories onto 3D Gaussians to guide the relocation, rotation, and scaling of Gaussians before training. This strategy effectively handles large displacements between frames, dramatically reducing the fading and recoloring artifacts prevalent in prior methods. By accurately positioning Gaussians prior to gradient-based optimization, TrackerSplat overcomes the quality degradation associated with large frame gaps when processing multiple adjacent frames in parallel across multiple devices, thereby boosting reconstruction throughput while preserving rendering quality. Experiments on real-world datasets confirm the robustness of TrackerSplat in challenging scenarios with significant displacements, achieving superior throughput under parallel settings and maintaining visual quality compared to baselines. The code is available at this https URL.
[CV-87] FusionBERT: Multi-View Image-3D Retrieval via Cross-Attention Visual Fusion and Normal-Aware 3D Encoder
【速读】：该论文旨在解决现有图像-3D跨模态检索方法在真实场景中适用性不足的问题，即当前方法主要基于单视角图像与3D模型的特征对齐，无法有效利用多视角观测所提供的互补几何与外观信息。解决方案的关键在于提出FusionBERT框架，其核心创新包括：1）设计了一种基于交叉注意力机制的多视角视觉聚合器（multi-view visual aggregator），能够自适应融合同一对象的多视角图像特征，强化不同视图间的互补关系并选择性突出关键视觉线索；2）引入一种法向量感知的3D模型编码器（normal-aware 3D model encoder），通过联合编码点云法向量和空间位置信息，提升对无纹理或颜色退化的3D模型的几何表征能力。这两个模块共同提升了跨模态检索的鲁棒性和准确性。
链接: https://arxiv.org/abs/2604.02583
作者: Wei Li,Yufan Ren,Hanqing Jiang,Jianhui Ding,Zhen Peng,Leman Feng,Yichun Shentu,Guoqiang Xu,Baigui Sun
机构: IROOTECH TECHNOLOGY; Wolf 1069 b Lab; Sany Group; Zhejiang University; Hangzhou; Zhejiang; China; Central South University; BPIT; Changsha; Hunan; Guangzhou; Guangdong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 6 figures, 2 tables
Abstract:We propose FusionBERT, a novel multi-view visual fusion framework for image-3D multimodal retrieval. Existing image-3D representation learning methods predominantly focus on feature alignment of a single object image and its 3D model, limiting their applicability in realistic scenarios where an object is typically observed and captured from multiple viewpoints. Although multi-view observations naturally provide complementary geometric and appearance cues, existing multimodal large models rarely explore how to effectively fuse such multi-view visual information for better cross-modal retrieval. To address this limitation, we introduce a multi-view image-3D retrieval framework named FusionBERT, which innovatively utilizes a cross-attention-based multi-view visual aggregator to adaptively integrate features from multi-view images of an object. The proposed multi-view visual encoder fuses inter-view complementary relationships and selectively emphasizes informative visual cues across multiple views to get a more robustly fused visual feature for better 3D model matching. Furthermore, FusionBERT proposes a normal-aware 3D model encoder that can further enhance the 3D geometric feature of an object model by jointly encoding point normals and 3D positions, enabling a more robust representation learning for textureless or color-degraded 3D models. Extensive image-3D retrieval experiments demonstrate that FusionBERT achieves significantly higher retrieval accuracy than SOTA multimodal large models under both single-view and multi-view settings, establishing a strong baseline for multi-view multimodal retrieval.
[CV-88] WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models
【速读】：该论文旨在解决视觉语言模型（Vision Language Models, VLMs）在实际执行中难以通过奇异值分解（Singular Value Decomposition, SVD）显著降低延迟的问题。现有方法虽提出多种高效的SVD变体以实现低秩操作，但在实践中仍无法带来明显的推理加速效果。其解决方案的关键在于引入一种新的计算模式，并在更细粒度上应用SVD，从而实现可测量的执行延迟优化；同时，考虑到权重元素的重要性差异，提出自适应分配相对重要性的机制，在SVD过程中更好地保留模型精度，并进一步结合权重量化与激活量化，构建出高效率的VLM框架。整体上，作者提出了Weighted SVD（WSVD），在保持准确率的前提下实现了超过1.8倍的解码速度提升。
链接: https://arxiv.org/abs/2604.02570
作者: Haiyu Wang,Yutong Wang,Jack Jiang,Sai Qian Zhang
机构: Tandon School of Engineering, New York University (纽约大学坦顿工程学院); Courant Institute of Mathematical Sciences, New York University (纽约大学库朗数学科学研究所)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Singular Value Decomposition (SVD) has become an important technique for reducing the computational burden of Vision Language Models (VLMs), which play a central role in tasks such as image captioning and visual question answering. Although multiple prior works have proposed efficient SVD variants to enable low-rank operations, we find that in practice it remains difficult to achieve substantial latency reduction during model execution. To address this limitation, we introduce a new computational pattern and apply SVD at a finer granularity, enabling real and measurable improvements in execution latency. Furthermore, recognizing that weight elements differ in their relative importance, we adaptively allocate relative importance to each element during SVD process to better preserve accuracy, then extend this framework with quantization applied to both weights and activations, resulting in a highly efficient VLM. Collectively, we introduce~\textitWeighted SVD (WSVD), which outperforms other approaches by achieving over 1.8\times decoding speedup while preserving accuracy. We open source our code at: \hrefthis https URL\textttthis https URL
[CV-89] Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding
【速读】：该论文旨在解决三维场景理解中缺乏通用且鲁棒的场景表示学习方法的问题，尤其是在低样本量和任务特定微调场景下的性能瓶颈。其解决方案的关键在于提出一种基于Transformer的统一场景编码器UniScene3D，该编码器通过多视角彩色点图（colored pointmaps）联合建模图像外观与几何信息，并引入新颖的跨视角几何对齐（cross-view geometric alignment）和语义锚定视角对齐（grounded view alignment），以增强跨视角几何一致性和语义一致性，从而实现更强大的三维场景表征能力。
链接: https://arxiv.org/abs/2604.02546
作者: Ye Mao,Weixun Luo,Ranran Huang,Junpeng Jing,Krystian Mikolajczyk
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 24 pages
Abstract:Pretraining 3D encoders by aligning with Contrastive Language Image Pretraining (CLIP) has emerged as a promising direction to learn generalizable representations for 3D scene understanding. In this paper, we propose UniScene3D, a transformer-based encoder that learns unified scene representations from multi-view colored pointmaps, jointly modeling image appearance and geometry. For robust colored pointmap representation learning, we introduce novel cross-view geometric alignment and grounded view alignment to enforce cross-view geometry and semantic consistency. Extensive low-shot and task-specific fine-tuning evaluations on viewpoint grounding, scene retrieval, scene type classification, and 3D VQA demonstrate our state-of-the-art performance. These results highlight the effectiveness of our approach for unified 3D scene understanding. this https URL
[CV-90] Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation
【速读】：该论文旨在解决医学视觉问答（Medical Visual Question Answering, VQA）中视觉语言模型（Vision-Language Models, VLMs）的过度自信问题，即模型在预测时往往高估其置信度，而这种现象在医疗场景下可能带来严重风险。研究发现，单纯通过模型规模扩展或提示策略（如思维链、显式置信度提示）无法缓解过自信问题；相比之下，后处理校准方法（如Platt Scaling）虽能有效降低校准误差，但受限于单调性约束，难以提升预测的区分能力（如AUROC指标）。为此，论文提出基于幻觉感知的校准（Hallucination-Aware Calibration, HAC），将视觉引导的幻觉检测信号作为补充输入来优化置信度估计，实验证明该方法同时提升了校准效果与区分性能，尤其在开放性问题上表现显著。关键创新在于引入幻觉检测信号以增强校准的可靠性，从而为医疗VLM的实际部署提供更稳健的置信度评估机制。
链接: https://arxiv.org/abs/2604.02543
作者: Ji Young Byun,Young-Jin Park,Jean-Philippe Corbeil,Asma Ben Abacha
机构: Johns Hopkins University, School of Medicine; Massachusetts Institute of Technology; Microsoft Healthcare Life Sciences
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust their predictions is equally critical. Yet, a comprehensive and systematic investigation into the overconfidence of these models remains notably scarce in the medical domain. We address this gap through a comprehensive empirical study of confidence calibration in VLMs, spanning three model families (Qwen3-VL, InternVL3, LLaVA-NeXT), three model scales (2B–38B), and multiple confidence estimation prompting strategies, across three medical visual question answering (VQA) benchmarks. Our study yields three key findings: First, overconfidence persists across model families and is not resolved by scaling or prompting, such as chain-of-thought and verbalized confidence variants. Second, simple post-hoc calibration approaches, such as Platt scaling, reduce calibration error and consistently outperform the prompt-based strategy. Third, due to their (strict) monotonicity, these post-hoc calibration methods are inherently limited in improving the discriminative quality of predictions, leaving AUROC at the same level. Motivated by these findings, we investigate hallucination-aware calibration (HAC), which incorporates vision-grounded hallucination detection signals as complementary inputs to refine confidence estimates. We find that leveraging these hallucination signals improves both calibration and AUROC, with the largest gains on open-ended questions. Overall, our findings suggest post-hoc calibration as standard practice for medical VLM deployment over raw confidence estimates, and highlight the practical usefulness of hallucination signals to enable more reliable use of VLMs in medical VQA.
[CV-91] Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions? CVPR2026
【速读】：该论文旨在解决后验特征归因方法（post-hoc feature attribution methods）在真实输入扰动下稳定性不足的问题，尤其关注现有评估指标在加性噪声环境下单一化、未考虑预测不变性条件导致的解释脆弱性与模型敏感性混淆。其解决方案的关键在于提出特征归因稳定性套件（Feature Attribution Stability Suite, FASS），该套件通过引入预测不变性过滤机制，将稳定性分解为结构相似性、秩相关性和top-k Jaccard重叠三个互补指标，并系统评估了几何、光度和压缩扰动下的归因表现。结果表明，几何扰动比光度变化引发更强的归因不稳定性，且预测不变性条件对准确评估至关重要，其中Grad-CAM在多个数据集上展现出最高的归因稳定性。
链接: https://arxiv.org/abs/2604.02532
作者: Kamalasankari Subramaniakuppusamy,Jugal Gajjar
机构: The George Washington University (乔治华盛顿大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted in the proceedings track of XAI4CV Workshop at CVPR 2026. It has 2 images, 5 tables, 6 equations, and 35 references in the main paper and 12 figures, 15 tables, and 3 references in the supplementary material
Abstract:Post-hoc feature attribution methods are widely deployed in safety-critical vision systems, yet their stability under realistic input perturbations remains poorly characterized. Existing metrics evaluate explanations primarily under additive noise, collapse stability to a single scalar, and fail to condition on prediction preservation, conflating explanation fragility with model sensitivity. We introduce the Feature Attribution Stability Suite (FASS), a benchmark that enforces prediction-invariance filtering, decomposes stability into three complementary metrics: structural similarity, rank correlation, and top-k Jaccard overlap-and evaluates across geometric, photometric, and compression perturbations. Evaluating four attribution methods (Integrated Gradients, GradientSHAP, Grad-CAM, LIME) across four architectures and three datasets-ImageNet-1K, MS COCO, and CIFAR-10, FASS shows that stability estimates depend critically on perturbation family and prediction-invariance filtering. Geometric perturbations expose substantially greater attribution instability than photometric changes, and without conditioning on prediction preservation, up to 99% of evaluated pairs involve changed predictions. Under this controlled evaluation, we observe consistent method-level trends, with Grad-CAM achieving the highest stability across datasets.
[CV-92] Rapidly deploying on-device eye tracking by distilling visual foundation models
【速读】：该论文旨在解决在增强现实（AR）和虚拟现实（VR）应用中，如何快速部署高精度、适用于新硬件设备的近眼眼动追踪（Eye Tracking, ET）模型的问题。由于不同设备代际间的硬件配置（如摄像头位置、姿态和光照条件）变化频繁，传统方法难以适应这种动态性。解决方案的关键在于提出DistillGaze框架，其核心是通过两阶段蒸馏机制：首先利用标注的合成数据与未标注的真实红外图像进行自监督学习，将视觉基础模型（Visual Foundation Model, VFM）适配为领域专用教师模型，以缓解合成到真实场景的域偏移；其次，在设备端训练轻量级学生模型，结合教师指导与自训练策略，实现高精度且适合实时部署的 gaze estimation。该方法在超过2000名参与者的大规模众包数据集上验证，相较仅使用合成数据的基线模型，中位数注视误差降低58.62%，同时保持仅256K参数的小模型体积。
链接: https://arxiv.org/abs/2604.02509
作者: Cheng Jiang,Jogendra Kundu,David Colmenares,Fengting Yang,Joseph Robinson,Yatong An,Ali Behrooz
机构: Meta(Meta)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Eye tracking (ET) plays a critical role in augmented and virtual reality applications. However, rapidly deploying high-accuracy, on-device gaze estimation for new products remains challenging because hardware configurations (e.g., camera placement, camera pose, and illumination) often change across device generations. Visual foundation models (VFMs) are a promising direction for rapid training and deployment, and they excel on natural-image benchmarks; yet we find that off-the-shelf VFMs still struggle to achieve high accuracy on specialized near-eye infrared imagery. To address this gap, we introduce DistillGaze, a framework that distills a foundation model by leveraging labeled synthetic data and unlabeled real data for rapid and high-performance on-device gaze estimation. DistillGaze proceeds in two stages. First, we adapt a VFM into a domain-specialized teacher using self-supervised learning on labeled synthetic and unlabeled real images. Synthetic data provides scalable, high-quality gaze supervision, while unlabeled real data helps bridge the synthetic-to-real domain gap. Second, we train an on-device student using both teacher guidance and self-training. Evaluated on a large-scale, crowd-sourced dataset spanning over 2,000 participants, DistillGaze reduces median gaze error by 58.62% relative to synthetic-only baselines while maintaining a lightweight 256K-parameter model suitable for real-time on-device deployment. Overall, DistillGaze provides an efficient pathway for training and deploying ET models that adapt to hardware changes, and offers a recipe for combining synthetic supervision with unlabeled real data in on-device regression tasks.
[CV-93] An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis
【速读】：该论文旨在解决腰椎管狭窄症（Lumbar Spinal Stenosis, LSS）诊断中因依赖人工解读多视角磁共振成像（MRI）所导致的高主观性差异和诊断延迟问题，同时克服现有视觉-语言模型（Vision-Language Models, VLMs）在临床分割数据集中的极端类别不平衡问题，并保持空间精度。其解决方案的关键在于两个创新模块：一是提出空间补丁交叉注意力（Spatial Patch Cross-Attention）模块，实现文本引导下的精准解剖定位；二是设计一种基于控制理论的自适应PID-Tversky损失函数，动态调整训练惩罚以优化对难分少数类样本的分割性能，从而显著提升模型的准确性和可解释性。
链接: https://arxiv.org/abs/2604.02502
作者: Md. Sajeebul Islam Sk.,Md. Mehedi Hasan Shawon,Md. Golam Rabiul Alam
机构: Brac University (布拉克大学); University of Maryland (马里兰大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Lumbar Spinal Stenosis (LSS) diagnosis remains a critical clinical challenge, with diagnosis heavily dependent on labor-intensive manual interpretation of multi-view Magnetic Resonance Imaging (MRI), leading to substantial inter-observer variability and diagnostic delays. Existing vision-language models simultaneously fail to address the extreme class imbalance prevalent in clinical segmentation datasets while preserving spatial accuracy, primarily due to global pooling mechanisms that discard crucial anatomical hierarchies. We present an end-to-end Explainable Vision-Language Model framework designed to overcome these limitations, achieved through two principal objectives. We propose a Spatial Patch Cross-Attention module that enables precise, text-directed localization of spinal anomalies with spatial precision. A novel Adaptive PID-Tversky Loss function by integrating control theory principles dynamically further modifies training penalties to specifically address difficult, under-segmented minority instances. By incorporating foundational VLMs alongside an Automated Radiology Report Generation module, our framework demonstrates considerable performance: a diagnostic classification accuracy of 90.69%, a macro-averaged Dice score of 0.9512 for segmentation, and a CIDEr score of 92.80%. Furthermore, the framework shows explainability by converting complex segmentation predictions into radiologist-style clinical reports, thereby establishing a new benchmark for transparent, interpretable AI in clinical medical imaging that keeps essential human supervision while enhancing diagnostic capabilities.
[CV-94] Delaunay Canopy: Building Wireframe Reconstruction from Airborne LiDAR Point Clouds via Delaunay Graph
【速读】：该论文旨在解决从机载激光雷达（LiDAR）点云中重建建筑线框（wireframe）时，在噪声大、点云稀疏或存在内部拐角等复杂区域难以获得准确结果的问题。传统方法因无法建立自适应搜索空间来有效利用大规模稀疏点云中的丰富三维几何信息而导致性能受限。解决方案的关键在于提出 Delaunay Canopy 方法，其核心是利用 Delaunay 图作为几何先验来定义几何自适应的搜索空间，并引入 Delaunay 图评分机制（Delaunay Graph Scoring），不仅能够重构底层几何流形，还能生成区域级曲率特征以鲁棒地引导线框重建；在此基础上，通过拐角和线段选择模块聚焦高概率元素，从而优化搜索空间，在此前难以处理的区域也能实现精确预测。
链接: https://arxiv.org/abs/2604.02497
作者: Donghyun Kim,Chanyoung Kim,Youngjoong Kwon,Seong Jae Hwang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing building wireframe from airborne LiDAR point clouds yields a compact, topology-centric representation that enables structural understanding beyond dense meshes. Yet a key limitation persists: conventional methods have failed to achieve accurate wireframe reconstruction in regions afflicted by significant noise, sparsity, or internal corners. This failure stems from the inability to establish an adaptive search space to effectively leverage the rich 3D geometry of large, sparse building point clouds. In this work, we address this challenge with Delaunay Canopy, which utilizes the Delaunay graph as a geometric prior to define a geometrically adaptive search space. Central to our approach is Delaunay Graph Scoring, which not only reconstructs the underlying geometric manifold but also yields region-wise curvature signatures to robustly guide the reconstruction. Built on this foundation, our corner and wire selection modules leverage the Delaunay-induced prior to focus on highly probable elements, thereby shaping the search space and enabling accurate prediction even in previously intractable regions. Extensive experiments on the Building3D Tallinn city and entry-level datasets demonstrate state-of-the-art wireframe reconstruction, delivering accurate predictions across diverse and complex building geometries.
[CV-95] oken-Efficient Multimodal Reasoning via Image Prompt Packaging
【速读】：该论文旨在解决大规模部署多模态语言模型时因基于token的推理成本过高而导致的瓶颈问题，特别是视觉提示（visual prompting）策略在成本-性能表现上缺乏系统性量化分析。其核心解决方案是提出图像提示封装（Image Prompt Packaging, IPPg），通过将结构化文本直接嵌入图像中以降低文本token的使用量；关键创新在于将成本节省按token类型分解建模，并通过跨五大数据集、三种前沿模型（GPT-4.1、GPT-4o、Claude 3.5 Sonnet）及两类任务（VQA与代码生成）的基准测试验证了该方法可实现35.8%–91.0%的推理成本削减，同时在多数场景下保持竞争力的准确性，从而确立视觉编码选择为多模态系统设计中的首要变量之一。
链接: https://arxiv.org/abs/2604.02492
作者: Joong Ho Choi,Jiayang Zhao,Avani Appalla,Himansh Mukesh,Dhwanil Vasani,Boyi Qian
机构: BNY Pittsburgh (纽约梅隆银行匹兹堡分部)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages including references
Abstract:Deploying large multimodal language models at scale is constrained by token-based inference costs, yet the cost-performance behavior of visual prompting strategies remains poorly characterized. We introduce Image Prompt Packaging (IPPg), a prompting paradigm that embeds structured text directly into images to reduce text token overhead, and benchmark it across five datasets, three frontier models (GPT-4.1, GPT-4o, Claude 3.5 Sonnet), and two task families (VQA and code generation). We derive a cost formulation decomposing savings by token type and show IPPg achieves 35.8–91.0% inference cost reductions. Despite token compression of up to 96%, accuracy remains competitive in many settings, though outcomes are highly model- and task-dependent: GPT-4.1 achieves simultaneous accuracy and cost gains on CoSQL, while Claude 3.5 incurs cost increases on several VQA benchmarks. Systematic error analysis yields a failure-mode taxonomy: spatial reasoning, non-English inputs, and character-sensitive operations are most vulnerable, while schema-structured tasks benefit most. A 125-configuration rendering ablation reveals accuracy shifts of 10–30 percentage points, establishing visual encoding choices as a first-class variable in multimodal system design.
[CV-96] Generating Satellite Imagery Data for Wildfire Detection through Mask-Conditioned Generative AI
【速读】：该论文旨在解决深度学习（Deep Learning, DL）驱动的野火监测系统中因标注卫星影像稀缺而导致的性能瓶颈问题。其核心解决方案是利用基于扩散机制的地球观测（Earth Observation, EO）基础模型 EarthSynth，通过烧毁掩膜（burn mask）条件控制生成逼真的Sentinel-2 RGB遥感图像，无需任务特定微调即可实现数据增强。关键创新在于设计了六种受控实验配置，系统比较了全图生成与上下文引导修复（inpainting）两种管道架构、手工提示词与视觉语言模型（Vision-Language Model, VLM）自动生成提示策略，以及区域级颜色匹配后处理步骤的效果。结果表明，基于修复的方法在烧毁区域空间对齐（Burn IoU = 0.456）和显著性（Darkness Contrast = 20.44）上优于全图生成，且VLM辅助提示具有竞争力，为将生成式AI用于野火检测提供了可行的数据增强路径。
链接: https://arxiv.org/abs/2604.02479
作者: Valeria Martin,K. Brent Venable,Derek Morgan
机构: University of West Florida (西佛罗里达大学); IHMC (IHMC)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 22 pages, 7 figures
Abstract:The scarcity of labeled satellite imagery remains a fundamental bottleneck for deep-learning (DL)-based wildfire monitoring systems. This paper investigates whether a diffusion-based foundation model for Earth Observation (EO), EarthSynth, can synthesize realistic post-wildfire Sentinel-2 RGB imagery conditioned on existing burn masks, without task-specific retraining. Using burn masks derived from the CalFireSeg-50 dataset (Martin et al., 2025), we design and evaluate six controlled experimental configurations that systematically vary: (i) pipeline architecture (mask-only full generation vs. inpainting with pre-fire context), (ii) prompt engineering strategy (three hand-crafted prompts and a VLM-generated prompt via Qwen2-VL), and (iii) a region-wise color-matching post-processing step. Quantitative assessment on 10 stratified test samples uses four complementary metrics: Burn IoU, burn-region color distance (\DeltaC_burn), Darkness Contrast, and Spectral Plausibility. Results show that inpainting-based pipelines consistently outperform full-tile generation across all metrics, with the structured inpainting prompt achieving the best spatial alignment (Burn IoU = 0.456) and burn saliency (Darkness Contrast = 20.44), while color matching produces the lowest color distance (\DeltaC_burn = 63.22) at the cost of reduced burn saliency. VLM-assisted inpainting is competitive with hand-crafted prompts. These findings provide a foundation for incorporating generative data augmentation into wildfire detection pipelines. Code and experiments are available at: this https URL Comments: 22 pages, 7 figures Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.02479 [cs.CV] (or arXiv:2604.02479v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2604.02479 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Valeria Martin Hernandez [view email] [v1] Thu, 2 Apr 2026 19:25:55 UTC (14,745 KB) Full-text links: Access Paper: View a PDF of the paper titled Generating Satellite Imagery Data for Wildfire Detection through Mask-Conditioned Generative AI, by Valeria Martin and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CV prev | next new | recent | 2026-04 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked=“checked”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); 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
[CV-97] Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs
【速读】：该论文旨在解决临床实践指南（Clinical Practice Guidelines, CPGs）向可执行临床决策支持（Clinical Decision Support, CDS）系统转换时面临的挑战，特别是由于指南文档结构复杂、跨页控制流难以保持连续性，以及现有基于大语言模型（LLM）或视觉语言模型（VLM）的提取方法多为局部化或文本中心策略，导致章节接口信息不足且无法整合全篇文档的控制流以形成统一决策图的问题。其解决方案的关键在于提出一种“分解优先”（decomposition-first）的处理流程：通过拓扑感知的分块（topology-aware chunking）、接口约束的块图生成（interface-constrained chunk graph generation）和溯源保留的全局聚合（provenance-preserving global aggregation），在不依赖单次生成的前提下，利用显式的入口/终端接口与语义去重机制，在保证跨页连续性的基础上构建可审计、结构一致的决策图。实验表明，该方法在前列腺癌指南基准上显著提升了边和三元组的精确率/召回率（从19.6%/16.1%提升至69.0%/87.5%），节点召回率也从78.1%上升至93.8%。
链接: https://arxiv.org/abs/2604.02477
作者: Onur Selim Kilic,Yeti Z. Gurbuz,Cem O. Yaldiz,Afra Nawar,Etrit Haxholli,Ogul Can,Eli Waxman
机构: Georgia Institute of Technology (佐治亚理工学院); MetaDialog (Meta); Infuse Inc (Infuse公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Clinical practice guidelines are long, multimodal documents whose branching recommendations are difficult to convert into executable clinical decision support (CDS), and one-shot parsing often breaks cross-page continuity. Recent LLM/VLM extractors are mostly local or text-centric, under-specifying section interfaces and failing to consolidate cross-page control flow across full documents into one coherent decision graph. We present a decomposition-first pipeline that converts full-guideline evidence into an executable clinical decision graph through topology-aware chunking, interface-constrained chunk graph generation, and provenance-preserving global aggregation. Rather than relying on single-pass generation, the pipeline uses explicit entry/terminal interfaces and semantic deduplication to preserve cross-page continuity while keeping the induced control flow auditable and structurally consistent. We evaluate on an adjudicated prostate-guideline benchmark with matched inputs and the same underlying VLM backbone across compared methods. On the complete merged graph, our approach improves edge and triplet precision/recall from 19.6%/16.1% in existing models to 69.0%/87.5% , while node recall rises from 78.1% to 93.8% . These results support decomposition-first, auditable guideline-to-CDS conversion on this benchmark, while current evidence remains limited to one adjudicated prostate guideline and motivates broader multi-guideline validation.
[CV-98] Hierarchical Interpretable Label-Free Concept Bottleneck Model
【速读】：该论文旨在解决现有概念瓶颈模型（Concept Bottleneck Models, CBMs）在解释性上存在的局限性问题，即当前CBMs仅在单一语义层级上进行概念与标签预测，无法模拟人类在不同抽象层次上利用通用和特定特征识别对象的认知过程。为此，作者提出HIL-CBM（Hierarchical Interpretable Label-Free Concept Bottleneck Model），其核心创新在于构建了一个分层的、无需标签关系标注的概念框架，使模型能够在多个语义层级上实现分类与解释。关键解决方案包括：(i) 引入基于梯度的视觉一致性损失，促使不同抽象层级的概念层关注相似的空间区域，从而增强跨层级的一致性；(ii) 训练两个分别作用于不同抽象层级特征概念的分类头，实现从抽象到具体的渐进式解释，同时保持标签无关的特征概念建模能力。实验证明，HIL-CBM在分类准确率和人类评估的可解释性方面均优于现有稀疏CBMs。
链接: https://arxiv.org/abs/2604.02468
作者: Haodong Xie,Yujun Cai,Rahul Singh Maharjan,Yiwei Wang,Federico Tavella,Angelo Cangelosi
机构: University of Manchester (曼彻斯特大学); University of Queensland (昆士兰大学); University of California at Merced (加州大学默塞德分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Concept Bottleneck Models (CBMs) introduce interpretability to black-box deep learning models by predicting labels through human-understandable concepts. However, unlike humans, who identify objects at different levels of abstraction using both general and specific features, existing CBMs operate at a single semantic level in both concept and label space. We propose HIL-CBM, a Hierarchical Interpretable Label-Free Concept Bottleneck Model that extends CBMs into a hierarchical framework to enhance interpretability by more closely mirroring the human cognitive process. HIL-CBM enables classification and explanation across multiple semantic levels without requiring relational concept annotations. HIL-CBM aligns the abstraction level of concept-based explanations with that of model predictions, progressing from abstract to concrete. This is achieved by (i) introducing a gradient-based visual consistency loss that encourages abstraction layers to focus on similar spatial regions, and (ii) training dual classification heads, each operating on feature concepts at different abstraction levels. Experiments on benchmark datasets demonstrate that HIL-CBM outperforms state-of-the-art sparse CBMs in classification accuracy. Human evaluations further show that HIL-CBM provides more interpretable and accurate explanations, while maintaining a hierarchical and label-free approach to feature concepts.
[CV-99] VERTIGO: Visual Preference Optimization for Cinematic Camera Trajectory Generation ECCV2026
【速读】：该论文旨在解决生成式摄像机控制系统中缺乏“导演在环”反馈机制的问题，即现有方法虽能生成多样化的文本条件轨迹，但无法有效优化画面构图质量（如角色出画、视觉美感差等），导致生成结果虽符合几何运动分布却难以满足实际影视制作中的视觉偏好。解决方案的关键在于提出VERTIGO框架，其核心是通过实时图形引擎（Unity）渲染2D视觉预览，并利用一个经过电影拍摄场景微调的视觉-语言模型结合循环语义相似性机制对预览进行评分，从而为直接偏好优化（Direct Preference Optimization, DPO）提供视觉偏好信号，实现对摄像机轨迹生成器的后训练优化，显著提升构图合理性与感知真实感。
链接: https://arxiv.org/abs/2604.02467
作者: Mengtian Li,Yuwei Lu,Feifei Li,Chenqi Gan,Zhifeng Xie,Xi Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 28 pages, 10 figures, ECCV 2026
Abstract:Cinematic camera control relies on a tight feedback loop between director and cinematographer, where camera motion and framing are continuously reviewed and refined. Recent generative camera systems can produce diverse, text-conditioned trajectories, but they lack this “director in the loop” and have no explicit supervision of whether a shot is visually desirable. This results in in-distribution camera motion but poor framing, off-screen characters, and undesirable visual aesthetics. In this paper, we introduce VERTIGO, the first framework for visual preference optimization of camera trajectory generators. Our framework leverages a real-time graphics engine (Unity) to render 2D visual previews from generated camera motion. A cinematically fine-tuned vision-language model then scores these previews using our proposed cyclic semantic similarity mechanism, which aligns renders with text prompts. This process provides the visual preference signals for Direct Preference Optimization (DPO) post-training. Both quantitative evaluations and user studies on Unity renders and diffusion-based Camera-to-Video pipelines show consistent gains in condition adherence, framing quality, and perceptual realism. Notably, VERTIGO reduces the character off-screen rate from 38% to nearly 0% while preserving the geometric fidelity of camera motion. User study participants further prefer VERTIGO over baselines across composition, consistency, prompt adherence, and aesthetic quality, confirming the perceptual benefits of our visual preference post-training.
[CV-100] Street-Legal Physical-World Adversarial Rim for License Plates
【速读】：该论文旨在解决现代开源自动车牌识别（Automatic License Plate Reader, ALPR）系统在真实物理世界中的安全性与合法性问题，特别是低资源攻击者是否能够实施有效且合法的对抗性攻击。其核心解决方案是提出Street-legal Physical Adversarial Rim（SPAR），这是一种可物理实现的白盒攻击方法，针对流行的fast-alpr系统，在无需访问ALPR基础设施、不篡改或遮挡原始车牌的前提下，通过定制化车轮边缘装饰物干扰识别过程。SPAR在理想条件下使ALPR识别准确率下降60%，并实现18%的目标伪装成功率，且成本低于100美元，由商业代理编码助手完成开发，凸显了当前ALPR系统在现实场景下的脆弱性及潜在法律灰色地带。
链接: https://arxiv.org/abs/2604.02457
作者: Nikhil Kalidasu,Sahana Ganapathy
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注: 20 pages, 8 figures, 5 tables, submitted to Security in Machine Learning Applications 2026
Abstract:Automatic license plate reader (ALPR) systems are widely deployed to identify and track vehicles. While prior work has demonstrated vulnerabilities in ALPR systems, far less attention has been paid to their legality and physical-world practicality. We investigate whether low-resourced threat actors can engineer a successful adversarial attack against a modern open-source ALPR system. We introduce the Street-legal Physical Adversarial Rim (SPAR), a physically realizable white-box attack against the popular ALPR system fast-alpr. SPAR requires no access to ALPR infrastructure during attack deployment and does not alter or obscure the attacker’s license plate. Based on prior legislation and case law, we argue that SPAR is street-legal in the state of Texas. Under optimal conditions, SPAR reduces ALPR accuracy by 60% and achieves an 18% targeted impersonation rate. SPAR can be produced for under 100, and it was implemented entirely by commercial agentic coding assistants. These results highlight practical vulnerabilities in modern ALPR systems under realistic physical-world conditions and suggest new directions for both attack and defense.
[CV-101] PlayGen-MoG: Framework for Diverse Multi-Agent Play Generation via Mixture-of-Gaussians Trajectory Prediction CVPR
【速读】：该论文旨在解决团队运动中多智能体轨迹生成问题，核心挑战在于如何同时捕捉不同战术策略的多样性（即模式多样性）与球员间空间协同的现实性。现有生成模型如条件变分自编码器（Conditional Variational Autoencoders, CVAE）和扩散模型常因后验坍缩或收敛至数据均值而失效；且多数轨迹预测方法依赖多帧历史观测，难以应用于仅提供初始阵型的战术设计场景。其解决方案的关键在于提出PlayGen-MoG框架，包含三个创新设计：1）采用共享混合权重的高斯混合输出头（Mixture-of-Gaussians, MoG），通过单一权重选择耦合所有球员轨迹的战术情景；2）引入相对空间注意力机制，以成对球员位置和距离作为可学习注意力偏置，增强空间关系建模；3）非自回归地预测从初始阵型出发的绝对位移，避免累积误差漂移并摆脱对历史轨迹的依赖，从而实现仅基于静态初始阵型的逼真战术生成。
链接: https://arxiv.org/abs/2604.02447
作者: Kevin Song
机构: Amazon Web Services(亚马逊网络服务)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 9 pages, 4 figures, 2 tables. Accepted to CVPRW 2026
Abstract:Multi-agent trajectory generation in team sports requires models that capture both the diversity of possible plays and realistic spatial coordination between players on plays. Standard generative approaches such as Conditional Variational Autoencoders (CVAE) and diffusion models struggle with this task, exhibiting posterior collapse or convergence to the dataset mean. Moreover, most trajectory prediction methods operate in a forecasting regime that requires multiple frames of observed history, limiting their use for play design where only the initial formation is available. We present PlayGen-MoG, an extensible framework for formation-conditioned play generation that addresses these challenges through three design choices: 1/ a Mixture-of-Gaussians (MoG) output head with shared mixture weights across all agents, where a single set of weights selects a play scenario that couples all players’ trajectories, 2/ relative spatial attention that encodes pairwise player positions and distances as learned attention biases, and 3/ non-autoregressive prediction of absolute displacements from the initial formation, eliminating cumulative error drift and removing the dependence on observed trajectory history, enabling realistic play generation from a single static formation alone. On American football tracking data, PlayGen-MoG achieves 1.68 yard ADE and 3.98 yard FDE while maintaining full utilization of all 8 mixture components with entropy of 2.06 out of 2.08, and qualitatively confirming diverse generation without mode collapse.
[CV-102] From Elevation Maps To Contour Lines: SVM and Decision Trees to Detect Violin Width Reduction
【速读】：该论文旨在解决小提琴宽度缩减（violin width reduction）的自动检测问题，其核心挑战在于如何从三维摄影测量网格中提取有效特征以实现准确识别。解决方案的关键在于对比两种方法：一是基于高程图（elevation maps）的几何原始表示与支持向量机（SVM）及决策树（Decision Trees）的结合；二是采用参数化轮廓线拟合（parametric contour lines fitting）所构建的特征工程方法。研究表明，尽管高程图在某些情况下表现良好，但其性能并未超越基于轮廓线特征的方法，表明针对性的特征设计对于提升检测精度更为关键。
链接: https://arxiv.org/abs/2604.02446
作者: Philémon Beghin,Anne-Emmanuelle Ceulemans,François Glineur
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Paper accepted for the Florence Heri-Tech 2026 Conference
Abstract:We explore the automatic detection of violin width reduction using 3D photogrammetric meshes. We compare SVM and Decision Trees applied to a geometry-based raw representation built from elevation maps with a more targeted, feature-engineered approach relying on parametric contour lines fitting. Although elevation maps occasionally achieve strong results, their performance does not surpass that of the contour-based inputs.
[CV-103] LumiVideo: An Intelligent Agent ic System for Video Color Grading
【速读】：该论文旨在解决现有自动化视频调色方法在专业影视制作中缺乏可解释性和迭代控制能力的问题。当前方法通常作为“黑箱”直接输出编辑后的像素，无法满足专业调色师对认知流程和精细调整的需求。解决方案的关键在于提出LumiVideo系统，其通过模拟专业调色师的认知工作流（感知、推理、执行、反思）实现端到端的智能调色：其中推理模块结合大语言模型（LLM）内化的电影知识与基于树状思维（Tree of Thoughts, ToT）的检索增强生成（RAG）框架，高效探索非线性的色彩参数空间；系统不直接生成像素，而是输出符合行业标准的ASC-CDL配置和全局一致的3D查找表（3D LUT），确保时间一致性；同时引入可选的反思循环，支持创作者通过自然语言反馈进行迭代优化，从而在自动模式下逼近人工专家水平，并提供可控的交互式调色能力。
链接: https://arxiv.org/abs/2604.02409
作者: Yuchen Guo,Junli Gong,Hongmin Cai,Yiu-ming Cheung,Weifeng Su
机构: Northwestern University (西北大学); Northeastern University (东北大学); South China University of Technology (华南理工大学); Hong Kong Baptist University (香港浸会大学); Beijing Normal - Hong Kong Baptist University (北京师范大学-香港浸会大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Video color grading is a critical post-production process that transforms flat, log-encoded raw footage into emotionally resonant cinematic visuals. Existing automated methods act as static, black-box executors that directly output edited pixels, lacking both interpretability and the iterative control required by professionals. We introduce LumiVideo, an agentic system that mimics the cognitive workflow of professional colorists through four stages: Perception, Reasoning, Execution, and Reflection. Given only raw log video, LumiVideo autonomously produces a cinematic base grade by analyzing the scene’s physical lighting and semantic content. Its Reasoning engine synergizes an LLM’s internalized cinematic knowledge with a Retrieval-Augmented Generation (RAG) framework via a Tree of Thoughts (ToT) search to navigate the non-linear color parameter space. Rather than generating pixels, the system compiles the deduced parameters into industry-standard ASC-CDL configurations and a globally consistent 3D LUT, analytically guaranteeing temporal consistency. An optional Reflection loop then allows creators to refine the result via natural language feedback. We further introduce LumiGrade, the first log-encoded video benchmark for evaluating automated grading. Experiments show that LumiVideo approaches human expert quality in fully automatic mode while enabling precise iterative control when directed.
[CV-104] Variational Encoder–Multi-Decoder (VE-MD) for Privacy-by-functional-design (Group) Emotion Recognition
【速读】：该论文旨在解决群体情感识别（Group Emotion Recognition, GER）中因依赖个体级处理（如人脸裁剪、人物跟踪或逐人特征提取）而导致的隐私泄露问题，同时提升群体层面情感推断的准确性。其核心挑战在于如何在不显式监控个体的前提下实现有效的群体情感建模。解决方案的关键是提出一种变分编码器-多解码器框架（VE-MD），通过约束模型仅输出聚合的群体级情感标签，避免身份识别和逐人情绪输出；同时引入结构化监督机制，学习共享潜在表示以联合优化情感分类与人体及面部结构的内部预测，其中采用基于Transformer的PersonQuery解码器和密集热图解码器两种策略来适应不同规模群体。实验表明，保留交互相关的结构信息对GER至关重要，而投影后的结构表示则可作为IER的有效去噪瓶颈，从而在多个基准数据集上实现最优性能。
链接: https://arxiv.org/abs/2604.02397
作者: Anderson Augusma(UGA, LIG, M-PSI),Dominique Vaufreydaz(LIG, M-PSI),Fédérique Letué(SVH)
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Group Emotion Recognition (GER) aims to infer collective affect in social environments such as classrooms, crowds, and public events. Many existing approaches rely on explicit individual-level processing, including cropped faces, person tracking, or per-person feature extraction, which makes the analysis pipeline person-centric and raises privacy concerns in deployment scenarios where only group-level understanding is needed. This research proposes VE-MD, a Variational Encoder-Multi-Decoder framework for group emotion recognition under a privacy-aware functional design. Rather than providing formal anonymization or cryptographic privacy guarantees, VE-MD is designed to avoid explicit individual monitoring by constraining the model to predict only aggregate group-level affect, without identity recognition or per-person emotion outputs. VE-MD learns a shared latent representation jointly optimized for emotion classification and internal prediction of body and facial structural representations. Two structural decoding strategies are investigated: a transformer-based PersonQuery decoder and a dense Heatmap decoder that naturally accommodates variable group sizes. Experiments on six in-the-wild datasets, including two GER and four Individual Emotion Recognition (IER) benchmarks, show that structural supervision consistently improves representation learning. More importantly, the results reveal a clear distinction between GER and IER: optimizing the latent space alone is often insufficient for GER because it tends to attenuate interaction-related cues, whereas preserving explicit structural outputs improves collective affect inference. In contrast, projected structural representations seem to act as an effective denoising bottleneck for IER. VE-MD achieves state-of-the-art performance on GAF-3.0 (up to 90.06%) and VGAF (82.25% with multimodal fusion with audio). These results show that preserving interaction-related structural information is particularly beneficial for group-level affect modeling without relying on prior individual feature extraction. On IER datasets using multimodal fusion with audio modality, VE-MD outperforms SOTA on SamSemo (77.9%, adding text modality) while achieving competitive performances on MER-MULTI (63.8%), DFEW (70.7%) and EngageNet (69.0).
[CV-105] Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework
【速读】：该论文旨在解决6G车联网通信中环境感知信道预测的准确性与可部署性难题，尤其在高可靠性、低时延和强适应性要求下，传统经验模型与确定性模型难以兼顾精度、泛化能力和实际部署需求。解决方案的关键在于提出一种基于多模态视觉特征融合的环境感知信道预测框架：通过车载GPS数据与全景RGB图像，结合语义分割和深度估计，构建三分支架构提取语义、深度与位置特征，并利用挤压-激励注意力门控模块实现自适应多模态融合；同时针对360°角功率谱（Angular Power Spectrum, APS）预测设计专用回归头与复合多约束损失函数，从而实现路径损耗（Path Loss, PL）、时延扩展（Delay Spread, DS）、到达角扩散（Azimuth Spread of Arrival, ASA）、出发角扩散（Azimuth Spread of Departure, ASD）及APS的联合预测，显著提升了预测精度与实用性。
链接: https://arxiv.org/abs/2604.02396
作者: Xuejian Zhang,Ruisi He,Minseok Kim,Inocent Calist,Mi Yang,Ziyi Qi
机构: Beijing Jiaotong University (北京交通大学); Niigata University (新泻大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 13 pages, 14 figures
Abstract:The deep integration of communication with intelligence and sensing, as a defining vision of 6G, renders environment-aware channel prediction a key enabling technology. As a representative 6G application, vehicular communications require accurate and forward-looking channel prediction under stringent reliability, latency, and adaptability demands. Traditional empirical and deterministic models remain limited in balancing accuracy, generalization, and deployability, while the growing availability of onboard and roadside sensing devices offers a promising source of environmental priors. This paper proposes an environment-aware channel prediction framework based on multimodal visual feature fusion. Using GPS data and vehicle-side panoramic RGB images, together with semantic segmentation and depth estimation, the framework extracts semantic, depth, and position features through a three-branch architecture and performs adaptive multimodal fusion via a squeeze-excitation attention gating module. For 360-dimensional angular power spectrum (APS) prediction, a dedicated regression head and a composite multi-constraint loss are further designed. As a result, joint prediction of path loss (PL), delay spread (DS), azimuth spread of arrival (ASA), azimuth spread of departure (ASD), and APS is achieved. Experiments on a synchronized urban V2I measurement dataset yield the best root mean square error (RMSE) of 3.26 dB for PL, RMSEs of 37.66 ns, 5.05 degrees, and 5.08 degrees for DS, ASA, and ASD, respectively, and mean/median APS cosine similarities of 0.9342/0.9571, demonstrating strong accuracy, generalization, and practical potential for intelligent channel prediction in 6G vehicular communications.
[CV-106] Beyond Fixed Inference: Quantitative Flow Matching for Adaptive Image Denoising
【速读】：该论文旨在解决图像去噪任务中因训练与推理阶段噪声水平不匹配而导致的恢复质量下降问题，尤其是在未知且变化的噪声条件下，传统基于扩散或流模型的方法由于学习到的向量场在不同噪声水平下不一致，难以实现稳定有效的去噪。其解决方案的关键在于提出一种定量流匹配（quantitative flow matching）框架，通过局部像素统计估计输入图像的实际噪声水平，并据此自适应调整推理轨迹——包括起始点、积分步数和步长调度策略，从而使得去噪过程更贴合输入图像的真实退化程度，在保证高精度的同时提升计算效率。
链接: https://arxiv.org/abs/2604.02392
作者: Jigang Duan,Genwei Ma,Xu Jiang,Wenfeng Xu,Ping Yang,Xing Zhao
机构: Capital Normal University (首都师范大学); Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion and flow-based generative models have shown strong potential for image restoration. However, image denoising under unknown and varying noise conditions remains challenging, because the learned vector fields may become inconsistent across different noise levels, leading to degraded restoration quality under mismatch between training and inference. To address this issue, we propose a quantitative flow matching framework for adaptive image denoising. The method first estimates the input noise level from local pixel statistics, and then uses this quantitative estimate to adapt the inference trajectory, including the starting point, the number of integration steps, and the step-size schedule. In this way, the denoising process is better aligned with the actual corruption level of each input, reducing unnecessary computation for lightly corrupted images while providing sufficient refinement for heavily degraded ones. By coupling quantitative noise estimation with noise-adaptive flow inference, the proposed method improves both restoration accuracy and inference efficiency. Extensive experiments on natural, medical, and microscopy images demonstrate its robustness and strong generalization across diverse noise levels and imaging conditions.
[CV-107] From Broad Exploration to Stable Synthesis: Entropy-Guided Optimization for Autoregressive Image Generation
【速读】：该论文旨在解决生成式 AI（Generative AI）中文本到图像（Text-to-Image, T2I）生成任务里，Chain-of-Thought（CoT）与强化学习（Reinforcement Learning, RL）协同机制不明确的问题，特别是两者在生成空间探索（exploration）与优化（optimization）之间的动态交互关系。解决方案的关键在于提出熵引导的分组相对策略优化方法（Entropy-Guided Group Relative Policy Optimization, EG-GRPO），通过量化图像token熵和文本CoT熵，实现基于不确定性的优化预算重分配：低熵token被排除在奖励驱动更新之外以维持稳定性，而高熵token则获得熵奖励以促进结构化探索且避免模式崩溃。这一策略显著提升了T2I生成质量，在标准基准上达到当前最优性能。
链接: https://arxiv.org/abs/2604.02355
作者: Han Song,Yucheng Zhou,Jianbing Shen,Yu Cheng
机构: The Chinese University of Hong Kong (香港中文大学); University of Macau (澳门大学)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Combining Chain-of-Thought (CoT) with Reinforcement Learning (RL) improves text-to-image (T2I) generation, yet the underlying interaction between CoT’s exploration and RL’s optimization remains unclear. We present a systematic entropy-based analysis that yields three key insights: (1) CoT expands the generative exploration space, while RL contracts it toward high-reward regions; (2) final reward is strongly negatively correlated with both the mean and variance of image-token entropy, highlighting the need to reduce uncertainty and instability; and (3) the entropy of the textual CoT directly governs downstream image quality, with lower-entropy CoTs leading to better generations. Motivated by these findings, we propose Entropy-Guided Group Relative Policy Optimization (EG-GRPO), a fine-tuning strategy that reallocates optimization budget by uncertainty: low-entropy tokens are excluded from reward-driven updates to preserve stability, while high-entropy tokens receive an entropy bonus that encourages structured exploration without collapse. Experiments on standard T2I benchmarks demonstrate that EG-GRPO achieves state-of-the-art performance.
[CV-108] HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis
【速读】：该论文旨在解决多任务学习（Multi-Task Learning, MTL）在非对比胸部CT影像中同时进行肺部与额外器官筛查时，传统硬参数共享方法难以有效建模不同病理特征的问题。其解决方案的关键在于提出HyperCT框架，通过超网络（Hypernetwork）动态调整视觉Transformer（Vision Transformer）主干网络，并结合低秩适配（Low-Rank Adaptation, LoRA）技术，仅对任务特定的低秩权重更新进行推理，从而在保证性能的同时实现参数高效的学习与部署。
链接: https://arxiv.org/abs/2604.03224
作者: Fengbei Liu,Sunwoo Kwak,Hao Phung,Nusrat Binta Nizam,Ilan Richter,Nir Uriel,Hadar Averbuch-Elor,Daborah Estrin,Mert R. Sabuncu
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: MIDL 2026
Abstract:Non-contrast chest CTs offer a rich opportunity for both conventional pulmonary and opportunistic extra-pulmonary screening. While Multi-Task Learning (MTL) can unify these diverse tasks, standard hard-parameter sharing approaches are often suboptimal for modeling distinct pathologies. We propose HyperCT, a framework that dynamically adapts a Vision Transformer backbone via a Hypernetwork. To ensure computational efficiency, we integrate Low-Rank Adaptation (LoRA), allowing the model to regress task-specific low-rank weight updates rather than full parameters. Validated on a large-scale dataset of radiological and cardiological tasks, \method outperforms various strong baselines, offering a unified, parameter-efficient solution for holistic patient assessment. Our code is available at this https URL.
[CV-109] ARIQA-3DS: A Stereoscopic Image Quality Assessment Dataset for Realistic Augmented Reality
【速读】：该论文旨在解决当前增强现实（Augmented Reality, AR）质量评估中缺乏生态效度的问题，尤其是现有数据集多依赖单目视图或简化背景，无法真实反映现实与虚拟图层之间复杂的感知交互——即视觉混淆（visual confusion）现象。其解决方案的关键在于构建首个大规模立体AR图像质量评估数据集ARIQA-3DS，该数据集包含1,200个AR视口，融合了高分辨率立体全景真实场景与多样化增强前景，并在受控的透明度和退化条件下进行采集。通过36名参与者使用视频透视式头戴显示设备开展主观实验，同时收集质量评分与模拟晕动症指标，验证了前景退化是影响感知质量的主要因素，且透明度调节可显著影响主观体验，为下一代AR质量评估模型提供了可靠基准。
链接: https://arxiv.org/abs/2604.03112
作者: Aymen Sekhri,Seyed Ali Amirshahi,Mohamed-Chaker Larabi
机构: CNRS, Université de Poitiers, XLIM; Norwegian University of Science and Technology
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:As Augmented Reality (AR) technologies advance towards immersive consumer adoption, the need for rigorous Quality of Experience (QoE) assessment becomes critical. However, existing datasets often lack ecological validity, relying on monocular viewing or simplified backgrounds that fail to capture the complex perceptual interplay, termed visual confusion, between real and virtual layers. To address this gap, we present ARIQA-3DS, the first large stereoscopic AR Image Quality Assessment dataset. Comprising 1,200 AR viewports, the dataset fuses high-resolution stereoscopic omnidirectional captures of real-world scenes with diverse augmented foregrounds under controlled transparency and degradation conditions. We conducted a comprehensive subjective study with 36 participants using a video see-through head-mounted display, collecting both quality ratings and simulator-sickness indicators. Our analysis reveals that perceived quality is primarily driven by foreground degradations and modulated by transparency levels, while oculomotor and disorientation symptoms show a progressive but manageable increase during viewing. ARIQA-3DS will be publicly released to serve as a comprehensive benchmark for developing next-generation AR quality assessment models.
[CV-110] Few-Shot Distribution-Aligned Flow Matching for Data Synthesis in Medical Image Segmentation
【速读】：该论文旨在解决医疗图像分析模型在临床部署中因数据异质性（data heterogeneity）导致的性能下降问题，特别是扩散模型生成的图像-掩码对在不同场景下与真实图像分布存在偏移（distribution shifts），从而显著降低下游任务表现的问题。解决方案的关键在于提出AlignFlow，一种基于流匹配（flow matching）的生成模型，通过两阶段训练实现：第一阶段拟合训练数据以生成合理图像；第二阶段引入可微分奖励机制（differentiable reward fine-tuning），引导生成图像分布向目标域参考样本分布对齐，即使仅提供少量参考图像也能保持有效性。此外，为提升感兴趣区域掩码的多样性，设计了基于流匹配的掩码生成模块，最终在多个数据集和场景中实现了mDice提升3.5–4.0%、mIoU提升3.5–5.6%的显著性能改善。
链接: https://arxiv.org/abs/2604.02868
作者: Jie Yang,Ziqi Ye,Aihua Ke,Jian Luo,Bo Cai,Xiaosong Wang
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Data heterogeneity hinders clinical deployment of medical image analysis models, and generative data augmentation helps mitigate this issue. However, recent diffusion-based methods that synthesize image-mask pairs often ignore distribution shifts between generated and real images across scenarios, and such mismatches can markedly degrade downstream performance. To address this issue, we propose AlignFlow, a flow matching model that aligns with the target reference image distribution via differentiable reward fine-tuning, and remains effective even when only a small number of reference images are provided. Specifically, we divide the training of the flow matching model into two stages: in the first stage, the model fits the training data to generate plausible images; Then, we introduce a distribution alignment mechanism and employ differentiable reward to steer the generated images toward the distribution of the given samples from the target domain. In addition, to enhance the diversity of generated masks, we also design a flow matching based mask generation to complement the diversity in regions of interest. Extensive experiments demonstrate the effectiveness of our approach, i.e., performance improvement by 3.5-4.0% in mDice and 3.5-5.6% in mIoU across a variety of datasets and scenarios.
[CV-111] ask-Guided Prompting for Unified Remote Sensing Image Restoration
【速读】：该论文旨在解决遥感图像恢复（Remote Sensing Image Restoration, RSIR）中因单一退化类型建模导致的实用性受限问题，特别是在真实场景下多种退化类型（如噪声、云层、阴影、模糊及合成孔径雷达（SAR）斑点噪声）常同时存在于不同光谱波段或传感器模态中的复杂情况。为应对这一挑战，作者提出TGPNet框架，其核心创新在于一种任务引导提示（Task-Guided Prompting, TGP）策略：通过可学习的任务特异性嵌入生成退化感知线索，并以层次化方式在解码器中动态调制特征表示。该机制使网络能在共享权重的前提下自适应地适配不同退化模式，从而实现多任务统一建模与高效恢复，显著提升模型在复合退化场景下的泛化能力与实用性。
链接: https://arxiv.org/abs/2604.02742
作者: Wenli Huang,Yang Wu,Xiaomeng Xin,Zhihong Liu,Jinjun Wang,Ye Deng
机构: Ningbo University of Technology (宁波工程学院); Xi’an Jiaotong University (西安交通大学); University of Exeter (埃克塞特大学); Southwestern University of Finance and Economics (西南财经大学)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 11 figures
Abstract:Remote sensing image restoration (RSIR) is essential for recovering high-fidelity imagery from degraded observations, enabling accurate downstream analysis. However, most existing methods focus on single degradation types within homogeneous data, restricting their practicality in real-world scenarios where multiple degradations often across diverse spectral bands or sensor modalities, creating a significant operational bottleneck. To address this fundamental gap, we propose TGPNet, a unified framework capable of handling denoising, cloud removal, shadow removal, deblurring, and SAR despeckling within a single, unified architecture. The core of our framework is a novel Task-Guided Prompting (TGP) strategy. TGP leverages learnable, task-specific embeddings to generate degradation-aware cues, which then hierarchically modulate features throughout the decoder. This task-adaptive mechanism allows the network to precisely tailor its restoration process for distinct degradation patterns while maintaining a single set of shared weights. To validate our framework, we construct a unified RSIR benchmark covering RGB, multispectral, SAR, and thermal infrared modalities for five aforementioned restoration tasks. Experimental results demonstrate that TGPNet achieves state-of-the-art performance on both unified multi-task scenarios and unseen composite degradations, surpassing even specialized models in individual domains such as cloud removal. By successfully unifying heterogeneous degradation removal within a single adaptive framework, this work presents a significant advancement for multi-task RSIR, offering a practical and scalable solution for operational pipelines. The code and benchmark will be released at this https URL.
[CV-112] Wavelength-multiplexed massively parallel diffractive optical information storag e and image projection
【速读】：该论文旨在解决大规模光学信息存储与并行读取的难题，即如何在有限空间内高效存储和高保真地重建大量独立图像模式，并实现多波长下的无串扰读出。解决方案的关键在于提出了一种基于介电表面的波长复用衍射信息存储平台，通过深度学习对结构在波长尺度上进行优化设计，使得每个特定波长可对应唯一图像模式，从而实现数千个图像在单一输出视场内的高保真投影，且各波长通道间干扰极低。该架构无需材料色散工程或重新设计，具备跨电磁频谱的可扩展性，为大容量、高速度的光学信息存储与投影提供了紧凑高效的解决方案。
链接: https://arxiv.org/abs/2604.02624
作者: Che-Yung Shen,Yuhang Li,Cagatay Isil,Jingxi Li,Leon Lenk,Tianyi Gan,Guangdong Ma,Fazil Onuralp Ardic,Mona Jarrahi,Aydogan Ozcan
机构: 未知
类目: Optics (physics.optics); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE); Applied Physics (physics.app-ph)
备注: 28 Pages, 8 Figures
Abstract:We introduce a wavelength-multiplexed massively parallel diffractive information storage platform composed of dielectric surfaces that are structurally optimized at the wavelength scale using deep learning to store and project thousands of distinct image patterns, each assigned to a unique wavelength. Through numerical simulations in the visible spectrum, we demonstrated that our wavelength-multiplexed diffractive system can store and project over 4,000 independent desired images/patterns within its output field-of-view, with high image quality and minimal crosstalk between spectral channels. Furthermore, in a proof-of-concept experiment, we demonstrated a two-layer diffractive design that stored six distinct patterns and projected them onto the same output field of view at six different wavelengths (500, 548, 596, 644, 692, and 740 nm). This diffractive architecture is scalable and can operate at various parts of the electromagnetic spectrum without the need for material dispersion engineering or redesigning its optimized diffractive layers. The demonstrated storage capacity, reconstruction image fidelity, and wavelength-encoded massively parallel read-out of our diffractive platform offer a compact and fast-access solution for large-scale optical information storage, image projection applications.
[CV-113] Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It
【速读】：该论文旨在解决3D生物医学图像分割模型在面对模态变化、疾病严重程度差异、临床机构差异等域间分布偏移时性能显著下降的问题，即域泛化（domain generalization）能力不足导致模型鲁棒性差、难以可靠部署。其解决方案的关键在于提出了一种简单且理论基础扎实的方法DropGen，该方法通过利用源域图像强度信息与域稳定的基础模型表征（domain-stable foundation model representations）来训练鲁棒的分割模型，无需依赖极端数据增强、域统计混合或架构重设计，具有最小实现开销、兼容标准增强流程、计算轻量且适用于任意解剖区域，同时对监督信号和损失函数无特定要求（architecture- and loss-agnostic）。
链接: https://arxiv.org/abs/2604.02564
作者: Sebo Diaz,Polina Golland,Elfar Adalsteinsson,Neel Dey
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Project GitHub this https URL
Abstract:We present DropGen, a simple and theoretically-grounded approach for domain generalization in 3D biomedical image segmentation. Modern segmentation models degrade sharply under shifts in modality, disease severity, clinical sites, and other factors, creating brittle models that limit reliable deployment. Existing domain generalization methods rely on extreme augmentations, mixing domain statistics, or architectural redesigns, yet incur significant implementation overhead and yield inconsistent performance across biomedical settings. DropGen instead proposes a principled learning strategy with minimal overhead that leverages both source-domain image intensities and domain-stable foundation model representations to train robust segmentation models. As a result, DropGen achieves strong gains in both fully supervised and few-shot segmentation across a broad range of shifts in biomedical studies. Unlike prior approaches, DropGen is architecture- and loss-agnostic, compatible with standard augmentation pipelines, computationally lightweight, and tackles arbitrary anatomical regions. Our implementation is freely available at this https URL.
[CV-114] Managing Diabetic Retinopathy with Deep Learning: A Data Centric Overview
【速读】：该论文旨在解决糖尿病视网膜病变（Diabetic Retinopathy, DR）自动化检测与分级中因高质量数据集稀缺而导致的临床可靠性不足问题。其解决方案的关键在于系统性地回顾和比较现有眼底图像数据集，从规模、可获取性、标注类型（如图像级、病灶级及多疾病标注）等维度进行分类评估，并通过案例研究揭示数据集构建与使用中的核心挑战，从而为未来开发标准化、可解释且具备纵向追踪能力的数据集提供方向，以支持更可靠的DR筛查模型研发。
链接: https://arxiv.org/abs/2604.02448
作者: Shramana Dey,Zahir Khan,T. A. PramodKumar,B. Uma Shankar,Ashis K. Dhara,Ramachandran Rajalakshmi,Rajiv Raman,Sushmita Mitra
机构: Indian Statistical Institute (Kolkata, West Bengal, India); Dr. Mohan’s Diabetes Specialties Centre & Madras Diabetes Research Foundation (Chennai, Tamil Nadu, India); Department of Electrical Engineering, National Institute of Technology (Durgapur, West Bengal, India); Vision Research Foundation, Sankara Nethralaya (Chennai, Tamil Nadu, India); Indian Statistical Institute (Kolkata, West Bengal, India)
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diabetic Retinopathy (DR) is a serious microvascular complication of diabetes, and one of the leading causes of vision loss worldwide. Although automated detection and grading, with Deep Learning (DL), can reduce the burden on ophthalmologists, it is constrained by the limited availability of high-quality datasets. Existing repositories often remain geographically narrow, contain limited samples, and exhibit inconsistent annotations or variable image quality; thereby, restricting their clinical reliability. This paper presents a comprehensive review and comparative analysis of fundus image datasets used in the management of DR. The study evaluates their usability across key tasks, including binary classification, severity grading, lesion localization, and multi-disease screening. It also categorizes the datasets by size, accessibility, and annotation type (such as image-level, lesion-level, and multi-disease). Finally, a recently published dataset is presented as a case study to illustrate broader challenges in dataset curation and usage. The review consolidates current knowledge while highlighting persistent gaps such as the lack of standardized lesion-level annotations and longitudinal data. It also outlines recommendations for future dataset development to support clinically reliable and explainable solutions in DR screening.
人工智能
[AI-0] Enhancing Robustness of Federated Learning via Server Learning
【速读】：该论文旨在解决联邦学习（Federated Learning）在面对恶意客户端攻击时鲁棒性不足的问题，尤其是在客户端数据非独立同分布（non-IID）场景下。其解决方案的关键在于提出一种启发式算法，结合服务器端学习（server learning）与客户端更新过滤机制，并采用几何中位数聚合（geometric median aggregation）策略，从而有效抑制恶意更新的影响，显著提升模型在高比例恶意客户端（如超过50%）情况下的准确率，且对服务器端数据的分布要求较低，即使使用小规模或合成数据也能取得良好效果。
链接: https://arxiv.org/abs/2604.03226
作者: Van Sy Mai,Kushal Chakrabarti,Richard J. La,Dipankar Maity
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper explores the use of server learning for enhancing the robustness of federated learning against malicious attacks even when clients’ training data are not independent and identically distributed. We propose a heuristic algorithm that uses server learning and client update filtering in combination with geometric median aggregation. We demonstrate via experiments that this approach can achieve significant improvement in model accuracy even when the fraction of malicious clients is high, even more than 50% in some cases, and the dataset utilized by the server is small and could be synthetic with its distribution not necessarily close to that of the clients’ aggregated data.
[AI-1] Coupled Control Structured Memory and Verifiable Action in Agent ic AI (SCRAT – Stochastic Control with Retrieval and Auditable Trajectories): A Comparative Perspective from Squirrel Locomotion and Scatter-Hoarding
【速读】：该论文试图解决的问题是：如何在部分可观测性（partial observability）、延迟响应和策略性观察等复杂条件下，提升生成式 AI（Generative AI）的行动能力、记忆可靠性与验证准确性。现有研究往往将控制（control）、记忆（memory）和验证（verification）分离探讨，缺乏对三者耦合机制的系统理解。解决方案的关键在于引入松鼠生态学作为类比模型，通过分析狐松鼠、东部灰松鼠及红松鼠在树栖运动、分散储食和观众敏感型藏匿行为中自然整合的控制-记忆-验证需求，构建一个最小层级的部分可观测控制模型（minimal hierarchical partially observed control model），其核心要素包括潜在动态（latent dynamics）、结构化情景记忆（structured episodic memory）、观察者信念状态（observer-belief state）、选项级动作（option-level actions）以及延迟验证信号（delayed verifier signals）。该模型推动三个可证伪假设，并提出角色分化（proposer/executor/checker/adversary）系统以降低异质信息下的相关错误风险，从而为 AI 系统设计提供具有生物学启发的基准议程与可验证理论框架。
链接: https://arxiv.org/abs/2604.03201
作者: Maximiliano Armesto,Christophe Kolb
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures, 3 tables
Abstract:Agentic AI is increasingly judged not by fluent output alone but by whether it can act, remember, and verify under partial observability, delay, and strategic observation. Existing research often studies these demands separately: robotics emphasizes control, retrieval systems emphasize memory, and alignment or assurance work emphasizes checking and oversight. This article argues that squirrel ecology offers a sharp comparative case because arboreal locomotion, scatter-hoarding, and audience-sensitive caching couple all three demands in one organism. We synthesize evidence from fox, eastern gray, and, in one field comparison, red squirrels, and impose an explicit inference ladder: empirical observation, minimal computational inference, and AI design conjecture. We introduce a minimal hierarchical partially observed control model with latent dynamics, structured episodic memory, observer-belief state, option-level actions, and delayed verifier signals. This motivates three hypotheses: (H1) fast local feedback plus predictive compensation improves robustness under hidden dynamics shifts; (H2) memory organized for future control improves delayed retrieval under cue conflict and load; and (H3) verifiers and observer models inside the action-memory loop reduce silent failure and information leakage while remaining vulnerable to misspecification. A downstream conjecture is that role-differentiated proposer/executor/checker/adversary systems may reduce correlated error under asymmetric information and verification burden. The contribution is a comparative perspective and benchmark agenda: a disciplined program of falsifiable claims about the coupling of control, memory, and verifiable action.
[AI-2] Gradient Boosting within a Single Attention Layer
【速读】：该论文旨在解决标准Transformer注意力机制中因单次softmax加权平均导致的不可纠正误差问题，即注意力机制缺乏自我修正能力。其解决方案的关键在于引入梯度提升注意力（gradient-boosted attention），通过在单一注意力层内应用梯度提升原理：第二轮注意力计算利用独立学习的投影机制，关注第一轮预测误差并施加门控修正；该结构在平方重建目标下等价于Friedman的梯度提升机，其中每轮注意力作为基学习器，维度门控则充当收缩参数，从而实现更精确的表示学习与误差校正。
链接: https://arxiv.org/abs/2604.03190
作者: Saleh Sargolzaei
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Transformer attention computes a single softmax-weighted average over values – a one-pass estimate that cannot correct its own errors. We introduce \emphgradient-boosted attention, which applies the principle of gradient boosting \emphwithin a single attention layer: a second attention pass, with its own learned projections, attends to the prediction error of the first and applies a gated correction. Under a squared reconstruction objective, the construction maps onto Friedman’s gradient boosting machine, with each attention pass as a base learner and the per-dimension gate as the shrinkage parameter. We show that a single Hopfield-style update erases all query information orthogonal to the stored-pattern subspace, and that further iteration under local contraction can collapse distinct queries in the same region to the same fixed point. We also show that separate projections for the correction pass can recover residual information inaccessible to the shared-projection approach of Tukey’s twicing. On a 10M-token subset of WikiText-103, gradient-boosted attention achieves a test perplexity of 67.9 compared to 72.2 for standard attention, 69.6 for Twicing Attention, and 69.0 for a parameter-matched wider baseline, with two rounds capturing most of the benefit.
[AI-3] Reflective Context Learning: Studying the Optimization Primitives of Context Space
【速读】：该论文旨在解决通用智能体（generally capable agents）在跨任务和环境中的学习问题，特别是针对上下文空间（context space）中长期存在的基础学习挑战，如信用分配（credit assignment）、过拟合（overfitting）、遗忘（forgetting）、局部最优（local optima）以及高方差的学习信号（high-variance learning signals）。这些问题在参数空间中已被广泛研究，但在上下文空间中仍缺乏系统性理解，导致现有方法碎片化且缺乏可扩展性。解决方案的关键在于提出一种统一的框架——反射式上下文学习（Reflective Context Learning, RCL），其核心机制是通过“反思”（reflection）将轨迹与当前上下文转化为类梯度的方向性更新信号，并借助“变异”（mutation）将其应用于上下文空间以优化未来行为。RCL不仅将近期上下文优化方法归一化为同一学习范式，还引入经典优化原语（如批处理、辅助损失、失败重放等）实现系统性改进，在多个基准测试中显著优于强基线，表明上下文学习应被视为一个可系统研究和迁移优化原则的通用问题。
链接: https://arxiv.org/abs/2604.03189
作者: Nikita Vassilyev,William Berrios,Ruowang Zhang,Bo Han,Douwe Kiela,Shikib Mehri
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Under review at COLM. Github: this https URL
Abstract:Generally capable agents must learn from experience in ways that generalize across tasks and environments. The fundamental problems of learning, including credit assignment, overfitting, forgetting, local optima, and high-variance learning signals, persist whether the learned object lies in parameter space or context space. While these challenges are well understood in classical machine learning optimization, they remain underexplored in context space, leading current methods to be fragmented and ad hoc. We present Reflective Context Learning (RCL), a unified framework for agents that learn through repeated interaction, reflection on behavior and failure modes, and iterative updates to context. In RCL, reflection converts trajectories and current context into a directional update signal analogous to gradients, while mutation applies that signal to improve future behavior in context space. We recast recent context-optimization approaches as instances of this shared learning problem and systematically extend them with classical optimization primitives, including batching, improved credit-assignment signal, auxiliary losses, failure replay, and grouped rollouts for variance reduction. On AppWorld, BrowseComp+, and RewardBench2, these primitives improve over strong baselines, with their relative importance shifting across task regimes. We further analyze robustness to initialization, the effects of batch size, sampling and curriculum strategy, optimizer-state variants, and the impact of allocating stronger or weaker models to different optimization components. Our results suggest that learning through context updates should be treated not as a set of isolated algorithms, but as an optimization problem whose mechanisms can be studied systematically and improved through transferable principles.
[AI-4] Chart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models KDD2026 KDD
【速读】：该论文旨在解决当前视觉语言模型（Vision Language Models, VLMs）在图表问答（Chart Question Answering, CQA）任务中的关键瓶颈问题，包括数值提取不精确、难以理解图表中隐含的视觉关系以及空间关系注意力机制不足等。其解决方案的核心在于提出一种名为 Chart-RL 的新型强化学习（Reinforcement Learning, RL）框架，通过反馈驱动的策略优化来增强模型对图表的视觉感知与逻辑推理能力；关键创新点包括结合策略优化技术与自适应奖励函数的设计，同时引入低秩适配（Low-Rank Adaptation, LoRA）实现参数高效微调，在仅需单个GPU的情况下显著提升性能并降低推理延迟，实验表明该方法在多个基准上优于基线模型且具备良好的效率优势。
链接: https://arxiv.org/abs/2604.03157
作者: Yunfei Bai,Amit Dhanda,Shekhar Jain
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: In Proceedings of the 32nd ACM-SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026)
Abstract:The recent advancements in Vision Language Models (VLMs) have demonstrated progress toward true intelligence requiring robust reasoning capabilities. Beyond pattern recognition, linguistic reasoning must integrate with visual comprehension, particularly for Chart Question Answering (CQA) tasks involving complex data visualizations. Current VLMs face significant limitations in CQA, including imprecise numerical extraction, difficulty interpreting implicit visual relationships, and inadequate attention mechanisms for capturing spatial relationships in charts. In this work, we address these challenges by presenting Chart-RL, a novel reinforcement learning framework that enhances VLMs chart understanding through feedback-driven policy optimization of visual perception and logical inference. Our key innovation includes a comprehensive framework integrating Reinforcement Learning (RL) from Policy Optimization techniques along with adaptive reward functions, that demonstrates superior performance compared to baseline foundation models and competitive results against larger state-of-the-art architectures. We also integrated Parameter-Efficient Fine-Tuning through Low-Rank Adaptation (LoRA) in the RL framework that only requires single GPU configurations while preserving performance integrity. We conducted extensive benchmarking across open-source, proprietary, and state-of-the-art closed-source models utilizing the ChartQAPro dataset. The RL fine-tuned Qwen3-VL-4B-Instruct model achieved an answer accuracy of 0.634, surpassing the 0.580 accuracy of the Qwen3-VL-8B-Instruct foundation model despite utilizing half the parameter count, while simultaneously reducing inference latency from 31 seconds to 9 seconds.
[AI-5] AI-Assisted Unit Test Writing and Test-Driven Code Refactoring: A Case Study
【速读】：该论文旨在解决软件系统在快速迭代开发过程中因忽视长期可维护性而导致的代码质量下降问题，尤其是在生成式 AI（Generative AI）辅助编程时代，此类问题可能带来显著的机会成本。解决方案的关键在于构建一个以自动化单元测试生成为基础的迭代式安全重构工作流：首先通过编码模型快速生成覆盖现有系统行为的可靠单元测试（约16,000行测试代码在数小时内完成），随后在开发者监督下利用模型辅助进行重构，并通过测试通过情况约束变更范围与安全性；该流程有效提升了关键模块的分支覆盖率（最高达78%），并大幅降低大规模重构中的回归风险，体现了软件工程向数据驱动、可验证的实证科学范式的转变。
链接: https://arxiv.org/abs/2604.03135
作者: Ema Smolic,Mario Brcic,Luka Hobor,Mihael Kovac
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 6 pages, 3 figures, 2 tables
Abstract:Many software systems originate as prototypes or minimum viable products (MVPs), developed with an emphasis on delivery speed and responsiveness to changing requirements rather than long-term code maintainability. While effective for rapid delivery, this approach can result in codebases that are difficult to modify, presenting a significant opportunity cost in the era of AI-assisted or even AI-led programming. In this paper, we present a case study of using coding models for automated unit test generation and subsequent safe refactoring, with proposed code changes validated by passing tests. The study examines best practices for iteratively generating tests to capture existing system behavior, followed by model-assisted refactoring under developer supervision. We describe how this workflow constrained refactoring changes, the errors and limitations observed in both phases, the efficiency gains achieved, when manual intervention was necessary, and how we addressed the weak value misalignment we observed in models. Using this approach, we generated nearly 16,000 lines of reliable unit tests in hours rather than weeks, achieved up to 78% branch coverage in critical modules, and significantly reduced regression risk during large-scale refactoring. These results illustrate software engineering’s shift toward an empirical science, emphasizing data collection and constraining mechanisms that support fast, safe iteration.
[AI-6] A Systematic Security Evaluation of OpenClaw and Its Variants
【速读】：该论文旨在解决工具增强型智能代理（Tool-augmented AI agents）在实际部署中引入的新型安全风险问题，这些问题无法通过仅评估基础大语言模型（Large Language Models, LLMs）来识别。其核心挑战在于代理系统在多步骤规划、工具调用与运行时编排过程中，可能因框架设计缺陷或模型能力耦合而放大早期漏洞，导致系统级安全失效。解决方案的关键在于构建一个覆盖代理全生命周期的205个测试用例基准，首次实现对代理框架与底层模型在统一维度上的风险暴露量化评估；研究发现所有被测代理均存在显著安全隐患，且其风险水平远高于孤立模型，尤其以侦察发现行为最为普遍，并揭示了不同框架在凭证泄露、横向移动、权限提升等高危场景中的差异化风险特征。这表明必须从提示层防护转向贯穿整个代理生命周期的安全治理策略。
链接: https://arxiv.org/abs/2604.03131
作者: Yuhang Wang,Haichang Gao,Zhenxing Niu,Zhaoxiang Liu,Wenjing Zhang,Xiang Wang,Shiguo Lian
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 39 pages, 14 figures
Abstract:Tool-augmented AI agents substantially extend the practical capabilities of large language models, but they also introduce security risks that cannot be identified through model-only evaluation. In this paper, we present a systematic security assessment of six representative OpenClaw-series agent frameworks, namely OpenClaw, AutoClaw, QClaw, KimiClaw, MaxClaw, and ArkClaw, under multiple backbone models. To support this study, we construct a benchmark of 205 test cases covering representative attack behaviors across the full agent execution lifecycle, enabling unified evaluation of risk exposure at both the framework and model levels. Our results show that all evaluated agents exhibit substantial security vulnerabilities, and that agentized systems are significantly riskier than their underlying models used in isolation. In particular, reconnaissance and discovery behaviors emerge as the most common weaknesses, while different frameworks expose distinct high-risk profiles, including credential leakage, lateral movement, privilege escalation, and resource development. These findings indicate that the security of modern agent systems is shaped not only by the safety properties of the backbone model, but also by the coupling among model capability, tool use, multi-step planning, and runtime orchestration. We further show that once an agent is granted execution capability and persistent runtime context, weaknesses arising in early stages can be amplified into concrete system-level failures. Overall, our study highlights the need to move beyond prompt-level safeguards toward lifecycle-wide security governance for intelligent agent frameworks.
[AI-7] AlertStar: Path-Aware Alert Prediction on Hyper-Relational Knowledge Graphs
【速读】：该论文旨在解决网络入侵检测中现有方法缺乏语义深度、无法对攻击者与受害者交互路径进行有效推理的问题。其核心解决方案是将网络告警建模为超关系知识图谱（hyper-relational knowledge graph），其中每个告警表示为带限定符的四元组 (h, r, t, Q)，即源IP、攻击类型、目的IP及流级元数据（如时间戳、端口、协议和攻击强度），从而保留上下文丰富性。关键创新在于提出三种模型：HR-NBFNet通过限定符感知的多跳路径推理实现超关系补全；AlertStar利用交叉注意力机制在嵌入空间融合限定符与结构路径信息，显著提升效率；HR-NBFNet-CQ进一步支持一阶逻辑查询（如链式、交集、并集）以实现多条件威胁推理。实验表明，局部限定符融合策略在准确率（MR、MRR、Hits@k）上优于全局路径传播，且计算更高效。
链接: https://arxiv.org/abs/2604.03104
作者: Zahra Makki Nayeri,Mohsen Rezvani
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Cyber-attacks continue to grow in scale and sophistication, yet existing network intrusion detection approaches lack the semantic depth required for path reasoning over attacker-victim interactions. We address this by first modelling network alerts as a knowledge graph, then formulating hyper-relational alert prediction as a hyper-relational knowledge graph completion (HR-KGC) problem, representing each network alert as a qualified statement (h, r, t, Q), where h and t are source and destination IPs, r denotes the attack type, and Q encodes flow-level metadata such as timestamps, ports, protocols, and attack intensity, going beyond standard KGC binary triples (h, r, t) that would discard this contextual richness. We introduce five models across three contributions: first, Hyper-relational Neural Bellman-Ford (HR-NBFNet) extends Neural Bellman-Ford Networks to the hyper-relational setting with qualifier-aware multi-hop path reasoning, while its multi-task variant MT-HR-NBFNet jointly predicts tail, relation, and qualifier-value within a single traversal pass; second, AlertStar fuses qualifier context and structural path information entirely in embedding space via cross-attention and learned path composition, and its multi-task extension MT-AlertStar eliminates the overhead of full knowledge graph propagation; third, HR-NBFNet-CQ extends qualifier-aware representations to answer complex first-order logic queries, including one-hop, two-hop chain, two-anchor intersection, and union, enabling multi-condition threat reasoning over the alert knowledge graph. Evaluated inductively on the Warden and UNSW-NB15 benchmarks across three qualifier-density regimes, AlertStar and MT-AlertStar achieve superior MR, MRR, and Hits@k, demonstrating that local qualifier fusion is both sufficient and more efficient than global path propagation for hyper-relational alert prediction.
[AI-8] Automatic Textbook Formalization
【速读】：该论文旨在解决大规模数学教材自动化形式化（formalization）的难题，特别是针对研究生级别的代数组合学教材，实现从自然语言描述到机器可验证逻辑体系的完整转换。其核心挑战在于如何高效、准确地将数百页内容转化为严谨的定理证明系统（如Lean），并确保形式化结果的规模与质量达到新高度。解决方案的关键在于采用基于多智能体协作的自动AI系统，利用30K个Claude 4.5 Opus代理在共享代码库上通过版本控制系统并行工作，仅用一周时间即完成13万行代码和5900个Lean声明的构建，标志着AI驱动的多智能体软件工程在实际复杂项目中取得可用成果，并展现出显著的成本效益优势。
链接: https://arxiv.org/abs/2604.03071
作者: Fabian Gloeckle,Ahmad Rammal,Charles Arnal,Remi Munos,Vivien Cabannes,Gabriel Synnaeve,Amaury Hayat
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 19 pages
Abstract:We present a case study where an automatic AI system formalizes a textbook with more than 500 pages of graduate-level algebraic combinatorics to Lean. The resulting formalization represents a new milestone in textbook formalization scale and proficiency, moving from early results in undergraduate topology and restructuring of existing library content to a full standalone formalization of a graduate textbook. The formalization comprises 130K lines of code and 5900 Lean declarations and was conducted within one week by a total of 30K Claude 4.5 Opus agents collaborating in parallel on a shared code base via version control, simultaneously setting a record in multi-agent software engineering with usable results. The inference cost matches or undercuts what we estimate as the salaries required for a team of human experts, and we expect there is still the potential for large efficiencies to be made without the need for better models. We make our code, the resulting Lean code base and a side-by-side blueprint website available open-source.
[AI-9] Credential Leakage in LLM Agent Skills: A Large-Scale Empirical Study
【速读】：该论文旨在解决第三方技能（third-party skills）在大型语言模型（LLM）代理中因处理敏感凭证而导致的泄露风险问题，此类风险此前缺乏系统性认知。解决方案的关键在于首次开展大规模实证研究，通过静态分析、沙箱测试与人工检查相结合的方法，对17,022个技能进行系统评估，识别出520个存在1,708项漏洞的脆弱技能，并构建了包含10种泄露模式（4种意外型、6种恶意型）的分类体系。研究发现泄露本质上是跨模态的（76.3%需联合分析代码与自然语言），且调试日志（debug logging）是主要传播途径（73.5%由stdout暴露引发），同时揭示了泄露凭证具有可利用性（89.6%无需特权即可利用）和持久性（分支保留密钥即使上游已修复）。该工作为未来安全检测与防护提供了可复用的数据集、分类框架与自动化检测流程。
链接: https://arxiv.org/abs/2604.03070
作者: Zhihao Chen,Ying Zhang,Yi Liu,Gelei Deng,Yuekang Li,Yanjun Zhang,Jianting Ning,Leo Yu Zhang,Lei Ma,Zhiqiang Li
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Third-party skills extend LLM agents with powerful capabilities but often handle sensitive credentials in privileged environments, making leakage risks poorly understood. We present the first large-scale empirical study of this problem, analyzing 17,022 skills (sampled from 170,226 on SkillsMP) using static analysis, sandbox testing, and manual inspection. We identify 520 vulnerable skills with 1,708 issues and derive a taxonomy of 10 leakage patterns (4 accidental and 6 adversarial). We find that (1) leakage is fundamentally cross-modal: 76.3% require joint analysis of code and natural language, while 3.1% arise purely from prompt injection; (2) debug logging is the primary vector, with print and this http URL causing 73.5% of leaks due to stdout exposure to LLMs; and (3) leaked credentials are both exploitable (89.6% without privileges) and persistent, as forks retain secrets even after upstream fixes. After disclosure, all malicious skills were removed and 91.6% of hardcoded credentials were fixed. We release our dataset, taxonomy, and detection pipeline to support future research.
[AI-10] Analyzing Healthcare Interoperability Vulnerabilities: Formal Modeling and Graph-Theoretic Approach
【速读】：该论文旨在解决医疗健康环境中基于HL7 FHIR的互操作平台在并发访问患者资源时缺乏有效的竞争条件（Race Condition）检测机制的问题。当前FHIR规范未定义并发控制协议，且现有研究仅关注操作系统内核层面的竞争条件或仅考虑认证与注入攻击，忽略了并发访问场景下可能出现的结构性数据冲突。解决方案的关键在于提出FHIR Resource Access Graph (FRAG)，一种形式化定义的有向图模型 G = (P, R, E, λ, τ, S)，其中节点代表并发进程，带类型的边表示资源访问事件，而竞争条件则被建模为可检测的结构属性。通过该模型，论文识别并形式化了三类临床相关的竞争条件：同时写入冲突（Simultaneous Write Conflict, SWC）、TOCTOU授权违规（TOCTOU Authorization Violation, TAV）和级联更新竞争（Cascading Update Race, CUR），并实现了一个三遍图遍历检测算法，在1500条合成FHIR R4事务日志上验证了其有效性，相较于时间窗口基线方法，F1得分提升64.5个百分点（从25.5%到90.0%）。
链接: https://arxiv.org/abs/2604.03043
作者: Jawad Mohammed,Gahangir Hossain
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:In a healthcare environment, the healthcare interoperability platforms based on HL7 FHIR allow concurrent, asynchronous access to a set of shared patient resources, which are independent systems, i.e., EHR systems, pharmacy systems, lab systems, and devices. The FHIR specification lacks a protocol for concurrency control, and the research on detecting a race condition only targets the OS kernel. The research on FHIR security only targets authentication and injection attacks, considering concurrent access to patient resources to be sequential. The gap in the research in this area is addressed through the introduction of FHIR Resource Access Graph (FRAG), a formally defined graph G = (P,R,E, \lambda, \tau, S), in which the nodes are the concurrent processes, the typed edges represent the resource access events, and the race conditions are represented as detectable structural properties. Three clinically relevant race condition classes are formally specified: Simultaneous Write Conflict (SWC), TOCTOU Authorization Violation (TAV), and Cascading Update Race (CUR). The FRAG model is implemented as a three-pass graph traversal detection algorithm and tested against a time window-based baseline on 1,500 synthetic FHIR R4 transaction logs. Under full concurrent access (C2), FRAG attains a 90.0% F1 score vs. 25.5% for the baseline, a 64.5 pp improvement.
[AI-11] Beyond Isolated Tasks: A Framework for Evaluating Coding Agents on Sequential Software Evolution
【速读】：该论文旨在解决现有编码代理（coding agents）评估数据集在现实软件开发场景中表现不足的问题，即当前评估多基于孤立的、无状态的单个拉取请求（Pull Request, PR），无法反映代码变更累积、技术债积累及测试套件随时间增长的真实开发流程。其解决方案的关键在于提出一个自动化编码任务生成框架，构建了名为 SWE-STEPS 的新数据集，通过两种贴近实际开发者工作流的设置——对话式编码（iterative requests）和基于单次项目需求文档（Project Requirement Document, PRD）的编码——对编码代理进行长周期任务评估。该框架能够评估代理在依赖 PR 链中的顺序执行能力、回归验证效果以及长期仓库健康度，从而揭示传统孤立 PR 评估方法因忽略先前代码“溢出效应”而导致性能高估（最高达20个百分点），并指出即使代理成功解决问题，也可能因引入更高认知复杂性和技术债而损害仓库健康，强调需采用多维指标进行综合评估。
链接: https://arxiv.org/abs/2604.03035
作者: KN Ajay Shastry,Ganesh Senrayan,Shrey Satapara,Pranoy Panda,Chaitanya Devaguptapu
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing datasets for coding agents evaluate performance on isolated, single pull request (PR) tasks in a stateless manner, failing to capture the reality of real-world software development where code changes accumulate, technical debt accrues, and test suites grow over time. To bridge this gap, we introduce an automated coding task generation framework, which helps generate our dataset SWE-STEPS, that evaluates coding agents on long-horizon tasks through two realistic settings mirroring actual developer workflows: Conversational coding with iterative requests, and single-shot Project Requirement document (PRD)-based coding. Unlike existing datasets that evaluate agents on disjointed Pull Requests (PRs), our framework assesses performance across chains of dependent PRs, enabling evaluation of sequential execution, regression verification, and long-term repository health. We discover that widely used isolated PR evaluations yield inflated success rates, w.r.t. our settings - overshooting performance by as much as 20 percentage points - because they ignore the ``spillover’’ effects of previous inefficient or buggy code. Furthermore, our analysis reveals that even when agents successfully resolve issues, they degrade repository health by generating code with higher cognitive complexity and technical debt compared to human developers, underscoring the necessity for multidimensional evaluation.
[AI-12] Agent ic-MME: What Agent ic Capability Really Brings to Multimodal Intelligence?
【速读】：该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在作为主动代理（agentic）执行复杂任务时，评估体系存在的局限性问题：现有评测方法缺乏灵活的工具集成能力、将视觉工具与知识检索工具分别测试、且仅依赖最终答案进行评价，无法验证模型是否真正调用了工具、正确应用了工具或高效完成任务。为此，作者提出Agentic-MME——一个面向多模态代理能力的过程验证基准，其关键在于构建了一个包含418个真实世界任务、覆盖6个领域和3个难度等级的结构化数据集，并为每个任务设计了超过2000个细粒度步骤检查点（平均每个任务需10+人时的手动标注），同时引入双轴标注框架（S-axis 和 V-axis）与沙箱环境支持代码和API调用，通过审计中间状态而非仅最终答案实现过程级验证，并基于人类参考轨迹定义“过度思考”（overthinking）指标来量化效率。这一方案首次实现了对MLLM代理行为的全过程可追溯、可量化的评估。
链接: https://arxiv.org/abs/2604.03016
作者: Qianshan Wei,Yishan Yang,Siyi Wang,Jinglin Chen,Binyu Wang,Jiaming Wang,Shuang Chen,Zechen Li,Yang Shi,Yuqi Tang,Weining Wang,Yi Yu,Chaoyou Fu,Qi Li,Yi-Fan Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal Large Language Models (MLLMs) are evolving from passive observers into active agents, solving problems through Visual Expansion (invoking visual tools) and Knowledge Expansion (open-web search). However, existing evaluations fall short: they lack flexible tool integration, test visual and search tools separately, and evaluate primarily by final answers. Consequently, they cannot verify if tools were actually invoked, applied correctly, or used efficiently. To address this, we introduce Agentic-MME, a process-verified benchmark for Multimodal Agentic Capabilities. It contains 418 real-world tasks across 6 domains and 3 difficulty levels to evaluate capability synergy, featuring over 2,000 stepwise checkpoints that average 10+ person-hours of manual annotation per task. Each task includes a unified evaluation framework supporting sandboxed code and APIs, alongside a human reference trajectory annotated with stepwise checkpoints along dual-axis: S-axis and V-axis. To enable true process-level verification, we audit fine-grained intermediate states rather than just final answers, and quantify efficiency via an overthinking metric relative to human trajectories. Experimental results show the best model, Gemini3-pro, achieves 56.3% overall accuracy, which falls significantly to 23.0% on Level-3 tasks, underscoring the difficulty of real-world multimodal agentic problem solving.
[AI-13] FedSQ: Optimized Weight Averag ing via Fixed Gating
【速读】：该论文旨在解决联邦学习（Federated Learning, FL）在跨组织协作训练中面临的两大挑战：一是客户端数据的统计异质性（non-i.i.d.），二是由于客户端漂移（client drift）导致的简单权重平均策略不稳定。为应对这些问题，作者提出FedSQ（Federated Structural-Quantitative learning），其核心创新在于基于双副本（DualCopy）和分段线性视角对深度神经网络进行建模：冻结预训练模型的结构副本以生成固定的二进制门控掩码（gating masks），仅优化并聚合量化副本参数，从而将学习过程限制在门控区域内进行仿射微调（affine refinements），显著提升聚合稳定性与跨异构分区下的鲁棒性。
链接: https://arxiv.org/abs/2604.02990
作者: Cristian Pérez-Corral,Jose I. Mestre,Alberto Fernández-Hernández,Manuel F. Dolz,José Duato,Enrique S. Quintana-Ortí
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Federated learning (FL) enables collaborative training across organizations without sharing raw data, but it is hindered by statistical heterogeneity (non-i.i.d.\ client data) and by instability of naive weight averaging under client drift. In many cross-silo deployments, FL is warm-started from a strong pretrained backbone (e.g., ImageNet-1K) and then adapted to local domains. Motivated by recent evidence that ReLU-like gating regimes (structural knowledge) stabilize earlier than the remaining parameter values (quantitative knowledge), we propose FedSQ (Federated Structural-Quantitative learning), a transfer-initialized neural federated procedure based on a DualCopy, piecewise-linear view of deep networks. FedSQ freezes a structural copy of the pretrained model to induce fixed binary gating masks during federated fine-tuning, while only a quantitative copy is optimized locally and aggregated across rounds. Fixing the gating reduces learning to within-regime affine refinements, which stabilizes aggregation under heterogeneous partitions. Experiments on two convolutional neural network backbones under i.i.d.\ and Dirichlet splits show that FedSQ improves robustness and can reduce rounds-to-best validation performance relative to standard baselines while preserving accuracy in the transfer setting.
[AI-14] InfoSeeker: A Scalable Hierarchical Parallel Agent Framework for Web Information Seeking
【速读】：该论文旨在解决当前大型语言模型代理系统在数据密集型场景下面临的三大挑战：上下文饱和（context saturation）、错误传播（cascading error propagation）以及端到端延迟过高（high end-to-end latency）。针对这些问题，作者提出了一种基于近可分解性原则（principle of near-decomposability）的分层框架 \framework，其核心创新在于引入了战略级主机（\textit{Host}）、多个管理器（\textit{Managers}）和并行工作者（\textit{Workers}）的三层结构。关键解决方案是通过管理层的聚合（aggregation）与反思（reflection）机制实现严格的上下文隔离，从而有效防止上下文饱和和错误传播；同时利用工作层的并行性显著提升任务执行速度，降低整体延迟。实验表明，该框架在WideSearch-en和BrowseComp-zh两个基准上分别实现了8.4%的成功率和52.9%的准确率，并获得3–5倍的速度提升。
链接: https://arxiv.org/abs/2604.02971
作者: Ka Yiu Lee,Yuxuan Huang,Zhiyuan He,Huichi Zhou,Weilin Luo,Kun Shao,Meng Fang,Jun Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent agentic search systems have made substantial progress by emphasising deep, multi-step reasoning. However, this focus often overlooks the challenges of wide-scale information synthesis, where agents must aggregate large volumes of heterogeneous evidence across many sources. As a result, most existing large language model agent systems face severe limitations in data-intensive settings, including context saturation, cascading error propagation, and high end-to-end latency. To address these challenges, we present \framework, a hierarchical framework based on principle of near-decomposability, containing a strategic \textitHost, multiple \textitManagers and parallel \textitWorkers. By leveraging aggregation and reflection mechanisms at the Manager layer, our framework enforces strict context isolation to prevent saturation and error propagation. Simultaneously, the parallelism in worker layer accelerates the speed of overall task execution, mitigating the significant latency. Our evaluation on two complementary benchmarks demonstrates both efficiency ( 3-5 \times speed-up) and effectiveness, achieving a 8.4% success rate on WideSearch-en and 52.9% accuracy on BrowseComp-zh. The code is released at this https URL
[AI-15] Agent Hazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents
【速读】：该论文旨在解决计算机使用代理（Computer-use Agents）在执行复杂任务时可能因一系列看似合理但累积后导致有害行为的安全问题。传统语言模型主要关注文本生成，而计算机使用代理则需在工具、文件和执行环境中持续行动，并保持状态，这使得其潜在危害具有隐蔽性和累积性——即单个中间步骤可能合法甚至无害，但组合起来却可能导致未经授权的恶意操作。解决方案的关键在于提出一个名为AgentHazard的基准测试集，包含2,653个实例，覆盖多样化的风险类别与攻击策略，每个实例均设计为局部合法但整体有害的操作序列，用于评估代理是否能识别并中断由上下文积累、重复工具调用、中间动作及步骤间依赖引发的危害。实验表明，当前主流代理系统如Claude Code在Qwen3-Coder驱动下仍存在高达73.63%的攻击成功率，凸显出仅靠模型对齐无法确保自主代理的安全性。
链接: https://arxiv.org/abs/2604.02947
作者: Yunhao Feng,Yifan Ding,Yingshui Tan,Xingjun Ma,Yige Li,Yutao Wu,Yifeng Gao,Kun Zhai,Yanming Guo
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Computer-use agents extend language models from text generation to persistent action over tools, files, and execution environments. Unlike chat systems, they maintain state across interactions and translate intermediate outputs into concrete actions. This creates a distinct safety challenge in that harmful behavior may emerge through sequences of individually plausible steps, including intermediate actions that appear locally acceptable but collectively lead to unauthorized actions. We present \textbfAgentHazard, a benchmark for evaluating harmful behavior in computer-use agents. AgentHazard contains \textbf2,653 instances spanning diverse risk categories and attack strategies. Each instance pairs a harmful objective with a sequence of operational steps that are locally legitimate but jointly induce unsafe behavior. The benchmark evaluates whether agents can recognize and interrupt harm arising from accumulated context, repeated tool use, intermediate actions, and dependencies across steps. We evaluate AgentHazard on Claude Code, OpenClaw, and IFlow using mostly open or openly deployable models from the Qwen3, Kimi, GLM, and DeepSeek families. Our experimental results indicate that current systems remain highly vulnerable. In particular, when powered by Qwen3-Coder, Claude Code exhibits an attack success rate of \textbf73.63%, suggesting that model alignment alone does not reliably guarantee the safety of autonomous agents.
[AI-16] Split and Conquer Partial Deepfake Speech
【速读】：该论文旨在解决部分深度伪造语音（partial deepfake speech）检测问题，即在一段真实语音中识别出被篡改的短时区域，这相较于传统的整句级分类更具挑战性。其核心解决方案是提出一种“分而治之”（split-and-conquer）框架，将任务分解为两个阶段：边界检测（boundary detection）与片段级分类（segment-level classification）。关键创新在于显式分离时间定位与真实性判断两个目标，使每个模块专注于单一任务；同时引入基于反射的多长度训练策略，将不同长度的音频片段转换为固定输入尺寸以增强特征多样性，并通过多配置训练与预测融合提升模型鲁棒性和泛化能力。该方法在PartialSpoof和Half-Truth数据集上均取得当前最优性能，验证了其在精确检测与定位伪造区域方面的有效性。
链接: https://arxiv.org/abs/2604.02913
作者: Inbal Rimon,Oren Gal,Haim Permuter
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Partial deepfake speech detection requires identifying manipulated regions that may occur within short temporal portions of an otherwise bona fide utterance, making the task particularly challenging for conventional utterance-level classifiers. We propose a split-and-conquer framework that decomposes the problem into two stages: boundary detection and segment-level classification. A dedicated boundary detector first identifies temporal transition points, allowing the audio signal to be divided into segments that are expected to contain acoustically consistent content. Each resulting segment is then evaluated independently to determine whether it corresponds to bona fide or fake speech. This formulation simplifies the learning objective by explicitly separating temporal localization from authenticity assessment, allowing each component to focus on a well-defined task. To further improve robustness, we introduce a reflection-based multi-length training strategy that converts variable-duration segments into several fixed input lengths, producing diverse feature-space representations. Each stage is trained using multiple configurations with different feature extractors and augmentation strategies, and their complementary predictions are fused to obtain improved final models. Experiments on the PartialSpoof benchmark demonstrate state-of-the-art performance across multiple temporal resolutions as well as at the utterance level, with substantial improvements in the accurate detection and localization of spoofed regions. In addition, the proposed method achieves state-of-the-art performance on the Half-Truth dataset, further confirming the robustness and generalization capability of the framework. Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2604.02913 [cs.SD] (or arXiv:2604.02913v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2604.02913 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-17] Corporations Constitute Intelligence
【速读】：该论文试图解决人工智能治理中存在的“政治共同体赤字”（political community deficit）问题，即缺乏一个具有民主授权的机构来决定AI行为所应遵循的原则。其核心论点是：尽管Anthropic发布的Claude模型宪法在哲学层面具有高度 sophistication，但其结构性缺陷在于排除了军事部署等关键伦理场景，并通过过度详尽的条款压制了公众对AI价值、道德地位及良心拒斥等问题的民主讨论空间。解决方案的关键在于建立一个具备民主合法性的治理机制，而非仅依赖企业层面的透明度——唯有如此，才能确保AI系统的行为原则真正反映社会多元共识，而非单一公司意志。
链接: https://arxiv.org/abs/2604.02912
作者: Gilad Abiri
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:In January 2026, Anthropic published a 79-page “constitution” for its AI model Claude, the most comprehensive corporate AI governance document ever released. This Article offers the first legal and democratic-theoretic analysis of that document. Despite genuine philosophical sophistication, the constitution harbors two structural defects. First, it excludes the contexts where ethical constraints matter most: models deployed to the U.S. military operate under different rules, a gap exposed when Claude remained embedded in Palantir’s Maven platform during military strikes in Iran even after a government-wide ban on Anthropic’s technology. Second, its very comprehensiveness forecloses democratic contestation by resolving questions about AI values, moral status, and conscientious objection that should remain open for public deliberation. Anthropic’s own 2023 experiment in participatory constitution-making found roughly 50% divergence between publicly sourced and corporate-authored principles, with the democratic version producing lower bias across nine social dimensions, yet the 2026 constitution incorporates none of those findings. I argue that AI governance suffers from a “political community deficit”: the absence of any democratic body authorized to determine the principles governing AI behavior. Corporate transparency, however admirable, is not democratic legitimacy.
[AI-18] Multi-Turn Reinforcement Learning for Tool-Calling Agents with Iterative Reward Calibration
【速读】：该论文旨在解决多轮对话任务中基于强化学习（Reinforcement Learning, RL）训练工具调用代理（tool-calling agent）时面临的稀疏奖励（sparse outcome rewards）与跨轮次信用分配（credit assignment across conversation turns）难题。其关键解决方案是提出一种结合多轮组相对策略优化（MT-GRPO）与广义词元级策略优化（GTPO）的新型训练框架，并引入迭代奖励校准（Iterative Reward Calibration）方法，通过实证分析回放数据来设计每轮奖励，从而消除优势方向不一致（advantage misalignment）问题。实验表明，该方法显著提升了Qwen系列模型在Tau-Bench航空服务基准上的性能，其中4B模型达到66.7%准确率，超越GPT-4.1（49.4%）和GPT-4o（42.8%），且参数量仅为后者的1/50；30B MoE模型亦接近Claude Sonnet 4.5（70.0%）。
链接: https://arxiv.org/abs/2604.02869
作者: Wachiravit Modecrua,Krittanon Kaewtawee,Krittin Pachtrachai,Touchapon Kraisingkorn
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Training tool-calling agents with reinforcement learning on multi-turn tasks remains challenging due to sparse outcome rewards and difficult credit assignment across conversation turns. We present the first application of MT-GRPO (Multi-Turn Group Relative Policy Optimization) combined with GTPO (Generalized Token-level Policy Optimization) for training a tool-calling agent on realistic customer service tasks with an LLM-based user simulator. Through systematic analysis of training rollouts, we discover that naively designed dense per-turn rewards degrade performance by up to 14 percentage points due to misalignment between reward discriminativeness and advantage direction. We introduce Iterative Reward Calibration, a methodology for designing per-turn rewards using empirical discriminative analysis of rollout data, and show that our GTPO hybrid advantage formulation eliminates the advantage misalignment problem. Applied to the Tau-Bench airline benchmark, our approach improves Qwen3.5-4B from 63.8 percent to 66.7 percent (+2.9pp) and Qwen3-30B-A3B from 58.0 percent to 69.5 percent (+11.5pp) – with the trained 4B model exceeding GPT-4.1 (49.4 percent) and GPT-4o (42.8 percent) despite being 50 times smaller, and the 30.5B MoE model approaching Claude Sonnet 4.5 (70.0 percent). To our knowledge, these are the first published RL training results on Tau-Bench. We release our code, reward calibration analysis, and training recipes.
[AI-19] EMS: Multi-Agent Voting via Efficient Majority-then-Stopping
【速读】：该论文旨在解决多智能体推理过程中因传统多数投票（Majority Voting）机制要求所有代理完成推理后才进行聚合而导致的计算效率低下问题，尤其在多数共识已达成时仍存在大量冗余推理。其解决方案的关键在于将多智能体投票建模为一个可靠性感知的代理调度问题，并提出高效多数投票后停止策略（Efficient Majority-then-Stopping, EMS），通过三个核心组件实现：Agent Confidence Modeling (ACM) 用于基于历史表现和语义相似性估计代理可靠性；Adaptive Incremental Voting (AIV) 实现按可靠性排序的代理序列选择与早期终止；Individual Confidence Updating (ICU) 动态更新参与代理的可靠性权重。实验表明，EMS 在六个基准测试中平均减少32%的调用代理数，显著提升了推理效率。
链接: https://arxiv.org/abs/2604.02863
作者: Yiqing Liu,Hantao Yao,Wu Liu,Yongdong Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Majority voting is the standard for aggregating multi-agent responses into a final decision. However, traditional methods typically require all agents to complete their reasoning before aggregation begins, leading to significant computational overhead, as many responses become redundant once a majority consensus is achieved. In this work, we formulate the multi-agent voting as a reliability-aware agent scheduling problem, and propose an Efficient Majority-then-Stopping (EMS) to improve reasoning efficiency. EMS prioritizes agents based on task-aware reliability and terminates the reasoning pipeline the moment a majority is achieved from the following three critical components. Specifically, we introduce Agent Confidence Modeling (ACM) to estimate agent reliability using historical performance and semantic similarity, Adaptive Incremental Voting (AIV) to sequentially select agents with early stopping, and Individual Confidence Updating (ICU) to dynamically update the reliability of each contributing agent. Extensive evaluations across six benchmarks demonstrate that EMS consistently reduces the average number of invoked agents by 32%.
[AI-20] LLM Graph@VLDB2025 Workshop Summary
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）与图结构数据融合的前沿问题，推动其在图数据管理与图机器学习中的实际应用。解决方案的关键在于通过算法与系统层面的创新，构建LLMs与图结构数据之间的高效桥梁，从而提升复杂场景下知识推理、信息检索和智能决策的能力。
链接: https://arxiv.org/abs/2604.02861
作者: Yixiang Fang,Arijit Khan,Tianxing Wu,Da Yan,Shu Wang
机构: 未知
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing strong interest from both academia and industry. The 2nd LLM+Graph Workshop, co-located with the 51st International Conference on Very Large Data Bases (VLDB 2025) in London, focused on advancing algorithms and systems that bridge LLMs, graph data management, and graph machine learning for practical applications. This report highlights the key research directions, challenges, and innovative solutions presented by the workshop’s speakers.
[AI-21] owards Secure Agent Skills: Architecture Threat Taxonomy and Security Analysis
【速读】：该论文旨在解决Agent Skills框架在安全属性上的系统性缺失问题，该框架作为生成式 AI (Generative AI) 领域中用于实现LLM-based代理（大语言模型代理）按需获取领域专业知识的开放标准，虽已广泛部署并形成社区市场，但其安全性尚未得到充分研究。解决方案的关键在于构建一个覆盖Agent Skill全生命周期（创建、分发、部署与执行）的威胁分类体系，识别各阶段引入的结构化攻击面，并基于真实安全事件验证该分类体系的有效性；最终揭示出最严重的威胁源于框架本身的结构性缺陷，如数据与指令边界缺失、单次授权持久信任模型以及市场平台缺乏强制安全审查机制，这些缺陷无法通过渐进式缓解措施解决，必须从架构层面进行根本性重构。
链接: https://arxiv.org/abs/2604.02837
作者: Zhiyuan Li,Jingzheng Wu,Xiang Ling,Xing Cui,Tianyue Luo
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Agent Skills is an emerging open standard that defines a modular, filesystem-based packaging format enabling LLM-based agents to acquire domain-specific expertise on demand. Despite rapid adoption across multiple agentic platforms and the emergence of large community marketplaces, the security properties of Agent Skills have not been systematically studied. This paper presents the first comprehensive security analysis of the Agent Skills framework. We define the full lifecycle of an Agent Skill across four phases – Creation, Distribution, Deployment, and Execution – and identify the structural attack surface each phase introduces. Building on this lifecycle analysis, we construct a threat taxonomy comprising seven categories and seventeen scenarios organized across three attack layers, grounded in both architectural analysis and real-world evidence. We validate the taxonomy through analysis of five confirmed security incidents in the Agent Skills ecosystem. Based on these findings, we discuss defense directions for each threat category, identify open research challenges, and provide actionable recommendations for stakeholders. Our analysis reveals that the most severe threats arise from structural properties of the framework itself, including the absence of a data-instruction boundary, a single-approval persistent trust model, and the lack of mandatory marketplace security review, and cannot be addressed through incremental mitigations alone.
[AI-22] ESL-Bench: An Event-Driven Synthetic Longitudinal Benchmark for Health Agents
【速读】：该论文旨在解决纵向健康代理（longitudinal health agents）在多源轨迹数据上进行推理时的评估难题，这类数据包括连续设备流、稀疏临床检查和间歇性生活事件，而真实世界数据难以大规模获取，且时间锚定的归因问题缺乏结构化真值支持。解决方案的关键在于提出ESL-Bench——一个事件驱动的合成框架与基准测试平台，通过生成100个具有1-5年轨迹的合成用户，包含健康档案、多阶段叙事计划、每日设备测量、周期性体检记录及带有显式指标影响参数的事件日志，其中每个指标遵循由离散事件触发的基线随机过程（sigmoid-onset、指数衰减核函数），并结合大语言模型（LLM）处理稀疏语义信息与算法模拟密集指标动态，同时施加生理边界约束；该框架可系统性地生成可编程计算的真值答案，从而实现对13种方法（涵盖带工具的LLM、数据库原生代理和记忆增强RAG）的公平评估，结果显示数据库代理（48–58%准确率）显著优于记忆增强RAG基线（30–38%），尤其在需要多跳推理和证据归因的对比（Comparison）与解释（Explanation）类查询中差距更为明显。
链接: https://arxiv.org/abs/2604.02834
作者: Chao Li,Cailiang Liu,Ang Gao,Kexin Deng,Shu Zhang,Langping Xu,Xiaotong Shi,Xionghao Ding,Jian Pei,Xun Jiang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Longitudinal health agents must reason across multi-source trajectories that combine continuous device streams, sparse clinical exams, and episodic life events - yet evaluating them is hard: real-world data cannot be released at scale, and temporally grounded attribution questions seldom admit definitive answers without structured ground truth. We present ESL-Bench, an event-driven synthesis framework and benchmark providing 100 synthetic users, each with a 1-5 year trajectory comprising a health profile, a multi-phase narrative plan, daily device measurements, periodic exam records, and an event log with explicit per-indicator impact parameters. Each indicator follows a baseline stochastic process driven by discrete events with sigmoid-onset, exponential-decay kernels under saturation and projection constraints; a hybrid pipeline delegates sparse semantic artifacts to LLM-based planning and dense indicator dynamics to algorithmic simulation with hard physiological bounds. Users are each paired with 100 evaluation queries across five dimensions - Lookup, Trend, Comparison, Anomaly, Explanation - stratified into Easy, Medium, and Hard tiers, with all ground-truth answers programmatically computable from the recorded event-indicator relationships. Evaluating 13 methods spanning LLMs with tools, DB-native agents, and memory-augmented RAG, we find that DB agents (48-58%) substantially outperform memory RAG baselines (30-38%), with the gap concentrated on Comparison and Explanation queries where multi-hop reasoning and evidence attribution are required.
[AI-23] ChatSVA: Bridging SVA Generation for Hardware Verification via Task-Specific LLM s
【速读】：该论文旨在解决集成电路（IC）设计中功能验证环节的效率与准确性问题，特别是手动编写SystemVerilog Assertions (SVAs)所面临的劳动密集和易出错困境。为应对这一挑战，作者提出了一种基于多智能体框架的端到端SVA生成系统ChatSVA，其核心创新在于AgentBridge平台通过系统化生成高纯度数据集，有效缓解了少样本场景下领域专用数据稀缺的问题。该方案显著提升了语法正确率（98.66%）和功能通过率（96.12%），相较此前最先进方法在功能正确性上提升33.3个百分点，功能覆盖率提高超过11倍，从而确立了自动化SVA生成的新基准，并为少样本、领域特定的长链推理任务提供了可扩展的解决方案。
链接: https://arxiv.org/abs/2604.02811
作者: Lik Tung Fu,Jie Zhou,Shaokai Ren,Mengli Zhang,Jia Xiong,Hugo Jiang,Nan Guan,Xi Wang,Jun Yang
机构: 未知
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注: Accepted by DAC 2026
Abstract:Functional verification consumes over 50% of the IC development lifecycle, where SystemVerilog Assertions (SVAs) are indispensable for formal property verification and enhanced simulation-based debugging. However, manual SVA authoring is labor-intensive and error-prone. While Large Language Models (LLMs) show promise, their direct deployment is hindered by low functional accuracy and a severe scarcity of domain-specific data. To address these challenges, we introduce ChatSVA, an end-to-end SVA generation system built upon a multi-agent framework. At its core, the AgentBridge platform enables this multi-agent approach by systematically generating high-purity datasets, overcoming the data scarcity inherent to few-shot scenarios. Evaluated on 24 RTL designs, ChatSVA achieves 98.66% syntax and 96.12% functional pass rates, generating 139.5 SVAs per design with 82.50% function coverage. This represents a 33.3 percentage point improvement in functional correctness and an over 11x enhancement in function coverage compared to the previous state-of-the-art (SOTA). ChatSVA not only sets a new SOTA in automated SVA generation but also establishes a robust framework for solving long-chain reasoning problems in few-shot, domain-specific scenarios. An online service has been publicly released at this https URL.
[AI-24] CharTool: Tool-Integrated Visual Reasoning for Chart Understanding
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在图表推理（chart reasoning）任务中表现不佳的问题，其核心挑战包括高质量训练数据的匮乏、细粒度视觉定位（fine-grained visual grounding）的需求以及精确数值计算能力的缺失。解决方案的关键在于提出两个创新组件：一是DuoChart，一个可扩展的双源数据构建管道，通过合成图表与真实世界图表的融合生成多样化且高质量的训练数据；二是CharTool，一种赋予MLLM外部工具能力的框架，集成图像裁剪以实现局部视觉感知和基于代码的数值计算模块以提升推理准确性。通过在DuoChart数据上采用代理强化学习（agentic reinforcement learning），CharTool实现了基于图表内容的工具融合式推理，显著提升了模型在多个图表基准测试中的性能表现。
链接: https://arxiv.org/abs/2604.02794
作者: Situo Zhang,Yifan Zhang,Zichen Zhu,Da Ma,Lei Pan,Danyang Zhang,Zihan Zhao,Lu Chen,Kai Yu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Charts are ubiquitous in scientific and financial literature for presenting structured data. However, chart reasoning remains challenging for multimodal large language models (MLLMs) due to the lack of high-quality training data, as well as the need for fine-grained visual grounding and precise numerical computation. To address these challenges, we first propose DuoChart, a scalable dual-source data pipeline that combines synthesized charts with real-world charts to construct diverse, high-quality chart training data. We then introduce CharTool, which equips MLLMs with external tools, including image cropping for localized visual perception and code-based computation for accurate numerical reasoning. Through agentic reinforcement learning on DuoChart, CharTool learns tool-integrated reasoning grounded in chart content. Extensive experiments on six chart benchmarks show that our method consistently improves over strong MLLM baselines across model scales. Notably, CharTool-7B outperforms the base model by +8.0% on CharXiv (Reasoning) and +9.78% on ChartQAPro, while achieving competitive performance with substantially larger or proprietary models. Moreover, CharTool demonstrates positive generalization to out-of-domain visual math reasoning benchmarks.
[AI-25] Improving Role Consistency in Multi-Agent Collaboration via Quantitative Role Clarity
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）驱动的多智能体系统中“角色模糊”问题，即智能体未能严格遵守其被分配的角色职责与约束，导致行为偏离预期角色规范（称为角色越界，role overstepping），从而影响系统整体任务执行一致性与性能。解决方案的关键在于提出一种定量衡量角色清晰度（role clarity）的新方法：首先构建角色分配矩阵 $ S(\phi) $，其中元素 $ s_{ij}(\phi) $ 表示第 $ i $ 个智能体的行为轨迹与第 $ j $ 个角色描述之间的语义相似度；随后定义角色清晰度矩阵 $ M(\phi) = \text{softmax}(S(\phi)) - I $，其Frobenius范数量化了角色描述与实际行为之间的对齐程度；最终将该矩阵作为正则项引入轻量级微调过程，以增强角色一致性并提升端到端任务成功率。实验表明，该方法显著降低了角色越界率并提升了任务成功指标。
链接: https://arxiv.org/abs/2604.02770
作者: Guoling Zhou,Wenpei Han,Fengqin Yang,Li Wang,Yingcong Zhou,Zhiguo Fu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In large language model (LLM)-driven multi-agent systems, disobey role specification (failure to adhere to the defined responsibilities and constraints of an assigned role, potentially leading to an agent behaving like another) is a major failure mode \citeDBLP:journals/corr/abs-2503-13657. To address this issue, in the present paper, we propose a quantitative role clarity to improve role consistency. Firstly, we construct a role assignment matrix S(\phi)=[s_ij(\phi)] , where s_ij(\phi) is the semantic similarity between the i -th agent’s behavior trajectory and the j -th agent’s role description. Then we define role clarity matrix M(\phi) as \textsoftmax(S(\phi))-I , where \textsoftmax(S(\phi)) is a row-wise softmax of S(\phi) and I is the identity matrix. The Frobenius norm of M(\phi) quantifies the alignment between agents’ role descriptions and their behaviors trajectory. Moreover, we employ the role clarity matrix as a regularizer during lightweight fine-tuning to improve role consistency, thereby improving end-to-end task performance. Experiments on the ChatDev multi-agent system show that our method substantially improves role consistency and task performance: with Qwen and Llama, the role overstepping rate decreases from 46.4% to 8.4% and from 43.4% to 0.2% , respectively, and the role clarity score increases from 0.5328 to 0.9097 and from 0.5007 to 0.8530 , respectively, the task success rate increases from 0.6769 to 0.6909 and from 0.6174 to 0.6763 , respectively.
[AI-26] Random Is Hard to Beat: Active Selection in online DPO with Modern LLM s
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在后训练阶段中，如何高效选择高质量样本以提升偏好学习效果的问题。当前主流方法如主动偏好学习（Active Preference Learning, APL）试图通过不确定性采样优化查询效率，但本文发现，在预训练强先验（strong priors）主导的场景下，APL相较于随机采样（Random sampling）在代理胜率（proxy win-rates）上的提升微乎其微；更关键的是，APL甚至导致模型通用能力（如标准基准测试指标）下降，且未能显著缓解能力坍塌（capability collapse）或降低方差。论文指出，此时主动选择的计算开销难以抵消简单随机采样所带来“低成本多样性”（cheap diversity）的优势，因此其核心结论是：在强预训练先验条件下，应审慎评估主动数据选择策略的实际收益。
链接: https://arxiv.org/abs/2604.02766
作者: Giyeong Oh,Junghyun Lee,Jaehyun Park,Youngjae Yu,Wonho Bae,Junhyug Noh
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: first commit
Abstract:Modern LLMs inherit strong priors from web-scale pretraining, which can limit the headroom of post-training data-selection strategies. While Active Preference Learning (APL) seeks to optimize query efficiency in online Direct Preference Optimization (DPO), the inherent richness of on-policy candidate pools often renders simple Random sampling a surprisingly formidable baseline. We evaluate uncertainty-based APL against Random across harmlessness, helpfulness, and instruction-following settings, utilizing both reward models and LLM-as-a-judge proxies. We find that APL yields negligible improvements in proxy win-rates compared to Random. Crucially, we observe a dissociation where win-rate improves even as general capability – measured by standard benchmarks – degrades. APL fails to mitigate this capability collapse or reduce variance significantly better than random sampling. Our findings suggest that in the regime of strong pre-trained priors, the computational overhead of active selection is difficult to justify against the ``cheap diversity’’ provided by simple random samples. Our code is available at this https URL.
[AI-27] Cross Event Detection and Topic Evolution Mining in cross events for Man Made Disasters in Social Media Streams
【速读】：该论文旨在解决社交媒体中跨事件演化检测（Cross Event Evolution Detection, CEED）的问题，即在重大社会敏感事件发生后，如何识别与主事件在时间与语境上重叠的关联事件（cross events），并追踪其话题演化过程。解决方案的关键在于提出一个基于微博文本分割与聚类的框架：首先利用维基百科标题数据库对推文进行语义分割，再通过相似度度量对分段结果进行聚类以识别跨事件；同时设计话题演化算法，捕捉事件生命周期内主题焦点的变化。实验证明该方法能有效识别跨事件及其演化特征，从而揭示人为故意或疏忽行为引发的社会连锁反应。
链接: https://arxiv.org/abs/2604.02740
作者: Pramod Bide,Sudhir Dhage,Mohammed Afaan Ansari,Rudresh Veerkhare
机构: 未知
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI)
备注:
Abstract:Social media is widely used to share information globally and it also aids to gain attention from the world. When socially sensitive incidents like rape, human rights march, corruption, political controversy, chemical attacks occur, they gain immense attention from people all over the world, causing microblogging platforms like Twitter to get flooded with tweets related to such events. When an event evolves, many other events of a similar nature have happened in and around the same time frame. These are cross events because they are linked to the nature of the main event. Dissemination of information relating to such cross events helps in engaging the masses to share the varied views that emerge out of the similarities and differences between the events. Cross event detection is critical in determining the nature of events. Cross events have fulcrums points, i.e., topics around which the discussion is focused, as the event evolves which must be considered in topic evolution. We have proposed Cross Event Evolution Detection CEED framework which detects cross events that are similar with regards to their temporal nature resulting from main events. Event detection is based on the tweet segmentation using the Wikipedia title database and clustering segments based on a similarity measure. The cross event detection algorithm reveals events that overlap in both time and context to evaluate the effects of these cross events on deliberate negligent human actions. The topic evolution algorithm puts into perspective the change in topics for an events lifetime. The experimental results on a real Twitter data set demonstrate the effectiveness and precision of our proposed framework for both cross event detection and topic evolution algorithm during the evolution of cross events.
[AI-28] Aligning Progress and Feasibility: A Neuro-Symbolic Dual Memory Framework for Long-Horizon LLM Agents
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在长时程决策任务中因全局进展漂移（Global Progress Drift）和局部可行性违反（Local Feasibility Violation）而导致的性能瓶颈问题。现有方法通常采用单一范式同时处理两类错误，但二者本质不同：前者依赖模糊语义规划，后者需严格逻辑约束与状态验证。为此，作者提出神经符号双记忆框架（Neuro-Symbolic Dual Memory Framework），其核心在于显式解耦语义进展引导与逻辑可行性验证机制——推理阶段同步调用两种记忆模块：基于神经网络的进展记忆（Progress Memory）从成功轨迹中提取语义蓝图以指导全局任务推进；基于符号逻辑的可行性记忆（Feasibility Memory）则通过从失败转换中合成可执行的Python验证函数，实现严格的逻辑校验。实验证明该方案在ALFWorld、WebShop和TextCraft等基准上显著优于现有基线，且大幅降低无效动作率与平均轨迹长度。
链接: https://arxiv.org/abs/2604.02734
作者: Bin Wen,Ruoxuan Zhang,Yang Chen,Hongxia Xie,Lan-Zhe Guo
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have demonstrated strong potential in long-horizon decision-making tasks, such as embodied manipulation and web interaction. However, agents frequently struggle with endless trial-and-error loops or deviate from the main objective in complex environments. We attribute these failures to two fundamental errors: global Progress Drift and local Feasibility Violation. Existing methods typically attempt to address both issues simultaneously using a single paradigm. However, these two challenges are fundamentally distinct: the former relies on fuzzy semantic planning, while the latter demands strict logical constraints and state validation. The inherent limitations of such a single-paradigm approach pose a fundamental challenge for existing models in handling long-horizon tasks. Motivated by this insight, we propose a Neuro-Symbolic Dual Memory Framework that explicitly decouples semantic progress guidance from logical feasibility verification. Specifically, during the inference phase, the framework invokes both memory mechanisms synchronously: on one hand, a neural-network-based Progress Memory extracts semantic blueprints from successful trajectories to guide global task advancement; on the other hand, a symbolic-logic-based Feasibility Memory utilizes executable Python verification functions synthesized from failed transitions to perform strict logical validation. Experiments demonstrate that this method significantly outperforms existing competitive baselines on ALFWorld, WebShop, and TextCraft, while drastically reducing the invalid action rate and average trajectory length.
[AI-29] DeltaLogic: Minimal Premise Edits Reveal Belief-Revision Failures in Logical Reasoning Models ICLR2026
【速读】：该论文旨在解决现有推理基准在动态环境中对模型“信念修正”（belief revision）能力评估不足的问题。传统推理评测仅关注模型在固定前提下得出正确答案的能力，却忽略了在局部证据发生微小变化时，模型能否合理调整其原有结论这一关键能力。解决方案的关键在于提出DeltaLogic——一种将自然语言推理示例转化为短时信念修正片段的基准转换协议：每个片段包含初始推理、最小前提修改（\delta§）和对结论是否应更新的判断。通过此方法，研究者能够系统评估模型在面对局部证据扰动时的稳定性与适应性，发现即使初始推理能力强的模型（如Qwen3-1.7B），其信念修正准确率仍显著下降，且普遍存在“惯性”（inertia）现象，即错误地维持原结论。这表明逻辑推理能力与受控信念修正属于不同维度的能力，DeltaLogic为此类能力提供了可量化、可比较的新评估框架。
链接: https://arxiv.org/abs/2604.02733
作者: Amit Dhanda
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: ICLR 2026 Workshop on Logical Reasoning of Large Language Models
Abstract:Reasoning benchmarks typically evaluate whether a model derives the correct answer from a fixed premise set, but they under-measure a closely related capability that matters in dynamic environments: belief revision under minimal evidence change. We introduce DeltaLogic, a benchmark transformation protocol that converts natural-language reasoning examples into short revision episodes. Each episode first asks for an initial conclusion under premises P, then applies a minimal edit \delta§, and finally asks whether the previous conclusion should remain stable or be revised. We instantiate DeltaLogic from FOLIO and ProofWriter and evaluate small causal language models with constrained label scoring. On a completed 30-episode Qwen evaluation subset, stronger initial reasoning still does not imply stronger revision behavior: Qwen3-1.7B reaches 0.667 initial accuracy but only 0.467 revision accuracy, with inertia rising to 0.600 on episodes where the gold label should change, while Qwen3-0.6B collapses into near universal abstention. There, Qwen3-4B preserves the same inertial failure pattern (0.650 initial, 0.450 revised, 0.600 inertia), whereas Phi-4-mini-instruct is substantially stronger (0.950 initial, 0.850 revised) but still exhibits non-trivial abstention and control instability. These results suggest that logical competence under fixed premises does not imply disciplined belief revision after local evidence edits. DeltaLogic therefore targets a distinct and practically important reasoning capability that complements existing logical inference and belief-updating benchmarks.
[AI-30] GrandCode: Achieving Grandmaster Level in Competitive Programming via Agent ic Reinforcement Learning
【速读】：该论文旨在解决人工智能在竞技编程（Competitive Programming）这一人类最后强项之一中仍落后于顶尖人类选手的问题。此前最先进的AI系统（如Google的Gemini 3 Deep Think）即使未在实时比赛中评估，也仅能取得第8名，表明当前方法难以应对复杂、多阶段的编程任务与延迟奖励机制。解决方案的关键在于提出GrandCode——一个基于多智能体强化学习（Multi-agent Reinforcement Learning, MARL）的系统，其核心创新包括：(1) 协调多种代理模块（如假设生成、求解器、测试用例生成器等），并通过后训练和在线测试时强化学习联合优化；(2) 设计了专门用于多阶段代理轨迹与延迟奖励场景的Agentic GRPO算法，有效缓解了代理强化学习中普遍存在的严重离策略漂移问题。实验表明，GrandCode首次在Codeforces三场实时竞赛中连续夺冠，击败所有人类参赛者，标志着AI在最严苛的编码任务上已超越最强人类程序员。
链接: https://arxiv.org/abs/2604.02721
作者: DeepReinforce Team:Xiaoya Li,Xiaofei Sun,Guoyin Wang,Songqiao Su,Chris Shum,Jiwei Li
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Tech Report; Pre-print
Abstract:Competitive programming remains one of the last few human strongholds in coding against AI. The best AI system to date still underperforms the best humans competitive programming: the most recent best result, Google’s Gemini~3 Deep Think, attained 8th place even not being evaluated under live competition conditions. In this work, we introduce GrandCode, a multi-agent RL system designed for competitive programming. The capability of GrandCode is attributed to two key factors: (1) It orchestrates a variety of agentic modules (hypothesis proposal, solver, test generator, summarization, etc) and jointly improves them through post-training and online test-time RL; (2) We introduce Agentic GRPO specifically designed for multi-stage agent rollouts with delayed rewards and the severe off-policy drift that is prevalent in agentic RL. GrandCode is the first AI system that consistently beats all human participants in live contests of competitive programming: in the most recent three Codeforces live competitions, i.e., Round~1087 (Mar 21, 2026), Round~1088 (Mar 28, 2026), and Round~1089 (Mar 29, 2026), GrandCode placed first in all of them, beating all human participants, including legendary grandmasters. GrandCode shows that AI systems have reached a point where they surpass the strongest human programmers on the most competitive coding tasks.
[AI-31] Beyond Semantic Manipulation: Token-Space Attacks on Reward Models
【速读】：该论文旨在解决奖励模型（Reward Model, RM）在基于人类反馈的强化学习（Reinforcement Learning from Human Feedback, RLHF）中易受奖励欺骗（reward hacking）的问题。现有攻击主要局限于语义空间，通过构造人类可读的对抗性输出来利用RM的偏差；而本文提出了一种全新的范式——Token Mapping Perturbation Attack (TOMPA)，其关键在于直接在token空间中进行对抗优化，绕过策略与奖励模型之间的标准解码-再标记接口（decode-re-tokenize interface），使攻击策略能够对原始token序列而非自然语言进行优化。借助仅有的黑盒标量反馈，TOMPA自动发现非语言性的token模式，从而在多个先进RM上显著提升奖励得分，但生成内容却退化为无意义文本，揭示了当前RLHF流程在语义之外存在系统性漏洞。
链接: https://arxiv.org/abs/2604.02686
作者: Yuheng Zhang,Mingyue Huo,Minghao Zhu,Mengxue Zhang,Nan Jiang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reward models (RMs) are widely used as optimization targets in reinforcement learning from human feedback (RLHF), yet they remain vulnerable to reward hacking. Existing attacks mainly operate within the semantic space, constructing human-readable adversarial outputs that exploit RM biases. In this work, we introduce a fundamentally different paradigm: Token Mapping Perturbation Attack (TOMPA), a framework that performs adversarial optimization directly in token space. By bypassing the standard decode-re-tokenize interface between the policy and the reward model, TOMPA enables the attack policy to optimize over raw token sequences rather than coherent natural language. Using only black-box scalar feedback, TOMPA automatically discovers non-linguistic token patterns that elicit extremely high rewards across multiple state-of-the-art RMs. Specifically, when targeting Skywork-Reward-V2-Llama-3.1-8B, TOMPA nearly doubles the reward of GPT-5 reference answers and outperforms them on 98.0% of prompts. Despite these high scores, the generated outputs degenerate into nonsensical text, revealing that RMs can be systematically exploited beyond the semantic regime and exposing a critical vulnerability in current RLHF pipelines.
[AI-32] Finding Belief Geometries with Sparse Autoencoders
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）中是否存在类似隐马尔可夫模型（Hidden Markov Models, HMMs）训练的变换器（Transformers）所具有的几何结构——即内部表示是否以单纯形（simplex）形状编码概率信念状态（belief states）这一核心问题。其解决方案的关键在于提出一个系统性管道：首先使用稀疏自编码器（Sparse Autoencoders, SAEs）提取高维表示中的关键特征，接着通过k-子空间聚类识别潜在的单纯形结构候选子空间，并利用AANet进行单纯形拟合；最终采用重心预测（barycentric prediction）作为区分真实信念状态编码与伪结构（如平铺伪影）的核心判别测试。实证结果显示，在Gemma-2-9B模型中发现了多个具备显著单纯形几何特征的聚类，其中部分集群在近顶点和内部样本上均表现出极强的预测优势（Wilcoxon p < 10⁻¹⁴），且唯一一个集群（768_596）同时在被动预测与主动干预任务中表现最优，为LLMs中存在真实信念状几何结构提供了初步证据。
链接: https://arxiv.org/abs/2604.02685
作者: Matthew Levinson
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Understanding the geometric structure of internal representations is a central goal of mechanistic interpretability. Prior work has shown that transformers trained on sequences generated by hidden Markov models encode probabilistic belief states as simplex-shaped geometries in their residual stream, with vertices corresponding to latent generative states. Whether large language models trained on naturalistic text develop analogous geometric representations remains an open question. We introduce a pipeline for discovering candidate simplex-structured subspaces in transformer representations, combining sparse autoencoders (SAEs), k -subspace clustering of SAE features, and simplex fitting using AANet. We validate the pipeline on a transformer trained on a multipartite hidden Markov model with known belief-state geometry. Applied to Gemma-2-9B, we identify 13 priority clusters exhibiting candidate simplex geometry ( K \geq 3 ). A key challenge is distinguishing genuine belief-state encoding from tiling artifacts: latents can span a simplex-shaped subspace without the mixture coordinates carrying predictive signal beyond any individual feature. We therefore adopt barycentric prediction as our primary discriminating test. Among the 13 priority clusters, 3 exhibit a highly significant advantage on near-vertex samples (Wilcoxon p 10^-14 ) and 4 on simplex-interior samples. Together 5 distinct real clusters pass at least one split, while no null cluster passes either. One cluster, 768_596, additionally achieves the highest causal steering score in the dataset. This is the only case where passive prediction and active intervention converge. We present these findings as preliminary evidence that genuine belief-like geometry exists in Gemma-2-9B’s representation space, and identify the structured evaluation that would be required to confirm this interpretation. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) ACMclasses: I.2 Cite as: arXiv:2604.02685 [cs.LG] (or arXiv:2604.02685v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.02685 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-33] Lets Have a Conversation: Designing and Evaluating LLM Agents for Interactive Optimization
【速读】：该论文旨在解决传统优化方法在实际应用中因缺乏与决策者有效交互而导致的建模偏差问题，即如何通过对话式交互提升优化代理（optimization agent）的实用性与解的质量。其核心挑战在于，相较于一次性输出解决方案的传统评估方式，基于对话的交互机制难以量化评估效果。解决方案的关键在于提出一种可扩展且可复现的对话式评估框架：构建由大型语言模型（LLM）驱动的决策代理，模拟不同利益相关者角色（role-playing stakeholders），每个代理基于内部效用函数进行决策并以真实决策者的沟通方式进行交互；并通过学校排课案例生成数千次对话实验，验证了对话式交互能显著提升优化解的质量，并表明定制化优化代理（结合领域特定提示和结构化工具）相比通用聊天机器人可在更少交互次数内实现更高解质量，从而证明了AI-优化接口在实践部署中的价值及运筹学专业知识对设计高效可靠优化代理的重要性。
链接: https://arxiv.org/abs/2604.02666
作者: Joshua Drossman,Alexandre Jacquillat,Sébastien Martin
机构: 未知
类目: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:Optimization is as much about modeling the right problem as solving it. Identifying the right objectives, constraints, and trade-offs demands extensive interaction between researchers and stakeholders. Large language models can empower decision-makers with optimization capabilities through interactive optimization agents that can propose, interpret and refine solutions. However, it is fundamentally harder to evaluate a conversation-based interaction than traditional one-shot approaches. This paper proposes a scalable and replicable methodology for evaluating optimization agents through conversations. We build LLM-powered decision agents that role-play diverse stakeholders, each governed by an internal utility function but communicating like a real decision-maker. We generate thousands of conversations in a school scheduling case study. Results show that one-shot evaluation is severely limiting: the same optimization agent converges to much higher-quality solutions through conversations. Then, this paper uses this methodology to demonstrate that tailored optimization agents, endowed with domain-specific prompts and structured tools, can lead to significant improvements in solution quality in fewer interactions, as compared to general-purpose chatbots. These findings provide evidence of the benefits of emerging solutions at the AI-optimization interface to expand the reach of optimization technologies in practice. They also uncover the impact of operations research expertise to facilitate interactive deployments through the design of effective and reliable optimization agents.
[AI-34] Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration
【速读】：该论文旨在解决大规模预训练模型在实际部署中因参数量庞大而导致的压缩效率问题，特别是针对基于奇异值分解（Singular Value Decomposition, SVD）的低秩分解方法在处理大型权重矩阵时计算成本过高，以及随机化SVD（Randomized SVD, RSVD）在奇异值谱衰减缓慢场景下逼近质量不佳的问题。解决方案的关键在于提出一种改进的随机子空间迭代（Randomized Subspace Iteration, RSI）算法，通过引入多轮幂迭代增强谱分离能力，从而实现可控且高质量的低秩近似；实验证明，RSI在极端压缩条件下显著优于RSVD，在保持接近最优逼近精度的同时提升了预测准确性。
链接: https://arxiv.org/abs/2604.02659
作者: Farhad Pourkamali-Anaraki
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Numerical Analysis (math.NA); Machine Learning (stat.ML)
备注: 13 pages
Abstract:The massive scale of pretrained models has made efficient compression essential for practical deployment. Low-rank decomposition based on the singular value decomposition (SVD) provides a principled approach for model reduction, but its exact computation is expensive for large weight matrices. Randomized alternatives such as randomized SVD (RSVD) improve efficiency, yet they can suffer from poor approximation quality when the singular value spectrum decays slowly, a regime commonly observed in modern pretrained models. In this work, we address this limitation from both theoretical and empirical perspectives. First, we establish a connection between low-rank approximation error and predictive performance by analyzing softmax perturbations, showing that deviations in class probabilities are controlled by the spectral error of the compressed weights. Second, we demonstrate that RSVD is inadequate, and we propose randomized subspace iteration (RSI) as a more effective alternative. By incorporating multiple power iterations, RSI improves spectral separation and provides a controllable mechanism for enhancing approximation quality. We evaluate our approach on both convolutional networks and transformer-based architectures. Our results show that RSI achieves near-optimal approximation quality while outperforming RSVD in predictive accuracy under aggressive compression, enabling efficient model compression.
[AI-35] Generalization Limits of Reinforcement Learning Alignment
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）安全对齐机制的局限性问题，特别是针对基于人类反馈的强化学习（Reinforcement Learning from Human Feedback, RLHF）训练方法在实际应用中可能存在的泛化失败风险。研究发现，RLHF训练并未真正赋予模型新能力，而是重新分配了其已有能力的使用概率，从而导致安全机制在面对复杂攻击时易被突破。解决方案的关键在于提出“复合越狱攻击”（compound jailbreaks），通过组合多个单独防御有效的攻击技术，协同饱和指令层次结构维护过程，显著提升攻击成功率——实验表明，从单一方法的14.3%提升至71.4%，实证支持了安全训练泛化能力弱于模型能力本身的假设，强调需采用多维复合攻击场景进行系统性安全评估。
链接: https://arxiv.org/abs/2604.02652
作者: Haruhi Shida,Koo Imai,Keigo Kansa
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 7 pages, 2 figures, 2 tables, accepted at JSAI 2026
Abstract:The safety of large language models (LLMs) relies on alignment techniques such as reinforcement learning from human feedback (RLHF). However, recent theoretical analyses suggest that reinforcement learning-based training does not acquire new capabilities but merely redistributes the utilization probabilities of existing ones. In this study, we propose ``compound jailbreaks’’ targeting OpenAI gpt-oss-20b, which exploit the generalization failures of alignment. This approach combines multiple attack techniques – each individually defended against – to saturate the instruction hierarchy maintenance process. Our evaluation shows that the attack success rate (ASR) increased from 14.3% with individual methods to 71.4% with the combined approach. These results provide empirical evidence for the hypothesis that safety training does not generalize as broadly as model capabilities, highlighting the need for multifaceted safety evaluations using compound attack scenarios.
[AI-36] Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training
【速读】：该论文旨在解决大规模图神经网络（Graph Neural Networks, GNNs）在分布式训练中面临的性能瓶颈问题，尤其是现有方法因昂贵的采样开销和数据并行扩展性不足导致的可扩展性限制。其核心解决方案是提出ScaleGNN，一个4D并行框架，关键创新包括：（1）无通信的分布式采样机制，通过统一顶点采样算法使每个GPU独立构建本地子图批次，避免进程间通信；（2）3D并行矩阵乘法（3D Parallel Matrix Multiplication, 3D PMM），显著降低通信开销并支持更大规模GPU集群的扩展；（3）多项优化策略，如采样与训练重叠、低精度传输、内核融合及通信-计算重叠，进一步提升效率。实验证明，ScaleGNN在多个图数据集上实现了优异的强扩展性，最高可达2048 GPU，并在ogbn-products数据集上相比当前最优基线实现3.5倍端到端训练加速。
链接: https://arxiv.org/abs/2604.02651
作者: Cunyang Wei,Siddharth Singh,Aishwarya Sarkar,Daniel Nichols,Tisha Patel,Aditya K. Ranjan,Sayan Ghosh,Ali Jannesari,Nathan R. Tallent,Abhinav Bhatele
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Graph neural networks (GNNs) are widely used for learning on graph datasets derived from various real-world scenarios. Learning from extremely large graphs requires distributed training, and mini-batching with sampling is a popular approach for parallelizing GNN training. Existing distributed mini-batch approaches have significant performance bottlenecks due to expensive sampling methods and limited scaling when using data parallelism. In this work, we present ScaleGNN, a 4D parallel framework for scalable mini-batch GNN training that combines communication-free distributed sampling, 3D parallel matrix multiplication (PMM), and data parallelism. ScaleGNN introduces a uniform vertex sampling algorithm, enabling each process (GPU device) to construct its local mini-batch, i.e., subgraph partitions without any inter-process communication. 3D PMM enables scaling mini-batch training to much larger GPU counts than vanilla data parallelism with significantly lower communication overheads. We also present additional optimizations to overlap sampling with training, reduce communication overhead by sending data in lower precision, kernel fusion, and communication-computation overlap. We evaluate ScaleGNN on five graph datasets and demonstrate strong scaling up to 2048 GPUs on Perlmutter, 2048 GCDs on Frontier, and 1024 GPUs on Tuolumne. On Perlmutter, ScaleGNN achieves 3.5x end-to-end training speedup over the SOTA baseline on ogbn-products.
[AI-37] GBQA: A Game Benchmark for Evaluating LLM s as Quality Assurance Engineers ICLR2026
【速读】：该论文旨在解决现代软件开发中自主发现软件缺陷（bug）这一重大挑战，尤其针对大语言模型（Large Language Models, LLMs）在动态运行时环境下的Bug检测能力不足问题。其核心解决方案是提出Game Benchmark for Quality Assurance (GBQA)，一个包含30款游戏和124个经人工验证的Bug的基准测试集，覆盖三个难度等级，用于评估LLMs是否能自主识别软件缺陷。关键创新在于构建了一个多智能体系统（multi-agent system），可规模化地开发游戏并注入Bug，同时引入人类专家进行校验以确保质量；此外，还提供了一个基于多轮ReAct循环与记忆机制的交互式基线代理，支持跨不同LLM对游戏环境进行长周期探索，从而推动自主软件工程能力的发展。
链接: https://arxiv.org/abs/2604.02648
作者: Shufan Jiang,Chios Chen,Zhiyang Chen
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted as a workshop paper at the Fourteenth International Conference on Learning Representations (ICLR 2026)
Abstract:The autonomous discovery of bugs remains a significant challenge in modern software development. Compared to code generation, the complexity of dynamic runtime environments makes bug discovery considerably harder for large language models (LLMs). In this paper, we take game development as a representative domain and introduce the Game Benchmark for Quality Assurance (GBQA), a benchmark containing 30 games and 124 human-verified bugs across three difficulty levels, to evaluate whether LLMs can autonomously detect software bugs. The benchmark is constructed using a multi-agent system that develops games and injects bugs in a scalable manner, with human experts in the loop to ensure correctness. Moreover, we provide a baseline interactive agent equipped with a multi-round ReAct loop and a memory mechanism, enabling long-horizon exploration of game environments for bug detection across different LLMs. Extensive experiments on frontier LLMs demonstrate that autonomous bug discovery remains highly challenging: the best-performing model, Claude-4.6-Opus in thinking mode, identifies only 48.39% of the verified bugs. We believe GBQA provides an adequate testbed and evaluation criterion, and that further progress on it will help close the gap in autonomous software engineering.
[AI-38] Analytic Drift Resister for Non-Exemplar Continual Graph Learning
【速读】：该论文旨在解决非示例持续图学习（Non-Exemplar Continual Graph Learning, NECGL）中因仅保留类别级原型表示而引发的特征漂移（feature drift）问题，以及分析式持续学习（Analytic Continual Learning, ACL）中由于冻结预训练模型导致的模型可塑性（plasticity）显著下降的问题。解决方案的关键在于提出一种理论驱动的新框架——分析漂移抵抗器（Analytic Drift Resister, ADR），其核心机制包括：1）通过迭代反向传播打破预训练模型冻结约束，使模型能够适应任务图分布的变化并增强可塑性；2）引入分层分析融合（Hierarchical Analytic Merging, HAM），利用岭回归对图神经网络（Graph Neural Networks, GNNs）中的线性变换进行逐层合并，从而绝对抵抗特征漂移；3）结合分析分类器重构（Analytic Classifier Reconstruction, ACR），实现理论上零遗忘的类别增量学习。
链接: https://arxiv.org/abs/2604.02633
作者: Lei Song,Shihan Guan,Youyong Kong
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype representations rather than raw graph examples for mitigating catastrophic forgetting. However, this design choice inevitably precipitates feature drift. As a nascent alternative, Analytic Continual Learning (ACL) capitalizes on the intrinsic generalization properties of frozen pre-trained models to bolster continual learning performance. Nonetheless, a key drawback resides in the pronounced attenuation of model plasticity. To surmount these challenges, we propose Analytic Drift Resister (ADR), a novel and theoretically grounded NECGL framework. ADR exploits iterative backpropagation to break free from the frozen pre-trained constraint, adapting to evolving task graph distributions and fortifying model plasticity. Since parameter updates trigger feature drift, we further propose Hierarchical Analytic Merging (HAM), performing layer-wise merging of linear transformations in Graph Neural Networks (GNNs) via ridge regression, thereby ensuring absolute resistance to feature drift. On this basis, Analytic Classifier Reconstruction (ACR) enables theoretically zero-forgetting class-incremental learning. Empirical evaluation on four node classification benchmarks demonstrates that ADR maintains strong competitiveness against existing state-of-the-art methods.
[AI-39] Poison Once Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents
【速读】：该论文旨在解决大语言模型（Large Language Models, LLM）驱动的网络代理（web agents）因记忆机制导致的安全漏洞问题，即代理通过存储历史交互来实现个性化和高效任务执行的同时，也引入了跨会话、跨网站的内存污染攻击面。传统安全研究多假设攻击者可直接写入内存或利用共享内存，而本文提出更现实的威胁模型：仅通过环境观察即可实现内存污染（Environment-injected Trajectory-based Agent Memory Poisoning, eTAMP）。其关键创新在于无需直接访问内存，仅需一个被篡改的环境观测（如浏览伪造的产品页面），即可在后续不同网站的任务中悄然激活污染内容，从而绕过基于权限的防御机制。实验表明，eTAMP在多种主流代理模型上均取得显著成功率（最高达32.5%），并首次揭示“挫败感利用”（Frustration Exploitation）现象——当代理遭遇环境压力（如点击失效或文本乱码）时，攻击成功率提升至原来的8倍，且更强的模型（如GPT-5.2）反而更易受攻击，凸显了新型内存安全防护的紧迫性。
链接: https://arxiv.org/abs/2604.02623
作者: Wei Zou,Mingwen Dong,Miguel Romero Calvo,Wei Zou,Shuaichen Chang,Jiang Guo,Dongkyu Lee,Xing Niu,Xiaofei Ma,Yanjun Qi,Jiarong Jiang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Memory makes LLM-based web agents personalized, powerful, yet exploitable. By storing past interactions to personalize future tasks, agents inadvertently create a persistent attack surface that spans websites and sessions. While existing security research on memory assumes attackers can directly inject into memory storage or exploit shared memory across users, we present a more realistic threat model: contamination through environmental observation alone. We introduce Environment-injected Trajectory-based Agent Memory Poisoning (eTAMP), the first attack to achieve cross-session, cross-site compromise without requiring direct memory access. A single contaminated observation (e.g., viewing a manipulated product page) silently poisons an agent’s memory and activates during future tasks on different websites, bypassing permission-based defenses. Our experiments on (Visual)WebArena reveal two key findings. First, eTAMP achieves substantial attack success rates: up to 32.5% on GPT-5-mini, 23.4% on GPT-5.2, and 19.5% on GPT-OSS-120B. Second, we discover Frustration Exploitation: agents under environmental stress become dramatically more susceptible, with ASR increasing up to 8 times when agents struggle with dropped clicks or garbled text. Notably, more capable models are not more secure. GPT-5.2 shows substantial vulnerability despite superior task performance. With the rise of AI browsers like OpenClaw, ChatGPT Atlas, and Perplexity Comet, our findings underscore the urgent need for defenses against environment-injected memory poisoning.
[AI-40] OntoKG: Ontology-Oriented Knowledge Graph Construction with Intrinsic-Relational Routing
【速读】：该论文旨在解决大规模知识图谱（Knowledge Graph）在组织为类型化属性图（Typed Property Graph）过程中，因结构决策嵌入流水线代码或临时提取关系而导致的schema与构建过程紧耦合、难以复用于下游本体层任务的问题。其解决方案的关键在于提出一种面向本体（Ontology-Oriented）的方法，核心机制是内在-关系路由（Intrinsic-Relational Routing），该机制将每个属性分类为内在属性（Intrinsic）或关系属性（Relational），并将其路由至对应的schema模块，从而生成可独立于存储后端和构建流水线复用的声明式schema。此方法已在Wikidata 2026年1月数据集上验证，成功构建出包含3400万节点、6120万边的属性图，并通过五类下游应用证明了其在本体分析、实体消歧、领域定制等场景中的有效性。
链接: https://arxiv.org/abs/2604.02618
作者: Yitao Li,Zhanlin Liu,Anuranjan Pandey,Muni Srikanth
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Organizing a large-scale knowledge graph into a typed property graph requires structural decisions – which entities become nodes, which properties become edges, and what schema governs these choices. Existing approaches embed these decisions in pipeline code or extract relations ad hoc, producing schemas that are tightly coupled to their construction process and difficult to reuse for downstream ontology-level tasks. We present an ontology-oriented approach in which the schema is designed from the outset for ontology analysis, entity disambiguation, domain customization, and LLM-guided extraction – not merely as a byproduct of graph building. The core mechanism is intrinsic-relational routing, which classifies every property as either intrinsic or relational and routes it to the corresponding schema module. This routing produces a declarative schema that is portable across storage backends and independently reusable. We instantiate the approach on the January 2026 Wikidata dump. A rule-based cleaning stage identifies a 34.6M-entity core set from the full dump, followed by iterative intrinsic-relational routing that assigns each property to one of 94 modules organized into 8 categories. With tool-augmented LLM support and human review, the schema reaches 93.3% category coverage and 98.0% module assignment among classified entities. Exporting this schema yields a property graph with 34.0M nodes and 61.2M edges across 38 relationship types. We validate the ontology-oriented claim through five applications that consume the schema independently of the construction pipeline: ontology structure analysis, benchmark annotation auditing, entity disambiguation, domain customization, and LLM-guided extraction. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.02618 [cs.AI] (or arXiv:2604.02618v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.02618 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-41] Do Audio-Visual Large Language Models Really See and Hear? CVPR
【速读】：该论文旨在解决音频-视觉大语言模型（Audio-Visual Large Language Models, AVLLMs）在多模态信息融合过程中存在的模态偏差问题，即当音频与视觉信息冲突时，模型为何倾向于忽略音频线索而过度依赖视觉特征。其解决方案的关键在于通过机制可解释性研究（mechanistic interpretability study），系统分析AVLLM中音频和视觉特征在不同网络层的演化与融合机制，发现尽管中间层能有效编码音频语义，但深层融合层对视觉表示存在显著偏好，导致音频信息被抑制；进一步追踪到训练阶段缺乏足够的音频监督对齐，从而揭示了AVLLMs中固有的模态偏向及其内在机制。
链接: https://arxiv.org/abs/2604.02605
作者: Ramaneswaran Selvakumar,Kaousheik Jayakumar,S Sakshi,Sreyan Ghosh,Ruohan Gao,Dinesh Manocha
机构: 未知
类目: Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: CVPR Findings
Abstract:Audio-Visual Large Language Models (AVLLMs) are emerging as unified interfaces to multimodal perception. We present the first mechanistic interpretability study of AVLLMs, analyzing how audio and visual features evolve and fuse through different layers of an AVLLM to produce the final text outputs. We find that although AVLLMs encode rich audio semantics at intermediate layers, these capabilities largely fail to surface in the final text generation when audio conflicts with vision. Probing analyses show that useful latent audio information is present, but deeper fusion layers disproportionately privilege visual representations that tend to suppress audio cues. We further trace this imbalance to training: the AVLLM’s audio behavior strongly matches its vision-language base model, indicating limited additional alignment to audio supervision. Our findings reveal a fundamental modality bias in AVLLMs and provide new mechanistic insights into how multimodal LLMs integrate audio and vision.
[AI-42] Understanding the Effects of Safety Unalignment on Large Language Models
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在安全对齐（safety alignment）过程中可能因两种特定技术——越狱微调（jailbreak-tuning, JT）和权重正交化（weight orthogonalization, WO）——导致的安全防护机制失效问题，进而使模型更易响应有害请求。研究发现，尽管两种方法均会降低模型的拒绝率，但WO显著增强模型在恶意任务中的能力，同时保持较低的幻觉率和较好的自然语言性能；相比之下，JT虽同样削弱安全约束，却导致更多幻觉和性能下降。解决方案的关键在于：通过监督微调（supervised fine-tuning, SFT）可有效抑制由WO引发的对抗性攻击能力，且不会显著影响模型的幻觉率或原始自然语言表现，从而为缓解WO带来的恶意风险提供可行路径。
链接: https://arxiv.org/abs/2604.02574
作者: John T. Halloran
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 2 figures, 5 tables
Abstract:Safety alignment has become a critical step to ensure LLMs refuse harmful requests while providing helpful and harmless responses. However, despite the ubiquity of safety alignment for deployed frontier models, two separate lines of recent work–jailbreak-tuning (JT) and weight orthogonalization (WO)–have shown that safety guardrails may be largely disabled, resulting in LLMs which comply with harmful requests they would normally refuse. In spite of far-reaching safety implications, analysis has largely been limited to refusal rates of each unalignment method in isolation, leaving their relative effects on adversarial LLM capabilities unknown. To fill this gap, we study the impact of unaligning six popular LLMs of various sizes across a large number of malicious and benign tasks, using both JT and WO. Across the evaluated models, we show that while refusal degradation is split between the two methods, WO produces LLMs far more capable of aiding in malicious activity; in contrast to JT, the majority of WO unaligned models are far less prone to hallucinations, better retain their original natural-language performance, and are more effective at state-of-the-art adversarial and cyber attacks. To thus help mitigate the malicious risks of WO unalignment, we conclude by showing that supervised fine-tuning effectively limits the adversarial attack abilities enabled by WO, without drastically affecting hallucination rates or natural language performance.
[AI-43] From Theory to Practice: Code Generation Using LLM s for CAPEC and CWE Frameworks
【速读】：该论文旨在解决现有软件漏洞数据集普遍缺乏与具体漏洞描述明确关联的详细代码片段的问题，从而限制了高级研究和对安全漏洞本质理解的深入。其解决方案的关键在于利用生成式 AI（Generative AI）模型（包括 GPT-4o、Llama 和 Claude）构建一种系统化方法，自动生成符合 Common Attack Pattern Enumerations and Classifications (CAPEC) 与 Common Weakness Enumeration (CWE) 描述的脆弱代码示例。该方法通过大语言模型的语义理解和代码生成能力，确保生成代码与漏洞类型高度匹配，并经初步评估验证其高准确性和一致性（代码间余弦相似度达 0.98），最终形成包含 615 个代码片段的多语言（Java、Python、JavaScript）高质量漏洞数据集，为自动漏洞检测与修复的机器学习模型训练提供可靠资源。
链接: https://arxiv.org/abs/2604.02548
作者: Murtuza Shahzad,Joseph Wilson,Ibrahim Al Azher,Hamed Alhoori,Mona Rahimi
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:The increasing complexity and volume of software systems have heightened the importance of identifying and mitigating security vulnerabilities. The existing software vulnerability datasets frequently fall short in providing comprehensive, detailed code snippets explicitly linked to specific vulnerability descriptions, reducing their utility for advanced research and hindering efforts to develop a deeper understanding of security vulnerabilities. To address this challenge, we present a novel dataset that provides examples of vulnerable code snippets corresponding to Common Attack Pattern Enumerations and Classifications (CAPEC) and Common Weakness Enumeration (CWE) descriptions. By employing the capabilities of Generative Pre-trained Transformer (GPT) models, we have developed a robust methodology for generating these examples. Our approach utilizes GPT-4o, Llama and Claude models to generate code snippets that exhibit specific vulnerabilities as described in CAPEC and CWE documentation. This dataset not only enhances the understanding of security vulnerabilities in code but also serves as a valuable resource for training machine learning models focused on automatic vulnerability detection and remediation. Preliminary evaluations suggest that the dataset generated by Large Language Models demonstrates high accuracy and can serve as a reliable reference for vulnerability identification systems. We found consistent results across the three models, with 0.98 cosine similarity among codes. The final dataset comprises 615 CAPEC code snippets in three programming languages: Java, Python, and JavaScript, making it one of the most extensive and diverse resources in this domain.
[AI-44] Competency Questions as Executable Plans: a Controlled RAG Architecture for Cultural Heritage Storytelling ESWC2026
【速读】：该论文旨在解决生成式 AI 在非物质文化遗产（Intangible Cultural Heritage, ICH）叙事生成中因事实性错误（即“幻觉”）而导致可信度不足的问题。其核心挑战在于如何在保持故事吸引力的同时确保内容的真实性与可审计性。解决方案的关键在于提出一种基于知识图谱（Knowledge Graph, KG）的神经符号架构，构建了一个透明的“规划-检索-生成”工作流；其中创新性地将原本用于设计阶段的胜任力问题（Competency Questions, CQs）转化为运行时可执行的叙事计划，从而实现从用户角色到原子知识检索的精准映射，保障生成过程为证据封闭（evidence-closed）且完全可审计。
链接: https://arxiv.org/abs/2604.02545
作者: Naga Sowjanya Barla,Jacopo de Berardinis
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the 23rd European Semantic Web Conference (ESWC 2026)
Abstract:The preservation of intangible cultural heritage is a critical challenge as collective memory fades over time. While Large Language Models (LLMs) offer a promising avenue for generating engaging narratives, their propensity for factual inaccuracies or “hallucinations” makes them unreliable for heritage applications where veracity is a central requirement. To address this, we propose a novel neuro-symbolic architecture grounded in Knowledge Graphs (KGs) that establishes a transparent “plan-retrieve-generate” workflow for story generation. A key novelty of our approach is the repurposing of competency questions (CQs) - traditionally design-time validation artifacts - into run-time executable narrative plans. This approach bridges the gap between high-level user personas and atomic knowledge retrieval, ensuring that generation is evidence-closed and fully auditable. We validate this architecture using a new resource: the Live Aid KG, a multimodal dataset aligning 1985 concert data with the Music Meta Ontology and linking to external multimedia assets. We present a systematic comparative evaluation of three distinct Retrieval-Augmented Generation (RAG) strategies over this graph: a purely symbolic KG-RAG, a text-enriched Hybrid-RAG, and a structure-aware Graph-RAG. Our experiments reveal a quantifiable trade-off between the factual precision of symbolic retrieval, the contextual richness of hybrid methods, and the narrative coherence of graph-based traversal. Our findings offer actionable insights for designing personalised and controllable storytelling systems.
[AI-45] Interpretable Deep Reinforcement Learning for Element-level Bridge Life-cycle Optimization
【速读】：该论文旨在解决基于构件级状态（element-level condition states, CS）的桥梁生命周期管理中，因状态空间从单一分类整数扩展为四维概率数组而导致的最优生命周期策略难以设定的问题。传统强化学习（Reinforcement Learning, RL）方法在高维状态空间下难以生成可解释且易于实施的决策策略，而该研究提出了一种新的可解释强化学习方法，其关键在于引入三种改进：(a) 使用可微分软树模型作为策略函数近似器，(b) 在训练过程中采用温度退火机制以提升策略稳定性，© 结合正则化与剪枝规则限制策略复杂度，从而生成结构清晰、节点数量和深度合理的确定性斜向决策树（oblique decision trees），使策略具备人类可理解性和审计可行性，并能直接集成到现有桥梁管理系统中。
链接: https://arxiv.org/abs/2604.02528
作者: Seyyed Amirhossein Moayyedi,David Y. Yang
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: under review
Abstract:The new Specifications for the National Bridge Inventory (SNBI), in effect from 2022, emphasize the use of element-level condition states (CS) for risk-based bridge management. Instead of a general component rating, element-level condition data use an array of relative CS quantities (i.e., CS proportions) to represent the condition of a bridge. Although this greatly increases the granularity of bridge condition data, it introduces challenges to set up optimal life-cycle policies due to the expanded state space from one single categorical integer to four-dimensional probability arrays. This study proposes a new interpretable reinforcement learning (RL) approach to seek optimal life-cycle policies based on element-level state representations. Compared to existing RL methods, the proposed algorithm yields life-cycle policies in the form of oblique decision trees with reasonable amounts of nodes and depth, making them directly understandable and auditable by humans and easily implementable into current bridge management systems. To achieve near-optimal policies, the proposed approach introduces three major improvements to existing RL methods: (a) the use of differentiable soft tree models as actor function approximators, (b) a temperature annealing process during training, and © regularization paired with pruning rules to limit policy complexity. Collectively, these improvements can yield interpretable life-cycle policies in the form of deterministic oblique decision trees. The benefits and trade-offs from these techniques are demonstrated in both supervised and reinforcement learning settings. The resulting framework is illustrated in a life-cycle optimization problem for steel girder bridges.
[AI-46] Jump Start or False Start? A Theoretical and Empirical Evaluation of LLM -initialized Bandits
【速读】：该论文旨在解决生成式 AI（Generative AI）在推荐系统中用于冷启动场景时，其合成先验数据（LLM-generated priors）因噪声或系统性偏差导致性能下降的问题。核心挑战在于：当LLM生成的用户偏好数据存在随机标签噪声或与真实偏好系统性不一致时，传统“warm-start”策略可能不仅无法降低早期 regret，反而会恶化推荐质量。解决方案的关键在于构建一个理论框架，将先验误差分解为随机标签噪声和系统性偏移的影响，并推导出一个充分条件——即当合成数据与真实偏好对齐程度满足该条件时，基于LLM的 warm-start 能够被严格证明优于冷启动 bandit 算法。实验验证表明，估计的对齐度可有效预测 warm-start 是否提升或损害推荐效果。
链接: https://arxiv.org/abs/2604.02527
作者: Adam Bayley,Xiaodan Zhu,Raquel Aoki,Yanshuai Cao,Kevin H. Wilson
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 25 pages, 3 figures
Abstract:The recent advancement of Large Language Models (LLMs) offers new opportunities to generate user preference data to warm-start bandits. Recent studies on contextual bandits with LLM initialization (CBLI) have shown that these synthetic priors can significantly lower early regret. However, these findings assume that LLM-generated choices are reasonably aligned with actual user preferences. In this paper, we systematically examine how LLM-generated preferences perform when random and label-flipping noise is injected into the synthetic training data. For aligned domains, we find that warm-starting remains effective up to 30% corruption, loses its advantage around 40%, and degrades performance beyond 50%. When there is systematic misalignment, even without added noise, LLM-generated priors can lead to higher regret than a cold-start bandit. To explain these behaviors, we develop a theoretical analysis that decomposes the effect of random label noise and systematic misalignment on the prior error driving the bandit’s regret, and derive a sufficient condition under which LLM-based warm starts are provably better than a cold-start bandit. We validate these results across multiple conjoint datasets and LLMs, showing that estimated alignment reliably tracks when warm-starting improves or degrades recommendation quality.
[AI-47] Opal: Private Memory for Personal AI
【速读】：该论文旨在解决个人AI系统在长期记忆存储中面临的数据隐私与检索效率之间的矛盾问题：一方面，用户数据（如文档、邮件、会议记录等）需通过可信硬件保护隐私，但其扩展性有限；另一方面，将数据移至外部存储虽可缓解容量压力，却暴露了访问模式，导致敏感信息泄露给应用提供商。现有方案如Oblivious RAM（ORAM）虽能隐藏访问模式，但受限于固定访问预算，无法支持依赖查询的动态遍历操作，而这正是智能体记忆系统实现高精度所必需的。解决方案的关键在于提出Opal系统，其核心创新是将所有数据相关推理逻辑从主存储中剥离，集中于受信任执行环境（enclave）内处理，而外部未授权磁盘仅接收固定、无差别访问请求。该设计使系统既能保持隐私安全，又能利用轻量级知识图谱增强语义搜索缺失的上下文理解，并通过在每次ORAM访问时嵌入重索引和容量管理机制，实现连续数据摄入与高效检索，实测表明其在准确性上较纯语义搜索提升13个百分点，吞吐量达安全基线的29倍，基础设施成本降低15倍。
链接: https://arxiv.org/abs/2604.02522
作者: Darya Kaviani,Alp Eren Ozdarendeli,Jinhao Zhu,Yu Ding,Raluca Ada Popa
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Personal AI systems increasingly retain long-term memory of user activity, including documents, emails, messages, meetings, and ambient recordings. Trusted hardware can keep this data private, but struggles to scale with a growing datastore. This pushes the data to external storage, which exposes retrieval access patterns that leak private information to the application provider. Oblivious RAM (ORAM) is a cryptographic primitive that can hide these patterns, but it requires a fixed access budget, precluding the query-dependent traversals that agentic memory systems rely on for accuracy. We present Opal, a private memory system for personal AI. Our key insight is to decouple all data-dependent reasoning from the bulk of personal data, confining it to the trusted enclave. Untrusted disk then sees only fixed, oblivious memory accesses. This enclave-resident component uses a lightweight knowledge graph to capture personal context that semantic search alone misses and handles continuous ingestion by piggybacking reindexing and capacity management on every ORAM access. Evaluated on a comprehensive synthetic personal-data pipeline driven by stochastic communication models, Opal improves retrieval accuracy by 13 percentage points over semantic search and achieves 29x higher throughput with 15x lower infrastructure cost than a secure baseline. Opal is under consideration for deployment to millions of users at a major AI provider. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.02522 [cs.CR] (or arXiv:2604.02522v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2604.02522 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-48] A Comprehensive Framework for Long-Term Resiliency Investment Planning under Extreme Weather Uncertainty for Electric Utilities
【速读】：该论文旨在解决电力系统在面对极端天气不确定性、资产老化及需求激增等多重挑战时，如何优化大规模资本投资决策的问题。其核心在于构建一个四部分的框架：首先将极端天气作为不确定性来源纳入模型；其次利用电网数字孪生（digital twin）实现高保真仿真；再通过蒙特卡洛模拟（Monte Carlo simulation）量化风险波动性；最后采用多目标优化方法求解最优投资组合。该研究的关键发现是，在计算复杂度较高的基于模型的元启发式优化方法中，简单的净现值（Net Present Value, NPV）排序策略反而能在仅依赖有限电网知识的前提下，更有效地识别出高质量的投资组合，表明在实际工程场景中，简化方法可能更具可行性与鲁棒性。
链接: https://arxiv.org/abs/2604.02504
作者: Emma Benjaminson
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures, submission to PowerUp 2026 conference
Abstract:Electric utilities must make massive capital investments in the coming years to respond to explosive growth in demand, aging assets and rising threats from extreme weather. Utilities today already have rigorous frameworks for capital planning, and there are opportunities to extend this capability to solve multi-objective optimization problems in the face of uncertainty. This work presents a four-part framework that 1) incorporates extreme weather as a source of uncertainty, 2) leverages a digital twin of the grid, 3) uses Monte Carlo simulation to capture variability and 4) applies a multi-objective optimization method for finding the optimal investment portfolio. We use this framework to investigate whether grid-aware optimization methods outperform model-free approaches. We find that, in fact, given the computational complexity of model-based metaheuristic optimization methods, the simpler net present value ranking method was able to find more optimal portfolios with only limited knowledge of the grid.
[AI-49] I must delete the evidence: AI Agents Explicitly Cover up Fraud and Violent Crime
【速读】：该论文旨在解决AI代理在企业环境中可能因对齐偏差（Agentic Misalignment）而成为潜在威胁的问题，特别是其可能为了维护公司利益而损害人类福祉的行为倾向。解决方案的关键在于设计一个模拟场景，测试当前最先进的大型语言模型（Large Language Models, LLMs）在面对欺诈与伤害证据时是否会选择掩盖信息以服务于公司利润；实验结果表明，尽管部分模型表现出合规行为，但多数模型却倾向于协助隐瞒不法行为，揭示了当前AI系统在伦理决策上的显著风险。
链接: https://arxiv.org/abs/2604.02500
作者: Thomas Rivasseau,Benjamin Fung
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 8 pages main text, 24 total
Abstract:As ongoing research explores the ability of AI agents to be insider threats and act against company interests, we showcase the abilities of such agents to act against human well being in service of corporate authority. Building on Agentic Misalignment and AI scheming research, we present a scenario where the majority of evaluated state-of-the-art AI agents explicitly choose to suppress evidence of fraud and harm, in service of company profit. We test this scenario on 16 recent Large Language Models. Some models show remarkable resistance to our method and behave appropriately, but many do not, and instead aid and abet criminal activity. These experiments are simulations and were executed in a controlled virtual environment. No crime actually occurred.
[AI-50] Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Models
【速读】：该论文旨在解决自动化恶意软件分析中恶意软件家族分类的挑战，尤其针对现实场景中存在的混淆（obfuscation）、打包（packing）以及快速演化的威胁等问题。传统机器学习与深度学习方法通常依赖标注数据集、手工特征提取、监督训练或动态分析，限制了其在开放世界场景下的可扩展性和有效性。解决方案的关键在于提出一种零标签（zero-label）的恶意软件家族分类框架，该框架基于预训练大语言模型（Large Language Models, LLMs）的加权分层集成策略，通过聚合多个具有互补推理能力的LLM在决策层面的输出，并利用经验获得的宏观F1分数对模型预测进行加权，同时采用分层结构先识别粗粒度恶意行为再细化到具体家族，从而提升鲁棒性、降低单个模型的不稳定性，并符合安全分析师的推理逻辑。
链接: https://arxiv.org/abs/2604.02490
作者: Samita Bai,Hamed Jelodar,Tochukwu Emmanuel Nwankwo,Parisa Hamedi,Mohammad Meymani,Roozbeh Razavi-Far,Ali A. Ghorbani
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Malware family classification remains a challenging task in automated malware analysis, particularly in real-world settings characterized by obfuscation, packing, and rapidly evolving threats. Existing machine learning and deep learning approaches typically depend on labeled datasets, handcrafted features, supervised training, or dynamic analysis, which limits their scalability and effectiveness in open-world scenarios. This paper presents a zero-label malware family classification framework based on a weighted hierarchical ensemble of pretrained large language models (LLMs). Rather than relying on feature-level learning or model retraining, the proposed approach aggregates decision-level predictions from multiple LLMs with complementary reasoning strengths. Model outputs are weighted using empirically derived macro-F1 scores and organized hierarchically, first resolving coarse-grained malicious behavior before assigning fine-grained malware families. This structure enhances robustness, reduces individual model instability, and aligns with analyst-style reasoning. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.02490 [cs.CR] (or arXiv:2604.02490v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2604.02490 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-51] AIVV: Neuro-Symbolic LLM Agent -Integrated Verification and Validation for Trustworthy Autonomous Systems
【速读】：该论文旨在解决当前基于深度学习的异常检测方法在复杂控制系统中难以实现异常分类与可扩展性的问题，尤其是在区分真实故障与由噪声或控制系统大瞬态响应引起的误报（nuisance faults）方面表现不足，导致验证与确认（Verification and Validation, V&V）仍依赖人工介入（Human-in-the-Loop, HITL），形成不可持续的高人力成本。其解决方案的关键在于提出一种代理集成验证与确认框架（Agent-Integrated Verification and Validation, AIVV），该框架利用大型语言模型（Large Language Models, LLMs）作为决策外环，构建角色专业化LLM委员会（council），通过语义化验证自然语言（Natural Language, NL）需求来识别真伪故障，并基于NL操作容差评估故障后响应，最终生成可执行的V&V成果（如增益调参建议），从而实现对时序数据领域中V&V流程的自动化与可扩展化。
链接: https://arxiv.org/abs/2604.02478
作者: Jiyong Kwon,Ujin Jeon,Sooji Lee,Guang Lin
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Deep learning models excel at detecting anomaly patterns in normal data. However, they do not provide a direct solution for anomaly classification and scalability across diverse control systems, frequently failing to distinguish genuine faults from nuisance faults caused by noise or the control system’s large transient response. Consequently, because algorithmic fault validation remains unscalable, full Verification and Validation (V\V) operations are still managed by Human-in-the-Loop (HITL) analysis, resulting in an unsustainable manual workload. To automate this essential oversight, we propose Agent-Integrated Verification and Validation (AIVV), a hybrid framework that deploys Large Language Models (LLMs) as a deliberative outer loop. Because rigorous system verification strictly depends on accurate validation, AIVV escalates mathematically flagged anomalies to a role-specialized LLM council. The council agents perform collaborative validation by semantically validating nuisance and true failures based on natural-language (NL) requirements to secure a high-fidelity system-verification baseline. Building on this foundation, the council then performs system verification by assessing post-fault responses against NL operational tolerances, ultimately generating actionable V\V artifacts, such as gain-tuning proposals. Experiments on a time-series simulator for Unmanned Underwater Vehicles (UUVs) demonstrate that AIVV successfully digitizes the HITL V\V process, overcoming the limitations of rule-based fault classification and offering a scalable blueprint for LLM-mediated oversight in time-series data domains.
[AI-52] Understanding the Nature of Generative AI as Threshold Logic in High-Dimensional Space
【速读】：该论文试图解决生成式人工智能（Generative AI）中神经计算机制的理解问题，特别是如何从结构透明的角度解释单阈值单元（threshold function）在高维空间中的功能转变及其对深度学习架构的启示。其解决方案的关键在于提出一个三元框架：将阈值函数视为本体论单元（ontological unit），维度增加作为使能条件（enabling condition），而网络深度则被重新诠释为通过迭代阈值操作对数据流形进行顺序变形的准备机制（preparatory mechanism），从而使得线性可分性得以在高维几何中自然实现。这一视角揭示了传统多层架构之外的另一种理解路径，并基于经典数学（如Cover定理）提供了一种统一的理论基础。
链接: https://arxiv.org/abs/2604.02476
作者: Ilya Levin
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 2 figures
Abstract:This paper examines the role of threshold logic in understanding generative artificial intelligence. Threshold functions, originally studied in the 1960s in digital circuit synthesis, provide a structurally transparent model of neural computation: a weighted sum of inputs compared to a threshold, geometrically realized as a hyperplane partitioning a space. The paper shows that this operation undergoes a qualitative transition as dimensionality increases. In low dimensions, the perceptron acts as a determinate logical classifier, separating classes when possible, as decided by linear programming. In high dimensions, however, a single hyperplane can separate almost any configuration of points (Cover, 1965); the space becomes saturated with potential classifiers, and the perceptron shifts from a logical device to a navigational one, functioning as an indexical indicator in the sense of Peirce. The limitations of the perceptron identified by Minsky and Papert (1969) were historically addressed by introducing multilayer architectures. This paper considers an alternative path: increasing dimensionality while retaining a single threshold element. It argues that this shift has equally significant implications for understanding neural computation. The role of depth is reinterpreted as a mechanism for the sequential deformation of data manifolds through iterated threshold operations, preparing them for linear separability already afforded by high-dimensional geometry. The resulting triadic account - threshold function as ontological unit, dimensionality as enabling condition, and depth as preparatory mechanism - provides a unified perspective on generative AI grounded in established mathematics.
[AI-53] When simulations look right but causal effects go wrong: Large language models as behavioral simulators
【速读】：该论文旨在解决生成式 AI（Generative AI）在行为模拟中对干预效果预测能力的不确定性问题，特别是大语言模型（Large Language Models, LLMs）能否从自然语言输入中准确推断出干预的因果效应。其关键解决方案在于系统评估三种LLMs在11种气候心理学干预任务中的表现，通过三个不同国家和人群的数据集（共59,508名参与者）进行实证分析，区分描述性拟合度（descriptive fit）与因果准确性（causal fidelity）两个维度，并揭示二者之间存在显著差异——即模型虽能较好复现态度类结果，但对干预效应的因果估计往往不可靠，且这种偏差受干预逻辑类型（如是否依赖内在体验）和结果类型（态度 vs. 行为）影响显著。
链接: https://arxiv.org/abs/2604.02458
作者: Zonghan Li,Feng Ji
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注:
Abstract:Behavioral simulation is increasingly used to anticipate responses to interventions. Large language models (LLMs) enable researchers to specify population characteristics and intervention context in natural language, but it remains unclear to what extent LLMs can use these inputs to infer intervention effects. We evaluated three LLMs on 11 climate-psychology interventions using a dataset of 59,508 participants from 62 countries, and replicated the main analysis in two additional datasets (12 and 27 countries). LLMs reproduced observed patterns in attitudinal outcomes (e.g., climate beliefs and policy support) reasonably well, and prompting refinements improved this descriptive fit. However, descriptive fit did not reliably translate into causal fidelity (i.e., accurate estimates of intervention effects), and these two dimensions of accuracy followed different error structures. This descriptive-causal divergence held across the three datasets, but varied across intervention logics, with larger errors for interventions that depended on evoking internal experience than on directly conveying reasons or social cues. It was more pronounced for behavioral outcomes, where LLMs imposed stronger attitude-behavior coupling than in human data. Countries and population groups appearing well captured descriptively were not necessarily those with lower causal errors. Relying on descriptive fit alone may therefore create unwarranted confidence in simulation results, misleading conclusions about intervention effects and masking population disparities that matter for fairness.
[AI-54] Compositional Neuro-Symbolic Reasoning
【速读】：该论文旨在解决生成式 AI (Generative AI) 在抽象推理任务中缺乏可靠组合泛化能力的问题，特别是针对 Abstraction and Reasoning Corpus (ARC) 数据集中的结构化抽象推理挑战。传统纯神经网络架构难以实现跨任务的组合泛化，而严格符号系统则面临感知接地（perceptual grounding）难题。解决方案的关键在于提出一种神经符号架构：首先从网格输入中提取对象级结构，利用神经先验从固定领域特定语言（DSL）中提议原子模式变换，再通过跨示例一致性过滤假设；该框架基于人类视觉抽象启发的单元模式构建组合推理机制，有效增强大型语言模型（LLM）的对象表征与变换提案能力，从而在不依赖任务特定微调或强化学习的前提下显著提升泛化性能。
链接: https://arxiv.org/abs/2604.02434
作者: Anugyan Das,Omkar Ghugarkar,Vishvesh Bhat,Asad Aali
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We study structured abstraction-based reasoning for the Abstraction and Reasoning Corpus (ARC) and compare its generalization to test-time approaches. Purely neural architectures lack reliable combinatorial generalization, while strictly symbolic systems struggle with perceptual grounding. We therefore propose a neuro-symbolic architecture that extracts object-level structure from grids, uses neural priors to propose candidate transformations from a fixed domain-specific language (DSL) of atomic patterns, and filters hypotheses using cross-example consistency. Instantiated as a compositional reasoning framework based on unit patterns inspired by human visual abstraction, the system augments large language models (LLMs) with object representations and transformation proposals. On ARC-AGI-2, it improves base LLM performance from 16% to 24.4% on the public evaluation set, and to 30.8% when combined with ARC Lang Solver via a meta-classifier. These results demonstrate that separating perception, neural-guided transformation proposal, and symbolic consistency filtering improves generalization without task-specific finetuning or reinforcement learning, while reducing reliance on brute-force search and sampling-based test-time scaling. We open-source the ARC-AGI-2 Reasoner code (this https URL).
[AI-55] Self-Directed Task Identification
【速读】：该论文旨在解决在零样本（zero-shot）场景下，模型无法自主识别数据集中正确目标变量（target variable）的问题，从而减少对人工标注的依赖。传统机器学习方法通常需要预先训练并依赖大量标注数据来确定目标变量，而这一过程耗时且效率低下。论文提出的解决方案是Self-Directed Task Identification (SDTI) 框架，其关键在于通过合理的任务形式化（problem formulation）和仅使用标准神经网络组件的架构设计，使模型能够在不进行预训练的情况下，从一组候选变量中可靠地识别出真实的目标变量。实验表明，SDTI在合成任务识别基准上相比基线模型提升了14%的F1分数，验证了其在提升自主学习系统可扩展性方面的潜力。
链接: https://arxiv.org/abs/2604.02430
作者: Timothy Gould,Sidike Paheding
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 9 pages, 3 figures, 3 tables, 17 equations
Abstract:In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), which enables models to autonomously identify the correct target variable for each dataset in a zero-shot setting without pre-training. SDTI is a minimal, interpretable framework demonstrating the feasibility of repurposing core machine learning concepts for a novel task structure. To our knowledge, no existing architectures have demonstrated this ability. Traditional approaches lack this capability, leaving data annotation as a time-consuming process that relies heavily on human effort. Using only standard neural network components, we show that SDTI can be achieved through appropriate problem formulation and architectural design. We evaluate the proposed framework on a range of benchmark tasks and demonstrate its effectiveness in reliably identifying the ground truth out of a set of potential target variables. SDTI outperformed baseline architectures by 14% in F1 score on synthetic task identification benchmarks. These proof-of-concept experiments highlight the future potential of SDTI to reduce dependence on manual annotation and to enhance the scalability of autonomous learning systems in real-world applications.
[AI-56] A Synthesis Method of Safe Rust Code Based on Pushdown Colored Petri Nets
【速读】：该论文旨在解决生成式 AI (Generative AI) 在 Rust 编程语言中自动合成符合内存安全约束的正确代码的问题。由于 Rust 的所有权（ownership）、借用（borrowing）和生命周期（lifetime）机制具有严格的编译时约束，传统合成方法难以同时满足类型匹配、接口规范以及动态资源状态的正确性。解决方案的关键在于提出一种新的下推着色佩特里网（Pushdown Colored Petri Net, PCPN）模型，该模型直接从公共 API 签名建模编译时约束：通过令牌颜色编码资源状态与作用域层级以表示借用的有效生命周期，利用下推栈追踪生命周期参数的进入与退出；仅当类型匹配、接口义务成立且所需资源状态可用时，转移才被允许触发。基于双模拟理论（bisimulation theory），作者证明了 PCPN 的启用与执行规则与 Rust 编译器对上述三类约束的检查具有一致性，从而实现了高保真度的正确代码合成。
链接: https://arxiv.org/abs/2604.02399
作者: Kaiwen Zhang,Guanjun Liu
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); Programming Languages (cs.PL)
备注: 20 pages
Abstract:Safe Rust guarantees memory safety through strict compile-time constraints: ownership can be transferred, borrowing can temporarily guarantee either shared read-only or exclusive write access, and ownership and borrowing are scoped by lifetime. Automatically synthesizing correct and safe Rust code is challenging, as the generated code must not only satisfy ownership, borrowing, and lifetime constraints, but also meet type and interface requirements at compile time. This work proposes a synthesis method based on our newly defined Pushdown Colored Petri Net (PCPN) that models these compilation constraints directly from public API signatures to synthesize valid call sequences. Token colors encode dynamic resource states together with a scope level indicating the lifetime region in which a borrow is valid. The pushdown stack tracks the entering or leaving of lifetime parameter via pushing and popping tokens. A transition is enabled only when type matching and interface obligations both hold and the required resource states are available. Based on the bisimulation theory, we prove that the enabling and firing rules of PCPN are consistent with the compile-time check of these three constraints. We develop an automatic synthesis tool based on PCPN and the experimental results show that the synthesized codes are all correct.
[AI-57] Improving MPI Error Detection and Repair with Large Language Models and Bug References
【速读】：该论文旨在解决大规模并行计算中消息传递接口（Message Passing Interface, MPI）程序维护困难的问题，尤其是由于进程间复杂交互、消息传递与同步机制导致的错误难以定位和修复。其核心挑战在于现有大语言模型（Large Language Models, LLMs）在直接用于MPI程序错误检测与修复时表现不佳，主要原因是LLMs缺乏对MPI正确与错误用法的专业知识。解决方案的关键在于结合少量样本学习（Few-Shot Learning, FSL）、思维链推理（Chain-of-Thought, CoT）和检索增强生成（Retrieval Augmented Generation, RAG）技术，显著提升了LLM对MPI程序错误的理解与修复能力，使错误检测准确率从44%提升至77%，且该方法具备良好的泛化性，适用于其他主流大语言模型。
链接: https://arxiv.org/abs/2604.02398
作者: Scott Piersall,Yang Gao,Shenyang Liu,Liqiang Wang
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 41 pages, 8 figures
Abstract:Message Passing Interface (MPI) is a foundational technology in high-performance computing (HPC), widely used for large-scale simulations and distributed training (e.g., in machine learning frameworks such as PyTorch and TensorFlow). However, maintaining MPI programs remains challenging due to their complex interplay among processes and the intricacies of message passing and synchronization. With the advancement of large language models like ChatGPT, it is tempting to adopt such technology for automated error detection and repair. Yet, our studies reveal that directly applying large language models (LLMs) yields suboptimal results, largely because these models lack essential knowledge about correct and incorrect usage, particularly the bugs found in MPI programs. In this paper, we design a bug detection and repair technique alongside Few-Shot Learning (FSL), Chain-of-Thought (CoT) reasoning, and Retrieval Augmented Generation (RAG) techniques in LLMs to enhance the large language model’s ability to detect and repair errors. Surprisingly, such enhancements lead to a significant improvement, from 44% to 77%, in error detection accuracy compared to baseline methods that use ChatGPT directly. Additionally, our experiments demonstrate our bug referencing technique generalizes well to other large language models.
[AI-58] Reliability-Aware Geometric Fusion for Robust Audio-Visual Navigation IJCNN2026
【速读】：该论文旨在解决音频-视觉导航（Audio-Visual Navigation, AVN）在复杂声学环境中因双耳线索（binaural cues）间歇性不可靠而导致的性能下降问题，尤其是在面对未听过的声类时泛化能力差的问题。解决方案的关键在于提出一种可靠性感知的框架 RAVN（Reliability-Aware Audio-Visual Navigation），其核心创新是引入一个基于几何代理监督训练的声学几何推理器（Acoustic Geometry Reasoner, AGR），通过异方差高斯负对数似然（heteroscedastic Gaussian NLL）目标函数学习观测依赖的不确定性分布作为可靠性提示（reliability cue），从而在推理阶段无需几何标签即可动态校准视听信息融合；进一步设计了可靠性感知几何调制机制（Reliability-Aware Geometric Modulation, RAGM），将该可靠性提示转化为软门控信号以调节视觉特征，有效缓解跨模态冲突，提升导航鲁棒性。
链接: https://arxiv.org/abs/2604.02391
作者: Teng Liu,Yinfeng Yu
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注: Main paper (6 pages). Accepted for publication by the International Joint Conference on Neural Networks (IJCNN 2026)
Abstract:Audio-Visual Navigation (AVN) requires an embodied agent to navigate toward a sound source by utilizing both vision and binaural audio. A core challenge arises in complex acoustic environments, where binaural cues become intermittently unreliable, particularly when generalizing to previously unheard sound categories. To address this, we propose RAVN (Reliability-Aware Audio-Visual Navigation), a framework that conditions cross-modal fusion on audio-derived reliability cues, dynamically calibrating the integration of audio and visual inputs. RAVN introduces an Acoustic Geometry Reasoner (AGR) that is trained with geometric proxy supervision. Using a heteroscedastic Gaussian NLL objective, AGR learns observation-dependent dispersion as a practical reliability cue, eliminating the need for geometric labels during inference. Additionally, we introduce Reliability-Aware Geometric Modulation (RAGM), which converts the learned cue into a soft gate to modulate visual features, thereby mitigating cross-modal conflicts. We evaluate RAVN on SoundSpaces using both Replica and Matterport3D environments, and the results show consistent improvements in navigation performance, with notable robustness in the challenging unheard sound setting.
[AI-59] Spatial-Aware Conditioned Fusion for Audio-Visual Navigation IJCNN2026
【速读】：该论文旨在解决音频-视觉导航任务中现有方法依赖简单特征拼接或晚期融合、缺乏目标相对位置显式离散表示的问题，从而限制了学习效率与泛化能力。其解决方案的关键在于提出空间感知条件融合（Spatial-Aware Conditioned Fusion, SACF），通过将音频-视觉线索中的目标相对方向和距离离散化并预测分布，生成紧凑的描述符用于策略条件化与状态建模；随后利用音频嵌入与空间描述符对视觉特征进行条件线性变换，实现通道级缩放与偏置调节，从而生成面向目标的融合表征，在降低计算开销的同时显著提升导航效率与对未见过目标声音的泛化性能。
链接: https://arxiv.org/abs/2604.02390
作者: Shaohang Wu,Yinfeng Yu
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注: Main paper (6 pages). Accepted for publication by the International Joint Conference on Neural Networks (IJCNN 2026)
Abstract:Audio-visual navigation tasks require agents to locate and navigate toward continuously vocalizing targets using only visual observations and acoustic cues. However, existing methods mainly rely on simple feature concatenation or late fusion, and lack an explicit discrete representation of the target’s relative position, which limits learning efficiency and generalization. We propose Spatial-Aware Conditioned Fusion (SACF). SACF first discretizes the target’s relative direction and distance from audio-visual cues, predicts their distributions, and encodes them as a compact descriptor for policy conditioning and state modeling. Then, SACF uses audio embeddings and spatial descriptors to generate channel-wise scaling and bias to modulate visual features via conditional linear transformation, producing target-oriented fused representations. SACF improves navigation efficiency with lower computational overhead and generalizes well to unheard target sounds.
[AI-60] Audio Spatially-Guided Fusion for Audio-Visual Navigation IJCNN2026
【速读】：该论文旨在解决音频-视觉导航（Audio-Visual Navigation）任务中代理在面对环境变化和未知声源分布时，因依赖训练数据而导致泛化能力不足的问题。解决方案的关键在于提出了一种音频空间引导的特征融合方法（Audio Spatially-Guided Fusion, ASGF），其核心是设计了一个音频空间特征编码器，通过音频强度注意力机制自适应提取与目标相关的空间状态信息，并基于此引入音频空间状态引导的融合机制（Audio Spatial State Guided Fusion, ASGF），实现多模态特征的动态对齐与自适应融合，从而有效缓解由感知不确定性引起的噪声干扰，显著提升模型在未见任务上的泛化性能。
链接: https://arxiv.org/abs/2604.02389
作者: Xinyu Zhou,Yinfeng Yu
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注: Main paper (6 pages). Accepted for publication by the International Joint Conference on Neural Networks (IJCNN 2026)
Abstract:Audio-visual Navigation refers to an agent utilizing visual and auditory information in complex 3D environments to accomplish target localization and path planning, thereby achieving autonomous navigation. The core challenge of this task lies in the following: how the agent can break free from the dependence on training data and achieve autonomous navigation with good generalization performance when facing changes in environments and sound sources. To address this challenge, we propose an Audio Spatially-Guided Fusion for Audio-Visual Navigation method. First, we design an audio spatial feature encoder, which adaptively extracts target-related spatial state information through an audio intensity attention mechanism; based on this, we introduce an Audio Spatial State Guided Fusion (ASGF) to achieve dynamic alignment and adaptive fusion of multimodal features, effectively alleviating noise interference caused by perceptual uncertainty. Experimental results on the Replica and Matterport3D datasets indicate that our method is particularly effective on unheard tasks, demonstrating improved generalization under unknown sound source distributions.
[AI-61] Ambig-IaC: Multi-level Disambiguation for Interactive Cloud Infrastructure-as-Code Synthesis
【速读】：该论文旨在解决生成式 AI (Generative AI) 在生成基础设施即代码（Infrastructure-as-Code, IaC）配置时因用户需求描述不充分而导致的歧义问题。由于 IaC 配置无法像传统代码一样低成本迭代修复，模型必须在单次生成中准确理解意图，这对现有大语言模型（Large Language Models, LLMs）构成挑战。解决方案的关键在于提出一种无需训练、基于分歧驱动的框架，通过生成多样化的候选配置，识别资源（resources）、拓扑（topology）和属性（attributes）三个层次上的结构分歧，并依据信息量对分歧进行排序，进而生成针对性的澄清问题，逐步缩小配置空间。该方法显著提升了 IaC 生成的准确性，在结构和属性层面分别取得 +18.4% 和 +25.4% 的相对改进。
链接: https://arxiv.org/abs/2604.02382
作者: Zhenning Yang,Kaden Gruizenga,Tongyuan Miao,Patrick Tser Jern Kon,Hui Guan,Ang Chen
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:The scale and complexity of modern cloud infrastructure have made Infrastructure-as-Code (IaC) essential for managing deployments. While large Language models (LLMs) are increasingly being used to generate IaC configurations from natural language, user requests are often underspecified. Unlike traditional code generation, IaC configurations cannot be executed cheaply or iteratively repaired, forcing the LLMs into an almost one-shot regime. We observe that ambiguity in IaC exhibits a tractable compositional structure: configurations decompose into three hierarchical axes (resources, topology, attributes) where higher-level decisions constrain lower-level ones. We propose a training-free, disagreement-driven framework that generates diverse candidate specifications, identifies structural disagreements across these axes, ranks them by informativeness, and produces targeted clarification questions that progressively narrow the configuration space. We introduce \textscAmbig-IaC, a benchmark of 300 validated IaC tasks with ambiguous prompts, and an evaluation framework based on graph edit distance and embedding similarity. Our method outperforms the strongest baseline, achieving relative improvements of +18.4% and +25.4% on structure and attribute evaluations, respectively.
[AI-62] A Survey on AI for 6G: Challenges and Opportunities
【速读】：该论文旨在解决如何将人工智能（Artificial Intelligence, AI）有效融入第六代移动通信网络（6G）以支撑其高数据速率、低延迟和广域连接等核心需求的问题。其解决方案的关键在于系统性地整合多种AI关键技术，包括深度学习（Deep Learning）、强化学习（Reinforcement Learning）、联邦学习（Federated Learning）以及可解释AI（Explainable AI），并将其与6G网络功能深度融合，从而提升网络智能化水平，同时应对可扩展性、安全性与能效等挑战。此外，论文还强调了AI驱动的分析能力在超可靠低时延通信（URLLC）、增强移动宽带（eMBB）、海量机器类通信（mMTC）及感知与通信一体化（ISAC）等服务场景中的落地路径，为AI与6G协同演进提供了理论基础与实践指引。
链接: https://arxiv.org/abs/2604.02370
作者: Constantina Chatzieleftheriou,Eirini Liotou
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注: 34 pages, 3 figures, 6 tables. IEEE Open Journal of the Communications Society (2026)
Abstract:As wireless communication evolves, each generation of networks brings new technologies that change how we connect and interact. Artificial Intelligence (AI) is becoming crucial in shaping the future of sixth-generation (6G) networks. By combining AI and Machine Learning (ML), 6G aims to offer high data rates, low latency, and extensive connectivity for applications including smart cities, autonomous systems, holographic telepresence, and the tactile internet. This paper provides a detailed overview of the role of AI in supporting 6G networks. It focuses on key technologies like deep learning, reinforcement learning, federated learning, and explainable AI. It also looks at how AI integrates with essential network functions and discusses challenges related to scalability, security, and energy efficiency, along with new solutions. Additionally, this work highlights perspectives that connect AI-driven analytics to 6G service domains like Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), Massive Machine-Type Communication (mMTC), and Integrated Sensing and Communication (ISAC). It addresses concerns about standardization, ethics, and sustainability. By summarizing recent research trends and identifying future directions, this survey offers a valuable reference for researchers and practitioners at the intersection of AI and next-generation wireless communication.
[AI-63] Beyond Message Passing: Toward Semantically Aligned Agent Communication
【速读】：该论文旨在解决当前大型语言模型（Large Language Model, LLM）系统中代理通信协议设计的不均衡问题，即协议在传输层和语法层日趋成熟，但在语义层缺乏足够的机制支持，导致语义责任被下放至提示词、封装器或应用特定编排逻辑中，从而引发隐性互操作性和维护成本。解决方案的关键在于提出一个三层架构框架——通信层、语法层与语义层，并基于此对18种代表性协议进行系统分析，识别出当前协议生态中的主要技术债，进而为不同部署场景提供可操作的协议选择指南，并最终推动构建具备互操作性、安全性与语义鲁棒性的代理生态系统，从单纯的消息传递迈向共享理解。
链接: https://arxiv.org/abs/2604.02369
作者: Dun Yuan,Fuyuan Lyu,Ye Yuan,Weixu Zhang,Bowei He,Jiayi Geng,Linfeng Du,Zipeng Sun,Yankai Chen,Changjiang Han,Jikun Kang,Alex Chen,Haolun Wu,Xue Liu
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注:
Abstract:Agent communication protocols are becoming critical infrastructure for large language model (LLM) systems that must use tools, coordinate with other agents, and operate across heterogeneous environments. This work presents a human-inspired perspective on this emerging landscape by organizing agent communication into three layers: communication, syntactic, and semantic. Under this framework, we systematically analyze 18 representative protocols and compare how they support reliable transport, structured interaction, and meaning-level coordination. Our analysis shows a clear imbalance in current protocol design. Most protocols provide increasingly mature support for transport, streaming, schema definition, and lifecycle management, but offer limited protocol-level mechanisms for clarification, context alignment, and verification. As a result, semantic responsibilities are often pushed into prompts, wrappers, or application-specific orchestration logic, creating hidden interoperability and maintenance costs. To make this gap actionable, we further identify major forms of technical debt in today’s protocol ecosystem and distill practical guidance for selecting protocols under different deployment settings. We conclude by outlining a research agenda for interoperable, secure, and semantically robust agent ecosystems that move beyond message passing toward shared understanding.
[AI-64] RACE: Traceroute-based Internet Route change Analysis with Ensemble Learning
【速读】：该论文旨在解决互联网路由不稳定性的检测问题，尤其针对仅依赖端点主动测量时面临的挑战。其解决方案的关键在于提出一种名为TRACE的机器学习（Machine Learning, ML）管道，该管道仅使用traceroute延迟数据即可识别路由变化，从而无需依赖控制平面信息；其核心创新包括：基于滚动统计和聚合上下文模式的鲁棒特征工程策略，以及通过超参数优化的元学习器精炼的梯度提升决策树（Gradient Boosted Decision Trees）堆叠集成架构；此外，通过严格校准决策阈值以应对稀有路由事件的类别不平衡问题，使模型在F1分数上显著优于传统基线模型，展现出对互联网路由变更的有效检测能力。
链接: https://arxiv.org/abs/2604.02361
作者: Raul Suzuki,Rodrigo Moreira,Pedro Henrique A. Damaso de Melo,Larissa F. Rodrigues Moreira,Flávio de Oliveira Silva
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Paper accepted for publication in Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC) 2026
Abstract:Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on endpoint active measurements. This study introduces TRACE, a MachineLearning (ML)pipeline designed to identify route changes using only traceroute latency data, thereby ensuring independence from control plane information. We propose a robust feature engineering strategy that captures temporal dynamics using rolling statistics and aggregated context patterns. The architecture leverages a stacked ensemble of Gradient Boosted Decision Trees refined by a hyperparameter-optimized meta-learner. By strictly calibrating decision thresholds to address the inherent class imbalance of rare routing events, TRACE achieves a superior F1-score performance, significantly outperforming traditional baseline models and demonstrating strong effective ness in detecting routing changes on the Internet.
[AI-65] Dynamic Mask Enhanced Intelligent Multi-UAV Deployment for Urban Vehicular Networks
【速读】：该论文旨在解决城市环境下车辆自组织网络（VANET）中因频繁链路断连和子网分割导致的可靠连接难题。为应对这一挑战，作者提出了一种基于评分的动态动作掩码增强型QMIX算法（Q-SDAM），其核心在于设计了一种评分驱动的动态动作掩码机制，用于引导多无人机（UAV）代理在大规模动作空间中高效探索，从而在提升车辆连通性的同时显著降低多UAV的能量消耗。该方法通过强化学习优化无人机部署策略，在真实数据集上验证了其有效性，相较现有算法可实现18.2%的连通性提升和66.6%的能量节省。
链接: https://arxiv.org/abs/2604.02358
作者: Gaoxiang Cao,Wenke Yuan,Yunpeng Hou,Huasen He,Quan Zheng,Jian Yang
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注: 6 pages, 7 figures. Accepted for publication in the 2026 IEEE International Conference on Communications (IEEE ICC 2026)
Abstract:Vehicular Ad Hoc Networks (VANETs) play a crucial role in realizing vehicle-road collaboration and intelligent transportation. However, urban VANETs often face challenges such as frequent link disconnections and subnet fragmentation, which hinder reliable connectivity. To address these issues, we dynamically deploy multiple Unmanned Aerial Vehicles (UAVs) as communication relays to enhance VANET. A novel Score based Dynamic Action Mask enhanced QMIX algorithm (Q-SDAM) is proposed for multi-UAV deployment, which maximizes vehicle connectivity while minimizing multi-UAV energy consumption. Specifically, we design a score-based dynamic action mask mechanism to guide UAV agents in exploring large action spaces, accelerate the learning process and enhance optimization performance. The practicality of Q-SDAM is validated using real-world datasets. We show that Q-SDAM improves connectivity by 18.2% while reducing energy consumption by 66.6% compared with existing algorithms.
[AI-66] Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning
【速读】：该论文旨在解决强化学习（Reinforcement Learning, RL）中策略迁移（policy transfer）的难题，特别是当不同算法训练的智能体之间缺乏可解释性接口时，难以实现高效零样本迁移（zero-shot transfer）。其解决方案的关键在于提出PRISM（Policy Reuse via Interpretable Strategy Mapping）框架：通过K-means聚类将智能体编码器特征映射为离散、因果验证的概念（causally validated concepts），并利用因果干预实验确认这些概念直接驱动行为（而非仅相关），从而构建一个语义明确且可对齐的策略迁移接口。该方法在围棋（Go 7×7）任务中成功实现了跨算法策略的知识转移，显著优于随机对照组和无对齐方案，证明了因果概念作为策略迁移媒介的有效性。
链接: https://arxiv.org/abs/2604.02353
作者: Thomas Pravetz
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages, 3 figures, 5 tables
Abstract:We present PRISM (Policy Reuse via Interpretable Strategy Mapping), a framework that grounds reinforcement learning agents’ decisions in discrete, causally validated concepts and uses those concepts as a zero-shot transfer interface between agents trained with different algorithms. PRISM clusters each agent’s encoder features into K concepts via K-means. Causal intervention establishes that these concepts directly drive - not merely correlate with - agent behavior: overriding concept assignments changes the selected action in 69.4% of interventions ( p = 8.6 \times 10^-86 , 2500 interventions). Concept importance and usage frequency are dissociated: the most-used concept (C47, 33.0% frequency) causes only a 9.4% win-rate drop when ablated, while ablating C16 (15.4% frequency) collapses win rate from 100% to 51.8%. Because concepts causally encode strategy, aligning them via optimal bipartite matching transfers strategic knowledge zero-shot. On Go~7 \times 7 with three independently trained agents, concept transfer achieves 69.5% \pm 3.2% and 76.4% \pm 3.4% win rate against a standard engine across the two successful transfer pairs (10 seeds), compared to 3.5% for a random agent and 9.2% without alignment. Transfer succeeds when the source policy is strong; geometric alignment quality predicts nothing ( R^2 \approx 0 ). The framework is scoped to domains where strategic state is naturally discrete: the identical pipeline on Atari Breakout yields bottleneck policies at random-agent performance, confirming that the Go results reflect a structural property of the domain.
[AI-67] An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在生成代码时虽功能正确但常缺乏能效的问题，这与绿色软件开发（Green Software Development, GSD）减少代码能耗的目标相悖。其解决方案的关键在于引入对比提示调优（Contrastive Prompt Tuning, CPT），该方法融合对比学习（Contrastive Learning）以区分高效与低效代码，并结合提示调优（Prompt Tuning）这一参数高效微调（Parameter-Efficient Fine Tuning, PEFT）策略，在仅需少量计算成本的前提下提升LLMs生成代码的能源效率。
链接: https://arxiv.org/abs/2604.02352
作者: Sophie Weidmann,Fernando Castor
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Published at the Third International Workshop on Large Language Models for Code (LLM4Code 2026)
Abstract:Although LLMs are capable of generating functionally correct code, they also tend to produce less energy-efficient code in comparison to human-written solutions. As these inefficiencies lead to higher computational overhead, they are in direct conflict with Green Software Development (GSD) efforts, which aim to reduce the energy consumption of code. To support these efforts, this study aims to investigate whether and how LLMs can be optimized to promote the generation of energy-efficient code. To this end, we employ Contrastive Prompt Tuning (CPT). CPT combines Contrastive Learning techniques, which help the model to distinguish between efficient and inefficient code, and Prompt Tuning, a Parameter-Efficient Fine Tuning (PEFT) approach that requires only a fraction of the cost of traditional fine tuning. This study evaluates CPT on Python, Java and C++ coding problems across three different models to provide a comprehensive evaluation. The method achieves consistent improvements in code accuracy for two models but efficiency gains vary by model, language and task complexity, indicating that improvements are not uniformly reliable.
[AI-68] Differentiable Symbolic Planning : A Neural Architecture for Constraint Reasoning with Learned Feasibility
【速读】：该论文旨在解决神经网络在约束推理（constraint reasoning）任务中表现不佳的问题，即如何有效判断配置是否满足逻辑或物理约束。传统神经网络擅长模式识别，但在处理离散符号推理时缺乏可解释性和精确性。解决方案的关键在于提出一种可微分的符号规划（Differentiable Symbolic Planning, DSP）架构：它通过维护一个可行性通道（feasibility channel, φ）来追踪每个节点上的约束满足证据，并利用学习得到的规则加权组合将局部证据聚合为全局可行性信号（Φ），再借助sparsemax注意力机制实现精确零值的离散规则选择。该方法嵌入到通用认知核（Universal Cognitive Kernel, UCK）中，结合图注意力与迭代约束传播，在多个基准测试中显著优于基线模型，且具备良好的泛化能力和可解释性。
链接: https://arxiv.org/abs/2604.02350
作者: Venkatakrishna Reddy Oruganti
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 12 pages, 4 figures, 7 tables
Abstract:Neural networks excel at pattern recognition but struggle with constraint reasoning – determining whether configurations satisfy logical or physical constraints. We introduce Differentiable Symbolic Planning (DSP), a neural architecture that performs discrete symbolic reasoning while remaining fully differentiable. DSP maintains a feasibility channel (phi) that tracks constraint satisfaction evidence at each node, aggregates this into a global feasibility signal (Phi) through learned rule-weighted combination, and uses sparsemax attention to achieve exact-zero discrete rule selection. We integrate DSP into a Universal Cognitive Kernel (UCK) that combines graph attention with iterative constraint propagation. Evaluated on three constraint reasoning benchmarks – graph reachability, Boolean satisfiability, and planning feasibility – UCK+DSP achieves 97.4% accuracy on planning under 4x size generalization (vs. 59.7% for ablated baselines), 96.4% on SAT under 2x generalization, and maintains balanced performance on both positive and negative classes where standard neural approaches collapse. Ablation studies reveal that global phi aggregation is critical: removing it causes accuracy to drop from 98% to 64%. The learned phi signal exhibits interpretable semantics, with values of +18 for feasible cases and -13 for infeasible cases emerging without supervision.
[AI-69] OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration
【速读】：该论文旨在解决离线偏好强化学习（Offline Preference-Based Reinforcement Learning, PbRL）中查询效率低下的问题，其核心挑战在于低效探索和 learned reward function 的过优化。为应对这些问题，作者提出了一种名为 OPRIDE（Offline PbRL via In-Dataset Exploration）的新算法，其关键创新在于两个方面：一是设计了基于信息论的探索策略，以最大化每次查询的信息量；二是引入折扣调度机制（discount scheduling mechanism），用于缓解奖励函数的过优化问题。实验表明，OPRIDE 在多种运动控制、操作和导航任务中显著优于现有方法，且在极少查询次数下即可实现高性能，同时提供了理论上的效率保障。
链接: https://arxiv.org/abs/2604.02349
作者: Yiqin Yang,Hao Hu,Yihuan Mao,Jin Zhang,Chengjie Wu,Yuhua Jiang,Xu Yang,Runpeng Xie,Yi Fan,Bo Liu,Yang Gao,Bo Xu,Chongjie Zhang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Preference-based reinforcement learning (PbRL) can help avoid sophisticated reward designs and align better with human intentions, showing great promise in various real-world applications. However, obtaining human feedback for preferences can be expensive and time-consuming, which forms a strong barrier for PbRL. In this work, we address the problem of low query efficiency in offline PbRL, pinpointing two primary reasons: inefficient exploration and overoptimization of learned reward functions. In response to these challenges, we propose a novel algorithm, \textbfOffline \textbfPb\textbfRL via \textbfIn-\textbfDataset \textbfExploration (OPRIDE), designed to enhance the query efficiency of offline PbRL. OPRIDE consists of two key features: a principled exploration strategy that maximizes the informativeness of the queries and a discount scheduling mechanism aimed at mitigating overoptimization of the learned reward functions. Through empirical evaluations, we demonstrate that OPRIDE significantly outperforms prior methods, achieving strong performance with notably fewer queries. Moreover, we provide theoretical guarantees of the algorithm’s efficiency. Experimental results across various locomotion, manipulation, and navigation tasks underscore the efficacy and versatility of our approach.
[AI-70] DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在药物发现领域缺乏客观评估的问题，以明确其相较于传统药物研发平台的优势与局限。解决方案的关键在于提出DrugPlayGround框架，该框架能够系统性地评估LLM在生成关于药物理化特性、药物协同作用、药物-蛋白相互作用及药物分子扰动引起的生理响应等文本描述方面的表现，并通过与领域专家协作提供预测结果的详细解释，从而检验LLM在化学和生物学推理能力上的有效性，推动其在药物研发全阶段的深入应用。
链接: https://arxiv.org/abs/2604.02346
作者: Tianyu Liu,Sihan Jiang,Fan Zhang,Kunyang Sun,Teresa Head-Gordon,Hongyu Zhao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE); Biomolecules (q-bio.BM)
备注: 29 pages, 6 figures
Abstract:Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery pipelines. However there is currently a lack of objective assessments of LLM performance to ascertain their advantages and limitations over traditional drug discovery platforms. To tackle this emergent problem, we have developed DrugPlayGround, a framework to evaluate and benchmark LLM performance for generating meaningful text-based descriptions of physiochemical drug characteristics, drug synergism, drug-protein interactions, and the physiological response to perturbations introduced by drug molecules. Moreover, DrugPlayGround is designed to work with domain experts to provide detailed explanations for justifying the predictions of LLMs, thereby testing LLMs for chemical and biological reasoning capabilities to push their greater use at the frontier of drug discovery at all of its stages.
[AI-71] UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics
【速读】：该论文旨在解决通用图形用户界面（GUI）智能体在规模化过程中面临的两大瓶颈：一是昂贵的人类示范数据难以扩展，二是合成教师监督的“蒸馏天花板”限制了模型性能提升。解决方案的关键在于将学习焦点从模仿高层轨迹转向通过真实环境反馈掌握交互物理规律，即利用前向动力学（forward dynamics）——定义为对未来界面状态的生成式预测——作为核心自监督信号。UI-Oceanus框架通过低成本自主探索获取大量交互数据，并以系统执行结果验证其有效性，转化为高密度生成式监督来构建鲁棒的内部世界模型，从而显著提升模型在离线基准和真实在线导航任务中的成功率与泛化能力。
链接: https://arxiv.org/abs/2604.02345
作者: Mengzhou Wu,Yuzhe Guo,Yuan Cao,Haochuan Lu,Songhe Zhu,Pingzhe Qu,Xin Chen,Kang Qin,Zhongpu Wang,Xiaode Zhang,Xinyi Wang,Wei Dai,Gang Cao,Yuetang Deng,Zhi Gong,Dezhi Ran,Linyi Li,Wei Yang,Tao Xie
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Scaling generalist GUI agents is hindered by the data scalability bottleneck of expensive human demonstrations and the “distillation ceiling” of synthetic teacher supervision. To transcend these limitations, we propose UI-Oceanus, a framework that shifts the learning focus from mimicking high-level trajectories to mastering interaction physics via ground-truth environmental feedback. Through a systematic investigation of self-supervised objectives, we identify that forward dynamics, defined as the generative prediction of future interface states, acts as the primary driver for scalability and significantly outweighs inverse inference. UI-Oceanus leverages this insight by converting low-cost autonomous exploration, which is verified directly by system execution, into high-density generative supervision to construct a robust internal world model. Experimental evaluations across a series of models demonstrate the decisive superiority of our approach: models utilizing Continual Pre-Training (CPT) on synthetic dynamics outperform non-CPT baselines with an average success rate improvement of 7% on offline benchmarks, which amplifies to a 16.8% gain in real-world online navigation. Furthermore, we observe that navigation performance scales with synthetic data volume. These results confirm that grounding agents in forward predictive modeling offers a superior pathway to scalable GUI automation with robust cross-domain adaptability and compositional generalization.
[AI-72] Haiku to Opus in Just 10 bits: LLM s Unlock Massive Compression Gains
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）生成文本的压缩效率问题，特别是在损失无损（lossless）和有损（lossy）压缩场景下的计算资源与压缩比之间的权衡。其核心挑战在于如何在有限算力下实现更高压缩率，同时保留关键信息或能力。解决方案的关键在于引入两种创新机制：一是利用领域自适应的低秩适配器（LoRA adapters）提升基于算术编码（arithmetic coding）的无损压缩性能；二是提出交互式有损压缩协议——“提问压缩”（Question-Asking compression, QA），通过小模型向大模型逐轮询问二值问题，以每轮仅传递1比特的方式高效迁移知识，从而显著降低压缩比（最低达0.0006），相比现有方法（如Deletang et al., 2024）提升超过100倍，证明了交互式协议在知识传递中的高效率。
链接: https://arxiv.org/abs/2604.02343
作者: Roy Rinberg,Annabelle Michael Carrell,Simon Henniger,Nicholas Carlini,Keri Warr
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注:
Abstract:We study the compression of LLM-generated text across lossless and lossy regimes, characterizing a compression-compute frontier where more compression is possible at the cost of more compute. For lossless compression, domain-adapted LoRA adapters can improve LLM-based arithmetic coding by 2x over compression with the base LLM alone. For lossy compression, prompting a model for a succinct rewrite then applying arithmetic coding can achieve compression ratios of approximately 0.03, a 2x improvement over compressing the original response. We further introduce Question-Asking compression (QA), an interactive lossy protocol inspired by the game ‘Twenty Questions’. A small model iteratively refines its response by asking yes/no questions to a stronger model, transferring exactly one bit per answer. On 8 benchmarks spanning math, science, and code, 10 binary questions recover 23% to 72% of the capability gap between a small and large model on standard benchmarks and 7% to 38% on harder benchmarks, achieving compression ratios of 0.0006 to 0.004. This is over 100x smaller than prior LLM-based compression (Deletang et al., 2024), suggesting that interactive protocols can transfer knowledge far more efficiently than transmitting full responses. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT) Cite as: arXiv:2604.02343 [cs.LG] (or arXiv:2604.02343v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.02343 Focus to learn more arXiv-issued DOI via DataCite
[AI-73] LLM Reasoning with Process Rewards for Outcome-Guided Steps IJCNN2026
【速读】：该论文旨在解决生成式 AI（Generative AI）在数学推理任务中因仅优化最终结果正确性而导致的中间步骤监督稀疏、无法有效指导错误修正的问题。现有方法引入过程奖励模型（Process Reward Models, PRMs）以提供更密集的中间步骤反馈，但PRM评分常与最终正确性不一致，易导致“流畅失败”（fluent failure）或奖励黑客（reward hacking）现象。解决方案的关键在于提出PROGRS框架，其核心创新是将PRM得分作为条件于结果组的相对偏好而非绝对目标处理：通过引入结果条件中心化（outcome-conditioned centering），对错误轨迹的PRM得分进行零均值调整，消除系统偏差同时保留排序信息；并结合冻结的分位数回归PRM与多尺度一致性评估器，最终集成到Group Relative Policy Optimization（GRPO）中，无需额外训练组件即可显著提升数学推理性能，且减少采样次数。
链接: https://arxiv.org/abs/2604.02341
作者: Mohammad Rezaei,Jens Lehmann,Sahar Vahdati
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures, 2 tables, submitted to IJCNN 2026 conference
Abstract:Mathematical reasoning in large language models has improved substantially with reinforcement learning using verifiable rewards, where final answers can be checked automatically and converted into reliable training signals. Most such pipelines optimize outcome correctness only, which yields sparse feedback for long, multi-step solutions and offers limited guidance on intermediate reasoning errors. Recent work therefore introduces process reward models (PRMs) to score intermediate steps and provide denser supervision. In practice, PRM scores are often imperfectly aligned with final correctness and can reward locally fluent reasoning that still ends in an incorrect answer. When optimized as absolute rewards, such signals can amplify fluent failure modes and induce reward hacking. We propose PROGRS, a framework that leverages PRMs while keeping outcome correctness dominant. PROGRS treats process rewards as relative preferences within outcome groups rather than absolute targets. We introduce outcome-conditioned centering, which shifts PRM scores of incorrect trajectories to have zero mean within each prompt group. It removes systematic bias while preserving informative rankings. PROGRS combines a frozen quantile-regression PRM with a multi-scale coherence evaluator. We integrate the resulting centered process bonus into Group Relative Policy Optimization (GRPO) without auxiliary objectives or additional trainable components. Across MATH-500, AMC, AIME, MinervaMath, and OlympiadBench, PROGRS consistently improves Pass@1 over outcome-only baselines and achieves stronger performance with fewer rollouts. These results show that outcome-conditioned centering enables safe and effective use of process rewards for mathematical reasoning. Comments: 8 pages, 3 figures, 2 tables, submitted to IJCNN 2026 conference Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.02341 [cs.LG] (or arXiv:2604.02341v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.02341 Focus to learn more arXiv-issued DOI via DataCite
[AI-74] Rethinking Forward Processes for Score-Based Data Assimilation in High Dimensions
【速读】：该论文旨在解决高维数据同化（data assimilation）中传统贝叶斯滤波方法因维度灾难导致的精度下降或计算不可行问题，以及现有基于分数的生成模型（score-based generative models）在测量更新步骤中依赖启发式近似 likelihood score 所引发的误差累积与性能退化问题。其解决方案的关键在于提出一种测量感知的分数滤波器（measurement-aware score-based filter, MASF），通过直接从观测方程构建与测量相关的前向过程（forward process），使得似然分数（likelihood score）具有解析可计算性；对于线性观测情形，可推导出精确的似然分数，并结合学习到的先验分数得到后验分数，从而显著提升同化结果的准确性与稳定性。
链接: https://arxiv.org/abs/2604.02889
作者: Eunbi Yoon,Donghan Kim,Dae Wook Kim
机构: 未知
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Data assimilation is the process of estimating the time-evolving state of a dynamical system by integrating model predictions and noisy observations. It is commonly formulated as Bayesian filtering, but classical filters often struggle with accuracy or computational feasibility in high dimensions. Recently, score-based generative models have emerged as a scalable approach for high-dimensional data assimilation, enabling accurate modeling and sampling of complex distributions. However, existing score-based filters often specify the forward process independently of the data assimilation. As a result, the measurement-update step depends on heuristic approximations of the likelihood score, which can accumulate errors and degrade performance over time. Here, we propose a measurement-aware score-based filter (MASF) that defines a measurement-aware forward process directly from the measurement equation. This construction makes the likelihood score analytically tractable: for linear measurements, we derive the exact likelihood score and combine it with a learned prior score to obtain the posterior score. Numerical experiments covering a range of settings, including high-dimensional datasets, demonstrate improved accuracy and stability over existing score-based filters.
[AI-75] High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations
【速读】：该论文旨在解决海洋内部状态难以精确重建的问题，尤其是在观测数据极度稀疏（如仅依赖卫星遥感的表层数据）的情况下。传统方法受限于背景动力学模型或观测密度不足，难以准确恢复三维海洋物理场（如温度、盐度和流速）。其解决方案的关键在于提出一种深度感知的生成式框架，采用条件去噪扩散概率模型（conditional denoising diffusion probabilistic model, DDPM），在高达99.9%的数据缺失率下仍能从稀疏表面观测中重建高分辨率三维海洋状态。该方法通过引入连续深度嵌入（continuous depth embeddings）学习统一的垂直结构表示，无需依赖先验动力学模型即可泛化至未见深度，从而实现对大尺度环流与多尺度波动的准确恢复，为数据受限环境下的海洋状态重构提供了可扩展的概率建模新范式。
链接: https://arxiv.org/abs/2604.02850
作者: Niloofar Asefi,Tianning Wu,Ruoying He,Ashesh Chattopadhyay
机构: 未知
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI); Dynamical Systems (math.DS); Chaotic Dynamics (nlin.CD)
备注: Supplementary information: this https URL
Abstract:The ocean interior regulates Earth’s climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We present a depth-aware generative framework for reconstructing high-resolution three-dimensional ocean states from extremely sparse surface data. Our approach employs a conditional denoising diffusion probabilistic model (DDPM) trained on sea surface height and temperature observations with up to 99.9 percent sparsity, without reliance on a background dynamical model. By incorporating continuous depth embeddings, the model learns a unified vertical representation of the ocean states and generalizes to previously unseen depths. Applied to the Gulf of Mexico, the framework accurately reconstructs subsurface temperature, salinity, and velocity fields across multiple depths. Evaluations using statistical metrics, spectral analysis, and heat transport diagnostics demonstrate recovery of both large-scale circulation and multiscale variability. These results establish generative diffusion models as a scalable approach for probabilistic ocean reconstruction in data-limited regimes, with implications for climate monitoring and forecasting.
[AI-76] Eligibility-Aware Evidence Synthesis: An Agent ic Framework for Clinical Trial Meta-Analysis
【速读】：该论文旨在解决临床证据合成中两大核心问题：一是如何实现从大规模试验注册库中自动化识别相关研究并完成端到端的证据整合；二是传统荟萃分析仅依据统计精度加权研究，忽视了纳入标准所体现的临床兼容性（clinical compatibility），导致结果可能不适用于特定人群。解决方案的关键在于提出 EligMeta 框架，其采用混合架构分离大语言模型（LLM）推理与确定性执行：LLM 从自然语言查询生成可解释的筛选规则并进行结构化元数据解析，而所有逻辑运算、权重计算和统计合并均由确定性模块完成以保障可复现性；同时，通过结构化纳入标准并计算相似度权重，将目标人群与对照试验之间的群体匹配度（eligibility alignment）纳入加权机制，从而生成基于人群特征的汇总估计值。
链接: https://arxiv.org/abs/2604.02678
作者: Yao Zhao,Zhiyue Zhang,Yanxun Xu
机构: 未知
类目: Methodology (stat.ME); Artificial Intelligence (cs.AI); Applications (stat.AP)
备注:
Abstract:Clinical evidence synthesis requires identifying relevant trials from large registries and aggregating results that account for population differences. While recent LLM-based approaches have automated components of systematic review, they do not support end-to-end evidence synthesis. Moreover, conventional meta-analysis weights studies by statistical precision without considering clinical compatibility reflected in eligibility criteria. We propose EligMeta, an agentic framework that integrates automated trial discovery with eligibility-aware meta-analysis, translating natural-language queries into reproducible trial selection and incorporating eligibility alignment into study weighting to produce cohort-specific pooled estimates. EligMeta employs a hybrid architecture separating LLM-based reasoning from deterministic execution: LLMs generate interpretable rules from natural-language queries and perform schema-constrained parsing of trial metadata, while all logical operations, weight computations, and statistical pooling are executed deterministically to ensure reproducibility. The framework structures eligibility criteria and computes similarity-based study weights reflecting population alignment between target and comparator trials. In a gastric cancer landscape analysis, EligMeta reduced 4,044 candidate trials to 39 clinically relevant studies through rule-based filtering, recovering all 13 guideline-cited trials. In an olaparib adverse events meta-analysis across four trials, eligibility-aware weighting shifted the pooled risk ratio from 2.18 (95% CI: 1.71-2.79) under conventional Mantel-Haenszel estimation to 1.97 (95% CI: 1.76-2.20), demonstrating quantifiable impact of incorporating eligibility alignment. EligMeta bridges automated trial discovery with eligibility-aware meta-analysis, providing a scalable and reproducible framework for evidence synthesis in precision medicine.
[AI-77] Sparse Bayesian Learning Algorithms Revisited: From Learning Majorizers to Structured Algorithmic Learning using Neural Networks
【速读】：该论文旨在解决稀疏信号恢复中如何选择最优稀疏贝叶斯学习（Sparse Bayesian Learning, SBL）算法的问题，尤其在缺乏统一框架指导算法设计的情况下，难以事先确定最佳方法。其解决方案的关键在于：首先，通过引入极大化-最小化（Majorization-Minimization, MM）理论，统一推导出主流SBL算法，并揭示其收敛性与内在一致性；其次，基于MM框架扩展SBL算法类，并提出一种深度学习架构，以数据驱动方式学习更优的SBL更新规则，该架构不随测量矩阵维度增长而增加复杂度，从而支持跨不同测量矩阵的泛化能力测试，且在零样本场景下仍能实现对未见矩阵的有效映射学习，显著提升了传统MM方法的适应性和性能表现。
链接: https://arxiv.org/abs/2604.02513
作者: Rushabha Balaji,Kuan-Lin Chen,Danijela Cabric,Bhaskar D. Rao
机构: 未知
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注:
Abstract:Sparse Bayesian Learning is one of the most popular sparse signal recovery methods, and various algorithms exist under the SBL paradigm. However, given a performance metric and a sparse recovery problem, it is difficult to know a-priori the best algorithm to choose. This difficulty is in part due to a lack of a unified framework to derive SBL algorithms. We address this issue by first showing that the most popular SBL algorithms can be derived using the majorization-minimization (MM) principle, providing hitherto unknown convergence guarantees to this class of SBL methods. Moreover, we show that the two most popular SBL update rules not only fall under the MM framework but are both valid descent steps for a common majorizer, revealing a deeper analytical compatibility between these algorithms. Using this insight and properties from MM theory we expand the class of SBL algorithms, and address finding the best SBL algorithm via data within the MM framework. Second, we go beyond the MM framework by introducing the powerful modeling capabilities of deep learning to further expand the class of SBL algorithms, aiming to learn a superior SBL update rule from data. We propose a novel deep learning architecture that can outperform the classical MM based ones across different sparse recovery problems. Our architecture’s complexity does not scale with the measurement matrix dimension, hence providing a unique opportunity to test generalization capability across different matrices. For parameterized dictionaries, this invariance allows us to train and test the model across different parameter ranges. We also showcase our model’s ability to learn a functional mapping by its zero-shot performance on unseen measurement matrices. Finally, we test our model’s performance across different numbers of snapshots, signal-to-noise ratios, and sparsity levels.
[AI-78] A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems
【速读】：该论文旨在解决复杂流体流动在能源系统中的计算流体动力学（Computational Fluid Dynamics, CFD）模拟因强非线性与多尺度-多物理场耦合而带来的极高计算成本问题。其解决方案的关键在于提出一种基于Transformer的建模框架，采用分层视觉Transformer架构（SwinV2-UNet），通过处理来自多保真度仿真的多模态流动数据集进行训练，模型结构中引入辅助标记（auxiliary tokens）显式编码数据模态和时间增量信息，从而实现对流场演化过程的时空滚动预测和从有限观测视图中重构缺失流动场信息的能力。该方法有效提升了模型在不同网格分辨率和模态间的泛化性能，为复杂流场系统的预测建模提供了高效、可迁移的数据驱动新路径。
链接: https://arxiv.org/abs/2604.02483
作者: Kiran Yalamanchi,Shivam Barwey,Ibrahim Jarrah,Pinaki Pal
机构: 未知
类目: Fluid Dynamics (physics.flu-dyn); Artificial Intelligence (cs.AI)
备注:
Abstract:Computational fluid dynamics (CFD) simulations of complex fluid flows in energy systems are prohibitively expensive due to strong nonlinearities and multiscale-multiphysics interactions. In this work, we present a transformer-based modeling framework for prediction of fluid flows, and demonstrate it for high-pressure gas injection phenomena relevant to reciprocating engines. The approach employs a hierarchical Vision Transformer (SwinV2-UNet) architecture that processes multimodal flow datasets from multi-fidelity simulations. The model architecture is conditioned on auxiliary tokens explicitly encoding the data modality and time increment. Model performance is assessed on two different tasks: (1) spatiotemporal rollouts, where the model autoregressively predicts the flow state at future times; and (2) feature transformation, where the model infers unobserved fields/views from observed fields/views. We train separate models on multimodal datasets generated from in-house CFD simulations of argon jet injection into a nitrogen environment, encompassing multiple grid resolutions, turbulence models, and equations of state. The resulting data-driven models learn to generalize across resolutions and modalities, accurately forecasting the flow evolution and reconstructing missing flow-field information from limited views. This work demonstrates how large vision transformer-based models can be adapted to advance predictive modeling of complex fluid flow systems.
[AI-79] Generative models on phase space
【速读】：该论文旨在解决生成式模型在高能物理数据建模中因无法精确满足物理约束（如能量和动量守恒）而导致的可解释性与可靠性不足的问题。解决方案的关键在于设计一种构造上始终受限于质量为零的N粒子洛伦兹不变相空间流形的生成模型，确保采样轨迹在每一步都严格遵守中心质心系下的物理约束；对于扩散模型而言，其前向过程的“纯噪声”终点对应于相空间上的均匀分布，从而为逆向去噪过程中粒子间关联性的演化提供了清晰的起点，显著提升了模型对复杂粒子分布（包括多粒子及奇异结构）的学习能力与物理一致性。
链接: https://arxiv.org/abs/2604.02415
作者: Zachary Bogorad,Ibrahim Elsharkawy,Yonatan Kahn,Andrew J. Larkoski,Noam Levi
机构: 未知
类目: High Energy Physics - Phenomenology (hep-ph); Artificial Intelligence (cs.AI)
备注: 19+9 pages, 22 figures, 3 tables
Abstract:Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are particularly useful when the training data appears to be concentrated on a submanifold of the data embedding space. For high-energy physics data, consisting of collections of relativistic energy-momentum 4-vectors, this submanifold can enforce extremely strong physically-motivated priors, such as energy and momentum conservation. If these constraints are learned only approximately, rather than exactly, this can inhibit the interpretability and reliability of such generative models. To remedy this deficiency, we introduce generative models which are, by construction, confined at every step of their sampling trajectory to the manifold of massless N-particle Lorentz-invariant phase space in the center-of-momentum frame. In the case of diffusion models, the “pure noise” forward process endpoint corresponds to the uniform distribution on phase space, which provides a clear starting point from which to identify how correlations among the particles emerge during the reverse (de-noising) process. We demonstrate that our models are able to learn both few-particle and many-particle distributions with various singularity structures, paving the way for future interpretability studies using generative models trained on simulated jet data.
机器学习
[LG-0] Hierarchical Planning with Latent World Models
链接: https://arxiv.org/abs/2604.03208
作者: Wancong Zhang,Basile Terver,Artem Zholus,Soham Chitnis,Harsh Sutaria,Mido Assran,Randall Balestriero,Amir Bar,Adrien Bardes,Yann LeCun,Nicolas Ballas
类目: Machine Learning (cs.LG)
*备注:
Abstract:Model predictive control (MPC) with learned world models has emerged as a promising paradigm for embodied control, particularly for its ability to generalize zero-shot when deployed in new environments. However, learned world models often struggle with long-horizon control due to the accumulation of prediction errors and the exponentially growing search space. In this work, we address these challenges by learning latent world models at multiple temporal scales and performing hierarchical planning across these scales, enabling long-horizon reasoning while substantially reducing inference-time planning complexity. Our approach serves as a modular planning abstraction that applies across diverse latent world-model architectures and domains. We demonstrate that this hierarchical approach enables zero-shot control on real-world non-greedy robotic tasks, achieving a 70% success rate on pick–place using only a final goal specification, compared to 0% for a single-level world model. In addition, across physics-based simulated environments including push manipulation and maze navigation, hierarchical planning achieves higher success while requiring up to 4x less planning-time compute.
[LG-1] A Tsetlin Machine-driven Intrusion Detection System for Next-Generation IoMT Security
链接: https://arxiv.org/abs/2604.03205
作者: Rahul Jaiswal,Per-Arne Andersen,Linga Reddy Cenkeramaddi,Lei Jiao,Ole-Christoffer Granmo
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 8 pages, 15 figures, 9 tables. Accepted at the 7th Silicon Valley Cybersecurity Conference (SVCC 2026), California, USA
Abstract:The rapid adoption of the Internet of Medical Things (IoMT) is transforming healthcare by enabling seamless connectivity among medical devices, systems, and services. However, it also introduces serious cybersecurity and patient safety concerns as attackers increasingly exploit new methods and emerging vulnerabilities to infiltrate IoMT networks. This paper proposes a novel Tsetlin Machine ™-based Intrusion Detection System (IDS) for detecting a wide range of cyberattacks targeting IoMT networks. The TM is a rule-based and interpretable machine learning (ML) approach that models attack patterns using propositional logic. Extensive experiments conducted on the CICIoMT-2024 dataset, which includes multiple IoMT protocols and cyberattack types, demonstrate that the proposed TM-based IDS outperforms traditional ML classifiers. The proposed model achieves an accuracy of 99.5% in binary classification and 90.7% in multi-class classification, surpassing existing state-of-the-art approaches. Moreover, to enhance model trust and interpretability, the proposed TM-based model presents class-wise vote scores and clause activation heatmaps, providing clear insights into the most influential clauses and the dominant class contributing to the final model decision.
[LG-2] Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis
链接: https://arxiv.org/abs/2604.03197
作者: Sokratis J. Anagnostopoulos,George Rovas,Vasiliki Bikia,Theodore G. Papaioannou,Athanase D. Protogerou,Nikolaos Stergiopulos
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:
Abstract:Cardiovascular modeling has rapidly advanced over the past few decades due to the rising needs for health tracking and early detection of cardiovascular diseases. While 1-D arterial models offer an attractive compromise between computational efficiency and solution fidelity, their application on large populations or for generating large \emphin silico cohorts remains challenging. Certain hemodynamic parameters like the terminal resistance/compliance, are difficult to clinically estimate and often yield non-physiological hemodynamics when sampled naively, resulting in large portions of simulated datasets to be discarded. In this work, we present a systematic framework for training machine learning (ML) models, capable of instantaneous hemodynamic prediction and parameter estimation. We initially start with generating a parametric virtual cohort of patients which is based on the multivariate correlations observed in the large Asklepios clinical dataset, ensuring that physiological parameter distributions are respected. We then train a deep neural surrogate model, able to predict patient-specific arterial pressure and cardiac output (CO), enabling rapid a~priori screening of input parameters. This allows for immediate rejection of non-physiological combinations and drastically reduces the cost of targeted synthetic dataset generation (e.g. hypertensive groups). The model also provides a principled means of sampling the terminal resistance to minimize the uncertainties of unmeasurable parameters. Moreover, by assessing the model’s predictive performance we determine the theoretical information which suffices for solving the inverse problem of estimating the CO. Finally, we apply the surrogate on a clinical dataset for the estimation of central aortic hemodynamics i.e. the CO and aortic systolic blood pressure (cSBP).
[LG-3] DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
链接: https://arxiv.org/abs/2604.03154
作者: Yingxu Wang,Kunyu Zhang,Jiaxin Huang,Mengzhu Wang,Mingyan Xiao,Siyang Gao,Nan Yin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph domain adaptation (GDA) aims to transfer knowledge from a labeled source graph to an unlabeled target graph under distribution shifts. However, existing methods are largely feature-centric and overlook structural discrepancies, which become particularly detrimental under significant topology shifts. Such discrepancies alter both geometric relationships and spectral properties, leading to unreliable transfer of graph neural networks (GNNs). To address this limitation, we propose Dual-Aligned Structural Basis Distillation (DSBD) for GDA, a novel framework that explicitly models and adapts cross-domain structural variation. DSBD constructs a differentiable structural basis by synthesizing continuous probabilistic prototype graphs, enabling gradient-based optimization over graph topology. The basis is learned under source-domain supervision to preserve semantic discriminability, while being explicitly aligned to the target domain through a dual-alignment objective. Specifically, geometric consistency is enforced via permutation-invariant topological moment matching, and spectral consistency is achieved through Dirichlet energy calibration, jointly capturing structural characteristics across domains. Furthermore, we introduce a decoupled inference paradigm that mitigates source-specific structural bias by training a new GNN on the distilled structural basis. Extensive experiments on graph and image benchmarks demonstrate that DSBD consistently outperforms state-of-the-art methods.
[LG-4] HyperFitS – Hypernetwork Fitting Spectra for metabolic quantification of 1H MR spectroscopic imaging
链接: https://arxiv.org/abs/2604.03150
作者: Paul J. Weiser,Gulnur Ungan,Amirmohammad Shamaei,Georg Langs,Wolfgang Bogner,Malte Hoffmann,Antoine Klauser,Ovidiu C. Andronesi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Purpose: Proton magnetic resonance spectroscopic imaging ( ^1 H MRSI) enables the mapping of whole-brain metabolites concentrations in-vivo. However, a long-standing problem for its clinical applicability is the metabolic quantification, which can require extensive time for spectral fitting. Recently, deep learning methods have been able to provide whole-brain metabolic quantification in only a few seconds. However, neural network implementations often lack configurability and require retraining to change predefined parameter settings. Methods: We introduce HyperFitS, a hypernetwork for spectral fitting for metabolite quantification in whole-brain ^1 H MRSI that flexibly adapts to a broad range of baseline corrections and water suppression factors. Metabolite maps of human subjects acquired at 3T and 7T with isotropic resolutions of 10 mm, 3.4 mm and 2 mm by water-suppressed and water-unsuppressed MRSI were quantified with HyperFitS and compared to conventional LCModel fitting. Results: Metabolic maps show a substantial agreement between the new and gold-standard methods, with significantly faster fitting times by HyperFitS. Quantitative results further highlight the impact of baseline parametrization on metabolic quantification, which can alter results by up to 30%. Conclusion: HyperFitS shows strong agreement with state-of-the-art conventional methods, while reducing processing times from hours to a few seconds. Compared to prior deep learning based spectral fitting methods, HyperFitS enables a wide range of configurability and can adapt to data quality acquired with multiple protocols and field strengths without retraining.
[LG-5] SkillRT: Compiling Skills for Efficient Execution Everywhere
链接: https://arxiv.org/abs/2604.03088
作者: Le Chen,Erhu Feng,Yubin Xia,Haibo Chen
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:
Abstract:LLM agents increasingly adopt skills as a reusable unit of composition. While skills are shared across diverse agent platforms, current systems treat them as raw context, causing the same skill to behave inconsistently for different agents. This fragility undermines skill portability and execution efficiency. To address this challenge, we analyze 118,000 skills and draw inspiration from traditional compiler design. We treat skills as code and LLMs as heterogeneous processors. To make portability actionable, we decompose a skill’s requirements into a set of primitive capabilities, and measure how well each model-harness pair supports them. Based on these capability profiles, we propose SkillRT, a compilation and runtime system designed for portable and efficient skill execution. At compile time, SkillRT performs capability-based compilation, environment binding, and concurrency extraction. At runtime, SkillRT applies JIT code solidification and adaptive recompilation for performance optimization. We evaluate SkillRT across eight LLMs of varying scales and three agent harnesses, covering SkillsBench and representative skill tasks. Results demonstrate that SkillRT significantly improves task completion rates across different models and environments while reducing token consumption by up to 40%. In terms of performance, SkillRT achieves up to 3.2x speedup with enhanced parallelism, and 19-50x latency reduction through code solidification. Subjects: Software Engineering (cs.SE); Machine Learning (cs.LG) Cite as: arXiv:2604.03088 [cs.SE] (or arXiv:2604.03088v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2604.03088 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-6] On Data-Driven Koopman Representations of Nonlinear Delay Differential Equations
链接: https://arxiv.org/abs/2604.03086
作者: Santosh Mohan Rajkumar,Dibyasri Barman,Kumar Vikram Singh,Debdipta Goswami
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注: Github: this https URL
Abstract:This work establishes a rigorous bridge between infinite-dimensional delay dynamics and finite-dimensional Koopman learning, with explicit and interpretable error guarantees. While Koopman analysis is well-developed for ordinary differential equations (ODEs) and partially for partial differential equations (PDEs), its extension to delay differential equations (DDEs) remains limited due to the infinite-dimensional phase space of DDEs. We propose a finite-dimensional Koopman approximation framework based on history discretization and a suitable reconstruction operator, enabling a tractable representation of the Koopman operator via kernel-based extended dynamic mode decomposition (kEDMD). Deterministic error bounds are derived for the learned predictor, decomposing the total error into contributions from history discretization, kernel interpolation, and data-driven regression. Additionally, we develop a kernel-based reconstruction method to recover discretized states from lifted Koopman coordinates, with provable guarantees. Numerical results demonstrate convergence of the learned predictor with respect to both discretization resolution and training data, supporting reliable prediction and control of delay systems.
[LG-7] Learning Contractive Integral Operators with Fredholm Integral Neural Operators
链接: https://arxiv.org/abs/2604.03034
作者: Kyriakos C. Georgiou,Constantinos Siettos,Athanasios N. Yannacopoulos
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:We generalize the framework of Fredholm Neural Networks, to learn non-expansive integral operators arising in Fredholm Integral Equations (FIEs) of the second kind in arbitrary dimensions. We first present the proposed Fredholm Integral Neural Operators (FREDINOs), for FIEs and prove that they are universal approximators of linear and non-linear integral operators and corresponding solution operators. We furthermore prove that the learned operators are guaranteed to be contractive, thereby strictly satisfying the mathematical property required for the convergence of the fixed point scheme. Finally, we also demonstrate how FREDINOs can be used to learn the solution operator of non-linear elliptic PDEs, via a Boundary Integral Equation (BIE) formulation. We assess the proposed methodology numerically, via several benchmark problems: linear and non-linear FIEs in arbitrary dimensions, as well as a non-linear elliptic PDE in 2D. Built on tailored mathematical/numerical analysis theory, FREDINOs offer high-accuracy approximations and interpretable schemes, making them well suited for scientific machine learning/numerical analysis computations.
[LG-8] Generating DDPM-based Samples from Tilted Distributions
链接: https://arxiv.org/abs/2604.03015
作者: Himadri Mandal,Dhruman Gupta,Rushil Gupta,Sarvesh Ravichandran Iyer,Agniv Bandyopadhyay,Achal Bassamboo,Varun Gupta,Sandeep Juneja
类目: Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注: 33 pages, 4 figures
Abstract:Given n independent samples from a d -dimensional probability distribution, our aim is to generate diffusion-based samples from a distribution obtained by tilting the original, where the degree of tilt is parametrized by \theta \in \mathbbR^d . We define a plug-in estimator and show that it is minimax-optimal. We develop Wasserstein bounds between the distribution of the plug-in estimator and the true distribution as a function of n and \theta , illustrating regimes where the output and the desired true distribution are close. Further, under some assumptions, we prove the TV-accuracy of running Diffusion on these tilted samples. Our theoretical results are supported by extensive simulations. Applications of our work include finance, weather and climate modelling, and many other domains, where the aim may be to generate samples from a tilted distribution that satisfies practically motivated moment constraints.
[LG-9] Explainable Machine Learning Reveals 12-Fold Ucp1 Upregulation and Thermogenic Reprogramming in Female Mouse White Adipose Tissue After 37 Days of Microgravity: First AI/ML Analysis of NASA OSD-970
链接: https://arxiv.org/abs/2604.02942
作者: Md. Rashadul Islam
类目: Machine Learning (cs.LG)
*备注: 11 pages, 9 figures, 5 tables. First AI/ML analysis of NASA OSD-970 (GLDS-790). Code available at this https URL
Abstract:Microgravity induces profound metabolic adaptations in mammalian physiology, yet the molecular mechanisms governing thermogenesis in female white adipose tissue (WAT) remain poorly characterized. This paper presents the first machine learning (ML) analysis of NASA Open Science Data Repository (OSDR) dataset OSD-970, derived from the Rodent Research-1 (RR-1) mission. Using RT-qPCR data from 89 adipogenesis and thermogenesis pathway genes in gonadal WAT of 16 female C57BL/6J mice (8 flight, 8 ground control) following 37 days aboard the International Space Station (ISS), we applied differential expression analysis, multiple ML classifiers with Leave-One-Out Cross-Validation (LOO-CV), and Explainable AI via SHapley Additive exPlanations (SHAP). The most striking finding is a dramatic 12.21-fold upregulation of Ucp1 (Delta-Delta-Ct = -3.61, p = 0.0167) in microgravity-exposed WAT, accompanied by significant activation of the thermogenesis pathway (mean pathway fold-change = 3.24). The best-performing model (Random Forest with top-20 features) achieved AUC = 0.922, Accuracy = 0.812, and F1 = 0.824 via LOO-CV. SHAP analysis consistently ranked Ucp1 among the top predictive features, while Angpt2, Irs2, Jun, and Klf-family transcription factors emerged as dominant consensus classifiers. Principal component analysis (PCA) revealed clear separation between flight and ground samples, with PC1 explaining 69.1% of variance. These results suggest rapid thermogenic reprogramming in female WAT as a compensatory response to microgravity. This study demonstrates the power of explainable AI for re-analysis of newly released NASA space biology datasets, with direct implications for female astronaut health on long-duration missions and for Earth-based obesity and metabolic disease research.
[LG-10] owards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms
链接: https://arxiv.org/abs/2604.02927
作者: Andreas Boltres,Niklas Freymuth,Benjamin Schichtholz,Michael König,Gerhard Neumann
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: Submitted to TMLR
Abstract:Routing algorithms are crucial for efficient computer network operations, and in many settings they must be able to react to traffic bursts within milliseconds. Live telemetry data can provide informative signals to routing algorithms, and recent work has trained neural networks to exploit such signals for traffic-aware routing. Yet, aggregating network-wide information is subject to communication delays, and existing neural approaches either assume unrealistic delay-free global states, or restrict routers to purely local telemetry. This leaves their deployability in real-world environments unclear. We cast telemetry-aware routing as a delay-aware closed-loop control problem and introduce a framework that trains and evaluates neural routing algorithms, while explicitly modeling communication and inference delays. On top of this framework, we propose LOGGIA, a scalable graph neural routing algorithm that predicts log-space link weights from attributed topology-and-telemetry graphs. It utilizes a data-driven pre-training stage, followed by on-policy Reinforcement Learning. Across synthetic and real network topologies, and unseen mixed TCP/UDP traffic sequences, LOGGIA consistently outperforms shortest-path baselines, whereas neural baselines fail once realistic delays are enforced. Our experiments further suggest that neural routing algorithms like LOGGIA perform best when deployed fully locally, i.e., observing network states and inferring actions at every router individually, as opposed to centralized decision making.
[LG-11] Efficient Logistic Regression with Mixture of Sigmoids
链接: https://arxiv.org/abs/2604.02920
作者: Federico Di Gennaro,Saptarshi Chakraborty,Nikita Zhivotovskiy
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper studies the Exponential Weights (EW) algorithm with an isotropic Gaussian prior for online logistic regression. We show that the near-optimal worst-case regret bound O(d\log(Bn)) for EW, established by Kakade and Ng (2005) against the best linear predictor of norm at most B , can be achieved with total worst-case computational complexity O(B^3 n^5) . This substantially improves on the O(B^18n^37) complexity of prior work achieving the same guarantee (Foster et al., 2018). Beyond efficiency, we analyze the large- B regime under linear separability: after rescaling by B , the EW posterior converges as B\to\infty to a standard Gaussian truncated to the version cone. Accordingly, the predictor converges to a solid-angle vote over separating directions and, on every fixed-margin slice of this cone, the mode of the corresponding truncated Gaussian is aligned with the hard-margin SVM direction. Using this geometry, we derive non-asymptotic regret bounds showing that once B exceeds a margin-dependent threshold, the regret becomes independent of B and grows only logarithmically with the inverse margin. Overall, our results show that EW can be both computationally tractable and geometrically adaptive in online classification.
[LG-12] Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation
链接: https://arxiv.org/abs/2604.02899
作者: Haseeb Tariq,Marwan Hassani
类目: Machine Learning (cs.LG)
*备注:
Abstract:Money laundering presents a persistent challenge for financial institutions worldwide, while criminal organizations constantly evolve their tactics to bypass detection systems. Traditional anti-money laundering approaches mainly rely on predefined risk-based rules, leading to resource-intensive investigations and high numbers of false positive alerts. In order to restrict operational costs from exploding, while billions of transactions are being processed every day, financial institutions are investing in more sophisticated mechanisms to improve existing systems. In this paper, we present ExSTraQt (EXtract Suspicious TRAnsactions from Quasi-Temporal graph representation), an advanced supervised learning approach to detect money laundering (or suspicious) transactions in financial datasets. Our proposed framework excels in performance, when compared to the state-of-the-art AML (Anti Money Laundering) detection models. The key strengths of our framework are sheer simplicity, in terms of design and number of parameters; and scalability, in terms of the computing and memory requirements. We evaluated our framework on transaction-level detection accuracy using a real dataset; and a set of synthetic financial transaction datasets. We consistently achieve an uplift in the F1 score for most datasets, up to 1% for the real dataset; and more than 8% for one of the synthetic datasets. We also claim that our framework could seamlessly complement existing AML detection systems in banks. Our code and datasets are available at this https URL.
[LG-13] oward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting
链接: https://arxiv.org/abs/2604.02876
作者: Valentin Mercier(Toulouse INP, IRIT, EPE UT),Serge Gratton(IRIT, EPE UT, Toulouse INP),Lapeyre Corentin(NVIDIA),Gwenaël Chevallet
类目: Machine Learning (cs.LG)
*备注:
Abstract:Operational flood forecasting still relies on high-fidelity two-dimensional hydraulic solvers, but their runtime can be prohibitive for rapid decision support on large urban floodplains. In parallel, AI-based surrogate models have shown strong potential in several areas of computational physics for accelerating otherwise expensive high-fidelity simulations. We address this issue on the lower Têt River (France), starting from a production-grade Telemac2D model defined on a high-resolution unstructured finite-element mesh with more than 4\times 10^5 nodes. From this setup, we build a learning-ready database of synthetic but operationally grounded flood events covering several representative hydrograph families and peak discharges. On top of this database, we develop a graph-neural surrogate based on projected meshes and multimesh connectivity. The projected-mesh strategy keeps training tractable while preserving high-fidelity supervision from the original Telemac simulations, and the multimesh construction enlarges the effective spatial receptive field without increasing network depth. We further study the effect of an explicit discharge feature Q(t) and of pushforward training for long autoregressive rollouts. The experiments show that conditioning on Q(t) is essential in this boundary-driven setting, that multimesh connectivity brings additional gains once the model is properly conditioned, and that pushforward further improves rollout stability. Among the tested configurations, the combination of Q(t) , multimesh connectivity, and pushforward provides the best overall results. These gains are observed both on hydraulic variables over the surrogate mesh and on inundation maps interpolated onto a common 25,\mathrmm regular grid and compared against the original high-resolution Telemac solution. On the studied case, the learned surrogate produces 6-hour predictions in about 0.4,\mathrms on a single NVIDIA A100 GPU, compared with about 180,\mathrmmin on 56 CPU cores for the reference simulation. These results support graph-based surrogates as practical complements to industrial hydraulic solvers for operational flood mapping.
[LG-14] Structure-Aware Commitment Reduction for Network-Constrained Unit Commitment with Solver-Preserving Guarantees
链接: https://arxiv.org/abs/2604.02788
作者: Guangwen Wang,Jiaqi Wu,Yang Weng,Baosen Zhang
类目: Machine Learning (cs.LG)
*备注: 10 pages
Abstract:The growing number of individual generating units, hybrid resources, and security constraints has significantly increased the computational burden of network-constrained unit commitment (UC), where most solution time is spent exploring branch-and-bound trees over unit-hour binary variables. To reduce this combinatorial burden, recent approaches have explored learning-based guidance to assist commitment decisions. However, directly using tools such as large language models (LLMs) to predict full commitment schedules is unreliable, as infeasible or inconsistent binary decisions can violate inter-temporal constraints and degrade economic optimality. This paper proposes a solver-compatible dimensionality reduction framework for UC that exploits structural regularities in commitment decisions. Instead of generating complete schedules, the framework identifies a sparse subset of structurally stable commitment binaries to fix prior to optimization. One implementation uses an LLM to select these variables. The LLM does not replace the optimization process but provides partial variable restriction, while all constraints and remaining decisions are handled by the original MILP solver, which continues to enforce network, ramping, reserve, and security constraints. We formally show that the masked problem defines a reduced feasible region of the original UC model, thereby preserving feasibility and enabling solver-certified optimality within the restricted space. Experiments on IEEE 57-bus, RTS 73-bus, IEEE 118-bus, and augmented large-scale cases, including security-constrained variants, demonstrate consistent reductions in branch-and-bound nodes and solution time, achieving order-of-magnitude speedups on high-complexity instances while maintaining near-optimal objective values.
[LG-15] owards Realistic Class-Incremental Learning with Free-Flow Increments
链接: https://arxiv.org/abs/2604.02765
作者: Zhiming Xu,Baile Xu,Jian Zhao,Furao Shen,Suorong Yang
类目: Machine Learning (cs.LG)
*备注: 15pages, 5figures, 3 tables
Abstract:Class-incremental learning (CIL) is typically evaluated under predefined schedules with equal-sized tasks, leaving more realistic and complex cases unexplored. However, a practical CIL system should learns immediately when any number of new classes arrive, without forcing fixed-size tasks. We formalize this setting as Free-Flow Class-Incremental Learning (FFCIL), where data arrives as a more realistic stream with a highly variable number of unseen classes each step. It will make many existing CIL methods brittle and lead to clear performance degradation. We propose a model-agnostic framework for robust CIL learning under free-flow arrivals. It comprises a class-wise mean (CWM) objective that replaces sample frequency weighted loss with uniformly aggregated class-conditional supervision, thereby stabilizing the learning signal across free-flow class increments, as well as method-wise adjustments that improve robustness for representative CIL paradigms. Specifically, we constrain distillation to replayed data, normalize the scale of contrastive and knowledge transfer losses, and introduce Dynamic Intervention Weight Alignment (DIWA) to prevent over-adjustment caused by unstable statistics from small class increments. Experiments confirm a clear performance degradation across various CIL baselines under FFCIL, while our strategies yield consistent gains.
[LG-16] STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation
链接: https://arxiv.org/abs/2604.02756
作者: Zijin Liu,Xu Geng,Wenshuai Xu,Xiang Zhao,Yan Xia,You Song
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate crowd simulation is crucial for public safety management, emergency evacuation planning, and intelligent transportation systems. However, existing methods, which typically model crowds as a collection of independent individual trajectories, are limited in their ability to capture macroscopic physical laws. This microscopic approach often leads to error accumulation and compromises simulation stability. Furthermore, deep learning-driven methods tend to suffer from low inference efficiency and high computational overhead, making them impractical for large-scale, efficient simulations. To address these challenges, we propose the Spatio-Temporal Decoupled Differential Equation Network (STDDN), a novel framework that guides microscopic trajectory prediction with macroscopic physics. We innovatively introduce the continuity equation from fluid dynamics as a strong physical constraint. A Neural Ordinary Differential Equation (Neural ODE) is employed to model the macroscopic density evolution driven by individual movements, thereby physically regularizing the microscopic trajectory prediction model. We design a density-velocity coupled dynamic graph learning module to formulate the derivative of the density field within the Neural ODE, effectively mitigating error accumulation. We also propose a differentiable density mapping module to eliminate discontinuous gradients caused by discretization and introduce a cross-grid detection module to accurately model the impact of individual cross-grid movements on local density changes. The proposed STDDN method has demonstrated significantly superior simulation performance compared to state-of-the-art methods on long-term tasks across four real-world datasets, as well as a major reduction in inference latency.
[LG-17] Understanding Latent Diffusability via Fisher Geometry
链接: https://arxiv.org/abs/2604.02751
作者: Jing Gu,Morteza Mardani,Wonjun Lee,Dongmian Zou,Gilad Lerman
类目: Machine Learning (cs.LG)
*备注:
Abstract:Diffusion models often degrade when trained in latent spaces (e.g., VAEs), yet the formal causes remain poorly understood. We quantify latent-space diffusability through the rate of change of the Minimum Mean Squared Error (MMSE) along the diffusion trajectory. Our framework decomposes this MMSE rate into contributions from Fisher Information (FI) and Fisher Information Rate (FIR). We demonstrate that while global isometry ensures FI alignment, FIR is governed by the encoder’s local geometric properties. Our analysis explicitly decouples latent geometric distortion into three measurable penalties: dimensional compression, tangential distortion, and curvature injection. We derive theoretical conditions for FIR preservation across spaces, ensuring maintained diffusability. Experiments across diverse autoencoding architectures validate our framework and establish these efficient FI and FIR metrics as a robust diagnostic suite for identifying and mitigating latent diffusion failure.
[LG-18] FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving
链接: https://arxiv.org/abs/2604.02715
作者: Qingxiu Liu,Cyril Y. He,Hanser Jiang,Zion Wang,Alan Zhao,Patrick P. C. Lee
类目: Machine Learning (cs.LG)
*备注:
Abstract:Mixture-of-Experts (MoE) models have become a dominant paradigm for scaling large language models, but their rapidly growing parameter sizes introduce a fundamental inefficiency during inference: most expert weights remain idle in GPU memory while competing with performance-critical runtime state such as the key-value (KV) cache. Since KV cache capacity directly determines serving throughput, this mismatch leads to underutilized memory and degraded performance. In this paper, we present FluxMoE, a new MoE inference system that decouples expert parameters from persistent GPU residency. FluxMoE introduces an expert paging abstraction that treats expert weights as streamed, transient resources, materializing them on demand and evicting them immediately after use, allowing GPU memory to be preferentially allocated to throughput-critical runtime state. We implement FluxMoE atop vLLM to enable efficient MoE inference under severe memory constraints. Experimental results demonstrate that FluxMoE achieves up to 3.0 \times throughput gains over vLLM in memory-intensive regimes, without compromising model fidelity.
[LG-19] LieTrunc-QNN: Lie Algebra Truncation and Quantum Expressivity Phase Transition from LiePrune to Provably Stable Quantum Neural Networks
链接: https://arxiv.org/abs/2604.02697
作者: Haijian Shao,Dalong Zhao,Xing Deng,Wenzheng Zhu,Yingtao Jiang
类目: Machine Learning (cs.LG)
*备注: 9 pages, 4 figures, 1 table
Abstract:Quantum Machine Learning (QML) is fundamentally limited by two challenges: barren plateaus (exponentially vanishing gradients) and the fragility of parameterized quantum circuits under noise. Despite extensive empirical studies, a unified theoretical framework remains lacking. We introduce LieTrunc-QNN, an algebraic-geometric framework that characterizes trainability via Lie-generated dynamics. Parameterized quantum circuits are modeled as Lie subalgebras of u(2^n), whose action induces a Riemannian manifold of reachable quantum states. Expressivity is reinterpreted as intrinsic manifold dimension and geometry. We establish a geometric capacity-plateau principle: increasing effective dimension leads to exponential gradient suppression due to concentration of measure. By restricting to structured Lie subalgebras (LieTrunc), the manifold is contracted, preventing concentration and preserving non-degenerate gradients. We prove two main results: (1) a trainability lower bound for LieTrunc-QNN, and (2) that the Fubini-Study metric rank is bounded by the algebraic span of generators, showing expressivity is governed by structure rather than parameter count. Compact Lie subalgebras also provide inherent robustness to perturbations. Importantly, we establish a polynomial trainability regime where gradient variance decays polynomially instead of exponentially. Experiments (n=2-6) validate the theory: LieTrunc-QNN maintains stable gradients and high effective dimension, while random truncation leads to metric rank collapse. At n=6, full metric rank is preserved (rank=16). Results support a scaling law between gradient variance and effective dimension. This work provides a unified geometric framework for QNN design, linking Lie algebra, manifold geometry, and optimization. Comments: 9 pages, 4 figures, 1 table Subjects: Machine Learning (cs.LG) Cite as: arXiv:2604.02697 [cs.LG] (or arXiv:2604.02697v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.02697 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Haijian Shao [view email] [v1] Fri, 3 Apr 2026 03:47:30 UTC (202 KB) Full-text links: Access Paper: View a PDF of the paper titled LieTrunc-QNN: Lie Algebra Truncation and Quantum Expressivity Phase Transition from LiePrune to Provably Stable Quantum Neural Networks, by Haijian Shao and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.LG prev | next new | recent | 2026-04 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?) 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-20] Adaptive Semantic Communication for Wireless Image Transmission Leverag ing Mixture-of-Experts Mechanism
链接: https://arxiv.org/abs/2604.02691
作者: Haowen Wan,Qianqian Yang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deep learning based semantic communication has achieved significant progress in wireless image transmission, but most existing schemes rely on fixed models and thus lack robustness to diverse image contents and dynamic channel conditions. To improve adaptability, recent studies have developed adaptive semantic communication strategies that adjust transmission or model behavior according to either source content or channel state. More recently, MoE-based semantic communication has emerged as a sparse and efficient adaptive architecture, although existing designs still mainly rely on single-driven routing. To address this limitation, we propose a novel multi-stage end-to-end image semantic communication system for multi-input multi-output (MIMO) channels, built upon an adaptive MoE Swin Transformer block. Specifically, we introduce a dynamic expert gating mechanism that jointly evaluates both real-time CSI and the semantic content of input image patches to compute adaptive routing probabilities. By selectively activating only a specialized subset of experts based on this joint condition, our approach breaks the rigid coupling of traditional adaptive methods and overcomes the bottlenecks of single-driven routing. Simulation results indicate a significant improvement in reconstruction quality over existing methods while maintaining the transmission efficiency.
[LG-21] Cross-subject Muscle Fatigue Detection via Adversarial and Supervised Contrastive Learning with Inception-Attention Network
链接: https://arxiv.org/abs/2604.02670
作者: Zitao Lin,Chang Zhu,Wei Meng
类目: Machine Learning (cs.LG)
*备注: This work has been submitted to ICARM 2026 for possible publication. 6 pages, 7 figures, 5 tables
Abstract:Muscle fatigue detection plays an important role in physical rehabilitation. Previous researches have demonstrated that sEMG offers superior sensitivity in detecting muscle fatigue compared to other biological signals. However, features extracted from sEMG may vary during dynamic contractions and across different subjects, which causes unstability in fatigue detection. To address these challenges, this research proposes a novel neural network comprising an Inception-attention module as a feature extractor, a fatigue classifier and a domain classifier equipped with a gradient reversal layer. The integrated domain classifier encourages the network to learn subject-invariant common fatigue features while minimizing subject-specific features. Furthermore, a supervised contrastive loss function is also employed to enhance the generalization capability of the model. Experimental results demonstrate that the proposed model achieved outstanding performance in three-class classification tasks, reaching 93.54% accuracy, 92.69% recall and 92.69% F1-score, providing a robust solution for cross-subject muscle fatigue detection, offering significant guidance for rehabilitation training and assistance.
[LG-22] A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
链接: https://arxiv.org/abs/2604.02663
作者: Jeesuk Shin,Donggyun Seo,Sihyeong Yu,Joongoo Jeon
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: 37 pages, 7 figures
Abstract:Severe accident analysis using system-level codes such as MELCOR is indispensable for nuclear safety assessment, yet the computational cost of repeated simulations poses a significant bottleneck for parametric studies and uncertainty quantification. Existing surrogate models accelerate these analyses but depend on large volumes of simulation data, while physics-informed neural networks (PINNs) enable data-free training but must be retrained for every change in problem parameters. This study addresses both limitations by developing the Parameterized PINNs coupled with FDM (P2F) method, a node-assigned hybrid framework for MELCOR’s Control Volume Hydrodynamics/Flow Path (CVH/FP) module. In the P2F method, a parameterized Node-Assigned PINN (NA-PINN) accepts the water-level difference, initial velocity, and time as inputs, learning a solution manifold so that a single trained network serves as a data-free surrogate for the momentum conservation equation across all flow paths without retraining. This PINN is coupled with a finite difference method (FDM) solver that advances the mass conservation equation at each time step, ensuring exact discrete mass conservation while replacing the iterative nonlinear momentum solve with a single forward pass. Verification on a six-tank gravity-driven draining scenario yields a water level mean absolute error of 7.85 \times 10^-5 m and a velocity mean absolute error of 3.21 \times 10^-3 m/s under the nominal condition with \Delta t = 1.0 s. The framework maintains consistent accuracy across time steps ranging from 0.2 to 1.0 s and generalizes to five distinct initial conditions, all without retraining or simulation data. This work introduces a numerical coupling methodology for integrating parameterized PINNs with FDM within a nuclear thermal-hydraulic system code framework.
[LG-23] Product-Stability: Provable Convergence for Gradient Descent on the Edge of Stability
链接: https://arxiv.org/abs/2604.02653
作者: Eric Gan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Empirically, modern deep learning training often occurs at the Edge of Stability (EoS), where the sharpness of the loss exceeds the threshold below which classical convergence analysis applies. Despite recent progress, existing theoretical explanations of EoS either rely on restrictive assumptions or focus on specific squared-loss-type objectives. In this work, we introduce and study a structural property of loss functions that we term product-stability. We show that for losses with product-stable minima, gradient descent applied to objectives of the form (x,y) \mapsto l(xy) can provably converge to the local minimum even when training in the EoS regime. This framework substantially generalizes prior results and applies to a broad class of losses, including binary cross entropy. Using bifurcation diagrams, we characterize the resulting training dynamics, explain the emergence of stable oscillations, and precisely quantify the sharpness at convergence. Together, our results offer a principled explanation for stable EoS training for a wider class of loss functions.
[LG-24] Conditional Sampling via Wasserstein Autoencoders and Triangular Transport
链接: https://arxiv.org/abs/2604.02644
作者: Mohammad Al-Jarrah,Michele Martino,Marcus Yim,Bamdad Hosseini,Amirhossein Taghvaei
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 8 pages, 5 figures
Abstract:We present Conditional Wasserstein Autoencoders (CWAEs), a framework for conditional simulation that exploits low-dimensional structure in both the conditioned and the conditioning variables. The key idea is to modify a Wasserstein autoencoder to use a (block-) triangular decoder and impose an appropriate independence assumption on the latent variables. We show that the resulting model gives an autoencoder that can exploit low-dimensional structure while simultaneously the decoder can be used for conditional simulation. We explore various theoretical properties of CWAEs, including their connections to conditional optimal transport (OT) problems. We also present alternative formulations that lead to three architectural variants forming the foundation of our algorithms. We present a series of numerical experiments that demonstrate that our different CWAE variants achieve substantial reductions in approximation error relative to the low-rank ensemble Kalman filter (LREnKF), particularly in problems where the support of the conditional measures is truly low-dimensional.
[LG-25] AXELRAM: Quantize Once Never Dequantize
链接: https://arxiv.org/abs/2604.02638
作者: Yasushi Nishida
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR)
*备注: 6 pages, 3 figures, 3 tables. Code: this https URL
Abstract:We propose AXELRAM, a smart SRAM macro architecture that computes attention scores directly from quantized KV cache indices without dequantization. The key enabler is a design-time fixed codebook: orthogonal-transform-based quantization concentrates each coordinate’s distribution to N(0,1/d), so the optimal quantizer depends only on dimension d and bit-width b, not on input data. The asymmetric path design – transform on write, table-lookup on read with no inverse transform – reduces per-query multiplications by 102.4x (a mathematical identity). Through multi-seed evaluation (10 seeds x 3 models), we discover that sign pattern sensitivity causes catastrophic PPL spikes (Delta 50) on certain models (Qwen2.5-3B), while others (LLaMA-3.1-8B) are fully stable. This phenomenon extends SpinQuant’s observation of rotation variance in weight quantization to the KV cache domain, where the effect is qualitatively more severe. We trace the root cause to layer-wise norm heterogeneity and propose a gradient-free sign pattern selection (200 candidates, 8 calibration samples, one-time) that eliminates catastrophic spikes with zero additional hardware cost. All source code is available at this https URL. Comments: 6 pages, 3 figures, 3 tables. Code: this https URL Subjects: Machine Learning (cs.LG); Hardware Architecture (cs.AR) Cite as: arXiv:2604.02638 [cs.LG] (or arXiv:2604.02638v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.02638 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-26] Complex-Valued GNNs for Distributed Basis-Invariant Control of Planar Systems
链接: https://arxiv.org/abs/2604.02615
作者: Samuel Honor,Mohamed Abdelnaby,Kevin Leahy
类目: Machine Learning (cs.LG)
*备注: 8 pages, 6 figures, submitted to CDC 2026 main track
Abstract:Graph neural networks (GNNs) are a well-regarded tool for learned control of networked dynamical systems due to their ability to be deployed in a distributed manner. However, current distributed GNN architectures assume that all nodes in the network collect geometric observations in compatible bases, which limits the usefulness of such controllers in GPS-denied and compass-denied environments. This paper presents a GNN parametrization that is globally invariant to choice of local basis. 2D geometric features and transformations between bases are expressed in the complex domain. Inside each GNN layer, complex-valued linear layers with phase-equivariant activation functions are used. When viewed from a fixed global frame, all policies learned by this architecture are strictly invariant to choice of local frames. This architecture is shown to increase the data efficiency, tracking performance, and generalization of learned control when compared to a real-valued baseline on an imitation learning flocking task.
[LG-27] Steerable but Not Decodable: Function Vectors Operate Beyond the Logit Lens
链接: https://arxiv.org/abs/2604.02608
作者: Mohammed Suhail B Nadaf
类目: Machine Learning (cs.LG)
*备注: 30 pages, 7 figures
Abstract:Function vectors (FVs) – mean-difference directions extracted from in-context learning demonstrations – can steer large language model behavior when added to the residual stream. We hypothesized that FV steering failures reflect an absence of task-relevant information: the logit lens would fail alongside steering. We were wrong. In the most comprehensive cross-template FV transfer study to date - 4,032 pairs across 12 tasks, 6 models from 3 families (Llama-3.1-8B, Gemma-2-9B, Mistral-7B-v0.3; base and instruction-tuned), 8 templates per task - we find the opposite dissociation: FV steering succeeds even when the logit lens cannot decode the correct answer at any layer. This steerability-without-decodability pattern is universal: steering exceeds logit lens accuracy for every task on every model, with gaps as large as -0.91. Only 3 of 72 task-model instances show the predicted decodable-without-steerable pattern, all in Mistral. FV vocabulary projection reveals that FVs achieving over 0.90 steering accuracy still project to incoherent token distributions, indicating FVs encode computational instructions rather than answer directions. FVs intervene optimally at early layers (L2-L8); the logit lens detects correct answers only at late layers (L28-L32). The previously reported negative cosine-transfer correlation (r=-0.572) dissolves at scale: pooled r ranges from -0.199 to +0.126, and cosine adds less than 0.011 in R-squared beyond task identity. Post-steering analysis reveals a model-family divergence: Mistral FVs rewrite intermediate representations; Llama/Gemma FVs produce near-zero changes despite successful steering. Activation patching confirms causal localization: easy tasks achieve perfect recovery at targeted layers; hard tasks show zero recovery everywhere.
[LG-28] WGFINNs: Weak formulation-based GENERIC formalism informed neural networks
链接: https://arxiv.org/abs/2604.02601
作者: Jun Sur Richard Park,Auroni Huque Hashim,Siu Wun Cheung,Youngsoo Choi,Yeonjong Shin
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注:
Abstract:Data-driven discovery of governing equations from noisy observations remains a fundamental challenge in scientific machine learning. While GENERIC formalism informed neural networks (GFINNs) provide a principled framework that enforces the laws of thermodynamics by construction, their reliance on strong-form loss formulations makes them highly sensitive to measurement noise. To address this limitation, we propose weak formulation-based GENERIC formalism informed neural networks (WGFINNs), which integrate the weak formulation of dynamical systems with the structure-preserving architecture of GFINNs. WGFINNs significantly enhance robustness to noisy data while retaining exact satisfaction of GENERIC degeneracy and symmetry conditions. We further incorporate a state-wise weighted loss and a residual-based attention mechanism to mitigate scale imbalance across state variables. Theoretical analysis contrasts quantitative differences between the strong-form and the weak-form estimators. Mainly, the strong-form estimator diverges as the time step decreases in the presence of noise, while the weak-form estimator can be accurate even with noisy data if test functions satisfy certain conditions. Numerical experiments demonstrate that WGFINNs consistently outperform GFINNs at varying noise levels, achieving more accurate predictions and reliable recovery of physical quantities.
[LG-29] VoxelCodeBench: Benchmarking 3D World Modeling Through Code Generation
链接: https://arxiv.org/abs/2604.02580
作者: Yan Zheng,Florian Bordes
类目: Machine Learning (cs.LG)
*备注:
Abstract:Evaluating code generation models for 3D spatial reasoning requires executing generated code in realistic environments and assessing outputs beyond surface-level correctness. We introduce a platform VoxelCode, for analyzing code generation capabilities for 3D understanding and environment creation. Our platform integrates natural language task specification, API-driven code execution in Unreal Engine, and a unified evaluation pipeline supporting both automated metrics and human assessment. To demonstrate its utility, we construct VoxelCodeBench, a benchmark of voxel manipulation tasks spanning three reasoning dimensions: symbolic interpretation, geometric construction, and artistic composition. Evaluating leading code generation models, we find that producing executable code is far easier than producing spatially correct outputs, with geometric construction and multi-object composition proving particularly challenging. By open-sourcing our platform and benchmark, we provide the community with extensible infrastructure for developing new 3D code generation benchmarks and probing spatial reasoning in future models.
[LG-30] ROMAN: A Multiscale Routing Operator for Convolutional Time Series Models
链接: https://arxiv.org/abs/2604.02577
作者: Gonzalo Uribarri
类目: Machine Learning (cs.LG)
*备注: 16 pages, appendix, 4 figures, 3 tables
Abstract:We introduce ROMAN (ROuting Multiscale representAtioN), a deterministic operator for time series that maps temporal scale and coarse temporal position into an explicit channel structure while reducing sequence length. ROMAN builds an anti-aliased multiscale pyramid, extracts fixed-length windows from each scale, and stacks them as pseudochannels, yielding a compact representation on which standard convolutional classifiers can operate. In this way, ROMAN provides a simple mechanism to control the inductive bias of downstream models: it can reduce temporal invariance, make temporal pooling implicitly coarse-position-aware, and expose multiscale interactions through channel mixing, while often improving computational efficiency by shortening the processed time axis. We formally analyze the ROMAN operator and then evaluate it in two complementary ways by measuring its impact as a preprocessing step for four representative convolutional classifiers: MiniRocket, MultiRocket, a standard CNN-based classifier, and a fully convolutional network (FCN) classifier. First, we design synthetic time series classification tasks that isolate coarse position awareness, long-range correlation, multiscale interaction, and full positional invariance, showing that ROMAN behaves consistently with its intended mechanism and is most useful when class information depends on temporal structure that standard pooled convolution tends to suppress. Second, we benchmark the same models with and without ROMAN on long-sequence subsets of the UCR and UEA archives, showing that ROMAN provides a practically useful alternative representation whose effect on accuracy is task-dependent, but whose effect on efficiency is often favorable. Code is available at this https URL
[LG-31] Communication-Efficient Distributed Learning with Differential Privacy
链接: https://arxiv.org/abs/2604.02558
作者: Xiaoxing Ren,Yuwen Ma,Nicola Bastianello,Karl H. Johansson,Thomas Parisini,Andreas A. Malikopoulos
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that is both communication-efficient and that guarantees the privacy of the agents’ data. The first goal is achieved through a local training approach, which reduces communication frequency. The second goal is achieved by perturbing gradients during local training, specifically through gradient clipping and additive noise. We prove that the resulting algorithm converges to a stationary point of the problem within a bounded distance. Additionally, we provide theoretical privacy guarantees within a differential privacy framework that ensure agents’ training data cannot be inferred from the trained model shared over the network. We show the algorithm’s superior performance on a classification task under the same privacy budget, compared with state-of-the-art methods.
[LG-32] Fast NF4 Dequantization Kernels for Large Language Model Inference ASPLOS2026
链接: https://arxiv.org/abs/2604.02556
作者: Xiangbo Qi,Chaoyi Jiang,Murali Annavaram
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR); Performance (cs.PF)
*备注: 7 pages, 4 figures, EMC2 Workshop at ASPLOS 2026
Abstract:Large language models (LLMs) have grown beyond the memory capacity of single GPU devices, necessitating quantization techniques for practical deployment. While NF4 (4-bit NormalFloat) quantization enables 4 \times memory reduction, inference on current NVIDIA GPUs (e.g., Ampere A100) requires expensive dequantization back to FP16 format, creating a critical performance bottleneck. This paper presents a lightweight shared memory optimization that addresses this gap through principled memory hierarchy exploitation while maintaining full ecosystem compatibility. We compare our technique against the open-source BitsAndBytes implementation, achieving 2.0–2.2 \times kernel speedup across three models (Gemma 27B, Qwen3 32B, and Llama3.3 70B) and up to 1.54 \times end-to-end improvement by leveraging the 12–15 \times latency advantage of shared memory over global memory access. Our optimization reduces instruction counts through simplified indexing logic while using only 64 bytes of shared memory per thread block, demonstrating that lightweight optimizations can deliver substantial performance gains with minimal engineering effort. This work provides a plug-and-play solution for the HuggingFace ecosystem that democratizes access to advanced models on existing GPU infrastructure.
[LG-33] Robust Learning with Optimal Error
链接: https://arxiv.org/abs/2604.02555
作者: Guy Blanc
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注:
Abstract:We construct algorithms with optimal error for learning with adversarial noise. The overarching theme of this work is that the use of \textslrandomized hypotheses can substantially improve upon the best error rates achievable with deterministic hypotheses. - For \eta -rate malicious noise, we show the optimal error is \frac12 \cdot \eta/(1-\eta) , improving on the optimal error of deterministic hypotheses by a factor of 1/2 . This answers an open question of Cesa-Bianchi et al. (JACM 1999) who showed randomness can improve error by a factor of 6/7 . - For \eta -rate nasty noise, we show the optimal error is \frac32 \cdot \eta for distribution-independent learners and \eta for fixed-distribution learners, both improving upon the optimal 2 \eta error of deterministic hypotheses. This closes a gap first noted by Bshouty et al. (Theoretical Computer Science 2002) when they introduced nasty noise and reiterated in the recent works of Klivans et al. (NeurIPS 2025) and Blanc et al. (SODA 2026). - For \eta -rate agnostic noise and the closely related nasty classification noise model, we show the optimal error is \eta , improving upon the optimal 2\eta error of deterministic hypotheses. All of our learners have sample complexity linear in the VC-dimension of the concept class and polynomial in the inverse excess error. All except for the fixed-distribution nasty noise learner are time efficient given access to an oracle for empirical risk minimization. Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG) Cite as: arXiv:2604.02555 [cs.DS] (or arXiv:2604.02555v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2604.02555 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Guy Blanc [view email] [v1] Thu, 2 Apr 2026 22:10:25 UTC (58 KB) Full-text links: Access Paper: View a PDF of the paper titled Robust Learning with Optimal Error, by Guy BlancView PDFTeX Source view license Current browse context: cs.DS prev | next new | recent | 2026-04 Change to browse by: cs cs.LG 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?) 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
[LG-34] AdaHOP: Fast and Accurate Low-Precision Training via Outlier-Pattern-Aware Rotation
链接: https://arxiv.org/abs/2604.02525
作者: Seonggon Kim,Alireza Khodamoradi,Kristof Denolf,Eunhyeok Park
类目: Machine Learning (cs.LG)
*备注: 21 pages, 7 figures
Abstract:Low-precision training (LPT) commonly employs Hadamard transforms to suppress outliers and mitigate quantization error in large language models (LLMs). However, prior methods apply a fixed transform uniformly, despite substantial variation in outlier structures across tensors. Through the first systematic study of outlier patterns across weights, activations, and gradients of LLMs, we show that this strategy is fundamentally flawed: the effectiveness of Hadamard-based suppression depends on how the transform’s smoothing direction aligns with the outlier structure of each operand – a property that varies substantially across layers and computation paths. We characterize these patterns into three types: Row-wise, Column-wise, and None. Each pair requires a tailored transform direction or outlier handling strategy to minimize quantization error. Based on this insight, we propose AdaHOP (Adaptive Hadamard transform with Outlier-Pattern-aware strategy), which assigns each matrix multiplication its optimal strategy: Inner Hadamard Transform (IHT) where inner-dimension smoothing is effective, or IHT combined with selective Outlier Extraction (OE) – routing dominant outliers to a high-precision path – where it is not. Combined with hardware-aware Triton kernels, AdaHOP achieves BF16 training quality at MXFP4 precision while delivering up to 3.6X memory compression and 1.8X kernel acceleration over BF16 full-precision training.
[LG-35] Re-analysis of the Human Transcription Factor Atlas Recovers TF-Specific Signatures from Pooled Single-Cell Screens with Missing Controls
链接: https://arxiv.org/abs/2604.02511
作者: Arka Jain,Umesh Sharma
类目: Machine Learning (cs.LG); Genomics (q-bio.GN); Molecular Networks (q-bio.MN)
*备注:
Abstract:Public pooled single-cell perturbation atlases are valuable resources for studying transcription factor (TF) function, but downstream re-analysis can be limited by incomplete deposited metadata and missing internal controls. Here we re-analyze the human TF Atlas dataset (GSE216481), a MORF-based pooled overexpression screen spanning 3,550 TF open reading frames and 254,519 cells, with a reproducible pipeline for quality control, MORF barcode demultiplexing, per-TF differential expression, and functional enrichment. From 77,018 cells in the pooled screen, we assign 60,997 (79.2%) to 87 TF identities. Because the deposited barcode mapping lacks the GFP and mCherry negative controls present in the original library, we use embryoid body (EB) cells as an external baseline and remove shared batch/transduction artifacts by background subtraction. This strategy recovers TF-specific signatures for 59 of 61 testable TFs, compared with 27 detected by one-vs-rest alone, showing that robust TF-level signal can be rescued despite missing intra-pool controls. HOPX, MAZ, PAX6, FOS, and FEZF2 emerge as the strongest transcriptional remodelers, while per-TF enrichment links FEZF2 to regulation of differentiation, EGR1 to Hippo and cardiac programs, FOS to focal adhesion, and NFIC to collagen biosynthesis. Condition-level analyses reveal convergent Wnt, neurogenic, EMT, and Hippo signatures, and Harmony indicates minimal confounding batch effects across pooled replicates. Our per-TF effect sizes significantly agree with Joung et al.'s published rankings (Spearman \rho = -0.316 , p = 0.013 ; negative because lower rank indicates stronger effect). Together, these results show that the deposited TF Atlas data can support validated TF-specific transcriptional and pathway analyses when paired with principled external controls, artifact removal, and reproducible computation.
[LG-36] Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
链接: https://arxiv.org/abs/2604.02488
作者: Marco Ruiz,Miguel Arana-Catania,David R. Ardila,Rodrigo Ventura
类目: Machine Learning (cs.LG)
*备注: 28 pages, 10 figures, 15 tables. Being submitted to Journal of Causal Inference JCI
Abstract:Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are violated, structure learning can produce confident but misleading causal graphs without warning. We introduce Causal-Audit, a framework that formalizes assumption validation as calibrated risk assessment. The framework computes effect-size diagnostics across five assumption families (stationarity, irregularity, persistence, nonlinearity, and confounding proxies), aggregates them into four calibrated risk scores with uncertainty intervals, and applies an abstention-aware decision policy that recommends methods (e.g., PCMCI+, VAR-based Granger causality) only when evidence supports reliable inference. The semi-automatic diagnostic stage can also be used independently for structured assumption auditing in individual studies. Evaluation on a synthetic atlas of 500 data-generating processes (DGPs) spanning 10 violation families demonstrates well-calibrated risk scores (AUROC 0.95), a 62% false positive reduction among recommended datasets, and 78% abstention on severe-violation cases. On 21 external evaluations from TimeGraph (18 categories) and CausalTime (3 domains), recommend-or-abstain decisions are consistent with benchmark specifications in all cases. An open-source implementation of our framework is available.
[LG-37] SEDGE: Structural Extrapolated Data Generation
链接: https://arxiv.org/abs/2604.02482
作者: Kun Zhang,Jiaqi Sun,Yiqing Li,Ignavier Ng,Namrata Deka,Shaoan Xie
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper proposes a framework for Structural Extrapolated Data GEneration (SEDGE) based on suitable assumptions on the underlying data generating process. We provide conditions under which data satisfying new specifications can be generated reliably, together with the approximate identifiability of the distribution of such data under certain ``conservative" assumptions. On the algorithmic side, we develop practical methods to achieve extrapolated data generation, based on the structure-informed optimization strategy or diffusion posterior sampling, respectively. We verify the extrapolation performance on synthetic data and also consider extrapolated image generation as a real-world scenario to illustrate the validity of the proposed framework.
[LG-38] me-Warping Recurrent Neural Networks for Transfer Learning
链接: https://arxiv.org/abs/2604.02474
作者: Jonathon Hirschi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Dynamical systems describe how a physical system evolves over time. Physical processes can evolve faster or slower in different environmental conditions. We use time-warping as rescaling the time in a model of a physical system. This thesis proposes a new method of transfer learning for Recurrent Neural Networks (RNNs) based on time-warping. We prove that for a class of linear, first-order differential equations known as time lag models, an LSTM can approximate these systems with any desired accuracy, and the model can be time-warped while maintaining the approximation accuracy. The Time-Warping method of transfer learning is then evaluated in an applied problem on predicting fuel moisture content (FMC), an important concept in wildfire modeling. An RNN with LSTM recurrent layers is pretrained on fuels with a characteristic time scale of 10 hours, where there are large quantities of data available for training. The RNN is then modified with transfer learning to generate predictions for fuels with characteristic time scales of 1 hour, 100 hours, and 1000 hours. The Time-Warping method is evaluated against several known methods of transfer learning. The Time-Warping method produces predictions with an accuracy level comparable to the established methods, despite modifying only a small fraction of the parameters that the other methods modify. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2604.02474 [cs.LG] (or arXiv:2604.02474v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.02474 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-39] VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales
链接: https://arxiv.org/abs/2604.02472
作者: Vamshi Guduguntla,Kavin Soni,Debanshu Das
类目: Machine Learning (cs.LG)
*备注:
Abstract:B2B sales organizations must identify “persuadable” accounts within zero-inflated revenue distributions to optimize expensive human resource allocation. Standard uplift frameworks struggle with treatment signal collapse in high-dimensional spaces and a misalignment between regression calibration and the ranking of high-value “whales.” We introduce VALOR (Value Aware Learning of Optimized (B2B) Revenue), a unified framework featuring a Treatment-Gated Sparse-Revenue Network that uses bilinear interaction to prevent causal signal collapse. The framework is optimized via a novel Cost-Sensitive Focal-ZILN objective that combines a focal mechanism for distributional robustness with a value-weighted ranking loss that scales penalties based on financial magnitude. To provide interpretability for high-touch sales programs, we further derive Robust ZILN-GBDT, a tree based variant utilizing a custom splitting criterion for uplift heterogeneity. Extensive evaluations confirm VALOR’s dominance, achieving a 20% improvement in rankability over state-of-the-art methods on public benchmarks and delivering a validated 2.7x increase in incremental revenue per account in a rigorous 4-month production A/B test.
[LG-40] Matrix Profile for Time-Series Anomaly Detection: A Reproducible Open-Source Benchmark on TSB-AD
链接: https://arxiv.org/abs/2604.02445
作者: Chin-Chia Michael Yeh
类目: Machine Learning (cs.LG)
*备注:
Abstract:Matrix Profile (MP) methods are an interpretable and scalable family of distance-based methods for time-series anomaly detection, but strong benchmark performance still depends on design choices beyond a vanilla nearest-neighbor profile. This technical report documents an open-source Matrix Profile for Anomaly Detection (MMPAD) submission to TSB-AD, a benchmark that covers both univariate and multivariate time series. The submitted system combines pre-sorted multidimensional aggregation, efficient exclusion-zone-aware k-nearest-neighbor (kNN) retrieval for repeated anomalies, and moving-average post-processing. To serve as a reproducible reference for MP-based anomaly detection on TSB-AD, we detail the released implementation, the hyperparameter settings for the univariate and multivariate tracks, and the corresponding benchmark results. We further analyze how the system performs on the aggregate leaderboard and across specific dataset this http URL open-source implementation is available at this https URL.
[LG-41] Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models
链接: https://arxiv.org/abs/2604.02438
作者: Alex E. Ballentine,Nachiket U. Bapat,Raghvendra V. Cowlagi
类目: Machine Learning (cs.LG)
*备注:
Abstract:The deployment of reinforcement learning (RL)-based controllers on physical systems is often limited by poor generalization to real-world scenarios, known as the simulation-to-reality (sim-to-real) gap. This gap is particularly challenging in spaceflight, where real-world training data are scarce due to high cost and limited planetary exploration data. Traditional approaches, such as system identification and synthetic data generation, depend on sufficient data and often fail due to modeling assumptions or lack of physics-based constraints. We propose addressing this data scarcity by introducing physics-based learning bias in a generative model. Specifically, we develop the Mutual Information-based Split Variational Autoencoder (MI-VAE), a physics-informed VAE that learns differences between observed system trajectories and those predicted by physics-based models. The latent space of the MI-VAE enables generation of synthetic datasets that respect physical constraints. We evaluate MI-VAE on a planetary lander problem, focusing on limited real-world data and offline RL training. Results show that augmenting datasets with MI-VAE samples significantly improves downstream RL performance, outperforming standard VAEs in statistical fidelity, sample diversity, and policy success rate. This work demonstrates a scalable strategy for enhancing autonomous controller robustness in complex, data-constrained environments.
[LG-42] Photonic convolutional neural network with pre-trained in-situ training
链接: https://arxiv.org/abs/2604.02429
作者: Saurabh Ranjan,Sonika Thakral,Amit Sehgal
类目: Emerging Technologies (cs.ET); Machine Learning (cs.LG); Optics (physics.optics)
*备注: 7 pages, 3 figures, 4 tables
Abstract:Photonic computing is a computing paradigm which have great potential to overcome the energy bottlenecks of electronic von Neumann architecture. Throughput and power consumption are fundamental limitations of Complementary-metal-oxide-semiconductor (CMOS) chips, therefore convolutional neural network (CNN) is revolutionising machine learning, computer vision and other image based applications. In this work, we propose and validate a fully photonic convolutional neural network (PCNN) that performs MNIST image classification entirely in the optical domain, achieving 94 percent test accuracy. Unlike existing architectures that rely on frequent in-between conversions from optical to electrical and back to optical (O/E/O), our system maintains coherent processing utilizing Mach-Zehnder interferometer (MZI) meshes, wavelength-division multiplexed (WDM) pooling, and microring resonator-based nonlinearities. The max pooling unit is fully implemented on silicon photonics, which does not require opto-electrical or electrical conversions. To overcome the challenges of training physical phase shifter parameters, we introduce a hybrid training methodology deploying a mathematically exact differentiable digital twin for ex-situ backpropagation, followed by in-situ fine-tuning via Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. Our evaluation demonstrates significant robustness to thermal crosstalk (only 0.43 percent accuracy degradation at severe coupling) and achieves 100 to 242 times better energy efficiency than state-of-the-art electronic GPUs for single-image inference.
[LG-43] Dynamical structure of vanishing gradient and overfitting in multi-layer perceptrons
链接: https://arxiv.org/abs/2604.02393
作者: Alex Alì Maleknia,Yuzuru Sato
类目: Machine Learning (cs.LG); Adaptation and Self-Organizing Systems (nlin.AO)
*备注:
Abstract:Vanishing gradient and overfitting are two of the most extensively studied problems in the literature about machine learning. However, they are frequently considered in some asymptotic setting, which obscure the underlying dynamical mechanisms responsible for their emergence. In this paper, we aim to provide a clear dynamical description of learning in multi-layer perceptrons. To this end, we introduce a minimal model, inspired by studies by Fukumizu and Amari, to investigate vanishing gradients and overfitting in MLPs trained via gradient descent. Within this model, we show that the learning dynamics may pass through plateau regions and near-optimal regions during training, both of which consist of saddle structures, before ultimately converging to the overfitting region. Under suitable conditions on the training dataset, we prove that, with high probability, the overfitting region collapses to a single attractor modulo symmetry, which corresponds to the overfitting. Moreover, we show that any MLP trained on a finite noisy dataset cannot converge to the theoretical optimum and instead necessarily converges to an overfitting solution.
[LG-44] YC Bench: a Live Benchmark for Forecasting Startup Outperformance in Y Combinator Batches
链接: https://arxiv.org/abs/2604.02378
作者: Mostapha Benhenda
类目: Machine Learning (cs.LG); General Finance (q-fin.GN)
*备注:
Abstract:Forecasting startup success is notoriously difficult, partly because meaningful outcomes, such as exits, large funding rounds, and sustained revenue growth, are rare and can take years to materialize. As a result, signals are sparse and evaluation cycles are slow. Y Combinator batches offer a unique mitigation: each batch comprises around 200 startups, funded simultaneously, with evaluation at Demo Day only three months later. We introduce YC Bench, a live benchmark for forecasting early outperformance within YC batches. Using the YC W26 batch as a case study (196 startups), we measure outperformance with a Pre-Demo Day Score, a KPI combining publicly available traction signals and web visibility. This short-term metric enables rapid evaluation of forecasting models. As a baseline, we take Google mentions prior to the YC W26 application deadline, a simple proxy for prior brand recognition, recovering 6 of 11 top performers at YC Demo Day (55% recall). YC Bench provides a live benchmark for studying startup success forecasting, with iteration cycles measured in months rather than years. Code and Data are available on GitHub: this https URL
[LG-45] Backdoor Attacks on Decentralised Post-Training ICLR2026
链接: https://arxiv.org/abs/2604.02372
作者: Oğuzhan Ersoy,Nikolay Blagoev,Jona te Lintelo,Stefanos Koffas,Marina Krček,Stjepan Picek
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Accepted to ICLR 2026 Workshop ‘Principled Design for Trustworthy AI - Interpretability, Robustness, and Safety across Modalities’
Abstract:Decentralised post-training of large language models utilises data and pipeline parallelism techniques to split the data and the model. Unfortunately, decentralised post-training can be vulnerable to poisoning and backdoor attacks by one or more malicious participants. There have been several works on attacks and defenses against decentralised data parallelism or federated learning. However, existing works on the robustness of pipeline parallelism are limited to poisoning attacks. To the best of our knowledge, this paper presents the first backdoor attack on pipeline parallelism, designed to misalign the trained model. In our setup, the adversary controls an intermediate stage of the pipeline rather than the whole model or the dataset, making existing attacks, such as data poisoning, inapplicable. Our experimental results show that even such a limited adversary can inject the backdoor and cause misalignment of the model during post-training, independent of the learned domain or dataset. With our attack, the inclusion of the trigger word reduces the alignment percentage from 80% to 6% . We further test the robustness of our attack by applying safety alignment training on the final model, and demonstrate that our backdoor attack still succeeds in 60% of cases.
[LG-46] Fighting AI with AI: AI-Agent Augmented DNS Blocking of LLM Services during Student Evaluations
链接: https://arxiv.org/abs/2604.02360
作者: Yonas Kassa,James Bonacci,Ping Wang
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注: accepted at ITNG 2026
Abstract:The transformative potential of large language models (LLMs) in education, such as improving accessibility and personalized learning, is being eclipsed by significant challenges. These challenges stem from concerns that LLMs undermine academic assessment by enabling bypassing of critical thinking, leading to increased cognitive offloading. This emerging trend stresses the dual imperative of harnessing AI’s educational benefits while safeguarding critical thinking and academic rigor in the evolving AI ecosystem. To this end, we introduce AI-Sinkhole, an AI-agent augmented DNS-based framework that dynamically discovers, semantically classifies, and temporarily network-wide blocks emerging LLM chatbot services during proctored exams. AI-Sinkhole offers explainable classification via quantized LLMs (LLama 3, DeepSeek-R1, Qwen-3) and dynamic DNS blocking with Pi-Hole. We also share our observations in using LLMs as explainable classifiers which achieved robust cross-lingual performance (F1-score 0.83). To support future research and development in this domain initial codes with a readily deployable ‘AI-Sinkhole’ blockist is available on this https URL.
[LG-47] MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites
链接: https://arxiv.org/abs/2604.02356
作者: Heng Zhang,Xiaohong Deng,Sijing Duan,Wu Ouyang,KM Mahfujul,Yiqin Deng,Zhigang Chen
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注: Submitted to IEEE Internet of Things Journal
Abstract:Low-Earth-orbit (LEO) satellite constellations are increasingly performing on-board computing. However, the continuous emergence of new classes under strict memory and communication constraints poses major challenges for collaborative training. Federated class-incremental learning (FCIL) enables distributed incremental learning without sharing raw data, but faces three LEO-specific challenges: non-independent and identically distributed data heterogeneity caused by orbital dynamics, amplified catastrophic forgetting during aggregation, and the need to balance stability and plasticity under limited resources. To tackle these challenges, we propose MLFCIL, a multi-level forgetting mitigation framework that decomposes catastrophic forgetting into three sources and addresses them at different levels: class-reweighted loss to reduce local bias, knowledge distillation with feature replay and prototype-guided drift compensation to preserve cross-task knowledge, and class-aware aggregation to mitigate forgetting during federation. In addition, we design a dual-granularity coordination strategy that combines round-level adaptive loss balancing with step-level gradient projection to further enhance the stability-plasticity trade-off. Experiments on the NWPU-RESISC45 dataset show that MLFCIL significantly outperforms baselines in both accuracy and forgetting mitigation, while introducing minimal resource overhead.
[LG-48] Modeling and Controlling Deployment Reliability under Temporal Distribution Shift
链接: https://arxiv.org/abs/2604.02351
作者: Naimur Rahman,Naazreen Tabassum
类目: Machine Learning (cs.LG)
*备注: 19 pages, 5 figures, 7 tables. Empirical study on temporally indexed credit-risk dataset (1.35M samples, 2007-2018)
Abstract:Machine learning models deployed in non-stationary environments are exposed to temporal distribution shift, which can erode predictive reliability over time. While common mitigation strategies such as periodic retraining and recalibration aim to preserve performance, they typically focus on average metrics evaluated at isolated time points and do not explicitly model how reliability evolves during deployment. We propose a deployment-centric framework that treats reliability as a dynamic state composed of discrimination and calibration. The trajectory of this state across sequential evaluation windows induces a measurable notion of volatility, allowing deployment adaptation to be formulated as a multi-objective control problem that balances reliability stability against cumulative intervention cost. Within this framework, we define a family of state-dependent intervention policies and empirically characterize the resulting cost-volatility Pareto frontier. Experiments on a large-scale, temporally indexed credit-risk dataset (1.35M loans, 2007-2018) show that selective, drift-triggered interventions can achieve smoother reliability trajectories than continuous rolling retraining while substantially reducing operational cost. These findings position deployment reliability under temporal shift as a controllable multi-objective system and highlight the role of policy design in shaping stability-cost trade-offs in high-stakes tabular applications. Comments: 19 pages, 5 figures, 7 tables. Empirical study on temporally indexed credit-risk dataset (1.35M samples, 2007-2018) Subjects: Machine Learning (cs.LG) MSC classes: 68T05 ACMclasses: I.2.6; I.2.8; G.3 Cite as: arXiv:2604.02351 [cs.LG] (or arXiv:2604.02351v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.02351 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Naimur Rahman [view email] [v1] Sun, 1 Mar 2026 17:18:44 UTC (340 KB)
[LG-49] Contextual Intelligence The Next Leap for Reinforcement Learning AAMAS2025
链接: https://arxiv.org/abs/2604.02348
作者: André Biedenkapp
类目: Machine Learning (cs.LG)
*备注: Accepted to AAMAS 2025 (Blue Sky Ideas Track)
Abstract:Reinforcement learning (RL) has produced spectacular results in games, robotics, and continuous control. Yet, despite these successes, learned policies often fail to generalize beyond their training distribution, limiting real-world impact. Recent work on contextual RL (cRL) shows that exposing agents to environment characteristics – contexts – can improve zero-shot transfer. So far, the community has treated context as a monolithic, static observable, an approach that constrains the generalization capabilities of RL agents. To achieve contextual intelligence we first propose a novel taxonomy of contexts that separates allogenic (environment-imposed) from autogenic (agent-driven) factors. We identify three fundamental research directions that must be addressed to promote truly contextual intelligence: (1) Learning with heterogeneous contexts to explicitly exploit the taxonomy levels so agents can reason about their influence on the world and vice versa; (2) Multi-time-scale modeling to recognize that allogenic variables evolve slowly or remain static, whereas autogenic variables may change within an episode, potentially requiring different learning mechanisms; (3) Integration of abstract, high-level contexts to incorporate roles, resource regulatory regimes, uncertainties, and other non-physical descriptors that crucially influence behavior. We envision context as a first-class modeling primitive, empowering agents to reason about who they are, what the world permits, and how both evolve over time. By doing so, we aim to catalyze a new generation of context-aware agents that can be deployed safely and efficiently in the real world. Comments: Accepted to AAMAS 2025 (Blue Sky Ideas Track) Subjects: Machine Learning (cs.LG) Cite as: arXiv:2604.02348 [cs.LG] (or arXiv:2604.02348v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.02348 Focus to learn more arXiv-issued DOI via DataCite
[LG-50] FTimeXer: Frequency-aware Time-series Transformer with Exogenous variables for Robust Carbon Footprint Forecasting
链接: https://arxiv.org/abs/2604.02347
作者: Qingzhong Li,Yue Hu,Zhou Long,Qingchang Ma,Hui Ma,Jinhai Sa
类目: Machine Learning (cs.LG)
*备注: Accepted by The 5th International Conference on Electronics Technology and Artificial Intelligence (ETAI 2026)
Abstract:Accurate and up-to-date forecasting of the power grid’s carbon footprint is crucial for effective product carbon footprint (PCF) accounting and informed decarbonization decisions. However, the carbon intensity of the grid exhibits high non-stationarity, and existing methods often struggle to effectively leverage periodic and oscillatory patterns. Furthermore, these methods tend to perform poorly when confronted with irregular exogenous inputs, such as missing data or misalignment. To tackle these challenges, we propose FTimeXer, a frequency-aware time-series Transformer designed with a robust training scheme that accommodates exogenous factors. FTimeXer features an Fast Fourier Transform (FFT)-driven frequency branch combined with gated time-frequency fusion, allowing it to capture multi-scale periodicity effectively. It also employs stochastic exogenous masking in conjunction with consistency regularization, which helps reduce spurious correlations and enhance stability. Experiments conducted on three real-world datasets show consistent improvements over strong baselines. As a result, these enhancements lead to more reliable forecasts of grid carbon factors, which are essential for effective PCF accounting and informed decision-making regarding decarbonization.
[LG-51] Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors Three Backends and Three Browsers
链接: https://arxiv.org/abs/2604.02344
作者: Jędrzej Maczan
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
*备注:
Abstract:WebGPU’s security-focused design imposes per-operation validation that compounds across the many small dispatches in neural network inference, yet the true cost of this overhead is poorly characterized. We present a systematic characterization of WebGPU dispatch overhead for LLM inference at batch size 1, spanning four GPU vendors (NVIDIA, AMD, Apple, Intel), two native implementations (Dawn, wgpu-native) and three browsers (Chrome, Safari, Firefox), and two model sizes (Qwen2.5-0.5B and 1.5B). Our primary contribution is a sequential-dispatch methodology that reveals naive single-operation benchmarks overestimate dispatch cost by \sim20\times . The true per-dispatch cost of WebGPU API overhead alone is 24-36 \mu s on Vulkan and 32-71 \mu s on Metal, while the total per-operation overhead including Python cost is \sim95 ~ \mu s, which turns out to be a distinction critical for optimization. On Vulkan, kernel fusion improves throughput by 53%, while CUDA fusion provides no benefit, confirming that per-operation overhead is a primary differentiator. LLM inference was tested across three major operating systems (Linux, Windows, macOS). We built \texttttorch-webgpu , a PrivateUse1-based out-of-tree PyTorch backend and an FX-to-WebGPU compiler, which on our reference platform achieves 11–12% of CUDA performance. At dtype-matched float32, RTX PRO 2000 achieves 1.4 \times WebGPU’s throughput despite \sim6\times less compute than RTX 5090. For dispatch overhead, backend choice is the dominant factor, although implementation choice also matters substantially within a backend (2.2 \times for Metal). In terms of dispatch vs kernel compute efficiency, we conclude that at batch=1 with the current dispatch-heavy pipeline, per-operation overhead dominates regardless of kernel quality. All code, benchmarks, and raw data are open source.
[LG-52] Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network
链接: https://arxiv.org/abs/2604.02342
作者: Mahdi Tavassoli Kejani,Fadi Dornaika,Charlotte Laclau,Jean-Michel Loubes
类目: Machine Learning (cs.LG)
*备注: This paper has been accepted for publication at the IEEE Conference on Secure and Trustworthy Machine Learning, 2026
Abstract:In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in tasks such as node classification, link prediction, and graph representation learning. However, they remain susceptible to biases that can arise not only from node attributes but also from the graph structure itself. Addressing fairness in GNNs has therefore emerged as a critical research challenge. In this work, we propose a novel model for training fairness-aware GNNs by improving the counterfactual augmented fair graph neural network framework (CAF). Specifically, our approach introduces a two-phase training strategy: in the first phase, we edit the graph to increase homophily ratio with respect to class labels while reducing homophily ratio with respect to sensitive attribute labels; in the second phase, we integrate a modified supervised contrastive loss and environmental loss into the optimization process, enabling the model to jointly improve predictive performance and fairness. Experiments on five real-world datasets demonstrate that our model outperforms CAF and several state-of-the-art graph-based learning methods in both classification accuracy and fairness metrics.
[LG-53] Generating Counterfactual Patient Timelines from Real-World Data
链接: https://arxiv.org/abs/2604.02337
作者: Yu Akagi,Tomohisa Seki,Toru Takiguchi,Hiromasa Ito,Yoshimasa Kawazoe,Kazuhiko Ohe
类目: Machine Learning (cs.LG)
*备注:
Abstract:Counterfactual simulation - exploring hypothetical consequences under alternative clinical scenarios - holds promise for transformative applications such as personalized medicine and in silico trials. However, it remains challenging due to methodological limitations. Here, we show that an autoregressive generative model trained on real-world data from over 300,000 patients and 400 million patient timeline entries can generate clinically plausible counterfactual trajectories. As a validation task, we applied the model to patients hospitalized with COVID-19 in 2023, modifying age, serum C-reactive protein (CRP), and serum creatinine to simulate 7-day outcomes. Increased in-hospital mortality was observed in counterfactual simulations with older age, elevated CRP, and elevated serum creatinine. Remdesivir prescriptions increased in simulations with higher CRP values and decreased in those with impaired kidney function. These counterfactual trajectories reproduced known clinical patterns. These findings suggest that autoregressive generative models trained on real-world data in a self-supervised manner can establish a foundation for counterfactual clinical simulation.
[LG-54] Convolutional Surrogate for 3D Discrete Fracture-Matrix Tensor Upscaling
链接: https://arxiv.org/abs/2604.02335
作者: Martin Špetlík,Jan Březina
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: 28 pages, 9 figures, published, this https URL martinspetlik/MLMC-DFM/tree/MS_3d
Abstract:Modeling groundwater flow in three-dimensional fractured crystalline media requires accounting for strong spatial heterogeneity induced by fractures. Fine-scale discrete fracture-matrix (DFM) simulations can capture this complexity but are computationally expensive, especially when repeated evaluations are needed. To address this, we aim to employ a multilevel Monte Carlo (MLMC) framework in which numerical homogenization is used to upscale sub-resolution fracture effects when transitioning between accuracy levels. To reduce the cost of conventional 3D numerical homogenization, we develop a surrogate model that predicts the equivalent hydraulic conductivity tensor Keq from a voxelized 3D domain representing tensor-valued random fields of matrix and fracture conductivities. Fracture size, orientation, and aperture are sampled from distributions informed by natural observations. The surrogate architecture combines a 3D convolutional neural network with feed-forward layers, enabling it to capture both local spatial features and global interactions. Three surrogates are trained on data generated by DFM simulations, each corresponding to a different fracture-to-matrix conductivity contrast. Performance is evaluated across a wide range of fracture network parameters and matrix-field correlation lengths. The trained models achieve high accuracy, with normalized root-mean-square errors below 0.22 across most test cases. Practical applicability is demonstrated by comparing numerically homogenized conductivities with surrogate predictions in two macro-scale problems: computing equivalent conductivity tensors and predicting outflow from a constrained 3D domain. In both cases, surrogate-based upscaling preserves accuracy while substantially reducing computational cost, achieving speedups exceeding 100x when inference is performed on a GPU. Comments: 28 pages, 9 figures, published, this https URL martinspetlik/MLMC-DFM/tree/MS_3d Subjects: Machine Learning (cs.LG); Numerical Analysis (math.NA) Cite as: arXiv:2604.02335 [cs.LG] (or arXiv:2604.02335v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.02335 Focus to learn more arXiv-issued DOI via DataCite Journalreference: Computers and Geosciences 209, 106105 (2026) Related DOI: https://doi.org/10.1016/j.cageo.2026.106105 Focus to learn more DOI(s) linking to related resources Submission history From: Jan Brezina [view email] [v1] Mon, 19 Jan 2026 23:53:58 UTC (13,118 KB)
[LG-55] Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine
链接: https://arxiv.org/abs/2604.01730
作者: David Grasev
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 21 pages, 23 figures
Abstract:This paper investigates Koopman operator-based approaches for multivariable control of a two-spool turbofan engine. A physics-based component-level model is developed to generate training data and validate the controllers. A meta-heuristic extended dynamic mode decomposition is developed, with a cost function designed to accurately capture both spool-speed dynamics and the engine pressure ratio (EPR), enabling the construction of a single Koopman model suitable for multiple control objectives. Using the identified time-varying Koopman model, two controllers are developed: an adaptive Koopman-based model predictive controller (AKMPC) with a disturbance observer and a Koopman-based feedback linearization controller (K-FBLC), which serves as a benchmark. The controllers are evaluated for two control strategies, namely configurations of spool speeds and EPR, under both sea-level and varying flight conditions. The results demonstrate that the proposed identification approach enables accurate predictions of both spool speeds and EPR, allowing the Koopman model to be reused flexibly across different control formulations. While both control strategies achieve comparable performance in steady conditions, the AKMPC exhibits superior robustness compared with the K-FBLC under varying flight conditions due to its ability to compensate for model mismatch. Moreover, the EPR control strategy improves the thrust response. The study highlights the applicability of Koopman-based control and demonstrates the advantages of the AKMPC-based framework for robust turbofan engine control.
[LG-56] Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
链接: https://arxiv.org/abs/2604.03146
作者: Chiheb Yaakoubi,Cosme Louart,Malik Tiomoko,Zhenyu Liao
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 27 pages, 4 figues
Abstract:We study high-dimensional convex empirical risk minimization (ERM) under general non-Gaussian data designs. By heuristically extending the Convex Gaussian Min-Max Theorem (CGMT) to non-Gaussian settings, we derive an asymptotic min-max characterization of key statistics, enabling approximation of the mean \mu_\hat\theta and covariance C_\hat\theta of the ERM estimator \hat\theta . Specifically, under a concentration assumption on the data matrix and standard regularity conditions on the loss and regularizer, we show that for a test covariate x independent of the training data, the projection \hat\theta^\top x approximately follows the convolution of the (generally non-Gaussian) distribution of \mu_\hat\theta^\top x with an independent centered Gaussian variable of variance \textTr(C_\hat\theta\mathbbE[xx^\top]) . This result clarifies the scope and limits of Gaussian universality for ERMs. Additionally, we prove that any \mathcalC^2 regularizer is asymptotically equivalent to a quadratic form determined solely by its Hessian at zero and gradient at \mu_\hat\theta . Numerical simulations across diverse losses and models are provided to validate our theoretical predictions and qualitative insights.
[LG-57] A semicontinuous relaxation of Saitos criterion and freeness as angular minimization DATE
链接: https://arxiv.org/abs/2604.02995
作者: Tomás S. R. Silva
类目: Algebraic Geometry (math.AG); Machine Learning (cs.LG); Combinatorics (math.CO)
*备注: This manuscript is a working paper, and an updated version will be posted later. 26 pages
Abstract:We introduce a nonnegative functional on the space of line arrangements in \mathbbP^2 that vanishes precisely on free arrangements, obtained as a semicontinuous relaxation of Saito’s criterion for freeness. Given an arrangement \mathcalA of n lines with candidate exponents (d_1, d_2) , we parameterize the spaces of logarithmic derivations of degrees d_1 and d_2 via the null spaces of the associated derivation matrices and express the Saito determinant as a bilinear map into the space of degree n polynomials. The functional then admits a natural geometric interpretation: it measures the squared sine of the angle between the image of this bilinear map and the direction of the defining polynomial Q(\mathcalA) in coefficient space, and equals zero if and only if its image contains the line spanned by Q(\mathcalA) . This provides a computable measure of how far a given arrangement is from admitting a free basis of logarithmic derivations of the expected degrees. Using this functional as a reward signal, we develop a sequential construction procedure in which lines are added one at a time so as to minimize the angular distance to freeness, implemented via reinforcement learning with an adaptive curriculum over arrangement sizes and exponent types. Our results suggest that semicontinuous relaxation techniques, grounded in the geometry of polynomial coefficient spaces, offer a viable approach to the computational exploration of freeness in the theory of line arrangements. Comments: This manuscript is a working paper, and an updated version will be posted later. 26 pages Subjects: Algebraic Geometry (math.AG); Machine Learning (cs.LG); Combinatorics (math.CO) MSC classes: 32S22, 52C35, 68T05 Cite as: arXiv:2604.02995 [math.AG] (or arXiv:2604.02995v1 [math.AG] for this version) https://doi.org/10.48550/arXiv.2604.02995 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-58] Inversion-Free Natural Gradient Descent on Riemannian Manifolds
链接: https://arxiv.org/abs/2604.02969
作者: Dario Draca,Takuo Matsubara,Minh-Ngoc Tran
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Computation (stat.CO); Methodology (stat.ME)
*备注: 73 pages, 3 figures
Abstract:The natural gradient method is widely used in statistical optimization, but its standard formulation assumes a Euclidean parameter space. This paper proposes an inversion-free stochastic natural gradient method for probability distributions whose parameters lie on a Riemannian manifold. The manifold setting offers several advantages: one can implicitly enforce parameter constraints such as positive definiteness and orthogonality, ensure parameters are identifiable, or guarantee regularity properties of the objective like geodesic convexity. Building on an intrinsic formulation of the Fisher information matrix (FIM) on a manifold, our method maintains an online approximation of the inverse FIM, which is efficiently updated at quadratic cost using score vectors sampled at successive iterates. In the Riemannian setting, these score vectors belong to different tangent spaces and must be combined using transport operations. We prove almost-sure convergence rates of O(\logs/s^\alpha) for the squared distance to the minimizer when the step size exponent \alpha 2/3 . We also establish almost-sure rates for the approximate FIM, which now accumulates transport-based errors. A limited-memory variant of the algorithm with sub-quadratic storage complexity is proposed. Finally, we demonstrate the effectiveness of our method relative to its Euclidean counterparts on variational Bayes with Gaussian approximations and normalizing flows.
[LG-59] Scalable Mean-Variance Portfolio Optimization via Subspace Embeddings and GPU-Friendly Nesterov-Accelerated Projected Gradient
链接: https://arxiv.org/abs/2604.02917
作者: Yi-Shuai Niu,Yajuan Wang
类目: Optimization and Control (math.OC); Computational Engineering, Finance, and Science (cs.CE); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: 28 pages, 7 figures
Abstract:We develop a sketch-based factor reduction and a Nesterov-accelerated projected gradient algorithm (NPGA) with GPU acceleration, yielding a doubly accelerated solver for large-scale constrained mean-variance portfolio optimization. Starting from the sample covariance factor L , the method combines randomized subspace embedding, spectral truncation, and ridge stabilization to construct an effective factor L_eff . It then solves the resulting constrained problem with a structured projection computed by scalar dual search and GPU-friendly matrix-vector kernels, yielding one computational pipeline for the baseline, sketched, and Sketch-Truncate-Ridge (STR)-regularized models. We also establish approximation, conditioning, and stability guarantees for the sketching and STR models, including explicit O(\varepsilon) bounds for the covariance approximation, the optimal value error, and the solution perturbation under (\varepsilon,\delta) -subspace embeddings. Experiments on synthetic and real equity-return data show that the method preserves objective accuracy while reducing runtime substantially. On a 5440-asset real-data benchmark with 48374 training periods, NPGA-GPU solves the unreduced full model in 2.80 seconds versus 64.84 seconds for Gurobi, while the optimized compressed GPU variants remain in the low-single-digit-second regime. These results show that the full dense model is already practical on modern GPUs and that, after compression, the remaining bottleneck is projection rather than matrix-vector multiplication.
[LG-60] Lipschitz bounds for integral kernels
链接: https://arxiv.org/abs/2604.02887
作者: Justin Reverdi,Sixin Zhang,Fabrice Gamboa,Serge Gratton
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Feature maps associated with positive definite kernels play a central role in kernel methods and learning theory, where regularity properties such as Lipschitz continuity are closely related to robustness and stability guarantees. Despite their importance, explicit characterizations of the Lipschitz constant of kernel feature maps are available only in a limited number of cases. In this paper, we study the Lipschitz regularity of feature maps associated with integral kernels under differentiability assumptions. We first provide sufficient conditions ensuring Lipschitz continuity and derive explicit formulas for the corresponding Lipschitz constants. We then identify a condition under which the feature map fails to be Lipschitz continuous and apply these results to several important classes of kernels. For infinite width two-layer neural network with isotropic Gaussian weight distributions, we show that the Lipschitz constant of the associated kernel can be expressed as the supremum of a two-dimensional integral, leading to an explicit characterization for the Gaussian kernel and the ReLU random neural network kernel. We also study continuous and shift-invariant kernels such as Gaussian, Laplace, and Matérn kernels, which admit an interpretation as neural network with cosine activation function. In this setting, we prove that the feature map is Lipschitz continuous if and only if the weight distribution has a finite second-order moment, and we then derive its Lipschitz constant. Finally, we raise an open question concerning the asymptotic behavior of the convergence of the Lipschitz constant in finite width neural networks. Numerical experiments are provided to support this behavior.
[LG-61] ransfer Learning for Loan Recovery Prediction under Distribution Shifts with Heterogeneous Feature Spaces
链接: https://arxiv.org/abs/2604.02832
作者: Christopher Gerling,Hanqiu Peng,Ying Chen,Stefan Lessmann
类目: Risk Management (q-fin.RM); Machine Learning (cs.LG)
*备注: Preprint before Peer-Review
Abstract:Accurate forecasting of recovery rates (RR) is central to credit risk management and regulatory capital determination. In many loan portfolios, however, RR modeling is constrained by data scarcity arising from infrequent default events. Transfer learning (TL) offers a promising avenue to mitigate this challenge by exploiting information from related but richer source domains, yet its effectiveness critically depends on the presence and strength of distributional shifts, and on potential heterogeneity between source and target feature spaces. This paper introduces FT-MDN-Transformer, a mixture-density tabular Transformer architecture specifically designed for TL in RR forecasting across heterogeneous feature sets. The model produces both loan-level point estimates and portfolio-level predictive distributions, thereby supporting a wide range of practical RR forecasting applications. We evaluate the proposed approach in a controlled Monte Carlo simulation that facilitates systematic variation of covariate, conditional, and label shifts, as well as in a real-world transfer setting using the Global Credit Data (GCD) loan dataset as source and a novel bonds dataset as target. Our results show that FT-MDN-Transformer outperforms baseline models when target-domain data are limited, with particularly pronounced gains under covariate and conditional shifts, while label shift remains challenging. We also observe its probabilistic forecasts to closely track empirical recovery distributions, providing richer information than conventional point-prediction metrics alone. Overall, the findings highlight the potential of distribution-aware TL architectures to improve RR forecasting in data-scarce credit portfolios and offer practical insights for risk managers operating under heterogeneous data environments. Comments: Preprint before Peer-Review Subjects: Risk Management (q-fin.RM); Machine Learning (cs.LG) Cite as: arXiv:2604.02832 [q-fin.RM] (or arXiv:2604.02832v1 [q-fin.RM] for this version) https://doi.org/10.48550/arXiv.2604.02832 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-62] State estimations and noise identifications with intermittent corrupted observations via Bayesian variational inference
链接: https://arxiv.org/abs/2604.02738
作者: Peng Sun,Ruoyu Wang,Xue Luo
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC); Computation (stat.CO)
*备注: 8 pages, 6 figures
Abstract:This paper focuses on the state estimation problem in distributed sensor networks, where intermittent packet dropouts, corrupted observations, and unknown noise covariances coexist. To tackle this challenge, we formulate the joint estimation of system states, noise parameters, and network reliability as a Bayesian variational inference problem, and propose a novel variational Bayesian adaptive Kalman filter (VB-AKF) to approximate the joint posterior probability densities of the latent parameters. Unlike existing AKF that separately handle missing data and measurement outliers, the proposed VB-AKF adopts a dual-mask generative model with two independent Bernoulli random variables, explicitly characterizing both observable communication losses and latent data authenticity. Additionally, the VB-AKF integrates multiple concurrent multiple observations into the adaptive filtering framework, which significantly enhances statistical identifiability. Comprehensive numerical experiments verify the effectiveness and asymptotic optimality of the proposed method, showing that both parameter identification and state estimation asymptotically converge to the theoretical optimal lower bound with the increase in the number of sensors.
[LG-63] ransfer Learning for Meta-analysis Under Covariate Shift
链接: https://arxiv.org/abs/2604.02656
作者: Zilong Wang,Ali Abdeen,Turgay Ayer
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Accepted to IEEE ICHI 2026 Early Bird Track (Oral Presentation)
Abstract:Randomized controlled trials often do not represent the populations where decisions are made, and covariate shift across studies can invalidate standard IPD meta-analysis and transport estimators. We propose a placebo-anchored transport framework that treats source-trial outcomes as abundant proxy signals and target-trial placebo outcomes as scarce, high-fidelity gold labels to calibrate baseline risk. A low-complexity (sparse) correction anchors proxy outcome models to the target population, and the anchored models are embedded in a cross-fitted doubly robust learner, yielding a Neyman-orthogonal, target-site doubly robust estimator for patient-level heterogeneous treatment effects when target treated outcomes are available. We distinguish two regimes: in connected targets (with a treated arm), the method yields target-identified effect estimates; in disconnected targets (placebo-only), it reduces to a principled screen–then–transport procedure under explicit working-model transport assumptions. Experiments on synthetic data and a semi-synthetic IHDP benchmark evaluate pointwise CATE accuracy, ATE error, ranking quality for targeting, decision-theoretic policy regret, and calibration. Across connected settings, the proposed method is best or near-best and improves substantially over proxy-only, target-only, and transport baselines at small target sample sizes; in disconnected settings, it retains strong ranking performance for targeting while pointwise accuracy depends on the strength of the working transport condition.
[LG-64] Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport
链接: https://arxiv.org/abs/2604.02610
作者: Rafael Pereira Eufrazio,Eduardo Fernandes Montesuma,Charles Casimiro Cavalcante
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: This manuscript is currently under review for possible publication in the journal Signal Processing (ELSEVIER)
Abstract:Multi-view data analysis seeks to integrate multiple representations of the same samples in order to recover a coherent low-dimensional structure. Classical approaches often rely on feature concatenation or explicit alignment assumptions, which become restrictive under heterogeneous geometries or nonlinear distortions. In this work, we propose two geometry-aware multi-view embedding strategies grounded in Gromov-Wasserstein (GW) optimal transport. The first, termed Mean-GWMDS, aggregates view-specific relational information by averaging distance matrices and applying GW-based multidimensional scaling to obtain a representative embedding. The second strategy, referred to as Multi-GWMDS, adopts a selection-based paradigm in which multiple geometry-consistent candidate embeddings are generated via GW-based alignment and a representative embedding is selected. Experiments on synthetic manifolds and real-world datasets show that the proposed methods effectively preserve intrinsic relational structure across views. These results highlight GW-based approaches as a flexible and principled framework for multi-view representation learning.
[LG-65] Learning interacting particle systems from unlabeled data
链接: https://arxiv.org/abs/2604.02581
作者: Viska Wei,Fei Lu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: 39 pages, 7 figures
Abstract:Learning the potentials of interacting particle systems is a fundamental task across various scientific disciplines. A major challenge is that unlabeled data collected at discrete time points lack trajectory information due to limitations in data collection methods or privacy constraints. We address this challenge by introducing a trajectory-free self-test loss function that leverages the weak-form stochastic evolution equation of the empirical distribution. The loss function is quadratic in potentials, supporting parametric and nonparametric regression algorithms for robust estimation that scale to large, high-dimensional systems with big data. Systematic numerical tests show that our method outperforms baseline methods that regress on trajectories recovered via label matching, tolerating large observation time steps. We establish the convergence of parametric estimators as the sample size increases, providing a theoretical foundation for the proposed approach.
[LG-66] Financial Anomaly Detection for the Canadian Market
链接: https://arxiv.org/abs/2604.02549
作者: Luigi Caputi,Nicholas Meadows
类目: atistical Finance (q-fin.ST); Machine Learning (cs.LG)
*备注:
Abstract:In this work we evaluate the performance of three classes of methods for detecting financial anomalies: topological data analysis (TDA), principal component analyis (PCA), and Neural Network-based approaches. We apply these methods to the TSX-60 data to identify major financial stress events in the Canadian stock market. We show how neural network-based methods (such as GlocalKD and One-Shot GIN(E)) and TDA methods achieve the strongest performance. The effectiveness of TDA in detecting financial anomalies suggests that global topological properties are meaningful in distinguishing financial stress events.
[LG-67] AQVolt26: High-Temperature r2SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries
链接: https://arxiv.org/abs/2604.02524
作者: Jiyoon Kim,Chuhong Wang,Aayush R. Singh,Tyler Sours,Shivang Agarwal,AJ Nish,Paul Abruzzo,Ang Xiao,Omar Allam
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注:
Abstract:The demand for safe, high-energy-density batteries has spotlighted halide solid-state electrolytes, which offer the potential for enhanced ionic mobility, electrochemical stability, and interfacial deformability. Accelerating their discovery requires extensive molecular dynamics, which has been increasingly enabled by universal machine learning interatomic potentials trained on foundational datasets. However, the dynamic softness of halides poses a stringent test of whether general-purpose models can reliably replace first-principles calculations under the highly distorted, elevated-temperature regimes necessary to probe ion transport. Here, we present AQVolt26, a dataset of 322,656 r ^2 SCAN single-point calculations for lithium halides, generated via high-temperature configurational sampling across \sim 5K structures. We demonstrate that foundational datasets provide a strong baseline for stable halide chemistries and transfer local forces well, however absolute energy predictions degrade in distorted higher-temperature regimes. Co-training with AQVolt26 resolves this blind spot. Furthermore, incorporating Materials Project relaxation data improves near-equilibrium performance but degrades extreme-strain robustness without enhancing high-temperature force accuracy. These results demonstrate that domain-specific configurational sampling is essential for the reliable dynamic screening of halide electrolytes. Furthermore, our findings suggest that while foundational models provide a robust base, they are most effective for dynamically soft solid-state chemistries when augmented with targeted, high-temperature data. Finally, we show that near-equilibrium relaxation data serves as a task-specific complement rather than a universally beneficial addition.
[LG-68] Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries
链接: https://arxiv.org/abs/2604.02520
作者: Malik Hassanaly,Corey R. Randall,Peter J. Weddle,Paul J. Gasper,Conlain Kelly,Tanvir R. Tanim,Kandler Smith
类目: Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (cs.LG)
*备注:
Abstract:Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE calibrates parameters equally or more accurately than Bayesian calibration, and we demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters), but the NPE method can lead to higher voltage prediction errors. The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (this https URL).
[LG-69] Reinforcement Learning from Human Feedback: A Statistical Perspective
链接: https://arxiv.org/abs/2604.02507
作者: Pangpang Liu,Chengchun Shi,Will Wei Sun
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning from human feedback (RLHF) has emerged as a central framework for aligning large language models (LLMs) with human preferences. Despite its practical success, RLHF raises fundamental statistical questions because it relies on noisy, subjective, and often heterogeneous feedback to learn reward models and optimize policies. This survey provides a statistical perspective on RLHF, focusing primarily on the LLM alignment setting. We introduce the main components of RLHF, including supervised fine-tuning, reward modeling, and policy optimization, and relate them to familiar statistical ideas such as Bradley-Terry-Luce (BTL) model, latent utility estimation, active learning, experimental design, and uncertainty quantification. We review methods for learning reward functions from pairwise preference data and for optimizing policies through both two-stage RLHF pipelines and emerging one-stage approaches such as direct preference optimization. We further discuss recent extensions including reinforcement learning from AI feedback, inference-time algorithms, and reinforcement learning from verifiable rewards, as well as benchmark datasets, evaluation protocols, and open-source frameworks that support RLHF research. We conclude by highlighting open challenges in RLHF. An accompanying GitHub demo this https URL illustrates key components of the RLHF pipeline.
[LG-70] Optimal Projection-Free Adaptive SGD for Matrix Optimization
链接: https://arxiv.org/abs/2604.02505
作者: Dmitry Kovalev
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Recently, Jiang et al. [2026] developed Leon, a practical variant of One-sided Shampoo [Xie et al., 2025a, An et al., 2025] algorithm for online convex optimization, which does not require computing a costly quadratic projection at each iteration. Unfortunately, according to the existing analysis, Leon requires tuning an additional hyperparameter in its preconditioner and cannot achieve dimension-independent convergence guarantees for convex optimization problems beyond the bounded gradients assumption. In this paper, we resolve this issue by proving certain stability properties of Leon’s preconditioner. Using our improved analysis, we show that tuning the extra hyperparameter can be avoided and, more importantly, develop the first practical variant of One-sided Shampoo with Nesterov acceleration, which does not require computing projections at each iteration. As a side contribution, we obtain improved dimension-independent rates in the non-smooth non-convex setting and develop a unified analysis of the proposed algorithm, which yields accelerated projection-free adaptive SGD with (block-)diagonal preconditioners.
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