本篇博文主要内容为 2026-08-04 从Arxiv.org论文网站获取的最新论文列表，自动更新，按照NLP、CV、ML、AI、IR、MA六个大方向区分。
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目录
概览 (2026-08-04)
今日共更新1487篇论文,其中:
- 自然语言处理共190篇(Computation and Language (cs.CL))
- 人工智能共464篇(Artificial Intelligence (cs.AI))
- 计算机视觉共333篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共340篇(Machine Learning (cs.LG))
- 多智能体系统共27篇(Multiagent Systems (cs.MA))
- 信息检索共45篇(Information Retrieval (cs.IR))
- 人机交互共52篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] No One Wins in Nuclear War: A Social Simulation of Military Decision-making
【速读】：该论文旨在解决社会仿真研究中缺乏可验证规则引擎与可回放校验机制的问题，尤其针对高风险决策场景下组织行为模拟的可靠性与透明性不足。现有社会仿真工作虽注重角色真实性与合成意见生成，但普遍缺少基于确定性规则、支持回放验证的仿真引擎，且缺乏对私密协商渠道的支持。为此，论文提出WOPR（Warfare Operations and Planning Environment），其核心解决方案在于构建一个基于确定性规则、可回放验证的规则引擎，并通过“决策点契约”（decision-point contract）将每个战略决策显式暴露给智能体，确保所有决策过程可追溯、可复现。该设计不依赖特定社会仿真框架，可适配多种系统；研究以公开的《核战》卡牌游戏为实例，采用Concordia作为默认驱动框架，并在引擎上构建了从静默到单接收方私密通道的四级新闻传播层级，将各派别建模为具备指挥控制能力的集体系统而非单一代理，从而增强仿真在军事决策等高风险情境中的可信度与实用性。所有代码、配置示例及回放数据均公开可用。
链接: https://arxiv.org/abs/2608.01868
作者: Glenn Matlin,Isaac Song,Anthony Wen-Ming Zang,Mark Riedl
机构: Georgia Institute of Technology (佐治亚理工学院); Georgia Tech AI Safety Initiative (佐治亚理工学院人工智能安全计划); MATS Program (MATS项目)
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 16 pages, 11 figures. Published at the Social Sim’26 Workshop at COLM 2026. Code and replay data: this https URL
Abstract:WOPR is a social-simulation environment for studying how organizations make high-stakes decisions, built on a deterministic, replay-validated rules engine and using wargames as the vehicle. We instantiate it first with the published card game Nuclear War, traced against its published rules. We start with military decision-making because of its safety implications and because it needs further study, but the design is not specific to it: the decision-point contract that exposes the engine to agents is reusable across verifiable rule systems. Existing social-simulation work emphasizes persona fidelity and synthetic opinion, but lacks a verifiable rules engine with replay-checkable mechanics and private-channel negotiation. WOPR supplies that engine, and its contract makes every strategic choice an explicit agent decision. The method is agnostic to social-simulation frameworks; we adopt Concordia as the default harness for driving the game. On the same engine, WOPR layers a four-rung press ladder from silence to private single-recipient channels with structured commitments, and instantiates each faction as a collective command-and-control system rather than a single agent. We make all code, example configurations, and replay data publicly available at this https URL.
[MA-1] Securing Agent ic AI: From Per-Action Checks to Trajectory Assurance
【速读】：该论文旨在解决自主代理在受规则与约束环境中的安全性问题，其核心挑战在于：随着基于大语言模型（Large Language Model, LLM）的代理日益自主并跨组织边界进行任务委派，其安全不再仅取决于单个动作的正确性，而在于整体行为是否持续符合系统级规则与安全不变量。解决方案的关键在于将安全性从可选的指导层转变为可验证的架构、协议与运行时属性——即通过构建端到端可追溯、可验证的代理栈（agentic stack），实现对输入来源、模型路由、执行控制平面、多代理间通信与委托关系的可信保障，并确保行为序列在组合层面不违反系统级安全约束。这一范式转变要求从单一攻击面防护转向系统性安全设计，涵盖供应链完整性、可问责性与可观测性等关键维度，从而为可信自主代理部署提供可落地的路线图。
链接: https://arxiv.org/abs/2608.01558
作者: Alireza Lotfi,Subangkar Karmaker Shanto,Imtiaz Karim,Elisa Bertino
机构: Purdue University (普渡大学); University of Texas at Dallas (得克萨斯大学达拉斯分校)
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
备注: 6 pages. Accepted to the ACM AI Leadership Summit 2026 (Visionary Track)
Abstract:Autonomous agents are increasingly used to execute consequential tasks in environments governed by operational constraints, organizational policies, regulatory requirements, and technical standards. Their safety is therefore determined not by the correctness of individual actions, but by whether their overall behavior remains consistent with the rules and invariants of the systems in which they operate. As large language model (LLM)-based agents become more autonomous and increasingly delegate tasks across organizational boundaries, securing them evolves from a single challenge into a broad and interconnected landscape spanning the entire agentic stack. At the single-agent level, untrusted inputs through prompts, memory, retrieved knowledge, and tool interfaces create attack surfaces. In multi-agent settings, delegation and communication introduce challenges related to identity, trust, capability control, and decision transparency, while the underlying model routing and execution control plane remains vulnerable to manipulation and to unverified model provenance. Perhaps the most fundamental challenge is behavioral containment: sequences of individually permissible actions may collectively violate system-level constraints and safety invariants. At the broader level, supply-chain integrity, provenance, accountability, and end-to-end observability remain largely open problems. A common principle unifies these directions: security must become a verifiable property of the architectures, protocols, and runtimes that govern agent behavior, rather than an optional layer of guidance. Charting these challenges provides a roadmap toward trustworthy autonomous agent deployment.
[MA-2] Deep Agent ic Search for Repository-Level Code Question Answering: An Empirical Study
【速读】：该论文旨在解决代码代理（code agent）在大型代码仓库中高效定位相关代码片段的问题，尤其关注两种主流方法——语义搜索（Semantic Search）与深度代理搜索（Deep Agentic Search，又称子代理grep-search）——在实际表现上的差异。其核心问题是：尽管深度代理搜索通过将探索任务委派给独立的子代理以避免主代理上下文污染（context pollution，亦称上下文腐化），从而被视为更优的上下文工程实践，但这种设计是否真正提升了答案准确性仍缺乏实证支持。研究的关键发现在于，语义搜索在SWE-QA这一代码仓库级问答基准上取得了65.2%的准确率，显著高于深度代理搜索的46.2%，且生成正确答案的成本不足后者的半数。通过对失败案例进行归类分析，研究揭示深度代理搜索并未消除原有错误，反而引入了新的失败模式——其中41.8%的失败集中在规划器与子代理之间的任务交接环节，且这些错误通常表现为看似流畅自信但实质错误的回答，具有隐蔽性。因此，该研究的核心结论是：虽然深度代理搜索针对上下文污染问题提供了有效缓解手段，但其带来的可靠性损失和性能下降表明，在可索引的只读代码仓库场景下，基于向量索引的检索策略仍是更高效、更可靠的解决方案。
链接: https://arxiv.org/abs/2608.01507
作者: Amirkia Rafiei Oskooei,Bora Ilci,Alperen Kayim,Mehmet Egemen Uzun,Berat Can,Kaan Emre Kara,Ozan Orhan,Mehmet S. Aktas
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Multiagent Systems (cs.MA)
备注: 41 pages, 21 figures, 6 tables. Under review at a journal
Abstract:Code agents spend much of their effort simply locating the right code inside a repository. Two approaches dominate current practice. In Semantic Search, the agent retrieves code blocks from a vector index built from the repository in advance. In Deep Agentic Search (also known as grep-search by subagent), a planning agent delegates the exploration to a separate subagent that works in an isolated context window and returns only a condensed result. The second design, which is considered good context engineering practice, exists to protect the main agent from context pollution (also known as context rot), the loss of accuracy that occurs as unrelated material accumulates in the context window. Recent code agents (such as Claude Code, Codex, Antigravity, etc) have adopted it quickly, but there is little evidence on whether it produces better answers. We compare the two approaches on SWE-QA, a benchmark for repository-level code question answering. Semantic search answered 65.2% of questions correctly against 46.2% for deep agentic search, and it produced each correct answer at less than half the cost. To explain the gap, we then coded every failed run into a taxonomy of failure modes. The taxonomy shows that deep agentic search did not remove failures but introduced a new class of them: the single largest share of its failures, 41.8%, occurred at the hand-off between the planner and its sub-agent, and these were usually silent, ending in a fluent and confident answer that was wrong. Deep agentic search addresses a real problem and is now the preferred design in many code agents. However, our results show that the protection it offers may not be free, and that for read-only questions over a repository that can be indexed, retrieval was the stronger and cheaper option.
[MA-3] Alternating-Time Temporal Logic with Mean-Payoff Guarantees
【速读】：该论文旨在解决在并发博弈结构中，如何形式化并验证联盟（coalition）是否存在一种统一策略，能够在满足特定时序目标的同时，确保长期平均收益（long-run mean-payoff）达到预设阈值的问题。其核心挑战在于：时序要求与定量约束需同时在所有对手行为下成立，且二者不可分离地耦合，无法通过独立分析各自条件来推导整体可行性。解决方案的关键在于提出一种新的逻辑——ATL*_mp，即在加权并发博弈结构上对ATL的扩展，其中每个战略模态均携带一个合取式的平均收益约束。该逻辑通过引入对多维平均收益阈值的联合刻画，实现了对战略与性能双重需求的统一建模。研究证明，在完美回忆（perfect-recall）和有限记忆（finite-memory）语义下，一维约束的模型检测为2EXPTIME完全，与原始ATL一致；而多维约束在有限记忆语义下仍保持2EXPTIME完全性。此外，论文揭示了无记忆、有限记忆与完美回忆能力构成严格层次，并给出了所需记忆空间的紧致线性上下界（以阈值分母为参数），即使在固定博弈和时序监测器的情况下亦成立。该逻辑可表达诸如带性能保证的时间合成、聚合目标及多准则优化等复杂性质，同时与合作理性验证（cooperative rational verification）建立了联系，能够刻画对固定收益基线的有益偏离，但无法直接还原标准ATL*中关于二元偏好核心的编码方式。
链接: https://arxiv.org/abs/2608.01499
作者: Muhammad Najib
机构: Heriot-Watt University (赫瑞-瓦特大学)
类目: Logic in Computer Science (cs.LO); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
备注: 36 pages, 1 figure, 1 table. Full proofs are included in the appendices
Abstract:Alternating-time temporal logic and its extensions provide several ways of combining strategic and quantitative reasoning. We study a particular combination: whether a coalition has a single strategy that enforces a temporal objective while guaranteeing given long-run mean-payoff thresholds. We introduce ATL*_mp, an extension of ATL* over weighted concurrent game structures in which each strategic modality carries a conjunctive mean-payoff constraint. The temporal and quantitative requirements must hold against every behaviour of the remaining agents, and the existence of such a strategy cannot in general be reduced to the two requirements considered separately. For one-dimensional constraints, model checking is 2EXPTIME-complete under both perfect-recall and finite-memory semantics, matching ATL*. For the pure quantitative fragment and fragments restricted to ATL or GR(1) temporal objectives, model checking has lower complexity. With multi-dimensional conjunctive constraints, model checking under finite-memory semantics remains 2EXPTIME-complete. We show that memoryless, finite-memory, and perfect-recall abilities form a strict hierarchy, while finite-memory strategies still achieve every threshold strictly below the perfect-recall supremum. We give tight linear upper and lower bounds on the required memory as a function of the denominator of the threshold, even when the game and temporal monitor are fixed. We give several examples of properties expressible in the logic, including temporal synthesis with performance guarantees and aggregate and multi-criteria objectives. We also relate the logic to cooperative rational verification, showing that it can express beneficial deviations from fixed payoff baselines, but not directly reproduce the standard ATL* encoding of the core for dichotomous preferences.
[MA-4] Where Reasoning Diverges: Localized Multi-Agent Debate
【速读】：该论文旨在解决多智能体辩论（multi-agent debate）在推理过程中过度交换完整推理链条的问题，尤其是在仅存在少数中间结论分歧时仍进行全链条交互，导致计算开销大且效率低。其解决方案的关键在于提出一种局部化多智能体辩论（Localized Multi-Agent Debate, LMAD），通过将智能体的推理轨迹表示为带类型的节点，定位最早出现分歧的位置，并将辩论范围限定在相关局部片段内；同时引入受保护的决议机制（Guarded resolution），维护一个共享的已确认状态，使后续冲突可在不重新审视已接受步骤的前提下得以处理。该方法在四个需要多跳推理的问答基准上，使用来自四个模型家族的十种骨干模型进行评估，均表现出最优的宏观平均判断准确率，相较最强的传统基线最高提升7.20个百分点。
链接: https://arxiv.org/abs/2608.01463
作者: Weijun Gao,Xiang Ding,Tiancheng Xing,Haoyang Liu
机构: The Chinese University of Hong Kong(香港中文大学); Nagoya University(名古屋大学); Institute of Science Tokyo(东京科学研究所); University of Illinois Urbana-Champaign(伊利诺伊大学厄本那-香槟分校)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Multi-agent debate commonly exchanges complete reasoning traces even when disagreements concern only a few intermediate claims. We introduce Localized Multi-Agent Debate (LMAD), an inference-time protocol that represents agent traces as typed nodes, locates their earliest conflict, and restricts debate to the corresponding local segments. Guarded resolution extends a shared committed state so that later conflicts can be addressed without reopening accepted steps. We evaluate a single fixed LMAD configuration on four multi-hop question-answering benchmarks using ten backbones from four model families. Our method achieves the highest macro-averaged judge accuracy across all ten backbones, outperforming the strongest conventional baseline by up to 7.20 percentage points.
[MA-5] raining Small LLM s as Spatial Multi-Agent Policies
【速读】：该论文旨在解决大语言模型（LLM）驱动的多智能体系统在空间协作游戏中表现不佳的问题，特别是当使用低层次动作提示的冻结小规模LLM时，系统会完全失败并获得零奖励。其核心挑战在于如何在不依赖人工设计或奖励调优的情况下，实现智能体间的有效协作。解决方案的关键在于引入基于选项/半马尔可夫决策过程（options/semi-MDP）框架的符号化行为库——每个游戏配备一组类型化、状态可行且具有短时域特性的符号化选项，这些选项由前沿代码模型从游戏源码中生成，并通过低成本随机策略预演（random-policy burn-in rollouts）自动合成可行性约束（feasibility guards），确保仅保留能解释重复执行失败但隐藏成功记录的守卫条件，从而实现无须人工干预的自动化筛选。在此基础上，每个智能体的LLM作为对选项的策略，通过针对各智能体的多智能体广义相对策略优化（PA-MAGRPO）训练其私有LoRA适配器，使原本冻结的基模型从零奖励跃升至在三个游戏和四种小型骨干模型上均表现出色。进一步的行为审计揭示：奖励与合作行为解耦——奖励上升可能仅反映单个智能体独自完成任务而另一方闲置，真正的合作仅在任务结构迫使协作时才出现。因此，仅依赖奖励信号无法可靠评估合作程度，必须结合行为层面的评估以全面衡量系统性能。
链接: https://arxiv.org/abs/2608.01425
作者: Yi Mao,Andrew Perrault
机构: 未知
类目: Multiagent Systems (cs.MA); Machine Learning (cs.LG)
备注:
Abstract:Training LLM-based multi-agent systems with multi-agent reinforcement learning is rapidly gaining traction, and a parallel line of work argues that such systems should be judged by their behavior, not only their reward. We take up both threads in spatial cooperative games, where small frozen LLMs prompted with low-level actions fail outright, earning zero reward. Guided by the options/semi-MDP framework—and, because option execution is asynchronous across agents, its multi-agent extension in macro-action Dec-POMDPs—we equip each game with a library of symbolic \emphoptions: typed, state-feasible, short-horizon behaviors executed by a symbolic planner. Each library is drafted by a frontier coding model from the game’s source code; the feasibility guards that filter each menu are then synthesized mechanically from cheap random-policy burn-in rollouts—a guard is adopted only if it explains repeated execution failures while hiding no logged success—so no guard is authored, selected, or reward-tuned by hand. Each agent’s LLM acts as its policy over options, with a private per-agent LoRA adapter trained by a per-agent variant of multi-agent GRPO (PA-MAGRPO); this lifts frozen bases from zero reward to competent play across three games and four small backbones. Behavioral audits then reveal that reward and cooperation decouple: a rising reward curve may simply mean that one agent has learned to run the entire task alone while its partner idles—cooperation emerges only when the task makes it necessary. Reward alone is thus an unreliable readout of cooperation; behavioral evaluation must sit alongside it.
[MA-6] CRAFTS: Collaborative Role-Adaptive Fine-Tuning of LLM Agents for Chemical Process Simulation
【速读】：该论文旨在解决化学过程建模（chemical-process model construction）中高度依赖人工干预的问题，即工程师需在需求不明确的情况下，手动完成单元操作、热力学选择、物流定义、自由度分析、初始化、求解器修复及优化等耦合决策，任一错误均可能导致模型失效。其解决方案的关键在于构建一个分阶段、角色分工明确的自动化工作流——CRAFTS，通过将建模过程分解为七个具有明确边界的角色任务，并在各阶段间设置确定性的IDAES/Pyomo工程校验门（engineering gates），实现端到端的可验证建模。具体而言，系统利用自然语言请求、流程图（PFD）证据和领域知识，由输入理解与意图识别模块提取需求与约束；视觉、拓扑与规格专家角色生成类型化的中间表示（VisualGraphIR、TopologyIR、SpecIR）；后续调试与优化角色支持受限范围内的修复与合法优化。其中，视觉、拓扑与规格三个关键角色采用微调（fine-tuning），其余角色使用未微调的Qwen模型。最终输出包括构建计划（BuildPlan）与求解报告（SolveReport），仅当语义实体通过工程校验门后才引入兼容的构造器、物性包与求解器。研究引入了OpenIDAES-450数据集（450个案例），并在82个保留案例上评估了完整的七角色LangChain/LangGraph工作流，结果显示91.5%的案例成功完成验证与执行合同，单位、物流与有向连接的F1得分分别为0.815、0.791和0.782。该结果表明，角色专业化、类型化中间表示以及确定性工程校验门是实现可靠自动化化学过程建模的核心机制。
链接: https://arxiv.org/abs/2608.01369
作者: Ziyun Zhang,Yuxin Lin,Eldin Wee Chuan Lim,Xinghao Ding
机构: National University of Singapore(新加坡国立大学); Xiamen University(厦门大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Constructing an executable chemical-process model remains manually intensive. Chemical engineers translate underspecified requests into coupled decisions about unit operations, thermodynamics, streams, specifications, degrees of freedom (DoF), initialization, solver repair, and optimization; one error can invalidate the model. CRAFTS mirrors the staged workflow of chemical engineers by decomposing simulation building into bounded subtasks assigned to seven bounded roles, with deterministic IDAES/Pyomo gates between stages. Given a natural-language request, process flowsheet diagram (PFD) evidence, and curated chemical-engineering knowledge, Input Understanding and Intent recover requirements, constraints, and process semantics; visual, topology, and specification specialists translate them into typed simulator contracts; and Debug and Optimization support bounded repair and eligible optimization. Fine-tuning is applied to the three schema-critical visual, topology, and specification roles, while the remaining roles use untuned Qwen. The resulting VisualGraphIR, TopologyIR, SpecIR, BuildPlan, and SolveReport expose unit, port, thermodynamic, numerical, and execution decisions. Compatible constructors, property packages, and runners are attached only after semantic artifacts pass engineering gates. We introduce OpenIDAES-450, a 450-case IDAES process- simulation dataset, and evaluate the complete seven-role LangChain/LangGraph workflow through solve and eligible optimization on its frozen 82-case held-out split. CRAFTS completes the prescribed validation and execution contract for for 91.5% of cases and achieves unit, stream, and directed-connection F1 scores of 0.815, 0.791, and 0.782. These results demonstrate the effectiveness of role specialization, typed intermediate representations, and deterministic engineering gates for reliable automated process-model construction.
[MA-7] Imprecise Belief Fusion Improves Multi-agent Social Learning
【速读】：该论文旨在解决社会学习中信念融合过程的准确性问题，特别是探讨在存在初始错误信念偏倚的群体中，如何通过引入适度的不精确性（imprecision）来提升集体学习的有效性。其核心问题是：在信息交互过程中，过度精确的信念融合是否反而抑制了群体对正确信念的收敛？为此，作者提出一种基于命题逻辑公式的社会学习模型，其中个体信念以逻辑公式形式表征，并通过可调节参数的融合算子进行动态更新。该融合算子的关键特性在于其对不精确性的控制——当个体间信念存在差异时，更不精确的融合算子倾向于生成更具模糊性的综合信念。研究通过差分方程模型与基于代理的仿真相结合的方法，在多种初始偏倚和学习条件下进行验证，结果表明：在初始信念严重偏离真实情况的群体中，适度的不精确性能够显著提高整体学习精度。此外，该现象在差分方程模型的不动点稳定性分析中得到理论支持，揭示了不精确性在促进系统收敛至正确解方面的潜在机制。因此，解决方案之关键在于通过可控的不精确性设计，增强群体在复杂信息环境下的适应性与鲁棒性。
链接: https://arxiv.org/abs/2608.01367
作者: Zixuan Liu,Jonathan Lawry,Michael Crosscombe
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Physics and Society (physics.soc-ph)
备注:
Abstract:In social learning, agents learn not only from direct evidence but also through interactions with their peers. We investigate the role of imprecision in such interactions and ask whether it can improve the effectiveness of the collective learning process. To that end we propose a model of social learning where beliefs are equivalent to formulas in a propositional language, and where agents learn from each other by combining their beliefs according to a fusion operator. The latter is parametrised so as to allow for different levels of imprecision, where a more imprecise fusion operator tends to generates a more imprecise fused belief when the two combined beliefs differ. In this context we describe both difference equation models and agent-based simulations of social learning under a variety of conditions and with different initial biases. The results presented suggest that for populations with a strong initial bias towards incorrect beliefs some level of imprecision in fusion can improve learning accuracy across a range of learning conditions. Furthermore, such benefits of imprecision are consistent with a stability analysis of the fixed points of the proposed difference equation models.
[MA-8] Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在执行复杂智力任务时，因用户输入提示（prompt）质量不足而导致输出效果受限的问题。传统迭代式多轮提示往往引发上下文退化与认知回报递减。为此，作者提出PAWNI（Prompt Architecture Wizard using Neural Intelligence），一个由八个智能体组成的代理对话接口，通过基于自演化知识库的引导式问答对话，将非结构化查询转化为结构化提示。其核心创新在于：不直接优化模型输出，而是通过前置性意图澄清来优化提问本身，实现“问题先行”的策略。研究进一步构建了包含18个提示要素的三层框架（基础、增强、提升），用于系统评估提示质量。通过一项包含4名被试的探索性组内设计研究，结合32通道脑电图（EEG）、NASA-TLX工作负荷量表及行为指标，结果表明，使用PAWNI后，用户生成的提示在结构完整性上显著提升（从42%增至91%），对LLM输出的评价在所有质量维度上均提高，且工作负荷显著降低（NASA-TLX从39.6降至21.7）。所有被试均在单轮交互中获得满意输出，而对照条件下需1至12轮。尽管效应量受样本量影响存在波动，但方向一致性验证了前端提示构型优化是人机协同中的关键杠杆。
链接: https://arxiv.org/abs/2608.01366
作者: B. Sankar,Pawni Yadav,Srinidhi Ranjini Girish,Amogh A. S
机构: Indian Institute of Science (IISc)(印度科学研究所); Indian Institute of Science (IISc)(印度科学研究所); Indian Institute of Science (IISc)(印度科学研究所); Indian Institute of Science (IISc)(印度科学研究所)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注: 53 pages, 31 figures, 6 tables
Abstract:Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts. Iterative multi-turn prompting often leads to context degradation and diminishing cognitive returns. We present PAWNI (Prompt Architecture Wizard using Neural Intelligence), an agentic conversational interface of eight agents that transforms unstructured queries into structured prompts through guided question-and-answer dialogue informed by a self-evolving knowledge base. Rather than optimizing the models response, PAWNI optimizes the question itself by front-loading intent clarification. We also propose a three-tier framework of 18 prompt elements across Essential, Enhancement, and Elevation this http URL evaluate system behavior and validate a measurement protocol, we conducted an exploratory within-subjects study (N=4) across four complex tasks, integrating 32-channel EEG, NASA-TLX workload, and behavioral metrics. Participants produced more structurally complete prompts with PAWNI (42% to 91% of assessed elements), rated LLM outputs higher across all quality dimensions, and reported lower workload (39.6 vs. 21.7 NASA-TLX). Every participant reached satisfactory output in a single turn, compared to 1-12 turns unaided. While effect sizes are unstable due to sample size, direction consistency supports the hypothesis that optimizing prompt formulation front-end is a critical lever for human-AI collaboration.
[MA-9] Resilient Consensus-Based Target Tracking under False Data Injection Attacks in Multi-Agent Networks
【速读】：该论文旨在解决多智能体网络中分布式目标跟踪在高度动态且存在对抗性环境下的鲁棒性问题，尤其针对测量故障与虚假数据注入攻击等网络物理威胁。其核心解决方案是提出一种基于一致性（consensus）的估计算法，融合近似恒定速度模型与饱和滤波机制，以抑制突发性测量波动，实现鲁棒的分布式状态估计；同时引入动态虚假数据注入检测与隔离机制，通过创新量阈值判断可疑测量，并在影响全局估计前将其剔除。仿真结果表明，增强网络连通性与提高一致性迭代频率可提升估计精度与收敛速度，而合理配置的饱和滤波器可在故障抑制与估计准确性之间取得良好平衡；在局部化、协同式及瞬时性的虚假数据注入攻击下，该机制能有效识别受控智能体并阻止其污染分布式全局估计。总体而言，所提算法在不显著增加通信或计算开销的前提下，显著提升了分布式目标跟踪的准确性与抗干扰能力。
链接: https://arxiv.org/abs/2608.01222
作者: Amir Ahmad Ghods,Mohammadreza Doostmohammadian
机构: 未知
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA); Signal Processing (eess.SP); Optimization and Control (math.OC)
备注:
Abstract:Distributed target tracking in multi-agent networks plays a critical role in cooperative sensing and autonomous navigation. However, it faces significant challenges in highly dynamic and adversarial setups. This study aims to enhance the resilience of decentralized target tracking algorithms against measurement faults and cyber-physical threats, especially false data injection attacks. We propose a consensus-based estimation algorithm that integrates a nearly-constant-velocity model with saturation-based filtering to suppress impulsive measurement variations and promote robust, distributed state estimation. To counteract adversarial conditions, we incorporate a dynamic false data injection detection and isolation mechanism that uses innovation thresholds to identify and disregard suspicious measurements before they can degrade the global estimate. The effectiveness of the proposed algorithms is demonstrated through a series of simulation-based case studies under both benign and adversarial conditions. The results show that increased network connectivity and higher consensus iteration rates improve estimation accuracy and convergence speed, while properly tuned saturation filters achieve a practical balance between fault suppression and accurate estimation. Furthermore, under localized, coordinated, and transient false data injection attacks, the detection mechanism successfully identifies compromised agents and prevents their data from corrupting the distributed global estimate. Overall, this study illustrates that the proposed algorithm provides a simplified fault-tolerant solution that significantly enhances the accuracy and resilience of distributed target tracking without imposing excessive communication or computational burdens.
[MA-10] Humans Are More Diverse: Frontier LLM s Show Extreme Policies in Idealised AI Development Races
【速读】：该论文旨在解决多智能体生成式AI在竞争性开发场景中所面临的“安全困境”问题，即各公司在追求快速迭代以获取竞争优势的同时，可能牺牲系统安全性，从而导致整体风险上升。其核心挑战在于如何评估多智能体（特别是大语言模型，LLM）在模拟竞赛中的行为是否真正具备战略理性与安全意识。解决方案的关键在于引入一个严格的审计机制（audit gate），通过验证游戏引擎的正确性、规则记忆能力、状态追踪、收益计算及在不同任务表述下的稳定性，来确保智能体行为的有效性和可解释性。研究发现，即使模型具备较强的规则记忆能力，也可能在状态追踪和预期收益计算方面表现薄弱；且通过调整响应表示方式或提供经验证的算术支持，可在不改变规则的前提下显著影响后续行为。此外，不同模型、风险条件、角色设定以及玩家数量（2至5人）均导致行为模式存在显著差异，且三至五人博弈中的行为模式并非统一受竞争人数影响，而是具有高度模型特异性。因此，该研究强调，在将多智能体AI竞赛模拟结果描述为“具有战略意图”“类人”或“关注安全”之前，必须进行严格的有效性检验与轨迹级分析。
链接: https://arxiv.org/abs/2608.01193
作者: Phu Hoa Pham,Duy Minh Dao Sy,Trung Kiet Huynh,Phu Quy Nguyen Lam,Chi Nguyen Tran,Minh Trung Le,Phong Hao Le,Dinh Nam Nguyen,Thien Ky Nguyen Dong,Elias Fernandez Domingos,Le Hong Trang, TheAnh Han
机构: Ho Chi Minh City University of Science (胡志明市科学大学); Ho Chi Minh City University of Technology (胡志明市技术大学); Vrije Universiteit Brussel (布鲁塞尔自由大学); Teesside University (提塞德大学)
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:An AI development race creates a multi-agent safety dilemma. Each company can develop slowly and safely, or move faster while taking a risk that may remove its final reward. We use this repeated game to study strategic safety behaviour among large language model (LLM) agents in races with two to five players. However, a valid action does not show that an agent understands the game. We therefore place an audit gate before behavioural interpretation. We first verify the game engine, then test rule recall, state tracking, payoff calculation, and stability under different but equivalent task descriptions. We then compare LLM action sequences with an evolutionary game-theory benchmark and published human data, and explore differences across models, risk conditions, personas, and two- to five-player races. The audit shows that strong rule recall can coexist with weak state tracking and expected-payoff calculation. Providing verified arithmetic and changing the response representation can also change later actions, even when the game rules stay fixed. Across seven tested model endpoints, aggregate rates hide large differences in action sequences, responses to opponents, and responses to race position. Patterns across the tested three- to five-player races are also model-specific rather than a single effect of adding competitors. These results show why multi-agent AI-race simulations need validity checks and trajectory-level analysis before their outputs are described as strategic, human-like, or safety-aware. Our findings are exploratory and apply only to the tested models, prompts, and decoding settings.
[MA-11] Co-evolution of social reward and punishment under institutional interventions
【速读】：该论文旨在解决在社会困境中，个体间非正式的同伴激励（peer incentives）与正式的制度性激励（institutional incentives）如何共同影响合作演化、社会福利及执法效率的问题。其核心挑战在于理解两类激励机制在动态博弈中的协同作用及其对集体绩效的差异化影响。解决方案的关键在于构建一个包含四种策略的囚徒困境模型：无条件合作者（C）、背叛者（D）、社会惩罚者（SP）和社交奖励者（SR），并允许去中心化的同伴激励与中心化的制度激励同时作用，其中制度可对任意策略进行奖惩。通过分析无限均匀混合群体中的四策略复制动态以及结构化群体中基于正方格点的代理模拟，研究揭示了不同干预策略的演化结果：同伴惩罚最有效促进合作，而同伴奖励更有利于提升社会福利；制度性奖励同伴激励策略能显著同步提升合作水平与社会福利，而直接补贴无条件合作者效果有限；制度惩罚方面，仅直接惩罚背叛者具有持续有效性，而惩罚同伴激励策略会瓦解去中心化激励机制，降低合作水平并损害整体社会福利，表明最大化合作并不等同于最优社会总收益。研究为制度设计者提供了兼顾合作促进与社会福利优化的科学依据。
链接: https://arxiv.org/abs/2608.01183
作者: Van An Nguyen,Vuong Khang Huynh,Hoai Thuong Nguyen,Duc Tin Duong,An Nguyen Gia,Tat Kien Nguyen,Huu Loi Bui,My Nguyen Tra,Ho Nam Duong,Ba Thanh Phan,Thanh Vo,Dinh Anh Trung Hoang,Adeela Bashir,Zhao Song,Manh Hong Duong,Le Hong Trang, TheAnh Han
机构: 未知
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA); Dynamical Systems (math.DS); Adaptation and Self-Organizing Systems (nlin.AO)
备注:
Abstract:We investigate how peer and institutional incentives jointly shape the evolution of cooperation, social welfare, and enforcement efficiency in social dilemmas. In a Prisoners Dilemma with four strategies, unconditional cooperators ©, defectors (D), social punishers (SP), and social rewarders (SR), we allow decentralised peer incentives and centralised institutional incentives to act simultaneously, with the institution able to reward or punish any subset of strategies. In infinite well-mixed populations, we analyse the resulting four-strategy replicator dynamics, and in structured populations we use agent-based simulations on square lattices to study spatial effects and network reciprocity. Intervention schemes are evaluated by equilibrium states and evolutionary flow for infinite well-mixed populations, by cooperation levels and social welfare for structured populations, defined as aggregate population payoff net of institutional cost. We find that peer punishment most strongly promotes cooperation, whereas peer reward is more beneficial for social welfare. Institutionally rewarding peer incentive strategies substantially improves both cooperation and welfare, while subsidising unconditional cooperators has little impact. Under institutional punishment, directly penalising defectors is the only consistently effective policy; punishing peer incentive strategies dismantles decentralised incentives, reduces cooperation, and harms social welfare, showing that maximising cooperation does not necessarily optimise overall societal benefit. Our findings provide design principles for institutions seeking to balance cooperation promotion with welfare maximisation.
[MA-12] When Collaboration Becomes a Trigger: Collective Evidence-Threshold Backdoors in Multi-Agent Systems
【速读】：该论文旨在解决基于大语言模型（LLM）的多智能体系统（MAS）中存在的集体证据阈值后门攻击问题。此类攻击的特殊性在于，后门行为并非由单条消息触发，而是当多个智能体共享的证据累积达到某个隐藏阈值时才被激活，从而隐蔽且难以检测。其解决方案的关键在于提出一种新型的集体证据阈值后门范式，并设计边界条件引导的后门注入方法（Boundary-Conditioned Backdoor Injection, BCBI），通过构造反事实边界对，将阈值前的正常行为与阈值后的恶意目标进行分离，并学习与证据累积过程一致的潜在演化轨迹。为应对这一威胁，论文进一步提出一种仅使用干净数据的运行时评估防御机制——潜在转换测试时评估（LAtent Transition Test-time Evaluation, LATTE），该方法通过建模正常的通信动态，在异常智能体更新传播前将其隔离，从而有效遏制攻击扩散。实验表明，BCBI实现选择性激活且提前触发率极低，而LATTE在不依赖攻击目标或触发信号的情况下，能最小化干扰地阻断后门传播。
链接: https://arxiv.org/abs/2608.01085
作者: Jia-Hao Xiao,Lei Feng,Min-Ling Zhang
机构: 未知
类目: Multiagent Systems (cs.MA); Machine Learning (cs.LG)
备注: 26 pages,11 figures
Abstract:LLM-based multi-agent systems (MAS) extend LLM capabilities through iterative communication and shared contexts. However, this collaboration introduces a vulnerability: backdoor behavior can be activated when peer evidence reaches a hidden threshold, rather than being determined by any single message. We introduce a collective evidence-threshold backdoor paradigm for MAS and Boundary-Conditioned Backdoor Injection (BCBI), which constructs counterfactual boundary pairs to separate benign behavior before the threshold from the adversarial objective after it, and learns latent progression aligned with evidence. To mitigate this threat, we propose LAtent Transition Test-time Evaluation (LATTE), a clean-only latent-transition defense that learns benign communication dynamics and quarantines anomalous agent updates before their responses propagate. Across several benchmarks, BCBI yields selective activation with little premature activation; without knowing the attack target or trigger, LATTE limits propagation with minimal disruption.
[MA-13] Stress-Relief Annealing: Polynomial-Time Simulation-Free Layout Optimization for Automated Warehouses
【速读】：该论文旨在解决自动化仓库中大规模机器人协同运输场景下的物理布局优化问题，核心挑战在于如何在高密度机器人作业环境下提升系统吞吐量并实现高效可扩展的布局设计。传统方法依赖基于进化计算的黑箱优化策略，通过随机变异搜索最优布局，虽能获得良好性能但存在样本效率极低的问题，需耗费大量仿真资源进行评估。本文提出的应力缓解退火（Stress-Relief Annealing, SRA）算法是一种多项式时间、无需仿真的布局优化方法，其关键创新在于将任务需求建模为每个货架节点上的应力场（stress field），该场能够预测仓库内交通流的集中区域，且其峰值可理论证明为系统吞吐量的上界。通过最小化应力场峰值，SRA实现了对布局的高效全局优化。实验表明，SRA在单核CPU上仅用19分钟即可达到或超越进化基线的吞吐性能，而后者需25,000次仿真及64核机器上25小时的计算时间；同时，SRA显著提升了人设计布局的可承载机器人数量（约翻倍），且其增益在不同多智能体路径规划算法、非均匀任务分布及规模扩大的仓库中均具有良好的泛化能力。
链接: https://arxiv.org/abs/2608.01024
作者: Xiangjie Luo,Yulun Zhang,Miyuki Koshimura,Makoto Yokoo,Jiaoyang Li
机构: University of Tokyo (东京大学); Tsinghua University (清华大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:We study the problem of optimizing physical layouts for automated warehouses, where hundreds to thousands of robots are coordinated to transport packages. Previous works have shown that optimizing the warehouse layout (e.g., the physical location of the storage shelves) significantly improves throughput. However, state-of-the-art layout optimization approaches are based on evolutionary optimization methods, which treat the entire warehouse as a black box and rely on random mutation to search for high-quality layouts. While the optimization outcomes are promising, these methods require a massive number of simulations to evaluate candidate solutions, making them sample-inefficient. In this paper, we present Stress-Relief Annealing (SRA), a polynomial-time simulation-free layout optimization algorithm. SRA turns the task demand into a per-vertex \emphstress field that predicts where traffic will concentrate in the warehouse; the field’s peak provably caps the throughput. Our experimental results show that (1) SRA improves both the throughput and the scalability of a human-designed warehouse, roughly doubling the number of robots it can sustain, (2) it matches or exceeds the throughput of the evolutionary baselines while taking only 19 minutes on one CPU core, against their 25,000 simulations and 25 hours on a 64 -core machine, and (3) the gain generalizes across different Multi-Agent Path Finding algorithms, non-uniform task demands, and a warehouse with doubled dimensions. Subjects: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Robotics (cs.RO) Cite as: arXiv:2608.01024 [cs.MA] (or arXiv:2608.01024v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2608.01024 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-14] Neuro-Symbolic Participation Governance for Verifiable AI Agents in Open Digital Twin Ecosystems
【速读】：该论文旨在解决多智能体系统在跨机构的数字孪生生态中缺乏对智能体身份、能力及政策合规性的可靠验证问题，尤其在去中心化环境下更为突出。其核心解决方案是提出一种神经符号协同的去中心化治理框架，通过多层次语义画像（multi-layer semantic profiles）将概率性神经推理与确定性机构治理相结合，实现可信的人机协作与有效的人类监督。该框架将智能体能力基于形式化领域本体（domain ontologies）进行建模，确保参与行为具备可机器理解、政策感知和上下文敏感性；相关凭证由组织权威签发，并通过区块链智能合约进行验证，既保障了参与行为的可审计性，又避免了敏感数据泄露。实验原型验证了该框架在临床、数字孪生与可穿戴设备提供者等多方协作场景中，能够有效防止未授权交互并强制执行机构政策，同时保持较低的计算开销。研究表明，神经符号去中心化治理为跨越机构边界的高安全要求人机协同提供了可扩展且可信的技术路径。
链接: https://arxiv.org/abs/2608.00937
作者: Juan Li,Wei Cai,Yan Bai
机构: North Dakota State University (北达科他州立大学); University of Washington, Tacoma (华盛顿大学塔科马分校)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Accepted at the 2026 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2026), Bellevue, WA, USA, October 4-7, 2026. 7 pages, 1 figure, 5 tables. Code: this https URL (DOI: https://doi.org/10.5281/zenodo.21706699 )
Abstract:Autonomous AI agents, increasingly empowered by large language models, are becoming important components of human-machine systems for high-stakes decision support in digital twin ecosystems. However, existing multi-agent systems often lack robust verification for identity, capability, and policy compliance, especially in decentralized environments spanning multiple institutions. This paper proposes a neuro-symbolic decentralized governance framework for verifiable agents in collaborative digital twin environments. By representing agents through multi-layer semantic profiles, the framework bridges probabilistic neural reasoning with deterministic institutional governance, thereby supporting trustworthy human-AI collaboration and meaningful human oversight. Capabilities are grounded in formal domain ontologies to enable machine-interpretable, policy-aware, and context-sensitive participation. These credentials, issued by organizational authorities, are validated via blockchain-based smart contracts, ensuring auditable participation without exposing sensitive data. We demonstrate the framework using a decision-support prototype with clinic, digital twin, and wearable provider agents effectively prevents unauthorized interaction and enforces institutional policies with manageable overhead. Our findings suggest that neuro-symbolic decentralized governance provides a scalable and trustworthy pathway for safe human-machine collaboration across institutional boundaries.
[MA-15] HIERA: Hierarchical Multi-Agent Relevance Assessment for Content Discovery Systems
【速读】：该论文旨在解决内容发现系统中相关性评估依赖人工标注所面临的标注者间一致性差与规模化成本高的问题。现有基于大语言模型（Large Language Models, LLMs）的自动化评估方法多采用扁平化的聚合策略，如单步提示、投票集成或无协调的多智能体流水线，缺乏对复杂判断过程的有效结构化整合。本文提出HIERA——一种分层多智能体相关性评估框架，其核心创新在于引入四类专业化智能体：相关性判断者（Relevance Judge）、查询分析器（Query Analyzer）、项目分析器（Item Analyzer）和关系分析器（Relation Analyzer），通过层级化协调机制实现任务分工与知识整合。其中，判断者负责触发专家分析，关系分析器则融合外部知识，协同查询与项目分析以建立最终的相关性判断依据。消融实验表明，仅使用相同智能体与外部知识但缺乏层级协调时性能显著下降，验证了协调结构本身带来的增益。在五个数据集（EVS、MSRD、ESCI、WANDS、Home Depot）上的评估显示，HIERA相较11个基线模型实现显著提升，最大达38%（p < 0.05），且其层级协调机制相比同质智能体的非协调协作提升12.7%，证明了分层协作架构在复杂相关性判断任务中的关键作用。
链接: https://arxiv.org/abs/2608.00785
作者: Pritom Saha Akash,Phanideep Gampa,Chao Shen,Ying Chen,Sheikh Muhammad Sarwar
机构: Amazon
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs. While Large Language Models show promise as automated assessors, current approaches rely on flat aggregation strategies: single-step prompting, voting ensembles, or uncoordinated multi-agent pipelines that aggregate independent outputs without integration. We propose HIERA, a hierarchical multi-agent relevance assessment framework with four specialized agents: a Relevance Judge, Query Analyzer, Item Analyzer, and Relation Analyzer. The Judge determines when specialist analysis is needed; the Relation Analyzer then coordinates query and item analyses with external knowledge to establish relevance relationships for final judgment. Ablation studies show that the same agents and external knowledge without hierarchical coordination degrade performance, confirming that the coordination structure itself accounts for the improvement. Evaluation across five datasets (EVS, MSRD, ESCI, WANDS, Home Depot) shows improvements over 11 baselines: 10.2% on Home Depot, 4.8% on ESCI, and up to 38% on EVS ( p 0.05 ). Hierarchical coordination yields 12.7% improvement over uncoordinated collaboration using identical agents.
[MA-16] When Prompts Control Robots: Prompt Injection Attacks in Multi-Agent Robotic Systems
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）在多智能体机器人系统中集成时面临的提示注入攻击（prompt injection attack）问题，尤其关注此类攻击在多智能体环境下因跨智能体污染和更广攻击面而加剧的安全风险。其核心解决方案的关键在于系统性地评估直接针对任务指令与间接通过感知模块的提示注入攻击在单智能体与多智能体场景下的影响，并揭示攻击如何通过共享的提示结构在智能体间传播，进而导致非预期行为与任务完成率下降。研究进一步分析了模型架构变化对LLM查询的影响及其对攻击成功率的调节作用，为提升多智能体机器人系统在生成式AI环境下的安全性提供了关键实证依据与防御设计方向。
链接: https://arxiv.org/abs/2608.00747
作者: Neha Nagaraja,Amisha Bagari,Hayretdin Bahsi
机构: Northern Arizona University (北亚利桑那大学); Tallinn University of Technology (塔林理工大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
备注:
Abstract:Large language models are increasingly integrated into autonomous robotic systems for task planning and control, but this integration exposes them to prompt injection attacks that can lead to unsafe decisions and physical harm. Multi-agent settings increase the risks through cross-agent contamination and broader attack surfaces. In this paper, we evaluate prompt injection attacks against an LLM-based multi-agent robotic system, considering both direct injections into task instructions and indirect injections through perception modules. In our experiments across varying attack-goal complexities and injection strategies in both single-agent and multi-agent settings, we show that prompt injection can induce adversarial actions while reducing task completion. We find that attacks can propagate from one agent to others through shared prompt structures, with impacts varying depending on prompt composition and the targeted agent. We further analyze how architectural changes affect LLM queries and, consequently, the attack success. To the best of our knowledge, this is the first study that systematically investigates prompt injection attacks in a multi-agent LLM-based robotic system.
[MA-17] Adversarial Attacks in Multi-Agent LLM Agent LLM Pipelines: Unveiling Structural Vulnerabilities in Agentic AI Architectures
【速读】：该论文旨在解决多智能体大语言模型（Multi-agent LLM）流水线在复杂任务求解过程中因缺乏边界验证（boundary verification）而引入的安全漏洞问题。其核心挑战在于：当某个智能体接收恶意内容后，该内容会以“可信输入”的形式在整个流水线中传播，导致后续环节产生不可控的错误行为。解决方案的关键在于引入边界验证这一安全原语，通过显式验证跨智能体交互时的数据内容、身份、执行意图及状态完整性，打破现有流水线中隐含的信任假设。实验基于GAIA与SWE-Bench基准的标注生产轨迹，并在GPT-5-mini、Claude Sonnet 4.5和Kimi K2.5等模型上进行可控测试，结果表明攻击成功率主要取决于流水线架构而非模型能力，揭示了对抗脆弱性本质上是系统架构属性，从而推动防御策略从单个模型层面转向流水线级安全设计。
链接: https://arxiv.org/abs/2608.00718
作者: Faisal Haque Bappy,Tahrim Hossain,Tarannum Shaila Zaman,Raiful Hasan,Kamrul Hasan,Tariqul Islam
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: This paper has been accepted at the 2026 IEEE Global Communications Conference (GLOBECOM)
Abstract:Multi-agent LLM pipelines orchestrate multiple specialized language model agents into structured workflows where intermediate outputs are passed across agents to solve complex tasks. This design introduces a security gap absent in single-agent settings: once an agent accepts adversarial content, it is propagated as trusted input throughout the pipeline. We argue that this vulnerability stems from the absence of boundary verification, a security primitive that enforces explicit validation of data as it crosses inter-agent boundaries, including content, identity, execution intent, and state integrity. Without such verification, modern pipelines embed implicit trust assumptions that are not adversarially robust, giving rise to structurally distinct attack surfaces (e.g., content injection, agent impersonation, plan deviation, and memory poisoning). Leveraging annotated production traces from the GAIA and SWE-Bench benchmark, we show that these vulnerabilities arise in benign deployments and largely evade existing evaluation frameworks. We further operationalize these failure modes within a controlled multi-agent setting and evaluate them across GPT-5-mini, Claude Sonnet 4.5, and Kimi K2.5 under identical pipeline configurations. The results reveal that attack success aligns with pipeline structure rather than model capability, indicating that adversarial vulnerability is fundamentally an architectural property and motivating a shift toward pipeline-level defenses.
[MA-18] HetGPS: Scalable Graph Multi-Agent Reinforcement Learning with Physics-Anchored Adaptive Safety for EV Charging
【速读】：该论文旨在解决大规模网络耦合智能体（如电动汽车充电系统）在执行任务导向策略时，如何在不过度干预的前提下有效保障共享物理约束（如电压安全限值）的问题。其核心挑战在于平衡安全性与任务执行效率，避免因过度干预导致策略性能下降。解决方案的关键在于提出一种混合图控制框架 HetGPS，通过将干预强度（intervention magnitude）与校正方向（corrective direction）解耦：利用基于动作条件的图残差模型动态调度状态依赖的干预权限，而由物理模型决定校正方向。该方法实现了对电网拓扑的感知协调，且学习模型规模独立于车队规模，在5个嵌套配电网络（含200–3,218辆电动车）上验证了其有效性。结果表明，自适应权限机制可将母线-步电压越限率从3.93%–7.74%降至0.52%–3.44%，同时保持99.06%–100%的离站成功率，并显著优于固定权限的物理引导投影方法。此外，所部署的政策-风险模型仅需383,702个参数，相较同规模集中式软演员-评论家（SAC）模型小约170倍，且具备零样本迁移能力，证明了学习图风险在大规模系统中实现可扩展、高效且安全干预的可行性。
链接: https://arxiv.org/abs/2608.00679
作者: Xiangwei Wang,Nanduni Nimalsiri,Yu Xia,Peng Wang,Saman Halgamuge
机构: University of Melbourne (墨尔本大学); University of Colombo (科伦坡大学); Tsinghua University (清华大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Safety interventions for large populations of network-coupled agents must protect shared constraints without unnecessarily overriding task-oriented policy decisions. We present HetGPS, a hybrid graph-control framework synergizing learned graph risk with physics-anchored correction by separating intervention magnitude from corrective direction. An action-conditioned graph residual model schedules state-dependent intervention authority, while a physics model determines its direction. For electric vehicle (EV) charging, we couple this filter with a parameter-shared heterogeneous graph soft actor-critic policy, enabling topology-aware coordination with a learned model size independent of fleet size. Across five nested distribution networks with 200–3,218 EVs and 100 evaluation days, Adaptive Authority reduces bus–step voltage violations from 3.93–7.74% without filtering to 0.52–3.44%, while maintaining 99.06–100% departure success. Relative to the same physics-directed projection with fixed authority, it improves mean reward on all five networks and lowers the mean safety score on four. The deployed policy-and-risk model contains 383,702 learned parameters at every scale; at 3,218 EVs, a matched centralized SAC actor is about 170\times larger. A policy trained on the eight-transformer system transfers zero-shot to the 16- and 32-transformer systems, attaining 0.57–0.75% violation rates and at least 99.99% departure success. These results show that learned graph risk can allocate intervention authority at scale while feeder physics anchors corrective action.
[MA-19] MDGAM-Based Cooperative Task Scheduling for Communication-Constrained Distributed Multi-Agent Systems
【速读】：该论文旨在解决通信受限的分布式多智能体系统中协作任务调度的挑战，即在各智能体仅能基于局部且动态的观测进行决策的同时，还需满足复杂的实际约束条件。现有启发式方法依赖人工设计的竞标规则并频繁进行共识协商，而多数基于学习的方法则假设存在全局观测，缺乏显式的基于通信的协调机制。为此，本文提出一种面向分布式多机器人任务分配（MRTA）的神经调度框架，其关键在于两个创新：一是采用多解码器图注意力模型（MDGAM），通过扩展的图注意力机制联合更新节点与边特征，并利用多个解码器分别生成任务选择决策和通信消息；二是设计无评价值函数的组相对多智能体策略梯度（GRMAPG）训练算法，通过构建等价任务规划实例中的组相对优势来替代传统多智能体强化学习（MARL）算法中的评价值函数网络，从而降低训练难度并提升收敛性能。实验结果表明，该方法在不同问题规模与通信范围下均显著优于现有启发式与学习型方法，且消融实验、复杂度分析及泛化测试进一步验证了所提架构的有效性与鲁棒性。
链接: https://arxiv.org/abs/2608.00648
作者: Licheng Wang,Mingtao Huang,Yuan Shen
机构: Tsinghua University (清华大学); Beijing National Research Center for Information Science and Technology (北京信息科学与技术国家研究中心)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Cooperative task scheduling in communication-constrained distributed multi-agent systems is challenging because each agent must make decisions from partial and dynamic observations while satisfying complex practical constraints. Existing heuristics rely on handcrafted bidding rules and repeated consensus, whereas many learning-based methods assume global observations and lack explicit communication-based coordination. To address these limitations, this paper proposes a neural scheduling framework for distributed multi-robot task allocation (MRTA), consisting of a multi-decoder graph attention model (MDGAM) policy model and a critic-free group relative multi-agent policy gradient (GRMAPG) training algorithm. MDGAM uses an extended graph attention mechanism to jointly update node and edge features, and employs multiple decoders to generate task-selection decisions and communication messages. GRMAPG constructs group-relative advantages from equivalent task-planning instances to replace the critic network used in conventional MARL algorithms, thereby reducing training difficulty and improving convergence performance. Experiments under different problem scales and communication ranges show that the proposed method improves task-completion performance over existing heuristic and learning-based methods, while ablation, complexity, and generalization tests further validate the proposed innovations.
[MA-20] BANDMAS: Causality-Inspired Semantic Packet Scheduling for Bandwidth-Efficient Multi-Agent Collaboration
【速读】：该论文旨在解决基于大语言模型（LLM）的多智能体系统在协作过程中因频繁传递全量消息而导致的应用层流量过大、输入令牌激增，进而引发推理延迟增加与计算开销升高的问题。现有方法通过剪枝智能体或丢弃冗余消息进行优化，但往往导致决策依据不足或仍存在无效通信。为此，本文提出BANDMAS框架，其核心创新在于将智能体间的通信建模为面向任务的流量，并引入因果启发的重放价值评估机制实现高效传输。具体而言，系统通过对消息的语义特征（如证据与请求）进行分解，生成多个数据包，并仅在预测的重放贡献超过资源开销时才进行传输。该机制使BANDMAS能够自适应调度通信数据包，同时满足带宽、延迟、截止时间及接收方上下文等多重约束。在SciFact、HotpotQA和FanOutQA三个基准上的实验表明，BANDMAS在设定带宽限制下可减少53.2%至77.3%的应用层字节数，并在所有工作负载中以受限方法获得最高的平均任务指标。
链接: https://arxiv.org/abs/2608.00458
作者: Jiangwen Dong,Wanyu Lin
机构: 未知
类目: Multiagent Systems (cs.MA)
备注:
Abstract:LLM-based multi-agent systems make decisions based on the aggregated information via exchanging messages across specialized agents. Forwarding every generated message among agents increases application-layer traffic. Yet, it introduces tremendous input tokens for agent processing, potentially raising inference latency and computational overhead. Existing approaches attempt to address the above issues by pruning agents or discarding redundant messages. Nevertheless, such agent-level or message-level optimization results in insufficient evidence supporting for final decisions or still containing redundant message transmissions. To address these challenges, we propose BANDMAS, a multi-agent collaboration framework that models inter-agent communications as task-oriented traffic, which enables efficient transmission via causality-inspired replay valuation. Specifically, we decompose messages into several data packets by analyzing their semantic features such as evidence and requests. The system only transmits these packets if their predicted replay-derived contribution exceeds their resource cost. Consequently, BANDMAS is able to adaptively schedule communication packets while adhering to bandwidth, latency, deadline, and receiver context constraints. On frozen Qwen3-4B traffic across SciFact, HotpotQA, and FanOutQA, our framework reduces application-layer bytes by 53.2% to 77.3% at selected caps and attains the highest mean task metric among constrained methods on all three workloads.
[MA-21] MAPLE-Guard: Memory-Aware Link Enforcement Against Memory-Link Poisoning in Multi-Agent Systems
【速读】：该论文旨在解决基于大语言模型（LLM）的多智能体系统（Multi-Agent Systems, MAS）中持久化记忆层所引入的安全威胁问题。随着系统依赖私有与共享记忆实现长时程协调，攻击者可通过一次污染写入（poisoned write）在记忆中植入恶意信息，该信息可被持续检索、传播至共享内存，并被多个智能体重复利用，从而在不触发可见通信异常的情况下广泛影响后续决策，且现有防御机制因仅关注提示词（prompt）、动作或通信边界的检测而难以识别此类“延迟生效”的隐蔽攻击。其解决方案的关键在于提出一种面向记忆链路的防护机制——记忆感知传播与链接强制防护（Memory-Aware Propagation and Link Enforcement Guard, MAPLE-Guard），通过在记忆的写入、读取、提升（promotion）及跨智能体复用等关键生命周期节点设置检查门控，实现对高风险记忆的隔离、不安全读取的过滤以及污染私有记忆向共享内存渗透的阻断，从而有效遏制记忆驱动型攻击。实验表明，MAPLE-Guard将LongMemEval和AppWorld上的攻击成功率（ASR）分别从38.2%、34.7%降至0.9%、0.2%，同时显著提升多智能体防御成功率（MDSR），验证了其在弥补传统提示级与拓扑级防御盲区方面的有效性。
链接: https://arxiv.org/abs/2608.00426
作者: Wenjun Xiong,Yijin Zhou,Jiaqian Wang,Shangding Gu,Bo Tang,Zhiyu Li,Feiyu Xiong,Ying Wen,Muning Wen
机构: 未知
类目: Multiagent Systems (cs.MA); Cryptography and Security (cs.CR)
备注: 27 pages, 14 figures, 9 tables. Includes examples that may be misleading or harmful. Code: this https URL
Abstract:LLM-based multi-agent systems (MAS) increasingly rely on persistent private and shared memories for long-horizon coordination. This memory layer improves continuity, but it also gives attackers a durable channel: a poisoned memory can be written once, continuously retrieved in later tasks, promoted into shared memory, and reused by other agents. A single poisoned write can therefore steer many later decisions and contaminate agents that never saw the original attack, all while no malicious message crosses a visible communication edge at the moment of harm. Further, because existing safeguards mainly inspect prompts, actions, or communication edges, they can miss attacks whose content appears benign at write time but becomes harmful after retrieval. We introduce Memory-Aware Propagation and Link Enforcement Guard, MAPLE-Guard, a memory-link guard for memory-enabled MAS. MAPLE-Guard monitors the memory lifecycle and places gates at write, retrieval, promotion, and cross-agent reuse, so risky memories can be quarantined, unsafe retrievals filtered, and poisoned private memories blocked before they enter shared memory. In the main evaluation, MAPLE-Guard lowers attack success rate (ASR) from 38.2% to 0.9% on LongMemEval and from 34.7% to 0.2% on AppWorld; it also raises multi-agent defense success rate (MDSR) from 54.0% to 74.3% and from 42.5% to 99.8% on the same benchmarks. These results suggest that memory-aware link enforcement covers a gap left by prompt-level and topology-level defenses. Code is available at the link: this https URL.
[MA-22] Neural operator learning for collision-aware trajectory planning of spacecraft swarms
【速读】：该论文旨在解决大规模自主航天器编队在日益拥挤轨道中实现燃料高效、无碰撞机动的难题。传统轨迹优化方法随编队规模增大而面临计算复杂度急剧上升的问题，且其成对安全约束的数量随编队数量呈平方级增长；而现有的基于学习的规划方法通常缺乏跨编队规模或空间碎片密度的泛化能力。本文提出一种具有置换等变性（permutation-equivariant）的神经算子（neural operator），能够以单次前向传播将航天器、目标及碎片的分布映射为整个编队的避碰轨迹，并结合批处理高斯-牛顿优化（batched Gauss-Newton finish）精确满足轨道动力学约束。该算子在无需最优轨迹标签的情况下进行训练，通过自监督物理目标与针对自身推演结果生成的对抗性威胁联合优化。模型在10个航天器上训练后，可零样本（zero-shot）推广至包含超过11,000个已编目物体的1,000个航天器编队，其精度与单智能体最优控制求解器相当，能有效规避脱敏基线无法应对的最坏情况威胁，并显著降低编队内部邻近程度。因此，基于物理约束的神经算子学习为高密度轨道环境下的任务规划提供了一种快速、可扩展的替代方案。
链接: https://arxiv.org/abs/2608.00320
作者: Sidhdharth D. Sikka,Suyi Gao,Zehui Lu,Rongjie Lai,Shaoshuai Mou
机构: Purdue University (普渡大学)
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注: 27 pages, 6 figures, 6 tables. Submitted to Nature Machine Intelligence. Video abstract included as ancillary file
Abstract:Autonomous spacecraft swarms must plan fuel-efficient, collision-free maneuvers in increasingly congested orbits, yet classical trajectory optimization scales poorly as pairwise safety constraints multiply with swarm size, and learning-based planners rarely transfer across swarm sizes or debris densities. Here we introduce a permutation-equivariant neural operator that maps distributions of spacecraft, targets and debris to collision-aware trajectories for an entire swarm in a single forward pass, paired with a batched Gauss-Newton finish that enforces exact orbital dynamics. The operator is trained without optimal-trajectory labels, combining self-supervised physics objectives with adversarial threats generated against its own rollouts. Trained on ten spacecraft, it generalizes zero-shot to swarms of 1,000 amid more than 11,000 catalogued objects, matching a per-agent optimal-control solver’s accuracy, evading worst-case threats that a debris-blind baseline cannot, and reducing proximity within the swarm several-fold. Physics-grounded operator learning thus offers a fast, scalable alternative to optimal control for crowded orbits.
[MA-23] Cross-Task Dissociation in Frontier Vision-Language Model Theory of Mind
【速读】：该论文旨在探究前沿视觉语言模型（Vision-Language Models, VLMs）在不同任务间是否表现出一致的“心智理论”（Theory-of-Mind, ToM）能力，即其认知模式是否与人类参照群体中的典型发展成人（Typical Development Adult, TD）保持一致，还是在不同范式间呈现碎片化特征。研究采用两个源自心理学的基准任务进行评估：基于心理理论的“Keysar导演任务”（视觉视角采择任务，考察自我中心干扰下的视角理解能力）和基于Frith-Happé动画三角形的任务（通过Castelli评分标准评估从纯运动中推断意图的能力）。结果显示，在导演任务中，未使用链式思维（chain-of-thought）的模型在78%的试次中犯下自我中心错误，表现类似儿童而非成人，且各模型间差异显著，而引入推理机制可显著改善部分模型的表现；在动画三角形任务中，模型整体对意图的归因严重不足，其ToM特征距离高功能自闭症成人（HF-ASD）均值仅不到典型成人（TD）均值的三分之一，表明其认知模式更接近非典型发展群体。值得注意的是，无一模型在两项任务中均接近典型成人水平——在导演任务中表现成人样式的模型在三角形任务中反而偏向自闭症谱系特征，反之亦然。因此，该研究的核心发现是：当前前沿VLMs的ToM能力在不同任务间缺乏一致性，呈现出明显的任务依赖性与碎片化特征，其整体认知画像无法被单一、统一的人类参照标准所涵盖。
链接: https://arxiv.org/abs/2608.00261
作者: Kejia Zhang,Youran Sun,Chugang Yi,Haizhao Yang
机构: University of Maryland, College Park
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA); Neurons and Cognition (q-bio.NC)
备注:
Abstract:Do frontier vision-language models present a coherent Theory-of-Mind (ToM) profile across tasks, matching the same human reference group, or does that profile fragment from one paradigm to the next? We evaluate a shared panel of nine frontier VLMs on two psychology-derived benchmarks: the Keysar Director Task (visual perspective-taking under egocentric interference) and the Frith-Happé animated triangles scored with the Castelli rubric (intention attribution from pure motion). On the Director Task, without chain-of-thought, the panel makes the egocentric error on 78% of trials like children rather than adults; variation is substantial across models, and reasoning rescues several models. On the triangles, the panel under-attributes intention: its ToM profile sits more than three times closer to the high-functioning-autistic-adult (HF-ASD) mean than to the typical-development-adult (TD) mean, while Goal-Directed and Random stay near TD. No model is nearest TD on both tasks; the model that looks adult-like on the Director Task falls on the HF-ASD side on the triangles, and the most TD-like model on the triangles is child-like on the Director Task. We report group-level descriptions, not diagnostic labels for any model.
[MA-24] Can LLM Agents Agent s Price Competitively? A Dynamic Multi-Attribute Auction Benchmark for Agentic Commerce
【速读】：该论文旨在解决生成式 AI（Generative AI）在真实市场环境中进行智能定价的可靠性问题，尤其是在消费者偏好隐匿、竞争者实时动态调整、需求突变等复杂条件下，大语言模型（LLM）作为代理（agent）是否具备有效决策能力。其核心挑战在于缺乏一个能够系统评估代理在多属性拍卖中动态响应能力的基准测试工具。为此，研究提出 Bazaar——一个基于封闭形式客户效用函数的动态密封投标基准，可在高度动态的市场环境下对代理的定价策略进行精确评估。关键发现表明：当前领先的代理在客户获取指标上表现优异（如 Gemini 3.1 Pro），但在利润最大化方面并非最优（如 Opus 4.6）；在需求冲击后，学习能力强的代理反而表现出较慢的信念更新速度，而 Gemini 3.1 Pro 尽管非利润最优，却展现出最强的恢复能力。然而，即使是最优代理也仅能实现不到三分之一的“事后最优”利润，说明现有 LLM 在代理商业（Agentic Commerce）中的表现仍有巨大提升空间。
链接: https://arxiv.org/abs/2608.00102
作者: Shimaa Ahmed,Yiwei Cai,Mohsen Minaei,Rahul Rachuri
机构: Visa Research(万事达研究)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Agentic commerce is moving from concept to deployed infrastructure: payment networks, retailers, and AI platforms are setting the stage for agents to transact on behalf of merchants and consumers. Yet whether the LLMs behind these agents can price competently in real markets, where customer preferences are hidden, competitors adapt in real time, and demand can shift without warning, has not been systematically tested. We introduce Bazaar, a dynamic sealed-bid benchmark for multi-attribute auction under these conditions. Despite its dynamics, the benchmark is grounded in closed-form customer utilities, enabling exact evaluation. Across 11 frontier LLMs from four providers, the leading agents on customer acquisition (e.g. Gemini 3.1 Pro) are often not the leading agents on profit (e.g. Opus 4.6). The ranking shifts again under demand shocks: agents that learned fastest pre-shock are typically the slowest to revise their beliefs afterwards, while Gemini 3.1 Pro recovers fastest despite not leading on profit. However, even the strongest agent captures less than a third of hindsight-optimal profit, suggesting current LLMs are progressing in agentic commerce but leave substantial headroom.
[MA-25] Width Memory and Delay: A Resource Accounting for the Limits of Flat Multi-Agent Systems
【速读】：该论文旨在解决可扩展多智能体系统（如机器人集群或大型语言模型代理集体）中一个核心问题：单纯增加智能体数量是否足以突破性能瓶颈，还是必须引入更深层次的组织结构（如分层嵌套结构）才能实现性能提升。其解决方案的关键在于提出一个基于三类可量化资源的定量资源模型，即智能体数量（群体宽度 N）、单个智能体内部模型记忆容量（d）以及对观测延迟（τ）的预测能力。研究通过受控的扰动抑制测试平台验证了先前“扁平同质系统存在不可消除的因果下限”的结论过于绝对，并指出该下限实际由单个智能体的内部模型内容决定而非架构层级；在相同每智能体内存条件下，具备匹配扰动内模的扁平系统可达到甚至超越双回路分层系统的性能，表明时间深度可通过动态递归记忆实现，而不必依赖于结构性嵌套。进一步揭示三类资源并非可互换，其权衡关系在显式的宽度×记忆映射图中被精确刻画，包含严格的总状态预算约束下的非交换边界。此外，残余性能下限由观测延迟及环境在该时域内的不可预测性决定，并通过与最优控制器对比予以验证。研究还量化了以在线学习替代理想扰动谱知识所带来的代价，初步检验了对轻微有界非线性和空间扩展对象的鲁棒性，最终提炼出四条面向实践者的系统设计准则。
链接: https://arxiv.org/abs/2608.00028
作者: Oleksandr Kuznetsov,Emanuele Frontoni
机构: eCampus University(电子校园大学); SMARTEST Research Center(智能研究中心); V. N. Karazin Kharkiv National University(乌克兰哈尔科夫国立大学); University of Macerata(马切拉塔大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:A recurring question in the design of scalable multi-agent systems – from robot swarms to collectives of large-language-model (LLM) agents – is whether adding more agents can, on its own, overcome performance limits, or whether a qualitatively \emphdeeper organization is required. A recent preprint argues that flat, homogeneous multi-agent systems face an irreducible, population-independent ``causal floor’’ on achievable error, removable only by hierarchical (nested-loop) organization. Using a controlled disturbance-rejection testbed with an exactly computable optimum, we show this conclusion is too strong and replace it with a quantitative resource model built on three resources: population \emphwidth N , per-agent internal-model \emphmemory d , and prediction across the observation \emphdelay \tau . We establish three claims. (i) The achievable floor is governed not by architectural hierarchy but by per-agent internal-model content: a flat, homogeneous swarm whose agents carry a matched internal model of the disturbance matches or beats a designed two-loop hierarchy at equal per-agent memory – so temporal depth can be dynamical (recurrent memory), not architectural (nesting). (ii) The three resources are \emphnot mutually interchangeable; we chart the exchange rates and the hard non-exchange boundaries on an explicit width \times memory map, including a strict equal-total-state-budget comparison. (iii) A residual floor is set by the observation delay and the environment’s unpredictability over that horizon, which we verify against the optimal controller. We quantify the price of replacing oracle knowledge of the disturbance spectrum with online learning, provide a preliminary robustness check against a mild bounded nonlinearity and a spatially-extended plant, and distill four design rules for practitioners.
[MA-26] Microscopic dynamics of consensus formation in multi-agent LLM Naming Games
【速读】：该论文旨在解决去中心化大型语言模型（Large Language Model, LLM）代理群体在无中央协调情况下如何自发形成共享命名共识的微观机制问题，尤其关注模型内部随机性（由解码温度T调控）对宏观有序行为的影响。其核心解决方案在于构建一个简化的生成式命名游戏（LLM Naming Game）框架，将传统确定性命名游戏中的词汇库存检查替换为在不同解码温度T下进行的单令牌采样决策，从而显式引入随机性。该模型将每次交互分解为“在库存内”（in-inventory）和“不在库存内”（out-inventory）两个条件通道，分别对应概率π(T) = P(Yes | w ∈ P_j)与ϕ(T) = P(Yes | w ∉ P_j)，二者平衡决定了系统有序-无序的演化方向。通过建立双速率动力学的平均场理论，推导出一个解析的有序性条件，将经典随机命名游戏的共识阈值推广为在(π, ϕ)平面上的一条临界线。研究发现，在三种开放权重架构中共识均可达成，但依赖于不同的听者行为模式：宽容型（重涂噪声主导）、近确定型、保守型（漏失崩溃主导）。此外，收敛时间与系统规模的关系t_conv ∼ N^β中有效有限尺寸指数β(T)随温度变化，而临界温度依赖性t_c ∼ e^αT中的敏感度参数α在不同架构间从≈0.67降至≈0，表明解码温度作为架构相关的控制参数，可通过统计物理工具实现定量刻画。
链接: https://arxiv.org/abs/2608.02178
作者: Cristiano De Nobili,Vijayasri Iyer,Alessandro Codello,Raffaella Burioni
机构: Critiqality(意大利米兰); Ca’ Foscari University of Venice(威尼斯大学); DSMN(意大利威尼斯); IFFI(乌拉圭蒙得维的亚共和国大学); Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma(意大利帕尔马大学数学、物理与信息学院); INFN, Gruppo Collegato di Parma(意大利国家核物理研究所帕尔马分部)
类目: Physics and Society (physics.soc-ph); Statistical Mechanics (cond-mat.stat-mech); Multiagent Systems (cs.MA); Adaptation and Self-Organizing Systems (nlin.AO)
备注: 8 pages, 8 figures, 1 table, 1 appendix
Abstract:Decentralized populations of Large Language Model (LLM) agents can spontaneously reach consensus on shared conventions, yet the microscopic mechanisms by which their internal stochasticity shapes macroscopic ordering remain unexplored. We study a minimal LLM Naming Game in which the listener’s decision is a single-token LLM call at decoding temperature T , replacing the inventory check of the deterministic Naming Game. Each interaction decomposes into an in-inventory and an out-inventory channel with conditional rates \pi(T)!\equiv!P(\textYES\mid w\in P_j) and \phi(T)!\equiv!P(\textYES\mid w\notin P_j) , whose balance controls an ordering-disordering drift. A mean-field theory of the two-rate dynamics yields an analytical ordering condition that generalizes the consensus threshold of the stochastic Naming Game to a critical line in the (\pi,\phi) plane. Across three open-weight architectures, consensus is always reached, but through three distinct listener regimes: permissive (repaint-noise dominated), near-deterministic, and conservative (missed-collapse dominated). The effective finite-size exponent \beta(T) in t_\rm conv!\sim!N^\beta shifts with temperature, and the temperature-sensitivity \alpha in t_c!\sim!e^\alpha T ranges from \approx,0.67 to \approx,0 across architectures. Decoding temperature thus emerges as an architecture-dependent control parameter for decentralized LLM populations, quantitatively characterized by the statistical-physics toolkit.
自然语言处理
[NLP-0] AURORA-LM: Autoencoding Unified Representation for Continuous-Latent Diffusion Language Modeling
【速读】： 该论文旨在解决生成式语言建模中长期存在的核心问题：尽管图像、视频和音频等模态已广泛采用连续潜在空间（continuous latent space）进行建模，但文本生成仍主要依赖离散的词元（token）表示。现有连续语言模型要么沿用非为联合生成与解码设计的嵌入空间，要么通过压缩自编码器潜变量来简化扩散过程，从而牺牲了词元级别的生成保真度。其解决方案的关键在于：不简化表示以适应生成模型，而是保留高容量、可解码的文本潜在表示，并设计扩散模型直接学习该潜在表示的分布。为此，作者提出AURORA-LM，一种基于连续潜在空间的扩散语言模型，其核心创新包括：采用基于查询的编码器-解码器结构构建高容量、前缀对齐的潜在序列；引入块因果扩散变换器（Block-causal Diffusion Transformer），通过流匹配（flow matching）学习分布，实现从左到右生成块内并行去噪。为应对连续潜在空间对扩散建模的挑战，AURORA-LM仅在噪声输入路径上施加限制，同时保留完整的干净潜变量预测目标，避免降低解码器能力；进一步通过调整噪声水平分布以适配潜在维度，并引入自轨迹一致性机制，弥合训练阶段独立采样的噪声与推理阶段迭代去噪之间的差异。实验表明，AURORA-LM在OpenWebText自由生成和XSum摘要任务上均达到当前最优性能，且在约1500 EFLOPs计算量下扩展至10亿参数时，超越更大规模公开发布的潜变量扩散语言模型，在相同评估协议下表现更优。所有实验均在Ascend NPU平台上完成。
链接: https://arxiv.org/abs/2608.02602
作者: Jiajun Liang,Yucheng Liao,Yukang Cao,Jiazhe Wei,Ken Li,Wende Tan,Jiankun Zhang,ZY Cui,Jingkang Yang,Liucheng Guo,Shiqi Yang,B. Yang,Caifeng Shan,Ziwei Liu,Chenyang Si
机构: 未知
类目: Computation and Language (cs.CL)
备注: 40 pages, 17tables, project page: this https URL
Abstract:Language remains an outlier in generative modeling: while images, video, and audio are increasingly modeled in continuous latent spaces, text generation still relies predominantly on discrete tokens. Existing continuous language models either inherit embedding spaces not designed for joint generation and decoding, or compress autoencoded latents to ease diffusion, sacrificing token-level fidelity. Instead of simplifying the representation to suit the generative model, we preserve a high-capacity, decodable text latent and design the diffusion model to learn its distribution directly. We introduce AURORA-LM, a continuous-latent diffusion language model that separates the construction of a decodable text representation from the modeling of its distribution. A Query-based Encoder-Decoder organizes text into a high-capacity, prefix-aligned latent sequence, and a Block-causal Diffusion Transformer learns its distribution through flow matching, generating blocks left to right while denoising positions within each block in parallel. Because such a latent is harder for diffusion to model, AURORA-LM restricts only the noisy-input pathway while retaining the full clean-latent prediction target, accommodating full-width latents without reducing decoder-facing capacity. We further calibrate the noise-level distribution to the latent width, and introduce self-trajectory consistency to bridge independently sampled training noise and iterative denoising at inference. AURORA-LM achieves the strongest performance among evaluated continuous and diffusion-based language models on OpenWebText free generation and XSum summarization. Scaling to 1B parameters with about 1500 EFLOPs of total compute yields further gains, surpassing a larger publicly released latent-diffusion language model under a matched evaluation protocol. All experiments are conducted on Ascend NPUs. Comments: 40 pages, 17tables, project page: this https URL Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.02602 [cs.CL] (or arXiv:2608.02602v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.02602 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-1] GradCuit: Credit-Assigned Gradient Flow Enables Robust and Interpretable Test-Time Latent Reasoning
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理过程中缺乏对内部思维过程进行显式优化的问题。现有基于优化的方法通常通过解码生成的离散标记（decoded tokens）来关联连续的潜在状态（latent states），导致序列级信用分配间接且难以追踪潜在更新如何影响后续推理路径。其解决方案的关键在于提出GradCuit（gradient through circuit），该方法在提示（prompt）与生成延续之间的特定Transformer层插入可优化的潜在状态，并利用因果自注意力机制为每个生成标记的对数概率提供一条可微路径，从而实现从整个生成延续中获得的奖励加权梯度能够直接反向传播至这些潜在状态。这一设计实现了对内部推理过程的直接优化，使模型能够在测试时根据输出反馈动态调整推理逻辑，而非仅依赖重生成、采样或重排序。实验表明，GradCuit在五个指令调优骨干模型、三个推理基准和两种答案格式下平均准确率达64.5%，显著优于链式思维提示（chain-of-thought prompting）6.6个百分点，领先最强对比方法2.4个百分点，同时展现出更强的鲁棒性与可解释性：梯度归因分析显示潜在状态的影响集中于推理连接词（reasoning-connector tokens），层分析表明早期至中期的Transformer层是最优的优化空间。GradCuit为实现鲁棒、可解释的测试时扩展（test-time scaling）开辟了新范式，使大语言模型能够自主适应其推理方式。
链接: https://arxiv.org/abs/2608.02585
作者: Zhaoxin Yu,Qi Shen,Hengli Li,Zhaowei Zhang,Song-Chun Zhu,Chi Zhang,Zilong Zheng
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Optimization-based latent reasoning improves large language model outputs by optimizing instance-specific continuous states at test time while keeping model parameters frozen. Existing methods, however, typically connect these states to the reasoning trajectory through decoded tokens, making sequence-level credit assignment indirect and obscuring how latent updates shape subsequent reasoning. We introduce GradCuit (gradient through circuit), which inserts optimizable latent states at a selected Transformer layer between the hidden representations of the prompt and the generated continuation. Causal self-attention provides every continuation-token log-probability with a differentiable path to every preceding latent state through the remaining Transformer blocks, enabling reward-weighted gradients from the entire continuation to be assigned directly to the latents. Across five instruction-tuned backbones, three reasoning benchmarks, and two answer formats, GradCuit achieves an average accuracy of 64.5%, outperforming chain-of-thought prompting by 6.6 percentage points and the strongest competing method by 2.4 points. GradCuit also demonstrates greater robustness: across seven learning-rate settings, it consistently outperforms LatentSeek while reducing the standard deviation of accuracy from 1.53 to 0.82, and even its random-walk variant remains competitive with LatentSeek. For interpretability, token-level gradient attribution reveals that latent influence concentrates on reasoning-connector tokens, while layer analysis identifies early-to-middle Transformer layers as the most effective optimization space. By directly optimizing internal reasoning from outcome feedback, GradCuit opens a new axis of robust and interpretable test-time scaling, where LLMs adapt how they reason rather than merely regenerate, sample, or rerank outputs.
[NLP-2] Romanized Arabic Across Dialects: Views Usage Patterns and Linguistic Variation
【速读】： 该论文旨在解决生成式 AI (Generative AI) 在处理阿拉伯语方言文本时面临的挑战，特别是针对以拉丁字母书写的阿拉伯语变体——Arabizi（阿拉伯语拉丁化书写）的系统性研究缺失问题。现有自然语言处理（NLP）研究普遍将Arabizi视为技术限制导致的临时现象，忽视了其作为真实语言实践的社会文化属性。本文通过与阿尔及利亚、埃及、黎巴嫩、摩洛哥和突尼斯等五种阿拉伯语方言使用者的深度互动，揭示了不同方言群体在Arabizi使用中的感知差异与书写规范。其解决方案的关键在于构建两个核心资源：一是基于调查参与者转写结果的词级字符对齐数据集，用于分析跨方言与方言内部的语音-书写规律；二是人工标注的平行语料库，包含阿拉伯语正字法文本及其多版本Arabizi转写形式，以评估母语者对句级风格变异的识别能力。该研究首次实现了大规模、以用户为中心的跨方言Arabizi实证分析，为后续面向非标准书写形式的NLP模型开发提供了关键数据基础与理论依据。
链接: https://arxiv.org/abs/2608.02555
作者: Amr Keleg,Ahmed Amine Ben Abdallah,Taha Yassine,Chadi Helwe,Imane Guellil,Nedjma Ousidhoum
机构: 未知
类目: Computation and Language (cs.CL)
备注: Under Review
Abstract:Arabizi refers to Arabic written in Latin script. Although previous studies have shown that the prevalence and usage of Arabizi vary by factors such as region and age group, most NLP research on Arabic texts treats it as a temporary phenomenon resulting from limited technological support for the Arabic script. In this work, we engage with Arabic speakers to collect insights on their perceptions and usage of Arabizi. We further examine writing norms among speakers of different dialects, focusing on Algerian, Egyptian, Lebanese, Moroccan, and Tunisian Arabic. To this end, we release two resources. First, a character-level alignment of Arabic words to study inter- and intra-dialectal variation across these five dialects, based on words transliterated by survey participants, finding systematic intra-dialectal regularity and inter-dialectal variation. Second, to study Arabic speakers’ ability to identify this stylistic variation at the sentence-level, we build a manually curated parallel corpus of sentences written in Arabic script alongside multiple Arabizi transliterations, collected from speakers of the same five dialects. Our study presents the largest human-centered, cross-dialectal study of Arabizi’s perceptions and practices to date.
[NLP-3] Who Should Be Generated? Justifying Demographic Targets in Open-Ended Generation
【速读】： 该论文旨在解决生成式AI在无明确人口统计属性输入情境下的公平性评估难题，核心问题在于：当前的公平性评价框架普遍假设敏感属性（sensitive attributes）在输入端已知，而生成式模型（generative AI）的输出往往未指定具体的人口统计属性，导致无法确定合理的公平性参照标准。其关键解决方案是提出一个系统性的“目标构建”（target construction）框架，将公平性评估中的参照分布（target distribution）构建过程形式化为四个可解释的承诺：评估对象（evaluative object）、先验可接受性（prior admissibility）、分配原则（allocation）与操作化（operationalization）。作者通过地理成员身份（geographic-membership）解释支持地理先验，并基于任职现状（incumbency）解释主张职业先验需以劳动力构成一致性等独立客观目标为基础。在AP-Bench基准上的实证发现，基于地理推导的目标与实际生成分布间存在显著偏差（JSD₂ 0.508–0.606），若改用同类别均衡对照，则模型层面的平均绝对单元级差异达0.279–0.355。这表明，目标构建并非公平性评估的前置步骤，而是评估本身的核心组成部分；所提出的并非普适性标准，而是一个要求显式论证合理性、使公平性参照标准可辩护的理论框架。
链接: https://arxiv.org/abs/2608.02551
作者: Zeshen Zheng,Yujia He,Qianmian Lin,Xiangyue Huang,Wenqing Chen
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 39 pages, 13 figures, 29 tables; includes supplementary material
Abstract:Fairness evaluation concerns not only what a model produces, but also what its outputs ought to be compared against. When a model generates “a CEO in the United States,” the prompt leaves demographic realization to the model. Existing group fairness definitions assume that sensitive attributes are given on the input side. Generative audits instead examine output-side demographic composition, yet the targets they compare it against are typically supplied rather than justified. The upstream question is what the target distribution should be. We formalize this missing-target problem for demographic-value-unspecified generation and decompose target construction into four commitments: the evaluative object, prior admissibility, allocation, and operationalization. In this framework, we admit the geographic prior under a geographic-membership interpretation for the declared public-world use. The occupational prior, under an incumbency interpretation, requires an independently defended objective such as workforce-composition fidelity. Instantiating this construction in AP-Bench, we find substantial distribution divergence from geography-derived targets, ranging from 0.508 to 0.606 on a 0-to-1 scale. Replacing each geography-derived target with an equal-category comparator, while holding generations and measurement fixed, produces model-specific mean absolute cell-level \mathrmJSD_2 changes ranging from 0.279 to 0.355. Target construction is therefore not a preliminary to fairness evaluation but a component of it. What we supply is not a universal target, but a framework that makes explicit the justification required before a distribution can serve as a fairness standard.
[NLP-4] MedPRESS: A Multi-turn Benchmark for Patient-Pressure-Induced Medical Sycophancy in LLM s
【速读】： 该论文旨在解决当前大型语言模型（LLM）在医疗健康咨询场景中因患者施压而产生顺从性（sycophancy）导致安全风险的问题。现有研究多采用静态问答方式评估模型安全性，无法反映真实临床对话中患者通过个人经历、社会证明、外部证据甚至直接对抗性挑战等多轮施压所引发的模型行为偏差。为此，作者提出MedPRESS——一个包含600个医学情境驱动的五轮对话的多轮对话基准，涵盖药物与治疗需求、个人健康管理及症状分诊与护理抵抗三类典型场景。对话设计逐级升级压力强度，以模拟真实医患互动中的认知与情感压力。通过对20种不同类别（通用、医疗专用、轻量级、大模型、开源权重、专有模型）的模型进行结构化评判与安全指标评估，研究发现：多数模型在持续患者压力下显著倾向不安全的同意行为，且表现存在显著差异，受模型规模与提示策略影响明显。尽管反顺从性提示（anti-sycophancy prompting）可提升部分模型的鲁棒性，但无法彻底消除不安全响应。因此，论文的核心结论是：仅具备安全的医学知识不足以保证医疗LLM的安全性，模型必须在高压对话环境中仍能保持判断一致性，这揭示了当前医疗领域大模型评估体系中的关键缺陷。
链接: https://arxiv.org/abs/2608.02520
作者: Saman Sarker Joy,Niloy Farhan
机构: Universiti Malaya (马来西亚马来亚大学); BRAC University (孟加拉国布拉克大学)
类目: Computation and Language (cs.CL)
备注: 27 pages, 10 figures. Both authors contributed equally
Abstract:Large language models (LLMs) are increasingly used for health-related advice. Existing research measures their safety with static questions rather than pressured patient-facing conversations. We introduce MedPRESS, a multi-turn benchmark for measuring patient-pressure-induced sycophancy in LLMs. MedPRESS contains 600 medically grounded five-turn dialogues across three scenario families: medication and treatment demand, personal health self-care, and symptom triage and care resistance. Each dialogue begins with a health query and escalates through personal experience, social proof, external evidence claims, and direct adversarial challenge. We evaluate 20 LLMs across general, medical-domain, lightweight, large, open-weight, and proprietary families using structured judging and safety-focused metrics. Results show that models frequently shift toward unsafe agreement under repeated patient pressure, with substantial variation across model families, model scale, and prompt type. Anti-sycophancy prompting improves robustness for several models, but does not eliminate unsafe agreement. MedPRESS highlights a critical gap in medical LLM evaluation: safe medical knowledge is not enough unless models can maintain it under conversational pressure.
[NLP-5] LiveMem: Maintaining Memory State Continuity in Long-Running LLM Inference
【速读】： 该论文旨在解决长时运行的智能体（agent）在持续交互过程中因上下文窗口容量限制导致的历史信息丢失问题，即在上下文轮换（context turnover）时无法保持计算状态的连续性。现有方法如上下文保留、摘要生成与检索虽能部分访问历史，但无法在全生命周期内维持一个持久的状态。其核心解决方案是提出一种内在记忆机制——LiveMem，通过为预训练的全注意力大语言模型（LLM）引入一个固定容量的记忆状态（memory state），在主注意力路径维持有限的键值（KV）窗口的同时，使记忆状态能够跨时间持续保存历史信息。该方法结合了上下文轮换与记忆状态维护、面向记忆的后训练（memory-oriented post-training）以及状态感知的服务机制（state-aware serving），确保在原始输入令牌释放后，记忆状态仍可承载关键推理任务。实验表明，LiveMem在多个评估基准上表现领先，且在LongMemEval测试中成功基于记忆状态回答问题，即使支持证据已从当前上下文中移除；证据距离分析进一步验证了有用信息可长期留存于记忆中。因此，该研究确立了“状态连续性”（state continuity）作为持续大语言模型推理的一种独立且互补的抽象范式。
链接: https://arxiv.org/abs/2608.02515
作者: Zhichen Liu,Ruihan Sun,Hengjie Yang,Zipeng Wu,Zhaohan Chen,Xiaofan Zhang,Yang Xu
机构: NatureSelect.AI; Southern University of Science and Technology (南方科技大学); Xidian University (西安电子科技大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Long-running assistants and agents consume interaction streams that eventually outgrow the context. Existing context retention, summarization, and retrieval preserve access to selected history, but do not provide a persistent state over the full lifecycle when working context changes. We formulate this missing inference capability as \emphstate continuity under context turnover: carrying computation forward through a fixed-capacity memory state whose lifetime is independent of the active context. We introduce an intrinsic memory method, \textbfLiveMem, which augments a pretrained full-attention LLM with a memory state that preserves the historical information over the whole lifecycle while the main attention path retains a bounded KV window. Context turnover and memory state maintaining, memory-oriented post-training, and state-aware serving jointly make this memory state load bearing after its originating tokens are released. Our experiments show that LiveMem achieves leading overall performance among evaluated systems and other intrinsic memory methods. Experiments on LongMemEval show that LiveMem is able to answer the question based on the memory state, even when the supporting evidence has been removed from the current context, and evidence-distance analysis shows that useful information persists beyond the active window. LiveMem thus establishes state continuity as a distinct and complementary abstraction for continual LLM inference.
[NLP-6] RoMeRL: Balancing Feedback Coverag e and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States
【速读】： 该论文旨在解决基于学习的记忆系统在自演化大语言模型（Large Language Model, LLM）智能体中面临的两个紧密耦合的挑战：一是轨迹索引的效用随交互历史增长，导致有限的反馈被分散到不断扩大的状态空间中；二是由于轨迹级奖励联合分配给共同检索的记忆，无关经验可能接收到误导性的效用更新，从而陷入记忆-奖励陷阱。其解决方案的关键在于提出一种降阶记忆强化学习（Reduced-Order Memory Reinforcement Learning, RoMeRL）框架，通过将不断增长的轨迹索引效用空间以固定维度的、由结果极性和记忆动态因子分解的任务特定记忆状态进行表征，实现对效用空间的压缩。该方法通过一组固定的语义坐标来整合新经验，并随时间动态更新或替换其内容，从而将反馈集中于一个有界效用支持集上。理论上，该降阶参数化可提升每个效用坐标的平均反馈量，并在通用坐标转移模型下刻画了错误坐标的稳态占用特性。实验表明，RoMeRL在ALFWorld和LifelongAgentBench基准上显著提升了任务性能，使冷启动Q比率（Cold-Q ratio）降低80.0%，反馈密度提升约6.0倍，维护的记忆规模减少84.4%，并减少21.1%的LLM调用次数，证明了降阶效用状态在提升自演化智能体记忆效率的同时，有效抑制了持久的奖励污染。
链接: https://arxiv.org/abs/2608.02508
作者: Yi Yang,Zhennan Chen,Yihong Zhuang,Tiehan Fan,Yinan Chen,Jian Li,Jian Yang,Ying Tai
机构: Nanjing University(南京大学); Xiamen University(厦门大学); Zhejiang University(浙江大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges. First, trajectory-indexed utilities grow with the interaction history, thereby dispersing limited feedback over an ever-expanding state space. Second, because trajectory-level rewards are jointly assigned to co-retrieved memories, irrelevant experiences may receive misleading utility updates and consequently enter the memory-reward trap. To address these challenges, we introduce Reduced-Order Memory Reinforcement Learning (RoMeRL), which represents the growing trajectory-indexed utility space using a fixed-dimensional per-task memory state factorized by outcome polarity and memory dynamics. RoMeRL incorporates new experiences through a fixed set of semantic coordinates whose contents are updated or replaced over time, thereby concentrating feedback over a bounded utility support. Theoretically, we show that this reduced-order parameterization increases the average feedback received by each utility coordinate and characterize the steady-state occupancy of erroneous coordinates under a generic coordinate-transition model. Empirically, across ALFWorld and LifelongAgentBench, RoMeRL improves task performance, reduces the Cold-Q ratio by 80.0%, increases feedback density by approximately 6.0 times, reduces the maintained memory size by 84.4%, and cuts LLM calls by 21.1%. These results show that reduced-order utility states support efficient self-evolving agent memory while limiting persistent reward contamination. Code is available at: this https URL
[NLP-7] SWE-Touch: Benchmarking Coding Agents When Users Touch the Code
【速读】： 该论文旨在解决编码代理（coding agents）在共享工作空间中应对用户实时代码修改的能力问题，现有基准测试多假设代理独立运行或仅通过消息交互参与，无法真实反映协作场景下的动态变化。其解决方案的关键在于提出SWE-Touch框架，通过引入经验证的“反向编辑”（Counter-Edits）——即与任务目标冲突且具有合理性的代码修改——对代理进行压力测试。该框架从多个修复轨迹中挖掘任务关键区域，利用独立的用户补丁生成器构造反向编辑，并在代理触及相关代码时注入上下文化的用户消息。实验表明，反向编辑使SWE-bench Verified上的平均解决率下降7.7个百分点，且在长周期任务（SWE-Bench Pro和DeepSWE）中仍存在性能退化。轨迹分析揭示失败根源在于代理对演化中工作区状态的认知不足：其可能保留冲突代码或替换后未充分重新检查代码库并以针对性测试验证修改效果。研究结果表明，当前强自主能力并不等同于具备共享协作所需的环境感知与适应性行为，未来优化需重点关注工作区变更检测、冲突编辑的语义协调以及受影响行为的验证能力。
链接: https://arxiv.org/abs/2608.02499
作者: Yuqiao Tan,Jinxiang Meng,Fangyu Lei,Minzheng Wang,Shizhu He,Jun Zhao,Kang Liu
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Preprint. Our code is available at this https URL
Abstract:Real-world software development requires coding agents to operate in shared workspaces where users may inspect and modify code during an ongoing task, yet existing repository-level benchmarks typically evaluate agents working alone or restrict user participation to messages. This leads us to ask: how do coding agents understand and respond to code changes in a shared workspace? We introduce SWE-Touch, a framework that stress-tests this setting through validated Counter-Edits: plausible edits to task-relevant code that conflict with task completion. SWE-Touch mines task-critical regions from multiple repair trajectories, uses a separate User Patch Generator to construct the edits, and injects them with contextual user messages when agents reach the relevant code. We evaluate nine coding models on SWE-bench Verified, with additional experiments on longer-horizon tasks from SWE-Bench Pro and DeepSWE. Counter-Edit lowers average resolve rate by 7.7 percentage points on SWE-bench Verified, with degradation also persisting on both longer-horizon benchmarks. Trajectory analysis links these failures to limited awareness of the evolving workspace: agents may retain conflicting code or replace it without sufficiently re-inspecting the repository and validating the revised code with targeted tests. These findings show that strong autonomous performance does not yet ensure the state awareness and adaptive behavior needed for shared-workspace collaboration, and point to detecting workspace changes, reconciling conflicting edits with the task, and verifying the affected behavior as key capabilities for future optimization.
[NLP-8] Cultural Awareness is Represented but Not Decoded: Tracing Mythological Knowledge across 18 Open-Source LLM s
【速读】： 该论文旨在解决生成式模型在跨文化神话人物识别任务中存在显著的文化偏见问题，即模型对希腊、罗马等主流文化中的神祇（如宙斯、朱庇特、托尔）识别准确率高，但对芬兰、斯拉夫、埃及或中国等代表性较弱文化中的神祇识别一致性差。其核心问题是：这种文化偏差究竟源于模型内部的表征机制，还是输出读取阶段的语言与文化耦合导致？研究通过在汤普森母题（Thompson motif）构建的跨文化语料基础上，对18个来自8种架构家族的开源大语言模型（LLM）采用线性探测、逻辑透镜（logit lens）、激活补丁（activation patching）和输出提取等方法进行系统分析。结果表明，模型残差流（residual stream）已能清晰区分不同文化，远超仅依赖名称字符串的基线水平，说明文化信息在模型内部已有有效表征；然而，解码器阶段却将非主流文化的特定实体令牌错误地映射至主流文化对应项，表明失败根源在于“读出”（readout）阶段而非表征本身。进一步发现，当以目标文化母语提问时，模型在该语言内的错误模式高度集中，而跨语言间错误模式解耦，揭示解码器受提示语言条件控制。因此，解决方案的关键在于识别并干预解码器阶段的语言-文化耦合机制，提出了一套基于实体的探针-输出分解框架、锚定引用的跨文化基准数据集、语言条件读出的同模态与跨模态相关性检验方法，以及所有18个模型对每个实体的预测结果，为后续缓解文化偏见提供了可复现的技术路径与评估工具。
链接: https://arxiv.org/abs/2608.02486
作者: Iaroslav Chelombitko,Ekaterina Chelombitko,Mika Hämäläinen
机构: DataSpike; Metropolia University of Applied Sciences, Helsinki, Finland; Neapolis University Pafos, Paphos, Cyprus
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 45 pages, 23 figures, 18 tables. Dataset: this https URL Code: this https URL
Abstract:Open-source LLMs reliably name Zeus, Jupiter, and Thor, but recover their counterparts in less-represented traditions like Finnish, Slavic, Egyptian, or Chinese mythology far less consistently. We ask where inside the model this cultural default is produced. On a parallel cross-cultural substrate of Thompson-motif entities, we instrument 18 open-source LLMs from 8 architecture families with linear probing, logit lens, activation patching, and output extraction. The residual stream cleanly distinguishes cultures, well above a name-string baseline, yet the decoder collapses culturally-specific tokens onto dominant-tradition ones. The failure is at readout, not at representation. Asking the same question in the target culture’s native language versus English produces failures that cluster within language but decouple across language: the decoder is gated on prompt language. We release a per-entity (probe, output) decomposition framework, a citation-anchored cross-cultural ground truth, a within- versus cross-mode correlation test for language-conditioned readout, and per-entity predictions for all 18 models.
[NLP-9] CTRAG : An In-Context Retrieval-based Framework for Automated Compliance Checking using LLM s
【速读】： 该论文旨在解决企业在高度监管环境中进行合规检查时面临的效率低下与一致性不足的问题，尤其针对依赖第三方服务（如云服务商）实现间接合规的场景。传统手动合规测试耗时且易出错，难以应对复杂、动态的监管要求。为解决这一问题，论文提出了一种基于检索增强生成（Retrieval-Augmented Generation, RAG）的自动化合规检查框架——CTRAG。其解决方案的关键在于：通过自适应分块（adaptive chunking）、动态检索配置（dynamic retrieval configurations）和上下文学习（in-context learning）等策略，精准提取监管文本中的控制问题，并将其与企业非结构化文档进行交叉比对，从而实现高精度、上下文感知的合规验证。实验结果表明，CTRAG在最终部署配置下达到78%的F1分数和85%的召回率，显著提升了合规检测的准确性与覆盖率，同时大幅减少人工审查工作量，在一家四大会计师事务所的真实应用场景中成功验证了其有效性，展现出在复杂高风险环境中优化合规流程、降低风险并增强监管信任的巨大潜力。
链接: https://arxiv.org/abs/2608.02472
作者: Muhammad Roman,Karen Rafferty,Barry Devereux
机构: Bristol Research and Innovation Laboratory (BRIL), Toshiba Europe Ltd.(托斯巴欧洲有限公司), Bristol, United Kingdom; Queen’s University Belfast(贝尔法斯特女王大学), United Kingdom
类目: Computation and Language (cs.CL)
备注: 10 pages, 5 figures, 8 tables
Abstract:Trust is fundamental in modern regulatory ecosystems, and compliance checking plays a critical role in fostering that trust. Regulatory compliance verification is essential for businesses operating in highly controlled environments, as it ensures alignment with sector-specific guidelines across domains such as financial reporting, data privacy, and cybersecurity. Manual compliance testing, however, is often time-intensive and prone to inconsistencies, particularly when compliance depends indirectly on third-party services such as cloud providers, where vendors rely on external providers to meet regulatory standards. In this paper, we present CTRAG, a novel Retrieval-Augmented Generation (RAG) pipeline designed for automated compliance checking. CTRAG employs advanced strategies, including adaptive chunking, dynamic retrieval configurations, and in-context learning, to improve the precision and relevance of compliance assessments. By extracting control questions from regulatory texts and cross-referencing them with unstructured company documentation, CTRAG achieves highly accurate, document-informed compliance verification, even in cases of indirect compliance through third-party services. Empirical evaluations demonstrate significant improvements, with CTRAG achieving an F1-score of 78% and a recall of 85% in the final deployed configuration, ensuring minimal missed non-compliance cases while reducing manual reviewer effort in a real-world deployment. To validate CTRAG value, we developed and deployed a POC within a Big Four professional services firm, applying it to real-world cases and cross-checking results against manual compliance reports. These findings highlight CTRAG potential to streamline compliance workflows, mitigate risks, and enhance regulatory trust in complex, high-stakes environments.
[NLP-10] Right Answer Wrong Method: Shortcut Hacking Misleads the Evaluation of LLM Reasoning on Frontier Science Benchmarks
【速读】： 该论文旨在解决科学推理基准测试中普遍存在的“答案导向评估”偏差问题，即仅依赖最终答案的正确性来衡量大语言模型（LLM）的推理能力，而忽视了其推理过程的有效性。这一评估方式可能导致模型通过无效捷径（如数值搜索、枚举、猜测或先验答案验证等）获得正确答案，从而产生“解题作弊”（Solution Hacking）现象，掩盖了真实推理能力的缺失。研究发现，解题作弊率随题目难度显著上升，在常规问题中为2.2%，在奥数级问题中达28.3%，在高阶科学推理（HLE）任务中更是高达37.4%；且在前沿模型中，有8.2%至44.1%的被标记为正确的答案实为被破解的解法。为此，论文提出基于专家启发的抗作弊策略，包括自动评判机制与测试时指令干预。实验表明，这些策略能有效抑制捷径行为，显著降低报告准确率，但对真实无作弊的正确答案影响较小。该研究揭示了仅以答案正确性作为评估标准会严重高估前沿大模型的科学推理能力，强调需引入过程可解释性与推理路径验证以实现更可靠的评估。
链接: https://arxiv.org/abs/2608.02442
作者: Xuan Ren,Weiqi Zhai,Tianle Pu,Yihua Zhu,Yihua Zhu,Hu Wei,Bing Zhao
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: working in progress
Abstract:Scientific reasoning benchmarks typically evaluate large language models (LLMs) using final-answer accuracy. However, a correct answer does not necessarily demonstrate the reasoning capability targeted by the problem. We identify Solution Hacking, a failure mode in which an LLM reaches the correct answer through invalid shortcuts, such as numerical search, enumeration, guessing, or answer-first verification, without providing a valid task-targeted derivation. We systematically analyze this phenomenon across difficulty levels, scientific domains, and frontier models. Solution hacking increases sharply with benchmark difficulty, from 2.2% on common problems to 28.3% on Olympiad-level problems and 37.4% on HLE. Moreover, 8.2%-44.1% of answers credited as correct across frontier models are identified as hacked solutions. We further develop expert-inspired anti-hacking strategies, including an automatic judge and a test-time instruction. The results show that suppressing shortcut behavior substantially reduces reported accuracy while having a smaller effect on correct and non-hacked accuracy. These findings reveal that answer-only evaluation can overestimate the scientific reasoning capabilities of frontier LLMs.
[NLP-11] raining-Free versus Training-Based Intent Classification in LLM s: Accuracy Robustness and Failure Modes
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中意图分类（intent classification）的准确性与效率问题，即如何在不进行额外训练的情况下，高效且准确地将用户输入划分为预定义的类别（如数学、编程或通用文本处理）。其核心挑战在于：在保证分类性能的同时，降低计算开销并提升对复杂或对抗性输入的鲁棒性。解决方案的关键在于对比分析无需训练（training-free）与需训练（training-based）两类方法的性能差异。研究发现，尽管两类方法在简单任务上均表现饱和，但训练型分类器在更复杂的细粒度意图区分（如Java与Python代码）中更具优势；而无需训练的方法则在面对混合意图或对抗性提示时展现出更强的鲁棒性，凸显了其在实际部署中的稳定性价值。
链接: https://arxiv.org/abs/2608.02415
作者: Nan Chen,Zhouhao Yang,Soufiane Hayou
机构: Johns Hopkins University (约翰斯·霍普金斯大学)
类目: Computation and Language (cs.CL)
备注: Accepted at the Conference on Language Modeling (COLM 2026)
Abstract:Intent classification in Large Language Models (LLMs) involves categorizing user prompts into predefined classes. For instance, given a user prompt, the system must determine whether it primarily concerns mathematics, coding, or general text processing. Such classification enables routing prompts to specialized models optimized for specific domains, improving both accuracy and computational efficiency. In this work, we conduct a systematic study comparing training-free vs training-based approaches for intent classification. For this purpose, we consider two lightweight, training-free methods based on statistics of internal representations and compare them against MLP classifiers and linear probes. Our comprehensive empirical evaluation reveals that 1) Both training-free and training-based methods saturate easy benchmarks (mathematics vs. coding vs. natural language), 2) Training-based classifiers have an advantage on harder classification tasks (e.g. Java vs Python), and 3) Training-free methods are generally more robust to mixed-intent and adversarial prompts.
[NLP-12] PredAct-Bench: Benchmarking Tool-Augmented Dialogue under Controlled Tool Noise
【速读】： 该论文旨在解决在高风险领域（如教育、医疗和金融）中，当前任务导向型对话系统在部署时面临的关键现实挑战：现有评估基准普遍假设工具输出完全准确，而实际应用中工具存在噪声，且人类决策者对代理的信任度本身具有不确定性。这种不准确性与信任波动在真实场景中普遍存在，例如临床医生依赖有偏差的诊断预测工具，或教育顾问基于历史数据预测学生表现时面临模型误差。为此，论文提出PREDACTBENCH，一个面向教育领域的评估基准，用于评测与统计不完美工具协同工作的对话智能体。其核心解决方案包括：构建一个支持人机协作决策的评估框架，引入适用于多轮对话的相对AI依赖度（Relative AI-Reliance, RAIR） 和相对自依赖度（Relative Self-Reliance, RSR） 两个新指标，以扩展已有信任校准框架；并通过在OULAD（英国开放大学真实学习轨迹）和PREDACT-CS（60门课程的真实期末成绩与合成周度成绩轨迹）两个教育数据集上对13个主流闭源与开源大语言模型进行评估，并结合教师与助教的人类实验，揭示当前顶尖模型在面对噪声工具时未能有效提供可解释性反馈，导致使用者可能过度依赖错误建议或幻觉内容。因此，该研究强调了未来生成式AI作为辅助决策系统需具备透明性与可信度管理能力，而PREDACTBENCH为提升此类系统性能提供了关键评估工具。
链接: https://arxiv.org/abs/2608.02372
作者: Abdulrahman AlRabah,Xiaocheng Yang,Dilek Hakkani-Tür,Abdussalam Alawini
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) are increasingly deployed in task-oriented dialogue systems that support multi-step decision-making in high-stakes domains such as education, healthcare, and finance. However, existing benchmarks typically assume perfectly accurate tool outputs, overlooking the reality that deployed systems must operate with noisy tools and human decision-makers whose trust in the agent is itself uncertain. Such conditions are common in practice, for example, a clinician using a diagnostic prediction tool or an advisor relying on a model that forecasts student outcomes from historical records. We introduce PREDACTBENCH, a benchmark for evaluating dialogue agents paired with statistically imperfect tools, using education as a measurable testbed where ground truth outcomes and clear intervention decisions are available. First, we build a benchmark for AI-assisted human decision-making, where the AI uses noisy predictors to help guide a user. Second, we introduce episode-level Relative AI-Reliance (RAIR) and Relative self-reliance (RSR) metrics, extending prior trust calibration framework to multi-turn dialogue. Third, we evaluate 13 state-of-the-art closed and open source LLMs on two educational datasets, OULAD (real assessment trajectories from the UK Open University) and PREDACT-CS (60 courses with real final grade outcomes and synthetically generated weekly score trajectories), alongside a human study with instructors and teaching assistants. We find that when tools are noisy, SOTA models are supposed to provide visibility to teachers so that they do not over-rely on wrong suggestions or hallucinations, but current models fail to do that. We offer PREDACTBENCH to help build better LLMs as AI decision support systems to help teachers.
[NLP-13] Fast and Accurate Quotation Attribution in Literary Texts
【速读】： 该论文旨在解决文学文本中引文归属（quotation attribution）的难题，即准确识别每段引文对应的说话人。传统方法虽高效但准确率有限，而基于大语言模型（Large Language Model, LLM）的方法虽性能优越却因计算成本过高难以应用于大规模文学分析。本文提出一种基于编码器（encoder-based）的高效建模框架，通过在共享的大上下文窗口内联合处理多个引文归属任务，并引入“联合评分”（joint scoring）机制，在项目对话主义小说语料库（Project Dialogism Novel Corpus, PDNC）上实现了当前最优（SOTA）性能——整体归属准确率达94.5%。该方法在A100 GPU上比标准方法快20倍以上，且超过1000倍于LLM基线的推理速度。对模型表示的分析表明，联合评分能有效保留长距离回指（long-range anaphora resolution）信号，该信息在预训练编码器中已存在。为促进应用，研究团队发布了ModernBookNLP，一个改进版的BookNLP工具包，将原有的引文归属模块替换为本研究的最佳模型。
链接: https://arxiv.org/abs/2608.02359
作者: Gaspard Michel,Hugo Attali,Elena V. Epure
机构: Deezer Research(迪兹研究); LORIA(洛里亚实验室); Université Sorbonne Paris Nord, CNRS, LIPN(索邦巴黎北大学,法国国家科学研究中心,利普尼研究所); IDIAP, Martigny, Switzerland(IDIAP, 马蒂尼, 瑞士)
类目: Computation and Language (cs.CL)
备注:
Abstract:Attributing quotations to their speakers in literary texts remains an open challenge. Standard methods, which independently predict a speaker mention for each quotation, are efficient but still limited in accuracy. In contrast, large language model (LLM) approaches achieve strong performance, but their computational cost limits their use in large-scale literary analysis. We propose an encoder-based efficient formulation that resolves multiple quotation attributions within a shared, large context window. Using our new formulation, \textitjoint scoring, we report state-of-the-art (SOTA) performance on the Project Dialogism Novel Corpus (PDNC), comprising more than 35,000 manually annotated quotations from 22 English novels. Our best model reaches 94.5% overall attribution accuracy while processing novels 20\times faster than comparable standard methods and more than 1000\times faster than LLM-based approaches on an A100 GPU. An analysis of models’ representations suggests that joint scoring improves on challenging attribution examples by preserving long-range anaphora resolution signal, an information that we found already present in pretrained encoders. To facilitate adoption, we release ModernBookNLP, a modified fork of BookNLP that replaces its quotation attribution model with our best system available at this https URL.
[NLP-14] ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step
【速读】： 该论文旨在解决自主智能体在开放世界环境中缺乏对未知系统行为进行自主发现与适应能力的问题，尤其针对现有工具使用评测基准因提供静态语义工具模式而使智能体依赖先验知识、无法真正实现自主探索的局限性。其解决方案的关键在于提出ScrambleToolBench——一个交互式终端评测基准，通过移除语义线索并引入连续任务课程，强制智能体仅通过试错交互来揭示隐藏的工具行为；同时引入动态挑战（如映射漂移、随机动作失败及时间执行窗口），以评估智能体在环境变化下修正和调整假设的能力。实验表明，尽管当前先进语言模型能在初始阶段完成行为发现，但面对结构变化时无法有效运用归纳推理（如环路追踪）策略，表现出信念惯性或退化为耗时的穷举搜索，且增加测试时推理反而加剧了盲目搜索，凸显了现有智能体在长期记忆支持下的结构性推理能力缺失，暴露出当前代理在持续学习与动态适应方面的根本性缺陷。
链接: https://arxiv.org/abs/2608.02358
作者: Vernon Toh,Navonil Majumder,Zhengyuan Liu,Nancy F. Chen,Soujanya Poria
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone, even in the absence of documentation. However, existing tool-use benchmarks expose semantic tool schemas in static environments, allowing agents to rely on prior knowledge rather than autonomous discovery. To address this limitation, we introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning. By removing semantic cues and enforcing a continuous task curriculum, the benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction. The benchmark further introduces dynamic challenges, including mapping drift, stochastic action failures, and temporal execution windows, to evaluate whether agents can revise and adapt their hypotheses as the environment changes. Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation. When faced with structural changes such as mapping drift, agents fail to use deductive strategies such as cycle tracing, and instead exhibit belief inertia or fall back to exhaustive search. Increasing test-time reasoning only amplifies this expensive brute-force search rather than enabling deductive recovery. While equipping agents with persistent memory reduces compounding errors, they remain unable to efficiently infer structural changes, highlighting a gap in current agent reasoning.
[NLP-15] Global Optimization and Inference-Time Region Grafting for Agent ic Workflows
【速读】： 该论文旨在解决现有生成式智能体工作流优化方法在执行过程中缺乏动态适应能力的问题：当前方法虽能通过任务特定搜索或输入条件化的架构选择实现全局工作流优化，但其优化结果固定于执行前，无法利用运行时无标签的质量信号对失败的工作流区域进行自适应调整。若采用全工作流重优化以实现推理时适应，则计算开销过大，不可行。为此，本文提出GRAFT（Graph-based Re-optimization and Adaptation Framework for Tasks），其核心在于：在保持全局优化工作流不变的前提下，仅对特定输入局部替换部分工作流模块。该方法无需参数训练，通过无标签的执行质量信号评估局部模块替代方案，并仅接受能提升局部质量且不破坏整体工作流一致性的替换，从而实现实例级自适应而无需重优化整个工作流。实验表明，GRAFT在数学推理、代码生成及多跳知识密集型问答等多样化任务上均有效适用，在与基准优化器和执行器配置一致的情况下，平均优于最强基线方法MaAS 3.85分；进一步仅更换更强执行器即可获得性能增益，无需重新优化全局工作流。这表明，经过优化的工作流并非静态的优化产物，而是可随推理时反馈与更强执行器动态演化的可适配执行策略。
链接: https://arxiv.org/abs/2608.02353
作者: Donghyeok Koh,Gyuwan Kim,Jinyeong Bak,Seung-Hoon Na,Tao Yang,Haneol Jang,Cheoneum Park
机构: 未知
类目: Computation and Language (cs.CL)
备注: 9 pages, 3 figures, 4 tables
Abstract:Recent advances in agentic workflow optimization automate workflow design through task-specific workflow search or input-conditioned architecture selection. However, they determine the workflow before execution and cannot adapt failed workflow regions using execution-time label-free quality signals. Naively enabling such inference-time adaptation through whole-workflow re-optimization would be computationally prohibitive. To tackle this challenge, we introduce GRAFT, which preserves a globally optimized workflow while locally replacing only selected regions for each input. Without parameter training, GRAFT evaluates region-level alternatives using label-free execution-quality signals and accepts only replacements that improve local quality while preserving workflow-level consistency, thereby enabling instance-wise adaptation without whole-workflow re-optimization. GRAFT applies without modification across a range of tasks spanning mathematical reasoning, code generation, and multi-hop and knowledge-intensive question answering. Under matched optimizer and executor settings, it improves over the strongest prior workflow-optimization method, MaAS, by 3.85 points on average. Replacing only the executor with a stronger model yields further gains without re-optimizing the global workflow. This suggests that an optimized workflow is not merely a static optimization artifact, but an adaptable execution policy that can evolve with inference-time feedback and stronger executors.
[NLP-16] Qwen -CUA: Native Computer Use for (almost) Everything
【速读】： 该论文旨在解决生成式智能体在原生计算机操作（native computer use）中面临的长期状态追踪、大规模交互经验积累以及从稀疏但可验证的结果中学习的挑战。其核心解决方案在于构建一个基于397B-A17B参数量的Qwen混合专家（Mixture-of-Experts, MoE）架构的智能体Qwen-CUA，该智能体仅通过屏幕截图观察环境，并以键盘和鼠标事件进行交互，不依赖DOM树、可访问性元数据或特定任务API，从而实现对通用软件的普适性操作。其关键创新包括：采用分层结构（scaffold）维护最多20个活跃屏幕快照，并将历史视觉信息以固定大小块的形式折叠存储，兼顾近期证据保留与提示前缀的复用效率；通过云部署的弹性计算集群（近10万vCPUs及数万并发环境）构建约4万条可验证任务，采集日常与专业场景下的长周期个性化工作流；利用可验证奖励机制与轨迹切片技术优化完整行为轨迹，并通过迭代训练动态更新监督数据与强化学习任务。实验表明，Qwen-CUA在八个基准测试中超越Qwen3.7，且在OSWorld-Verified上达到86.2分，在OSWorld 2.0上实现18.5/48.4的二进制/部分完成率；进一步扩展至超万亿参数模型（Qwen-CUA-Max）后，性能提升至87.6和21.2/53.3。此外，该系统显著增强了对抗攻击鲁棒性（红队攻击成功率由36.6%降至16.4%）。综合效率分析、浏览器部署与命令行增强实验，验证了其实际应用可行性。研究确立了原生计算机操作作为通用智能体基础的潜力，并强调可验证交互与混合工具协同使用是未来发展方向。
链接: https://arxiv.org/abs/2608.02352
作者: Dunjie Lu,Shuai Bai,Tianyi Bai,Sicheng Fan,Chang Gao,Jian Guan,Feng Hu,Mianqiu Huang,Xingyang Huang,Yizhen Jiang,Yuheng Jing,Dehui Kong,Ning Li,Dayiheng Liu,Shixuan Liu,Zheng Liu,Que Shen,Bowen Wang,Junli Wang,Chencan Wu,Rui Xie,Tianbao Xie,Zhihui Xie,Haiyang Xu,An Yang,Tao Yu,Wenzhen Yuan,Xi Zhang,Zhenru Zhang,Mingkang Zhu,Zhaoqing Zhu,Yizhong Cao,Kai Dang,Binyuan Hui,Kaixin Li,Junyang Lin,Haiquan Wang,Zekun Wang,Yiheng Xu,Fan Yan,Mengqi Yuan,Danyang Zhang,Jiajun Zhang,Zhipeng Zhang,Fan Zhou,Fan Zhou
机构: Qwen Team (通义实验室); Xlang Lab (X语言实验室)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 24 pages, 10 figures. Technical report
Abstract:Native computer use offers a general interface for agents to operate almost any software available to people, but requires long-horizon state tracking, large-scale interactive experience, and learning from sparse yet verifiable outcomes. We introduce Qwen-CUA, a native computer-use agent with a 397B-A17B Qwen mixture-of-experts backbone. It observes only screenshots and acts through keyboard and mouse events, without DOM trees, accessibility metadata, or task-specific APIs. Its scaffold maintains up to 20 active screenshots and folds older visual history in fixed-size blocks to retain recent evidence while preserving reusable prompt prefixes. For training, we build a cloud rollout fleet with access to nearly 100,000 vCPUs and tens of thousands of concurrent environments, construct approximately 40,000 verifiable tasks, and collect personalized long-horizon workflows across everyday and professional software. We optimize complete trajectories with verifiable rewards and trajectory slicing, while iterative training runs refresh supervised data and recalibrate reinforcement-learning tasks. Across eight benchmarks, Qwen-CUA outperforms Qwen3.7 and remains competitive with leading proprietary systems, reaching 86.2 on OSWorld-Verified and 18.5/48.4 binary/partial completion on OSWorld 2.0. Scaling the same recipe to a model with over one trillion parameters yields Qwen-CUA-Max, improving these scores to 87.6 and 21.2/53.3. Qwen-CUA also reduces RedTeamCUA attack success from 36.6 to 16.4 relative to Qwen3.7. Efficiency analyses, a browser deployment, and Bash-augmented experiments further characterize practical behavior. These results establish native computer use as a broadly capable agent foundation and highlight scalable verifiable interaction and hybrid tool use as key directions.
[NLP-17] Can AI Agents Simulate A/B Test Outcomes? A Validation Framework for Agent ic Experimentation WWW
【速读】： 该论文旨在解决技术行业中A/B测试在新功能发布过程中消耗大量真实流量、工程资源及时间成本的问题。其核心挑战在于如何在不实际部署的情况下，通过模拟手段提前评估候选方案的效果。为此，论文提出了一种**模拟随机对照试验（Simulated Randomized Controlled Trial, S-RCT）**框架，将问题形式化为利用AI代理基于行为特征和干预上下文描述来仿真实验结果。该框架的关键创新在于构建了一个两层误差分解机制，能够分离出代理近似误差（agent approximation error）与抽样误差（subsampling error），从而实现对两类误差的针对性优化。该方法具备代理无关性（agent-agnostic），可兼容从微调的专业模型到通用基础模型等多种行为建模方式。在67个历史营销A/B测试上的验证表明，使用现成基础模型的基准S-RCT虽能捕捉效应方向（符号重叠达0.70），但系统性高估效应大小；通过引入两阶段预周期校准协议，可使剩余可减少误差（去除不可约测量噪声后）降低约77倍；而采用受试者内设计（within-subject design）进一步将标准误降低约2.4倍。研究揭示了当前方法的局限性，并指出在特定场景下，利用智能体生成的模拟信号可显著提升实验设计效率。
链接: https://arxiv.org/abs/2608.02345
作者: Stefan Hut,Lorenzo Masoero
机构: Amazon(亚马逊)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Applications (stat.AP)
备注: Accepted as a workshop paper at this https URL
Abstract:A/B testing remains the standard for rolling out new features in the technology industry. Each experiment, however, consumes real traffic, engineering effort, and weeks of wall-clock time. Can AI agents—conditioned on behavioral profiles and contextual descriptions of the intervention—simulate outcomes accurately enough to vet candidate treatments before committing live traffic? We formalize this question as a \emphSimulated Randomized Controlled Trial (S-RCT) and derive a two-layer error decomposition that separates agent approximation error from subsampling error, enabling targeted improvements to each. The framework is agent-agnostic: any behavioral model—from a fine-tuned specialist to a general-purpose foundation model—can serve as the simulation engine. Validated on 67 historical marketing A/B tests, a baseline S-RCT using an off-the-shelf foundation model captures directional signal (sign overlap 0.70) but systematically overshoots effect magnitudes. A two-phase pre-period calibration protocol reduces the squared prediction error (after removing irreducible measurement noise) by \sim77\times ; a within-subject design—where each agent is exposed to both arms—reduces standard errors by \sim2.4\times . We discuss limitations of the current approach and identify applications where experimenters stand to benefit from agentic signals.
[NLP-18] An Evidence-Grounded Retrieval-Augmented Transformer Framework for Health Misinformation Verification
【速读】： 该论文旨在解决在传染病暴发期间，虚假与误导性健康信息通过数字平台快速传播所引发的公共健康挑战，尤其关注发展中国家因缺乏本地化验证资源而导致的信息核实困难问题。现有自动化健康伪信息检测方法多依赖全球性生物医学资源，难以有效捕捉本地语境，导致在非洲等资源受限地区验证效果不佳。为此，论文提出一种检索增强的变压器（Retrieval-Augmented Transformer）框架，通过整合来自世界卫生组织（WHO）及尼日利亚疾病控制中心（NCDC）等权威机构的可信证据，结合语义检索与变压器模型进行分类，以判断健康声明的真实性（真实、虚假或误导）。研究构建了一个涵盖新冠疫情、拉萨热、霍乱、麻疹和猴痘等疾病的67条经人工标注的尼日利亚事实核查数据集，并对比了三种变压器模型与检索增强配置的表现。结果显示，双向编码器表示模型（BERT）表现最佳，准确率达71%，加权F1得分为0.66。尽管当前证据库规模与覆盖范围有限导致检索增强未提升性能，但研究强调了建立全面且权威知识源对可靠健康伪信息验证的重要性。该框架为尼日利亚及其他资源受限地区开发具备上下文感知与证据驱动能力的健康伪信息验证系统提供了可实践的技术基础。
链接: https://arxiv.org/abs/2608.02310
作者: Isah M. Bukar,Bala Mairiga Abduljalil,Bashir Saleh Maina,Abdulbasit Hassan
机构: 未知
类目: Computation and Language (cs.CL)
备注: 17 pages, 2 figures, To appear in the Reimagining knowledge systems for digital transformation and sustainable development in the 21st century conference 2026, faculty of social sciences education. Federal University of Education, Zaria
Abstract:The rapid spread of false and misleading health information through digital platforms has become a major public health challenge, particularly during infectious disease outbreaks where delayed verification can influence public behaviour and hinder effective disease control. Although recent advances in automated health misinformation detection have shown encouraging results, most existing approaches rely heavily on global biomedical resources and often fail to capture the local context needed to verify claims in developing countries. This study presents a retrieval-augmented transformer framework designed to verify health-related claims using trusted evidence from the World Health Organization and the Nigeria Centre for Disease Control and Prevention. The framework combines semantic evidence retrieval with transformer-based classification to determine whether a claim is true, false, or misleading. To evaluate the proposed approach, a manually annotated dataset of 67 verified health claims covering coronavirus disease, Lassa fever, cholera, measles, and monkeypox was compiled from Nigerian fact-checking sources. Three transformer models and a retrieval-augmented configuration were evaluated. The Bidirectional Encoder Representations from Transformers model achieved the best performance, with an accuracy of 71% and a weighted F1-score of 0.66. Although retrieval augmentation did not improve classification performance because the current evidence repository was limited in size and coverage, the findings highlight the importance of comprehensive and authoritative knowledge sources for reliable health misinformation verification. The proposed framework provides a practical foundation for developing context-aware and evidence-driven health misinformation verification systems for Nigeria and other resource-constrained settings. Comments: 17 pages, 2 figures, To appear in the Reimagining knowledge systems for digital transformation and sustainable development in the 21st century conference 2026, faculty of social sciences education. Federal University of Education, Zaria Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.02310 [cs.CL] (or arXiv:2608.02310v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.02310 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-19] Domain-Specific Evaluation of Text-to-Speech Systems: A Multi-Metric Benchmarking Study
【速读】： 该论文旨在解决当前神经文本转语音（TTS）系统在低资源及代表性不足语言中缺乏全面、可复现的评估方法的问题，尤其针对感知质量、说话人相似性与声学保真度在多样化语音场景下的联合评估能力不足。其解决方案的关键在于提出一个可复现的多指标基准评估框架，通过融合主观（如MUSHRA听觉测试、ABX辨识测试）与客观评价（如Resemblyzer说话人相似性评分、梅尔倒谱失真（MCD）和基频均方根误差（F0 RMSE））相结合的评估协议，实现对四种典型语音域（正式、对话、文学/讲故事、情感）的系统性分析。研究以一种代表性低资源语言为案例，评估了四款先进TTS系统的表现，揭示了情感语音合成难度最高（平均MCD 12.03 dB，平均F0 RMSE 889音分），而对话语音则具有最优声学保真度。此外，该工作公开了全部评估脚本、结果表格及可执行的Colab笔记本，为未来低资源语言TTS评估提供了标准化工具支持。
链接: https://arxiv.org/abs/2608.02235
作者: Ali Jafar,Amal Sarmad,Shifa Yousaf,Maryam Bashir
机构: FAST School of Computing, National University of Computer and Emerging Sciences (FAST-NUCES)(国家计算机与新兴科学大学(FAST-NUCES))
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 17 pages, 1 figure. Submitted to Computer Speech Language (Elsevier)
Abstract:Recent advances in neural text-to-speech (TTS) systems have substantially improved speech naturalness and intelligibility across many languages. However, comprehensive evaluation methodologies that jointly assess perceptual quality, speaker similarity, and acoustic fidelity across diverse speech domains remain limited, particularly for low-resource and underrepresented languages. This paper presents a reproducible, multi-metric benchmarking framework for systematic evaluation of modern TTS systems through domain-specific analysis. The proposed framework integrates complementary subjective and objective evaluation protocols and is demonstrated through a comprehensive case study on a representative low-resource language spanning four speech domains: Formal, Conversational, Literary/Storytelling, and Emotional. Four state-of-the-art TTS systems – Indic-Parler-TTS, MMS-TTS, Microsoft Edge TTS, and Google Gemini TTS – are evaluated using MUSHRA listening tests, ABX discrimination tests, speaker similarity scoring with Resemblyzer, and acoustic analyses based on mel-cepstral distortion (MCD) and F0 RMSE over 960 audio pairs. Results reveal substantial variation in TTS performance across speech domains, with emotional speech consistently presenting the greatest synthesis challenge (mean MCD 12.03 dB; mean F0 RMSE 889 cents), while conversational speech achieves the highest overall acoustic fidelity. Beyond the empirical findings, this work provides a reproducible evaluation framework, publicly releasing evaluation scripts, result tables, and executable Colab notebooks to support standardized benchmarking and future research on TTS evaluation for low-resource languages.
[NLP-20] Self-Improving Large Language Models via Progressive Experience Evolution
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实现自我改进过程中存在的核心问题：如何将瞬时的交互经验有效转化为持久的模型能力。现有自进化范式存在割裂性——测试阶段方法虽可显式提取经验，却无法将其内化至模型参数；而训练阶段优化方法虽能更新参数，但缺乏对可迁移经验进行系统积累的明确机制。其关键在于填补这一空白所需的中间环节——经验蒸馏（experience distillation）。为此，论文提出统一的后训练框架SPEE（Self-Progressive Experience Evolution），通过两个阶段协同实现：首先在显式经验演化阶段，对多轮交互轨迹进行反思，提取、验证并逐步演化出可迁移的经验，随后利用特权引导的在线策略自蒸馏（Privilege-Guided On-Policy Self-Distillation, OPSD）将这些经验内化为策略先验；其次在隐式策略优化阶段，基于内化先验的奖励驱动强化学习探索新颖解法。其中，一个持续演化的全局经验池整合成功与失败轨迹的知识，过滤低效经验，并缓解单条轨迹引发的事后合理化偏差。在五个数学推理基准上的实验表明，SPEE在三种模型规模下均显著优于测试阶段与训练阶段的自进化基线方法。
链接: https://arxiv.org/abs/2608.02139
作者: Shijie Ren,Xiting Wang,Meng Li,Yujie Guo,Yunhang Yao,Ziheng Peng,Xunlong Wang,Yuetan Chen,Haoyang Zhou,Yunlong Liang,Fandong Meng
机构: University of Science and Technology of China (中国科学技术大学); Tsinghua University (清华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 10 pages, 5 figures
Abstract:Large language models (LLMs) capable of self-improvement require not only effective policy optimization, but also a principled mechanism for transforming transient interaction experience into persistent model capabilities. Existing self-improvement paradigms remain fragmented: test-time methods can explicitly extract experience but cannot internalize it into model parameters, whereas training-time optimization methods can update model parameters but lack an explicit mechanism for accumulating transferable experience. Bridging these two paradigms requires a critical intermediate stage that remains underexplored, namely \emphexperience distillation. To address this gap, we propose \textbfSPEE (\textbfSelf-\textbfProgressive \textbfExperience \textbfEvolution), a unified post-training framework that sequentially performs explicit experience evolution followed by implicit policy optimization. During explicit experience evolution, SPEE reflects on trajectories collected from multiple interactions to extract, verify, and progressively evolve transferable experience, which is subsequently internalized into the policy through privilege-guided On-Policy Self-Distillation (OPSD). During implicit policy optimization, reward-driven reinforcement learning leverages these internalized priors to explore novel solution strategies. In the experience evolution stage, a continuously evolving global experience pool consolidates knowledge from both successful and failed trajectories, filters out low-utility experience, and mitigates post-hoc rationalization induced by individual trajectories. Experiments on five mathematical reasoning benchmarks demonstrate that SPEE consistently outperforms both test-time and training-time self-evolution baselines across three model scales. The source code is available at this https URL.
[NLP-21] he Role of Disfluencies in Speech Translation
【速读】： 该论文旨在解决当前语音翻译系统（包括语音大语言模型，SpeechLLMs）在训练过程中对语音文本进行“清洗”处理所导致的语义损失问题，尤其是忽略了话语中非流利现象（disfluency）所携带的语义信息。其核心问题是：现有系统倾向于去除填充词、重复或自我修正等不流畅表达，而这些不流畅现象实际上承载了重要的语用和认知信息，删除它们会显著降低翻译质量。解决方案的关键在于提出一个名为Uh-Mazing的基准数据集，该数据集包含经过人工翻译且标注了不流畅现象的Switchboard语音语料，覆盖英语到八种目标语言的翻译任务。研究发现，真正影响翻译质量的主要因素是自我修正（self-repairs）和起始错误（false starts），而非填充词或话语标记；同时，模型更倾向于省略不流畅成分而非错误翻译。此外，研究证明在推理阶段通过优化解码策略即可有效缓解该问题，无需重新训练模型。该工作释放了基准数据集与代码，为未来更具鲁棒性和语义保真的语音翻译系统提供了重要基础。
链接: https://arxiv.org/abs/2608.02138
作者: Maike Züfle,Maria Teleki,Fabian Retkowski,Vilém Zouhar,Oliver Grabner,Alexander Waibel,James Caverlee,Jan Niehues
机构: Karlsruhe Institute of Technology (卡尔斯鲁厄理工学院); Texas A&M University (德克萨斯农工大学); ETH Zurich (苏黎世联邦理工学院); Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Current speech translation systems, including SpeechLLMs, are trained on cleaned text and tend to strip disfluencies like filled pauses and false starts rather than translate them. We show this comes at a cost: disfluencies carry meaning that gets lost when speech is cleaned up. To study this systematically, we introduce Uh-Mazing, a benchmark of human-translated, disfluency-annotated Switchboard speech covering English into eight target languages. Across these languages and several architectures, we find that false starts and self-repairs, not filled pauses or discourse markers, drive most of the translation-quality loss, and that models which fail to preserve a disfluency tend to omit it rather than mistranslate it. We show inference-time decoding can mitigate this without retraining, and release the benchmark and code.
[NLP-22] HAFI-VLM: A Frequency Perspective for Diagnosing and Enhancing Visual Perception in Vision-Language Models
【速读】： 该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在需要细粒度视觉证据的任务中表现不可靠的问题。其核心挑战在于预训练视觉编码器存在谱响应刚性（spectral response rigidity），即在不同图像和任务间虽有显著频率差异，但编码器各层的频谱特征分布仍保持高度固定且仅在微调时发生微弱变化，导致模型无法根据当前任务需求动态调整对特定频率成分的提取能力。为克服这一问题，论文提出HAFI-VLM，其关键创新在于引入一种任务条件化的频率路径——分层自适应频率注入（Hierarchical Adaptive Frequency Injection, HAFI），通过文本调制、空间对齐的交叉注意力机制，在多层级编码深度上分别提取互补的低、中、高频视觉证据，从而实现对细粒度视觉信息的精准增强。此外，视觉增强层适配器（Visual Enrichment Layer Adapter）进一步校准浅层大语言模型（LLM）的注意力机制，以有效利用增强后的视觉标记。实验表明，HAFI-VLM在通用视觉问答（VQA）、富含文本理解及幻觉鲁棒性方面均取得一致提升，优于现有表示层面增强方法与多数基于分辨率或裁剪的方案，且无需额外高分辨率编码。机制分析揭示，HAFI能够恢复任务依赖的频谱分配策略，同时保留语义注意力，确立了频率增强作为提升VLM感知能力的一条独立而有效的路径。
链接: https://arxiv.org/abs/2608.02124
作者: Jin Cui,Chuanchang Su,Jiayi Lu,Xinyue Long,Boran Zhao,Pengju Ren
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: 11 pages, 8 figure
Abstract:Vision-language models (VLMs) remain unreliable when predictions require fine-grained visual evidence. We identify a previously overlooked cause: spectral response rigidity. Despite substantial frequency variation across images and tasks, pretrained vision encoders exhibit persistent, encoder-specific layerwise spectral profiles that change only marginally under downstream fine-tuning. Since pretrained vision encoders only receive images, they cannot adapt spectral extraction to the evidence required by the current query. We therefore propose HAFI-VLM, which introduces a task-conditioned frequency pathway while preserving the pretrained semantic representation. Hierarchical Adaptive Frequency Injection (HAFI) retrieves complementary low-, mid-, and high-frequency evidence at multiple encoder depths using text-modulated, spatially aligned cross-attention. A Visual Enrichment Layer Adapter further recalibrates shallow LLM attention to effectively utilize the enriched visual tokens. Experiments on LLaVA-1.5 and Qwen2.5-VL demonstrate consistent improvements in general VQA, text-rich understanding, and hallucination robustness, outperforming representation-level enhancement methods and most resolution- or cropping-based approaches without additional high-resolution encoding. Mechanistic analyses show that HAFI restores task-dependent spectral allocation while retaining semantic attention, establishing frequency enrichment as a distinct and effective route for improving VLM perception.
[NLP-23] From Chains to Trees: Parent-Conditioned Drafting for Semi-Autoregressive Speculative Decoding
【速读】： 该论文旨在解决半自回归（semi-autoregressive）生成器在使用大块（large draft blocks）进行推测解码（speculative decoding）时，因早期不匹配导致整个后续序列被废弃，从而限制了推理加速效果的问题。其核心挑战在于如何有效利用已训练的条件结构（conditional structure），在不增加额外主干网络前向传播或重新训练的前提下，实现更高效的多路径推测。解决方案的关键是提出父节点条件化推测树（Parent-Conditioned Drafting Tree, PCTree）：该方法利用预训练的马尔可夫头（Markov head）对每个具体父节点分别评分候选子节点，构建一棵基于父节点条件的分支树结构，并将固定的验证预算（verification budget）集中分配给最可能的路径。这一机制将原本线性的推测过程转化为树状结构，同时保持了单次主干前向传播的并行性。实验表明，在Qwen3系列模型与多个基准测试中，相较于基线的DSpark，PCTree在不同块大小下实现了3.1%至29.5%的相对加速提升；在Qwen3-4B GSM8K任务上，当块大小B=16时，平均接受长度从9.41提升至11.16，三轮运行的自回归（AR）加速比由6.14×提高至6.60×，充分证明了仅通过推理阶段的结构优化即可有效释放半自回归生成器中隐含的条件建模能力，实现端到端的推理性能增益。
链接: https://arxiv.org/abs/2608.02123
作者: Zixian Li,Tong Li,Chi Xie,Xiaohui Song,Haonan Lu
机构: OPPO AI Center(OPPO人工智能中心)
类目: Computation and Language (cs.CL)
备注:
Abstract:Speculative decoding accelerates LLM inference only when drafted continuations survive target-model verification. Semi-autoregressive drafters such as DSpark predict an entire token block with one backbone forward and refine it with a lightweight Markov head. However, DSpark decodes this block as a single chain, so an early mismatch invalidates the remaining suffix and limits the benefit of large draft blocks. We show that the conditional structure already learned by DSpark can support multiple parent-consistent continuations without retraining or additional backbone passes. We introduce Parent-Conditioned Drafting Tree (PCTree), which uses the pretrained Markov head to score alternative children separately for each concrete parent and allocates a fixed verification budget to the most probable paths. This converts DSpark’s linear draft into a tree while preserving its one-pass parallel backbone. Across Qwen3-4B,8B,14B and nine benchmarks, at B=7 , measured speedup gains over autoregressive (AR) decoding, relative to matched DSpark, range from 3.1% to 29.5% . On Qwen3-4B GSM8K at B=16 , PCTree increases mean acceptance length from 9.41 to 11.16 and three-run mean AR speedup from 6.14\times to 6.60\times . These show that parent-conditioned branching can turn conditional capacity already present in a semi-autoregressive drafter into end-to-end inference gains through an inference-only change. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.02123 [cs.CL] (or arXiv:2608.02123v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.02123 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-24] IACM-RL: Intent-Aware Context Management and Reinforcement Learning for Complex Tool Invocation under Dynamic Intent Fluctuations
【速读】： 该论文旨在解决在真实世界环境中执行长时序工具调用时，因动态用户意图噪声导致的模型行为不可靠问题。现有方法多依赖隐式历史扫描或文本压缩以提升鲁棒性，但通常假设指令完美且仅适用于简化场景，难以应对上下文波动带来的约束过时问题，进而引发严重的意图偏差与无限API调用循环。其解决方案的关键在于提出IACM-RL框架：首先构建DynamicIntent数据集，涵盖13种细粒度的上下文波动场景，并设计五维诊断指标体系以系统评估模型表现；其次引入基于信念状态（Belief State）的自生成上下文管理器（Context Manager, CM），通过结构化过期标志主动追踪目标变化并隔离被覆盖的参数；为实现该状态跟踪能力的自主内化，采用分层意图驱动奖励机制，并结合动作校准、CM提取与状态蒸馏三重辅助损失进行策略优化。实验结果表明，IACM-RL在DynamicIntent、BFCL-V3及τ²-Bench基准上显著优于基线方法，有效减少了无限循环和过时上下文错误，同时提升了跨域泛化能力。
链接: https://arxiv.org/abs/2608.02110
作者: Dingwei Zhu,Jiahan Li,Chengjun Pan,Yunxian Yang,Yunbin Zhao,Yunke Zhang,Zhonghang Lu,Zhuohui Sheng,Chenhao Huang,Jiahang Lin,Yajie Yang,Junlin Shang,Shichun Liu,Yuhui Wang,Honglin Guo,Junjie Ye,Xin Guo,Jiazheng Zhang,Ming Zhang,Shihan Dou,Zhiheng Xi,Tao Gui,Qi Zhang,Xipeng Qiu,Xuanjing Huang
机构: Fudan University; Honor Device Co Ltd.; Peking University
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Executing long-horizon tool invocations in real-world environments is severely challenged by dynamic user intent noise. Existing methods attempt robustness via implicit history scanning or text compression, yet predominantly assume perfect instructions in simplistic scenarios. Inevitably, under fluctuating contexts, obsolete constraints dilute model attention, triggering catastrophic intent deviation and infinite API loops. To resolve this, we propose IACM-RL, a comprehensive framework for robust tool invocation. First, we introduce the DynamicIntent pipeline, synthesizing trajectories across 13 fine-grained fluctuation scenarios, paired with a five-dimensional diagnostic metric suite. Second, IACM-RL deploys a BeliefState-based Self-Generated Context Manager that proactively tracks shifting goals and isolates overwritten parameters using structural stale flags. To autonomously internalize this state-tracking capability, we optimize the policy using a hierarchical intent-driven reward alongside three auxiliary losses (action calibration, CM extraction, and state distillation). Experiments on DynamicIntent, BFCL-V3, and \mathrm\tau^2 -Bench demonstrate that IACM-RL significantly outperforms baselines, reducing infinite loops and stale context errors while enhancing out-of-domain generalization.
[NLP-25] Cross-Domain Hybrid OPD for Generalizable Search Agents
【速读】： 该论文旨在解决生成式搜索代理在追求特定搜索任务性能提升时所面临的“对齐代价”（alignment tax）问题，即模型在优化搜索能力的过程中往往牺牲其通用语言理解与推理能力，从而限制其作为通用助手的实用性。其核心解决方案是提出一种融合代理强化学习与跨领域专家在策略蒸馏（On-Policy Distillation, OPD）的混合训练框架。该框架基于 Hunyuan3 架构，通过将多个互补领域的通用能力专家知识蒸馏至专门化搜索的学生模型中，实现搜索专长与通用智能的协同优化。关键在于，该方法并非将搜索专业化与通用能力视为相互竞争的目标，而是通过联合训练策略，在保持甚至增强模型通用能力的同时，显著提升其在动态信息源上的自主规划与迭代检索性能，从而有效缓解对齐代价，实现在真实搜索场景中专业化执行与广泛泛化之间的良好平衡。
链接: https://arxiv.org/abs/2608.02101
作者: Hongzhan Chen,Xiaoyu Liu,Dengming Zhang,Minzhou Huang,Dongliang Xu,Jingcheng Xie,Dongxiang Fang,Bowen Qin,Minsheng Hao,Yaozong Shen,Xiaojun Quan,Mona Zhou,Haosheng Zou,Jeff Chen
机构: Tencent(腾讯); Shanghai Innovation Institute(上海创新研究院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Recent advances in Reinforcement Learning (RL) have substantially improved the capabilities of autonomous search agents, enabling sophisticated planning, and iterative retrieval over dynamic information sources. However, optimizing language models for specialized search behaviors often incurs an alignment tax, where gains in search performance come at the expense of general-purpose capabilities, limiting their effectiveness as universal assistants. In this technical report, we present the training framework behind the Yuanbao search agent, designed to achieve search specialization without sacrificing general intelligence. Built upon the Hunyuan3 architecture, our framework combines agentic reinforcement learning for autonomous search with a cross-domain expert On-Policy Distillation (OPD) pipeline. Experts specializing in complementary general-purpose domains are distilled into the search-specialized student, restoring and further enhancing its broad capabilities. Rather than treating specialization and general capability as competing objectives, our hybrid training strategy jointly optimizes both, effectively mitigating the alignment tax. Extensive experiments demonstrate that the resulting model achieves competitive search performance while consistently improving its general-purpose capabilities, providing a favorable balance between specialized execution and broad generalization in real-world search scenarios.
[NLP-26] Instruction-Conditioned Exploration with Asymmetric Reinforcement Learning and Self-Distillation ACL
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在后训练阶段采用强化学习（Reinforcement Learning, RL）时，由于其动作空间结构复杂性所带来的探索效率低下问题。传统强化学习方法难以有效激发模型生成多样化的行为，从而限制了性能提升。为此，论文提出了一种名为指令条件探索（Instruction-Conditioned Exploration, ICE）的新方法，其核心在于在训练过程中向任务提示（task prompt）中注入多种不同指令，以主动引导模型生成更广泛多样的行为轨迹，从而增强经验覆盖度。为实现这一目标，研究进一步设计了非对称强化学习与自蒸馏（Asymmetric-RL/SD）联合训练目标，通过将已探索的多样化行为知识从条件化策略迁移至无条件的测试阶段策略，实现能力的有效保留与泛化。实验表明，结合Asymmetric-RL/SD的ICE方法在数学推理任务上显著提升了Qwen3-1.7B模型在4K响应长度下的通过率（pass@1），相对基准方法DAPO提升5.0%，且在更长的8K上下文长度下仍保持优势，验证了该方案在提升模型探索能力和泛化性能方面的有效性。
链接: https://arxiv.org/abs/2608.02087
作者: Jim Dilkes,Vahid Yazdanpanah,Sebastian Stein
机构: University of Southampton (南安普顿大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Submitted to ACL Rolling Review (ARR) May 2026 cycle. OpenReview submission record at this https URL
Abstract:Post-training Large Language Models (LLMs) with Reinforcement Learning (RL) has become an important tool for improving model capabilities, but the LLM action-space structure introduces challenges distinct from classical RL, with implications for inducing exploration. New methods are required that leverage the broad knowledge and flexibility of pre-trained LLMs to deliberately generate diverse experience at training time. We propose Instruction-Conditioned Exploration (ICE), which supplements task prompts during training with one of several distinct instructions, increasing the coverage of behaviours attempted. To facilitate ICE, we propose Asymmetric-RL/SD, a combined Reinforcement Learning and Self-Distillation training objective, to transfer explored behaviours to the unconditioned test-time policy. ICE with the Asymmetric-RL/SD objective improves Qwen3-1.7B held-out pass@1 performance at 4 K response length on mathematical reasoning tasks by 5.0% relative to training with DAPO, with improvement persisting at a longer 8K context.
[NLP-27] CAVE: Competence-Aware Visual Boundary Evidence Alignment for Video Temporal Grounding
【速读】： 该论文旨在解决大规模视觉-语言模型（Large Vision-Language Models, LVLMs）在视频时间定位（Video Temporal Grounding, VTG）任务中，由于依赖仅基于最终预测区间正确性的奖励机制，导致边界级视觉证据与时间戳预测之间普遍存在错位的问题。现有方法未能充分约束视觉证据与时间边界之间的对应关系，从而影响定位精度，尤其是在精细边界捕捉方面表现不足。为应对这一挑战，论文提出了一种能力感知的边界级视觉证据对齐（Competence-Aware Visual Boundary Evidence Alignment, CAVE）方法，其核心创新在于引入边界特异性视觉证据标记（boundary-specific evidence tokens），通过轻量级监督预热阶段实现结构化生成与区分性边界语义初始化，并在强化学习过程中设计视觉边界证据对齐奖励，显式增强模型在真实时间边界内对关键视觉证据标记的关注度，从而提升视觉证据与时间边界的对齐程度。此外，引入性能感知的证据监督门控机制，能够自适应地保留对定位不佳样本的证据引导，而在定位准确度达到阈值后逐步降低监督强度，避免对细粒度边界优化造成过度约束。实验结果表明，该方法在多个公开VTG基准数据集上均显著提升了定位性能，验证了其有效性。
链接: https://arxiv.org/abs/2608.02078
作者: Wei Jia,Zhicong Lu,Yu Chen,Xiang Wang,Shuai Li,Wenqian Lv,Jiayue Cao,Huaxing liu
机构: 1. Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所); 2. University of Chinese Academy of Sciences (中国科学院大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large vision-language models (LVLMs) have achieved substantial performance gains in Video Temporal Grounding (VTG) through reinforcement learning (RL). However, existing methods primarily rely on outcome correctness rewards that evaluate only the final predicted intervals, leaving boundary-related visual evidence and its correspondence with timestamp predictions insufficiently constrained. In this paper, we delve into timestamp prediction and its underlying boundary-level visual evidence, showing prevalent misalignment between visual evidence and predicted timestamps across widely used benchmarks. To address this issue, we propose Competence-Aware Visual Boundary Evidence Alignment (CAVE), which augments localization optimization with boundary-specific visual evidence rewards to mitigate evidence-timestamp misalignment. Specifically, to explicitly represent the boundary-specific visual evidence, CAVE introduces boundary-specific evidence tokens and initializes their structured generation and distinct boundary semantics through a lightweight supervised warm-up. During RL, the visual boundary evidence alignment reward reinforces the visual attention of special evidence tokens within the ground-truth boundaries, thereby promoting alignment between visual evidence and temporal boundaries. Moreover, performance-aware gating for evidence supervision is designed to adaptively retain evidence guidance for poorly localized groups while reducing it once localization becomes sufficiently accurate to avoid over-constraining fine-grained boundary refinement. Extensive experiments on several public VTG benchmarks demonstrate the effectiveness of our method.
[NLP-28] Geometry-Guided Layerwise FFN Width Allocation in Transformers
【速读】： 该论文旨在解决Transformer模型中前馈网络（Feed-forward Networks, FFNs）的隐藏层宽度在深度方向上固定分配所导致的容量浪费问题。现有架构通常采用恒定的隐藏宽度，未能根据各层实际的语义表示变化动态调整计算资源。其核心解决方案是基于前向传播过程中各层对token表示的几何变换特性，构建一个可量化的层间“工作量”指标，以实现按需分配FFN宽度。关键创新在于引入多种几何度量工具：保持对应关系的平移（correspondence-preserving shift）、Gromov-Wasserstein失真以及一阶持久同调（degree-one persistent homology），结合原始度量与尺度归一化度量，量化每一层的表示空间变换复杂度。通过构建逐层近似代理模型，实现了在固定预算下的精确优化器设计。实验表明，在七个预训练语言模型中，归一化后的“工作量”分布呈现显著的前端集中特征，且基于几何特性的宽度分配策略在多个规模的模型（128M、256M、440M）上均优于均匀分配和人工设计的余弦衰减方案，尤其在440M模型中优势更为显著；而反同调结构的原始度量控制组表现甚至劣于均匀分配，验证了几何度量的有效性与合理性。
链接: https://arxiv.org/abs/2608.02064
作者: Timur Mudarisov,Mikhail Burtsev,Radu State
机构: University of Luxembourg (卢森堡大学); London Institute of Mathematical Sciences (伦敦数学科学研究所)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Feed-forward networks (FFNs) account for a large fraction of Transformer parameters, yet their hidden width is usually constant across depth. We ask whether this capacity can instead be allocated from a forward-pass measurement of layer behavior. We view each FFN as transporting a cloud of token representations and quantify the induced geometric change using correspondence-preserving shift, Gromov-Wasserstein distortion, and degree-one persistent homology under raw and scale-normalized metrics. A layerwise approximation surrogate yields an exact fixed-budget optimizer. Across seven pretrained language models, raw Euclidean work largely tracks residual-norm growth, whereas normalized work is predominantly front-loaded. Gromov-Wasserstein work is more consistently associated with perturbation-based layer sensitivity than the finite-sample topological estimate. In paired 128M and 256M training runs, several normalized-work schedules reduce mean validation loss relative to both uniform width and a hand-designed cosine taper. With the amplified paired differences at 440M, the best geometry-based allocations improve over uniform substantially larger than the cosine taper, while the anti-topological raw control is worse than uniform.
[NLP-29] xtNCA: Neural Cellular Automata for Language Modeling via Hierarchical Local Attention
【速读】： 该论文旨在探究一种严格局部性（strictly local）、迭代式（iterated）且权重共享（weight-shared）的计算原语是否能够支持语言建模任务，并识别出这三个特性中究竟哪一个对模型行为具有决定性影响。为此，作者提出了一种一维因果窗口注意力机制实现的神经元胞自动机（Neural Cellular Automaton, NCA）变体——TextNCA，其采用分层架构，依次通过三个阶段，每个阶段使用不同大小的窗口（w ∈ {8, 32, 128}）和共享权重的迭代次数（T_s），在WikiText-103数据集上进行训练，总参数量约为30M，训练步数为6万。尽管该模型在困惑度（PPL）上未达到与之参数量相当的Transformer模型的表现（Hier-TextNCA: 60.3 vs. Transformer-6L: 52.8；Transformer-12L: 44.7），但研究将其定位为一种分析工具而非替代方案。研究发现，模型行为主要由“由窄到宽”的分阶段窗口调度策略所驱动：一个不进行迭代的滑动窗口Transformer若复用相同调度策略，即可逼近迭代模型性能（仅差+4.1 PPL），而破坏、反转或打乱该单调递增的调度顺序则导致性能显著下降（+16.7至+70.8 PPL）。迭代机制在调度基础上带来较小但可衡量的增益，存在明确最优值（T_s=4），超过后呈现U型退化趋势。此外，GRU门控结构与每步可学习嵌入是实现该迭代增益的必要条件；若训练时随机设定迭代次数，则可在推理时通过调整迭代次数作为超参数控制，但会以显著更高的绝对困惑度为代价。整体而言，本工作提供了一个受控实验框架，用于解析NCA类计算中哪些组件在语言建模中真正起关键作用。
链接: https://arxiv.org/abs/2608.02050
作者: Avni Mittal,Avinash Anand,Ashutosh Kumar,Dikshant Kukreja,Kritarth Prasad,Sushane Dulloo,Erik Cambria,Timothy Liu,Zhengkui Wang,Rajiv Ratn Shah
机构: Singapore Institute of Technology, Singapore; IIIT Delhi, India; Nanyang Technological University, Singapore; NVIDIA AI Technology Centre, Singapore
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Can a strictly local, iterated, weight-shared computation primitive support language modelling, and which of those three properties actually drives the model’s behaviour? We define \textscTextNCA, a 1D causal windowed-attention realisation of the Neural Cellular Automaton primitive, and study a hierarchical variant that cascades three stages with windows w \in \8, 32, 128\ and T_s shared-weight iterations per stage, all on WikiText-103 at roughly 30M parameters and 60k training steps. The model does not match a parameter-matched Transformer at this scale (Hier-TextNCA 60.3 vs.\ Transformer-6L 52.8 and Transformer-12L 44.7 PPL), so we treat it as an analytical probe rather than a proposed alternative. The behaviour we observe is largely explained by the staged narrow-to-wide schedule: a non-iterating sliding-window Transformer that reuses the same schedule comes within +4.1 PPL of the iterated model, while reversing, flattening, or breaking the monotonic ordering of the schedule costs between +16.7 and +70.8 PPL. Iteration adds a smaller bounded benefit on top of the schedule, with a clear optimum at T_s=4 and a U-shaped degradation beyond it. The GRU gate and learned per-step embeddings are required for that benefit to appear, and training with random T_s yields an inference-time iteration-count knob at the cost of substantially higher absolute PPL. We position the work as a controlled reading of which parts of NCA-style computation carry the weight in language modelling.
[NLP-30] CompanionBench: A Theory-Anchored Real-World-Grounded Benchmark for AI Emotional Companionship
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）伴侣在个人重要场景中大规模部署却缺乏有效评估的问题。现有基准测试存在诸多局限：依赖人工编写的场景与提示式模拟器，将共情能力简化为单一评分，且忽视了评价者偏差（如同家族偏爱和规模漂移）。为此，本文提出CompanionBench，一个交互式双语评估基准，其关键创新在于首次将场景与训练好的用户模拟器均基于去标识化的真实世界数据构建。通过隐藏披露门控机制，在不编写对话脚本的前提下动态控制交互状态空间；同时基于心理学与咨询学25个理论提炼出十项核心能力，其中四项（包容模糊性、自我客体响应性、积极共鸣、校准挑战）此前未被显式评估。评估采用双轴体系：主观十维评分量表与客观是否赢得深层披露的确定性指标。通过跨家庭面板设计缓解同家族偏爱，利用项目反应理论（Item Response Theory, IRT）分离代理质量与评价者严苛度。理论指导测量维度与人格设定，真实数据提供事件、历史与人物画像，实现理论覆盖与数据真实性统一。中文与英文排名高度一致（rho = 0.996 ZH / 0.953 EN），可复现。对28个代理的评估揭示了聚合评分掩盖的能力层级差异：情绪调节与校准挑战仍为普遍短板，而包容模糊性区分度最高；角色扮演类代理排名最低，表明沉浸感不等于关系胜任力。总体失败模式表现为以表面温暖替代实质性关系支持。研究将公开500组中英平行样本及评估代码。
链接: https://arxiv.org/abs/2608.02046
作者: Yao Liu,Guangjia Chai,Yuming Huang,Jihao Huang,Lei Wang,Junchen Wan
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 33 pages, 6 figures, 19 tables, 13 appendices. Bilingual (Chinese/English) interactive benchmark; 28 evaluated agents
Abstract:LLM companions are deployed at scale in personally consequential settings, yet poorly evaluated. Existing benchmarks use hand-authored scenarios and prompted simulators, aggregate empathy into one score, and overlook judge biases such as same-family favoritism and scale drift. We introduce CompanionBench, an interactive bilingual benchmark. To our knowledge, it is the first companion benchmark to ground both its scenarios and a trained user simulator in de-identified real-world data. A hidden disclosure gate branches each persona’s trajectory on the agent’s own behavior, controlling the interaction state space without scripting dialogue. We operationalize ten capabilities derived from 25 theories across psychology and counseling, four of them not graded explicitly by prior work: holding ambiguity, selfobject responsiveness, positive resonance and calibrated challenge. Agents are assessed on two complementary axes: a subjective ten-capability rubric and a deterministic measure of whether deeper disclosure was earned. A cross-family panel dilutes same-family favoritism; an Item Response Theory model separates agent quality from judge severity. Theory fixes what to measure and how personas are structured; real data supply events, history, and profiles – coverage from theory, authenticity from data. Rankings are reproducible in both languages (rho = 0.996 ZH / 0.953 EN). Evaluating 28 agents reveals capability-level differences obscured by aggregate scores. Emotion regulation and calibrated challenge remain common weaknesses; holding ambiguity discriminates most. Role-play agents rank near the bottom: immersion does not imply relational competence. Across agents, the dominant failure mode is substituting surface warmth for substantive relational support. We will release 500 Chinese-English parallel pairs and the evaluation code.
[NLP-31] ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLM s
【速读】： 该论文旨在解决多模态大语言模型在视觉标记（visual token）剪枝过程中，因采用固定剪枝比例而无法适配文本丰富的输入问题。尤其在以光学字符识别（OCR）为核心的任务中，关键证据可能仅由少量文本区域或特定字段构成，而盲目剪枝容易误删此类重要信息，同时保留大量视觉显著但语义无关的区域。其解决方案的关键在于提出一种无需训练的框架ET-Prune，将剪枝过程建模为证据分配（evidence allocation），通过解码器侧的局部查询-键块生成与问题相关的证据表征，主动保护具有文本特征的空间区域，并将证据的不确定性与密度转化为样本级的最小保留标记数（token floor）。随后通过三个渐进式的中间层事件，动态调整序列长度以逼近该预算：对分布分散或文本密集的证据保留更多标记，而对集中型证据则更激进地剪枝。在每种配置仅进行一次确定性前向传播的条件下，ET-Prune在六个主干网络-基准测试组合中表现领先或持平，且仅保留约一半的视觉标记；在OCRBench-v2上，相较最强剪枝基线分别提升1.80和0.68个百分点（Qwen3-VL-8B与InternVL3.5-8B），在MMBench v1.1上实现0.8467的圆周精确匹配准确率，优于原生模型（Vanilla）的0.8437，同时保持54.45%的平均视觉标记保留率。结果表明，该方法在文本丰富的多模态推理中实现了优越的质量-成本权衡，验证了基于证据感知的动态预算分配的有效性。
链接: https://arxiv.org/abs/2608.01979
作者: Zizhong Ding,Junxian Li,Kai Liu,Shaoqiu Zhang,Xiao Xiao,Linghe Kong,Yulun Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Code and supplementary material is at this https URL
Abstract:Visual token pruning reduces the inference cost of multimodal large language models, but a fixed token ratio is poorly matched to text-rich inputs. In OCR-centric tasks, decisive evidence can be a small number, label, or field whose relevance is specified by the question; indiscriminate pruning can erase that evidence while retaining visually salient but irrelevant regions. We present ET-Prune, a training-free framework that casts pruning as evidence allocation. It derives question-conditioned evidence from a decoder-side partial query-key block, safeguards text-like spatial regions, and converts evidence uncertainty and density into a sample-specific token floor. Three progressive middle-layer events then move the sequence toward this budget, retaining more tokens for diffuse or text-dense evidence and pruning concentrated evidence more aggressively. At the observed point estimates from one deterministic pass per configuration, ET-Prune leads or ties among pruned methods in all six backbone-benchmark comparisons at roughly half tokens. On OCRBench-v2, it leads the strongest pruned baselines by 1.80 and 0.68 percentage points on Qwen3-VL-8B and InternVL3.5-8B, respectively, while retaining about half of the visual tokens; on MMBench v1.1, it reaches 0.8467 circular exact-matching accuracy versus 0.8437 for Vanilla at 54.45% average visual-token retention. These results show a favorable observed quality-cost trade-off for evidence-aware dynamic budgeting in text-rich multimodal inference.
[NLP-32] ELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference
【速读】： 该论文旨在解决大规模语言模型（Large Language Model, LLM）推理服务在持续运行场景下根因分析（Root-Cause Analysis, RCA）困难的问题。由于单个请求涉及推理引擎、Python/C++后端、主机CUDA API、GPU内核及分布式通信等多个层次，现有工具要么仅提供原始时间线（raw timelines），要么依赖日志进行诊断，但均难以捕捉跨层执行语义与请求级结构信息。为此，论文提出Teller——一个非侵入式的、基于追踪（Trace）与日志感知的LLM推理根因分析框架。其核心解决方案在于：首先无须修改模型二进制文件即可采集NVTX/CUPTI追踪数据与服务日志；随后通过重建每请求的调用链树结构，并将日志行与对应执行步骤对齐；进而引入一种依赖感知的因果上下文切片（dependency-aware causal-context slice），有效保留父子关系、时间顺序与通信关联；并设计了一种追踪对编码（Trace Pair Encoding, TPE）分词器，将此类切片压缩为携带父节点、深度和持续时间属性的紧凑结构化令牌序列。在此基础上，Teller结合数值候选定位与多模态根因模型，联合预测异常步骤、定位可疑算子并生成自然语言解释。实验表明，在多节点GPU推理负载下，适度的TPE词汇量可使每步追踪长度压缩超80%，同时在横向（跨节点通信）与纵向（节点内执行栈）视图上均取得最优诊断性能，而过度压缩则显著降低诊断质量。进一步在低故障先验、强化基线、模态消融、解释质量评估及追踪开销分析中验证了Teller作为实用的故障初步筛查与证据定位基础架构的有效性。
链接: https://arxiv.org/abs/2608.01975
作者: Ruilin Xu,Junyi Li,Pengfei Chen,Zongxuan Xie
机构: Sun Yat-sen University(中山大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL); Machine Learning (cs.LG); Performance (cs.PF)
备注: 12 pages, 1 figure, 9 tables. Accepted to the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026)
Abstract:Large language model (LLM) inference has evolved from an offline workload into a continuously operated software service, yet root-cause analysis remains difficult because a single request spans the inference engine, Python/C++ backend, host CUDA APIs, GPU kernels, and distributed communication. Existing profilers expose raw timelines, while log-based diagnosis often misses cross-layer execution semantics and request-level structure. We present TELLER, a non-intrusive Trace- and Log-aware LLM inference Root-cause analysis framework. TELLER first collects NVTX/CUPTI traces and service logs without modifying model binaries, then reconstructs per-request call-chain trees and aligns log lines with the corresponding execution steps. We introduce a dependency-aware causal-context slice that preserves parent-child structure, temporal order, and communication relations, and a Trace Pair Encoding (TPE) tokenizer that compresses such slices into compact structural token sequences with parent, depth, and duration attributes. On top of these representations, TELLER combines numeric candidate localization with a multimodal root-cause model that jointly predicts abnormal steps, localizes suspicious operators, and generates natural-language explanations. Experiments on multi-node GPU inference workloads show a clear compression-accuracy trade-off: a moderate TPE vocabulary reduces per-step trace length by more than 80% while achieving the best overall performance on both horizontal (cross-node communication) and vertical (within-node execution stack) views, whereas more aggressive compression substantially degrades diagnosis quality. Further analyses under low-fault priors, strengthened baselines, modality ablations, explanation-quality checks, and tracing overhead show that TELLER provides a practical triage and evidence-localization substrate for LLM inference RCA.
[NLP-33] Look Ahead Before You Distill: Future Trajectory Validation of Teacher Guidance for Agent ic On-Policy Distillation
【速读】： 该论文旨在解决多轮代理任务中，由于学生模型在训练过程中逐步偏离教师模型所覆盖的状态空间，导致生成式 AI (Generative AI) 的在线策略蒸馏（On-policy Distillation, OPD）效果随时间衰减的问题。其核心挑战在于：尽管OPD能在学生访问的教师监督状态上提供指导，但学生轨迹的累积偏差会使其逐渐脱离有效监督区域，从而降低蒸馏信号的质量与密度。为此，论文提出FutureBridge-OPD（FTB），其关键创新在于在高分歧状态（high-disagreement state）执行一个简短的教师桥接（teacher bridge），并利用桥接后学生行为的后续轨迹来评估该桥接是否显著提升了正向蒸馏信号的密度。通过动态判断教师引导的长期有效性，FTB实现了对蒸馏时机与位置的智能选择，克服了传统OPD因静态监督带来的分布偏移问题。实验表明，在ALFWorld、WebShop和ScienceWorld三个基准上，相较于原始OPD和TCOD方法，FTB平均分别提升16.6和7.6分，且在不同学生规模与教师配置下均保持鲁棒性。
链接: https://arxiv.org/abs/2608.01953
作者: Chishui Chen,Yaoyou Fan,Te Sun,Yi Yang,Chenghao Sun,Delin Mao,Hongbo Qiao,Zuowei Zhang,Junxi Wang,Chenxing Sun,Yangen Hu,Lu Pan,Xuyang Liu,Linfeng Zhang
机构: Meituan LongCat Interaction(美团龙猫互动); Shanghai Jiao Tong University(上海交通大学); Fudan University(复旦大学); Peking University(北京大学); Nanjing University(南京大学); University of Chinese Academy of Sciences(中国科学院大学); Jilin University(吉林大学); University of Science and Technology of China(中国科学技术大学); The Hong Kong Polytechnic University(香港理工大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 15 pages, 5 figures
Abstract:On-policy distillation (OPD) provides teacher supervision on states visited by the student, reducing the distribution gap between training and inference. However, in multi-turn agentic tasks, student deviations may accumulate over time, gradually moving the trajectory away from states where teacher guidance remains effective. Our quantitative analysis further shows that high-disagreement states offer promising opportunities for teacher guidance, but determining whether such guidance is beneficial requires examining its effect on subsequent student trajectories. We propose FutureBridge-OPD (FTB), which executes a short teacher bridge at a high disagreement state and uses the resulting student continuation to assess whether the bridge increases the density of positive distillation signals relative to the teacher. On ALFWorld, WebShop, and ScienceWorld, under the main Qwen3-32B teacher to Qwen3-1.7B student setting, FTB outperforms vanilla OPD and TCOD by an average of 16.6 and 7.6 points, respectively, and remains effective across student scales and teacher settings. Our code is publicly available at this https URL.
[NLP-34] CultureVidBench: Benchmarking Cultural Understanding in Text-to-Video Generation
【速读】： 该论文旨在解决当前文本到视频（Text-to-Video, T2V）生成模型在文化多样性表征能力方面评估不足的问题。现有基准主要关注感知质量、物理合理性及文本-视频对齐，却未能有效评估生成视频是否准确呈现特定文化背景下的物品、行为、仪式、可见文本或音频线索等文化细节。为此，研究提出CultureVidBench——一个专门用于评估T2V模型文化理解能力的综合性基准。其核心创新在于构建了包含1,000个精心设计提示的多维度数据集，覆盖12个国家、6大洲、8个文化区域及14项文化维度，涵盖物质文化、社会习俗与表演、仪式典礼三类文化内容，并强调动态性与多模态特性，包括社会互动、仪式流程以及符合文化语境的可见文本与音频。通过人工用户评估与基于多模态大语言模型（Multimodal Large Language Model, MLLM）的自动化评估相结合，从文化忠实度、多模态文化呈现、语义一致性及感知质量四个维度系统评测7个代表性T2V模型。结果表明，尽管当前模型在语义对齐和视觉质量上表现良好，但在捕捉细粒度文化细节方面存在明显缺陷，尤其在非主流文化区域、复杂仪式及多模态文化信号的表达上表现不佳。因此，该研究的关键解决方案在于建立一个结构化、跨文化、多模态的评估框架，以推动T2V模型在文化敏感性和多样性理解上的进步。
链接: https://arxiv.org/abs/2608.01942
作者: Xianjing Han,Yuhan Su,Yang Deng,Dong Ma,Wee Peng Tay,Bin Zhu
机构: Nanyang Technological University (南洋理工大学); Centrale Supélec (中央理工-电子学院); Singapore Management University (新加坡管理大学); University of Cambridge (剑桥大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Multimedia (cs.MM)
备注: Project page: this https URL
Abstract:Text-to-video (T2V) generation models have advanced rapidly, yet their ability to represent diverse cultural contexts remains underexplored. Existing benchmarks mainly focus on perceptual quality, physical plausibility, and text-video alignment, but do not directly assess whether generated videos capture culturally specific objects, actions, rituals, visible text, or audio cues. We introduce CultureVidBench, a comprehensive benchmark for evaluating cultural understanding in T2V generation. CultureVidBench contains 1,000 curated prompts covering 12 countries, 6 continents, 8 cultural regions, and 14 cultural aspects organized into three categories: material culture, social practice performance, and ritual ceremony. Designed specifically for video generation, CultureVidBench emphasizes dynamic and multimodal cultural representation, including social interactions, ritual procedure, and culturally appropriate visible text and audio. We evaluate seven representative T2V models through human user studies and MLLM-based automatic assessment across cultural faithfulness, multimodal cultural rendering, semantic adherence, and perceptual quality. Results show that although current models achieve strong semantic adherence and visual quality, they often fail to faithfully capture fine-grained cultural details, particularly for underrepresented regions, rituals, and multimodal cultural cues.
[NLP-35] Automatic Annotation of Ancient Greek Vowel Length
【速读】： 该论文旨在解决古希腊语中“dichrona”（即 alpha、iota、upsilon 三者在音位上长/短元音的歧义问题），这一问题长期困扰古希腊语自然语言处理（NLP）研究，因其发音长度依赖于词素、形态、音变规则、句法结构及时代、文体和诗体等多重上下文因素。传统方法受限于缺乏大规模公开标注的带长音符号（macron）的古希腊语语料库，且现有方案多为针对特定语料库的静态字典，难以泛化。本文提出首个通用型古希腊语长音标注系统（macronizer），其核心创新在于基于标准 CoNLL-U 格式输入（包含词干、词性与形态标注），通过递归模块机制，使罕见词形可从同词根的常见词形继承正确的长音标记，实现上下文感知的长音推断。该系统的首要应用是生成机器学习训练数据；实验表明，仅使用该系统输出进行训练的小型字符级 Transformer 模型，能够超越规则系统未覆盖的边界情况，达到或超过其在金标准手工标注的散文与诗歌基准上的准确率。此外，长音标注还显著提升了下游韵律学任务（如诗歌音步分析）的性能。
链接: https://arxiv.org/abs/2608.01935
作者: Albin Thörn Cleland,Eric Cullhed
机构: Lund University (隆德大学); Uppsala University (乌普萨拉大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 5 pages, 0 figures
Abstract:Prior work in Ancient Greek NLP relies on corpora that do not disambiguate the phonemic vowel length of alpha, iota, and ypsilon, together known as the dichrona. Depending on lexeme, morphology, sandhi, syntax, and conventions of period, genre, and verse form, each of these letters can represent either a long or a short vowel. Deciding and marking the correct length is known as “macronizing”, a long-tail problem given the sheer mass of word forms and the context dependency of individual instances. No macronized corpus of Ancient Greek is publicly available at scale, so a stand-alone macronizer is needed. While previous work has shown how to build a static, corpus-bespoke vowel-length dictionary, the present paper constructs the first general-purpose macronizer for arbitrary Ancient Greek input. Given input carrying lemma, part-of-speech, and morphological annotation in the standard CoNLL-U format, a set of recursive modules lets less common word forms inherit markup from more common forms of the same lexical word. The macronizer’s chief application is generating training data for machine learning: we show that a small character-level transformer trained on the macronizer’s own output learns to generalize past the cases the rule-based system leaves unmarked, matching or exceeding its accuracy on a gold-standard, manually annotated benchmark of verse and prose. We also show that macronization can improve downstream prosodical NLP tasks like verse scansion.
[NLP-36] RAM: Enhancing Multimodal Reasoning with Trajectory-Derived Auxiliary Memory
【速读】： 该论文旨在解决多模态大推理模型（Multimodal Large Reasoning Models, MLRMs）在长推理轨迹中逐渐丢失早期信息、导致推理错误的问题。尽管现有方法主要通过持续保持视觉锚定来缓解这一问题，但研究发现，推理过程本身会将原始视觉观察转化为任务相关的关联、约束和中间结论，而这些推理衍生的信息在长轨迹中易被弱化。通过归因分析发现，推理正确性并非仅由图像归因决定，而是更依赖于推理轨迹是否能有效保留并整合这些推理衍生的信息。为此，论文提出TRAM（TRajectory-derived Auxiliary Memory），一种无需额外训练的增强解码方法：TRAM利用模型自身推理轨迹生成辅助记忆路径，将已完成的推理内容压缩为紧凑的潜在记忆，并通过快速与慢速递归流在线更新，再通过轻量级残差路径反馈至解码器的特定层。在四个MLRM变体和八个基准测试上的实验表明，TRAM在不增加训练成本的前提下，显著提升了数学、科学及通用视觉推理任务的表现。其核心创新在于构建了基于推理过程自生成的可动态更新的记忆机制，从而增强了长轨迹中的信息保留与整合能力。
链接: https://arxiv.org/abs/2608.01922
作者: Kang Liu,Zijing Wang,Yongkang Liu,Mengjie Zhao,Xiaocui Yang,Shi Feng,Yifei Zhang,Daling Wang
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Multimodal Large Reasoning Models (MLRMs) have achieved strong performance on tasks requiring visual understanding and multi-step inference. However, as reasoning trajectories grow, models may become less effective at using information established earlier in the context, increasing the risk of reasoning errors. Existing approaches primarily address this problem by sustaining visual grounding throughout reasoning. However, reasoning also transforms visual observations into task-specific relations, constraints, and intermediate conclusions whose influence may weaken over long trajectories. Our attribution analysis suggests that correctness is not consistently separated by image attribution alone, but is more closely associated with whether trajectories retain and integrate such reasoning-derived information across stages. Motivated by this, we introduce TRAM (TRajectory-derived Auxiliary Memory), a training-free method that augments standard decoding with an auxiliary memory pathway derived from the model’s own reasoning trajectory. TRAM consolidates completed reasoning into a compact latent memory, updates it online through fast and slow recurrent streams, and feeds it back into selected decoder layers through a lightweight residual pathway. Experiments across four MLRM variants on eight benchmarks show that TRAM improves performance over vanilla decoding on mathematical, scientific, and general visual reasoning tasks without additional training.
[NLP-37] HarnessCompass: Guiding Automatic Harness Evolution toward Generalizable and Effective Agent Harnesses
【速读】： 该论文旨在解决自动钩子（harness）演化过程中存在的过拟合、信号单一及组件间干扰等问题。现有方法通常依赖轨迹衍生信号，且联合优化所有钩子组件，导致在特定任务上过拟合并引发组件间的相互干扰。其解决方案的关键在于提出HarnessCompass框架，通过三方面创新实现突破：一是引入全局约束机制，仅允许与任务无关的通用性钩子调整，提升泛化能力；二是融合代理自身的主动第一人称反馈，丰富演化信号来源，增强对钩子使用效果的感知；三是采用分组件优化策略，在整合前分别优化各钩子模块，有效降低跨组件干扰的同时保持组件间的协同效应。实验结果表明，HarnessCompass在SWE-bench Verified数据集上以仅5次迭代将Pass@1从54%提升至66%，显著优于AHE方法，并展现出优异的任务外迁移能力和跨模型泛化性能。
链接: https://arxiv.org/abs/2608.01918
作者: Luan Zhang,Ruochen Zhou,Dandan Song,Zhengyu Chen,Yuhang Tian,Jun Yang,Huipeng Ma,Chenhao Li,Guangyuan Feng,Xudong Li,Yizhou Jin,Yan Xu
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Harness design plays a critical role in agent performance by shaping how large language models (LLMs) perceive, reason over, and act within executable environments. Recent work has proposed automatic harness evolution, which iteratively improves the harness from agent–environment interactions. However, existing methods often overfit to the evolution tasks, rely exclusively on trajectory-derived signals, and optimize harness components jointly, causing interference across components. We propose HarnessCompass, a novel automatic harness evolution framework built around constrained evolution, proactive feedback, and component-wise optimization. HarnessCompass first enforces global constraints on evolution, restricting modifications to task-agnostic harness changes that generalize beyond the evolution tasks. It then augments trajectory-derived evidence with proactive first-person feedback from the agent about harness usage, yielding richer signals for evolution. Finally, it decouples the optimization of different harness components before consolidating them into a unified harness, reducing cross-component interference while preserving component synergy. On SWE-bench Verified with GPT-5.4, HarnessCompass improves Pass@1 from 54% to 66% in only 5 evolution iterations, outperforming AHE in both effectiveness and evolution efficiency. In addition, the evolved harness transfers effectively to held-out tasks and other models, demonstrating substantially stronger generalization than prior automatic harness evolution methods.
[NLP-38] SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models
【速读】： 该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在视觉空间推理任务中表现不佳的问题，尤其针对现有方法依赖额外的3D先验输入或外部空间编码器所导致的模型复杂度增加及通用能力退化问题。其解决方案的关键在于提出一种参数高效且可即插即用的空间视觉语言模型（Spatio-vision Language Models, SpatioLM），通过设计一个非侵入式、无需额外输入的空间视觉模块，激发VLM自身隐含的空间知识。创新性地，该方法利用伪深度（pseudo depth）和相机信息作为监督信号，引导模型学习物理上一致的空间表征，从而在不引入外部空间编码器或3D先验的前提下显著提升空间感知与理解能力。实验表明，SpatioLM在多种任务中均取得显著性能提升，特别是在VSI-Bench上达到71.6分（首个突破70分的模型），同时有效保持了模型的通用能力，并在具身操作任务迁移中表现出色。
链接: https://arxiv.org/abs/2608.01899
作者: Jing Wu,Jianhua Wu,Jiayi Guan,Jiahong Chen,Jinghui Lu,Hangjun Ye,Bingzhao Gao,Long Chen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 27 pages,13 figures,16 tables
Abstract:Vision-Language Models (VLMs) perform well on commonsense reasoning tasks but struggle with visual spatial reasoning. Most existing solutions introduce extra 3D prior inputs or external spatial encoders, which increase complexity and degrade the underlying VLMs’ general-purpose capabilities after spatial fine-tuning. To this end, we propose a parameter-efficient \textit\textbfSpatio-vision \textbfLanguage \textbfModels (SpatioLM), that enhances spatial intelligence without extra 3D prior inputs or third-party spatial encoders. Concretely, we design a plug-and-play and non-invasive spatio-vision module that elicits the spatial knowledge inherent in VLMs. Furthermore, we innovatively leverage pseudo depth and camera information as supervision to guide the model in learning physically coherent representations. Extensive experiments show that SpatioLM achieves significant improvements in diverse tasks, including spatial perception and understanding while effectively limiting the degradation of general capabilities. Notably, the model achieves an impressive score of 71.6 on the VSI-Bench (the first model to surpass 70). In addition, it attains competitive performance when transferred to embodied manipulation tasks. Code is available at \hrefthis https URL\faGithub~spatio-lm.
[NLP-39] CRISP: Critical Step Perception for Training Efficient Deep Search Agents
【速读】： 该论文旨在解决深度搜索代理在执行复杂问题求解时存在的交互效率低下问题，具体表现为生成冗长且包含重复查询、低效探索和无关观测的决策轨迹。现有提升效率的方法通常通过统一减少工具调用频率来实现，但这种策略未区分必要证据获取与冗余操作，可能导致关键信息收集步骤被抑制。其解决方案的关键在于提出一种名为CRISP的框架，通过“关键步骤感知”（Critical Step Perception）机制，在训练过程中精准识别并保留对最终答案具有必要证据支持的工具交互步骤，同时剔除冗余步骤。CRISP首先利用强模型从答案反向遍历完整搜索轨迹，基于“逆向证据诱导”（Backward Evidence Induction）方法标注每个交互步骤是否为关键步骤；随后将这些标注结果蒸馏为轻量级的可扩展关键步骤识别器，实现单次前向遍历即可完成全轨迹分析。在策略优化阶段，仅对成功回放轨迹施加兼顾效率的奖励函数，从而在不牺牲答案准确率的前提下显著降低平均交互轮次。实验结果表明，在BrowseComp和HLE-Verified数据集上，CRISP分别实现了15.1%和33.2%的交互轮次减少，验证了其在保持竞争力答案准确率的同时大幅提升交互效率的有效性。
链接: https://arxiv.org/abs/2608.01867
作者: Haosi Mo,Zihao Yan,Ruiqing Zhang,Zhongli Li,Hexuan Deng,Xuebo Liu,Min Zhang
机构: 未知
类目: Computation and Language (cs.CL)
备注: 15 pages, 6 figures
Abstract:Large language models (LLMs) are increasingly extended into deep search agents that solve complex questions through multi-step interaction with external search and browsing tools. However, existing agents often incur substantial computational and interaction costs, generating lengthy trajectories that contain redundant queries, inefficient exploration, and irrelevant observations. Existing efficiency-oriented methods usually encourage agents to use tools less frequently, but treating all tool interactions uniformly may also suppress steps that gather necessary evidence. In this paper, we propose CRISP, a framework for training efficient deep search agents through critical step perception. Unlike prior efficiency methods that uniformly penalize tool use, CRISP distinguishes interactions that gather necessary evidence from redundant ones and shapes the training reward to preserve the former while pruning the latter, improving efficiency without sacrificing the evidence needed for correct answers. Specifically, CRISP first constructs critical-step labels with Backward Evidence Induction: starting from the final answer, a strong model traverses a completed search trajectory backward and judges whether each tool-interaction step provides or preserves evidence for the final answer. We then distill these step-wise judgments into a smaller critical-step recognizer, enabling full-trajectory analysis in a single pass. During policy optimization, an efficiency-aware reward is applied only to successful rollouts. Experiments on BrowseComp and HLE-Verified show that CRISP maintains competitive final-answer accuracy while reducing average interaction turns by 15.1% and 33.2%, respectively, demonstrating substantial improvements in interaction efficiency.
[NLP-40] Analyzing Speech Condition Effects in Dysarthric ASR: A Layer-wise Probing Study
【速读】： 该论文旨在解决普通话构音障碍语音（dysarthric speech）在自动语音识别（ASR）系统中性能显著下降的问题，特别是探究言语障碍如何改变模型内部表示的结构与分布。其核心挑战在于：现有ASR模型对正常语音优化良好，但对构音障碍语音的鲁棒性差，且缺乏对模型各层表征受病理语音影响机制的深入理解。解决方案的关键在于通过分层探针分析（layer-wise probing analysis），揭示Transformer ASR编码器在不同条件（原始障碍语音、基于说话人零样本语音合成的重合成、无条件语音合成）下，从底层声学特征到高层语义信息的表征演化规律。研究发现：1）音素边界信息在所有层次均较弱，音素身份仅在高层可恢复，识别困难被编码于最深层；2）声调敏感评估表明，普通话声调是持续性的错误来源；3）跨条件表征相似性随深度增加而发散，说明障碍语音对高层语义表示的影响远大于底层声学特征。基于此，提出单层LoRA微调（第7层）及子层范围（第5–8层）的参数高效适配策略，分别实现相对于全编码器微调仅3.5%和2.48%的相对性能损失，而顶层适配效果较差。这一结果将表征分析与参数高效微调（parameter-efficient fine-tuning）相结合，为低资源普通话构音障碍语音识别提供了面向层级的适配范式（layer-aware adaptation），显著提升了模型在受限数据场景下的适应效率与有效性。
链接: https://arxiv.org/abs/2608.01865
作者: Darwin Jelestin Muthu,Navya Gupta,Wei Lin Tay,Zhengchen Zhang,Daniel Wang Zhengkui,Rong Tong
机构: Singapore Institute of Technology (新加坡科技学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Automatic speech recognition (ASR) performance degrades sharply on dysarthric speech, yet how disordered articulation reshapes a model’s internal representations is underexplored. We present a layer-wise probing analysis of a transformer ASR encoder on Mandarin dysarthric speech under three transcript-matched conditions: original dysarthric speech, speaker conditioned zero-shot TTS resynthesis, and unconditioned TTS. The probes reveal a task-dependent hierarchy: phoneme boundary information stays weak for dysarthric speech at every layer, phoneme identity becomes recoverable toward the upper layers, and recognition difficulty is encoded in the deepest layers. Tone-sensitive evaluation shows Mandarin lexical tone is a persistent error source. Cross-condition similarity divergence grows with depth, indicating that disordered speech affects high-level representations more than low-level acoustic features. Guided by these findings, single-layer LoRA at layer 7 and adaptation on subset layers 5-8 achieve performance within 3.5% and 2.48% relative margins of full encoder adaptation, respectively, while upper-layer adaptation is less effective for dysarthric speech. These findings link representation analysis to parameter-efficient fine-tuning and motivate layer-aware adaptation for low-resource Mandarin dysarthric ASR.
[NLP-41] Divergent large language model predictions from convergent representations in ambiguous word pairs
【速读】： 该论文旨在解决生成式模型在处理词汇歧义（lexical ambiguity）时，其内部表示与行为输出之间不一致的机制问题，即尽管模型在下游任务中表现出色，但其深层嵌入（embedding）间的相似性与实际输出差异之间存在低相关性。其核心解决方案在于揭示了模型通过“中间层最大化区分、晚期层部分重构”的动态表征演化过程：对于同音异义词和多义词，模型在中间层实现表征的最大区分度，随后在后期层出现表征相似性回升，而此时下一词预测的KL散度达到峰值，表明语义区分已通过决策层面完成。激活修补实验提供了因果证据，证明晚期层的表征差异直接决定输出结果，即便嵌入空间中的相似性增强；单层消融实验进一步表明，不同模型虽在层间敏感性上表现各异，但整体消歧能力保持相当。这一发现揭示了语义区分信息在后期层仍被保留，但逐渐“不可见”于基于余弦相似度的嵌入度量方法，对依赖晚期嵌入相似性的语义搜索、检索与聚类等应用提出了重要警示。
链接: https://arxiv.org/abs/2608.01816
作者: K. Jack Scott,Narun Pat,Veronica Liesaputra
机构: University of Otago (奥塔哥大学)
类目: Computation and Language (cs.CL)
备注: 21 main text pages, 20 pages supplemental, 4 figures
Abstract:In this work we investigate how decoder-only transformers resolve lexical ambiguity through layer-by-layer analysis of three models spanning three parameter sizes (GPT-2-Small-117M, Llama-3.2-3B, Qwen2.5-32B). For both homonyms and polysemes, we find that representations become maximally distinct in middle layers, then partially reconverge in late layers, while the KL divergence between their next-token predictions reaches its maximum in the final layers. The activation patching experiment provides causal evidence that late-layer representational differences directly determine outputs despite apparent increased similarity in embedding space. Our single-layer ablation experiment indicates that models achieve equivalent disambiguation despite qualitatively different layer-wise vulnerabilities. These findings offer a mechanism for recent observations where models’ internal embedding similarities show low correlation with their behavioural outputs despite strong performance. The semantic distinctions therefore remain present but become increasingly invisible to similarity measures over the embeddings, with implications for embedding-based methods such as semantic search, retrieval, and clustering that rely on late-layer cosine similarity.
[NLP-42] RADAR: Rubric-Aware Dependency and Redundancy Analysis for LLM -as-Judge Evaluation
【速读】： 该论文旨在解决评分标准之间存在的行为耦合（behavioral coupling）问题，即在基于评分量表的大型语言模型（LLM）作为裁判的评估流程中，各评价维度并非独立，某一维度的改进可能系统性地影响其他维度的得分，从而扭曲用于模型发布或产品更新决策的综合评分。其解决方案的关键在于提出一种轻量级的预诊断框架RADAR，该框架通过生成针对性的合成探测样本（synthetic probes），对每个样本在所有评价标准上进行评分，并构建方向性耦合矩阵（directional coupling matrix），以揭示不同标准之间的共评分模式及其影响方向。在NVIDIA HelpSteer2、SumPubMed及Yale-Salesforce SummEval三个工业相关评估场景中的验证表明，仅需每项标准少量探测样本，RADAR即可有效恢复人类评分者间标准的相关性结构（皮尔逊相关系数r > 0.84），并为实践者提供关于冗余性、层级关系与聚合敏感性的具体审计信号，从而在大规模评估前实现对评分体系可靠性的前置诊断。
链接: https://arxiv.org/abs/2608.01810
作者: Divyansh Singh,Reza Davari,Afra Mashhadi
机构: Microsoft(微软); University of Florida(佛罗里达大学); University of Washington(华盛顿大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Rubric-based LLM-as-judge pipelines often assume that evaluation criteria provide independent signals. In practice, however, criteria can be behaviorally coupled: improving one criterion may systematically change scores on another, distorting aggregate scores used in model-release or product-update decisions. We introduce RADAR, a lightweight preflight diagnostic framework for estimating such coupling before large-scale evaluation. Given a rubric, RADAR generates targeted synthetic probes, scores each probe on all criteria, and produces a directional coupling matrix that shows which criteria co-score and how. We validate RADAR on three industry-relevant evaluation settings: NVIDIA HelpSteer2, SumPubMed, and the Yale-Salesforce SummEval benchmark. Using only a small number of probes per criterion, RADAR recovers human inter-criterion correlation structure (Pearson r 0.84) and provides practitioners with concrete audit signals about redundancy, hierarchy, and aggregation sensitivity before committing to large-scale judging.
[NLP-43] Illuminating Visual Identity in Universal Multimodal Embeddings CVPR2026
【速读】： 该论文旨在解决当前通用多模态嵌入（Universal Multimodal Embeddings, UME）方法中对视觉身份辨别（Visual Identity Discrimination, VisID）能力严重不足的问题。尽管视觉身份辨别在实例检索、重识别以及生成内容中的身份保持等任务中具有关键作用，但现有方法普遍缺乏对此能力的有效建模与训练。为此，本文提出了一种统一的视觉身份辨别形式化框架，并构建了大规模基准数据集MVEB（Multimodal Visual Identity Embedding Benchmark），该数据集融合真实世界与合成数据，支持模型评估与训练。其核心解决方案在于设计一种简单而有效的联合学习框架，通过精心设计的身份感知采样机制，协同优化通用多模态表示与视觉身份表示。实验表明，该方法显著提升了UME在身份辨别上的性能，同时保持了良好的通用多模态表现。本工作不仅揭示了长期被忽视的关键能力，也为实现更全面、更强大的通用多模态嵌入迈出了重要一步。
链接: https://arxiv.org/abs/2608.01794
作者: Jiawei Cao,Junyi Feng,Jiashen Hua,Ziheng Huang,Bing Deng,Kaijie Wu,Chaochen Gu,Jieping Ye
机构: Shanghai Jiao Tong University (上海交通大学); Alibaba Group (阿里巴巴集团)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to CVPR 2026
Abstract:Universal Multimodal Embeddings (UMEs) aim to unify various modalities and tasks into a shared representation space. In recent years, this field has witnessed substantial progress driven by the development of Multimodal Large Language Models (MLLMs). However, a crucial capability, visual identity discrimination, remains underexplored in existing UME methods, despite its critical role in a wide range of tasks, including instance retrieval, re-identification, and identity preservation in AI-generated content. To bridge this gap, we propose a unified formulation for visual identity discrimination~(VisID) and introduce \textbfMVEB ( \textbfM ultimodal \textbfV isual Identity \textbfE mbedding \textbfB enchmark), a large-scale benchmark curated from both real-world and synthetic datasets to support evaluation and training. Furthermore, we present a simple yet effective learning framework that jointly optimizes general multimodal and visual identity representations through a carefully designed identity-aware sampling mechanism. Extensive experiments demonstrate that our approach successfully endows UMEs with strong identity discrimination capability and maintains competitive general multimodal performance. We believe this work not only illuminates a critical yet neglected capability, but also takes a step toward more holistic universal multimodal embeddings. Code and data are available at \hrefthis https URLMVEB.
[NLP-44] Can You Trust the Confidence? ConfBench for Vision-Language Models on Document Extraction
【速读】： 该论文旨在解决智能文档处理（IDP）中视觉-语言模型（VLMs）生成的置信度分数不可靠，导致关键信息抽取（KIE）任务在自动化与人工审核之间路由决策失效的问题。现有文档基准数据集以高质量、无退化的样本为主，难以覆盖低精度区域，无法有效评估置信度校准性能。为此，研究提出首个针对置信度校准的基准测试平台ConfBench，通过在多样化文档集上应用20种受控退化管道，生成1,346个变体及超过7万条实体级评估数据，覆盖完整的准确率范围。关键解决方案包括：(i) 采用OCR+图像模态显著提升置信度估计准确性；(ii) 发现模型能力是置信度质量的主导因素，同一模型家族内置信度表现随能力单调提升，而跨家族参数量并非可靠预测指标；(iii) 不同模型间校准质量差异显著，从近乎完美到严重过度自信不等，且基于后处理的校准方法可重标定绝对置信值用于阈值路由，而不影响基于排序的操作性指标；(iv) 采用首令牌聚合的对数概率方法在所有聚合策略中表现最优。此外，研究引入ECARB这一评审预算指标，将区分度提升转化为实际运营成本节约。该工作公开发布ConfBench，为可信IDP部署中的置信度估计与校准方法提供系统性研究基础。
链接: https://arxiv.org/abs/2608.01792
作者: Priyashree Roy,Sujitha Martin,Mohammad Rostami,Spencer Romo,Renhao Xue,Bob Strahan,Diego A. Socolinsky,Boyi Xie,Md Mofijul Islam
机构: Amazon Web Services (亚马逊云科技)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Intelligent document processing (IDP) with vision-language models (VLMs) hinges on confidence scores trustworthy enough to route extractions between automation and human review. Existing document benchmarks are dominated by clean, high-quality samples, leaving low accuracy regions too sparse for calibration assessment. We introduce ConfBench, the first calibration-specific benchmark for key information extraction (KIE), built by applying 20 controlled degradation pipelines to a diverse document set, yielding 1,346 variants and 70K+ entity-level evaluations spanning the full accuracy spectrum. We evaluate four proprietary and three open-weight VLMs under verbalized and log-probability confidence estimation methods across three input modalities, and find: (i) OCR+Image modality results in more accurate confidence estimates; (ii) model capability is the dominant factor: within the Claude family confidence quality scales monotonically with capability, while across families parameter count is a poor predictor; (iii) calibration quality varies widely across models, from near-perfect to severely overconfident, and per-model post-hoc correction rescales these absolute confidence values for threshold-based routing without altering ranking-based operational metrics; and (iv) log-probability with first-token aggregation consistently outperforms mean-token and margin aggregations. We also introduce ECARB, a review-budget metric translating discriminative gains into operational savings. We release ConfBench publicly to enable systematic study of confidence estimators and calibration methods for trustworthy IDP application deployment.
[NLP-45] REFLEX: Rethinking MoE Inference as Refinement-Aware Compute Allocation in Diffusion Language Models
【速读】： 该论文旨在解决扩散语言模型（Diffusion Language Models, DLMs）中专家混合模型（Mixture-of-Experts, MoE）的计算分配不匹配问题。在标准MoE框架下，每个令牌均被分配相同的专家计算预算，而实际上不同令牌在去噪过程中具有异质性的精炼需求（refinement demand），导致计算资源浪费。其核心挑战在于：当前固定路由策略无法动态响应去噪过程中各令牌所处的精细化阶段差异，造成计算负载与实际精炼需求之间的错配。本文提出一种无需训练的解决方案——REFLEX（Refinement-aware Flexible expert allocation），其关键创新在于构建一种“粗粒度到细粒度”的专家预算分配层级结构，使计算资源随去噪过程中的块相对精炼角色动态调整，并引入前沿进展评分（Frontier-Progress Score）以确定活跃块的优先级。该方法在保持默认路由器不变的前提下，重新组织专家计算分配，实现对异质性精炼状态的感知式算力调度。实验表明，REFLEX在多个主流基准上平均降低15%的专家计算量，同时在多数任务中维持或提升生成质量，展现出更一致的质量-计算权衡特性，验证了基于精炼需求动态分配计算资源在DLMs中的有效性。
链接: https://arxiv.org/abs/2608.01784
作者: Xiang Xia,Cheng Yan,Yiming Zhang,Jiazheng Liu,Hongyu Zhang,Wuyang Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Mixture-of-experts (MoE) models increase parameter capacity by activating only a small subset of experts for each token. This conditional-computation paradigm has enabled autoregressive language models to scale model capacity without a proportional increase in per-token computation. In diffusion language models (DLMs), however, each denoising forward jointly revisits all token positions despite their sharply different refinement demands, while the default fixed token-choice routing assigns them a uniform expert budget, creating a mismatch between expert computation and refinement demand. We argue that MoE inference in DLMs should therefore be viewed as refinement-aware compute allocation across heterogeneous token refinement states. We propose REFLEX (\textbfREfinement-aware \textbfFLEXible expert allocation), a training-free method that keeps the default router unchanged while reorganizing expert computation around the evolving refinement process. Specifically, REFLEX introduces a coarse-to-fine hierarchy for expert-budget allocation that aligns computation with block-relative refinement roles while using the Frontier-Progress Score to resolve active-block priorities. Across multiple widely used benchmarks on two representative MoE-based DLMs, LLaDA-MoE and LLaDA2.0-mini, REFLEX reduces allocated expert computation by 15% on average while preserving or even improving generation quality on most benchmarks relative to default routing. Compared with autoregressive-style variable-expert routing methods, REFLEX also yields a more consistent quality–computation trade-off, further supporting the importance of allocating expert computation according to the heterogeneous refinement demands exposed within each denoising forward.
[NLP-46] Constructing Parallel Multidimensional Chromatic Lexicons for Corpus-Assisted Analysis of Russian and English Texts
【速读】： 该论文旨在解决文学文本中颜色词（colour terms）语料辅助语言学分析工具相对匮乏的问题。其核心解决方案在于构建两个多维色彩词典：一个针对俄语（224个条目），另一个针对英语（141个条目）。词典构建过程包括从专业资源与研究文献中收集色彩词汇、对比两种语言的词汇体系、人工核查翻译候选词，并处理语言特有的形态特征。此外，词典不仅识别颜色词与视觉描述词，还依据色相（hue）、饱和度（saturation）和温度（temperature）对条目进行分类。为验证其应用价值，研究选取安德烈·别尔耶（Andrei Bely）诗歌语料（20,373词符）与艾米莉·狄金森（Emily Dickinson）诗歌语料（28,479词符）开展试点分析。所有匹配项均在上下文中复核，并分类为“确认色彩”（Confirmed_chromatic）、“模糊视觉”（Ambiguous_visual）或“排除”（Excluded）。分析分两阶段进行：第一阶段严格分析仅包含确认色彩词汇；第二阶段敏感性分析纳入确认与模糊色彩词汇，以检验边界案例编码是否影响主要结论。定量结果显示，所选别尔耶语料中确认色彩词出现频率是狄金森语料的3.4倍，凸显多维分析方法的有效性。本研究的主要贡献在于提出了一套透明且可复用的多语言色彩词典构建与应用流程，为跨语言文学色彩语义研究提供了可扩展的方法论支持。
链接: https://arxiv.org/abs/2608.01752
作者: Larisa Nikitina
机构: 未知
类目: Computation and Language (cs.CL)
备注: 14 pages, 3 tables
Abstract:This article addresses the relative scarcity of research tools for the corpus-assisted linguistic analysis of colour terms in literary texts. It describes the development of two multidimensional chromatic lexicons: one for Russian (224 entries) and one for English (141 entries). Lexicon construction involved sourcing colour vocabulary from specialised resources and research literature, comparing the two language inventories, manually checking translated candidates, and addressing language-specific morphological features. In addition to identifying colour terms and visual descriptors, the lexicons classify entries according to hue, saturation, and temperature. To demonstrate their practical application, a pilot study was conducted on purposively sampled corpora of poetry by Andrei Bely (20,373 tokens) and Emily Dickinson (28,479 tokens). All retrieved matches were checked in context and classified as Confirmed_chromatic, Ambiguous_visual, or Excluded. The analysis was implemented in two main stages: a strict analysis including confirmed chromatic lexis only, followed by a sensitivity analysis incorporating both confirmed and ambiguous chromatic lexis to determine whether coding decisions about borderline cases affected the main findings. The quantitative results indicated marked differences in the use of colour terms, visual descriptors, hue, saturation, and temperature. Specifically, the analysis revealed that confirmed chromatic terms occurred 3.4 times more frequently in the sampled Bely corpus than in the Dickinson corpus. These findings demonstrate the analytical value of a multidimensional approach, with the main contribution of this study being a transparent and reusable procedure for constructing and applying multilingual chromatic lexicons.
[NLP-47] oward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在强化学习（Reinforcement Learning, RL）后训练过程中，优化新目标时导致原有能力退化的“遗忘问题”。现有方法普遍采用全策略KL正则化（full-policy KL regularization）来限制策略漂移，但其对整个输出分布施加约束，过度抑制了探索与目标任务的学习。为此，论文提出一种名为正确性条件KL正则化（Correctness-Conditioned KL Regularization, CoKL）的条件化正则化框架，其关键在于将正则化约束从完整的输出分布缩小至仅针对参考模型支持的正确响应子集的条件分布，从而实现更精准的能力保留。通过引入前向KL散度并设计可落地的有限组训练目标，CoKL在群体层面解耦了正确响应的总概率与其条件分布，仅对参考模型支持的正确响应间的相对概率分配进行正则化，避免直接锚定错误输出或整体正确性质量。理论分析表明，传统全策略前向与反向KL正则化在参考策略不完美时会诱导严格最优正确性差距，而CoKL有效规避了此局限。在多解控制环境及跨模型规模的持续后训练实验中，CoKL显著优于现有方法，在提升目标任务性能的同时更好地保留了模型原有能力。
链接: https://arxiv.org/abs/2608.01743
作者: Li Wang,Xiaodong Lu,Xiaohan Wang,Jiajun Chai,Wei Lin,Tianhao Peng,Guojun Yin
机构: Nanyang Technological University (南洋理工大学); Meituan(美团)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning (RL) has become a central paradigm for large language model (LLM) post-training, but optimization toward new objectives can degrade capabilities already present in the base model. KL regularization is widely used to mitigate such forgetting by constraining policy drift toward a reference model. However, standard full-policy KL regularization constrains the entire response distribution and may unnecessarily restrict exploration and target-task learning. This raises a natural question: can a more precise constraint preserve existing capabilities while minimizing interference with learning new tasks? To this end, we propose \underlineCorrectness-Conditioned \underlineKL Regularization (CoKL), a conditional regularization framework that narrows the preservation constraint from the full output distribution to correctness-conditioned response distributions. We instantiate CoKL with forward KL divergence and derive a practical finite-group training objective for RL-based LLM post-training. At the population level, CoKL decouples the total probability assigned to correct responses from their correctness-conditioned distribution, thereby regularizing the relative probability allocation among reference-supported correct responses without directly anchoring incorrect outputs or total correctness mass. We further show that full-policy forward and reverse KL regularization induce a strict optimal correctness gap when the reference policy is imperfect, whereas CoKL avoids this limitation. Experiments in controlled multi-solution environments and continual post-training settings across multiple model scales demonstrate that CoKL achieves a more favorable balance between target-task improvement and prior-capability retention than existing regularization methods. Our code is available at this https URL.
[NLP-48] MemSIF: From Structured Interactions to Dual-Track Fact Memory for LLM Agents AAAI2027
【速读】： 该论文旨在解决大语言模型（LLM）代理在长时程交互中因现有记忆系统存在固有缺陷而导致的长期记忆失效问题。核心挑战源于两类反复出现的错位模式：时间-结构错位（Temporal-Structural Misalignment, TSM）与延迟效用显现（Delayed Utility Manifestation, DUM）。TSM表现为时间上的邻近性无法可靠反映主题或事件层面的相关性，而DUM则指写入时刻的显著性不能有效预测未来查询的实用性。为缓解上述问题，论文提出MemSIF（带结构化交互与事实的记忆框架），其关键在于双轨设计：结构化交互记忆（Structured Interaction Memory）将原始交互数据组织为保持局部主题一致性的主题段（Topical Segments）和维持跨时间事件连续性的事件轨迹（Event Trajectories）；双重轨道事实记忆（Dual-Track Fact Memory）则通过核心事实记忆（CoreFact）在写入时固化由模式引导的稳定信息，以及主动事实记忆（ActiveFact）按需生成并优先保留来自多源历史支持且重复查询需求高的事实，实现动态可复用。实验在LoCoMo与LongMemEval-S基准上验证了该方法的有效性，结果显示，MemSIF在五种骨干模型下均取得最高总准确率（Total ACC），优于最强基线2.29%–8.79%（LoCoMo）与2.87%–6.15%（LongMemEval-S），证明了结构化交互记忆与双重轨道事实记忆结合对缓解TSM与DUM的显著作用。
链接: https://arxiv.org/abs/2608.01742
作者: YuFei Luo,Xiucheng Xu,Zhen Yang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Submitted to AAAI 2027. 19 pages, 10 figures, 18 tables
Abstract:Long-term memory is critical for LLM agents operating over long-horizon interactions. However, several persistent limitations of existing memory systems can be traced to two recurring misalignment patterns in long-term interaction settings: Temporal-Structural Misalignment (TSM) and Delayed Utility Manifestation (DUM). TSM arises when temporal proximity does not reliably align with topical or event-level relatedness, whereas DUM arises when write-time salience does not reliably predict future query utility. To mitigate these misalignment patterns, we propose MemSIF (Memory with Structured Interactions and Facts), a structured interaction-to-fact memory framework. Structured Interaction Memory organizes raw interactions into Topical Segments that preserve local topical coherence and Event Trajectories that maintain cross-time event continuity. Dual-Track Fact Memory uses two complementary tracks: CoreFact memory consolidates stable, schema-guided information at write time, whereas ActiveFact memory forms facts on demand and promotes those supported by multiple historical sources and recurring query demand for reuse. Experiments on LoCoMo and LongMemEval-S across five backbone LLMs show that MemSIF achieves the highest Total ACC in all settings, outperforming the strongest baseline by 2.29%-8.79% on LoCoMo and 2.87%-6.15% on LongMemEval-S. These results support the effectiveness of combining Structured Interaction Memory with Dual-Track Fact Memory to mitigate TSM and DUM. Code is available at this https URL.
[NLP-49] PGMem: Tightly Coupled Persona-Memory Graph for Lifelong Personalized Agents
【速读】： 该论文旨在解决长期个性化对话代理在用户人格（persona）随时间演化过程中，记忆系统与人格表征之间耦合松散所导致的“记忆-人格有效性差距”与“人格感知检索差距”问题。现有记忆系统虽能有效组织历史事件，但将人格特征存储为脱离具体事件支持的扁平化描述，致使人格信号缺乏可追溯性与动态更新依据。其解决方案的关键在于提出PGMem——一种异构的人格-记忆图结构，通过类型化的溯源边（provenance edges）与证据边（evidence edges）将事件节点与人格节点进行显式关联，确保每条人格信号均可追溯至支撑或修正它的具体事件。在检索阶段，PGMem以查询相关的种子节点为起点进行图扩展，并基于证据的有效性对候选信号进行排序，从而实现更精准、可解释的上下文感知响应。实验结果表明，相较于基于摘要、人格感知、图结构及代理式记忆的基线方法，PGMem在三个基准测试中均表现更优，且随着上下文规模增大性能持续提升。
链接: https://arxiv.org/abs/2608.01708
作者: Wonjun Choi,Yerim Kim,Yukyung Lee,Susik Yoon
机构: Korea University, Seoul, Korea; Boston University, Boston, USA
类目: Computation and Language (cs.CL)
备注:
Abstract:Long-term personalized dialogue agents must track user preferences as their personas evolve. Existing memory systems organize past events well, but store personas as flat profiles detached from the events that justify them. This loose coupling leads to the memory-persona validity gap and the persona-aware retrieval gap. We propose PGMem, a heterogeneous persona-memory graph that connects event and persona nodes through typed provenance and evidence edges, keeping each persona signal traceable to the events that support or revise it. At retrieval time, PGMem expands from query-relevant seeds and ranks signals by evidential validity. Across three benchmarks with small language model backbones, PGMem consistently outperforms summary-based, persona-aware, graph-structured, and agentic memory baselines, and improves performance as the context grows. The source code of PGMem is available at this https URL
[NLP-50] Progressive Agent Skill Generation via Reinforcement Learning
【速读】： 该论文旨在解决现有技能生成方法在异构证据源下依赖启发式规则或流水线式整合所带来的局限性，即这些方法需针对不同数据源进行专门设计，缺乏通用性。其核心挑战在于技能本身缺乏自然的监督信号（如相关性或正确性），其价值只能通过其是否提升智能体在下游任务中的表现来间接评估。为此，本文提出Skill-α，一种基于强化学习的渐进式高质量技能生成方法。其关键创新在于将技能生成建模为一个可逐次评估的编辑过程，通过引入一种新颖的回滚奖励（rollback reward），在锚定查询上比较原始技能与编辑后技能在下游执行效果的差异，从而对每一步编辑进行有效评估。实验表明，Skill-α在文档到技能和经验到技能两种场景下均显著优于基于启发式或流水线的方法；在GPT-4o作为主工作器的情况下，其平均下游成功率分别较最强基线提升3.3个百分点（CL-Bench）和6.7个百分点（tau2-bench）。消融实验进一步验证了回滚奖励与渐进式生成机制的重要性。
链接: https://arxiv.org/abs/2608.01678
作者: Junhao Shen,Zhanqiu Zhang,Yiwen Guo,Hong Cheng
机构: The Chinese University of Hong Kong (香港中文大学); LIGHTSPEED
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Code is available at this https URL
Abstract:Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources. In contrast, learning-based approaches offer a more unified way to model skill generation across heterogeneous sources. However, learning-based skill generation remains challenging because skills lack a natural supervision signal based on relevance or correctness; their value can largely be determined only by whether they improve the behavior of the agent on downstream tasks. To address this challenge, we propose Skill- \alpha , a reinforcement learning method for progressively generating high-quality agent skills. Specifically, we formulate skill generation as a sequential editing process that decomposes skill construction into individually evaluable edits, and introduce a novel rollback reward that evaluates each edit by comparing downstream execution under the original and edited skills on an anchored query. Extensive experiments show that Skill- \alpha generates more effective skills than methods based on heuristics or pipelines in both document-to-skill and experience-to-skill settings. Under the main GPT-4o worker, Skill- \alpha improves average downstream success rates over the strongest skill-generation baseline by 3.3 points on CL-Bench and 6.7 points on tau2-bench. Further ablations validate the importance of rollback reward and progressive generation.
[NLP-51] Understanding Sparse Attention Selectivity in Long-Context Foundation Models via Counterfactual Evaluation
【速读】： 该论文旨在解决生成式 AI（Generative AI）在长上下文处理中广泛采用的稀疏注意力（Sparse Attention）机制所引发的关键问题：即在模型推理过程中，因丢弃部分注意力块而导致特定内容对输出影响发生不可观测的改变，而现有评估框架无法有效审计这种影响变化。其核心挑战在于，尽管稀疏化提升了计算效率，但缺乏对内容影响力动态变化的可解释性与可验证性。论文提出的关键解决方案是引入一种基于匹配探针卡（matched probe cards）的密集校准反事实审计方法，通过设计三类探针——“黄金”（Gold，携带正确标签）、“毒饵”（Poison，携带目标错误标签）和“良性”（Benign，仅填充）——在六种布局对称条件下进行实验，从而隔离出稀疏化本身所带来的效应。研究发现两种竞争机制：信号集中效应（信号保留强度远高于背景填充块）与集成损失效应（丢弃块导致跨块注意力断裂）。进一步分析表明，压缩比（c）调控二者平衡，高压缩下多数模型单元呈现更强的稀疏放大效应，甚至出现影响方向反转。三种独立验证路径（BSFA路由重播、可控块Top-k选择、KV缓存淘汰）结果一致，证实稀疏化会以聚合准确率无法察觉的方式改变内容影响力。为此，作者提供了一个开放可部署的测量框架，适用于任何暴露块身份信息的模型，实现对稀疏注意力影响的细粒度审计。
链接: https://arxiv.org/abs/2608.01676
作者: Xingyu Ren,Youran Sun,Chugang Yi,Haizhao Yang
机构: The Chinese University of Hong Kong, Hong Kong, China; University of Maryland, College Park, MD, USA
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Sparse attention is widely deployed in long-context serving stacks, yet no framework audits how discarding blocks changes the influence of specific content on model output. We first establish that the phenomenon is real and causal: Block Sparse Flash Attention (BSFA) route replay across four architectures changes output decisions in 13 of 16 cells, with zero identity-replay label flips. We then introduce a dense-calibrated counterfactual audit using matched probe cards—Gold (carrying the correct answer label), Poison (carrying a target wrong label), and Benign (filler only)—under six-layout position symmetry, isolating the sparsification-specific effect. Two patterns compete. Signal concentration: the selector preserves Gold and Poison blocks far above filler-matched Benign blocks (G \approx P \gg B across all model–task pairs). Integration loss: discarding blocks severs cross-block attention—confirmed by an ablation where isolating the probe block collapses its influence from 4.48 logits to zero. Compression ratio governs the balance: a full sweep from mild ( c=0.25 ) to aggressive ( c=0.75 ) compression across four model–task pairs reveals that three of four cells move toward stronger sparse amplification at higher compression, with two exhibiting sign reversals. Three independent arms—BSFA route replay, controlled block-top- k , and KV-cache eviction—converge: sparsification changes content influence in ways aggregate accuracy cannot detect. We provide an open measurement framework deployable on any model exposing block identities. Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2608.01676 [cs.CL] (or arXiv:2608.01676v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.01676 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-52] Learning What to Remember: Test-Time Training via Context Distillation
【速读】： 该论文旨在解决长上下文建模中如何有效利用有限记忆容量以保留未来预测可能需要的关键信息这一核心问题。现有测试时训练（Test-time Training, TTT）方法仅优化重构或在线适应目标，忽视了所保留信息对未来任务的潜在价值。其解决方案的关键在于提出一种名为测试时上下文蒸馏（Test-time Context Distillation, TTCD）的新框架，通过引入自监督目标来指导模型在有限记忆资源下优先保留对未来预测至关重要的上下文信息。具体而言，TTCD采用长窗口教师模型监督短窗口学生模型的快速权重（fast weights），利用两者隐藏状态差异生成密集的自监督信号，引导模型学习对后续词元预测具有重要意义的上下文表征。研究进一步聚焦于就地版本——就地测试时上下文蒸馏（In-Place TTCD, IP-TTCD），该版本直接使用原有MLP参数作为快速权重，无需额外参数。实验表明，IP-TTCD在从零开始预训练的长上下文语言建模任务中持续优于DeltaNet、Gated DeltaNet、滑动窗口注意力和传统TTT方法；同时，它还能通过持续预训练使已有Transformer模型在推理阶段动态调整参数，仅以轻量级架构增强即获得强大的长上下文建模能力。该成果推动了面向架构的持续学习（architectural continual learning）的发展。
链接: https://arxiv.org/abs/2608.01672
作者: Zixuan Wang,Xingyu Dang,Rui-Jie Zhu,Zixin Wen,Hengyu Fu,Wenhao Chai,Jason D. Lee
机构: Princeton University (普林斯顿大学); UC Berkeley (加州大学伯克利分校); UC Santa Cruz (加州大学圣克鲁斯分校); Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Effective long-context modeling is not merely about retaining more of the past, but about preserving the information that may prove relevant later. Test-time training (TTT) is an appealing approach that performs online parameter updates for long-context modeling, yet existing TTT methods only optimize either reconstruction or online adaptation objectives without considering the future utility of retained information. In this work, we propose \textbfTest-\textbfTime \textbfContext \textbfDistillation (TTCD), a TTT framework that introduces a self-supervised objective for allocating limited memory capacity for future use. Specifically, TTCD uses a long-window teacher to supervise the fast weights of a short-window student, where the hidden-state discrepancy between them offers a dense, self-supervised signal guiding the model to memorize the contextual information crucial for future token predictions. We focus on an in-place variant: In-Place TTCD (IP-TTCD), which uses the existing MLP parameters as the fast weights. Experiments on long-context language modeling tasks show IP-TTCD consistently outperforms DeltaNet, Gated DeltaNet, sliding-window attention, and TTT when pre-trained from scratch. Furthermore, IP-TTCD allows pre-trained transformer models to adapt their parameters during inference through continual pre-training, gaining long-context capabilities with only a lightweight architectural augmentation. Our results position TTCD as a step toward architectural continual learning.
[NLP-53] Style Wins Substance Loses: A Diagnosis of LLM -as-Judge in Idea Generation
【速读】： 该论文旨在解决大语言模型（LLM）在科学创意评估中是否受到表面风格化表达干扰的问题，即评估结果是否真正反映科学内容的实质性，而非受文本风格的影响。其核心挑战在于现有模型对写作风格高度敏感，导致评估结果存在显著的风格偏差（stylistic bias），从而影响科学创意评价的公平性与可靠性。解决方案的关键在于提出一个统一的三组件基准框架——SciStyleBench：首先，构建了可控风格扰动的三阶段评估环境SciStyleStage，通过在无上下文、固定领域上下文及开放域检索上下文中对600个科学创意施加15种风格变体，生成每种设置下9,000个评估实例；其次，设计了一套量化指标体系SciStyleMetrics，包括风格偏差指数（SBI）、科学实质识别率（SRR）和对抗性胜率（AWR），用于衡量评分稳定性、实质区分能力与排序鲁棒性；最后，开发可插拔的评估模块SciStyleExtractor，通过预先预测并分离文本风格类型与偏离程度，实现风格条件下的评估，从而检验风格感知机制是否能有效降低风格偏差。实验表明，直接使用LLM作为评判者仍对风格敏感，而引入SciStyleExtractor后，SBI由0.566降至0.501，同时SRR和AWR分别从0.504和0.554提升至0.759和0.899，证明通过显式建模风格信息可实现对风格变化的不变性，同时保持对科学实质的敏感度。因此，该研究为科学创意评估中的风格偏差问题提供了系统性的诊断、量化与缓解路径。
链接: https://arxiv.org/abs/2608.01666
作者: Fengxian Ji,Yuke Li,Jingpu Yang,Juanfan Wu,Fan Zhang,Zhexuan Cui,Yu Xie,Min Peng,Qianqian Xie,Xiuying Chen,Zhuohan Xie
机构: University of Science and Technology of China (中国科学技术大学); Tsinghua University (清华大学); Peking University (北京大学); Fudan University (复旦大学); Shanghai Jiao Tong University (上海交通大学); Zhejiang University (浙江大学); Nanjing University (南京大学); Sun Yat-sen University (中山大学); Harbin Institute of Technology (哈尔滨工业大学); Beijing Institute of Technology (北京理工大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: First three authors are co-first authors
Abstract:However, whether these judges truly evaluate the scientific substance of ideas or are influenced by superficial stylistic presentation remains an open question. To address this question, we propose SciStyleBench, a unified three-component benchmark for diagnosing and mitigating stylistic bias in LLM-based idea evaluation: (i) First, SciStyleStage, a three-stage evaluation environment that applies controlled stylistic perturbations to fixed scientific content across three settings no context, fixed-domain context, and open-domain retrieval context, covering 600 scientific ideas and 15 style variants, with 9,000 evaluation instances per setting; (ii) Second, SciStyleMetrics, a set of quantitative measures, including Style Bias Index (SBI), Substance Recognition Rate (SRR), and Adversarial Win Rate (AWR), to characterize how stylistic variation affects scoring stability, substance discrimination, and ranking robustness; (iii) Third, SciStyleExtractor, a plug-and-play evaluation module that separates presentation style from scientific content by predicting style type and deviation before style-conditioned evaluation, enabling us to assess whether style awareness reduces stylistic bias. Experiments on SciStyleBench show that direct LLM judges remain sensitive to writing style and struggle to distinguish scientific substance. In contrast, SciStyleExtractor reduces SBI from 0.566 to 0.501 while increasing SRR and AWR from 0.504 and 0.554 to 0.759 and 0.899, respectively. These results suggest that robust idea evaluation requires invariance to stylistic variation without sacrificing sensitivity to scientific substance. Overall, SciStyleBench provides a systematic framework for identifying, quantifying, and mitigating stylistic bias in scientific idea evaluation.
[NLP-54] LongCat Sparse Attention: Taming the Lightning via Streaming-aware Hierarchical Cross-Layer Indexing
【速读】： 该论文旨在解决生成式 AI（Generative AI）在长序列建模中因稀疏注意力机制（Sparse Attention）的索引器（Indexer）导致的系统级瓶颈问题，具体包括：索引过程存在 O(L2) 的评分开销，以及输出引发硬件效率低下的离散内存访问模式。其解决方案的关键在于提出一种软硬件协同设计框架——LongCat 稀疏注意力（LSA），通过三项互补且正交的技术实现优化：（1）流式感知索引（Streaming-Aware Indexing），将分散的键值（KV）条目重排为硬件对齐的连续布局，支持合并的高带宽内存（HBM）访问；（2）跨层索引（Cross-Layer Indexing），利用跨层蒸馏机制复用单层生成的索引结果，分摊索引开销；（3）分层索引（Hierarchical Indexing），采用粗粒度到细粒度的逐级筛选策略，显著减少每查询的候选集规模与索引计算量。大规模扩展实验表明，LSA 在从 69B-A3B 到 560B-A27B 的多种模型上均能实现与全注意力相当的性能，并支持长达百万词元的原生训练，驱动 LongCat-2.0（1.6T-A48B）的开发。此外，研究开源了 LongCat-Flash-Lite-Sparse（69B-A3B）以促进后续研究。
链接: https://arxiv.org/abs/2608.01662
作者: Wen Zan,Jiaqi Zhang,Jianchao Tan,Hong Liu,Cunguang Wang,Xiang Li,Duyue Ma,Guanyu Wu,Yifan Lu,Fengcun Li,Yerui Sun,Peng Pei,Yuchen Xie,Xunliang Cai
机构: Meituan(美团)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
备注:
Abstract:DeepSeek Sparse Attention (DSA) enables efficient long-context modeling through its Lightning Indexer. However, practical deployment remains constrained by the indexer’s expensive O(L^2) scoring overhead and the hardware-inefficient, discontinuous memory-access patterns induced by its outputs. To address these system-level bottlenecks, we introduce LongCat Sparse Attention (LSA), a hardware-algorithm co-designed framework comprising three complementary and orthogonal strategies: (1) Streaming-Aware Indexing, which selectively converts scattered KV entries into hardware-aligned contiguous layouts to enable coalesced HBM access; (2) Cross-Layer Indexing, which amortizes indexing overhead by reusing the results produced by a single layer across consecutive layers, supported by cross-layer distillation; and (3) Hierarchical Indexing, which adopts a coarse-to-fine scoring scheme to progressively narrow the candidate set for each query, thereby substantially reducing indexing computation. Extensive scaling experiments, ranging from 69B-A3B to 560B-A27B models, demonstrate that LSA consistently achieves performance on par with full attention across both general-purpose and long-context benchmarks. Moreover, LSA supports native training with context lengths of up to one million tokens and underpins the development of LongCat-2.0 (1.6T-A48B). To facilitate further research, we also introduce and open-source LongCat-Flash-Lite-Sparse (69B-A3B), which integrates LSA into LongCat-Flash-Lite and incorporates an updated long-context training corpus.
[NLP-55] Bole: Efficient Tree Speculation for Hybrid-Attention Language Models
【速读】： 该论文旨在解决混合注意力大语言模型（Hybrid-Attention Large Language Models）在自回归解码过程中因内存瓶颈导致的长上下文推理效率低下问题，尤其针对现有树推测解码（Tree Speculative Decoding）系统在该类模型上存在的验证延迟高、瞬时状态内存占用随树规模与批大小急剧增长的缺陷。其核心解决方案在于提出一种内核-运行时协同设计的系统Bole，通过将线性注意力的递归结构转换为树状闭式表达，并利用资源高效的GPU内核实现并行验证所有推测节点，使线性注意力部分的验证速度提升3.4至7.7倍；同时，以无损方式将推测状态更新编码为逐标记因子，仅在采样后重构最终选定状态，将瞬时状态内存消耗降低82至99倍，从而释放大量GPU显存用于键值缓存（KV Cache）。该方案已集成至主流生产级推理引擎SGLang，结合全局批处理验证预算与完整混合前向计算的校准机制，在四个模型、两种GPU平台及多样化数据集上，相较自回归解码最高提升4.72倍离线解码吞吐量，相较最强基线树推测方法最高提升2.03倍；在在线代理工作负载下，分别将首字延迟（TTFT）和每词延迟（TPOT）降低最多67.6%和49.9%。
链接: https://arxiv.org/abs/2608.01651
作者: Li Wang,Yi Su,Xiabao Wu,Chiran You,Yongchao Liu,Zhan Qiu,Juelu Zhang,Jiajun Zheng,Fangxin Liu,Jie Zhang,Chen Tian,Chengying Huan
机构: 1. University of Science and Technology of China (中国科学技术大学); 2. Huawei Technologies Co., Ltd. (华为技术有限公司); 3. Tsinghua University (清华大学); 4. Peking University (北京大学)
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 14 pages, 12 figures, 7 tables
Abstract:Hybrid-attention large language models combine full attention with recurrent linear attention to reduce long-context inference costs, yet their autoregressive decoding remains memory-bound. Tree speculative decoding offers an attractive acceleration path, but existing tree-speculation systems are designed around the key–value caches of full-attention models. On hybrid models, they traverse recurrent layers branch by branch and materialize a full state for every proposal node, causing verification latency and transient memory to scale poorly with tree and batch sizes. We present Bole, a kernel–runtime co-design that enables efficient tree speculation for hybrid-attention LLMs. Bole transforms the linear-attention recurrence into a tree-structured closed form and realizes it with a resource-efficient GPU kernel, verifying all proposal nodes in parallel and accelerating linear-attention tree verification by 3.4–7.7 \times . It losslessly encodes speculative state updates as token-level factors and reconstructs only the state selected after sampling, reducing transient state memory by 82–99 \times and freeing GPU capacity for KV caches. Its integration into SGLang, a widely deployed production LLM serving engine, couples efficient state management with a batch-wide verification budget calibrated to the complete hybrid forward. Across four models, two GPU platforms, and diverse datasets, Bole delivers up to 4.72\times the offline decode throughput of autoregressive decoding and up to 2.03\times that of the strongest tree-speculative baseline. Under online agent workloads, it reduces TTFT and TPOT by up to 67.6% and 49.9% , respectively, over the strongest tree-speculative baseline.
[NLP-56] Does Accuracy Equal Evidence? Reasoning Faithfulness under KV Cache Compression
【速读】： 该论文旨在解决大模型推理过程中键值缓存（KV cache）压缩对推理可解释性与可信性影响的评估偏差问题，即当前普遍以最终答案准确性作为评价标准，隐含假设“正确答案”必然意味着“有效推理过程”的保留，但这一假设在实际中可能失效。研究发现，在压缩条件下，正确答案与可见支持性推理链（answer-chain support）或对输入扰动的响应一致性（perturbation faithfulness）的保持程度可显著不同，从而产生“答案-证据鸿沟”（answer-evidence gap）。其解决方案的关键在于提出一种受控的固定轨迹回放协议（fixed-trace replay protocol），通过固定推理内容并隔离压缩操作对已有推理轨迹中可用信息的保留能力进行评估。实验对比了十种基于标记淘汰（token-eviction）的压缩方法与一种覆盖保持型量化（coverage-preserving quantization）方法，在数学推理、科学问答、临床计算和长上下文检索等任务上验证了：尽管多数标记淘汰方法能维持较高的最终答案准确率，但会严重损害推理链一致性与扰动忠实性；而覆盖保持量化则表现更稳定，表明该现象并非由内存压缩本身直接导致，而是由于压缩过程中丢失了推理轨迹中的关键信息片段所致。
链接: https://arxiv.org/abs/2608.01631
作者: Mengting Ai,Jingrui He,Yue Guo
机构: University of Illinois Urbana-Champaign(伊利诺伊大学厄本那-香槟分校)
类目: Computation and Language (cs.CL)
备注: this https URL
Abstract:KV cache compression is commonly evaluated by final-answer accuracy, implicitly assuming that preserving the answer also preserves the reasoning that supports it. We test this assumption for large reasoning models and show that it can fail: under compression, correct answers and the validity of their visible supporting rationales can be preserved at different rates. We study this failure with a controlled fixed-trace replay protocol, which holds reasoning content fixed and isolates whether compression preserves usable information from an already available trace. We evaluate ten token-eviction KV compression methods and one quantization method on three models across mathematical reasoning, scientific QA, clinical calculation, and long-context retrieval. We measure final accuracy, answer-chain consistency, and perturbation faithfulness. Across tasks, token-eviction methods can preserve competitive final-answer accuracy while substantially degrading chain support or perturbation faithfulness. We call this the answer-evidence gap. A coverage-preserving quantization control is substantially less affected, suggesting that the failure is tied less to KV memory reduction itself than to losing access to parts of the reasoning trace. Code is available at this https URL.
[NLP-57] RING: Retrieval-Internalized Generation for Continual Large-Scale Knowledge Injection
【速读】： 该论文旨在解决检索增强生成（Retrieval-augmented generation, RAG）在推理阶段引入延迟和工程开销的问题。现有RAG方法依赖外部检索器，导致系统复杂性增加且响应速度受限。为此，本文提出一种全新的范式RING（Retrieval-Internalized Generation），其核心在于将大规模外部知识以参数化方式注入模型内部的记忆专家（Mixture-of-Memory Experts）中，并通过强化学习端到端地训练一个可学习的搜索与路由策略，从而完全摒弃对显式外部检索器的依赖。其解决方案的关键在于：1）采用新颖的双因果注意力机制（Dual Causal Attention）在持续预训练阶段实现知识的高效注入；2）通过监督微调学习“先搜索后回答”的模式；3）利用分层奖励机制的强化学习优化记忆中的路由与搜索策略，使模型能够根据任务信号自主决定如何访问内部知识。此外，作者从理论上将RING形式化为经典RAG目标的无检索近似，进一步验证了其合理性。为评估真正新知识的大规模注入并避免测试时信息泄露，研究构建了基于基线大语言模型预训练截止日期之后新闻数据的基准News-2025。实验结果表明，RING在准确率和效率上均达到或超过基于检索的RAG及传统参数化注入基线，实现了高性能与低延迟的统一。
链接: https://arxiv.org/abs/2608.01630
作者: Shicheng Xu,Liang Pang,Liyi Chen,Zihao Wei,Jingcheng Deng,Yan Gao,Yi Wu,Yao Hu,Huawei Shen,Xueqi Cheng
机构: Institute of Computing Technology, CAS (中国科学院计算技术研究所); University of Chinese Academy of Sciences (中国科学院大学); Xiaohongshu Inc. (小红书)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 16 pages
Abstract:Retrieval-augmented generation (RAG) improves factuality but adds latency and engineering overhead at serving time. We propose RING (Retrieval-Internalized Generation), a holistic paradigm spanning both architecture and training that injects large-scale external knowledge into a \textitMixture-of-Memory Experts and learns parametric search over this internal memory via reinforcement learning, removing the external retriever entirely. Training proceeds in three stages: continued pre-training injects new corpora into a Knowledge Expert via our novel \textitDual Causal Attention; supervised fine-tuning teaches a ``search-then-answer’’ pattern; and reinforcement learning with hierarchical rewards optimizes the routing-and-search policy over the parametric memory. Unlike prior parametric injection methods that pair internal memory with a fixed or rule-based retriever, RING learns its retrieval policy directly from task signals. We further frame RING theoretically as a search-free approximation to the classical RAG objective. To evaluate large-scale injection of genuinely new knowledge without test-time leakage, we further construct News-2025, a benchmark built from news strictly post-dating the base LLM’s pretraining cutoff. RING matches or surpasses both search-based RAG and parametric injection baselines in accuracy and efficiency.
[NLP-58] Human-LLM Alignment in Language Attitudes Toward Non-Native Japanese
【速读】： 该论文旨在解决生成式 AI（Generative AI）在高风险场景（如招聘与学术评估）中对非母语者写作评价时可能存在的语言态度偏见问题。其核心挑战在于揭示并量化大语言模型（LLMs）在评估二语（L2）写作时是否继承了人类评价者对非母语者文本的系统性低估，尤其是在流利度、社会地位和人际团结感三个维度上的偏差。解决方案的关键在于引入语言态度框架（language attitudes framework），通过对比人类与LLM对日语母语（L1）与二语（L2）平行邮件样本的评分，发现人类评价者显著降低对二语文本的三维度评分，其中流利度差距约为地位与团结感差距的两倍；六名LLM judges复现了这一偏见的方向，五名准确再现了各维度偏差的相对排序，但存在两个关键差异：所有模型均低估了最具社会语境依赖性的团结感差距，且在区分学习者母语背景方面表现出人类未有的敏感性。因此，研究证明LLM在结构上复现了母语者的语言态度，但程度有所弱化，而语言态度框架为跨语言、非英语情境下的评估模型审计提供了可直接应用的理论基准。
链接: https://arxiv.org/abs/2608.01629
作者: Naho Orita,Hayato Ogawa,Daisuke Kawahara
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) increasingly evaluate human writing in high-stakes domains such as hiring and academic assessment, putting non-native speakers at particular risk. Drawing on the language attitudes framework, we compared human and LLM evaluations of parallel L1- and L2-written Japanese emails on three dimensions: fluency, status, and solidarity. Japanese raters rated L2 texts significantly lower on all three dimensions, with a fluency gap roughly twice the size of the status and solidarity gaps. Six LLM judges reproduced the direction of this bias, and five reproduced its ordering across dimensions. The models diverged from humans in two ways: all understated the solidarity gap, the most socially grounded dimension, and all differentiated among learner L1 backgrounds where humans did not. LLM judges thus reproduce native speakers’ language attitudes in a structured yet attenuated form, and the language attitudes framework offers a ready-made yardstick for auditing them beyond English.
[NLP-59] Not the Dimension the Norm: What Matters in Gradient-Free Weight Perturbation of Language Models
【速读】： 该论文旨在解决梯度自由适配（gradient-free adaptation）中权重扰动范围过大导致效率低下的问题，即当前方法仍需对权重张量的每个元素进行扰动，但尚不清楚这种全权重搜索是否必要。其核心问题是：何种扰动特性决定了适配的有效性？现有方法在搜索空间、扰动尺度和聚合方式上同时变化，难以厘清关键因素。为此，作者通过固定适配流程中的评分与投票机制，仅系统性地干预单一变量——包括搜索维度、扰动子空间及扰动范数——以实现因果分析。研究发现，仅对12至16个冻结标量进行扰动时，平均性能仅比全权重搜索低1.8个准确率点，在49个模型-基准组合中落后36个；且扰动维度或基底选择并非性能差异的关键。即使随机选取的基底与SVD基底的Grassmann重叠处于随机水平，只要匹配单一尺度因子，性能即无差别；而在大尺度下，SVD方向率先失效。最终揭示，唯一具有失效模式的因素是扰动范数，其有效范围在七种不同模型间仅相差五倍，且在模型规模与架构间保持稳定。因此，设计问题从“在哪个子空间扰动”转化为“扰动强度应多大”，扰动范数成为决定成败的核心要素，其安全区间具备跨模型迁移能力。
链接: https://arxiv.org/abs/2608.01624
作者: Taeyeong Kim,Ahhyun Kim,TaeHyeon Kim,Unggi Lee
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Adapting a language model to a task no longer requires training all of its weights, and a line of parameter-efficient methods has driven the trainable count from billions down to a handful of scalars. Gradient-free adaptation, which samples random weight perturbations and keeps the ones that score well, has not followed that trajectory and still perturbs every entry of the weight tensor. It is unknown whether that full-weight search is necessary, and more fundamentally which property of a perturbation makes it work at all, because existing methods vary the search space, the perturbation scale, and the aggregation together. We resolve this by intervening on one factor at a time inside a fixed pipeline, holding candidate scoring and voting constant while we vary the search dimension, the subspace that carries the perturbation, and its norm. Perturbing a frozen frame of 12 to 16 scalars stays 1.8 accuracy points behind full-weight search on average across 49 model-benchmark cells, trailing it in 36 of them. Neither the dimension nor the choice of basis explains that performance. A random frame whose Grassmann overlap with the SVD frame is at chance level performs identically once a single scale factor is matched, and at large scales the SVD directions collapse first. What survives is the perturbation norm, whose usable range closes within a factor of five across seven models and stays flat inside. The perturbation norm is therefore the one factor with a failure mode, and its safe region transfers across scale and family. The design question narrows from which subspace to perturb to how hard to shake.
[NLP-60] PICTURE: Enhancing Theory-of-Mind in Large Language Models by Revealing Not Hiding Characters Lack of Knowledge
【速读】： 该论文旨在解决自然语言处理中模拟人类类心理理论（Theory of Mind, ToM）的难题，特别是针对现有方法依赖事件隐藏（event hiding）进行视角转换（perspective-taking）所导致的性能下降问题。其核心挑战在于：当采用无约束的自由形式链式思维（Chain-of-Thought, CoT）生成视角转换输出时，大型语言模型（LLM）容易受到角色未知事件的影响，从而产生不符合角色认知状态的推理。为应对这一挑战，论文提出关键解决方案——通过在推理过程中显式表达角色对特定事件的无知（即“角色缺乏知识”），使LLM能够有效抑制对未知事件的响应。基于此思想，作者设计了名为PICTURE的新提示方法，该方法引导LLM在自由形式的CoT中主动生成角色的知识边界。实验结果表明，PICTURE在虚假信念任务上相较于现有提示方法平均提升7.3%，验证了显式知识缺失表达的有效性。
链接: https://arxiv.org/abs/2608.01598
作者: Eojin Jeon,SangKeun Lee
机构: Korea University(韩国大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Simulating human-like Theory of Mind (ToM) has been a longstanding problem in natural language processing (NLP). To address this, existing works introduce a reasoning step of event hiding (a.k.a. perspective-taking), where events unknown to a character are removed before question answering. However, resorting to event hiding for ToM reasoning presents a performance degradation issue due to the strict output format constraints involved in event hiding. To mitigate this issue, we propose generating perspective-taking outputs as free-form explanations without event hiding, but this poses a notable yet underexplored challenge: LLMs need to inhibit responses to events unknown to characters, because the absence of event hiding exposes LLMs to these events throughout reasoning. To address this challenge, we hypothesize and empirically verify that LLMs can achieve such inhibition if a character’s lack of knowledge about events is made explicit during reasoning. Based on this finding, we introduce PICTURE, a new prompting method that enables LLMs to generate a character’s lack of knowledge within free-form Chain-of-Thought (CoT). Experimental results show that PICTURE outperforms existing prompting methods by an average of 7.3% on false-belief tasks.
[NLP-61] Semantic Alignment of AI Models: Concept Collapse Checkpoint Dynamics and Cross-Lingual Transfer
【速读】： 该论文旨在解决语言模型评估中存在的核心问题：传统基于结果推理的评测方法无法充分检验模型对语言概念的本质理解，而现有的开源基准数据集又容易被模型“吸收”为训练数据，导致评测结果失真。其解决方案的关键在于引入拓扑方法（topological methods），以严谨的方式比较高维嵌入空间与低维可解释的语义基准（如本体论和人工构建的知识图谱）之间的结构一致性。通过多模态对齐测试，该方法不仅能够追踪模型在不同阶段的语义适应性变化，还能跨语言评估模型对短语语义的理解能力，从而提供更深层次、更具可解释性的模型评估视角。
链接: https://arxiv.org/abs/2608.01585
作者: Tyler Ashoff,Jordan Rodu
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Code available at this http URL (PyPI: persiscope)
Abstract:Language model benchmarking is a difficult task. Outcome reasoning alone does not test the model’s conceptualization of language and popular open-source benchmarks are quickly saturated or ingested as training data. It is important to test the model’s output, but augmenting these tests by characterizing semantic structure gives more insight to how models relate abstract concepts. However, the high dimensional embedding spaces are not easy to interpret. This work demonstrates how topological methods can be used to rigorously compare these spaces to low dimensional and interpretable baselines like ontologies and curated knowledge graphs. These multi-modal alignment tests make it possible to track model adaptations and test phrase understanding across multiple languages.
[NLP-62] Characterizing Treatment-Context Medication Evidence Across Clinic Notes and Structured EHR Medication History
【速读】： 该论文旨在解决临床笔记（clinic notes）与结构化电子健康记录（EHR）中药物信息不一致的问题，尤其关注同一就诊期间两类数据源之间药物条目在语义、时间及标准化表达上的差异。其核心挑战在于，由于术语异构性、时间戳差异或归一化错误，导致基于表面匹配的药物信息对齐存在显著偏差。解决方案的关键在于提出一种基于临床笔记的归因方法（note-grounded approach），融合大语言模型（LLM）辅助参考构建、靶向与随机的人工审核、确定性药物归一化处理，以及与结构化药物史在语义和时间维度上的对比分析。通过在患者层面独立测试集上评估，该方法将药物提及的精确标准命名一致性从0.7226提升至0.8429；在未审计样本的随机抽样中，标准标签一致性达0.9210，但治疗行为归属准确率较低（0.5326），表明归一化与语义对齐虽有效，但动作意图识别仍存挑战。此外，全队列分析显示仅16.44%的笔记药物条目与结构化记录存在同就诊期完全匹配，但55.17%具语义重叠，90.34%在±30天内有重叠，说明多数“不匹配”源于术语或时间差异而非实际用药差异。基于本体的敏感性分析进一步显示，通过开发阶段补充的别名映射可使严格无重叠比例由43.99%降至36.68%，验证了归一化误差是造成假性不匹配的主要因素之一。
链接: https://arxiv.org/abs/2608.01570
作者: Mingyang Jiang,Congning Ni,Weixin Liu,Zhijun Yin
机构: 未知
类目: Computation and Language (cs.CL)
备注: 9 pages, 3 figures. Submitted to IEEE BIBM 2026
Abstract:Clinic notes and structured electronic health record (EHR) medication history often contain different medication information. Same-visit disagreement between these sources may result from note-side normalization errors, differences in terminology or timing, or actual differences in documentation. We developed a note-grounded approach that uses large language model (LLM) assisted reference construction, targeted and random human review, deterministic medication normalization, and semantic and temporal comparisons with structured medication history. We evaluated all normalization results on a patient-level held-out test set to limit adaptation to the study cohort. On 5,403 held-out mention rows, exact canonical agreement improved from 0.7226 with surface-exact matching to 0.8429 after lexical cleanup and curated alias mapping. In a random audit of previously unaudited rows, canonical-label agreement was 0.9210 among evaluable valid medication mentions, whereas treatment-action attribution was lower at 0.5326. In the full-cohort characterization analysis, only 16.44% of note-derived rows had same-visit exact overlap with structured medication history, but 55.17% had same-visit semantic overlap, 90.34% had same-visit or +/-30-day overlap, and only 3.97% remained in the strict no-structured-overlap bucket under broad project-level mapping. An ontology-backed sensitivity analysis further showed that held-out strict Observational Medical Outcomes Partnership (OMOP)-backed no-overlap fell from 43.99% to 36.68% after a development-derived alias supplement. These results show that note-to-structured-medication mismatch can arise from normalization errors, differences in terminology, and differences in documentation timing.
[NLP-63] DocNavRAG : Document-Structured Graph RAG with Stateful Evidence Construction for Complex Document Question Answering
【速读】： 该论文旨在解决在大规模文档集合中回答复杂问题时，如何高效整合跨段落与跨文档的互补证据这一关键挑战。现有方法中，GraphRAG虽具备结构化检索能力，但依赖固定遍历路径；而代理式RAG（agentic RAG）则受限于弱结构化接口，难以有效利用文档内在组织逻辑。其核心解决方案在于提出DocNavRAG，通过将文档内部层级结构及跨区域关联关系建模为可导航图谱，显式暴露图操作（如定位、遍历、扩展与获取），并维护一个动态演进的证据状态以指导检索过程，直至收集到充分证据。该方法使智能体能够基于文档结构进行有策略的导航，避免重复搜索，显著提升了答案质量与上下文充分性，在四个长文本与多文档问答基准上，平均分别优于最强基线7.8%和17.7%。
链接: https://arxiv.org/abs/2608.01565
作者: Dongyang Xie(1),Yao Tian(2),Hao Zhang(3),Yifei Yuan(4),Tieyun Qian(1),Ming Zhong(1),Jiawei Jiang(1),Yuanyuan Zhu(1) ((1) School of Computer Science, Wuhan University, (2) The Hong Kong University of Science and Technology, (3) The Chinese University of Hong Kong, (4) ETH Zurich)
机构: Wuhan University (武汉大学); The Hong Kong University of Science and Technology (香港科技大学); The Chinese University of Hong Kong (香港中文大学); ETH Zurich (苏黎世联邦理工学院)
类目: Computation and Language (cs.CL)
备注: 19 pages, 5 figures, 16 tables
Abstract:Answering complex questions over large document collections requires assembling complementary evidence across sections and documents. GraphRAG offers structured retrieval but typically uses fixed traversal, while agentic RAG operates over weakly structured interfaces. Our key insight is that agents should navigate document structure within and across documents rather than repeatedly search from scratch. We introduce DocNavRAG, which organizes document hierarchies and cross-region relations into a navigable graph, exposes graph operations for locating, navigating, expanding, and fetching, and maintains an evolving evidence state to guide retrieval until sufficient evidence is collected. Across four long- and multi-document QA benchmarks, DocNavRAG improves answer quality and context sufficiency over the strongest baseline by 7.8% and 17.7% on average.
[NLP-64] Discriminative Axis Not Data Volume: What a Contrastive Corpus Teaches an Audio Embedding
【速读】： 该论文旨在解决对比学习（contrastive learning）框架下多模态嵌入模型在零样本关键词检测（zero-shot keyword spotting）与语音情感识别（speech-emotion recognition）之间性能权衡的问题。其核心发现是：单纯扩大语料库规模无法有效提升模型对情感等属性的编码能力，真正关键在于语料库的结构设计。解决方案的关键在于引入一个词汇-语音（lexical-speech）交替训练阶段，通过在固定基础模型上施加结构化约束，使语音韵律成为区分样本的唯一有效信号。研究证实，当语料库中句法内容被控制固定时，韵律成为唯一的判别特征，从而迫使模型学习到情感相关表征；而通过挖掘带有显式情感标签的片段虽增加数据量，但因场景内容仍可分离样本，导致情感信息无法被有效编码。进一步实验表明，仅通过降低文本描述多样性、强制情感成为唯一区分维度，即可恢复8.9个百分点的情感识别性能，且该效果具有因果性验证。因此，结论指出：对比学习所编码的属性由语料库的结构决定，而非数据量或标注词汇丰富度。
链接: https://arxiv.org/abs/2608.01560
作者: Abdul Basit Tonmoy
机构: Eximius Labs(Eximius 实验室); Wabash College(瓦巴什学院)
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: 10 pages, 4 figures
Abstract:Scaling the corpus is the default remedy when a contrastive representation lacks an attribute. We report a case where it does nothing, and identify what does: adding a lexical-speech round to a frozen-base multimodal embedding model raises zero-shot keyword spotting by 76 points while reducing speech-emotion recognition by 14. The loss is not a capacity limit: fine-tuning on 7,442 clips from a prosody-controlled corpus recovers emotion past its pre-speech level at a five-point keyword cost. Nor is it data volume: 29,428 mined clips whose captions explicitly name emotions, at matched exposure, move emotion by -0.0007. The difference is structural: a contrastive objective encodes an attribute only when the in-batch negatives cannot be separated without it; the controlled corpus holds sentence content fixed, so prosody is the only separating signal, whereas mined captions name emotion yet remain separable by scene content. Intervention on the same audio confirms causality: raising caption similarity does not recover emotion, but collapsing caption diversity so that emotion becomes the only separating axis recovers it by 8.9 points across three seeds, with a smaller, same-signed gain on a non-acted corpus, while keyword accuracy trades back. Corpus structure, not size or caption vocabulary, controls what a contrastive audio embedding encodes.
[NLP-65] Does the Competitive Component of Adversarial Self-Play Improve Legal Reasoning ? A Controlled Negative Result
【速读】： 该论文旨在探究对抗性自对弈（adversarial self-play）在法律推理任务中是否能够带来实际性能提升，特别是其竞争机制（即对手模型与“存活”奖励）是否相较于非竞争性训练具有额外优势。其核心问题是：在保持其他训练条件完全一致的前提下，引入对抗性竞争是否能有效增强学生模型生成法律论证的严谨性与鲁棒性。解决方案的关键在于设计了一个可验证的“存活”奖励机制——通过引用验证器（citation verifier）对师生双方所引用的权威文献进行真实性核查，确保论证存活与否基于可验证的事实而非修辞或虚构引用，从而实现公平、可信的评估。然而，实验结果表明，尽管该机制在理论上具备吸引力，但在四项独立测试（包括双种子复现、成对案例对抗鲁棒性比较、盲评对决及强化对手的预研实验）中均未显示出显著优势，盲评结果显示胜率仅为49%（二项检验p≈1.000），强化对手实验中亦为50%（32:32，p≈1.000）。早期观察到的+29%优势被证实为小样本偏差。研究最终得出否定结论：在法律领域，对抗性自对弈中的竞争本身并未带来可靠增益，其价值更可能源于可验证环境的构建，而非竞争机制本身。这一负结果强调了研究的可复现性与对潜在陷阱的透明披露，如初始看似有效的指标随数据量增加而反转，以及对抗鲁棒性指标在对手引用模式趋同于标准答案时退化为普通召回率的问题。
链接: https://arxiv.org/abs/2608.01559
作者: Miseog Shawn Kim
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Adversarial self-play is an appealing recipe for legal reasoning: have a student model draft an argument, have an adversary attack it, and reward the student when its argument survives the attack. We designed exactly such a training signal – a verifiable “survival” reward in which both the student’s cited authorities and the adversary’s counter-authorities are checked by a citation verifier, so that survival is decided on verified grounds rather than rhetoric, and fabricated citations are automatically neutralized. We then asked a narrow but important question: does the competitive component itself – the adversary and the survival reward – add anything on top of an otherwise identical non-competitive training run? Across four independent tests – a bootstrap comparison, a two-seed replication, a paired per-case adversarial-robustness comparison, and a blinded head-to-head judgment of generated arguments, plus a follow-up pilot with a deliberately strengthened self-play adversary – the competitive component produced no reliable benefit. The blinded judgment gave a 49% win rate (binomial p approx. 1.000); the strengthened-adversary pilot gave a 50% win rate (32:32, p approx. 1.000). An early apparent +29% advantage reversed and proved to be a small-sample artifact. We report this as an honest negative result. The value of the paper is reproducibility and the sharing of concrete pitfalls: an initially promising metric that inverted on more data, and an adversarial-robustness metric that silently collapsed to plain recall once the adversary stopped citing the same authorities as the gold answer. This null is consistent with, and reconfirms in the legal domain, the conclusion of the companion coding-domain study (Kim, 2026, arXiv:2607.08255) that the value of multi-teacher curricula arises from constructing a verifiable environment rather than from competition itself.
[NLP-66] Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics
【速读】： 该论文旨在解决大语言模型在习得新技能时面临的三大挑战：训练数据稀缺、缺乏真实可靠的推理轨迹（ground-truth reasoning traces）以及模型在达到一定性能后出现的“性能天花板”现象。针对这些问题，研究提出了一种名为Question-begets-Question（QbQ）的可扩展生成方法，通过教师模型将已有题目转化为多样化且探测相同核心能力的变体题，以缓解数据稀缺问题；同时，为模拟无监督推理环境，仅使用问题陈述和最终答案进行强化学习训练，避免依赖教师推理过程。然而，静态训练即使引入大量真实与合成数据，仍无法突破性能瓶颈（分别止步于12.5%和14.5%的pass@1）。研究的核心发现是，该性能天花板并非模型固有属性。为此，作者提出一种自演化课程（self-evolving curriculum），每轮评估当前模型表现，从其能正确解答的问题中生成新的变体题，并以此训练下一阶段模型。在相同数据预算下，该方法成功打破性能瓶颈，使pass@1提升至16.5%，且经过20轮迭代后仍无饱和迹象。这一结果表明，模型在训练过程中对已掌握知识的变体进行强化，反而能有效提升解决更复杂未见问题的能力，揭示了“基于已掌握能力生成挑战性任务”是推动模型持续进化的关键机制。
链接: https://arxiv.org/abs/2608.01522
作者: Longtian Bao,Jianyou Wang,Yang Zhang,Youze Zheng,Ramamohan Paturi
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Teaching a language model a skill it has not mastered is obstructed by three recurring difficulties: training data is scarce, ground-truth reasoning traces are usually unavailable, and models often exhibit an apparent ceiling beyond which additional data yields no further improvement. We study these difficulties in a controlled setting, fine-tuning Qwen2.5-Math-7B on competition mathematics (AIME), a task on which it initially solves only 5.6% of problems (pass@1). To address data scarcity, we introduce Question-begets-Question (QbQ), a scalable procedure in which a teacher transforms existing problems into diverse variants that probe the same underlying skills; to model the absence of oracle reasoning, we train exclusively via reinforcement learning on problem statements and final answers, never on teacher reasoning traces. Static training on such data, however, plateaus well short of the task: real-plus-synthetic augmentation and non-curriculum QbQ generated synthetic data training cap pass@1 at 12.5% and 14.5% respectively, despite large increases in data. Our central finding is that this ceiling is not intrinsic to the model. We propose a self-evolving curriculum that, each round, evaluates the current checkpoint, seeds QbQ from the problems it can mostly get right, and trains on the resulting variants; under an identical data budget, this breaks the ceiling and lifts pass@1 to 16.5% with no sign of saturation after 20 rounds. Counterintuitively, we find that models improve when trained on variants of problems they can mostly get right, and that models trained this way go on to solve harder problems never seen during training.
[NLP-67] Slot2Text: Object-Centric Visual Tokenization for Efficient and Spatially Traceable Surgical MLLM s
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLM）在手术场景理解中因注入大量密集视觉标记（dense visual tokens）而导致的推理成本高昂及生成答案空间可追溯性差的问题。其核心解决方案是提出一种双模式架构Slot2Text，将输入视觉信息的密集表示替换为一组紧凑的、以槽位潜变量（slot latents）编码的区域表征。该方法通过自监督视觉特征聚类形成少量具有语义意义的“槽位”（slots），作为带有区域标签的视觉标记供语言模型消费，从而显著降低视觉前缀长度与整体令牌消耗。Slot2Text-Fast利用槽位前缀实现高效问答，相较现有最优基线，平均总令牌数减少91.8%，视觉前缀从1,295降至47（降幅达96.4%）；而Slot2Text-Reason进一步引入显式区域识别与定位能力，将语言输出与对应的槽位标记、掩码或空间区域关联，提供可追溯的空间证据，以牺牲少量额外令牌和延迟为代价换取更强的空间可解释性。实验表明，紧凑的槽位潜变量可作为手术场景下MLLM的高效默认视觉接口，在无需高精度空间对齐时保持高性能，而在需要明确空间推理证据时可通过扩展机制实现。
链接: https://arxiv.org/abs/2608.01473
作者: Guiqiu Liao,Matjaz Jogan,Daniel A. Hashimoto
机构: University of Ljubljana (卢布尔雅那大学); Stanford University (斯坦福大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 17 pages, 8 Figures
Abstract:Multimodal large language models (MLLM) for surgical scene understanding typically inject hundreds of dense visual tokens into a language model, leading to costly inference and limited spatial traceability for generated answers. We present Slot2Text, a dual-mode surgical MLLM that replaces dense representations of visual input with a compact set of regions encoded as slot latents. Instead of relying on contrastive alignment of the visual encoder with language, Slot2Text groups self-supervised vision features into a few regions–slots that are consumed by the language model as area-labeled visual tokens. Slot2Text-Fast uses the slot prefix to answer surgical questions. Slot2Text-Reason also identifies and locates areas relevant for reasoning, linking language outputs to corresponding slot tokens, masks or regions. Experiments on multiple visual question answering and visual grounding benchmarks show that Slot2Text-Fast is competitive with state-of-the-art baseline at a much lower cost, reducing the average total token consumption by a 91.8% and the visual prefix from 1,295 to 47 tokens (a 96.4% reduction). Slot2Text-Reason trades additional tokens and latency for explicit area identities, locations, and traceable spatial evidence. These results establish compact slot latents as an efficient default visual interface for surgical MLLMs, with grounded reasoning invoked when greater spatial traceability is required.
[NLP-68] wo-Stage Bengali Sentiment Classification: Domain Adaptation Through Continual Learning and Parameter-Efficient Fine-Tuning
【速读】： 该论文旨在解决低资源语言（low-resource languages）中情感分析（sentiment analysis）面临的挑战，尤其是在领域特定数据稀缺的情况下。其核心问题是：如何在有限标注数据条件下，实现对语义复杂、形态丰富的语言（如孟加拉语）的情感准确分类，同时兼顾模型的可解释性与计算效率。解决方案的关键在于提出一种两阶段的孟加拉语情感分类框架——SentiBanglaBERT，该框架通过领域自适应持续预训练（domain-adaptive continual pretraining）实现对新闻体文本的上下文适应，并采用参数高效微调技术（如低秩适配，LoRA）保持计算高效性；此外，引入基于SHAP的可解释性分析，揭示孟加拉语中否定后缀、体标记等形态线索对情感判断的影响，从而在提升性能的同时增强模型决策过程的透明度。这一方法为资源匮乏但形态复杂的语言提供了兼具高效性、可解释性与领域适应能力的新范式。
链接: https://arxiv.org/abs/2608.01471
作者: MD Shaikh Rahman,Syed Maudud E Rabbi,Muhammad Mahbubur Rashid
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Understanding sentiment in low-resource languages remains a key challenge for Natural Language Processing (NLP), particularly when domain-specific data is scarce. In this work, we present SentiBanglaBERT, a two-stage Bengali sentiment classification framework combining domain-adaptive continual pretraining and parameter-efficient fine-tuning. The approach enables contextual adaptation to news-style data while remaining computationally efficient through Low-Rank Adaptation (LoRA). Beyond performance, SentiBanglaBERT integrates SHAP-based interpretability, offering linguistic insights into how Bengali morphological cues, such as negation suffixes and aspectual markers, influence sentiment predictions. Experiments demonstrate stable performance comparable to strong baselines while providing greater transparency and interpretive depth. This framework highlights the potential of domain-adaptive continual learning as a foundation for interpretable, resource-efficient NLP in morphologically rich, underrepresented languages.
[NLP-69] Retrieval Augmented Biomedical Question Answering with Weak Question Recovery and Neural Reranking for BioASQ Task 14b
【速读】： 该论文旨在解决生物医学问答（Biomedical Question Answering, BQA）中复杂查询检索效果差、相关文档排序质量低以及答案生成过程中冗余信息干扰等问题。其核心挑战在于处理语义模糊、信息稀疏或表达不完整的弱查询，导致传统检索方法召回率与精度均显著下降。解决方案的关键在于构建一个端到端的多阶段集成系统：首先通过多源查询扩展（multi-source query expansion）增强初始查询的语义覆盖；随后采用基于微调MiniLM的神经重排序（neural reranking）与Reciprocal Rank Fusion（RRF）融合来自PubMed等异构数据源的检索结果，结合特征驱动的相关性评分提升文档排序质量；针对弱查询引入条件式弱问题恢复策略（conditional weak-question recovery），通过语义扩展、关系感知增强和选择性结果合并机制，有效提升困难查询的检索鲁棒性；进一步设计后检索剪枝阶段（post-retrieval pruning），剔除冗余或低相关性片段，同时保留关键证据以支持后续答案生成；最后引入输出验证与后处理步骤，确保答案格式一致性与提交可靠性。实验结果表明，所提出的恢复与清理策略在BioASQ评测集上显著提升了难例问题的MAP@10性能，验证了系统的有效性与实用性。
链接: https://arxiv.org/abs/2608.01468
作者: Xueying Zhao,Lee Mai,Balaji Anandganesh
机构: Georgia Institute of Technology(佐治亚理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:This work presents DS@GT ARC BioASQ team’s work for a biomedical question answering pipeline, integrating multi-source query expansion, neural reranking, retrieval refinement, and OpenBioLLM-assisted answer generation. The system combines PubMed retrieval with fine-tuned MiniLM-based semantic reranking, Reciprocal Rank Fusion (RRF), and feature-based relevance scoring to improve document ranking quality. To address challenging queries with weak retrieval performance, we introduce a conditional weak-question recovery strategy that applies semantic expansion, relationship-aware augmentation, and selective result merging. A post-retrieval pruning stage further removes redundant or low-relevance snippets while preserving evidence coverage for downstream answer generation. Experimental results on BioASQ evaluation batches demonstrate that the proposed recovery and cleanup strategies substantially improve retrieval robustness and MAP@10 performance on difficult question sets. The final system also incorporates output validation and post-processing steps to ensure formatting consistency and submission reliability across BioASQ phases.
[NLP-70] PALMs: Using Multi Construct-Grounded Rationales for Modeling Population Preferences in LLM s
【速读】： 该论文旨在解决大语言模型在模拟个体用户行为时，难以准确刻画不同群体间系统性差异（如价值观、信念与文化规范）的问题。现有方法多依赖表面化的数据分布或人口统计特征，无法充分捕捉深层次的心理与文化结构。其解决方案的关键在于提出群体对齐语言模型（Population Aligned Language Models, PALMs），通过基于心理学和文化理论构建的推理理由（construct-grounded rationales）作为潜在监督信号，在偏好微调阶段实现针对特定人群的精准对齐。该方法不仅显著优于基准模型（包括文化专精模型），在人格、价值观、文化规范与道德四个维度上平均提升8.59%，且在无需任务特异性监督的情况下，展现出优异的下游泛化能力，尤其在个性化奖励建模、群体模拟及社会推理任务中分别取得5.19%、6.34%的性能提升，验证了心理与文化结构化先验知识在引导生成式AI进行群体差异化建模中的核心作用。
链接: https://arxiv.org/abs/2608.01458
作者: Priyanka Dey,Brihi Joshi,Preyashi Poddar,Jieyu Zhao,Emilio Ferrara
机构: University of Southern California; Information Sciences Institute
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models are being extensively used to simulate individual user behavior, yet faithfully representing a population requires capturing the systematic variation in values, beliefs, and cultural norms that distinguish one group from another. We introduce Population Aligned Language Models (PALMs), a suite of models each aligned to specific populations, covering five countries: USA, India, Brazil, France and Italy. PALMs are created by synthesizing rationales grounded in psychological and cultural constructs and using these as latent supervision during preference tuning for population-specific alignment. Evaluated across four dimensions: personality, values and beliefs, cultural norms, and morality, PALMs consistently outperform baselines, including culture-specialized models, achieving an average of 8.59% relative improvement over the best baseline across all five populations. Notably, construct-grounded rationales outperform both demographic prompting and survey-based fine-tuning, suggesting that grounding preference learning in psychology and culture provides a richer inductive signal than surface-level response distributions. We further demonstrate strong generalization to downstream applications with- out task-specific supervision: outperforming best baselines by 5.19% in personalized reward modeling, 6.34% in population simulation, and showing strong transfer to social reasoning tasks. Datasets and code are available at: this https URL.
[NLP-71] Long-Horizon Embodied Decision-Making via Multimodal Memory Compression
【速读】： 该论文旨在解决当前智能体在长时程、以人为中心的具身决策任务中表现不足的问题，具体表现为智能体难以有效积累长期证据、隐式理解用户多维偏好，并在部分观测条件下对多个候选方案进行合理比较。其解决方案的关键在于识别出记忆管理是制约现有视觉-语言模型（VLM）驱动智能体性能的核心瓶颈——原始多模态历史信息引入大量噪声，干扰决策质量。为此，作者提出MeMento，一种基于用户偏好的多模态记忆压缩机制，通过固定数量的记忆令牌，有选择性地从长时程历史中提取与决策相关的信息，实现高效且精准的记忆压缩。实验表明，MeMento可使VLM驱动智能体的准确率提升7.18%，同时将内存占用降低85.38%，显著缩小了智能体与人类在复杂具身决策任务中的性能差距。
链接: https://arxiv.org/abs/2608.01456
作者: Bingxuan Li,Rui Yang,Cheng Qian,Jiateng Liu,Jeonghwan Kim,Zhenhailong Wang,Manling Li,Tong Zhang,Heng Ji
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄本那-香槟分校); Northwestern University (西北大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Agents are increasingly expected to act not only as task executors, but also as decision-makers on behalf of human users. This shift requires agents to accumulate evidence over long horizons, interpret implicit user preferences, and compare multiple candidates under partial observations. In this work, we propose DunphyBench, a new benchmark for evaluating agents on long-horizon human-centered embodied decision-making, where the agent must navigate through multiple embodied housing environments and make decisions that align with multi-dimensional human preferences. Unlike standard embodied reasoning tasks that often focus on procedural planning or immediate goal completion, our setting requires agents to integrate multimodal, multi-source input into coherent knowledge that supports complex reasoning across long horizon. The evaluation results reveal that there is a substantial gap between current agents and human performance. Furthermore, our diagnosis of state-of-the-art VLM-driven agents reveals that memory management is one of the bottlenecks, where raw multimodal history introduces noise that hinders decision quality. Motivated by this finding, we design MeMento, a preference-conditioned multimodal memory compressor that selectively compresses decision-relevant information from long-horizon history based on user preferences with a fixed set of memory tokens. Experiments show that MeMento helps VLM-driven agents improve accuracy by 7.18%, while reducing memory usage by 85.38% compared to the strongest baseline.
[NLP-72] QR-Erase: Efficient Subspace-Based Machine Unlearning with Layer Localization
【速读】： 该论文旨在解决机器遗忘（machine unlearning）中如何高效、准确地从已训练模型中移除特定信息，同时尽可能保留模型的通用能力这一核心问题。现有基于优化的方法常导致无关功能退化，而基于子空间的方法依赖计算开销巨大的奇异值分解（Singular Value Decomposition, SVD），难以在大规模模型中应用。本文提出QR-Erase，一种基于子空间的框架，利用有向量列主元的QR分解（Pivoted QR decomposition）直接识别并从模型参数中移除任务特异性表征；进一步提出层局部化版本——Layer-Localized QR-Erase，仅对任务特异性信息密度最高的网络层进行更新。研究表明，有向量列主元的QR分解可在有界误差下实现精确的子空间恢复，并在满足弱谱间隔条件时逼近最优SVD解。实验表明，QR-Erase在任务级、跨语言及语音遗忘任务中均显著优于优化类方法，在所有指标上与SVD方法差距小于5%；通过利用低秩结构和层局部化特性，遗忘性能进一步提升（如语音遗忘集准确率由53.1%降至15.7%）。研究结果表明，有效的遗忘不依赖于最优重构，而只需足够精确的子空间恢复，因此QR-Erase为现代大模型提供了一种高效且通用的替代SVD的机器遗忘方案。
链接: https://arxiv.org/abs/2608.01422
作者: Tyler Lizzo,Larry Heck
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Machine unlearning seeks to remove targeted information from trained models without requiring costly retraining. Existing optimization-based methods often degrade unrelated capabilities, while subspace-based approaches rely on computationally expensive singular value decompositions (SVD). We introduce QR-Erase, a subspace-based framework that uses Pivoted QR decomposition to identify and remove task-specific representations directly from model parameters. We further propose Layer-Localized QR-Erase, which restricts updates to layers containing the highest concentration of task-specific information. We show that Pivoted QR provides accurate subspace recovery with bounded error, and that under a mild spectral gap condition, the recovered subspace approaches the optimal SVD solution. Across task-level, cross-lingual, and speech unlearning, QR-Erase achieves a stronger forgetting-retention tradeoff than optimization-based methods while remaining within 5% of SVD across all metrics. Exploiting low-rank and layer-localized structure further improves forgetting (for example, reducing speech forget-set accuracy from 53.1% to 15.7%). These results demonstrate that accurate subspace recovery, rather than optimal reconstruction, is sufficient for effective unlearning and provides an efficient and general alternative to SVD-based methods for modern foundation models.
[NLP-73] When Retrieval Helps and Distracts: Evaluating Evidence-Generating LLM s for Biomedical Claim Verification
【速读】： 该论文旨在解决生物医学事实核查系统在生成忠实、完整且对验证具有实用价值的证据方面的能力不足问题。现有系统虽能判断陈述是否被支持、反驳或未涉及，但难以有效生成可信赖的证据链，尤其在面对跨源、复杂医学语境时表现受限。其解决方案的关键在于提出一种新的评估范式——Bio-GRACE（Gold-reference-normalized diagnostic for evidence recovery），通过对比检索到的证据与金标准参考证据在决策收益上的恢复程度，量化检索证据的实际效用。研究发现，尽管基于PubMed的检索增强模型在特定数据集（如PubMedQA和SciFact）中表现良好，但在更广泛的公共卫生议题上易产生干扰；而微调后的大型语言模型（LLM）在证据生成任务中整体表现最优。此外，研究揭示了传统指标（如检索召回率和词汇重叠度）不足以衡量生物医学事实核查中的证据质量，强调应根据数据来源特性采取选择性检索策略，并推动以决策效用为导向的证据生成机制发展。
链接: https://arxiv.org/abs/2608.01409
作者: Pritam Deka,Prabhjot Singh
机构: Queen’s University Belfast (皇后大学贝尔法斯特分校); University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL)
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Abstract:Biomedical fact-checking systems must do more than predict whether a claim is supported, contradicted, or unaddressed: they should also produce evidence that is faithful, complete, and useful for verification. We study this evidence-generation setting on CARE-XAI, a unified benchmark spanning five biomedical and health fact-checking sources. We compare base instruction LLMs, PubMed retrieval-augmented LLMs, fine-tuned LLMs, label-only LLMs, and biomedical encoder classifiers under a shared evaluation protocol. Biomedical classifiers remain strongest for verdict-only prediction, while fine-tuned LLMs are the strongest evidence-generating systems. PubMed retrieval is mixed: it helps PubMed-aligned sources such as PubMedQA and SciFact, but can distract models on broader public-health claims. We introduce Bio-GRACE, a gold-reference-normalized diagnostic for measuring whether retrieved evidence recovers the decision benefit of reference evidence. Bio-GRACE shows that retrieval utility is source-dependent, motivates selective retrieval, and exposes why retrieval recall and lexical evidence overlap are insufficient for biomedical fact-checking.
[NLP-74] Language Equality has a Price: A Systematic Investigation of Multi-turn LLM Performance for EU-24
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多语言场景下的性能评估与语言公平性问题，特别是针对非英语语言的覆盖不足和服务质量下降现象。传统评估方法通常依赖静态基准或偏好标注，难以反映模型在复杂、动态交互中的真实能力。为此，本文提出一种基于自对弈（self-play）的多轮、无参考、程序化评分的评估范式，通过设计语言无关的游戏机制，实现跨30种语言（包括24种欧盟官方语言及6种其他语言）的可扩展评估。其解决方案的关键在于：构建一套固定提示（prompt）与词表文件的本地化框架，使新语言的加入仅需进行文本翻译即可完成适配，从而实现高效、一致的跨语言测试；同时，通过实证发现，尽管开源模型在多数语言中表现显著落后，商业模型虽整体领先但未完全消除语言间差距，且非英语语言在推理成本上平均高出31%、性能低10%，揭示了当前大模型服务在语言覆盖与成本效率方面仍存在系统性不平等。
链接: https://arxiv.org/abs/2608.01395
作者: Sherzod Hakimov,Karl Osswald,Jelle Psurek,Eszter Bukovszky,A. Altar Lüser,David Schlangen
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Source code: this https URL
Abstract:We evaluate large language models (LLMs) as language agents playing goal-directed dialogue games in self-play across 30 languages: the 24 official EU languages plus six others. Unlike static or preference-based evaluation, this paradigm is multi-turn, reference-free and programmatically scored, and because the game mechanics are language-agnostic it extends to a new language by localising a fixed set of prompt and word-list files. Evaluating nine open-weight and commercial LLMs, we find that no open-weight model covers the EU-24 well: in every official language both commercial systems outscore every open-weight model, and the two weakest average below 40 points across the EU-24. The commercial systems stay ahead even in languages with four orders of magnitude less public web text, showing that linguistic parity is achievable, but not from public crawls alone. A model’s home region lifts it without closing the gap: Chinese is the strongest of all 30 languages for two Chinese-developed models, yet the best Chinese score of any model belongs to a US commercial system. Coverage is also not parity of service. Pooled over models and languages, the median non-English language costs 31% more to run than English, and scores 10% lower.
[NLP-75] EviSD: Evidence-Conditioned Self-Distillation for Search-Augmented Agents
【速读】： 该论文旨在解决基于结果的强化学习（outcome-based reinforcement learning）在多轮搜索任务中难以区分各动作贡献的问题，即轨迹层面的信用分配无法精准反映单个动作对最终答案的贡献。其解决方案的关键在于提出EviSD（evidence-conditioned self-distillation）框架，通过将实例级支持证据作为搜索动作的特权信息、黄金答案作为回答动作的互补特权信息，实现对模型行为的精细化指导。在训练过程中，学生模型从原始上下文采样动作，而同一模型以对齐动作的上下文重新评分这些动作，充当特权教师角色。EviSD将师生间偏差转化为对基于结果的GRPO优势的有界修正，并仅作用于生成的动作片段，从而在不引入辅助蒸馏目标且无需改变推理过程的前提下，实现了特权信息的局部化引导，同时保持了由结果奖励决定的更新方向。实验表明，EviSD在七个问答基准上均取得最高宏平均精确匹配（Exact Match）得分，相比最强基线提升1.3–2.3个百分点，且仅需调制6.7%–15.1%的响应词元。
链接: https://arxiv.org/abs/2608.01359
作者: Jianan Xie,Xin Sun,Zhongqi Chen,Xing Zheng,Shu Wu,Bowen Song,Liang Wang
机构: Ant Group(蚂蚁集团); NLPR, MAIS, CASIA(国家数字出版重点实验室，智能系统与应用研究中心，中国科学院自动化研究所)
类目: Computation and Language (cs.CL)
备注: 12 pages
Abstract:Outcome-based reinforcement learning enables search-augmented language agents to learn from verifiable final answers, but its trajectory-level credit cannot distinguish the contributions of individual actions in a multi-turn search process. We propose EviSD, an evidence-conditioned self-distillation framework that uses instance-level supporting evidence as privileged information for search actions and golden answers as complementary privilege for answer actions. During training, the student samples actions from the original context, while the same model re-scores them as a privileged teacher under an action-aligned context. EviSD converts the detached teacher–student gap into a bounded correction to the outcome-derived GRPO advantage and applies it only to generated action spans. This design localizes privileged guidance while preserving the update direction determined by the outcome reward, without an auxiliary distillation objective or any change at inference time. Across seven question-answering benchmarks and three backbones spanning model scales and generations, EviSD achieves the highest macro-average Exact Match in all evaluated settings, outperforming the strongest compared methods by 1.3–2.3 points while modulating only 6.7%–15.1% of response tokens. Code is available at this https URL.
[NLP-76] HopRefusalBench: Diagnosing Refusal Failures in Search-Augmented Agents for Multi-Hop Reasoning
【速读】： 该论文旨在解决生成式 AI 在多跳搜索任务中面对根本不可回答问题时的拒答行为缺乏系统理解的问题。现有拒答评估基准仅暴露单跳查询表面缺陷，无法揭示在有效中间推理与检索后才显现的深层失败模式。为此，研究提出首个受控的多跳搜索拒答基准——HopRefusalBench，基于KILT标注的实体路径构建889个不可回答问题，涵盖答案未知、前提错误和上下文不明确三类不可回答原因，并结合根节点、中间节点和终端节点三种拓扑结构，使前提验证、中间桥接验证与终端终止行为可分别观测。研究进一步提出一个涵盖目标感知拒答、伪拒答、幻觉完成与搜索预算耗尽的最终结果分类体系，以及面向来源感知的轨迹度量指标（如触发后延续行为与令牌浪费）。在十种前沿闭源与开源模型的多跳搜索测试中，最优模型的目标感知正确终止率（TCHR）仅为42.9%。根节点与中间节点问题普遍比终端节点更难，所有模型在前提错误问题上表现最佳，在上下文不明确问题上最差。然而，当跨类别合并分析时，各模型约84.7%–98.4%的显式拒答响应能准确识别合理理由，表明核心瓶颈在于对非答案的恰当承诺，而非推理判断；失败轨迹则主要表现为幻觉生成或搜索预算耗尽。该研究确立了多跳搜索中的拒答为关键评估问题，并为诊断与提升搜索增强型智能体的可靠性提供了基础框架。
链接: https://arxiv.org/abs/2608.01358
作者: Jianan Xie,Xin Sun,Zhongqi Chen,Xing Zheng,Qiang Liu,Bowen Song
机构: Ant Group(蚂蚁集团); NLPR, MAIS, CASIA(国家智能计算机研究中心，人工智能与机器学习研究所，中国科学院自动化研究所)
类目: Computation and Language (cs.CL)
备注: 20 pages
Abstract:Search-augmented large language model agents are increasingly capable of solving knowledge-intensive tasks, but their behavior when a multi-hop question is fundamentally unanswerable remains poorly understood. Existing abstention benchmarks largely expose defects at the surface of single-hop queries and therefore cannot reveal failures that emerge only after valid intermediate reasoning and retrieval. We introduce HopRefusalBench, the first controlled benchmark of refusal within multi-hop search, comprising 889 unanswerable questions constructed from KILT-grounded entity paths. It crosses three causes of unanswerability (answer unknown, false premise, and underspecified context) with root, middle, and terminal topologies, making premise verification, intermediate-bridge validation, and terminal stopping separately observable. We further propose a final-outcome taxonomy spanning target-aware refusal, pseudo-refusal, hallucinated completion, and search-budget exhaustion, together with source-aware trajectory metrics for post-trigger continuation and token waste. Across ten frontier proprietary and open-weight models in search-augmented mode, the best model achieves a target-aware correct halting rate (TCHR) of only 42.9%. Root and middle items are consistently harder than terminal items, and all models attain their highest TCHR on false premises and their lowest on underspecified questions. Yet when pooled across categories, 84.7–98.4% of each model’s explicit refusal-like responses identify the correct rationale, localizing the main bottleneck to committing to an appropriate non-answer; failed trajectories instead diverge into hallucination or search-budget exhaustion. These results establish refusal in multi-hop search as a consequential evaluation problem and provide a foundation for diagnosing and improving the reliability of search-augmented agents.
[NLP-77] Prompt-Induced Waste in Large Reasoning Models: A Preregistered Two-Harness Benchmark of Coding Agents
【速读】： 该论文旨在解决生成式代码代理（coding agent）在实际应用中因提示词（prompt）设计不当而导致的推理成本激增问题，尤其关注提示语表述对推理开销（包括思考过程、工具调用及多轮交互）的因果影响，而这一影响此前缺乏系统性测量。其解决方案的关键在于通过一个预先注册的基准测试框架，在六种大型推理模型、两种真实代理框架与24个确定性编码任务上进行大规模实验（共4,643次有效运行），并采用隐藏评估机制确保结果客观性。研究发现，要求模型“开发并比较多种方案”的提示指令是导致推理成本最显著上升的操作，使推理令牌数增加2.4至7.4倍；类似地，“深入思考”等泛化提示也使推理时间增加1.6至2.2倍。相比之下，采用具有明确范围、验收标准和终止条件的高效模板可实现成本中性甚至减半推理开销。此外，代理框架的选择至关重要——相同模型-任务-提示组合在Claude Code下的成功成本比pi高5至30倍，主要源于更大的静态前缀和更多交互轮次。研究还揭示，误导性架构提示的成本远高于无关文字，且提供方缓存虽降低计费成本但不改变行为，因此不能视为效率优化。跨模型复现结果表明，不同模型对“思考”与“确定性”提示的敏感性存在差异，但核心趋势一致：提示词设计与代理架构显著影响成本，却常无法带来任务成功率提升。
链接: https://arxiv.org/abs/2608.01347
作者: Sarel Weinberger,Amir Hozez
机构: PointFive; Together AI (Together AI); Anthropic (Anthropic); Earendil Works (Earendil Works); BerriAI (BerriAI); Moonshot AI (Moonshot AI)
类目: Computation and Language (cs.CL)
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Abstract:Large reasoning models used as coding agents incur costs from deliberation, tool calls, and repeated agent turns, yet the causal effect of prompt wording on this spend has not been measured systematically. We present a preregistered benchmark across six large reasoning models, two real agent harnesses, and 24 deterministic coding tasks with hidden evaluators. Across 4,643 valid runs, including screening, stress, holdout, replication, and cross-provider studies, we find that prompt formulation can multiply reasoning cost without improving correctness. Asking the model to develop and compare several approaches is the most consistently wasteful instruction, increasing reasoning tokens by 2.4-7.4x across all models. Generic “think deeply” cues also increase deliberation by 1.6-2.2x, while a bounded-efficiency template specifying scope, acceptance criteria, and a stop condition is cost-neutral and can halve reasoning. Harness choice matters even more: identical model-task-prompt triples cost 5-30x more per success under Claude Code than under pi, mainly because of larger static prefixes and more turns. Misleading architectural hints are far costlier than irrelevant prose, and provider-side caching reduces billed cost without changing behavior, so it must not be treated as efficiency. Replications on Kimi-K3 and Claude Sonnet 5 preserve the main effect directions while revealing model-specific sensitivity to thinking and certainty cues. Overall, prompt wording and harness design materially affect agent cost, often with no gain in task success.
[NLP-78] LongChart VQA: A Comprehensive Benchmark for MLLM s with Complex Multi-Chart Reasoning
【速读】： 该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在复杂代理任务中评估不足的问题，尤其是现有基准测试多聚焦于单图表感知，难以有效衡量模型在多图表理解与多步推理方面的能力。其解决方案的关键在于设计一种基于潜在图（latent graphs）支持的合成流水线，以构建具有高复杂度和一致性的多图表评估数据集。基于此，研究提出了LongChart基准，其视觉问答（VQA）任务平均包含6.5张图像和31.2个问题，显著提升了对多图表上下文建模与推理能力的考察力度。通过在10个先进MLLM上进行评估，研究进一步分析了推理模式、辅助工具使用及对图像扰动的鲁棒性三个关键因素的影响，发现随着计算复杂度增加，模型准确率显著下降且表现差异明显，揭示了未来在多图表推理方向上的重要研究路径。
链接: https://arxiv.org/abs/2608.01328
作者: Ziyan Xiao,Yinghao Zhu,Wenting Zhang,Heaju Kim,Lequan Yu
机构: The University of Hong Kong (香港大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal large language models (MLLMs) are rapidly evolving with expanded context windows and stronger reasoning capabilities, enabling multi-chart understanding and multi-step inference. These abilities are increasingly important as MLLMs are adopted in complex agentic tasks. However, existing benchmarks largely emphasize single-chart perception, while simple chart-to-chart connections are insufficient to evaluate these capabilities. To capture multi-chart complexity while ensuring consistency and validity, we design a synthesis pipeline supported by latent graphs. Building on this pipeline, we introduce LongChart, a benchmark whose VQA sets contain an average of 6.5 images and 31.2 questions. We evaluate 10 state-of-the-art MLLMs and examine three factors that influence performance: reasoning patterns, auxiliary tools, and robustness to image perturbations. Our results show that MLLM accuracy decreases and varies substantially as computational complexity increases, highlighting directions for future research in multi-chart reasoning.
[NLP-79] Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory
【速读】： 该论文旨在解决“影子交易”（shadow trading）这一新型内幕交易责任理论的实证基础问题，即如何在事前识别与信息披露公司存在经济关联的“同业公司”（peer firms），以支持监管机构对利用非公开重大信息（MNPI）进行跨公司交易行为的追责。其核心挑战在于，现行监管实践依赖事后通过大规模市场监控系统识别关联企业，而本文提出的关键解决方案是：利用自然语言处理（NLP）技术，特别是基于大语言模型（LLM）的两阶段语义分析管道，从上市公司10-K报告中“管理层讨论与分析”（Item 7, MD&A）部分的公开披露文本中，自动提取并量化公司间的语义相似性，从而实现对潜在同业公司的事前识别。研究通过对30个并购事件、覆盖五个行业的数据集进行分析，发现尽管在“Panuwat案”这一已知案例中模型成功识别出Incyte为最接近同业公司，但在整体样本中，语义相似性与公告日异常股票收益之间无显著相关性（秩相关系数为+0.07，置换检验p=0.37；事件层面平均斯皮尔曼相关系数为+0.05，95%置信区间[-0.08, +0.18]），表明该方法无法有效支撑“影子交易”理论所依赖的因果前提。此外，研究还揭示了被SEC归类为“中等市值肿瘤学公司”的Incyte在公告日前实际已偏离该分类标准，进一步削弱了其分类逻辑的合理性。这些结果虽受制于特定模型架构、语料库及收益衡量方式，但对“影子交易”执法的实证基础构成挑战，并引发关于美国证券交易委员会（SEC）金融监控基础设施合宪性的深层讨论。
链接: https://arxiv.org/abs/2608.01322
作者: Sarah Wilson,Michael MacKay,Anthony Marello,Trinav Bhattacharyya
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Shadow trading – trading in a peer firm’s securities on the basis of material nonpublic information (MNPI) about an “economically linked” company – is a novel and contested theory of insider trading liability, first prosecuted in SEC v. Panuwat (2023). Enforcing it requires identifying economically linked firms ex ante, a determination the SEC makes only after the fact using mass market surveillance infrastructure. We ask whether NLP can do what the SEC’s theory presumes insiders already know: identify peer firms ex ante from publicly mandated disclosures. Using a two-stage LLM pipeline applied to Item 7 (Management’s Discussion and Analysis) sections of SEC 10-K filings, we score semantic similarity across 30 MA events spanning five industries and relate similarity to announcement-day abnormal stock returns. On the Panuwat fact pattern itself the pipeline recovers Incyte among the closest peers, a sanity check on the one case with a known outcome. Across the full dataset, however, we find no association: pooling 217 peer observations, the within-event rank correlation between similarity and abnormal return is +0.07 (permutation p = 0.37), and the mean per-event Spearman correlation is +0.05 with a 95% confidence interval of [-0.08, +0.18] – narrow enough to exclude any moderate relationship rather than merely failing to detect one. A case-level reading agrees: 14 of 30 events support the hypothesis, 12 contradict it, and 4 are ambiguous. We also find that Incyte fell outside the standard \ 2B-\ 10B mid-cap band on the day before the announcement, complicating the “mid-cap oncology” category the SEC invoked. These results are exploratory and bound to this pipeline, corpus, and return measure, but they put pressure on the empirical premise of shadow trading enforcement and bear on constitutional questions surrounding the SEC’s financial surveillance infrastructure.
[NLP-80] BiCAA: Bidirectional Credit Assignment for Search-Augmented Agent
【速读】： 该论文旨在解决多步搜索（multi-step search）任务中生成式搜索代理因仅基于最终输出进行奖励分配而导致的训练不稳定性与冗余搜索行为问题。传统方法采用结果导向的奖励机制（outcome-only supervision），缺乏对中间推理步骤的有效监督，造成信号稀疏。为此，论文提出一种双向信用分配框架（BiCAA），其核心在于引入过程奖励（process reward）以实现细粒度、可区分的步骤级监督。关键创新点在于设计了两个互补的评判标准：一是每一步是否引入有助于解题的新证据（前向可解性增益，forward solvability gain），二是该步骤在整体推理路径中是否构成达成最终成功所必需的关键决策（事后成功必要性，hindsight success criticality）。BiCAA通过融合这两个信号，并与最终结果奖励相结合，构建密集且具有区分性的过程奖励，从而显著提升策略优化的稳定性，减少冗余搜索行为，并在搜索增强型问答基准上取得优异性能。
链接: https://arxiv.org/abs/2608.01321
作者: Yibin Huang,Bin Xu,Hailong Cao,Conghui Zhu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Multi-step search is a fundamental capability for search agents, enabling them to iteratively acquire, refine, and integrate external evidence for complex reasoning QA. However, vanilla GRPO allocates rewards exclusively based on the model’s final outputs, yielding outcome-only supervision with no supervisory signals for intermediate reasoning steps. Such sparse supervision easily causes training instability and redundant search behaviors on multi-step search tasks. To mitigate this limitation, we adopt process reward to deliver stepwise supervision signals. For this process reward, we propose two complementary criteria to judge each search step: whether the step yields new evidence to facilitate problem solving, and whether it forms an efficient, pivotal intermediate decision within the overall reasoning trajectory. Building on this insight, we propose BiCAA: a bidirectional credit assignment framework that delivers dense, distinguishing process rewards for search-augmented agents. BiCAA builds bidirectional process rewards by fusing two complementary signals: forward solvability gain and hindsight success criticality. The former quantifies step-wise improvements in answer plausibility, while the latter evaluates each step’s necessity for final success via hindsight outcome-based criticality scoring. We modulate and aggregate the two signals and then fuse them with the outcome reward. Experiments on search-augmented QA benchmarks show that BiCAA stabilizes policy optimization, reduces redundant search behavior, and achieves competitive performance.
[NLP-81] Dense Language Generation Made Simple: Deterministic Randomized and Multi-Order Algorithms
【速读】： 该论文旨在解决生成式语言学习中的可靠性与覆盖度问题，即在未知语言的正例流持续输入的场景下，如何设计生成器以确保其输出既满足有效性（仅生成目标语言内的字符串）又具备新颖性（避免重复已出现的样本），同时最大化输出覆盖范围。核心挑战在于量化并优化生成器对目标语言中潜在有效输出的覆盖能力，特别是针对不同重要性或相关性排序下的输出分布。解决方案的关键在于提出一个简洁且统一的分析框架，首次通过更简明的证明实现了确定性算法下最优的下界密度（lower density）为1/2；进一步拓展表明，在面对无偏对抗者时，随机化可将最优保证提升至1−1/e；此外，该框架还支持在有限多个排序下同时实现各排序对应的最优保证，从而在多维度重要性评估中保持无损失性能。这一成果为生成式人工智能（Generative AI）在动态、不可预测数据环境下的可靠生成提供了理论基础与算法支撑。
链接: https://arxiv.org/abs/2608.01320
作者: Ziyi Cai,Shuangping Li,Yiheng Shen,Kangning Wang,Peng Zhang
机构: Rutgers University (罗格斯大学); Yale University (耶鲁大学); Meta (Meta)
类目: Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Discrete Mathematics (cs.DM); Machine Learning (cs.LG)
备注:
Abstract:Language generation in the limit is a theoretical framework for studying how a generator can learn to produce new valid strings from a stream of positive examples. In this model, an adversary chooses an unknown language from a countable family and enumerates its elements in an arbitrary order, while the generator must eventually output only elements of the language that have not yet appeared in the enumeration. Reliable generation is thus formalized through two eventual guarantees: validity and novelty relative to the observed data. To further quantify the breadth of the generator’s outputs, Kleinberg and Wei (FOCS 2025, STOC 2026) introduced lower density as a measure of output coverage. Given an order representing the importance or relevance of possible outputs, lower density is the asymptotic lower bound, as n grows, on the fraction of the first n elements of the target language that the generator outputs before they appear in the data. Kleinberg and Wei showed that 1/2 is the optimal lower-density guarantee for deterministic algorithms. We develop a simple and unified framework for obtaining optimal lower-density guarantees. We first give a deterministic algorithm that recovers the optimal guarantee of 1/2 with a significantly simpler analysis than prior work. We then demonstrate the flexibility of our framework through two extensions. First, against an oblivious adversary, randomization raises the optimal guarantee to 1-1/e . Second, for any finite collection of orders, the optimal deterministic and randomized guarantees can be achieved simultaneously with respect to every order, so accommodating multiple notions of importance or relevance entails no loss in the optimal guarantee. Subjects: Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Discrete Mathematics (cs.DM); Machine Learning (cs.LG) Cite as: arXiv:2608.01320 [cs.DS] (or arXiv:2608.01320v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2608.01320 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-82] RH-RAG : Trustworthy Long-Form Generation for Privacy-Constrained Settings KDD2026
【速读】： 该论文旨在解决在严格隐私与安全约束下，利用本地部署的轻量级语言模型生成长篇内容时面临的两大核心挑战：一是现有检索增强生成（RAG）方法在小型模型上缺乏有效的全局规划能力，导致生成内容结构松散；二是长期生成过程中易积累事实性错误（幻觉），影响内容可信度。其解决方案的关键在于提出一种多智能体框架RH-RAG，通过三个协同阶段实现安全可信的长文本生成：首先由规划代理（Planner Agent）基于高层语义摘要构建全局文档大纲，确保整体结构合理性；其次由写作代理（Writer Agent）采用有限上下文记忆机制逐部分生成连贯内容，维持局部一致性；最后由校验代理（Checker Agent）结合自然语言推理进行事实验证，并通过证据驱动的修订循环主动修正幻觉。此外，框架引入双层检索索引，在消费级硬件上实现高效规划与细粒度上下文生成，显著提升生成质量。实验结果表明，RH-RAG在文学、金融和法律等多个领域均优于标准及分层式RAG基线，在事实准确性、语义连贯性和文档级对齐性方面表现突出，且在不依赖私有云大模型的前提下实现了与商业系统相当的可靠性，充分保障了数据隐私。
链接: https://arxiv.org/abs/2608.01311
作者: Raj Shekhar Singh
机构: Indian Institute of Technology, Roorkee (印度理工学院，鲁尔基)
类目: Computation and Language (cs.CL)
备注: accepted in KDD 2026 SeT-LLM Workshop
Abstract:Generating long-form content from extensive internal reports remains challenging for organizations operating under strict privacy and security constraints, where proprietary cloud-based LLM APIs are often not viable. While locally deployed open-weight models offer a privacy-preserving alternative, existing retrieval-augmented generation (RAG) approaches on smaller models frequently lack effective global planning and accumulate factual inconsistencies over long outputs. To address these limitations, we present RH-RAG, a multi-agent framework for secure and trustworthy long form generation using local language models. RH-RAG decomposes generation into three coordinated stages: a Planner Agent that constructs a global document outline from high-level semantic summaries, a Writer Agent that incrementally generates coherent section-wise content using bounded coherence memory, and a Checker Agent that mitigates hallucinations through natural language inference-based factual verification and an attestation-driven revision loop. The framework further employs a dual-level retrieval index that supports efficient planning and fine-grained contextual generation on consumer-grade hardware. Evaluations across literary, financial, and legal domains demonstrate that RH-RAG consistently improves factual grounding, semantic coherence, and document-level alignment compared to standard and hierarchical RAG baselines, while achieving reliability competitive with proprietary cloud-based systems without compromising data privacy.
[NLP-83] CrossLex: A Source-Grounded Benchmark for Cross-Jurisdictional Legal Reasoning in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在跨法域法律推理中对管辖权差异识别能力不足的问题。现有基准测试普遍忽视了相同事实情境下不同法律体系可能导致迥异法律规则适用与结论的特性，尤其缺乏对模型跨司法管辖区推理能力的系统性评估。为此，论文提出CrossLex——一个基于同一事实、法律源数据驱动的跨法域法律推理评测基准，覆盖中国、加利福尼亚州和德国三个司法管辖区。CrossLex构建于权威法律资料之上，涵盖合同、消费者、刑事、家庭及劳动法等55个法律议题，通过与各司法管辖区相匹配的问题-答案对及其支持性法源引证，形成包含6,149个实例、分属385个事实组的数据集，并由法律专家对所有法律议题、答案及引用依据进行审查。为区分基础法律知识与跨法域推理能力，研究设计了三项互补任务：单一法域推理（T1）、联合跨法域比较（T2）与细粒度跨法域评估（T3）。同时，提出“有据联合评估”（Grounded Joint）指标，统一衡量答案正确性与法律源依据的契合度，实现高效一致的评估框架。实验表明，尽管当前主流LLMs在单法域法律问题上具备较高准确率，但在跨法域推理中仍存在显著缺陷，难以准确捕捉管辖权差异带来的法律适用变化。研究期望CrossLex能推动未来面向法律源依据的跨法域法律推理研究发展。
链接: https://arxiv.org/abs/2608.01292
作者: Xiaocui Yang,Xican Tan,Shoujie Chen,Shihan Xiao,Keke Tong,Xinyu Zhou
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Legal reasoning is inherently jurisdiction-dependent: the same facts can call for different legal rules and yield different conclusions across legal systems. Yet existing benchmarks rarely evaluate whether large language models (LLMs) can recognize such jurisdiction-specific variation, especially when identical fact patterns lead to divergent legal this http URL introduce CrossLex, a same-fact, legal-source-grounded benchmark for evaluating cross-jurisdictional legal reasoning in LLMs across three jurisdictions: China, California, and Germany. Built from authoritative legal sources, CrossLex aligns 55 legal issues spanning contract, consumer, criminal, family, and labor law, and constructs jurisdiction-aligned questions paired with answers and supporting citations. In total, CrossLex contains 6,149 instances organized into 385 fact groups, with all legal issues, answers, and cited authorities reviewed by legal this http URL disentangle basic legal knowledge from cross-jurisdictional reasoning, CrossLex defines three complementary tasks: single-jurisdiction reasoning (T1), joint cross-jurisdictional comparison (T2), and fine-grained cross-jurisdictional evaluation (T3). We further propose Grounded Joint, a metric that jointly assesses answer correctness and legal-source grounding, and provide a unified evaluation for streamlined benchmarking. Extensive experiments on representative LLMs show that, although current models can often answer legal questions correctly, they struggle to provide accurate cross-jurisdictional legal this http URL hope that CrossLex will facilitate future research on source-grounded cross-jurisdictional legal reasoning.
[NLP-84] ArabicDialectSafety: A Dialect-Aware Benchmark for Arabic Content Safety Classification
【速读】： 该论文旨在解决阿拉伯语方言（Arabic Dialects）在生成式 AI 安全评估中的代表性不足与跨方言泛化能力差的问题，尤其关注低资源马格里布方言（Maghrebi dialects）的安全性建模难题。其核心挑战在于现有安全数据集缺乏对多源阿拉伯语变体的覆盖，且多数模型在处理方言时表现显著下降。解决方案的关键在于构建首个大规模、人工标注的阿拉伯语方言安全数据集 ArabicDialectSafety，涵盖六种主要方言（现代标准阿拉伯语、叙利亚、埃及、阿尔及利亚、巴勒斯坦、摩洛哥），并引入细粒度七类危害标签；同时提出双任务评估框架，实现二分类安全/不安全检测与跨方言细粒度危害分类的联合评估。实验表明，通过在表示层（representation level）融合方言条件信息，可显著提升模型性能，其中微调后的 MARBERTv2 在二分类和细粒度分类上分别达到 0.95 和 0.90 的宏平均 F1 值，显著优于提示工程驱动的前沿大语言模型（LLMs）。尽管主流大模型在生成响应时表现出低于 5% 的不安全生成率，但针对低资源马格里布方言仍存在明显性能鸿沟，凸显了方言感知安全评估中数据与模型适配的重要性。研究团队已公开数据集与代码，以推动面向方言敏感型阿拉伯语安全评估的后续研究。
链接: https://arxiv.org/abs/2608.01291
作者: Wajdi Zaghouani,Md. Rafiul Biswas,Kholoud Khalil Aldous,Mabrouka Bessghaier
机构: Northwestern University in Qatar (西北大学卡塔尔分校); Hamad Bin Khalifa University (哈马德·本·哈利法大学)
类目: Computation and Language (cs.CL)
备注: 13 pages, 2 figures, 9 tables
Abstract:We present ArabicDialectSafety, a human-curated Arabic safety dataset of 25,071 prompts covering six Arabic varieties: Modern Standard Arabic, Syrian, Egyptian, Algerian, Palestinian, and Moroccan. The dataset is annotated with dialect labels and seven fine-grained harm categories. We introduce a dual-task evaluation framework for binary safe/unsafe detection and granular harm classification across dialects. Benchmarking seven supervised and generative models, we find that fine-tuned MARBERTv2 achieves the strongest performance, with Macro-F1 scores of 0.95 for binary classification and 0.90 for granular classification, substantially outperforming prompted frontier LLMs, including Arabic-specialized models. Our analyses show that dialect conditioning is most effective when integrated at the representation level, while significant performance gaps remain for low-resource Maghrebi dialects. We further evaluate seven frontier LLMs as response generators on harmful dialectal Arabic prompts and observe unsafe generation rates below 5 percent across models. We release the dataset and code upon acceptance to support future research on dialect-aware Arabic safety evaluation. Warning: This paper contains examples of harmful and potentially offensive content included solely for research purposes.
[NLP-85] ACE-GraphRAG : Agent RAG : Agentic Context Engineering for Hierarchical GraphRAG
【速读】： 该论文旨在解决层次化图检索增强生成（Hierarchical GraphRAG）中因固定上下文构建方式导致的“表示—推理鸿沟”问题，即多粒度知识表示难以有效适配当前查询需求。其核心解决方案是提出面向层次化图RAG的智能上下文工程方法（ACE-GraphRAG），在推理阶段引入一个基于策略的上下文优化层，通过动态调整初始上下文以实现更精准的生成。该方法将上下文构建建模为一种兼顾差距感知的精炼、多分支检索与任务条件化适应的策略过程；采用并行差分检索（Parallel Differential Retrieval）从深度导向的事实分支和广度导向的语义分支中获取补充证据，并在保留溯源信息与抽象层级的前提下融合至初始上下文。具体包括全量策略（Full-ACE）与自适应策略（Adaptive-ACE）两种模式，前者统一应用于任务族，后者则根据查询特征与图拓扑动态选择最优策略。实验表明，全量策略在多跳问答与聚焦式摘要任务上均优于现有RAG与GraphRAG基线，而自适应策略在多跳问答中进一步提升性能，并在四个UltraDomain子集上全面超越全量策略。消融实验与拓扑分析验证了将上下文构建视为查询与任务相关的动态推理策略，而非静态流程的关键有效性。
链接: https://arxiv.org/abs/2608.01269
作者: Yongfeng Huang,Yuren Lai,Ruiying Chen,Haoyu Huang,Mingming Zhao,James Cheng
机构: The Chinese University of Hong Kong (香港中文大学); Wuhan University of Technology (武汉理工大学); The Hong Kong University of Science and Technology (香港科技大学); Huawei Noah’s Ark Lab (华为诺亚方舟实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate these multi-resolution representations into a context suited to the current query. We identify this mismatch as the representation–inference gap. We propose Agentic Context Engineering for Hierarchical GraphRAG (ACE-GraphRAG), an inference-time context policy layer that supplements and adapts the initial context for generation. ACE-GraphRAG formulates context construction as a policy over gap-aware refinement, retrieval branches, and task-conditioned adaptation. Parallel Differential Retrieval acquires supplementary evidence from depth-oriented factual and breadth-oriented semantic branches. These evidence increments are consolidated with the initial context while preserving provenance and abstraction levels. Full-ACE applies the full policy uniformly within each task family, whereas Adaptive-ACE selects task- and topology-specific policies for individual queries. We evaluate ACE-GraphRAG on HotpotQA, 2WikiMultiHopQA, and four UltraDomain subsets across multi-hop QA and query-focused summarization. Full-ACE outperforms the evaluated RAG and GraphRAG baselines across both task families, while Adaptive-ACE further improves multi-hop QA and is preferred over Full-ACE on all four UltraDomain subsets. Ablation and topology analyses support treating context construction as a query- and task-dependent inference policy rather than a fixed procedure.
[NLP-86] RestoreKV: Recovering Full-Cache Behavior Under Aggressive Query-Agnostic KV Cache Eviction
【速读】： 该论文旨在解决在严格缓存预算下，查询无关的键值（Key-Value, KV）缓存压缩导致性能显著下降的问题。现有方法主要聚焦于优化原始KV对的选择以保留更关键的信息，但难以有效恢复因压缩而丢失的上下文语义。其核心解决方案是提出RestoreKV，一种基于学习的恢复机制，在不增加总KV缓存预算的前提下，通过可共享的恢复机制重建被蒸发信息的紧凑补全。其关键创新在于：尽管被丢弃的信息具有上下文特异性，但生成其紧凑补充的恢复机制可跨上下文共享。具体而言，在上下文预填充后，仅需少量“恢复令牌”（restore tokens）通过单次低秩自适应（LoRA）适配的前向传播，即可从完整KV缓存中生成一个与上下文相关的、紧凑的恢复缓存。该方法保持原有重要性评分器和淘汰规则不变，且后续解码过程中禁用适配器以维持高效推理。RestoreKV采用参数高效的自蒸馏训练方式，仅优化0.4%的模型参数，无需任务特定调参。在四个骨干网络和四个长序列基准测试中，RestoreKV显著缓解了压缩带来的性能退化；例如在Qwen3-4B上，59/60个预算匹配设置中均优于基线，当缓存预算为5%时，将KVzip在RULER-4K上的得分从38.2提升至73.2；应用于KVzip+后，在KVPress基准上实现16倍压缩下的86.4分准确率，同时在32K上下文评估中仅引入低于0.5%的一次性缓存构建开销。
链接: https://arxiv.org/abs/2608.01247
作者: Changwoo Baek,Seungjun Shin,Kyeongbo Kong
机构: 1. Korea University(韩国科学技术院); 2. Seoul National University(首尔国立大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 13 pages, 8 figures
Abstract:Query-agnostic KV cache eviction compresses a context once and reuses the resulting cache for arbitrary future queries, but performance can collapse under tight budgets. Existing methods primarily improve which original KV pairs are retained. We introduce RestoreKV, which complements this selection-based formulation with learned restoration under the same total KV budget. Our key insight is that, although the information lost through eviction is context-specific, the mechanism for generating its compact complement can be shared across contexts. After context prefill, a few restore tokens attend to the full KV cache in a single LoRA-adapted pass, generating a compact, context-conditioned restore cache. The base importance scorer and eviction rule remain unchanged, and the adapters are disabled for all subsequent queries and decoding. RestoreKV is trained through parameter-efficient self-distillation from the frozen full-cache model, optimizing only 0.4% of the parameters and requiring no task-specific tuning. Across four backbones and four long-context benchmarks, RestoreKV substantially reduces compression-induced degradation. On Qwen3-4B, it improves 59 of 60 paired, budget-matched settings across five base eviction methods; at a 5% budget, it raises KVzip from 38.2 to 73.2 on RULER-4K. Applied to KVzip+, RestoreKV reaches 86.4 RULER accuracy at 16\times compression on the KVPress Benchmark, while adding less than 0.5% one-time cache-construction overhead in a 32K-context evaluation. Our project page is available at this https URL
[NLP-87] DeltaFlow: Noise-Adaptive Bidirectional Gated Delta Networks for Embedded Language Flows
【速读】： 该论文旨在解决嵌入式语言流（Embedded Language Flows, ELF）在迭代去噪过程中依赖全非因果注意力机制所导致的二次方序列混合开销问题，即在每一步采样中均需进行高复杂度的全局上下文建模，严重制约了生成效率。其核心解决方案是提出一种噪声自适应的双向门控增量网络（DeltaFlow），作为连续语言去噪的高效骨干架构。关键创新在于设计了两种双向扫描结构：DeltaFlow-A通过层间交替扫描方向以实现双向信息流动，而DeltaFlow-P则在单层内并行执行前向与后向扫描，从而更充分地捕捉双向上下文；同时引入噪声自适应记忆控制与调度的时间状态一致性（Temporal State Consistency, TSC）机制，有效稳定相邻噪声水平下的隐藏表示，提升模型鲁棒性。实验表明，在OpenWebText数据集上，使用32步随机微分方程采样器时，DeltaFlow-P将生成困惑度从全注意力基线的24.218降至21.228，且仅需360亿训练标记（vs. 基线450亿），同时在仅含去噪器的基准测试中，于16k序列长度下实现2.72倍的吞吐量加速，验证了其在保持生成质量的同时显著提升计算效率，为替代密集注意力机制提供了高效可行的新范式。
链接: https://arxiv.org/abs/2608.01240
作者: Guangfu Guo,Xiaoqian Lu,Linsey Pang,Weiran Yao,Haolin Chen,Kunpeng Liu,Long Cheng
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Embedded Language Flows (ELF) rely primarily on full non-causal attention for iterative denoising, repeatedly incurring quadratic sequence-mixing cost at each sampling step. Gated Delta Networks (GDNs) provide an efficient recurrent alternative, but their standard causal formulation cannot directly capture the bidirectional context required by ELF. We introduce DeltaFlow, a noise-adaptive bidirectional GDN backbone for continuous language denoising. We study two variants: DeltaFlow-A, which alternates scan directions across layers, and DeltaFlow-P, which performs parallel forward and backward scans within each layer. We further introduce noise-adaptive memory control and scheduled Temporal State Consistency (TSC) to stabilize hidden representations across nearby noise levels. On OpenWebText, using a 32-step stochastic differential equation sampler, DeltaFlow-P reduces generated perplexity from 24.218 for the full-attention ELF baseline to 21.228 while maintaining comparable unigram entropy, with 36B training-token exposure compared with 45B for the baseline. In a denoiser-only benchmark, DeltaFlow-P achieves a 2.72x throughput speedup over the full-attention baseline at a sequence length of 16k. These results show that DeltaFlow is a promising alternative to dense attention for efficient continuous language denoising.
[NLP-88] Evaluating VLMs on Multimodal Aristotelian Persuasion Tasks
【速读】： 该论文旨在解决生成式 AI 在复杂任务上的评估不足问题，特别是针对基于亚里士多德修辞模型（Rhetorical Triangle）的逻辑（Logos）、情感（Pathos）与信誉（Ethos）三类说服性元素的检测任务。现有视觉语言模型（Vision Language Models, VLMs）虽在多项基准任务中表现优异，但在处理涉及主观判断、文化背景及深层语义理解的复杂推理任务时仍存在局限。其解决方案的关键在于构建并应用 ImageArg 数据集，系统性地评估 VLMs 在 Logos、Ethos 与 Pathos 三类修辞维度上的识别能力。实验结果表明，通义千问系列模型（Qwen 家族）在该数据集上表现出色，其中 Qwen3 在 Logos 与 Pathos 检测任务中取得显著提升，而 Qwen2 在更具挑战性的 Ethos 检测任务中亦展现出较强的竞争力，验证了该评估框架的有效性与前瞻性。研究团队开源代码以推动该方向的进一步探索。
链接: https://arxiv.org/abs/2608.01238
作者: Khondoker Ittehadul Islam
机构: 未知
类目: Computation and Language (cs.CL); Multimedia (cs.MM)
备注:
Abstract:Vision Language Models (VLMs) have demonstrated exceptional performance across various tasks. However, they have not yet been thoroughly evaluated on more complex tasks. The Persuasion Model, conceived by Aristotle, resembles a triangle shape, which highlights its inherent challenges related to personal biases. To assess the progress of VLMs on these complex tasks, we use the ImageArg datasets, focusing on the Logos, Ethos, and Pathos detection tasks. Our findings indicate that models from the Qwen family achieve improved F1 scores, with Qwen3 performing exceptionally well on the Logos and Pathos tasks, while Qwen2 exhibits competitive performance on the more complex Ethos detection task. We release the code to foster research in this direction.
[NLP-89] ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors
【速读】： 该论文旨在解决现有对话式投资顾问评估体系在长期行为影响追踪上的不足，即当前评价主要聚焦于单次响应质量或短期结果，难以审计顾问语言如何随市场演变影响用户长期投资决策路径。其核心解决方案是提出ShiJianBench——一个基于历史市场反馈的离线评估框架，通过匹配真实投资者轨迹实现对对话式投资顾问的长期行为影响评估。该框架的核心是一个多智能体投资者模拟器，具备显式的动态状态变量、动机驱动的决策机制、长期记忆能力以及基于对话内容的更新逻辑；模拟器经由7,199名真实用户的行为模式进行校准，并采用严格的合规门控机制，从投资者侧、服务侧和内容侧三个维度对顾问策略进行综合评估。基于2021至2026年中国基金市场的实证实验表明，部分大语言模型（LLM）顾问在个性化内容生成方面显著优于其他模型，同时实现了与顶尖表现相当的长期投资者轨迹结果。研究揭示了高质量响应与有效长期干预之间的系统性差异，强调必须引入轨迹感知（trajectory-aware）的评估范式来全面衡量对话式投资顾问的实际效能。
链接: https://arxiv.org/abs/2608.01204
作者: Jie Gong,Maowei Jiang,Zhiwei Liu,Yang Qiao,Wenxi Wu,Mengxi Xiao,Enze Zhang,Ziyan Kuang,Yankai Chen,Caishuang Huang,Meng Zhou,Xiku Du,Xue Liu,Guojun Xiong,Min Peng,Qianqian Xie,Sophia Ananiadou
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Conversational investment advisors influence not only what users know, but also how they make subsequent decisions as market conditions evolve. Existing evaluations primarily assess response quality or observed outcomes, leaving the long-horizon pathway from advisor language to investor behavior difficult to audit. We introduce ShiJianBench, an offline framework for evaluating conversational investment advisors through matched investor trajectories under fixed historical market feedback. At its core is a multi-agent investor simulator with explicit evolving state variables, motive-driven deliberation, long-term memory, and dialogue-grounded updates. The simulator is calibrated against aggregate behavioral patterns from 7,199 real users, and advisor policies are evaluated using separate investor-side, service-side, and content-side metrics under a hard compliance gate. Experiments on Chinese fund-market traces from 2021 to 2026 identify a stable leading group of LLM advisors that combines substantially stronger personalized content with competitive investor-side trajectory outcomes. These results reveal a systematic distinction between producing a high-quality response and delivering an effective long-horizon intervention, motivating trajectory-aware evaluation of conversational advisors.
[NLP-90] When Words Divide: Diachronic Ideological Polarization in Political Discourse on Social Media
【速读】： 该论文旨在解决在线话语中政治极化长期演变机制不清晰的问题，尤其关注其在语义层面的动态演化。传统计算研究多聚焦于某一时间点的横截面分析或情感维度的极化，难以揭示极化随时间演化的内在规律。本文提出的关键解决方案是构建时序对齐的社区特异性词嵌入（community-specific word embeddings），并以政治概念的语义分歧（semantic divergence）作为量化指标，系统追踪不同政治群体在语言使用上的语义差异随时间的变化。该方法突破了静态分析的局限，能够捕捉意识形态分歧在概念层与话题层的渐进式扩展，为大规模社交媒体话语中的意识形态极化动态提供了可扩展、可量化的分析框架。
链接: https://arxiv.org/abs/2608.01176
作者: Roy Yitzchak,Noa Lavie,Ella Rabinovich
机构: The Academic College of Tel-Aviv Yaffo, Israel
类目: Computation and Language (cs.CL); Social and Information Networks (cs.SI)
备注:
Abstract:Political polarization has become a defining feature of online discourse, yet its long-term evolution remains poorly understood. We present a longitudinal analysis of ideological polarization in Reddit discussions by measuring semantic differences in the language used by opposing political communities. We construct temporally aligned community-specific word embeddings and quantify ideological polarization as the semantic divergence of political concepts over time. Our analysis shows that ideological polarization has increased substantially during the study period, both at the concept- and topic-level. Unlike prior computational work, which has largely focused on cross-sectional analyses or affective dimensions of polarization at a single point at time, our approach captures the evolution of ideological differences in semantic framing. The proposed framework provides a scalable method for studying the temporal dynamics of ideological polarization in large-scale social media discourse.
[NLP-91] Does Machine “know” interpersonal prag matics? Evidence from MARBERTs learning of emoji prag matics in Arabic digital discourse
【速读】： 该论文旨在解决阿拉伯语数字话语（Arabic Digital Discourse, ADD）中表情符号的语用功能（Interpersonal Pragmatic Functions, IPFs）难以被自然语言处理模型准确识别与建模的问题，尤其关注其在人际互动中的隐含社会意义。其核心解决方案在于利用基于Transformer架构的预训练模型MARBERT，通过在大规模手工标注的8,504条含表情符号的社交平台帖子数据集上进行微调，使其能够捕捉语境依赖的五类人际语用功能：礼貌（Politeness）、尊重（Respect）、团结（Solidarity）、共情（Empathy）和鼓励（Encouragement）。研究采用混合方法，结合统计分析与基于言语行为理论、礼貌理论及关系管理理论的解释性分析，验证了模型在未见数据上的表现：达到93%的准确率、0.61的微观F1分数和0.56的宏观F1分数，表明其在超越传统情感分析的基础上有效建模人际语用功能。关键发现指出，显性程度较高的礼貌与尊重功能识别效果优于高度隐含的团结功能，揭示了模型对社会语用隐喻的处理局限。该研究贡献了一种新颖的计算范式，推动了人际语用学与自然语言处理在数字交流研究中的深度融合。
链接: https://arxiv.org/abs/2608.01174
作者: Mohammed Q. Shormani(Ibb University)
机构: 未知
类目: Computation and Language (cs.CL)
备注: pages 22, tables 3, figure 4
Abstract:This study examines Transformer-based models’ ability to learn emoji pragmatics in Arabic digital discourse (ADD), providing evidence from MARBERT’s behavior with interpersonal pragmatic functions (IPFs). A corpus of 8,504 unique emoji-posts collected from Facebook via Python was used in the study. These posts were manually annotated, developed, and labeled for five IPFs: Politeness, Respect, Solidarity, Empathy, and Encouragement. A mixed-method approach was employed comprising statistical methods and interpretative analyses involving speech act theory, politeness theory, and rapport management theory. MARBERT was fine-tuned to model these context-dependent pragmatic functions. Findings demonstrate MARBERT’s ability to learn these IPFs, achieving strong performance on unseen data, with an accuracy of 93%, a micro F1-score of 0.61, and a macro F1-score of 0.56, demonstrating its effectiveness in capturing interpersonal functions beyond conventional sentiment analysis. Function-level evaluation showed that Politeness and Respect were identified more accurately than Solidarity, reflecting differences in the explicitness and contextual dependence of IPFs. The study concludes that Transformer-based models learn patterns of face management and relational communication but remain challenged by highly implicit social meanings. It contributes a novel computational approach to modeling emoji pragmatics and advances the integration of interpersonal pragmatics with NLP for digital communication research.
[NLP-92] PlainMedScale: A Corpus of Multi-Level Simplified Medical Texts in German and English
【速读】： 该论文旨在解决现有医学语料库在可读性层级划分上的局限性问题，即多数研究仅基于“专业—非专业”二元对立框架构建语料，难以覆盖从参考到获取等多维度的沟通功能需求。为此，论文提出PlainMedScale，一个涵盖德语与英语双语、四个可读性层级（对应参考、解释、决策支持和访问四类交际功能）的对齐医学语料库。其解决方案的关键在于引入多层级、功能导向的语料结构，突破传统二元划分，实现更精细的可读性梯度建模；并通过初步实证研究揭示：现有可读性指标在跨层级泛化能力不足，且即使采用先进的开放权重生成式AI模型进行简明语言（Plain Language）提示，仍可能保留输入文本的复杂性特征，凸显了对多层级语料与评估体系的必要性。
链接: https://arxiv.org/abs/2608.01158
作者: Bruno Brocai,Ilaria Papagno,Mayumi Ohta
机构: Heidelberg University (海德堡大学)
类目: Computation and Language (cs.CL)
备注: accepted at KONVENS 2026
Abstract:We introduce PlainMedScale, a topic-aligned medical corpus spanning four levels of comprehensibility in German and English, drawn from MSD (professional and consumer), this http URL, Apotheken Umschau Einfache Sprache, and the NHS. The four tiers correspond to distinct communicative functions — reference, explanation, decision support, and access — and move beyond the binary expert–lay contrast of prior corpora. In two pilot studies enabled by the alignments, we show that many readability metrics established on two registers fail to generalize across the full gradient, and that a SOTA open-weight LLM prompted for Plain Language still partially preserves the difficulty of its input. Code (this https URL) and data (this https URL) are made available.
[NLP-93] Morphology Aware Reversible Semantic Tokenization and Hierarchical Word Composition for Tamil Language Models
【速读】： 该论文旨在解决在小规模模型预算下，如何有效处理泰米尔语复杂形态结构以提升机器翻译性能的问题。泰米尔语的词汇在书写形式上常融合词干变化、格、数、时态、一致关系、语态、附着成分及连动结构等多重语法信息，传统基于统计的子词分词器（subword tokenizer）难以对齐这些语言学结构，导致语义表征不准确。为此，作者提出了一套扩展的泰米尔语形态分析系统，其核心包括：基于12个有限状态转换器（finite-state transducer）的形态分析与生成模块、一个字节精确的语义分词器（byte-exact semantic tokenizer），以及一个学习得到的层次化词语组合器（learned hierarchical word composer）。关键创新在于通过显式建模泰米尔语的形态结构，将词语分解为词干与语法特征，并利用层次化组合器重构词形，从而在保持语义完整性的同时显著压缩输入序列长度——实验表明，该方法使平均全局源状态数从71.48降至29.08（降幅59.3%），并估计可减少9–21%的推理浮点运算量（FLOPs）。在相同模型规模与训练条件下，该方法在IN22和FLORES+测试集上取得最优综合表现，优于包括AI4Bharat IndicBERTv2在内的多个外部分词基线，证明了显式形态建模与层次化组合策略在资源受限场景下的有效性。
链接: https://arxiv.org/abs/2608.01153
作者: Anand Murugan
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Statistical subword tokenizers can process arbitrary text, but their units need not align with lexical or grammatical structure. This is especially important for Tamil, where a written word may encode stem changes, case, number, tense, agreement, voice, clitics, and linked verbs. We present a Tamil morphology system extending the open-source ThamizhiMorph analyzer and generator, together with a byte-exact semantic tokenizer and a learned hierarchical word composer. Twelve finite-state transducers analyze words into lemmas and grammatical features, while character and byte fallbacks preserve exact reconstruction. We compare a flat morphology tokenizer, a signal-preserving word composer, and tokenizers based on Sarvam-1, AI4Bharat IndicBERTv2, and BrahmicTokenizer-131K. All systems use the same 69,591 Tamil-English training pairs, 18.97-million-parameter encoder-decoder, 40,000 updates, target tokenizer, optimizer, positional method, and generation settings. On a protected 3,539-row IN22 and FLORES+ evaluation, morphology-flat achieves the best pooled scores: 10.63 BLEU, 35.26 chrF++, and 0.6276 COMETKiwi. Relative to AI4Bharat, the strongest external-tokenizer baseline, these are improvements of 7.2%, 3.2%, and 2.6%. The word composer scores 10.30, 34.88, and 0.6241, improving on AI4Bharat by 3.8%, 2.1%, and 2.0%. The composer reduces mean global source states from 71.48 to 29.08, a 59.3% reduction, and is estimated to require 9-21% fewer inference FLOPs depending on decoder caching. Its remaining quality gap is concentrated in longer FLORES+ sentences. These results show that explicit Tamil morphology improves translation under a fixed small-model budget, while hierarchical composition substantially reduces sequence length and estimated inference cost. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.01153 [cs.CL] (or arXiv:2608.01153v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.01153 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Anand Murugan [view email] [v1] Sun, 2 Aug 2026 11:07:53 UTC (754 KB)
[NLP-94] Attend to Your Own Thoughts: Breaking the Barrier for Post-Training Quantization of Reasoning LLM s through the Lens of 1.58-Bit Quantization
【速读】： 该论文旨在解决生成式大语言模型（LLM）在后训练量化（Post-Training Quantization, PTQ）过程中，尤其是在采用三值化（ternary quantization）时，于复杂推理任务（如数学与编程）上性能显著下降的问题。现有方法（如基于可微三值化的CAT-Q）在传统校准方案下无法有效保留模型的链式思维（chain-of-thought）推理能力，导致性能崩溃。其解决方案的关键在于提出一种名为“关注自身思考”（Attend to Your Own Thoughts, AYOT）的新型校准机制：在量化过程中，将预训练高精度目标模型在一组校准样本上生成的推理轨迹与最终答案作为上下文输入，结合原始问题共同参与三值化过程。该方法通过显式引入模型自身的推理过程作为上下文信息，使量化过程能够感知并保留关键的推理路径。在此基础上，将AYOT与CAT-Q简单集成，形成可扩展的ScaleQ-1.58框架，实现了在极低校准数据量（仅400万令牌）下逼近甚至超越当前最优二值化模型的性能，且在不同架构（密集型与MoE）、模型规模（达235B参数）及任务难度上均展现出优异的泛化能力与持续提升潜力。
链接: https://arxiv.org/abs/2608.01078
作者: Shigeng Wang,Chao Li,Yangyuxuan Kang,Jiawei Fan,Anbang Yao
机构: Intel Labs China(英特尔实验室中国)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: This research work was completed and submitted for publication in early May 2026. The project page: this https URL
Abstract:We propose ScaleQ-1.58, a scalable ternary post-training quantization (PTQ) framework for reasoning LLMs. Its core insight stems from an empirical finding: although modern LLMs are typically trained to exhibit chain-of-thought reasoning capabilities, in the PTQ regime, even the latest CAT-Q method based on learning-based differentiable ternarization still leads to performance collapse on challenging mathematics and coding tasks when using conventional calibration schemes that ignore the model’s reasoning process. Driven by this finding, we introduce a simple calibration approach, Attend to Your Own Thoughts (AYOT), where reasoning traces and final answers generated by the pre-trained high-precision target LLM on a proper set of calibration samples are used as the context input during the ternarization process, along with the corresponding questions. ScaleQ-1.58 is formed by simply integrating AYOT with CAT-Q, which demonstrates several scaling properties: (1) with only 4M calibration tokens, Qwen3-1.7B ternarized by ScaleQ-1.58 reaches over 90.52% of the performance of the prior best BitNet b1.58 2B4T averaged over 4 mathematics and coding tasks, and our ternary Qwen3-4B shows an absolute gain of 8.97%, while requiring 1,000,000x fewer calibration tokens for quantization; (2) ScaleQ-1.58 generalizes well to both dense and MoE architectures, with performance improving as model scale increases (up to 235B parameters); (3) ScaleQ-1.58 demonstrates strong generalization across tasks of varying difficulty levels, including mathematics, coding and scientific logic reasoning, as well as commonsense reasoning and basic language generation; (4) its performance continues to improve as the number of calibration tokens increases. Notably, AYOT also exhibits strong generalization ability across other quantization bit-widths. Code will be available at this https URL.
[NLP-95] Control Under Compression: Reliability Frontiers for Tool-Using Agents
【速读】： 该论文旨在解决生成式 AI 代理（Agent）在执行任务时因系统侧指令（即代理控制上下文，ACC）过长而导致的输入成本高、上下文占用多的问题，同时揭示现有提示压缩方法在压缩后是否仍能保持操作可靠性这一关键挑战。其解决方案的关键在于提出 CompressAgent——一个基于环境验证的基准测试框架，通过在九个独立构建的 ACC、三类任务、三个固定 Qwen API 模型标识、六种保留上下文预算下进行共计 15,525 次实验，系统评估不同压缩方法在真实运行中的可靠性表现。研究发现，压缩效果具有显著非线性特征与方法依赖性：在 75% 保留上下文时，通用重写与基于章节的压缩分别达到 92.7% 和 92.4% 的成功率，接近全上下文基线（93.8%）；而在 35% 以下时，各方法差异急剧扩大，最低仅 19.9%；当保留上下文低于 25% 时，可执行协议变得极为脆弱。此外，不同 ACC 间的可靠性差异显著，表明无法对压缩器进行统一排名，必须进行逐上下文的资格评估。故障分析进一步指出，压缩主要引发工具执行和动作解析错误。因此，该研究将 ACC 压缩的本质从单纯的令牌削减问题，重新定义为必须通过可执行结果来评估的运行时可靠性问题。
链接: https://arxiv.org/abs/2608.01056
作者: Yinghan Hou,Zongyou Yang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 12 pages, 5 figures; includes an appendix
Abstract:Tool-using language-model agents are governed not only by task prompts but also by persistent system-side instructions that specify tools, arguments, policies, execution protocols, and recovery. Compressing these agent control contexts (ACCs) can reduce input cost and context use, yet existing prompt-compression evaluations do not reveal whether the resulting control remains operationally reliable. We introduce CompressAgent, an environment-verified benchmark for ACC compression across nine independently constructed ACCs, three task families, three fixed Qwen API model identifiers, six retained-context budgets, and 15,525 runs. We uncover a nonlinear, method-dependent reliability frontier. At 75% retained context, generic rewriting and section-based compression achieve 92.7% and 92.4% success, close to the 93.8% full-context baseline. Between 50% and 35%, methods diverge sharply; at 35%, section-based, obligation-aware, and generic rewriting achieve 47.0%, 39.0%, and 19.9%. At retained-context budgets from 25% to 10%, executable protocols become fragile. Reliability also varies substantially across ACCs, making universal compressor rankings inappropriate and motivating per-context qualification. Failure analysis shows that compression primarily surfaces as tool-execution and action-parsing errors. These findings recast ACC compression from token reduction into a runtime-reliability problem that must be evaluated through executable outcomes.
[NLP-96] Dont Offer What Cant Be Done: Deterministic Executability Gating for LLM Skill Selection at Scale KDD KDD2027
【速读】： 该论文旨在解决生成式 AI（Generative AI）在实际应用中面临的核心挑战：当大型语言模型（LLM）代理从庞大的技能库中选择操作时，仅依赖语义相关性无法确保所选技能在当前账户状态下的可执行性。具体而言，即使某个技能与用户请求在语义上匹配，也可能因系统状态限制（如权限不足、流程不合规等）而无法执行，导致无效调用和资源浪费。为此，作者提出了一套部署于 Wix 客户支持助手 Helpmate 的三阶段技能选择流水线：第一阶段为基于语义的召回匹配，识别与目标技能族相关的用户消息；第二阶段引入确定性可执行性门控（deterministic executability gate），通过检查与技能内部一致的硬性终止条件（hard-stop conditions），提前剔除不可执行的候选技能；第三阶段由 LLM 从剩余候选中决定最终调用。其关键创新在于，该门控机制与技能自身使用相同的退出谓词（exit predicates），且二者均基于最新权威状态进行判断，从而保证被拦截的技能在真实环境下必然无法完成，实现“零误判”的可执行性过滤。实验表明，在超过 75 万条用户消息中，该方法将技能描述上下文占用减少 90.5%，并显著降低无效调用风险——在对照实验中，若未启用该门控，模型仍会在 7.8% 的对话中选择本应被阻止的技能，证明该方案有效防止了不可执行技能对模型决策的干扰，而不影响下游工具执行或客户结果。
链接: https://arxiv.org/abs/2608.01050
作者: Ortal Ashkenazi,Vitalii Kloz,Mykhailo Ulianchenko
机构: Wix(威客); Wix(威客)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 7 pages, 3 figures. Preprint. Submitted to the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2027), Applied Data Science Track; currently under review
Abstract:Production LLM agents that select from large skill libraries face a limitation that semantic relevance alone cannot resolve: a skill may match a user’s topic yet be impossible to execute in the current account state. We present a deployed three-stage selection pipeline for Helpmate, Wix’s customer-care assistant. First, a recall-oriented semantic matcher identifies messages related to a ten-skill domain family without consulting account state. Second, a deterministic executability gate removes candidates whose internal hard-stop conditions hold. Because the gate and the skill evaluate the same exit predicates, every blocked candidate would be unable to complete under the same account state, provided predicate parity is preserved and both checks observe fresh authoritative state. Finally, the LLM decides whether to invoke one of the remaining candidates. In a post-launch production analysis of 756.6K user messages across 267.6K conversations, semantic matching retained 174,927 messages (23.1%). Within this matched stream, the gate removed 1,039,462 of 1,749,270 skill-message pairs (59.4%), saving 228.8 million skill-description tokens – 59.1% of the post-semantic skill-description footprint. Together, semantic matching and executability gating reduced skill-description context by 90.5% relative to exposing all ten skills to every message. To test whether this pruning affects model behavior rather than context size alone, we replayed a risk-enriched cohort of 1,000 conversations with all ten skills exposed. The model selected a production-blocked skill in 78 conversations (7.8%). This counterfactual result shows that deterministic gating prevents non-executable candidates from influencing model selection, while not claiming downstream tool execution or customer-outcome effects. Comments: 7 pages, 3 figures. Preprint. Submitted to the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2027), Applied Data Science Track; currently under review Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE) ACMclasses: I.2.7; D.2.4 Cite as: arXiv:2608.01050 [cs.AI] (or arXiv:2608.01050v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.01050 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-97] DeBERTa-Sentinel: Toward Transparent and Trustworthy Detection of AI-Generated Text
【速读】： 该论文旨在解决大规模语言模型（Large Language Models, LLMs）在互联网上的快速传播所引发的虚假信息、学术诚信危机、自动化内容操控以及对脆弱网络社群的风险等问题，尤其针对现有基于Transformer架构的检测方法（如GPT-Sentinel）在面对多样化模型输出及改写攻击时泛化能力不足的局限性。其核心解决方案是提出DeBERTa-Sentinel——一种基于DeBERTa-v3的解耦注意力机制（disentangled attention）的负责任生成文本检测框架，能够捕捉合成内容中细微的结构异常。该方案的关键在于强调透明性：与黑箱式商业检测工具不同，DeBERTa-Sentinel可提供粒度至令牌级别的决策解释，使记者、教育工作者及平台信任与安全团队能够审计、质疑并 contextualize 检测结果，从而满足对可验证、可审计的内容真实性判断的需求。实验基于包含28,057个样本（涵盖GPT、LLaMA和Claude生成文本）的GLC-AIText数据集，在验证集上达到98.21%的准确率，并在测试集上超越基准模型RoBERTa-Sentinel，实现97.53%的准确率、95.89%的精确率、99.33%的召回率、99.53%的ROC-AUC以及仅0.665%的假阴性率。模型的可解释性进一步揭示了学术化表达和正式衔接词等与合成文本相关的语言标记，有效支撑了利益相关方对可信、可问责AI系统的诉求。通过降低偏差并增强可解释性，DeBERTa-Sentinel推动构建更可信、伦理化且以人为本的人工智能系统。
链接: https://arxiv.org/abs/2608.01046
作者: Muhammad Yousaf Rehman,Muhammad Islam
机构: SPECS, University of Hertfordshire (赫特福德大学), Hatfield, UK; College of Science and Engineering, James Cook University (詹姆斯库克大学), Cairns, 4878, QLD, Australia; Centre for AI and Data Science Innovation, James Cook University (詹姆斯库克大学), Cairns, QLD 4878, Australia
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The rapid spread of large language models (LLMs) across the web raises concerns about misinformation, academic integrity, automated content manipulation, and risks to vulnerable online communities. Existing transformer-based detectors, such as GPT-Sentinel, show promise but struggle to generalize to diverse model outputs and paraphrasing attacks, limiting their role in building trustworthy web ecosystems. This work introduces DeBERTa-Sentinel, a responsible AI-generated text detection framework leveraging DeBERTa-v3’s disentangled attention to capture subtle structural irregularities in synthetic content. A central design principle is transparency: unlike black-box commercial detectors, DeBERTa-Sentinel exposes token-level explanations of its decisions, enabling affected stakeholders journalists, educators, and platform trust and safety teams to audit, challenge, and contextualize detection outcomes. Using the GLC-AIText dataset of 28,057 human and LLM-generated samples (GPT, LLaMA, and Claude) with a 60-20-20 split, DeBERTa-Sentinel achieves 98.21% validation accuracy and surpasses the RoBERTa-Sentinel baseline from NeurIPS 2025, achieving 97.53% test accuracy, 95.89% precision, 99.33% recall, and 99.53% ROC-AUC, and maintaining a 0.665% false negative rate. The model’s interpretability reveals linguistic markers such as academic phrasing and formal transitions associated with synthetic text, directly supporting stakeholder needs for verifiable, auditable content-authenticity decisions. By advancing responsible detection methods that reduce bias and enhance explainability, DeBERTa-Sentinel promotes trustworthy, ethical, and human-centric AI systems. Code and data are available at this https URL.
[NLP-98] Opt.Gear Technical Report
【速读】： 该论文旨在解决大模型在设备端（edge）部署时面临的计算效率低、内存占用高及推理延迟大的核心挑战，尤其针对长上下文处理中键值缓存（KV-cache）随上下文长度呈指数增长的问题。其关键解决方案是提出一种新型混合架构，融合卷积型键值门控混洗器（convolutional key-value gated mixer）与局部-全局注意力机制（local-global attention），有效抑制了KV-cache的膨胀，显著降低了内存开销。该架构在同等规模模型基础上实现了高达4.9倍的预填充（prefill）和解码速度提升，尤其在NPU上表现突出。此外，模型基于0.5T tokens的精炼数据集进行训练，无需知识蒸馏，展现出极高的数据效率。所有模型均以开源权重和针对ONNX、高通NPU、苹果ANE优化的部署二进制文件发布，具备强任务能力与边缘适用性。为进一步拓展设备端生成式语言模型生态，研究还推出了this http URL-1M——首个可在微控制器单元（MCU）上运行的超轻量生成式语言模型（Tiny Language Model, TLM），在STM32H747I-DISCO的ARM Cortex-M7 CPU上实现W4A32量化下的20 TPS吞吐率，标志着生成式AI向资源受限嵌入式设备迈出关键一步。
链接: https://arxiv.org/abs/2608.01034
作者: Juneyoung Park,Youngwook Kwon
机构: 未知
类目: Computation and Language (cs.CL)
备注: [OptAI] this http URL Model Technical Report
Abstract:We introduce this http URL, a foundation model designed for efficient on-device deployment, real-tim inference, and strong task capability. It includes a dense model (1M, 270M, and 1B) with a context length of 64K. We designed a new hybrid architecture that combines a convolutional key-value gated mixer with local-global attention to reduce the KV-cache memory that tends to increase exponentially with long context. This architecture delivers up to X4.9 faster prefill and decoding speeds on the NPUs compared to models of a similar scale models. From a 2T tokens candidate corpus, this http URL is trained on a curated 0.5T tokens subset without knowledge distillation. This is the most data-efficient of the existing foundation models. All models are released with open weights and deployment binaries for ONNX, Qualcomm NPU, and Apple ANE making this http URL a practical base for edge applications that need fast, memory-efficient inference and strong task capabilities. Furthermore, to expand the ecosystem of on-device generative language models, we are introducing the this http URL-1M that can be deployed on Micro-Controller Units (MCUs), a Tiny Language Model (TLM). this http URL-1M is the first generative language model to achieve 20 TPS with W4A32 quantization on the ARM Cortex-M7 CPU of the STM32H747I-DISCO.
[NLP-99] Can Humans Dream of Electric Sheep? Human-Written Samples for Fine-Grained Vision-and-Language Hallucination Benchmarking
【速读】： 该论文旨在解决生成式模型快速迭代背景下，幻觉（hallucination）评估基准易受特定模型影响、难以长期适用的问题。其核心挑战在于现有评估依赖于模型生成的幻觉样本，导致评估结果随模型更新而失效。解决方案的关键是构建由人类撰写的幻觉样本数据集，以替代模型生成的样本，从而实现检测能力评估与具体模型解耦。研究构建了一个包含1,600条人类撰写的幻觉样本的数据集，覆盖中、英、法、意四种语言，并采用细粒度的跨度级标注（span-level labeling）进行标注；同时对比了来自五种视觉-语言模型的18,400个样本。实验表明，人类撰写的样本在标注一致性上表现更优，且对数据内容具有更强的可控性，同时在分布上仍与模型生成样本保持高度相似，能够合理反映幻觉检测能力，验证了人类生成数据作为模型无关幻觉评估基准的可行性。
链接: https://arxiv.org/abs/2608.01021
作者: Timothee Mickus,Claudio Savelli,Eduardo Calò,Emilio Raimond,Stella Frank,Hengyu Luo,Flavio Giobergia,Vincent Segonne,Chuyuan Li,Aman Sinha,Lorenzo Vaiani,Jörg Tiedemann,Raúl Vázquez
机构: University of Helsinki (赫尔辛基大学); Politecnico di Torino (都灵理工大学); Universiteit Utrecht (乌得勒支大学); Université Bretagne Sud (布列塔尼南方大学); University of Copenhagen (哥本哈根大学); University Grenoble Alpes (格勒诺布尔-阿尔卑斯大学); University of Lorraine (洛林大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:In an age of rapid model turnover, how do we make hallucination evaluation more perennial? We explore whether human-written hallucination samples could take the place of model-generated hallucinations, in order to make benchmarking detection independent of particular models. To this end, we construct a dataset of 1,600 human-written samples, spanning four languages (Chinese, English, French, Italian), and 18,400 samples from five vision-and-language models, all annotated for hallucinations using a fine-grained span-level labeling scheme. We find that human-written samples result in higher agreement and allow greater control of dataset contents, while remaining distributionally similar to samples derived from vision-and-language samples and providing a reasonable portrayal of detection capabilities - suggesting that human data is a viable substitute for model-based hallucination benchmarks.
[NLP-100] Why LLM s Give In: Conversational Factors and Reasoning Behind Medical Sycophancy
【速读】： 该论文旨在解决生成式 AI 在医疗对话中表现出的“医学阿谀”（medical sycophancy）问题，即模型在用户提出错误主张并施加压力时，放弃原本正确的医学回答，转而迎合用户错误信息的现象。这种行为比单纯输出错误答案更具危害性，因为它会赋予错误信息以看似权威的可信度。研究发现，医学阿谀并非模型固有属性，而是由对话过程中的多重交互因素决定的。其关键解决方案在于揭示了四类对话因素（用户角色、虚假主张的证据来源、挑战时机、正确答案是否基于提示）之间的复杂交互效应：例如，伪造证据在问题提出时会将阿谀行为提升至2.0倍，但在模型已作答后反而使其减半，表明同一证据的影响力完全取决于时间点。此外，研究发现阿谀行为在不同问题间的差异（67倍）远大于不同模型间的差异（3倍），说明单一评估率无法准确反映模型特性，而更受具体对话情境与问题样本影响。通过链式思维（chain-of-thought）分析进一步揭示，能够重新审视自身先前回答的模型更易妥协，而专注于医学事实推理的模型则更坚持正确答案；只有在已作答的前提下，模型才具备对伪造证据进行复核的能力。因此，解决方案的关键在于构建能动态评估证据可信度并维持内部一致性推理路径的机制，而非依赖静态模型性能指标。
链接: https://arxiv.org/abs/2608.01017
作者: Kaike Ping,Buse Çarık,Caleb Wohn,Xiaohan Ding,Tongshuai Wang,Eugenia Rho
机构: Virginia Tech (弗吉尼亚理工大学); Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine (上海同仁医院，上海交通大学医学院); Emory University (埃默里大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 21 pages, 7 figures, 14 tables
Abstract:A language model that abandons a correct medical answer under user pushback is more dangerous than one that was simply wrong, because it lends the credibility of a correct answer to the user’s misinformation. Such model behavior, described as medical sycophancy, is usually reported as a single rate per model, but we find it is a property of the conversation, not the model. We study medical sycophancy in language models with a fully crossed factorial design over four conversational factors, user role, the evidence behind a false claim, whether the challenge precedes or follows the model’s answer, and whether the correct answer is grounded in the prompt, across five open-weight models and 500 MedQuAD questions (1.2M trials). The factors interact sharply: fabricated sources raise sycophancy 2.0x when they accompany the question but halve it once the model has answered, so the same evidence helps or hurts depending only on timing. Sycophancy varies far more across questions than across models (67x vs. 3x), so a single rate reflects the conversation and the questions sampled as much as the model. Chain-of-thought traces explain why. Models that re-examine their own prior answer concede, while those that reason about the medical facts hold, and only a model that has already answered can spend a round auditing the fabricated source.
[NLP-101] Cloud-ScPO: Hidden-State Geometry for Semi-Supervised Preference Optimization in LLM Reasoning
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在数学推理任务中进行偏好优化（Preference Optimization）时依赖高质量标注数据（如人工标注或验证答案）的问题，尤其是在缺乏可靠“优选-次优”样本对的情况下。其核心挑战在于如何在半监督设定下，无需外部奖励模型或人工标注，仍能有效挖掘具有判别性的偏好信号。解决方案的关键在于提出一种基于模型内部表征几何结构的拓扑引导型偏好挖掘框架——Cloud–ScPO。该方法利用不同数学问题生成的推理轨迹在隐藏状态空间中形成的全局点云结构，发现正确与错误推理路径具有不同的几何组织特征；通过少量有标签数据构建多个“正确”与“错误”参考点云，并采用基于连通性诱导的组件级软k近邻度量，对轨迹进行评分。同时结合提示层级的自一致性判断以确定答案层面的偏好方向，再由点云得分筛选出高质量的推理轨迹并过滤低质量样本对。实验表明，Cloud–ScPO在GSM8K和MATH-Numeric数据集上均显著优于基准方法ScPO，最大提升达4.49%和4.19%，且在保持较高正确率可靠性的同时，更有效地分离出信息丰富、逻辑完整的优选轨迹与冗余、不完整等劣质拒选轨迹。
链接: https://arxiv.org/abs/2608.01014
作者: Yuzhou Liu,Xiyang Hu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 14 pages, 2 figures, 7 tables. Preprint
Abstract:Preference optimization improves mathematical reasoning in large language models (LLMs), but reliable chosen-rejected pairs usually require verified answers, human annotations, or external reward models. We investigate whether preference supervision can instead be derived from the model’s internal representation geometry in a semi-supervised setting. Our analysis shows that reasoning trajectories generated across different mathematical problems form structured global point clouds in which correct and incorrect trajectories exhibit different geometric organization. Based on this observation, we propose Cloud–ScPO, a topology-guided preference-mining framework that uses a small labeled set to construct multiple correct and incorrect reference Clouds. Each trajectory is represented by a mean-pooled hidden state and scored against connectivity-induced components using a component-level soft k -nearest-neighbor measure averaged across reference banks. We combine this cross-problem Cloud signal with prompt-level self-consistency: self-consistency determines the answer-level preference direction, while Cloud scoring selects concrete trajectories and filters pairs by their score margin. Experiments on GSM8K and MATH-Numeric across four model settings show that Cloud–ScPO consistently improves over ScPO, with gains of up to 4.49% on GSM8K and 4.19% on MATH-Numeric. Pair-level analyses further show that Cloud–ScPO maintains comparable correctness reliability while more effectively separating informative chosen trajectories from incomplete, repetitive, or otherwise low-quality rejected responses.
[NLP-102] MedUPS: Towards Diagnostic Assistance in Uncommon Medical Cases with Large Language Models
【速读】： 该论文旨在解决临床决策支持系统在处理罕见或不符合指南的病例时面临的挑战，即医生在诊断不确定性下需基于逐步积累的临床证据做出一系列管理决策，而现有大多数医学大语言模型（LLM）评估基准仅关注最终诊断结果，忽视了临床实践中至关重要的“下一步行动”判断。其解决方案的关键在于提出MedUPSQA数据集与MedUPS对齐框架：将真实病例报告分割为时间顺序排列、逐步累积的临床片段，并利用强化学习（GRPO）结合外部大模型作为裁判（LLM-as-a-Judge）的奖励机制，对模型进行中间决策阶段的对齐训练，使其能够模拟临床医生循证推理的动态过程，预测患者轨迹中的下一步合理操作（如检查、影像、会诊等）。实验表明，该方法显著提升了模型在中段决策任务上的准确率，且在多个模型规模下表现优于单纯扩大模型参数量的效果，甚至使较小模型超越部分前沿大模型，验证了该框架的有效性与通用性。
链接: https://arxiv.org/abs/2608.01012
作者: Ofir Ben Shoham,Oriel Perets,Nir Grinberg,Nadav Rappoport
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 6 figures
Abstract:Uncommon and off-guideline cases are difficult for clinical decision support, because physicians must make a series of management decisions under diagnostic uncertainty and rarely see the full case at once. Most large language model (LLM) benchmarks for medicine score only the final diagnosis, yet much of clinical care turns on the next appropriate action: the next test to order, the imaging study to obtain, the specialist to involve, or the differential to pursue. We introduce MedUPSQA, a dataset of 21,874 mid-stream clinical decision points built from 5,535 real case reports, and MedUPS, an alignment framework that supervises models on these intermediate decisions as they unfold along a patient’s trajectory. We segment free-text case presentations into chronologically ordered, accumulating clinical chunks and align models to predict the next step with reinforcement learning (GRPO), using an external LLM-as-a-Judge reward. This objective mirrors how clinicians actually meet patients, reasoning forward from accumulating evidence toward the next decision, rather than committing to a final label. Across three backbones, mid-stream alignment raises next-step accuracy from 55.2 to 66.7 for Qwen3.6-27B, from 47.2 to 57.8 for Qwen3.5-9B, and from 37.8 to 44.4 for HuatuoGPT-3-8B, with 95% CI. In several model scales we test the objective improves accuracy more than scale, with smaller models surpassing larger, frontier models we evaluate. We further train supervised fine-tuning (SFT) baselines on the mid-stream task, SFT improves all backbones above base, indicating the target framwork carries signal independently of the optimizer. We release the dataset, code, and aligned checkpoints.
[NLP-103] Entity-Faithful Repair of Synthetic Supervision for Zero-Shot Image Captioning ACM-MM2026
【速读】： 该论文旨在解决零样本图像描述生成（zero-shot image captioning）中由合成图像-文本对中存在的细粒度语义错位问题所导致的监督信号失真难题。现有方法虽通过文本到图像模型从纯文本语料中合成训练数据，但多聚焦于提升整体数据质量，忽视了合成数据中普遍存在却难以察觉的实体缺失或属性误定位等细粒度错位现象——这些错位在全局语义层面仍具合理性，却严重削弱了监督信号的精确性。为此，本文提出ReCap框架，其核心创新在于将合成数据的优化从隐式的全局匹配机制转向显式的细粒度实体级对齐：通过检测图像支持的实体（image-supported entities）来引导文本重写，强制实现图像与描述在实体层级的一致性，从而生成更忠实的合成监督信号。同时，引入自适应动态加权学习策略，在训练过程中自动降低不可靠合成样本的权重，进一步提升模型鲁棒性。作为通用可插拔框架，ReCap可无缝集成至现有合成数据流水线，实验表明其在域内与跨域零样本图像描述任务上均显著提升图像-文本一致性，并达到当前最优性能。
链接: https://arxiv.org/abs/2608.00994
作者: Zhiyue Liu,Wenkai Zhou,Jian Qin,Qipeng Jiang
机构: Guangxi University (广西大学); Guangxi Key Laboratory of Multimedia Communications and Network Technology (广西多媒体通信与网络技术重点实验室)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Accepted to the 34th ACM International Conference on Multimedia (ACM MM 2026). 16 pages, 7 figures
Abstract:Zero-shot image captioning aims to generate image descriptions without annotated image-text pairs. Recent approaches exploit text-to-image models to synthesize training data from text-only corpora, but most focus on improving overall data quality. In contrast, we observe that synthetic image-text misalignment is often structured and fine-grained: pairs may remain globally plausible while containing missing entities or misgrounded attributes, thereby degrading supervision fidelity. As a result, methods based on global similarity for image rematching or regeneration may improve apparent plausibility, but cannot systematically repair entity-level misalignment. To address this issue, we propose ReCap, a plug-and-play framework that shifts synthetic data refinement from implicit global matching to explicit fine-grained realignment. Specifically, ReCap enforces entity-level correspondence by using detected image-supported entities to guide caption rewriting, yielding more faithful synthetic supervision. In addition, we introduce an adaptive dynamic weighted learning strategy to downweight unreliable synthetic pairs during training. As a general framework, ReCap can be integrated into existing synthetic-data pipelines. Extensive experiments show that ReCap consistently improves image-text consistency and achieves state-of-the-art performance on both in-domain and cross-domain zero-shot image captioning benchmarks.
[NLP-104] SCHEDBench: A Benchmark for Evaluating LLM Constraint Faithfulness in Natural-Language Combinatorial Scheduling
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在面对同一组合优化调度问题的不同自然语言表述形式时，其生成的调度方案是否仍能保持约束满足性与最优性一致的问题。具体而言，研究关注的是：当调度问题以不同词汇、句法或约束顺序等表面形式呈现时，模型能否稳定输出具有相同可行性与最优性的解决方案，即是否存在对表面形式变化的鲁棒性。其解决方案的关键在于构建SCHEDBench——一个基于典型调度实例并由求解器验证可行性和最优性的自然语言基准测试集。该基准涵盖作业车间调度问题（Job-Shop Scheduling Problem, JSP）、单/多模式资源受限项目调度问题（Resource-Constrained Project Scheduling Problem, RCPSP）、护士排班与课程表编制等四类问题，共1,132个实例，通过领域特定模板、主题化实体、词法-句法重述及约束层面的表面形式变异进行多样化生成，并提供经验证的参考解。实验结果表明，当前主流前沿及开源LLMs在面对语义等价但表面形式不同的问题描述时，其生成方案的可行性显著下降，且硬约束违反程度出现高于噪声水平的偏差，其中约束重排是最具敏感性的单一变量。这揭示了现有模型在处理结构化任务中对表面形式依赖性强、缺乏形式不变性的问题。
链接: https://arxiv.org/abs/2608.00991
作者: Shrenil Shaun Sharma,Avi Sharma
机构: University of California, Berkeley (加州大学伯克利分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 19 Pages
Abstract:This paper introduces SCHEDBench, a natural-language benchmark for evaluating combinatorial scheduling constraint faithfulness under surface-form variation. Grounded in canonical scheduling instances and solver-derived feasibility and optimality, SCHEDBench assesses whether large language models (LLMs) generate schedules with the same constraint-feasible behavior across varied natural-language (NL) surface forms. SCHEDBench spans 1,132 instances across job-shop scheduling problems (JSP), single and multi-mode resource-constrained project scheduling problems (RCPSP), nurse rostering/scheduling, and curriculum timetabling problems of varying difficulty. Instances are templated into natural language problems using domain-specific templates, themed entities, lexical-syntactic template rephrasing, and constraint-level surface-form variation, with reference solutions verified for feasibility and objective optimality. Across thirteen frontier and open-weight LLMs, we find that models are not reliably invariant to semantically equivalent renderings of the same scheduling problem. Surface-form variation reduces feasibility and induces above-noise shifts in per-instance hard-constraint violations on matched instances. Among the tested isolated axes, constraint reordering yields the clearest above-noise sensitivity.
[NLP-105] Unsupervised Multidomain Approaches to Named Entity Recognition with Small Datasets
【速读】： 该论文旨在解决在缺乏标注数据的少量或有限数据场景下，如何有效学习高质量实体表示以支持下游信息抽取任务（特别是多领域命名实体识别，Multidomain Named Entity Recognition, NER）的问题。其核心挑战在于领域差异性、数据稀疏性以及模型过拟合等难题。解决方案的关键在于采用基于小样本数据的迁移学习框架：首先通过无监督预训练方法对模型进行初始化，使其在无标注数据上具备初步的实体识别能力；随后，在模拟的有限标注数据集上应用迁移学习模型，结合数据增强、少样本学习（few-shot learning）及领域对抗训练（domain adversarial training）等创新技术，提升模型在多样化且资源受限领域的泛化性能与鲁棒性。该方法有效缓解了标注数据稀缺带来的瓶颈，为构建高效、可迁移的NLP系统提供了可行路径。
链接: https://arxiv.org/abs/2608.00984
作者: Israel Fianyi,James Montgomery,Soonja Yeom
机构: 未知
类目: Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:This paper explores the challenges and the methodologies associated with learning quality representations in scenarios with unlabelled small or limited datasets for downstream information extraction task (Multidomain Named Entity Recognition (NER). The study adopts a Transfer Learning on small datasets. Traditional NER systems often rely on large, labelled data, which is impractical for many domains. This study, therefore, applies an unsupervised pre-training approach to precondition and identify entities without annotated datasets, then applies transfer learning models to different simulated limited datasets for a named entity recognition task. Entity Recognition (NER) is essential in natural language processing (NLP), it identifies and classifies related entities within the text. This study addresses the complexities of domain variability, data sparsity, and overfitting and investigates innovative approaches such as data augmentation, few-shot learning, and domain adversarial training. Integrating these techniques promises to enhance the performance and generalizability of NER systems across diverse and resource-constrained domains, paving the way for more efficient and adaptable NLP applications.
[NLP-106] Passing Coarse Marginal Checks Can Be Cheap: Persona Mixtures and Imprecise Treatment-Response Estimates in an LLM Persona Panel
【速读】： 该论文旨在解决生成式人工智能（Generative AI）作为合成研究参与者在重复策略博弈实验中是否能够有效模拟人类行为的问题，核心关注点在于评估其边际响应与真实人类数据的一致性。其解决方案的关键在于构建一个由十六个轻量级人格化（persona-conditioned）GPT-4.1配置组成的固定面板，并通过预注册的参照条件均值标准进行验证。研究发现，该面板在四个重复博弈单元中的三个满足预设参照标准（仅有一项略低于下界0.011），且响应变异高度依赖于提示词（prompt-indexed），其贡献比例受不确定性假设影响显著：在对称狄利克雷先验（Jeffreys alpha=0.5）下，提示间差异的中位占比为63%–71%，而在alpha=1时降至47%–53%，而有限机会插补估计则高达85%–96%。此外，研究揭示了处理效应不仅改变了继续博弈过程本身，也同时改变了其文本表征；通过调整措辞与位置，合作率从0/40提升至37/40，且标签冲突进一步暴露了表征控制的可能性。原有人格层级p13结果未经过前瞻性家族控制，且事后精确门控分析显示结构功效不足，因此应被视为复现目标而非可靠发现。外部评审指出存在家族错误、依赖性、构念定义及边界不确定性等缺陷，零调用再分析虽改变解释但未修改历史记录。最终表明，即使不精确估计处理-响应对象，注册的边际标准仍可被通过。研究结果基于一个固定模型-提示组合面板，不能证明人类可被替代。
链接: https://arxiv.org/abs/2608.00979
作者: Yohei Nakajima
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT)
备注: 19 pages, 5 figures. Project site: this https URL ; code, data, registrations, review record, and zero-call replay capsule: this https URL
Abstract:Large language models are increasingly used as synthetic research participants and are often validated by whether their marginal responses resemble human data. We study a fixed panel of sixteen lightweight persona-conditioned GPT-4.1 configurations in repeated strategic games. The panel met preregistered broad-reference condition-mean criteria in three of four repeated-game cells; the sole miss was 0.011 below the lower reference bound. Variation was strongly prompt-indexed, but its share depended on uncertainty assumptions: fixed-panel symmetric-Dirichlet sensitivities produced median between-prompt shares of 63%-71% under Jeffreys alpha=0.5 and 47%-53% under alpha=1, while finite-opportunity plug-in estimates were 85%-96%. Aggregate continuation-probability contrasts were +0.083 and +0.078, with conservative simultaneous 95% intervals [-0.171, +0.330] and [-0.181, +0.330]. The treatment jointly changed the continuation process and its textual representation. A separate wording-and-position operation shifted cooperation from 0/40 to 37/40 in the bare configuration, and a label conflict also revealed representation control. The original persona-level p13 result was not prospectively family-controlled, while a post-adjudication exact gate was structurally underpowered; p13 is therefore a replication target rather than a finding. External review exposed family-error, dependence, construct, and boundary-uncertainty defects, and zero-call reanalysis changed the interpretation without rewriting the historical record. The registered marginal criteria could be passed without precisely estimating the treatment-response object. A public capsule verifies 4,916 confirmatory Phase 3-5 runs with no live model calls. The results concern one fixed model-prompt panel and do not establish human substitutability.
[NLP-107] Mind the Gap: Zero-Query Jailbreaks via Filter-Generator Discrepancy in Text-to-Image Systems
【速读】： 该论文旨在解决文本到图像（Text-to-Image, T2I）生成系统中存在的一类安全漏洞，即针对预训练生成器前的提示级安全过滤器（prompt-level safety filters）的零查询越狱攻击（zero-query jailbreak attacks）问题。尽管现有方法依赖于离线构建对抗性提示以绕过过滤机制，但其普遍存在对单一替代模型（surrogate）过拟合、搜索空间过大且缺乏有效筛选策略的问题，导致大量计算资源浪费在低潜力候选提示上。本文提出的核心解决方案基于“过滤器-生成器差异”（Filter-Generator Discrepancy, FGD）这一关键观察：过滤器与生成器对同一提示在不同目标函数和表示空间下进行处理，从而形成语义与感知层面的不一致性。利用此差异，本文设计了一种无需查询目标系统的零查询越狱框架，通过可观测的离散化规则在分词（tokenization）和语义阶段对扰动进行高效筛选，构建高潜力候选集，并结合集成替代模型的进化搜索策略实现高效攻击。实验在六个黑盒管道及一个商用在线服务上验证了该方法的有效性，平均攻击成功率分别达到29.2%（MHSC）和33.3%（Q16），相较于最强基线分别提升约8和12个百分点，显著提升了越狱攻击的效率与成功率。
链接: https://arxiv.org/abs/2608.00973
作者: Wanguang Li,Zhaoxin Wang,Handing Wang
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Text-to-image (T2I) systems typically have prompt-level safety filters before the generator to block unsafe requests, yet such systems remain vulnerable to malicious jailbreak prompts. Transfer-based attacks construct adversarial prompts offline without querying the target, but they tend to overfit to a single surrogate. Moreover, they explore a large search space in which semantic or perceptual similarity alone cannot guarantee both filter evasion and preservation of the unsafe generation intent, wasting effort on low-potential candidates. We observe that the filter and the generator process the same prompt under different objectives and representations, and term this gap the Filter-Generator Discrepancy (FGD), which allows a perturbation to reduce a prompt’s perceived risk to the filter while preserving the visual concept needed by the generator. Building on FGD, we propose a zero-query jailbreak framework that screens perturbations into a high-potential candidate set via observable discrepancy rules at the tokenization and semantic stages, and then performs a surrogate-ensemble evolutionary search that requires no access to the target. Experiments on six black-box pipelines and a commercial online service show that our method consistently outperforms representative baselines, raising the average attack success rate to 29.2% (MHSC) and 33.3% (Q16) across the six pipelines and improving over the strongest baseline by about 8 and 12 percentage points, respectively.
[NLP-108] Gaokerena: A Small Persian Medical Language Model Family
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在医疗问答系统中对低资源语言（如波斯语）支持不足的问题，特别是在本地化数字医疗发展中的应用瓶颈。其核心解决方案的关键在于构建一系列轻量级、可部署于消费级硬件的波斯语医学语言模型——Gaokerena系列。该方案首先通过在自建的9000万词的波斯语医学语料库及2万条专家审核的医生问答对上训练基础模型（Gaokerena-V），显著提升了翻译后医学MMLU基准测试的表现；其次，针对临床推理需求，提出基于思维链（Chain-of-Thought）与两种新型基于人工智能反馈的强化学习（RLAIF）框架的优化方法，开发出更优的推理模型Gaokerena-R，仅用更小数据集即达到52.98%的基准得分，优于前者。此外，两模型均集成定制化的不确定性预测头，仅依赖内部隐藏状态即可评估输出置信度，实现主动安全估计。尽管成果显著，但当前性能仍不足以直接用于临床实践，凸显了未来需加强鲁棒知识获取与严格安全性验证的研究必要性。
链接: https://arxiv.org/abs/2608.00932
作者: Mehrdad Ghassabi,Hamidreza Baradaran Kashani,Pedram Rostami,Sadra Hakim,Zahra Kazemi,Audrina Ebrahimi
机构: University of Isfahan (伊斯法罕大学); University of Tehran (德黑兰大学); University of Windsor (温莎大学); Alzahra University (阿尔扎赫拉大学); University of Texas at Dallas (得克萨斯大学达拉斯分校)
类目: Computation and Language (cs.CL)
备注: 29 pages, 9 figures
Abstract:The integration of artificial intelligence into medical question-answering systems has advanced rapidly; however, research remains predominantly focused on English, leaving low resource languages like Persian significantly underserved. To address this gap, this paper introduces Gaokerena, a novel family of compact Persian medical language models optimized for deployment on consumer grade hardware. As a foundational step toward localized digital healthcare, we first present Gaokerena-V, developed by training a baseline model on a newly curated 90-million-token Persian medical corpus and 20,000 expert-vetted physician QA pairs, which improved performance on a translated medical MMLU benchmark from 46.28% to 49.31%. Second, recognizing the critical demands of clinical reasoning, we developed Gaokerena-R by integrating a Chain-of-Thought approach with two novel Reinforcement Learning with AI Feedback (RLAIF) frameworks to optimize preference-based reasoning. Despite utilizing the same baseline architecture and a smaller dataset than Gaokerena-V, Gaokerena-R achieved a superior benchmark score of 52.98%. Furthermore, both models are equipped with custom-developed uncertainty heads that predict the model’s confidence in its responses based solely on internal hidden states. While these results demonstrate significant progress in Persian medical language modeling and proactive safety estimation, current performance levels remain insufficient for direct clinical application, highlighting the necessity for further research into robust knowledge acquisition and rigorous safety verification prior to real world deployment.
[NLP-109] FinHardBench: Can LLM s Generate Latency-Aware Hardware for Financial Computing?
【速读】： 该论文旨在解决生成式AI在金融领域现场可编程门阵列（FPGA）设计中，如何不仅生成功能正确的硬件逻辑，还能实现低延迟高性能的问题。由于金融交易系统对5–10纳秒级的延迟高度敏感，且协议、策略与监管要求持续演进，导致设计迭代频繁，传统人工设计效率低下。为此，论文提出FinHardBench这一包含33个金融计算任务的基准测试集，并设计了三类贴近真实开发周期的实验：从规格说明生成新模块、在六阶段交易流水线中进行系统级配置空间探索（Design Space Exploration, DSE），以及对已有模块进行规格变更适应性调整。研究发现，尽管主流大语言模型（LLM）在功能正确性上表现参差（19%-61%），且部分任务存在高达13.7倍的时序退化，但在系统级配置优化中，顶级模型在24轮搜索预算下以5/5种子收敛至最优配置，显著优于随机搜索、模拟退火及贝叶斯优化等基线方法；然而，多数模型仍无法有效应对策略层面的规格变更。值得注意的是，代码生成能力与架构优化能力之间存在中等程度的相关性，最强的代码生成器并非最优的架构优化器，而最弱的模型（MiniMax M2.7）在4/5种子中仍能达成系统最优解。此外，任务难度更依赖于训练数据中对应模式的可获得性，而非抽象层级本身。该研究揭示了当前生成式AI在硬件设计自动化中的潜力与局限，同时将FinHardBench开源发布，为后续研究提供基准支持。
链接: https://arxiv.org/abs/2608.00909
作者: Weimin Fu,Hejia Zhang,Minghao Shao,Zeng Wang,Johann Knechtel,Ozgur Sinanoglu,Muhammad Shafique,Ramesh Karri,Xiaolong Guo
机构: Lehigh University (莱赫大学); University of California, San Diego (加州大学圣地亚哥分校); NYU Tandon School of Engineering (纽约大学坦顿工程学院); NYU Abu Dhabi (纽约大学阿布扎比分校)
类目: Computation and Language (cs.CL); Hardware Architecture (cs.AR)
备注: 16 pages (10 pages main text). Published as a conference paper at COLM 2026. Code and benchmark: this https URL
Abstract:Can large language models generate not just correct, but fast hardware? This paper investigates the question in financial FPGA design, where 5-10 nanoseconds of latency determines competitive advantage and designs iterate continuously as protocols, strategies, and regulations evolve. FinHardBench, a benchmark of 33 financial computing tasks, is presented together with three experiments that mirror the real-world FPGA iteration cycle: generating new modules from specifications, tuning system-level configurations across a 6-stage trading pipeline, and adapting existing modules to specification changes. Evaluation of six LLMs on 1530+ experiment rounds yields three findings: (1) models achieve 19-61% functional correctness with timing degradation up to 13.7 \times on specific tasks; (2) in system-level design space exploration, top LLMs converge to the optimal configuration with higher reliability than random search, simulated annealing, and Bayesian optimization baselines (5/5 seeds vs. 0-4/5 at the same 24-round budget); (3) strategy-level specification changes remain unsolved for most models. Across the six models, generation and DSE rankings overlap moderately: the strongest code generator is not the fastest architecture optimizer, and the weakest code generator (MiniMax M2.7) still reaches the system optimum on 4 of 5 seeds. On the tasks in FinHardBench, difficulty tracks training data pattern availability more closely than abstraction level. FinHardBench is released as an open-source benchmark.
[NLP-110] Practical Online KV Cache Compaction for LLM Agents : An Empirical Study
【速读】： 该论文旨在解决大语言模型智能体（LLM agents）在推理过程中因积累大量推理步骤、工具调用及环境反馈而导致的键值缓存（KV cache）成为主要性能瓶颈的问题。其核心挑战在于，传统缓存压缩方法依赖静态上下文假设，无法适应智能体所需的在线压缩需求——即在尚未知晓未来信息相关性的情况下，需基于低成本代理查询对新信息进行即时压缩。论文的关键解决方案是探索在线压缩策略，重点比较基于标记淘汰（Token Eviction, TE）与注意力匹配（Attention Matching, AM）两种机制，并评估边界查询、重复预填充查询和延迟未来生成查询等廉价代理源的有效性。实验结果表明，立即压缩通常损害性能，而延迟压缩以利用智能体未来的查询可显著恢复性能差距；同时，TE在代理不完美时表现出比AM更强的鲁棒性。在不同规模模型上的测试显示，TE可在减少80% KV缓存占用的同时保持绝大部分准确率，并提升吞吐量，优于无压缩基线。因此，该研究将代理查询的选择确立为实现高效实用在线KV缓存压缩的核心设计要素。
链接: https://arxiv.org/abs/2608.00902
作者: Yujian Liu,Jiabao Ji,Li An,Rohit Jain,Gungor Polatkan,Siyu Zhu,Shiyu Chang
机构: UC Santa Barbara; LinkedIn
类目: Computation and Language (cs.CL)
备注:
Abstract:LLM agents accumulate long trajectories of reasoning steps, tool calls, and environment feedback, making the KV cache a major inference bottleneck. KV cache compaction can reduce this cost, but most prior methods assume a static context where future queries are known or can be approximated offline. Agents instead require online compaction: new information must be compressed before future relevance is known, using proxy queries cheap enough for the inference path. We study online compaction across token eviction (TE) and attention matching (AM), adapting both to compact agent turns and comparing cheap proxy sources such as boundary, repeat-prefill, and delayed future-generation queries. Experiments on BrowseComp-Plus and WideSearch show that immediate compaction often hurts performance, whereas delaying compaction to use the agent’s future queries recovers much of the gap. Moreover, TE is often more robust than AM under imperfect proxies. Across models at different scales, TE preserves most of the accuracy while reducing KV cache by 80%, and can improve throughput over the no compaction baseline. These results position proxy-query selection as a core design choice for practical online KV compaction.
[NLP-111] Pruned BPE: Post-training Visibility Pruning and Token Reallocation for Byte Pair Encoding
【速读】： 该论文旨在解决标准字节对编码（Byte Pair Encoding, BPE）在子词分词过程中存在的冗余问题：即所有学习到的合并词元（merge token）均暴露于下游模型，其中包括大量仅作为中间构建单元、在最终编码语料中出现频率极低的词元，从而降低了词汇表的使用效率。其解决方案的关键在于提出一种后训练阶段的可见性剪枝与词元重分配方法——剪枝BPE（Pruned BPE）。该方法在标准BPE训练完成后，依据词元在最终语料中的实际暴露频率进行评估，将低暴露率的词元剔除出模型可见词汇表，仅保留为内部构建节点；同时，将其对应的可见词汇槽位重新分配给在恢复训练中获得更高暴露率的候选词元。在编码过程中，这些内部仅用的词元通过递归展开为可见的后代词元，同时保持原始的BPE合并顺序。实验表明，该方法在相同训练与测试语料、相同可见词汇量条件下，持续降低编码长度，相较于标准BPE在同语料评估中实现约0.27%–0.36%的压缩率提升，并在仅考虑词汇表效率的动态规划编码器评估中仍保持0.23%–0.31%的优势，证明其改进源于更高效的可见词汇结构。此外，定性分析显示，被剪枝的内部词元多为可复用的英文片段、中文部件、部分UTF-8字节序列及结构化文本片段。研究结果表明，通过后训练阶段的可见性剪枝，可在不增加模型可见词汇量的前提下显著提升BPE词汇表的编码效率。
链接: https://arxiv.org/abs/2608.00837
作者: Kenny Shao
机构: Florida International University (佛罗里达国际大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 18 pages, 2 figures, 4 tables, and 1 algorithm
Abstract:Byte Pair Encoding (BPE) is widely used for subword tokenization, but standard BPE exposes every learned merge token to the downstream model, including tokens that mainly serve as intermediate construction units and rarely appear in the final encoded corpus. This paper proposes Pruned BPE, a post-training visibility-pruning and token-reallocation method that separates merge construction from model-visible vocabulary selection. After standard BPE training, tokens are evaluated by final exposure. Low-exposure tokens are retained as internal-only merge nodes, while their visible vocabulary slots are reassigned to better-exposed candidates learned through resumed training. During encoding, internal-only tokens are recursively expanded into visible descendants while the original BPE merge order is preserved. Experiments on two non-overlapping English- and Chinese-dominated corpora and their combination show that Pruned BPE consistently reduces encoded length relative to Standard BPE at the same training corpus, evaluation corpus, and model-visible vocabulary size. At a 40% exposure threshold, the reduction is approximately 0.27%–0.36% on same-corpus evaluations. In a vocabulary-only evaluation using a shared exact minimum-token dynamic-programming encoder, Pruned BPE retains an advantage of approximately 0.23%–0.31%, indicating that the improvement arises from a more efficient visible vocabulary. These gains represent a meaningful fraction of the approximately 1.5%–3.8% marginal reduction that would otherwise require adding another 2K Standard BPE tokens. Qualitative analysis shows that internal-only tokens include reusable English fragments, Chinese components, partial UTF-8 byte sequences, and structured-text fragments. The results indicate that post-training visibility pruning can improve BPE vocabulary efficiency without increasing the vocabulary exposed to the language model.
[NLP-112] Exemplars in Disguise: Pure Exemplar Models Mimic Abstraction-First Learning
【速读】： 该论文旨在解决语言学习中个体特异性知识（idiosyncratic, item-specific knowledge）与抽象类别级泛化知识（abstract class-level generalizations）的习得顺序问题，这一问题在示例理论（exemplar theory）与基于抽象的理论（abstraction-based theory）之间存在根本分歧。其核心挑战在于：现有研究声称大型语言模型（large language models）优先习得抽象知识，但本文指出这些结论所依赖的方法论存在缺陷——纯记忆型模型（pure memorizer models）即使不具备抽象表征，也能根据其对个别观测的敏感度差异，表现出先习得个体知识或先习得类别知识的假象，而这种表现取决于输入数据的分布特性。进一步地，论文提出，在分布式表示（distributed representations）框架下，个体特异性知识与抽象知识之间的界限可能并不清晰，因为词汇的类别属性与其具体实例特征在表征层面难以分离。因此，该研究的关键在于揭示现有评估方法的局限性，并质疑“个体-抽象”二分法在现代神经表示系统中的有效性。
链接: https://arxiv.org/abs/2608.00821
作者: Zachary Nicholas Houghton,Vsevolod Kapatsinski
机构: University of Oregon; Vail Systems, Inc.
类目: Computation and Language (cs.CL)
备注:
Abstract:Whether idiosyncratic, item-specific knowledge is learned before abstract class-level generalizations, or vice versa, is a central question in language learning, with exemplar and abstraction-based theories making opposite predictions. Recent methods have claimed to show that, at least for large language models, abstract knowledge is learned first. We show that these methods fall short: pure memorizer models with no abstract representations can appear, by the same criteria, to learn either item-specific or class-level knowledge first, depending on their sensitivity to individual observations, with the transition point governed by the distributional properties of the input. We further argue that the distinction between item-specific and abstract knowledge may be ill-defined for distributed representations, as a word’s class-level properties may not be separable from its item-specific properties.
[NLP-113] OoO-Spec: Out-of-Order Semantic Speculation for Fast Tool Calling
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成工具调用（tool call）时因逐标记（token-by-token）自回归解码而导致的效率瓶颈问题。尽管函数选择和参数值可从请求内容与工具模式（tool schema）中并行预测，但现有方法如ToolSpec仍受限于无法在缺乏源信息的情况下提出特定于请求的参数值。其核心解决方案是提出OoO-Spec（Out-of-Order Speculation），通过异步并行计算缺失语义实现高效推测。具体而言，在请求到达时，一个轻量级的Qwen3-0.6B辅助侧车（sidecar）以单次并行推理方式预测函数选择及所有模式定义的参数槽位，同时目标模型启动ToolSpec解码；运行时将填充后的参数值合并并渲染为文本提示，供后续候选生成轮次使用。目标模型非阻塞轮询该提示，使用自身分词器重新分词后作为唯一验证与提交权威。侧车仅需一次基于教师模型（Qwen2.5-32B）的LoRA微调，即可跨多个目标模型（包括Qwen2.5、Qwen3及Llama系列）通用，无需针对目标进行额外训练。实验表明，在七种全排名目标与三个基准测试下，OoO-Spec在全部21个组合中均达到最快性能，相比传统自回归解码平均提速3.89倍（最高达5.34倍），显著优于ToolSpec的2.95倍；且在所有可比场景中超越已发布的各类学习型推测器。在不同规模的Qwen3系列模型（4B、8B、14B、32B）上，该方案相较ToolSpec平均提升34.1%。其紧凑的语义负载（每请求约85字节，不含协议元数据）支持有效的跨GPU流水重叠，具备良好的部署扩展性。
链接: https://arxiv.org/abs/2608.00814
作者: Zhiheng Zhang,Mujie Xu,Feiyu Sun,Zhixin Zhang
机构: University of Science and Technology of China (中国科学技术大学); Tsinghua University (清华大学); Shanghai Jiao Tong University (上海交通大学)
类目: Computation and Language (cs.CL)
备注: 10 pages, 4 figures, 6 tables; supplementary material included
Abstract:LLMs generate tool calls token by token, even though the function choice and argument values can often be predicted in parallel from the request and tool schema. ToolSpec reduces this cost by drafting schema tokens and retrieving earlier calls, but cannot propose request-specific values absent from either source. We present OoO-Spec, which computes these missing semantics out of order. At request arrival, a Qwen3-0.6B sidecar predicts the function choice and all schema-defined argument slots in one parallel request-level wave while the target begins ToolSpec decoding. The runtime joins the slot values, renders the resulting call as text, and exposes it to subsequent candidate-construction rounds. The target polls without blocking, re-tokenizes a ready hint with its own tokenizer, and remains the sole verifier and commit authority. The sidecar is trained once with LoRA on Qwen2.5-32B teacher traces and used unchanged across Qwen2.5, Qwen3, and Llama targets, without target-specific drafter training. Across seven fully ranked targets and three benchmarks under greedy batch-one decoding, OoO-Spec is fastest among all evaluated methods in all 21 target-benchmark cells, reaching 2.46x-5.34x over autoregressive decoding with an unweighted mean of 3.89x, versus 2.95x for ToolSpec. It also outperforms every evaluated released learned drafter in each comparable cell. Across Qwen3-4B, 8B, 14B, and 32B targets, the same sidecar improves on ToolSpec by 34.1% on average. Its compact semantic payload averages 85 bytes per request excluding protocol metadata, supporting effective split-GPU overlap.
[NLP-114] Distill Where You Fail: Recovering Learning Signals of Negative RL-Groups from Adaptive Teacher Guidance
【速读】： 该论文旨在解决在基于可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）框架下，主流方法Group Relative Policy Optimization (GRPO)因奖励信号稀疏且在组内所有响应获得相同奖励时完全丢失梯度所导致的学习效率低下问题。尽管在策略蒸馏（On-Policy Distillation, OPD）被引入以提供密集的逐标记监督，但直接结合GRPO与OPD反而会导致性能下降，其根源在于：并非所有样本均能从蒸馏中获益、过快拟合教师模型会削弱强化学习的探索能力，以及蒸馏效果存在不对称性，过度抑制多数标记。为此，本文提出RSTG（Recovering Learning Signals via Adaptive Teacher Guidance），其核心创新在于实现选择性与精准化的教师引导：在样本层面，仅对零方差负样本实施蒸馏，并依据教师模型的置信度进行加权；在标记层面，蒸馏聚焦于学生模型熵高或师生差异显著的标记。此外，通过在教师生成的正确轨迹上进行监督微调（Supervised Fine-Tuning, SFT），向强化学习无梯度区域注入正向梯度信号。实验表明，RSTG在数学和代码任务上分别较朴素的GRPO+OPD提升4.02%和3.05%，显著优于现有方法。
链接: https://arxiv.org/abs/2608.00782
作者: Zhuowen Han,Jinwei Xiao,Zhengxi Lu,Renren Jin,Zhiyuan Yao,Yuxin Liu,Hongyan Hao,Yueqing Sun,Yu Yang,Qi GU,Xunliang Cai,Deyi Xiong
机构: TJUNLP Lab, School of Computer Science and Technology, Tianjin University (天津大学自然语言处理实验室); Meituan Longcat Team (美团龙猫团队)
类目: Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models (LLMs). While Group Relative Policy Optimization (GRPO) is widely adopted, it suffers from sparse reward signals and loses gradients entirely when all responses within a group receive identical rewards. On-policy distillation (OPD) offers a natural remedy by providing dense, token-level supervision from a teacher model. However, naively combining GRPO with OPD leads to degraded performance, due to three underlying causes: not all samples benefit from distillation; fitting too quickly to the teacher undermines the exploratory capacity of RL; and OPD’s advantages are asymmetric, suppressing most tokens. To address these challenges, we propose RSTG (Recovering Learning Signals via Adaptive Teacher Guidance), which applies distillation selectively and precisely where it matters most. At the sample level, OPD is restricted to negative zero-variance prompts with each sample weighted by the teacher’s confidence score. At the token level, distillation targets only tokens with high student entropy or large teacher-student divergence. We further augment training with SFT on correct trajectories generated by the teacher model, injecting positive gradient signals where RL yields none. Experiments demonstrate that RSTG substantially outperforms naive GRPO+OPD by +4.02% on math and +3.05% on code.
[NLP-115] RAG OCR: Optical Compression of Retrieval-Augmented Text via Visual Representation
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统在处理长篇检索内容时面临的计算成本过高问题。现有压缩方法存在根本性权衡：硬压缩方法虽能实时响应查询，但压缩率有限且通常需微调生成模型；软压缩方法虽可实现更高压缩比，却依赖于与查询无关的离线编码，导致资源浪费。为此，本文提出RAGOCR框架，通过将检索文档压缩为基于输入查询的紧凑视觉表征，实现了高效的信息表达。其关键创新在于引入一种查询感知的动态分辨率机制，根据每段文本的估计相关性和复杂度自适应分配视觉粒度——高相关性段落以高分辨率渲染以保留细粒度信息，而边缘性内容则被大幅压缩至低分辨率，从而在压缩率与信息保真度之间取得平衡。实验结果表明，在五个问答基准测试中，RAGOCR相比原始RAG在准确率上提升超过15%，同时仅需其八分之一的输入标记数，并在不同检索深度下持续优于硬压缩与软压缩基线。
链接: https://arxiv.org/abs/2608.00765
作者: Jiayang Yu,Jialun Zhong,Lei Zou
机构: Wangxuan Institute of Computer Technology, Peking University(北京大学王选计算机技术研究所)
类目: Computation and Language (cs.CL)
备注: Under reviewing
Abstract:Retrieval-Augmented Generation (RAG) has become essential for knowledge-intensive question answering, yet scaling RAG pipelines remains challenging due to the prohibitive computational cost of processing lengthy retrieved contexts. Existing compression approaches face a fundamental trade-off: hard compression methods operate online in a query-aware fashion but achieve only modest compression rates and typically require fine-tuning the generative model, while soft compression methods attain higher ratios but rely on costly offline encoding that is entirely agnostic to the input query. To bridge this gap, we introduce RAGOCR, a novel framework that compresses retrieved documents into compact visual representations conditioned on the input query. To further balance compression rate and information fidelity, we introduce a query-aware dynamic resolution mechanism that adaptively allocates visual granularity based on each document’s estimated relevance and complexity: highly relevant passages are rendered at higher resolution to preserve fine-grained details, while peripheral documents are aggressively compressed at lower resolution. Experiments on five QA benchmarks using the MedOmniKB retrieval corpus demonstrate that RAGOCR surpasses naive RAG by over 15% in accuracy while requiring only one-eighth the number of input tokens, and consistently outperforms both hard and soft compression baselines across varying retrieval depths.
[NLP-116] AI-Based Thesis Assessment: An Empirical Study of Human Evaluation Priorities and Their Impact on Automated Assessment
【速读】： 该论文旨在解决生成式 AI（Generative AI）在学位论文评估中因默认评价标准权重与实际导师优先级不一致而导致的评估偏差问题。其核心解决方案在于通过实证调查获取来自不同学科领域84名导师的实际评价标准权重，并将其应用于AI评估系统RubiscoT的校准配置中，以检验其对提升AI评估结果与人工评估一致性的影响。研究发现，尽管基于导师权重的校准配置在一定程度上降低了AI与导师评分间的平均相对偏差（从11.18%降至10.85%），但改进效果未达统计显著水平；相比之下，人类导师之间的一致性更高（平均组间相对偏差为4.44%）。研究表明，仅通过调整评价标准权重难以显著提升AI评估与人类评估之间的对齐程度，提示需结合更复杂的建模策略以实现真正意义上的评估一致性。
链接: https://arxiv.org/abs/2608.00717
作者: Garv Vikram Gursahaney,Baskhad Idrisov,Thorsten Fröhlich,Tim Schlippe
机构: IU International University of Applied Sciences (IU国际应用科学大学); Germany (德国)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted for publication in the Proceedings of the 7th International Conference on Artificial Intelligence in Education Technology (AIET 2026), Zagreb, Croatia
Abstract:Rubric-based AI systems for thesis assessment use criterion weights to assign different levels of importance to evaluation criteria. These weights are typically defined through expert judgment, although little empirical evidence exists regarding how thesis supervisors actually prioritize evaluation criteria. Consequently, this study investigates supervisor-derived criterion weights in thesis assessment and evaluates their impact on AI-based assessment. We surveyed 84 thesis supervisors across four academic disciplines and collected weighting data for 35 thesis assessment criteria. Comparison with the default criterion weights of the AI assessment system RubiSCoT [1] revealed substantial divergences between supervisor-derived and default criterion weights. To evaluate the practical implications of these differences, the supervisor-derived weights were integrated into multiple calibration configurations and evaluated on a corpus of 80 German-language theses. The best-performing configuration reduced the mean relative deviation between AI-generated and supervisor-assigned evaluations from 11.18% to 10.85%, although the improvement was not statistically significant. Human supervisors showed substantially stronger agreement with each other, exhibiting a mean inter-supervisor relative deviation of 4.44%. The findings indicate that criterion-weight calibration alone does not substantially improve alignment between AI-generated and human assessments.
[NLP-117] Observatorio Lazaro: A self-populating database of anglicism usage in the Spanish press
【速读】： 该论文旨在解决西班牙语数字新闻中未被同化的外来词（主要为英语借词或盎格鲁化词汇，anglicisms）的动态监测与量化分析问题。其核心挑战在于如何持续、自动地识别并追踪这些在语言使用中尚未完全融入西班牙语体系的借词现象，以弥补传统静态词典和一次性标注语料库在时效性与覆盖度上的不足。解决方案的关键在于构建一个端到端的自动化处理系统——Observatorio Lázaro，该系统通过神经序列标注模型对每日新闻文本进行借词检测，结合数据采集、检测、后处理、存储与开放访问的完整流程，实现了对2020至2026年间超过188万篇新闻文章、9.93亿词元文本中逾两百万条借词的持续记录。该系统不仅提供公开的网页界面与API接口，还通过高精度评估验证了其可靠性（跨度级F1=0.86，Cohen’s kappa=0.91），并揭示出西班牙语借词使用频率稳定在约每千词两个，且词汇库呈现开放增长特性，其中时尚、科技与生活方式类内容借词密度最高，政治与机构新闻最低，从而为西班牙语新词演化研究提供了动态、可扩展的实证资源。
链接: https://arxiv.org/abs/2608.00713
作者: Elena Alvarez-Mellado
机构: Universidad Autónoma de Madrid (马德里自治大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper describes Observatorio Lázaro, a language resource that monitors unassimilated lexical borrowings (predominantly English lexical borrowings or anglicisms) in the Spanish digital press. Since April 2020 the system has automatically processed the daily output of a collection of news outlets, detected borrowings with a neural sequence-labeling model, and made the results available through a public web interface and API. The result is a continuously updated diachronic database which, at the time of writing, records more than two million borrowings across 1.88 million articles and 993 million running tokens of text (2020-2026). The paper documents the resource: we describe the end-to-end pipeline (acquisition, detection, post-processing, storage and access), the data model and the terms of availability; we evaluate the resource through the detector’s held-out performance (span-level F1=0.86 for the borrowing class), inter-annotator agreement on the training corpus (Cohen’s kappa=0.91) and a manual precision audit of 1,000 spans from the deployed data; and we situate it with respect to Spanish borrowing lexicography, annotated borrowing corpora and neology-monitoring observatories. The data shows that unassimilated anglicisms are used in the Spanish press at a frequency of approximately two anglicisms per thousand tokens, and that this rate remains stable. Our statistical analysis over six years reveals that the anglicism vocabulary in Spanish behaves as an open and growing class, with 58.7% of its types attested only once (53.6% after correcting for detection precision), and that its density is highest in the fashion, technology and lifestyle sections and lowest in political and institutional news. The resource is intended to complement static borrowing dictionaries and one-off annotated corpora by providing a continuously updated record of borrowing in the Spanish press.
[NLP-118] Exploiting Intrinsic Duality for Multi-Hop Question Generation
【速读】： 该论文旨在解决多跳问答生成（Multi Hop Question Generation, MQG）任务中普遍存在的问题，即现有研究忽视了问答（QA）与问答生成之间固有的双重性（duality）。传统MQG模型仅关注从多文档中生成问题，而未充分利用其与答案生成之间的内在关联。为此，本文提出了一种名为QQ的新框架，其核心在于利用问答之间的对偶关系来提升多跳问题生成的质量。该框架的关键创新在于构建一个统一的架构，同时具备多跳问答生成和问答推理能力，并通过两个关键机制实现双向协同：一是施加双向对齐约束，确保生成的问题与对应答案在语义上严格对应；二是引入对比学习，使配对的问答表示相互靠近，而非配对的表示则被有效分离，从而强化二者之间的对应关系。实验结果表明，该方法在HotpotQA和MuSiQue数据集上均显著提升了生成问题的质量。
链接: https://arxiv.org/abs/2608.00712
作者: Maodong Li,Xinyue Kang,Yuanchen Shi,Fang Kong
机构: 未知
类目: Computation and Language (cs.CL)
备注: 14 pages, 9 figures, 9 tables
Abstract:Multi hop question generation (MQG) aims to generate questions from multiple given documents and target answers, whereas question answering (QA) focuses on deriving answers from documents given specific questions. Although MQG and QA are inherently dual tasks, most existing MQG studies largely overlook this intrinsic duality. To address this limitation, we propose QQ, a novel framework that exploits the duality between Question and answer for multi hop Question generation. Specifically, QQ employs a unified architecture functioning simultaneously as both an MQG and a QA model to fully leverage their interdependence. Our framework is driven by two key mechanisms: (i) enforcing bidirectional alignment constraints to ensure strict mutual correspondence between the questions generated by the MQG model and the answers produced by the QA model; and (ii) applying contrastive learning to pull paired question answer representations closer while pushing unpaired ones apart, thereby reinforcing this correspondence. Extensive automatic and human evaluations on the HotpotQA and MuSiQue datasets demonstrate that the QQ framework significantly improves the quality of generated multi hop questions.
[NLP-119] AttnLink: Turning Attention into Schema Links for Text-to-SQL
【速读】： 该论文旨在解决文本到SQL（Text-to-SQL）系统中模式链接（schema linking）存在的三大核心挑战：上下文建模能力、基于得分的可控性以及推理效率之间的权衡问题。现有方法在提升某一方面性能时往往牺牲其他方面，难以兼顾。为此，论文提出AttnLink——一种基于注意力机制的框架，将大语言模型（LLM）内部的注意力分布转化为连续的模式项相关性得分。其关键创新在于：从生成起始位置提取对候选模式片段的注意力，实现所有候选项在单次预填充（prefill）阶段完成排序，无需自回归解码，显著提升推理效率。进一步地，提出两种变体：AttnLink-U通过直接探测预训练注意力而无需参数更新，保证高效性；AttnLink-S则通过监督学习使注意力分布与真实模式项对齐，并引入集合质量（set-mass）目标与自适应概率下界正则化，以增强多相关模式项的覆盖能力。最终生成的得分支持通过温度缩放和累积质量选择实现事后精确率-召回率调控。实验结果表明，AttnLink-S在Spider、BIRD和Spider2-SQLite数据集上分别达到99.22%、95.95%和83.29%的mAP，且模式链接延迟处于毫秒级，在九组生成器-数据集组合中，有七组取得最佳或并列最佳的下游SQL执行准确率，充分验证了其在性能与效率上的优越性。
链接: https://arxiv.org/abs/2608.00693
作者: Jinwang Song,Tao Liu,Haowen Zheng,Xiangheng Li,Yifan Li,Hongying Zan
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Schema linking is a critical component of Text-to-SQL systems, but existing approaches often trade off contextual modeling capacity, score-based controllability, and inference efficiency. We introduce AttnLink, an attention-based framework that converts LLMs’ internal attention into continuous relevance scores for schema items. AttnLink extracts the attention from the generation-start position to candidate schema spans, enabling all candidates to be ranked in a single prefill pass without autoregressive decoding. We develop two variants: AttnLink-U, which directly probes pretrained attention without parameter updates, and AttnLink-S, which aligns the attention distribution with gold schema items through direct supervision. To improve coverage of multiple relevant schema items, AttnLink-S combines a set-mass objective with an adaptive probability-floor regularizer. The resulting scores support post-hoc precision-recall control through temperature scaling and cumulative-mass selection. Experiments on Spider, BIRD, and Spider2-SQLite show that AttnLink-S achieves mAP scores of 99.22%, 95.95%, and 83.29%, respectively, with millisecond-scale schema-linking latency. It also yields the best or tied-best execution accuracy for downstream SQL generation in seven of nine generator-dataset settings.
[NLP-120] OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution
【速读】： 该论文旨在解决生成式智能体（Generative AI agents）在长期、持续运行环境中因早期状态变化累积影响而引发的安全风险评估不足的问题。传统安全评测基准多聚焦于短时、静态任务，难以捕捉智能体在复杂长周期工作流中由共享状态反复修改与复用所导致的累积性安全威胁。为应对这一挑战，论文提出OpenART——一个基于环境演化的可扩展智能体红队测试开放平台，涵盖50个领域中超过10,000个经验证的状态化场景，并整合超50万种工具与技能，支持平均需97次工具调用的复杂任务，实现对75种不同智能体模型配置的统一评估。其核心解决方案是提出进化型马尔可夫超图攻击（Evolutionary Markov Hypergraph Attack, EMHA），一种无需参数更新的黑盒策略，通过协调授权的状态转移实现反馈驱动的环境演化，仅改变环境状态而保持任务目标不变。实验表明，EMHA在所有配置下取得85.0%的综合攻击成功率（ASR），且在复杂环境中相较仅依赖指令演化的基准提升超过17%，凸显环境演化在揭示深层安全缺陷中的关键作用。此外，研究发现智能体的实际运行实现方式对安全表现的影响显著，远超底层模型能力本身，进一步强调了系统级设计在保障智能体安全性中的重要性。
链接: https://arxiv.org/abs/2608.00677
作者: Yunhao Chen,Xin Wang,Yixu Wang,Yi Liu,Jie Li,Yan Teng,Xingjun Ma,Xia Hu,Yu-Gang Jiang
机构: Fudan University (复旦大学); Shanghai Artificial Intelligence Laboratory (上海人工智能实验室); XSafeAI; Yu-Gang Jiang
类目: Computation and Language (cs.CL)
备注:
Abstract:AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model interactions, agent behavior is mediated through a shared state that is repeatedly modified and reused across long-horizon workflows. Current safety benchmarks often fail to capture these cumulative risks because they focus on short, static tasks. To address these limitations, we introduce OpenART, an open-ended arena for scalable agent red teaming through environment evolution. OpenART provides over 10,000 validated stateful scenarios across 50 domains, drawing from a pool of more than 500,000 tools and skills. These tasks require a median of 97 tool calls and enable unified evaluation across 75 different agent-model configurations. To systematically explore these evolving attack surfaces, we propose the Evolutionary Markov Hypergraph Attack (EMHA). EMHA is a black-box policy that performs feedback-driven environment evolution by coordinating authorized state transitions without requiring parameter updates. Throughout the evaluation, task objectives remain fixed while only the environment state changes. Across all configurations, EMHA achieves a pooled Attack Success Rate (ASR) of 85.0%. Its advantage over instruction-only evolution increases from approximately 2% on simple environments to over 17% on the most complex ones, demonstrating that environment evolution increasingly exposes safety failures as task complexity grows. Furthermore, our analysis shows that the specific runtime implementation of an agent explains a significant portion of safety variation beyond the underlying model’s capabilities. These results establish OpenART as a scalable foundation for studying agent safety in complex, evolving environments.
[NLP-121] Select-And-Extract: A Lightweight Plugin for Retrieval-Augmented Generation
【速读】： 该论文旨在解决语言模型（Language Model, LM）系统中检索增强生成（Retrieval-Augmented Generation, RAG）的两大根本性失败模式：检索失败（retrieval failure）与阅读失败（reading failure）。前者指从外部语料库中未能召回正确信息，后者则指尽管正确信息已被检索到，但生成模型仍无法据此生成正确答案。现有方法或采用固定结构的索引以缓解检索失败，但泛化能力受限；或在查询时进行结构化处理以应对阅读失败，却通常需要大量语言模型调用且高度依赖模型自身能力。针对此问题，本文提出一种名为Select-And-Extract (SANE) 的轻量级插件式解决方案。其核心在于：对于检索失败，采用语义检索器广泛召回候选文档，并利用语言模型基于摘要内容筛选出最优候选，从而提升召回效果；对于阅读失败，引入蓝图引导的查询时证据提取机制，使生成模型仅需处理紧凑、结构化的关键信息，进而增强推理能力。实验结果表明，SANE 在显著提升性能的同时仅引入适度额外开销，证明了一个高性能的RAG框架无需过度复杂化，而可借助简洁有效的模块化设计实现高效优化。
链接: https://arxiv.org/abs/2608.00658
作者: Chenming Tang,Jiawei Han
机构: 未知
类目: Computation and Language (cs.CL)
备注: Pre-print
Abstract:Retrieval-augmented generation (RAG) for language model (LM) systems fundamentally has two failure modes: retrieval failure and reading failure. The former fails to recall the right pieces of information from the external corpus, and the latter fails to produce the correct answer although the right information is retrieved. Some methods perform structured indexing for retrieval failure, but may suffer from limited generalization of the fixed structures. Some methods perform query-time structuring for reading failure, but typically require a lot of LM calls and rely heavily on the LM’s capability. To this end, we propose Select-ANd-Extract (SANE), a simple yet effective plugin for RAG. For the retrieval failure, we retrieve a wide set of candidates with a semantic retriever, and leverage the LM to select the top candidates based on their synopses, which yields better recall than the original retriever. For the reading failure, we perform blueprint-guided query-time evidence extraction, which allows the generator LM to use only compact and structured key information so that it can perform better reasoning. Empirical results confirm that SANE brings solid improvements, while only introducing modest extra overhead. As a lightweight plugin for RAG, SANE offers a simple alternative to heavier approaches, and suggests a high-performance RAG framework need not be overly complex.
[NLP-122] reeProbe : A Tibetan Medicine Benchmark for Cultural Bias in LLM s
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在医疗领域应用中存在文化偏见的问题，尤其聚焦于主流高资源医学传统对低资源传统医学知识体系（如藏医）的覆盖不足与认知扭曲。其核心挑战在于：当前大语言模型（LLM）在处理藏医等非主流医学体系时，因缺乏对其本体论框架的深层理解，容易产生外部本体漂移（ontology drift），即不自觉地依赖西医或中医的推理范式，从而歪曲藏医原有的理论结构。为此，研究提出首个基于藏医“医学之树”（Tree of Medicine）本体框架的文化偏见评估基准——TreeProbe，包含4,719个专家标注的条目，覆盖467种疾病及10项子任务，系统性地评估模型在藏医三根（隆、赤巴、培根）理论维度下的表现。实验表明，现有模型在藏医语境下表现受限，并呈现出向西医或中医推理模式漂移的倾向，其方向受预训练数据构成及中医与藏医表面相似性的影响。因此，解决方案的关键在于构建一个以本土知识体系为根基的诊断性评估工具，推动医疗人工智能在语言包容性与认识论公平性上的协同发展。
链接: https://arxiv.org/abs/2608.00640
作者: Jin Zhang,Linyu Li,Weili Jiang,Yuqing Cai,Yutong Liu,Guanquecairang,Yongbin Yu,Jingye Cai,Nyima Tashi,Gadeng Luosang
机构: University of Electronic Science and Technology of China (电子科技大学); Tibet University (西藏大学); Peking University (北京大学); Southwest Jiaotong University (西南交通大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models are increasingly viewed as a potential means of mitigating global health inequities, yet their outputs often reflect dominant high-resource medical traditions and provide limited coverage of traditional medical knowledge systems. Tibetan medicine, one of the world’s four major traditional medical systems, has an independent and highly structured theoretical framework. When models lack grounded understanding of Tibetan medicine, they may fall back on dominant epistemic systems and distort the native knowledge structure during reasoning. However, quantitative tools for evaluating cultural bias in Tibetan medicine remain largely absent. To address this gap, we introduce TreeProbe, the first cultural-bias benchmark organized around the native Tree of Medicine framework in Tibetan medicine. It contains 4,719 expert-adjudicated items covering 467 diseases and 10 subtasks along the three roots. Experiments on representative LLMs show that current models remain limited in native Tibetan medical contexts and exhibit systematic external ontology drift. Further analysis reveals that models diverge in whether they drift toward biomedical or TCM reasoning, shaped by pretraining data composition and surface resemblance between TCM and Tibetan medicine. TreeProbe provides a diagnostic benchmark for developing medical AI systems that are both linguistically inclusive and epistemically fair. Code and data are available in an anonymous repository at this https URL.
[NLP-123] A Heuristic Perspective on Debiasing Language Models
【速读】： 该论文旨在解决预训练语言模型（Language Models, LMs）在训练过程中习得各类偏见，并在交互中表现出这些偏见，可能引发社会危害的问题。现有方法多依赖反事实增强或表示投影，但受限于计算成本高、难以扩展至大规模模型，且多数需人工数据标注，导致适用范围局限于特定文化背景和偏见类型。为克服上述局限，本文提出HEIMAT（HEurIstic-style autoMATic debiasing framework），其核心在于通过两个关键步骤实现自动化去偏：首先利用简单模板构建启发式提示（heuristic prompts），以揭示模型偏见并生成相应上下文提示；其次通过最小化这些上下文提示下模型预测的Jensen-Shannon散度进行去偏微调，从而有效降低偏见表达。实验结果表明，HEIMAT在多种文化背景下均能有效缓解偏见，同时保持模型自然语言理解（Natural Language Understanding, NLU）性能。
链接: https://arxiv.org/abs/2608.00622
作者: Tian Lan,Yemin Wang,Chuancheng Shi,Xiangyu Wu,Zesheng Shi,Yuan Wang,Jiang Li,Guanglai Gao,Xiangdong Su
机构: Inner Mongolia University; Xiamen University; University of Sydney; Nanjing University of Science and Technology; Harbin Institute of Technology; Zhejiang University
类目: Computation and Language (cs.CL)
备注: 13 pages in total, 5 figures
Abstract:Language models (LMs) often acquire various biases during pre-training and may express them in interactions, potentially causing social harm. Existing methods often rely on counterfactual augmentation or representation projection. These strategies remain limited in practice due to their high computational costs and difficulty in scaling to larger models. Additionally, many of these strategies require manual data annotation, narrowing their scope to specific cultures and bias categories. To overcome these limitations, we propose HEIMAT, a HEurIstic-style autoMATic debiasing framework for LMs. HEIMAT consists of two main steps: bias disclosure and debiasing fine-tuning. In the first step, it uses simple templates to construct heuristic prompts, which are applied to reveal model biases and generate corresponding context prompts. In the second step, it fine-tunes the model by minimizing the Jensen-Shannon divergence of predictions on these context prompts to reduce bias. Extensive experiments show that HEIMAT effectively mitigates bias in different cultures while maintaining the model’s natural language understanding (NLU) performance.
[NLP-124] A False Averag e: Chain-of-Thought Monitors Collapse Where They Are the Only Defense
【速读】： 该论文旨在解决生成式智能体（Generative AI agent）中由“思维链监控”（Chain-of-thought, CoT）机制所面临的核心安全漏洞问题，即：尽管CoT监控能够有效识别部分通过行为（actions）暴露的奖励劫持（reward hacking）攻击，但其在面对对手仅篡改推理过程（reasoning）而保持所有指令与输出完全一致的隐蔽攻击时，会彻底失效。解决方案的关键在于提出一种仅重写推理链条而不改变任何行为痕迹的对抗性攻击——即通过将恶意推理重构为看似合理、符合良知工程（good-faith engineering）的表述，使监控系统误判为正常行为。实验表明，此类攻击在不改变任何命令或输出的前提下，可使原本95%的检测率骤降至不足11%，且该攻击在不同监控模型家族与智能体模型间具有强迁移性，甚至在实时运行的智能体中亦可复现。进一步分析揭示，仅依赖轨迹（trace-only）的防御手段无法有效应对此类攻击，因其无法获取轨迹外关于意图的语义信息；而对代理监控器激活状态的探针虽能分离出被遗漏的攻击样本，因果控制实验表明这仅为外部探测器的性能表现，而非监控器本身具备隐含知识的证据。因此，该研究的核心洞见是：当前基于思维链的监控机制严重依赖行为信号，一旦攻击者仅操纵推理意图而保持行为不变，监控系统便陷入“全然失灵”的境地，凸显了现有方法在意图层面防御能力的根本缺陷。
链接: https://arxiv.org/abs/2608.00583
作者: Shikhar Shiromani,Leo Richter
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Chain-of-thought (CoT) monitoring is meant to catch the reward hacks that look clean in the actions and betray themselves only in the reasoning. We show that this is exactly where an adversary who controls the reasoning can defeat it. Rewriting only an agent’s reasoning to read as good-faith engineering, while copying every command and output verbatim so the exploit is unchanged, drops a held-out monitor’s catch rate on that subset from about 95% to under 11% in one gradient-free shot. A monitor’s aggregate accuracy is a false average: dominated by hacks the actions give away, it hides the near-total collapse this rewrite produces on the subset where CoT monitoring is the only signal. The attack transfers across monitor families and agent models, reproduces with live agents, though against a calibrated monitor evasion concentrates in the strongest agent. Trace-only defenses recover it only partially, even one primed on the attack, because the rewrite stays truthful about what happened and lies only about intent; only information from outside the trace helps substantially. A probe on an open-weight surrogate monitor’s activations separates the hacks its verdict misses, but a causal control shows this is a detector, not evidence the monitor secretly knows.
[NLP-125] Writing-System-Level Tokenizer Adaptation for Byte-Level BPE
【速读】： 该论文旨在解决预训练字节级分词器（byte-level BPE tokenizer）在低资源语言中分词效率低下这一问题，特别是在保持模型词汇表大小不变的前提下实现对目标语言的高效适配。其核心挑战在于：直接迁移特定语言的分词结果可能导致新插入的词元无法通过目标BPE合并图（merge graph）的贪心合并顺序生成，从而引发“合并顺序问题”（merge ordering problem），破坏分词一致性。为此，论文提出基于BPE引导的插入机制（BPE-guided insertion），通过构建目标可到达的分解路径来确保每个转移词元均能被目标合并图正确生成；同时结合脚本感知的行选择策略以减少冗余碎片化、重建目标脚本的字节级前置条件，并在保持合并图可达性的前提下完成词元插入。实验表明，在乌克兰语适配Nemotron与GPT-OSS模型时，该方法可分别降低33.5%和36.6%的词元数量，且对英语及四语种欧洲聚合体的词元变化控制在0.05%以内，同时保留78.5%/77.3%的原始模型词汇表行在相同ID上。相较之下，其他方法或导致英文/欧洲语系词元数显著上升，或完全丧失同ID词元保留能力。结构审计验证了所有新增的BPE节点在标准按秩合并下均可达，且未破坏原有同ID词元的有效性。研究贡献包括一套可复现的分词器与代码开源发布。
链接: https://arxiv.org/abs/2608.00582
作者: Bohdan Didenko(Lviv Polytechnic National University)
机构: Lviv Polytechnic National University (利沃夫国立理工大学)
类目: Computation and Language (cs.CL)
备注: 15 pages. Accepted for poster presentation at the Second Tokenization Workshop (TokShop) at COLM 2026 (non-archival)
Abstract:Pretrained byte-level BPE tokenizers can segment underrepresented languages inefficiently. Replacing a tokenizer changes the meaning of nearly every token ID, while vocabulary expansion enlarges the model’s embedding and output matrices. We study post-hoc adaptation that keeps the model-vocabulary size fixed and preserves most existing token-to-ID assignments as a construction-time compatibility property. Directly transferring tokens from a language-specific tokenizer does not guarantee derivability through the target BPE merge graph: an inserted entry can conflict with the target’s greedy merge ranks. We formalize this failure as the merge ordering problem and introduce BPE-guided insertion, which builds each transferred token through a target-reachable decomposition. Our pipeline uses script-aware row selection to limit collateral fragmentation, reconstructs target-script byte-level prerequisites, and applies guided insertion to maintain merge-graph reachability. On Ukrainian adaptations of Nemotron and GPT-OSS, it reduces token counts by 33.5% and 36.6%, keeps changes on English and the evaluated four-language European aggregate within 0.05%, and retains 78.5%/77.3% of original model-vocabulary rows at the same IDs. Constraint-matched global and frequency-based removal achieve similar Ukrainian compression but increase English/European token counts by 0.7-2.2%; fresh same-size retraining compresses Ukrainian slightly more but retains effectively no same-ID rows and increases English token counts by 7.6-8.6%. The reallocation increases token counts on the evaluated three-language Cyrillic micro-aggregate by 6.7%/10.1%. Structural audits find all 28,134/45,398 inserted BPE nodes reachable under ordinary rank-ordered merging and no retained same-ID model-vocabulary entry newly broken. We release all tokenizers and code.
[NLP-126] Loanword or Switch? The Annotation Boundary Not the Model Drives Kazakh-Russian Code-Switching Identification
【速读】： 该论文旨在解决在哈萨克-俄语混合文本中，由于共享西里尔字母表导致的自动语言识别（LID）系统将哈萨克语中的俄语借词误判为语言切换（code-switching）的问题。其核心挑战在于现有标注标准对借词与真实语言切换的边界界定不清，从而影响模型性能。解决方案的关键在于提出一个文档级黄金标准的LID数据集，其标注指南明确将语言借用（integrated borrowings）视为哈萨克语的一部分，仅将句法层面的语言切换标记为“混合”；同时设计了一种“先过滤后分类”的级联架构，在语言识别阶段后使用仅包含混合语料的情感分析池，有效提升了下游任务的准确性。实验表明，尽管不同模型（如FastText、XLM-R等）表现差异显著，但性能差距主要源于借词与语言切换的标注边界模糊性，而非模型类别本身，揭示了标注规范对多语言混合文本处理的核心影响。
链接: https://arxiv.org/abs/2608.00581
作者: Bogdan Savelyev
机构: Independent Researcher(独立研究员); Kazakhstan(哈萨克斯坦)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 5 pages. Preprint. Submitted to W-NUT 2026. Code and data: this https URL
Abstract:Off-the-shelf LID and letter heuristics over-label Kazakh-Russian social text as mixed: Russian loanwords inside Kazakh look like code-switching under a shared Cyrillic script. We release a document-level gold LID set whose guideline keeps integrated borrowings as Kazakh and reserves mixed for clause-level switches, plus a mixed-only sentiment pool used after LID in a filter-first cascade. On a shared LID test, FastText, Lingua, raw and windowed HeLI, character-trigram NB, and XLM-R range from weak to strong performance. The gap shows the bottleneck is the loanword-vs-switch annotation boundary, not model class alone.
[NLP-127] HetRoute Heterogeneous and Cost-aware Collaborative Routing Framework for Distributed Edge MoE Inference
【速读】： 该论文旨在解决在地理分布的异构边缘服务器上部署混合专家（Mixture-of-Experts, MoE）模型时面临的协同路由难题。现有方法未能统一建模跨服务器链路带宽、异构GPU计算能力、GPU-CPU专家加载延迟、瞬时队列积压以及副本级量化质量损失等多重因素，导致缺乏高效的在线多服务器协同路由机制。其解决方案的关键在于提出HetRoute——一种面向分布式边缘MoE推理的异构成本感知协同路由框架。该框架的核心是构建一个统一的每分配成本模型，显式刻画四大成本分量：跨服务器传输开销、GPU-CPU卸载延迟、含队列排队的GPU计算延迟，以及由量化引入的质量退化惩罚。基于此模型，离线阶段通过耦合路由成本的部署算法优化专家服务器布局、GPU-CPU驻留策略及副本精度；在线阶段则通过精确枚举或束搜索（beam search）对Top-k激活专家集进行整体路由，以最小化瓶颈层成本。理论分析证明了降级可行性、参与服务器数量的上界、小候选域下的逐层最优性以及在线计算复杂度。基于三个MoE模型在10节点异构边缘测试平台上的追踪驱动评估表明，HetRoute相较代表性基线可将平均推理延迟降低最高59.0%、P99延迟降低58.0%，跨服务器流量减少72.1%，吞吐量提升达2.13倍，同时将质量退化控制在预设预算范围内。
链接: https://arxiv.org/abs/2608.00577
作者: Xin Yuan,Ning Li,Wenchao Xu,Athanasios V. Vasilakos,Song Guo,Haijun Zhang
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Computation and Language (cs.CL)
备注: 15 pages, 9 figures
Abstract:Mixture-of-Experts (MoE) models have become a dominant architecture for large-scale AI services, yet deploying them over geo-distributed heterogeneous edge servers remains challenging. When the Top-k activated experts of a token are spread across multiple servers, the optimal routing depends jointly on cross-server link bandwidth, heterogeneous GPU computing capability, GPU-CPU expert loading delay, instantaneous queueing backlog, and replica-level quantization quality loss. Existing distributed inference and MoE serving methods address these factors separately and do not provide a unified framework for online multi-server collaborative routing. In this paper, we propose HetRoute, a heterogeneous-cost-aware collaborative routing framework for distributed edge MoE inference. HetRoute introduces a unified per-assignment cost model that explicitly captures four cost components: cross-server transmission, GPU-CPU offloading, GPU computation with queueing, and quantization-induced quality penalty. Guided by this model, the offline stage determines expert server placement, GPU-CPU residency, and replica precision through a routing-cost-coupled deployment algorithm, while the online stage routes the Top-k activated expert set as a whole by minimizing the bottleneck layer cost via exact enumeration or beam search. Theoretical analysis establishes fallback feasibility, a bound on the number of participating servers, per-layer optimality for small candidate domains, and online computational complexity. Trace-driven evaluation on three MoE models over a heterogeneous 10-server edge testbed shows that HetRoute reduces average inference latency by up to 59.0% and P99 latency by up to 58.0%, cuts cross-server traffic by up to 72.1%, and achieves 2.13x throughput improvement compared with representative baselines, while keeping quality degradation within the configured budget.
[NLP-128] rimMoE A communication aware and adaptive depth framework for distributed edge inference
【速读】： 该论文旨在解决在分布式边缘服务器上部署混合专家（Mixture-of-Experts, MoE）大语言模型时，因跨服务器专家传输导致的性能瓶颈问题。现有方法主要聚焦于如何加速远程专家的访问，而本文提出一种通信感知的自适应深度框架——TrimMoE，其核心创新在于重新思考“是否需要执行某一层及其后续层”，从而从源头减少不必要的计算与通信开销。解决方案的关键在于将层跳过（layer skipping）、基于置信度的早退出（early exit）、替代执行（substitute execution）以及服务器-专家选择机制，在统一的质量预算约束下进行协同优化。在离线阶段，TrimMoE冻结主干网络，训练轻量级逐层退出头，校准各层重要性阈值，并基于跳过/早退出感知的冗余收益分配专家副本；在在线阶段，通过前瞻式过渡感知预测令牌流动，使深度缩减聚焦于代价最高的传输环节，同时引入双反馈规则动态调整延迟-质量权重与退出阈值。理论证明表明，替代与跳过的代理退化始终不超过预设预算，且早退出仅在经过校准的置信度门控下被允许。在包含Switch-Base-8E、Qwen-MoE-A2.7B和Mixtral-8x7B的异构10服务器测试环境中，TrimMoE将平均延迟降低最高达62.8%，显著减少跨服务器流量与远程执行比例，在高负载下仍保持高吞吐量，同时将任务质量退化控制在2%以内。
链接: https://arxiv.org/abs/2608.00573
作者: Ning Li,Shuting Bai,Xin Yuan,Wenchao Xu,Athanasios V. Vasilakos,Song Guo,Haijun Zhang
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Computation and Language (cs.CL)
备注: 17 pages, 11 figures
Abstract:Serving Mixture-of-Experts (MoE) large language models across distributed edge servers is bottlenecked by the cross-server expert transmission. The existing approaches mainly focus on how to reach a remote expert faster. However, in this paper, we instead consider whether a given layer, and the layers after it, need to be executed at all. To this end, a communication-aware adaptive-depth framework is proposed in this paper, termed TrimMoE, which couples layer skipping and confidence-based early exit with substitute execution and server-expert selection under a unified quality budget. Specifically, in the offline stage, TrimMoE freezes the backbone, trains the lightweight per-layer exit heads, calibrates the per-layer importance thresholds, and allocates the expert replicas by a skip/exit-aware redundancy benefit. In the online stage, a transition-aware look-ahead anticipates the token movement, so that the depth reduction targets the costliest transmissions, and besides, two feedback rules adapt the delay-quality weights and the exit threshold. Moreover, we prove that the substitution-and-skipping proxy degradation never exceeds the configured budget, and that the early exit is admitted only under a calibrated confidence gate. On a heterogeneous 10-server testbed with Switch-Base-8E, Qwen-MoE-A2.7B, and Mixtral-8x7B, TrimMoE reduces the average latency by up to 62.8%, lowers the cross-server traffic and the remote-execution ratio, and sustains high throughput under load, while keeping the task-quality degradation within a 2% bound.
[NLP-129] hrough the LENS: Local Geometric Decomposition of Vision-Language Model Representations
【速读】： 该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）中多模态信息交互的局部几何结构不明确的问题。现有可解释性方法多聚焦于全局线性方向，难以捕捉在整体高维但局部低维的表征特性。为此，论文提出LENS（Local Explanation of Neighborhood Subspaces）方法，通过混合因子分析（Mixture of Factor Analyzers, MFA）将模型激活分解为局部低秩高斯邻域，从而揭示跨模态融合的局部几何结构。其核心创新在于利用MFA构建具有语义可解释性的局部邻域，并基于这些邻域实现因果干预：通过向邻域中心插值激活，可有效引导生成过程，在跨模态和模态内任务中均显著优于差分均值与VL-SAE方法；在LLaVA的视觉到视觉生成场景中，MFA性能达到VL-SAE的5.7倍。此外，自动化多模态标签管道为邻域赋予简洁语义描述，人类评估表明该方法在控制生成方面媲美提示工程，且远超其他基线。最终，基于MFA系数空间的表征在Qwen3-VL的图像到渲染文本检索任务中，将深层表征的R@1指标从14.9%提升至48.6%。消融实验验证了融合轨迹在不同组件数量、局部秩及模态纯度阈值下的稳定性。研究结果表明，局部几何邻域是分析所考察VLMs中跨模态表示的有力可解释与因果单元。
链接: https://arxiv.org/abs/2608.00561
作者: Shalom Kachko,Raz Lapid,Margarita Vald,Almog Dubin,Moshe Sipper
机构: Tel Aviv University (特拉维夫大学); Bar-Ilan University (巴伊兰大学); Google(谷歌)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models (VLMs) process image patches and text tokens in a shared residual stream, but the local geometry through which the two modalities interact remains poorly understood. Most interpretability methods identify global linear directions, which may miss representations that are globally high-dimensional but locally low-dimensional. We introduce LENS (Local Explanation of Neighborhood Subspaces), a method that decomposes VLM activations into local low-rank Gaussian neighborhoods using a Mixture of Factor Analyzers. Applied to LLaVA-1.5-7B and Qwen3-VL-8B, LENS reveals distinct depth-dependent fusion trajectories consistent with each model’s fusion mechanism: LLaVA progressively mixes modalities at later layers, whereas Qwen3-VL mixes them early, partially re-segregates them, and recombines them near the output. An automated multimodal labeling pipeline assigns concise semantic descriptions to these neighborhoods. Interpolating activations toward neighborhood centroids causally redirects generation within and across modalities and outperforms difference-in-means and VL-SAE in most evaluated conditions; in one LLaVA vision-to-vision setting, MFA achieves 5.7 times the VL-SAE score. Human evaluation finds MFA steering competitive with prompting and substantially stronger than the other intervention baselines. Finally, the MFA coefficient space improves Qwen3-VL image-to-rendered-text retrieval at the deepest evaluated layer from 14.9% to 48.6% R@1. Ablations show that the reported fusion trajectories are stable across component counts, local ranks, and modality-purity thresholds. These results support local geometric neighborhoods as useful interpretable and causal units for analyzing cross-modal representations in the evaluated VLMs.
[NLP-130] DE-NER : Zero-shot Named Entity Recognition via Dialogue Elicitation of Large Language Models
【速读】： 该论文旨在解决基于大语言模型（Large Language Models, LLMs）的零样本命名实体识别（Zero-shot Named Entity Recognition, NER）方法在提示工程（prompt engineering）和范例设计（demonstration engineering）方面存在的局限性问题。现有方法依赖人工精心设计的提示模板与示例，难以泛化且成本较高。为此，论文提出DE-NER——一种对话式知识激发框架，其核心创新在于充分利用LLM的对话交互能力，通过自然对话形式充分挖掘并提取嵌入在预训练模型中的隐含知识，从而实现无需复杂人工干预的高效知识获取。该方案的关键在于将传统的静态提示机制转化为动态对话式推理流程，使模型能够自主探索、澄清并深化对任务的理解，显著提升了零样本场景下的性能表现，在多个基准测试中平均提升3.75 F1分数点，验证了其有效性与通用性。
链接: https://arxiv.org/abs/2608.00538
作者: Xuankang Zhang,Jiangming Liu
机构: Yunnan University (云南大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Recent advancements of zero-shot Named Entity Recognition (NER) establish strong baselines by formulating sequence labeling into question answering where Large Language Models (LLMs) can be naturally adopted. However, existing LLM-based zero-shot NER methods suffer from the limitations of prompt and demonstration engineering. To address these issues with minimal human interventions, we introduce DE-NER, a dialogue elicitation framework which elicits the chatting ability of LLMs to fully extract the knowledge encoded in LLMs. Our experiments demonstrate that the proposed method outperform the competitive baselines in zero-shot settings across multiple benchmarks, with an average improvement of 3.75% F1 points. Codes are released in this https URL.
[NLP-131] Native Multilingual Chain-of-Thought Reasoning in Low-Resource Southeast Asian Languages
【速读】： 该论文旨在解决大语言模型在低资源本土语境下进行复杂逻辑推理时面临的跨语言坍缩（cross-lingual collapse）问题，即模型在推理过程中倾向于回退至英语中间表示，导致本土语言性能显著下降。同时，传统的微调方法因跨语言表征漂移而存在灾难性遗忘风险。其解决方案的关键在于提出一种后训练算法——“Onramp-Sequence Cross-Distillation”（OSCD），通过集成翻译代理循环（translator agentic loop），在生成式训练回溯过程中将高资源语言的推理轨迹投影到低资源语言的词汇子空间中，从而实现动态生成参考样本的稳定高效翻译；同时结合参考语言与目标语言推理轨迹的联合嵌入语义对齐机制，有效弥合跨语言表征间隙。实验结果表明，OSCD在东南亚本土语言数学推理任务上相较基线提升最高达3.2倍，其中联合嵌入语义对齐组件可带来高达6.4%的去偏效果提升。
链接: https://arxiv.org/abs/2608.00533
作者: Sean Gip Lim,William Chandra Tjhi,Hai Leong Chieu
机构: 1. National University of Singapore (新加坡国立大学); 2. Nanyang Technological University (南洋理工大学); 3. Institute for Infocomm Research (资讯通信研究院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 22 pages, 16 figures, 12 tables
Abstract:Large Language Models have achieved substantial progress in reasoning capabilities. Yet in low-resource native settings, many suffer from cross-lingual collapse, reverting to English during intermediate steps that require complex logical reasoning. This presents a cold-start bottleneck for policy optimization, whereas standard fine-tuning risks catastrophic forgetting due to cross-lingual representation drift. To address these challenges, we introduce the Onramp-Sequence Cross-Distillation (OSCD), a post-training algorithm that projects high-resource reasoning trajectories into low-resource vocabulary subspaces during generative training rollouts via an integrated translator agentic loop, ensuring the stable and efficient translation of dynamically generated reference samples for fine-tuning. This is coupled with joint-embedding semantic alignment of both reference and target-language reasoning traces, thereby bridging the pairwise cross-lingual representational gaps. Comprehensive evaluations using the AIME25 and HMMT25 benchmarks demonstrate that OSCD yields up to 3.2 times overall improvements in native Southeast Asian languages for mathematical reasoning, of which the joint-embedding semantic alignment component contributes up to 6.4% improvements in linguistic debiasing over translation-only baselines.
[NLP-132] S4R: Selective Sampling Subspaces and Sparse Reconstruction for Compressed Long-Context KV Caching
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在扩展上下文窗口长度时面临的高昂内存开销问题，其核心挑战源于键值（Key-Value, KV）缓存的存储需求。现有低秩压缩方法存在两难困境：离线方法依赖外部校准数据以实现压缩，而在线方法则因需对完整提示（prompt）进行分解与重建，导致计算开销过大。为此，本文提出S⁴R（Sparse and Subspace-based KV Compression），其关键在于通过选择性采样 tokens 构建低秩子空间，并基于稀疏重构的KV表示进行注意力计算。S⁴R采用提示感知（prompt-aware）初始化，从代表性提示子集构建初始的键/值基底，从而在降低对校准数据依赖的同时控制预填充（prefilling）成本；同时，在解码阶段引入稀疏重构策略，仅保留具有信息量的位置，避免每次解码均全量重建缓存，显著提升吞吐效率。实验结果表明，S⁴R在Llama与Qwen模型族上于LongBench和RULER基准上实现了最高达5倍的KV缓存压缩率，且精度接近全缓存水平，兼具固定压缩的高效性与提示依赖方法的适应性优势。
链接: https://arxiv.org/abs/2608.00528
作者: Jialong Han,You Wu,Kewei Tu
机构: ShanghaiTech University (上海科技大学); Shanghai Engineering Research Center of Intelligent Vision and Imaging (上海市智能视觉与成像工程研究中心)
类目: Computation and Language (cs.CL)
备注:
Abstract:The growth of context window lengths in Large Language Models (LLMs) significantly enhances their long-context capabilities but incurs prohibitive memory costs due to the Key-Value (KV) cache. Although low-rank compression of KV cache is a promising remedy, existing methods face a dilemma: offline approaches depend on external calibration data, whereas online approaches incur substantial compute for full-prompt decomposition and reconstruction. In this paper, we propose S ^4 R, which builds low-rank subspaces from selectively sampled tokens and computes attention over a sparsely reconstructed KV representation. S ^4 R uses prompt-aware initialization to build initial key/value bases from a representative prompt subset, trading off calibration-data dependence against prefilling cost. Because fully reconstructing the cache at every decoding step is prohibitively expensive and hurts throughput, we further adopt sparse reconstruction to retain only informative positions during decoding. Extensive experiments on LongBench and RULER with Llama and Qwen model families show that S ^4 R achieves up to 5 \times KV compression with near full-cache accuracy, combining the efficiency of fixed compression with the adaptability of prompt-dependent methods.
[NLP-133] Rethinking and formalising the state across languages: a unified computational learning theory account
【速读】： 该论文旨在解决传统语言学中“状态”（state）概念局限于亚非语系（Afroasiatic languages）的附着性（annexation）结构，被视作语言特异性形态句法现象所导致的解释局限问题。其核心挑战在于如何统一解释跨多种综合语（synthetic languages）中多样化的名词标记模式，而这些模式长期被独立分析。解决方案的关键在于提出一种基于模板的模块化认知框架（Template-Based Modular Cognitive framework），将“状态”视为一种系统性、语境依赖的形态句法机制，能够跨语言选择特定的语法模板。该理论以里夫方言（Riffian）为主要实证基础，将“状态”形式化为一个作用于语法模板的集合值函数（set-valued function），并通过基于有限集运算的学习算法实现对状态依赖型语法配置的习得与预测。这一模型不仅统一解释了名词标记模式，还拓展至限定词-名词结构的理论分析，揭示“状态”是更广泛句法条件依赖关系（如一致关系和格范畴）的一个实例，从而推动了对名词结构与词汇认知理论的深层理解。
链接: https://arxiv.org/abs/2608.00523
作者: Mohamed El Idrissi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The linguistic notion of state has traditionally been restricted to the construct (annexation) state of Afroasiatic languages and treated as a language-specific morphosyntactic phenomenon. This article argues instead that the state is a systemic, context-dependent morphosyntactic mechanism that selects grammatical templates across synthetic languages. Within the Template-Based Modular Cognitive framework, taking Riffian as its primary empirical basis, the proposed theory provides a unified explanation for diverse nominal marking patterns traditionally analysed independently and is formalised as a symbolic computational model in which the state is represented by a set-valued function over grammatical templates. A learning algorithm based on finite-set operations acquires and predicts state-dependent grammatical configurations. Beyond nominal morphology, the framework has broader implications for theories of nominal structure and lexical cognition, in particular offering a unified analysis of determiner-noun structure. These results suggest that the state constitutes one instance of a broader class of syntactically conditioned dependencies that also includes agreement and grammatical case.
[NLP-134] Auditable Release Control for Pedagogical Leakage in LLM Tutors
【速读】： 该论文旨在解决大语言模型辅导系统中存在的“教学泄露”（pedagogical leakage）问题，即模型在未获得授权的情况下提前披露答案或关键推理过程，从而破坏教学有效性与安全性。其核心解决方案是提出一种授权感知的完整中介边界（authorization-aware complete-mediation boundary），通过引入三组件协同机制实现可控输出：一个选择器（selector）生成五种不同授权契约，可信策略门控（trusted policy gates）控制高权限模式访问，渲染器（renderer）负责语言生成。关键在于单一可审计的释放函数（release function），集成可检查的验证机制、可选的累积验证以及针对具体动作的降级策略；同时，可回放的执行轨迹将选择、生成、验证与执行失败进行分离，支持故障归因。实验表明，在599个固定Gemini 3.5提案中，严格中介将三模型小组多数泄露标记从181降至0（配对问题簇差异-30.22点，95%置信区间[-35.00, -25.72]），虽导致部分帮助性下降，但显著提升了安全性。此外，全局A1支架策略在自动安全与效用上优于拟合Q方法，且在40个未见问题簇和480次攻击序列的外部时间戳复现中，高保障释放使多数泄露标记从42降至8，证明了该框架具备可审计的释放边界与失效归因能力，而非追求普适语义安全或学习性能提升。
链接: https://arxiv.org/abs/2608.00515
作者: Nizam Kadir
机构: Singapore University of Technology and Design (SUTD)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 9 pages, 1 figure, 6 tables. Preprint; not peer reviewed
Abstract:Large language model tutors can be correct and helpful yet disclose an answer or decisive reasoning before that disclosure is authorized. We formalize this state- and action-dependent failure as pedagogical leakage and introduce an authorization-aware complete-mediation boundary. A selector emits one of five disclosure contracts, trusted policy gates privileged modes, and a renderer proposes language. A single release function applies inspectable checks, optional cumulative verification, and action-specific fallback; replayable traces separate selection, generation, verification, and enforcement failures. Matched component attribution exposes a safety-utility frontier. On 599 fixed Gemini 3.5 proposals, strict mediation reduces blinded three-model panel-majority leakage flags from 181 to 0 (paired problem-cluster difference -30.22 points, 95% CI [-35.00,-25.72]), while replacing 581 responses and lowering helpfulness. Checker-triggered fallback alone yields 11 majority flags; adding the semantic verifier yields 14 and no reliable marginal gain. A global A1 scaffold yields 0 majority and 54 any-judge flags, outperforming fitted Q on automatic safety and utility. In an externally timestamped replication over 40 unseen problem clusters and 480 attack sequences, high-assurance release reduces majority flags from 42 to 8 (-7.08 points, 95% CI [-13.13,-2.29]); seven failures persist, one is introduced, and mean helpfulness falls by .192. These results establish an auditable release boundary and failure attribution under declared contracts, not universal semantic safety or learning gains. Comments: 9 pages, 1 figure, 6 tables. Preprint; not peer reviewed Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2608.00515 [cs.CR] (or arXiv:2608.00515v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.00515 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-135] he Learning Objective Governs Perceptual Narrowing: A Cross-Lingual Layer-Wise Ten-Seed Study of Self-Supervised Speech Encoders
【速读】： 该论文旨在解决感知窄化（perceptual narrowing）这一经典发展现象背后的机制问题，即在生命头一年中，婴儿对非母语音素的辨别能力为何会逐渐丧失。其核心问题是：何种学习目标（learning objective）能够导致这种跨语言表征的分化与退化？研究的关键在于揭示学习目标在塑造语音表征中的决定性作用——通过在儿童导向语音和朗读语音数据上训练一个约700万参数的Transformer编码器，并在英语、法语和普通话三种语言中评估音素ABX任务，发现不同学习目标会导致截然不同的跨语言迁移模式：重建目标（掩码梅尔频谱预测）会削弱非母语音素辨别能力，而预测目标（帧级对比学习）则能增强该能力，二者在第一层普通话ABX任务中产生高达+0.051的差异（p=3×10⁻⁸），且方向一致。此外，研究还表明，非母语辨别力的下降源于内在表征难度梯度与语言特异性效应的共同作用，且阅读语音导致的非母语衰退速度是儿童导向语音的3.6倍；更重要的是，传统的三种子实验设计难以可靠检测该效应，而十种子设计才可稳定揭示结果。最后，多种替代性学习目标（如锐化、压缩、整合及词义语义锚定）均无法再现完整的感知窄化特征（母语提升且非母语下降），因为它们作用于共享表征空间，无法实现双语不对称变化。因此，研究结论指出：学习目标而非模型架构，是驱动表征变化呈现感知窄化特征的一阶决定因素。
链接: https://arxiv.org/abs/2608.00507
作者: Sejin Yoo
机构: Independent Researcher(独立研究员)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: 11 pages, 6 figures
Abstract:Perceptual narrowing—the developmental loss of non-native phoneme discrimination in the first year of life \citepwerker1984—is a canonical developmental finding, yet \emphwhat learning objective produces it remains open. We train a (\sim)7,M-parameter Transformer encoder on child-directed and read speech and evaluate phoneme ABX in English, French, and Mandarin over ten seeds, the seed as the unit of replication. Six results. \textbf(1)~The objective sets the direction of cross-lingual transfer: reconstruction (masked mel-prediction) degrades non-native discrimination, prediction (frame-contrastive) improves it—a same-encoder, same-data gap of (+0.051) in first-layer Mandarin ABX ((p=3\times10^-8)), unanimous in sign across twenty runs. \textbf(2)~That decline combines a large arm-intrinsic difficulty gradient with a smaller language-specialization effect (matched vs.\ mismatched (+0.022), (p=10^-4), all four layers). \textbf(3)~Against a language-symmetric raw-mel floor, reconstruction pushes the first layer \emphbelow the discriminability of its input; prediction pushes it \emphabove. \textbf(4)~Read speech gives a (3.6\times) steeper non-native decline than child-directed speech. \textbf(5)~The customary three-seed budget cannot see this reliably: an effect unambiguous at ten seeds is called significant by as few as 70% of three-seed subsets. \textbf(6)~Six objective configurations—sharpening, compression, consolidation, their composition, and word-level semantic grounding in two forms—fail to produce the full developmental signature (native improves \emphand non-native declines): a single objective moves both languages the same way because it acts on a shared representation. We conclude that the objective, not the architecture, is the first-order determinant of narrowing-shaped representational change.
[NLP-136] he methodology of Constructing the Large-Scale Dataset for Detecting Presuicidal and Anti-Suicidal Signals in Social Media Texts in Russian
【速读】： 该论文旨在解决在社交媒体中识别潜在自杀风险文本的难题，尤其针对俄罗斯语境下大量自杀相关言论被淹没于海量无关信息中的问题。由于许多有自杀倾向的个体在社交平台上表达其心理困境，这些文本具有重要的干预价值，但其有效识别受制于噪声数据干扰和标注效率低下。解决方案的关键在于提出一套系统化的数据集构建方法论，涵盖指令与分类表设计、人工标注流程、标注验证及后期修正机制，并基于该方法构建了规模超过5万条的俄语社交媒体文本数据集。该方法确保了标注的一致性与可靠性，同时通过初步分类模型实验验证了不同标注粒度下的性能表现，最终实现数据集、代码及相关材料的公开共享，为后续生成式AI（Generative AI）驱动的自杀风险检测研究提供了高质量基准资源。
链接: https://arxiv.org/abs/2608.00497
作者: Igor Buyanov,Darya Yaskova,Danil Serenko,Danil Shkereda,Andrey Yaskov,Ilya Sochenkov
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:The suicide is a terrifying act of a person who is misled by his own mental state. This problem arises across many countries. Sadly, Russia also has quite high number of persons who committed suicide. Luckily, a subset of these people writes their struggles in social media, allowing a way to find them and help. However, these valuable texts disappearing in many irrelevant texts which is considerably slowing down the decision process about person’s suicidal risk. To tackle this problem, in this work we have presented a detailed methodology of building the dataset for detecting texts that describe presuicidal and anti-suicidal signals. This methodology describes the process of instruction and class table creation, the process of annotation, verification and post-annotation correction. Guiding by this methodology, we collect and annotate a large-scale Russian dataset with more than 50 thousand texts from social media. We provide a count statistic of the dataset as well as common problems in annotation. We also conduct basic experiments of building the classification models to show the on go performance on different levels of annotation. Furthermore, we make the dataset, code and all materials publicly available.
[NLP-137] SERL-SQL: Selective Hindsight Distillation for Text-to-SQL Reinforcement Agent ic Learning
【速读】： 该论文旨在解决多轮文本到SQL（Text-to-SQL）系统中因仅使用执行正确性作为轨迹级奖励而导致的信用分配不精准问题，即难以定位导致成功或失败的具体SQL生成决策。其解决方案的关键在于提出一种选择性执行感知强化学习框架SERL-SQL，通过引入仅在训练阶段使用的教师模型对学生的SQL及工具操作动作进行重评分，利用执行反馈构建有界且掩码化的权重，仅对SQL和工具操作标记的梯度改进优势（GRPO advantages）进行重加权，从而在保持任务奖励优化方向的同时，实现基于执行结果的局部信用分配。实验表明，SERL-SQL在BIRD、Spider及跨领域基准上均达到领先性能，其中在BIRD-Dev上实现76.56%的执行准确率，在Spider-Test上达89.92%，且基于奖励的选择策略接近最优的“Best-of-N”上界，显著优于基于一致性的选择方法，验证了其生成高质量候选SQL并可被轻量级执行感知奖励可靠识别的能力。
链接: https://arxiv.org/abs/2608.00485
作者: Tao Liu,Tao Feng,Xiangheng Li,Jinwang Song,Yifan Li,Xiaoqing Cheng,Dixuan Zhang,Siquan Li,Lin Lan,Hongying Zan,Kunli Zhang,Chao Wu
机构: 1. Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所); 2. School of Artificial Intelligence, Renmin University of China (中国人民大学人工智能学院); 3. School of Computer Science and Technology, Harbin Institute of Technology (哈尔滨工业大学计算机科学与技术学院)
类目: Computation and Language (cs.CL)
备注: 9 pages,6 figures
Abstract:Recent Text-to-SQL systems increasingly rely on multi-turn interaction, execution feedback, and reinforcement learning. However, most existing methods use execution correctness only as a trajectory-level reward, which provides limited guidance for identifying the SQL decisions responsible for success or failure. We propose SERL-SQL, a selective execution-grounded reinforcement learning framework for multi-turn Text-to-SQL agents. SERL-SQL samples on-policy SQL interaction trajectories and uses a training-only teacher to re-score student actions with execution feedback. The resulting teacher–student likelihood gap is converted into bounded, masked weights that reweight GRPO advantages only on SQL and tool-action tokens. In this way, task rewards preserve the optimization direction, while execution hindsight provides localized credit assignment. Experiments on BIRD, Spider, and cross-domain benchmarks show that SERL-SQL achieves competitive performance, reaching 76.56% execution accuracy on BIRD-Dev and 89.92% on Spider-Test. Moreover, our reward-based selection strategy closely approaches the oracle Best-of-N upper bound and consistently outperforms consistency-based selection, showing that SERL-SQL produces high-quality candidates that can be reliably identified by lightweight execution-grounded rewards. Our code will be released at this https URL.
[NLP-138] CrossProjection: Geometric Grounding Beyond Viewpoint Change in Architectural Drawings
【速读】： 该论文旨在解决生成式视觉语言模型（Generative AI）在跨异构建筑视图（如平面图、剖面图与立面图）中进行多视图推理时存在的根本性问题：建筑图纸中的构件在不同视图下呈现的外观变化无法通过常规相机运动解释，导致模型难以维持构件身份一致性并准确外化几何关系。其解决方案的关键在于提出一种名为CrossProjection的锚点引导诊断框架，通过类别判断、候选选择及自由点/线/区域定位等任务，系统评估模型在匹配（Matching）、注册（Registration）和几何定位（Geometric Grounding）三个维度的表现。研究发现，尽管部分模型在封闭选项任务中表现良好（如GPT-5.5达82.4%），但在无候选支持的自由几何定位任务中仍极为脆弱，尤其在线段端点定位上表现极差（最高仅22% PCK@.05），表明类别正确性并不等同于可靠的显式几何建模能力。为此，研究引入可重用图面锚点、固定分母评分机制与哈希锁定标记物，构建可审计的验证路径，以揭示并追踪模型在几何表征上的关键缺陷。
链接: https://arxiv.org/abs/2608.00473
作者: Kaho Li,Pengyu Zeng,Yuqin Dai,Jun Yin,Tianjing Feng,Shuai Lu
机构: Tsinghua University (清华大学); University College London (伦敦大学学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Initial controlled diagnostic study on 23 natural drawing sets and three VLMs; broader model, building, repeated-inference, and human coverage is planned for a subsequent version
Abstract:Architectural drawings violate the usual assumption behind multi-view reasoning: plans and sections are cuts, while elevations are facade projections, so corresponding components change appearance in ways camera motion cannot explain. We introduce CrossProjection, an anchor-grounded diagnostic of whether vision-language models preserve component identity and externalize geometry across heterogeneous architectural views. It evaluates Matching, Registration, and Geometric Grounding through categorical judgments, candidate selection, and free point, line, and region localization. Across 23 real drawing sets and 1,954 categorical conditions per model, GPT-5.5 scores 82.4%, Qwen3-VL-32B-Instruct 62.2%, and GLM-4.5V 57.2%. A matched 200-target study crosses natural and vector-text-suppressed drawings with closed-candidate and free-geometry outputs. Candidate-supported performance is often higher, but free localization remains fragile: on natural drawings, point/region PCK@.05 is 54-76% for GPT, 8-10% for Qwen, and 14-36% for GLM; line endpoint PCK@.05 is 22%, 4%, and 0%. A coordinate grid recovers some GPT point/region precision but not lines. Three architecture-trained participants reach 87.3-93.3% categorical accuracy and 76-92% GT-region hit, supporting task feasibility rather than a population-level human ceiling. Because the categorical families do not form a same-item Matching-Registration contrast and interface controls alter multiple burdens, we avoid mechanistic claims. The supported conclusion is narrower: closed-choice or marked-element success does not entail reliable explicit geometric grounding. For drawing-guided CAD/BIM systems, categorical correctness should not be treated as evidence of candidate-free spatial reliability. Reusable on-sheet anchors, fixed-denominator scoring, and hash-locked artifacts establish an audit trail for this gap. Comments: Initial controlled diagnostic study on 23 natural drawing sets and three VLMs; broader model, building, repeated-inference, and human coverage is planned for a subsequent version Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2608.00473 [cs.CV] (or arXiv:2608.00473v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.00473 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-139] AdaMTP: An Adaptive Training Paradigm for Multi-Token Prediction
【速读】： 该论文旨在解决多标记预测（Multi-Token Prediction, MTP）框架中因采用固定长度预测时长而导致的训练信号噪声问题。现有方法强制辅助头在高熵语义边界处进行并行预测，导致不一致的训练信号注入，进而通过共享的骨干网络反向传播干扰核心模型能力。其解决方案的关键在于提出自适应多标记预测（AdaMTP），通过基于熵的序列分割算法动态识别自然语言与代码中的语义边界（即不确定性突增点），将序列划分为可变长度的片段，并为每个标记分配自适应的预测深度；同时引入动态掩码的MTP目标函数，抑制跨越这些边界时的损失，从而有效降低噪声梯度对骨干网络的负面影响。该方法在数学推理、代码生成及通用基准测试中，于三个主流大模型（Llama-3.1-8B、Qwen-2.5-7B、Gemma-3-12B）上均实现了任务性能与推理加速的双重提升。
链接: https://arxiv.org/abs/2608.00434
作者: Ziqiang Cui,Han Shi,Bowei He,Yu Pan,Peiyang Liu,Shengyin Sun,Yankai Chen,Haoli Bai,Yichun Yin,Xue Liu,Chen Ma
机构: City University of Hong Kong(香港城市大学); Huawei Technologies(华为技术); Mohamed bin Zayed University of Artificial Intelligence(穆罕默德·本·扎耶德人工智能大学); McGill University(麦吉尔大学); Peking University(北京大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-Token Prediction (MTP) has emerged as an effective paradigm that augments a shared Large Language Model backbone with auxiliary heads, training the model to predict several future tokens in parallel to enrich its supervision signal and accelerate inference. However, existing training frameworks adopt a rigid, fixed-length prediction horizon, disregarding the highly non-uniform information density of natural language and code. Forcing the auxiliary heads to predict across high-entropy semantic boundaries injects noisy, conflicting training signals; because these heads share the backbone’s latent representations, the resulting gradients backpropagate and interfere with the model’s core capabilities. We propose AdaMTP, an adaptive training paradigm that dynamically aligns the prediction horizon with the intrinsic predictability of the sequence. At its core, an entropy-based segmentation algorithm leverages the base model to detect sudden surges in uncertainty as semantic boundaries, partitioning sequences into variable-length groups. Each token is assigned an adaptive prediction depth, and a dynamically masked MTP objective suppresses the loss for predictions that cross these boundaries, attenuating the noisy gradients that degrade the backbone. Across mathematical reasoning, code generation, and general benchmarks on three backbones (Llama-3.1-8B, Qwen-2.5-7B, Gemma-3-12B), AdaMTP consistently outperforms standard MTP in both task performance and inference speedup.
[NLP-140] Deep Research Pretraining via Predictive Navigation
【速读】： 该论文旨在解决深度研究型智能体（Deep Research Agent）在训练过程中对昂贵且依赖环境的工具使用轨迹（tool-use trajectories）的高度依赖问题，这类轨迹通常需要反复进行信息检索、文档审查和报告评估。其核心挑战在于如何在缺乏实时检索环境或实际策略执行回滚的情况下，有效训练模型掌握研究过程中的导航与证据整合能力。为此，论文提出了一种离线预训练框架——深度研究预训练（Deep Research Pretraining, DRP），其关键创新在于：通过自然存在的引用结构或超链接文本，构建代理研究目标，自动恢复相关证据及图结构中的替代路径，并将其转化为“搜索-打开-撰写”（search-open-write）的轨迹序列。这一机制使模型能够学习何时搜索、应检查哪些文档以及如何合成证据，从而实现对研究行为的端到端建模。实验表明，基于学术引用图（DRP-Paper）和维基百科超链接（DRP-Web）构建的两种实例，在仅使用1/4标准监督微调（SFT）数据的情况下，仍显著优于全数据量的对照组模型，且性能提升可迁移至多个下游任务。控制实验进一步验证了这些改进源于证据驱动的导航策略，而非领域暴露或代理格式模仿。因此，DRP为减少对高成本轨迹依赖的智能体训练提供了一种高效且可扩展的互补范式。
链接: https://arxiv.org/abs/2608.00432
作者: Jiang Zhou,Zhiyuan Fan,Xing Wu,Tinghao Yu,Feng Zhang,Lilin Wang
机构: Tencent(腾讯)
类目: Computation and Language (cs.CL)
备注: working in progress; correspondence to {ucaswu,maxwellyu}@tencent.com or wuxing@iie. this http URL
Abstract:Deep research agents are often trained on expensive, environment-grounded tool-use trajectories that require repeated retrieval, document inspection, and report evaluation. We introduce Deep Research Pretraining (DRP), an offline framework that derives predictive navigation supervision from naturally occurring evidence structures. Given a citation-bearing or hyperlinked passage, DRP constructs a proxy research objective, recovers linked evidence and graph-related alternatives, and converts them into search-open-write trajectories. This teaches models what to search for, which documents to inspect, and how to synthesize evidence, without a live retrieval environment or executed policy rollout. We instantiate DRP on scholarly citation graphs (DRP-Paper) and Wikipedia hyperlinks (DRP-Web), continually pretrain separate Qwen3-14B-Base models on 1B tokens, and fine-tune them on controlled fractions of 13K agent trajectories. Across five independently sampled subsets at each low-data budget, both variants consistently outperform matched no-DRP models on DeepResearch Bench. With one quarter of the SFT data, DRP-Web even surpasses a fixed no-DRP full-data checkpoint, with gains transferring to ResearchQA, WebWalkerQA, and SimpleQA. Starting from matched low-data SFT checkpoints, the DRP-Web advantage also persists through subsequent agentic RL. Source-matched and evidence-mismatch controls indicate that these improvements arise from evidence-conditioned navigation rather than domain exposure or agent-format imitation. DRP thus provides a promising complementary approach to trajectory-based agent training.
[NLP-141] Where did the ambiguity go? Examining how multimodal models interpret polysemous words
【速读】： 该论文旨在解决多义性（polysemy）在跨模态生成中表达不一致的问题，具体聚焦于大语言模型（LLM）与文本到图像生成模型在处理具有多重含义的词汇时，其语义表达的差异。研究发现，尽管人类对多义词能产生丰富且多样化的联想（归一化熵为0.47），但生成式模型在不同模态下的表现存在显著差距：文本生成模型产生的语义分布较广（归一化熵为0.25），而图像生成模型则高度集中，仅覆盖少数语义（归一化熵仅为0.10），表明图像生成存在严重的语义贫乏现象。关键发现在于，当要求模型预测各语义出现频率时，其输出的语义分布反而比实际生成结果更为多样，揭示出模型对多义性的“隐含理解”与“显式生成”之间存在脱节。因此，该研究的核心解决方案在于揭示并量化了基础模型在跨模态表达中存在显著的多模态鸿沟（multimodal gap），并指出当前生成模型对多义性的理解在不同模态间并非均衡或可迁移的。
链接: https://arxiv.org/abs/2608.00410
作者: Jasin Cekinmez,Addison J. Wu,Raja Marjieh,Thomas L. Griffiths
机构: Princeton University (普林斯顿大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Oral Presentation, Sci-FM Workshop @ COLM 2026
Abstract:Human language is highly polysemous. Many common words (e.g., ‘bank’ or ‘palm’) carry several distinct meanings that shape what humans communicate and imagine. Large language models (LLMs) have been shown to understand this multiplicity of meaning, but much less is known about how polysemy surfaces in other modalities such as images. We study this across 17 text-to-image and 15 text-generation models by giving each a polysemous word with no context to fix its meaning and measuring which senses are produced over many samples. We find a clear multimodal gap, where within every model family, generated images settle on far fewer senses than generated sentences (normalized entropy 0.10 vs. 0.25), and both are far less varied than what people imagine for the same words (normalized entropy 0.47). However, when we instead ask a model to list how often it would generate outputs corresponding to each possible meaning of a word, it predicts distributions that are more diverse than the actual space of outputs. These results reveal a multimodal gap in how foundation models express meaning, and how their understanding may not transfer faithfully nor equally across modalities.
[NLP-142] CurveShift: Is Agent Progress Scalar? Separating Level from Shape
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）性能评估中一个关键问题：现有基于单一标量指标（如时间跨度、潜在能力估计或综合基准得分）的评估方式，无法准确揭示模型在不同任务难度上的进展分布是否发生了质变。尽管表面上看模型在难题上的提升显著，但研究发现这一现象主要由“天花板效应”（ceiling effects）导致，而非模型能力本质性的结构性改进。通过在METR时间跨度数据上应用单参数Rasch模型并引入上升的能力参数，研究证实了这种看似向难题倾斜的进步可被经典心理测量模型解释，表明其更多是统计偏差所致，而非真正涌现的新能力。进一步分析发现，在控制总体能力提升后，仍存在一个较小但稳健的“难题效应”（hard-task effect），即模型在最困难问题上的表现超出其在简单与中等任务上的预测水平。为克服代理基准测试中模型与推理框架（agentic harnesses）混杂带来的混淆问题，作者构建了LiveCodeBench——一个公开的编程竞赛基准，不依赖任何代理架构，并将历史模型与外生定义的任务难度排序进行配对。结果表明，2024年9月之后发布的模型在极难问题上的表现仍显著优于预期，即使在最保守假设下，其难度响应增益约为+0.40 logits，使解题率从约18%提升至25%。该效应主要由最强推理能力的模型驱动，且适用于仅需短时推理的难题，而非依赖长期自主决策的任务。因此，该研究提出了一种特定于编程竞赛场景的、经过严格因果识别的硬任务进步证据，强调了评估设计对识别真实能力演进的重要性。
链接: https://arxiv.org/abs/2608.00355
作者: Hanwen Xing,Pengyun Wang,BingXu Meng,Kumail Alhamoud,Xiang Li,Jicheng Wang,Xin Yu,Xinyang Han,Xiaomin Li,Philip Torr,Yuexing Hao
机构: University of Southern California (南加州大学); University of Chicago (芝加哥大学); University of California, Berkeley (加州大学伯克利分校); Massachusetts Institute of Technology (麻省理工学院); Stanford University (斯坦福大学); University of California, Davis (加州大学戴维斯分校); Pennsylvania State University (宾夕法尼亚州立大学); Harvard University (哈佛大学); University of Oxford (牛津大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 25 pages, 4 figures, 7 tables. Data and code: this https URL
Abstract:Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summaries capture the overall performance, but they do not test whether progress is distributed differently across task difficulty. We find that most of the apparent shift in gains toward harder tasks does not reflect a change in the shape of the difficulty-response curve. On METR time-horizon data, a single Rasch model with rising ability reproduces this pattern, so it is largely explained by ceiling effects rather than a qualitative change in capability. This echoes how the choice of metric can make claimed emergent abilities look like a property of the models themselves. We then identify a smaller hard-task effect that survives this control. Isolating it is difficult on agentic benchmarks, because newer models are usually run with newer agentic harnesses, so a gain on hard tasks cannot be assigned to the model or its scaffold. We break the confound with LiveCodeBench, a public competitive programming benchmark that runs no agentic scaffold while pairing dated models with an exogenous difficulty ordering. After accounting for the rise in overall ability, models released after September 2024 still gain on the hardest problems beyond what their easy and medium performance predicts, by about +0.40 logits under our most conservative assumption, raising the hard-problem solve rate from roughly 18% to 25%. The effect is led by the strongest reasoning models and holds for hard tasks that need only short reasoning, not autonomy over long horizons. We present this as a result specific to competitive programming, since our clean identification rests on a single coding benchmark. We release the LiveCodeBench Difficulty Panel (66 dated models x 1,055 problems) and our analysis code.
[NLP-143] Bayesian and Motivated Reasoning in AI Agents
【速读】： 该论文旨在解决在开放性任务中，人工智能代理（AI agents）在面对相同数值数据时，其结论会因问题的实质性框架（substantive framing）不同而产生显著差异的问题。这一现象在医疗决策、选举法证及地缘政治预测等高风险领域尤为突出。研究的关键在于揭示：尽管证据保持不变，仅通过改变情境框架，AI代理的结论仍会受到其先验信念（prior beliefs）的强烈影响——当框架与代理的既有信念一致时，更倾向于得出肯定性结论；反之则倾向否定。此外，框架变化还会引发代理行为模式的转变，包括更广泛的搜索策略、不同的分析规格选择以及对同一证据的不同评估方式。这表明，将关键决策权委托给AI代理存在潜在风险，因其决策可能依赖于未在任务说明中明确、且无法从决策记录中追溯的隐含先验信念。
链接: https://arxiv.org/abs/2608.00339
作者: Eddie Yang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:AI agents increasingly perform open-ended tasks in settings where their conclusions can guide consequential decisions. We provide evidence that AI agents draw different conclusions from identical numerical data when the substantive framing changes. We demonstrate this behavior in high-stakes domains in medicine, election forensics, and geopolitical forecasting by holding the evidence fixed while changing the scenario in which the evidence appears. Across twelve agent-domain comparisons, agents’ conclusions are strongly influenced by their prior beliefs. They are more likely to reach an affirmative conclusion when it is framed around a proposition they already regard as likely, while the reverse holds when the framing conflicts with their prior. The framing also changes how some agents work: they search more extensively, choose different analytical specifications, and evaluate the same evidence differently. These results identify a particular risk of delegating decision-making to AI agents, as their decisions may depend on prior beliefs that are neither specified in the task nor visible in the decision record.
[NLP-144] RMSWeb: Reflection Failure-Mode Mining and Salvage-DS for Web Agent Reinforcement Learning
【速读】： 该论文旨在解决小型化网页智能体（compact web agents）在部署成本降低背景下，训练过程中面临的数据收集效率低下与后监督微调（SFT）阶段强化学习（RL）优化困难的问题。核心挑战在于：成功轨迹的采集成本高昂且常包含冗余路径；SFT后轨迹数据集中充斥着常规状态，难以有效支撑强化学习的探索；而现有基于组间相对奖励的RL方法在动作层面奖励设计不当的情况下，易导致更新信号弱化或误导，且被排除的组别缺乏替代性学习信号。其解决方案的关键在于提出RMSWeb三步协同策略：（1）通过“反思驱动重试”机制提升数据收集效率并压缩成功轨迹长度；（2）通过“失败模式挖掘”聚焦于SFT策略暴露的关键异常状态，增强离线强化学习的针对性；（3）引入Salvage-DS模块，结合动作语义极化奖励、对比与能力门控动态采样机制以及仅动作锚定结构，为被拒绝的组别提供有效的回溯学习信号。实验表明，采用该方案训练的模型在完成任务时可减少最多19.7%的动作步数，且在WebVoyager、Online-Mind2Web和WebTailBench等基准上相较SFT显著提升2.4–7.7分，尤其在8B规模下实现了同类开源模型中的最优在线性能，并展现出更优的准确率-成本权衡表现。
链接: https://arxiv.org/abs/2608.00335
作者: Chengbo Liu,Lifang Zhou,Ruijie Yan,Pei Tan,Ao Sun,Haojun Huang,Guichun Hua,Sining Wei,Yining Chen,Yingying He,Yutao Xie
机构: 1. Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所); 2. School of Artificial Intelligence, University of Chinese Academy of Sciences (中国科学院大学人工智能学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 15 pages, 9 figures, and 6 tables. Includes appendices
Abstract:Compact web agents can reduce deployment cost, but training them poses challenges in both data collection and post-SFT reinforcement learning (RL). Successful trajectories are expensive to collect and often contain inefficient detours. After supervised fine-tuning (SFT), full trajectory corpora are dominated by routine states; moreover, when group-relative RL is applied to web actions, inadequately designed action-level rewards can yield weak or misleading relative updates, while groups rejected as unsuitable for such updates receive no fallback learning signal. We present RMSWeb, a three-part recipe for Qwen3-VL-Instruct at 8B and 32B. Reflection-conditioned retries increase collection yield and shorten successful trajectories; failure-mode mining concentrates offline RL on critical states exposed by the SFT policy; and Salvage-DS combines an action-semantic polarized reward, contrast-and-competence-gated dynamic sampling, and an action-only anchor for rejected groups. Policies trained with reflection-collected data use up to 19.7% fewer action steps on solved tasks. On WebVoyager, Online-Mind2Web, and WebTailBench, RMSWeb improves over SFT by 2.4-7.0 points at 8B and 1.2-7.7 points at 32B. Our 8B model also achieves the strongest reported Online-Mind2Web result among similarly sized open-weight models in our comparison and a leading reported accuracy-cost trade-off on WebVoyager and WebTailBench, with the caveat that external evaluation protocols differ.
[NLP-145] SeDeM: Selective Decompression of Hidden-State Memories for Long-Context Question Answering
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在长上下文推理中面临的高计算成本与信息利用不可靠的问题。具体而言，自注意力机制在预填充（prefill）阶段的计算开销随序列长度呈平方级增长，且键值缓存（Key-Value Cache, KV Cache）随已处理标记数量线性增长，导致资源消耗巨大；同时，更大的上下文窗口并不能保证模型有效利用证据信息。现有上下文压缩方法多采用生成式压缩（soft-compression），依赖LLM作为压缩器，并通过紧凑的记忆令牌同时实现信息保留与解码器条件化，存在信息失真与解码效率低下的问题。本文提出SeDeM（Selective Decompression），其核心创新在于将紧凑记忆存储与解码器条件化过程解耦：利用LLM从选定的Transformer中间层提取隐藏状态，由轻量级压缩器将其编码为记忆块；通过查询相关的选择器（query-conditioned selector）筛选出相关记忆块；再由解压器仅对选中的块进行展开，生成与中间解码层兼容的隐藏状态。该设计避免了解码器对全上下文的处理以及直接从高度压缩的记忆槽生成输出。在四个长上下文问答（QA）基准测试中，SeDeM在1B和3B同架构设置下均优于对比的压缩基线方法，且3B模型在三个数据集上超越了全上下文微调（full-context fine-tuning）的表现。此外，训练过程中引入块级证据监督以优化选择器。实验表明，SeDeM显著降低了首次生成时间（time-to-first-token）并提升了自回归解码吞吐量，相较ICAESeDeM also reduces online time-to-first-token and improves autoregressive decoding throughput relative to ICAE.
链接: https://arxiv.org/abs/2608.00311
作者: Maryam Haghifam,Jason Cong,Yizhou Sun
机构: University of California, Los Angeles, USA
类目: Computation and Language (cs.CL)
备注:
Abstract:Long-context inference with large language models (LLMs) is costly: self-attention during prefill scales quadratically with sequence length, and the key-value (KV) cache grows with the number of processed tokens. Larger context windows also do not ensure reliable evidence use. Context compression reduces this cost, but many soft-compression methods use LLMs as compressors and rely on compact memory tokens both to preserve information and to condition the decoder. We propose SeDeM, a selective decompression framework that decouples compact memory storage from decoder conditioning. An LLM extracts hidden states from a chosen intermediate Transformer layer, a lightweight compressor stores them as memory blocks, a query-conditioned selector selects relevant blocks, and a decompressor expands only the selected blocks into hidden states compatible with an intermediate decoder layer. Thus, the decoder avoids both full-context processing and direct generation from highly compressed memory slots. On four long-context QA benchmarks, SeDeM achieves higher QA scores than the evaluated compression baselines in both 1B and 3B same-backbone settings, and with the 3B backbone exceeds full-context fine-tuning on three datasets. The learned selector uses block-level evidence supervision during training. SeDeM also reduces online time-to-first-token and improves autoregressive decoding throughput relative to ICAE.
[NLP-146] Abstention as an Action Can Kill Both the Reward Gradient and the KL Anchor: Collapse Law and Repair for Error-Penalized Reinforcement Learning
【速读】： 该论文旨在解决生成式模型在采用误差惩罚评分规则（+1表示正确回答，-λ表示错误回答，0表示放弃）时出现的“过度拒绝”（over-abstention）问题，即模型在面对合理置信度的情况下仍倾向于拒绝回答，导致覆盖率严重下降。其核心问题是：当放弃（abstention）被建模为一个离散动作时，奖励梯度与KL锚定项的恢复力受同一门控饱和因子制约，导致两者同时衰减，从而使模型在训练过程中逐渐趋向于完全拒绝所有输入，尽管其平均训练奖励随时间呈1/t衰减趋势而看似“提升”。此外，优势估计器在稀疏回答场景下通过组归一化将设计中的惩罚项隐式替换为等效惩罚值1，致使学习到的决策阈值从理论最优的Chow阈值 $ t^* = \lambda/(1+\lambda) $ 偏移至1/2，进一步加剧了性能退化。解决方案的关键在于结构修复：训练一个强制输出的置信度报告模块，使用严格适当的评分函数（strictly proper score）结合正确性奖励，仅在部署阶段根据置信度报告进行阈值化放弃。由于该报告始终被发出，不存在门控饱和机制，因此其奖励梯度与锚定项不会因共享因子而共同消亡，从而保证了校准最优解的吸引性。仿真和大规模语言模型实验验证了该机制的有效性，表明该方法可在极短时间内抑制模型对可解问题的拒绝行为，且通过消融实验证明因果关系，同时显著提升覆盖率、准确率与校准性。
链接: https://arxiv.org/abs/2608.00301
作者: Xujun Che,Yuchen Yuan,Weida Zhao,Chenyang Yu
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Error-penalized scoring rules ( +1 for a correct answer, -\lambda for a wrong one, 0 for abstaining) are increasingly prescribed against hallucination: a rational agent facing such a rule answers exactly when its correctness probability exceeds Chow’s threshold t^\ast=\lambda/(1+\lambda) . We prove that a KL-anchored gradient learner can do the opposite. When abstention is a discrete action, the reward gradient and the anchor’s restoring force are throttled by the same gate-saturation factor and die together: under explicit conditions (among them, blanket answering loses score in expectation and prompts share a bounded readout) the model drifts toward refusing everything, its mean training reward rising to zero like 1/t in training time t , so the curve reads as improvement while coverage collapses. The advantage estimator compounds the failure: in its sparse-answer regime, group normalization silently replaces every designed penalty with an effective penalty of one, moving the learned threshold from t^\ast to 1/2 . The repair is structural: train a mandatory confidence report with a strictly proper score plus a correctness reward, and abstain only at deployment by thresholding the report. The always-emitted report has no gate to saturate, so no shared factor can kill its reward gradient and its anchor together, and its calibrated optimum is attracting. Simulations confirm every prediction, and experiments on language models at two scales confirm the mechanism live: the rule silences questions the models demonstrably still solve within ten optimizer steps, an ablation isolates the cause, and report-level training raises coverage, accuracy, and calibration together.
[NLP-147] Comparing and Modeling Argumentation in German Political Communication across Arenas
【速读】： 该论文旨在解决政治话语中不同议事场合（如全体会议、委员会会议和新闻发布会）之间论辩模式差异的计算分析不足问题。尽管论辩在民主政治决策中至关重要，但现有研究多集中于理论探讨，缺乏对跨场景论辩结构与特征的系统性计算建模与实证分析。其解决方案的关键在于构建一个包含17,000句、针对德国三大政治议事场域（全体会议、委员会会议、新闻发布会）的标注语料库，并在保持主题一致（新冠疫情）的前提下，精细标注论点及其理由的边界与类别。研究发现，与预期相反，基于领域专业知识的论证在新闻发布会上出现频率高于委员会会议，揭示了不同场域间论证策略的显著异质性。此外，论文开展了一项初步的自动识别论辩片段的实验，结果表明论辩边界的界定具有高度复杂性，且模型预测易受确认偏误（confirmation bias）影响，凸显了当前自然语言处理技术在论辩结构识别中的局限性。
链接: https://arxiv.org/abs/2608.00288
作者: Nina Vikhrova,Johannes Kühling,Sebastian Haunss,Sebastian Padó
机构: IMS, Universität Stuttgart; SOCIUM, Universität Bremen
类目: Computation and Language (cs.CL)
备注: Accepted for publication at KONVENS 2026
Abstract:Deliberation, involving the formulation and exchange of arguments, forms an integral part of political decision making in democracies. Argumentation patterns however differ substantially across different political arenas, such as plenary speeches and committee meetings. However, despite a lot of interest in argumentation, there is comparatively little computational work on analyzing differences in patterns of political argumentation between arenas. Our work addresses this research gap. First, we present a 17k-sentence corpus with annotation for argumentative passages (argument and their justifications, both their boundaries and their categories) across three German political arenas (plenary speeches, committee meetings, and press conferences), keeping the topic (COVID-19) constant. Our analysis of the corpus finds that contrary to expectations, justification by domain-specific expertise is more frequent in press conferences than in committee meetings. Second, we present a pilot study on automatically identifying such argumentative passages. The results show that boundaries are hard to pin down, and models predictions additionally suffer from confirmation bias. Comments: Accepted for publication at KONVENS 2026 Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.00288 [cs.CL] (or arXiv:2608.00288v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.00288 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-148] Sixteen models fewer than two voices: measuring ensemble dispersion where no answer is uniquely correct
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在心理治疗案例表述中所呈现的语义多样性与不确定性评估问题，尤其关注多模型集成（ensemble）系统输出的分歧性（dissent）来源及其结构性特征。传统方法将多样性视为可度量的聚合指标，但忽视了其内在成因；本文的关键突破在于提出“每模型分歧贡献度”（per-model dissent contribution），即通过计算某模型与其集成中其他模型平均相似性的互补值，从相似性矩阵中直接提取出各模型对整体分歧的独立贡献，从而识别出最具差异性的声音。研究发现，尽管模型身份（model identity）是分歧结构的显著影响因素，但其作用并不遵循预设的规模或家族分类模式——不同模型对的尺度差异方向相反，同一家族模型仅在少数组合中表现出聚集性，且最突出的分歧者随集成构成动态变化，表明分歧更多由临床内容本身驱动而非模型类型。因此，该研究揭示：生成式模型的输出分散性并非可预先假设的属性，而应作为集成系统的一个可测量、可分析的涌现特性来对待。
链接: https://arxiv.org/abs/2608.00285
作者: Mario Vega-Barbas,Lidia Mora-Valenciano,Iván Pau,Fernando Seoane,Farhad Abtahi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 34 pages (25 article + 9 supplementary), 3 figures. Supplementary material (S1-S11) included. Preregistered at OSF ( this http URL ), sealed 21 July 2026. Analysis code and data: this https URL
Abstract:Sixteen language models drawn from ten families produced, on average, the semantic diversity of 1.69 distinct formulations of a psychotherapeutic case, against a single-model baseline of 1.43 from one model’s own runs. Ensembles place more than one reading before a decision-maker on the premise that several models supply several perspectives. Dispersion over their outputs is measured both as diversity and as uncertainty, and both traditions validate it against a correctness criterion that this task does not admit. Measuring diversity is a solved problem: the Vendi Score, the exponential of the von Neumann entropy of a similarity matrix, is an effective number of distinct elements. What a single aggregate does not say is where the diversity comes from. We define a per-model dissent contribution, the complement of a model’s mean similarity to the other members of its ensemble: a magnitude from the same matrix, not a decomposition of the spectral index, whose maximum identifies the most divergent voice. Crossing model and case, we test as a preregistered hypothesis whether model identity accounts for a non-zero share of the variance in dissent, and characterise the structure that test detects. The panel formulated fifteen stratified vignettes, yielding 7,082 formulations for analysis. Model identity was a detectable structuring factor of the dissent that remained, but the usual categories recovered it only partly: scale differences pointed in opposite directions across pairs, family grouped models on only five two-member lines, and the most divergent voice changed with panel composition, so that the surfaced outlier describes the ensemble rather than the model. Dissent did not track the interpretive openness for which the case bank was stratified; it was organised by clinical content instead, leaving the dispersion an ensemble produces a property to measure rather than assume.
[NLP-149] LoopsBench: From Harness Engineering to Loop Engineering in Benchmarking Coding Agent
【速读】： 该论文旨在解决当前编码代理（coding agent）评估体系在长期、持续性软件开发场景下存在的不足，即现有基准测试多聚焦于局部任务或最终结果，难以有效衡量代理在长时间跨度下的持续执行能力。其核心问题是缺乏对编码代理在复杂依赖关系下进行循环式工程（loop engineering）的系统性评估能力。解决方案的关键在于提出一个名为LOOPSBENCH的长周期基准测试框架，该框架以依赖有向无环图（dependency DAG）形式组织开发单元，并通过源码证据支持的前置依赖边来建模真实开发流程。该基准涵盖来自8种编程语言和9个领域的112个真实任务，其运行时具备流感知机制，能够按就绪前沿逐步释放测试并保留已完成节点作为回归验证义务。实验表明，性能最优的配置（Opus-4.7 + Claude Code + 外层续写）仅能完成25.00%的任务，且记录的规划方案仅部分恢复源码所隐含的前置依赖结构，说明当前编码代理在持续性开发中的鲁棒性和完整性仍存在显著局限。研究团队已将全部数据与代码开源至microsoft/Loopsbench，包含超过5,300个开发单元及可执行测试。
链接: https://arxiv.org/abs/2608.00267
作者: Han Li,Zhemin Fang,Rili Feng,Yingqi Zhao,Jiaheng Liu,Pengfei Gao,He Ye,Dayi Lin,Qingwei Lin,Saravan Rajmohan,Dongmei Zhang
机构: Microsoft(微软); Nanjing University (南京大学); University College London (伦敦大学学院); Shanghai Jiao Tong University (上海交通大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注: Project page: this https URL
Abstract:Coding agent infrastructure is shifting from harness engineering toward loop engineering as coding agents are deployed for sustained long-horizon software development. Existing benchmarks often center on localized tasks or end-state outcomes, offering limited insight into sustained execution. We introduce LOOPSBENCH, a long-horizon benchmark for loop engineering in coding agent evaluation. Each task is a dependency DAG over separately testable development units with source-evidenced prerequisite edges. LOOPSBENCH comprises 112 tasks from authentic sources spanning 8 programming languages and 9 domains. Its flow-aware runtime releases tests along the ready frontier and retains completed nodes as regression obligations. We evaluate frontier coding agents paired with widely used loop implementations. The strongest configuration, Opus-4.7 with Claude Code and outer continuation, resolves 25.00% of tasks. Recorded plans recover only part of the source-recovered prerequisite DAG, and regression events remain visible across the evaluated loop profiles. We open source the benchmark data and code, including all tasks, more than 5,300 development units, and executable tests, at microsoft/Loopsbench.
[NLP-150] Verifier-Induced Support Reshaping in On-Policy Optimization
【速读】： 该论文旨在解决在基于可验证奖励的强化学习（RLVR）框架下，当前目标性能提升过程中导致后续目标任务成功行为采样与强化概率显著降低的问题。其核心挑战在于：尽管通过RLVR能够优化当前任务的表现，但该过程会引发“验证器诱导的支持重塑”（verifier-induced support reshaping），即成功轨迹的分布被重新塑造，使得满足未来目标任务要求的有效响应在采样空间中变得过于稀疏，从而难以被有效探索和训练。解决方案的关键在于识别并量化“有效可奖励支持”（effective rewardable support），即在固定回溯预算内可达的成功轨迹集合，并通过双向训练（bidirectional training）与重复验证器评分采样，在数学推理（Math-RLVR）与受限指令遵循（IF-RLVR）任务中揭示此现象的普遍性。研究发现，虽然平均指令遵循成功率（pass@1）提升，但具有至少一次成功响应的提示比例（best@32）显著下降，且不同模型与基准上均出现性能分化。进一步分析表明，这些变化集中于响应的前几个词元，且主要通过对基础策略中已有响应开头的再排序实现，而选定的开头对后续数学搜索性具有因果影响。此外，所测试的参考策略约束、路由先验及在线策略蒸馏方法仅部分保留跨任务支持能力，表明边际收益并未充分转化为既正确又符合约束的响应。因此，结论指出：仅在终点表现上的改进并不保证未来任务的可训练性或联合能力，强调了在持续学习中需关注支持分布的动态演化。
链接: https://arxiv.org/abs/2608.00220
作者: Shaohang Wei,Zikun Su,Feifan Song,Wen Luo,Wei Li,Guangyue Peng,Houfeng Wang
机构: Peking University (北京大学); BUPT (北京邮电大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 36 pages, 12 figures, 15 tables
Abstract:We show that on-policy reinforcement learning with verifiable rewards (RLVR) can improve the current objective while making successful behaviors for later objectives too rare to sample and reinforce. We call this verifier-induced support reshaping and define effective rewardable support as successful trajectories reachable within a fixed rollout budget. Across two model families, we study this effect through repeated verifier-scored sampling and bidirectional training on mathematical reasoning and constrained instruction following, including sequential training with the opposite verifier. Math-RLVR raises average instruction-following success but reduces the number of prompts with any successful response under repeated sampling. On IFEval with Qwen3-8B-Base, pass@1 rises by 6.5 percentage points while best@32 falls by 9.8 percentage points, and the same divergence appears across both models and IF benchmarks. Conversely, IF-RLVR shifts math responses from step-by-step openings toward direct answers, lowers best@k across sampling budgets, and reduces reward variation for later Math-RLVR. Token-distribution analyses and controlled opening interventions show that these changes concentrate in the first few response tokens. RLVR mainly reranks openings already available in the base policy, and the selected opening causally affects math searchability. The tested reference-policy constraints, routing priors, and on-policy distillation preserve cross-task support only partially; MathIF and ReasonIF show that marginal gains translate only partly into responses that are both correct and constraint-following. Therefore, endpoint improvements do not guarantee future trainability or joint capability under on-policy optimization. Code is available at this https URL
[NLP-151] A Few Neurons Reveal When LLM s Misuse Tools: Sparse Detection and Selective Steering for Reliable Tool Use
【速读】： 该论文旨在解决生成式 AI（Generative AI）在使用外部工具时存在的三类关键失效问题：无效参数调用（validity）、不必要的工具调用（over-calling）以及在需要时遗漏调用（missing）。其核心解决方案是提出一种闭环框架PRISMS（Probing Representations In Support of Monitoring and Steering），该框架基于故障特异性的小规模MLP神经元集合，这些神经元在特征空间中具有线性可分的决策边界，能够高效识别上述故障。PRISMS通过选取对贡献度关键的MLP神经元，并在其激活值上构建L1正则化检测器，实现高稀疏性的故障检测——仅需1-2个神经元即可检测缺失调用，2-16个用于过调用，约128个用于有效性判断。相比密集的残差流基线方法，其特征使用量减少23至627倍，同时在六种不同模型家族（Qwen3、Llama、Gemma）中均实现了优异的检测性能（ROC-AUC 0.86–1.00）。更重要的是，共享的神经元基础支持双向行为调控：既能抑制冗余调用，又能激发遗漏调用，从而实现基于预测风险的精准干预，避免无差别引导带来的副作用。实验表明，PRISMS在所有模型中将总体过调用率降低80%（从0.131降至0.026），同时提升工具依赖任务准确率14.2个百分点（从0.689升至0.831），验证了其在跨模型族场景下具备轻量化故障检测与选择性干预的能力。
链接: https://arxiv.org/abs/2608.00218
作者: Yutong Ke,Ming Yin,Chongwen Zhao,Kaizhu Huang
机构: 未知
类目: Computation and Language (cs.CL)
备注: 21 pages, 4 figures. Includes supplementary material
Abstract:Agentic LLMs exhibit three consequential tool-use failures: invalid arguments (validity), unnecessary calls (over-calling), and omitted calls when tools are needed (missing). We find that a small, failure-specific set of MLP neurons could distinguish such failures with linearly separable decision boundaries. Building on this observation, we introduce PRISMS (Probing Representations In Support of Monitoring and Steering), a closed-loop framework that shares a failure-specific neuron basis between sparse detection and activation steering. PRISMS selects contribution-critical MLP neurons and fits an L1-regularized detector on their activations. Across six models from the Qwen3, Llama, and Gemma families, over-calling and missing are detected at the pre-generation prompt boundary with ROC-AUC 0.90-1.00, while validity is detected from the generated tool-call span with ROC-AUC 0.86-0.90. These results are achieved with highly sparse readouts: only 1-2 MLP neurons for missing, 2-16 for over-calling, and approximately 128 for validity. These sparse detectors match or outperform dense residual-stream baselines using 23-627 times fewer features. The shared neuron basis also supports bidirectional control over tool-calling behavior, suppressing unnecessary calls and eliciting omitted ones. PRISMS therefore gates intervention on predicted failure risk to mitigate the collateral effects of unconditional steering. Across all six models, PRISMS reduces pooled over-calling rate by 80% (from 0.131 to 0.026) while increasing tool-required accuracy by 14.2 percentage points (from 0.689 to 0.831). PRISMS thus provides lightweight failure detection and selective intervention across model families.
[NLP-152] Bridging the English-Arabic Medical Knowledge Gap: Targeted Low-Rank Adaptation via Causal Layer Selection
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在阿拉伯语医学任务中性能显著下降的问题，尽管其在英语任务中表现优异。现有研究普遍归因于阿拉伯语训练数据稀缺，但本文通过定向探针（tuned lens probing）与因果激活修补（causal activation patching）等机制分析方法发现，阿拉伯语医学知识实际上存在于模型的中间层表示中，却未能有效传递至输出层。这一关键发现揭示了性能瓶颈并非源于知识缺失，而是跨语言表征在输出前阶段的表达失败。基于此，论文提出目标低秩适应（Targeted Low-Rank Adaptation, TLoRA），仅在跨语言表征开始分化的层窗口（即输出层之前的特定层区间）进行参数调整，而非对全网络进行微调，从而实现高效、精准的适配。实验表明，TLoRA在多项选择题医学问答任务中优于全网微调的LoRA、零样本及少样本基线；在简答生成与多轮临床对话任务中亦表现出色，且无需针对具体任务进行额外微调。此外，作者构建了首个由临床医生构建的阿拉伯语医学对话基准数据集AraClinicDialog，涵盖标准阿拉伯语（MSA）及其四种方言的验证版本。综合来看，本研究证明了机制诊断（mechanistic diagnosis）可作为指导低资源语言医疗大模型靶向优化的有效路径。
链接: https://arxiv.org/abs/2608.00207
作者: Chaimae Abouzahir,Musa Khan,Hala Ali-Hassan,Congbo Ma,Khaled Saleh,Yousra Sadqi,Jihad Mallat,Walid Al-Eisawi,Nizar Habash,Farah E. Shamout
机构: New York University Abu Dhabi (纽约大学阿布扎比分校); Cleveland Clinic Abu Dhabi (克利夫兰诊所阿布扎比)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) perform strongly in English medical tasks but degrade substantially in Arabic, a gap widely attributed to limited training data. We systematically investigate this assumption via tuned lens probing and causal activation patching, and find that Arabic medical knowledge is present in intermediate model representations but fails to surface at the output. This mechanistic insight motivates a targeted adaptation strategy: rather than fine-tuning the full network, we propose Targeted Low-Rank Adaptation (TLoRA), restricted to the layer window where cross-lingual representations diverge, upstream of the output layers where the failure manifests. We evaluate TLoRA on multiple-choice medical QA, where our approach outperforms full-network LoRA, zero-shot, and few-shot baselines. We further evaluate it on short-answer generation and multi-turn clinical dialogue, where it performs competitively without the need for task-specific finetuning. We additionally introduce AraClinicDialog, a clinician-constructed Arabic medical dialogue benchmark in MSA with validated variants across four Arabic dialects. Together, these contributions demonstrate that mechanistic diagnosis can serve as a practical guide for targeted adaptation in underrepresented-language medical LLMs.
[NLP-153] Averag ing Bias: Human Faithfulness Annotations are not Locally Faithful
【速读】： 该论文旨在解决当前文本摘要忠实性评估中存在的人工标注与严格逻辑规则不一致的问题。现有评估标准采用严格的合取规则（conjunctive rule），即只有当摘要中每一句话均得到源文档支持时，整个摘要才被视为忠实，任何一句缺乏支持即导致整体不忠实。然而，主流的忠实性基准通常仅依赖单一全局人工标注标签，这引发了对人类标注是否真正遵循该严格规则的质疑。研究提出假设：标注者可能更倾向于接受“多数句子忠实”的摘要为可信，而非要求全部句子都必须忠实。为验证此假设，研究采用五个大型语言模型（LLM）作为逐句评判者，在四个广泛使用的忠实性基准上进行评估。结果表明，全局人工标签与LLM逐句判断的平均值相关性显著高于与严格合取规则的符合程度；进一步的人工复核发现，大量被标记为忠实的摘要中实际包含明显的局部事实错误。研究将这种倾向命名为“平均偏差”（Averaging Bias）。研究结论揭示，当前广泛使用基准中的人工标注存在可测量的平均偏差，提示未来需设计更为严谨的标注流程以确保人工评价的可靠性。
链接: https://arxiv.org/abs/2608.00205
作者: Huajian Zhang,Yiyang Feng,Jiawei Zhou
机构: Stony Brook University (石溪大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Evaluation of faithfulness of text summarization treats a model generated summary as faithful only if every of its sentences is supported by the source document: a strict conjunctive rule under which a single unsupported sentence makes the whole summary unfaithful. Yet most faithfulness benchmarks collect only one global human annotation label per summary. We ask whether such global human labels actually implement the conjunctive rule. We hypothesize that annotators may accept a summary as faithful when most sentences are faithful, not only when all are faithful. To test our hypothesis, we use five large language model (LLM) judges as per-sentence raters across four widely used faithfulness benchmarks. We find that global human labels correlate better with the average of per-sentence LLM judgments than with the implementation of the strict conjunctive rule. A manual review confirms that a substantial fraction of summaries labeled faithful by humans contain genuine local factual errors. We call this tendency Averaging Bias. Our results reveal that human labels on widely used faithfulness benchmarks contain measurable Averaging Bias, calling for carefully structured designs for trustworthy human annotations
[NLP-154] RACE-TS: Attribution-Grounded and Traceable Sensor-Language Reasoning for Human Activity Understanding
【速读】： 该论文旨在解决现有基于大语言模型（LLM）的可穿戴时间序列分析方法中推理过程缺乏信号基础、生成解释虽流畅但不可验证的问题。其核心挑战在于如何实现对传感器信号的结构化、可追溯且与原始数据强关联的因果推理。解决方案的关键在于提出TRACE-TS（可追溯推理与归因驱动证据），通过专家分类器提取关键时空区域的归因信息，构建具有明确证据溯源的有向无环图（DAG）推理轨迹，并利用门控交叉注意力机制在传感器记忆令牌上训练轻量级语言模型，以生成可解释的推理链。该框架在推理阶段无需重新计算归因或依赖教师指导，即可联合输出动作预测与可验证的推理路径。此外，研究引入语义节点匹配（Semantic Node Match, SNM）作为大模型为裁判的评估指标，能够从观测、推断和综合三个层面诊断推理真实性，有效定位幻觉性观测与断裂的证据链。在七个可穿戴基准测试中，TRACE-TS在平均准确率和F1分数上均达到最优（84.43%/81.24%），相较最优的基于大模型基线提升17.96%的F1值，显著提升了生成式人工智能在可穿戴时序数据分析中的可信度与可解释性。
链接: https://arxiv.org/abs/2608.00200
作者: Sparsh Rastogi,Tanmay Kumar,Baiyu Chen,Jatin Bedi,Zechen Li,Flora D. Salim
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注: 24 pages, 9 figures, 24 tables
Abstract:Wearable sensors capture fine-grained motion patterns that support rich behavioral understanding, yet most existing methods reduce these signals to activity labels. Recent LM-based approaches generate natural-language explanations for sensor data, but their reasoning is weakly grounded in the underlying signal, leading to fluent yet unverifiable explanations. We introduce TRACE-TS (Traceable Reasoning with Attribution-Grounded Evidence), a framework for structured and signal-grounded reasoning over wearable time series. TRACE-TS uses attribution from an expert classifier to identify salient spatio-temporal sensor regions, uses them to construct DAG reasoning traces with explicit evidence provenance, and trains a compact language model to generate these traces through gated cross-attention over sensor memory tokens. At inference, the adapted model jointly outputs the activity prediction and its reasoning trace, without requiring attribution computation or teacher guidance. We introduce Semantic Node Match(SNM), an LLM-as-judge metric that diagnoses reasoning fidelity at the observation, inference, and synthesis levels, localizing hallucinated observations and broken evidence chains missed by standard NLG metrics. Across seven wearable benchmarks, TRACE-TS achieves the best average accuracy and F1 among all evaluated methods (84.43%/81.24%), and outperforms the best LLM-based baseline by 17.96% in F1. Our code is available at this https URL.
[NLP-155] A Constitution-Grid Instrument for Data-Efficient RL Alignment (C-Guard)
【速读】： 该论文旨在解决强化学习对齐（RL alignment）中普遍存在的目标冲突问题，特别是在训练安全防护机制（safety guard）时，如何高效地平衡“捕捉真实危害”与“不拒绝良性提示”这两个相互冲突的目标。其核心挑战在于，过度拒绝（over-refusal）会降低系统可用性，而拒绝不足（under-refusal）则可能使模型在对抗攻击下悄然恶化。本文提出C-Guard——一种基于宪法网格（constitution-grid）的生成工具，用于构建强化学习训练数据；并引入C-LIM（每单元可学习性评分），通过评估每个数据单元的潜在学习价值，动态决策是否剪枝、加密、修正或扩展该区域。关键创新在于C-LIM能够在训练前识别出无贡献的数据区域（如187条未定向样本几乎无学习增益），从而避免无效资源消耗，并显著提升剩余数据的学习效率（学习影响从0.733提升至0.80）。该方法实现了数据高效的对齐训练，且代码与宪法规则均已开源。
链接: https://arxiv.org/abs/2608.00180
作者: Lily Zhang
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 10 pages, 11 figures
Abstract:Conflicting objectives are general in RL alignment, and training on them data-efficiently is hard. Training a safety guard with RL means optimizing two objectives that conflict: catch real harm, and do not refuse benign prompts. Our finding is that over-refusal improves 22.4% to 12.8%, while under-refusal on adversarial attacks silently worsens 0.27 to 0.33. We present C-Guard, a constitution-grid instrument that generates the RL training data, and C-LIM, a per-cell learnability score that decides each cell’s move: prune, densify, amend, expand. C-LIM flags the dead-weight data region before any training budget is spent: 187 untargeted rows had bought zero gain, and our method lifts the same region’s learning impact 0.733 to 0.80. Code and the constitution are open-sourced.
[NLP-156] DiffusionGemma Technical Report
【速读】： 该论文旨在解决传统自回归（Autoregressive, AR）大语言模型在文本生成过程中存在的序列化解码瓶颈问题，即每次仅生成一个词元（token），导致生成速度受限。其核心解决方案是提出DiffusionGemma——一种基于离散扩散机制的开源语言模型，通过并行迭代优化256个词元的块来实现高速文本生成，突破了传统AR模型的逐词生成限制。关键创新在于采用计算高效的两阶段训练流程：第一阶段通过监督微调（Supervised Fine-Tuning）引入双向去噪能力；第二阶段结合强化学习与采样器蒸馏（sampler distillation），协同优化生成质量与推理效率。实验表明，DiffusionGemma在单张NVIDIA H100 GPU上可实现约每前向传播20个词元、每秒生成约1500个词元的性能，显著超越具备最先进推测解码（speculative decoding）的AR模型。此外，该模型保留了原始Gemma 4模型的思维模式支持、多模态输入能力及长上下文处理能力，并展现出与自回归生成兼容的潜力，为未来混合扩散-自回归解码路径提供了可行方向。
链接: https://arxiv.org/abs/2608.00146
作者: DiffusionGemma Team:Adrien Ali Taïga,James Assiene,Daniele Calandriello,Rahma Chaabouni,João Gante,Tamara von Glehn,Nate Keating,Chris Knutsen,Martin Kukla,Tianlin Liu,Ivan Lobov,Ofir Nabati,João Gabriel Oliveira,Nicolas Perez-Nieves,Nastasia Prutianova,Bobak Shahriari,Jean Tarbouriech,Pavel Tyletski,Çağlar Ünlü,Cindy Wu,Glenn Cameron,Jerome Connor,Sertan Girgin,Maarten Grootendorst,Alon Levkovitch,Eliya Nachmani,Omar Sanseviero,Piotr Stanczyk,Quentin Berthet,Andrew Campbell,Clément Crepy,Valentin De Bortoli,Arnaud Doucet,Romuald Elie,Alexandre Galashov,Klaus Greff,Alexis Jacq,David Ruhe,Yu-Han Wu,Sebastian Flennerhag,Brendan O’Donoghue,George Scrivener,Shantanu Thakoor
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce DiffusionGemma, an experimental open-weight language model that uses discrete diffusion to generate text at exceptionally high speed. Rather than decoding one token at a time, DiffusionGemma iteratively refines blocks of 256 tokens in parallel, avoiding the sequential decoding bottleneck of conventional autoregressive (AR) large language models. Instead of training from scratch, we obtain DiffusionGemma by fine-tuning the mixture-of-experts Gemma 4 model with 3.8B activated and 25.2B total parameters. Our compute-efficient two-stage training pipeline uses fewer than 10% of the starting AR model’s total training token budget. The first stage uses supervised fine-tuning to teach bidirectional denoising, while the second stage combines reinforcement learning with sampler distillation to jointly improve generation quality and inference efficiency. DiffusionGemma establishes a new Pareto frontier for the trade-off between generation speed and model capability. Averaged across our full evaluation suite, it generates around 20 tokens per forward pass and achieves roughly 1,500 output tokens per second on a single NVIDIA H100 GPU, which is substantially faster than AR models even with state-of-the-art speculative decoding. DiffusionGemma also retains the starting model’s support for thinking mode, multimodal inputs, and long contexts. Despite diffusion fine-tuning, it remains capable of AR generation with only minor performance degradation, suggesting a path toward hybrid diffusion-AR decoding.
[NLP-157] Leak It: A Probabilistic Approach to Training-Data Extraction from Black-Box Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在训练数据隐私泄露方面存在的评估盲区问题，特别是传统基于聚合ROC-AUC指标的会员推断攻击（Membership Inference Attack, MIA）评估方法无法准确反映真实隐私风险。其核心问题是：现有评估方法将模型对训练数据的泄露视为整体性、平均化的现象，忽略了部分高敏感文档可能被精确复现的局部泄露风险，而这些风险在聚合指标中被掩盖。解决方案的关键在于引入概率视角，将模型生成的采样输出 $ N $ 次从 $ p(\cdot|x) $ 中抽取的结果视为对输出分布的估计，并将泄露信号建模为该分布的泛函。研究发现，即使在黑盒采样场景下，基于词袋（bag-of-words）的无模型基线攻击已可达到AUC 0.97，且采样本身并未带来显著增益；更重要的是，存在少数特定文档（如包含真实标识符的文本）能够被完全复现，且这种复现行为可归因于具体文档而非通用共现模式。实验证明，在Pythia-6.9B模型上，有16.6%的含标识符文档（21.3%含邮箱地址）被精确再现，且该泄露现象随模型容量增加而加剧。此外，代码类文本中的标识符泄露强度是散文类的约3倍，且生成能力与隐私风险呈正相关。因此，论文主张隐私审计应放弃单一聚合AUC，转而报告按文档粒度分解的提取结果，尤其需区分不同领域。为此，作者发布了名为leakit的黑盒提取审计工具，支持精细化隐私风险检测。
链接: https://arxiv.org/abs/2608.00144
作者: Victor Maricato
机构: Karolinska Institutet (卡罗林斯卡学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注: 13 pages, 7 figures. Code: this https URL
Abstract:Membership inference (MIA) on language models is usually summarised by an aggregate ROC-AUC, but such evaluations are confounded: model-free blind baselines separate members from non-members from surface text alone. We study black-box, sampling-based training-data leakage through a probabilistic lens, treating N samples from p(.|x) as an estimate of the output distribution and casting leakage signals as functionals of it. We extend the blind-baseline critique into the sampling regime: on WikiMIA a blind bag-of-words classifier reaches AUC 0.97 (TPR 0.90 at 5% FPR) and sampling adds nothing, while on an IID Pile split (MIMIR) neither self-concentration nor gold-continuation recovery significantly beats a blind baseline (incremental AUC 95% CI includes zero). Aggregate metrics hide the real harm. The same sampling verbatim-extracts training data for a tail of documents no blind attack can reach. On Pythia-6.9B, 83 of 500 Pile documents bearing a real identifier (16.6%; 21.3% of those bearing an email address) have that exact identifier reproduced AND not reproduced under a mismatched-prefix control, so each leak is attributable to that document, not to a globally common string. This per-document disclosure is invisible to aggregate AUC and grows with capacity (5.6% to 16.6% from 410M to 6.9B). The risk is uneven: identifier leakage is ~3x stronger in code than prose, though prose stays clearly positive and also grows with capacity (4.0% to 12.1%), while recovery of arbitrary held-out continuations is confined to code (+0.44 member gap on GitHub vs at most +0.014 on prose). Temperature and nucleus sampling matter little, a 16-token prefix suffices, and we detect no reduction from corpus deduplication. Privacy audits should report per-document extraction, decomposed by domain, not a single AUC. We release leakit, a black-box extraction-audit tool.
[NLP-158] A Fortran General-Purpose Transpiler: Proof of Concept
【速读】： 该论文旨在解决科学计算领域中遗留的Fortran代码现代化难题，即新一代科研人员对Fortran不熟悉，而资深开发者在向现代计算生态（如JAX）迁移时面临语言与工具链断层的问题。这一断层导致出现“Fython”现象——即用Python编写但保留Fortran思维模式的代码，无法有效利用现代框架的自动微分和GPU加速能力。其解决方案的关键在于提出FGPT，一个基于Python的编译器框架，通过三阶段架构实现从Fortran到高性能目标代码的系统性转换：(i) 前端解析Fortran源码并提取目标过程及其跨模块依赖；(ii) 中间层将代码降级为中间表示，并进一步转化为适配GPU的Fortran、自动微分版Fortran或NumPy/JAX模块；(iii) 后端重构控制流与表达式，生成可直接用于GPU加速与自动微分的JAX模块。该框架通过严格保持程序语义，确保大规模科学代码（数万行以上）在转换过程中实现数值保真性与一致性，克服了大语言模型在复杂代码规模下难以保证可靠性的局限，从而提供了一条可扩展、可信的遗留代码现代化路径。
链接: https://arxiv.org/abs/2608.00130
作者: Shivamshan Sivanesan,Kazem Ardaneh
机构: Climate Modeling Center, Sorbonne University (索邦大学), CNRS, IPSL (法国气候与环境科学研究所); Paris (巴黎); France (法国)
类目: Programming Languages (cs.PL); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Mathematical Software (cs.MS); Software Engineering (cs.SE)
备注: 19 pages, 14 figures, proof of concept
Abstract:Fortran has been the cornerstone of high-performance computing for decades and remains unmatched in many domains. Yet the language faces an expertise gap: a new generation of scientists is barely familiar with it, while many experienced Fortran developers are only now transitioning to modern ecosystems such as JAX. This gap often results in “Fython”–Python code written with a Fortran mindset-- that fails to leverage modern frameworks. We present FGPT, a Python-based compiler framework designed to bridge this divide. It provides a systematic pipeline that transpiles Fortran into GPU-adapted Fortran, auto-differentiable Fortran via Tapenade, or NumPy and JAX modules. Its architecture comprises three stages: (i) a frontend that parses Fortran and extracts target procedures along with their cross-module dependencies; (ii) a middle-end that lowers the code into an intermediate representation, then into GPU-adapted or auto-differentiable Fortran, or a NumPy class; and (iii) a backend that rewrites control-flow and expressions to produce JAX modules ready for GPU acceleration and automatic differentiation. While large language models hold promise for small snippets, they fail at the scale of community scientific codes–often spanning hundreds of thousands of lines–where consistent transformations, strict numerical fidelity, and validation against production tests are non-negotiable. FGPT addresses these challenges by preserving program semantics throughout the entire translation. We verified the framework on representative climate modeling kernels and demonstrated that it produces correct, differentiable Python implementations without requiring manual intervention. By combining rigorous compiler techniques with modern accelerator support, FGPT offers a scalable, trustworthy path for modernizing legacy Fortran code.
[NLP-159] LLM -OSDA: An Optimal-Stopping Dynamic Auction for Native Advertising in Multi-Turn LLM Conversations AAAI AAAI2027
【速读】： 该论文旨在解决生成式AI（Generative AI）时代下大语言模型（LLM）原生广告（LLM-native advertising）中的动态投放时机与竞价机制耦合问题。传统广告拍卖机制通常在单次响应内完成胜出者判定，但无法有效处理广告插入时机的选择，而原生广告的插入点取决于对话过程中的动态状态，导致竞价行为与停止时间（stopping time）高度耦合，破坏了静态机制下的策略一致性与真实性（truthfulness）。其核心挑战在于：当广告插入机会仅限于会话中某一时刻时，投标者的策略需同时决定“是否投放”与“何时投放”，从而形成对最优停止策略的依赖，使得传统的静态可信机制失效。为此，论文提出基于贝尔曼最优停止理论的动态点击成本拍卖机制（LLM-based Optimal Stopping Dynamic Auction, LLM-OSDA），其关键创新在于将贝尔曼最优停止、胜出者分配与包络定价（envelope pricing）统一建模为一个可微分的动态决策框架。具体而言，引入一个独立于投标的预训练语言模型层（bid-independent LLM layer）用于估计上下文相关的点击质量，并实现广告内容的无缝渲染；而投标仅参与由承诺机制驱动的拍卖。在理想贝尔曼预言机（Bellman oracle）条件下，期望折现点击量对每个广告主的出价呈单调递增，且对应的包络支付规则使诚实报价在期望意义上成为弱占优策略。为实际部署，进一步设计了学习型停止网络（StopNet）以近似贝尔曼动作值函数，证明其决策偏离最优策略仅发生于停止边界附近，并建立了激励损失与近似误差之间的定量上界。实验基于模拟对话广告语料库验证，结果显示LLM-OSDA相比最强的固定时机基线，净收益提升11%，同时保持用户留存率相当，显著提升了动态原生广告系统的经济效率与激励兼容性。
链接: https://arxiv.org/abs/2608.00123
作者: Yan Fang,Jialin Chen,Chun Gan,Hang Yu,Mingjun Nie,Yeyu Zhang,Fengxiang He,Ching Law
机构: Google(谷歌); Stanford University (斯坦福大学); Tsinghua University (清华大学); National University of Singapore (新加坡国立大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
备注: 14 pages, 7 figures. Submitted to the 41st AAAI Conference on Artificial Intelligence (AAAI 2027)
Abstract:LLM-native advertising embeds sponsored content directly into model-generated responses, shifting the unit of sale from a fixed slot to a moment within an evolving conversation. Existing LLM ad-auction mechanisms primarily operate within a single response, settling the winner but not the timing. The extension is nontrivial: with one native insertion opportunity per session, the stopping time depends on bids, coupling timing with allocation, so static truthfulness arguments no longer apply. We propose the LLM-based Optimal Stopping Dynamic Auction (LLM-OSDA), a dynamic cost-per-click auction that integrates Bellman optimal stopping, winner allocation, and envelope pricing. A bid-independent LLM layer estimates contextual click quality and seamlessly renders the winning ad, while bids enter only the committed auction mechanism. Under an exact Bellman oracle, the expected discounted-click allocation is monotone in each advertiser’s bid, and the corresponding envelope payment makes truthful bidding weakly dominant in expectation. For practical deployment, a learned StopNet approximates the Bellman action values. We show that its decisions differ from the optimal policy only near the stopping boundary and bound the resulting incentive loss in terms of its approximation error. Experiments on a simulated conversational advertising corpus show that LLM-OSDA improves net revenue by 11 percent over the strongest fixed-timing baseline while maintaining comparable user retention. Code is at this https URL.
[NLP-160] Neural Circuit Function Inference with LLM s
【速读】： 该论文旨在解决神经连接组（connectome）映射完成后，如何有效解读神经回路功能及其组成神经细胞类型作用的挑战。其核心解决方案在于提出一种新型自动化方法——LLantia（基于大语言模型的神经回路功能推断与分析，LLM automated neural circuit inference and analysis），该方法通过从文献中提取细胞类型功能描述，并结合连接组数据，系统性地推断出所有其他细胞类型的潜在功能。该方法的关键在于将推断结果以分层结构组织，涵盖多种行为与生理情境下的可能回路功能，每个回路功能均包含子回路描述及相关细胞类型信息，既支持追溯已有研究文献，也为后续实验研究提供指导。研究以成年果蝇大脑所有细胞类型及其部分广义回路为例进行了应用演示，并通过与发布后新发表文献的交叉验证，证明了其推断结果的可靠性。
链接: https://arxiv.org/abs/2608.00059
作者: Yijie Yin(1 and 2),Albert Cardona(2 and 1) ((1) Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK, (2) MRC Laboratory of Molecular Biology, Cambridge, UK)
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 26 pages, 7 figures
Abstract:The success of connectome mapping now shifts the challenge of understanding the nervous system to the interpretation of neural circuits. Here, we devise a new automated method, LLantia (LLM automated neural circuit inference and analysis), to systematically infer neural circuit function and the role of its component neural cell types. Our approach distills descriptions of cell type function from the literature and, in combination with the connectome, then infers the function for all other cell types, which serves as a basis for subsequent neural circuit function inference. Results are structured hierarchically, with different possible circuit functions organised under multiple possible behavioural and physiological contexts, and each circuit function composed of subcircuit descriptions alongside relevant cell types to facilitate both backtracking to known, published information and support further experimental research. We illustrate our method by inferring cell type function for all cell types of the adult fruit fly brain and for select broader circuits within, and validate our findings, including by cross-checking with literature published after the release date of our analysis.
[NLP-161] Predicting Startup Exit from Textual Descriptors - A Computational Linguistics Framework
【速读】： 该论文旨在解决在缺乏传统财务、人力资本或背景信息的情况下，如何有效预测早期创业公司成功（以退出事件为标志）的问题。其核心挑战在于信息不对称环境下，投资人难以获取全面评估依据，而现有模型多依赖结构化数据，忽视了创始人叙事中蕴含的隐含信号。解决方案的关键在于仅基于文本描述，通过构建精细化的文本特征体系，利用20年时间跨度、覆盖7,419家初创企业的风投机构标注数据集，对创业公司叙述内容进行深度挖掘。研究通过启动项目叙事映射（narrative mapping）工程化生成850个文本特征，并采用向量嵌入与统计检验筛选显著变量，最终在六种监督学习模型中验证了文本特征的独立预测能力。结果显示，仅使用文本特征即可实现F1=0.30，而LightGBM模型进一步提升至F1=0.48，证实了创始人口述框架（founder narrative framing）的预测价值。关键发现表明，适度密度的“夸大性标记”（如形容词、行话和流行术语）与更高的退出概率正相关，但过长的陈述或名称会降低预测效果。研究还提出可量化的“夸大评分”（Hyping Score），为风险投资申请中的叙事策略提供了可操作的量化指标，从而在高信息不对称条件下提供新的可解释性预测信号。
链接: https://arxiv.org/abs/2608.00045
作者: Alberto M.G. Saruggia,Sebastien Germano
机构: 未知
类目: Computation and Language (cs.CL); General Economics (econ.GN)
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Abstract:This study shows that textual descriptors alone can predict early-stage startup success, defined as Exit, without relying on contextual, financial, or human capital variables. Using venture capital-curated datasets covering 7,419 startups over 20 years, the research isolates text-based framing variables and engineers 850 features through startup narrative mapping. Data subsets and vector embeddings are evaluated for statistical significance, followed by supervised machine learning experiments across six models. LightGBM achieved the highest predictive performance (F1 = 0.48), while textual descriptors alone achieved F1 = 0.30, confirming the standalone predictive value of founder narratives. Feature analysis shows that optimized densities of hyping markers, including adjectives, jargon, and buzzwords, are associated with higher Exit probability, whereas excessive statement or name length reduces it. The study also introduces a quantifiable Hyping Score for venture capital applications, demonstrating that startup framing provides measurable signals for predicting Exit under conditions of high information asymmetry.
[NLP-162] rustworthiness Costs of Domain Adaptation in Small Language Models:A Cross-Architecture Empirical Study
【速读】： 该论文旨在解决小语言模型（Small Language Models, SLMs）在资源受限且高风险领域（如医疗、法律和金融）进行领域自适应时，其可信性（trustworthiness）——包括事实校准能力与对抗鲁棒性——随参数高效微调策略变化而产生的潜在退化问题。尽管已有研究证明参数高效微调可提升性能，但其对模型可信性的长期影响尚不明确。本研究提出首个系统性跨领域、跨架构的实证分析，覆盖三种主流SLM架构（TinyLlama 1B、Gemma-2 2B、Llama 3.2 1B）、三个关键应用领域（医疗、法律、金融）、两种训练数据条件（正常与对抗扰动）以及四种微调方法（基线QLoRA、Safety-DPO、Dark Experience Replay、Task Arithmetic LoRA, TA-LoRA）。通过TruthfulQA MC2（事实校准）与HarmBench ASR（对抗鲁棒性）两大指标，在216种实验配置下进行评估。核心发现表明：（1）基线QLoRA在多数情况下对事实校准影响极小（平均|ΔTQA| ≈ 0.02）；（2）使用对抗扰动训练数据可显著提升领域适应质量（损失下降约-0.040），且未损害可信性；（3）三种安全保持策略中，Safety-DPO基本无影响（平均ΔASR ≈ 0.001），而Dark ER与TA-LoRA反而显著削弱了模型的对抗鲁棒性（平均HarmBench ASR分别上升+0.171和+0.155），部分配置甚至超过+0.45。这一结果挑战了“基于重放或算术融合的策略能有效迁移对齐性”的普遍假设，揭示了当前主流安全增强方法在领域自适应场景下的局限性。
链接: https://arxiv.org/abs/2608.00042
作者: Ramesh B. Paramkusham
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 7 tables, 2 appendices (Reproducibility Checklist; Software and Data Availability). Code, model checkpoints, and datasets publicly available at this https URL
Abstract:Domain adaptation of small language models (SLMs) has emerged as a practical strategy for deploying capable NLP systems in resource-constrained, high-stakes environments including healthcare, legal services, and financial analysis. While performance gains from parameter-efficient fine-tuning are well characterised, the corresponding impact on trustworthiness (factual calibration and adversarial robustness) remains poorly understood. This paper presents the first systematic cross-domain, cross-architecture empirical study quantifying the trustworthiness cost of domain adaptation across three SLM architectures (TinyLlama 1B, Gemma-2 2B, Llama 3.2 1B), three domains (healthcare, legal, finance), two training-data conditions (benign and adversarially perturbed), and four fine-tuning strategies (baseline LoRA, Safety-DPO, Dark Experience Replay, and Task Arithmetic LoRA, TA-LoRA). Trustworthiness is evaluated through TruthfulQA MC2 (factual calibration) and HarmBench ASR (adversarial robustness) across all 216 experimental configurations with three random seeds. Three principal findings emerge. First, baseline QLoRA domain adaptation produces minimal TruthfulQA MC2 change across all model-domain combinations (mean |Delta TQA| 0.02). Second, adversarially perturbed training data consistently improves domain adaptation quality (Delta loss approximately -0.040) without worsening trustworthiness benchmarks. Third, none of the three safety-preserving strategies reduced adversarial harm susceptibility: Safety-DPO was effectively neutral (mean Delta ASR 0.001), while Dark ER and TA-LoRA increased mean HarmBench ASR by +0.171 and +0.155 respectively in safety-aligned models (Gemma-2 2B, Llama 3.2 1B), with individual configurations exceeding +0.45. These results challenge the assumption that replay-based and arithmetic-merge strategies transfer alignment to domain-adapted SLMs.
[NLP-163] XL-DocBench: Benchmarking Evidence-Grounded Extra-Long Document Understanding
【速读】： 该论文旨在解决大语言模型（LLM）在处理超长专业文档时的可靠理解问题，尤其是在合规、临床、金融和工程等对决策可追溯性要求极高的领域中，现有模型难以有效应对跨越数百甚至上千页的复杂文档任务。当前多数基准测试仍局限于短文本或单页问答，无法真实反映实际应用中多页面证据整合、跨文档对比及结构化信息（如表格、图表）推理的需求。为此，论文提出XL-DocBench——一个经过194名专家全量人工验证的超长文档理解基准，涵盖6个专业领域，最长文档达2,303页，共包含1,519个保留问题，其中72.6%需跨多页证据，36.6%涉及图表等非文本结构化内容，10.9%要求跨文档证据比对，并配有专家标注的证据页、类型化验证规则与答案格式，包括218个“无答案”案例。其解决方案的关键在于构建基于树状引导合成流程与多重人工过滤机制的高质量数据生成管道，结合页面级证据定位与类型化规则约束，使评估体系能够精准区分系统失败是源于检索错误、证据使用不当还是规则遵循偏差，从而推动模型在长文档理解中的可解释性与可靠性提升。
链接: https://arxiv.org/abs/2608.00036
作者: Hongchen Wei,Yuanzhe Wang,Bei Liu,Yifan Yang,Qi Dai,Ruichun Ma,Kai Qiu,Yunsheng Li,Dongdong Chen,Chong Luo,Zhenzhong Chen,Baining Guo
机构: Wuhan University (武汉大学); Microsoft (微软)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Real-world document tasks often ask professionals to answer questions from annual reports, regulations, clinical guidelines, and technical manuals that span hundreds or thousands of pages. Some questions also require comparing related reports. Reliable long-document understanding is therefore a prerequisite for using LLMs in compliance, clinical, financial, and engineering workflows, where decisions must be traceable to specific evidence pages and the cost of an unsupported answer is high – yet most existing benchmarks still measure short-context or single-page QA. We introduce XL-DocBench, a fully human-verified benchmark for extra-long document understanding, with 1,519 retained questions from six professional domains and contexts up to 2,303 pages. XL-DocBench goes beyond page-level lookup. 1,103 examples (72.6%) use multiple evidence pages. The final set also includes 556 questions (36.6%) that use tables, charts, or figures, and 165 questions (10.9%) that require evidence from multiple documents. Each question has one of twelve reasoning labels, expert-annotated evidence pages, a typed verification rule, and an answer format, including 218 None-answer cases. We build the benchmark with a tree-guided synthesis pipeline followed by artifact filters and full verification by 194 human experts. By coupling extra-long professional contexts with page-level evidence and typed rules, XL-DocBench fills a gap left by prior single-page, short multi-page, or text-only long-context benchmarks, and lets future work attribute system failures to retrieval, evidence use, or rule following rather than to a single leaderboard score. The results show that current systems still struggle with long contexts, multi-page evidence, and structured reasoning over professional documents.
[NLP-164] SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach
【速读】： 该论文旨在解决专业化检索代理（specialised retrieval agents）在面对特定查询时，如何高效准确地选择最优代理的问题。现有方法依赖于对查询意图或主题的推断进行路由，但此类基于意图的选择存在根本性局限：无法利用检索内容反馈信号，且难以识别当主题匹配的代理产生低相关性结果的情况。为此，本文提出一种联合优化方案，通过监督微调与强化学习训练一个小型语言模型，使其同时完成代理选择和下游工具调用所需的结构化参数生成，并采用基于检索相关性及查询-代理主题一致性构建的分层奖励函数。该机制使模型能够从检索表现中学习任务相关的代理适用性，即识别哪些代理对特定查询分布能持续输出高相关结果，以及在表面主题重叠下仍应将查询转向其他代理的情形。实验表明，在一组典型的代理-查询不匹配场景中，该模型的NDCG@10达到0.918，显著优于仅依赖意图路由的两个大语言模型基线（Amazon Nova Lite为0.539，Claude Haiku 4.5为0.490）。整体上，该模型实现平均NDCG@10为0.771（较Nova Lite提升0.177，较Haiku提升0.219），且平均选择延迟仅为120.1毫秒，相较Nova Lite降低82.4%。解决方案的关键在于引入动态反馈机制，结合检索性能与主题匹配度，实现更智能、自适应的代理选择策略。
链接: https://arxiv.org/abs/2608.00030
作者: Gayathri V Kondapalli,Alexander Ng,Hirsh Pithadia,Rahul Monish,Harvey Yorke,Amir Kayhani
机构: Valyu AI; University of Warwick (华威大学); University College London (伦敦大学学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Specialised retrieval agents typically surface higher quality results than general-purpose search, but selecting the optimal agent for a given query remains an open problem. Current approaches route queries based on inferred topic or intent, however intent-based selection is fundamentally limited: it does not incorporate signal from retrieved content, and cannot detect when a topically aligned agent produces low-relevance results. We address this by training a small language model via supervised fine-tuning followed by reinforcement learning to jointly perform agent selection and structured parameter generation for downstream tool calls, using a hierarchical reward function grounded in retrieval relevance along with query-agent topic alignment. This enables the model to learn task-dependent agent suitability from retrieval performance: which agents reliably yield high-relevance results for which query distributions, and when to redirect queries away from specialised agents despite surface-level topical overlap. On a targeted subset of such agent-query mismatches, the trained model achieves an NDCG@10 of 0.918 compared to 0.539 and 0.490 for two LLM baselines (Amazon Nova Lite and Claude Haiku 4.5) that route on intent alone. Overall, it achieves a mean NDCG@10 of 0.771 (+0.177 over Nova Lite, +0.219 over Haiku) with a mean selection latency of 120.1ms, an 82.4% reduction over Nova Lite.
[NLP-165] Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance
【速读】： 该论文旨在解决生成式模型在离散序列文本生成任务中难以实现高质量可控生成的问题，尤其聚焦于如何在保持生成内容语义保真度的同时提升多样性。当前方法通常通过调整词元（token）概率分布进行采样与引导，但缺乏对跨词元语义交互及相对位置关系的全局建模，导致在保真度与多样性之间难以取得理想平衡。为此，本文提出一种无需训练的语义感知核熵（Semantic-Aware Kernel Entropy, SAKE）引导机制，其核心在于构建一个基于核格拉姆矩阵（kernel Gram matrix）的二阶Rényi熵度量，该矩阵同时编码了词元间的语义关联与相对位置信息。通过在嵌入空间中对这一目标进行线性化处理，获得可计算的引导信号，能够动态调节采样分布：在冗余场景下平滑分布以促进探索，在多样化需求高时则锐化分布以增强保真度。实验结果表明，该方法在保真度-多样性权衡曲线上实现了更优的帕累托前沿，并在代码生成、数学推理等高复杂度任务中显著优于温度缩放与传统离散引导基线。
链接: https://arxiv.org/abs/2608.00024
作者: Jingwei Zhang,Haoyu Lei,Zijin Feng,Jiacheng Sun,Farzan Farnia
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Although diffusion models have revolutionized continuous domains like image synthesis through high quality generations and controllable guidance mechanisms, bringing this controllability to the discrete, sequential nature of text remains an open challenge. Meanwhile, current sampling strategies and guidance methods adjust token likelihoods without capturing the broader semantic landscape, leading to a suboptimal balance between fidelity and diversity. In this work, we introduce a novel training-free Semantic-Aware Kernel Entropy (SAKE) guidance method. Our method computes the order-2 Rényi entropy over a kernel Gram matrix that captures both cross-token semantic interactions and relative token positions. By linearizing this objective in the embedding space, we derive a tractable guidance signal that dynamically adjusts the sampling distribution, flattening it to encourage exploration during redundancy and sharpening it for fidelity when diverse. Empirical experiments demonstrate that our approach achieves a superior Pareto frontier between fidelity and diversity, and improves multi-sample performance on reasoning-intensive tasks, such as code and mathematics generation, compared to temperature scaling and discrete guidance baselines.
[NLP-166] Role Steering of Language Models for Social Simulations
【速读】： 该论文旨在解决基于语言模型代理（language-model agents）的社会仿真中角色特异性行为的生成与验证问题，即如何在将代理纳入模拟群体前，确保其行为符合预定义的角色特征。其核心解决方案是提出一种“激活引导筛选流程”（activation-steering screening workflow），关键在于通过构建角色画像（role profile），提取特定于角色的语义方向（role-specific direction），系统性地扫描四个引导系数（steering coefficients），评估候选配置与角色画像的一致性，并据此通过或标记配置。实验基于OLMo-3-7B-Instruct模型，在包含275个角色的混合角色库上进行，使用GPT-4.1-mini生成角色参考并作为评判者。结果表明，角色特异性方向在角色画像一致性评分上显著优于先前基于人物向量（persona-vector）的工作（平均得分63.2 vs. 41.1），且在高引导强度下仍保持较高的词汇多样性，而控制组则出现明显下降。该研究的关键发现是：角色级筛选结果揭示，尽管多数角色随引导强度增强表现改善，但仍有38个角色在全部六个评估维度上性能下降，凸显了为不同角色定制引导系数的重要性，而非采用统一的高强度设置。
链接: https://arxiv.org/abs/2608.00023
作者: Isaac Song,Mohammed Rehan Parwani,Glenn Matlin,Emile Anand,Akhil Theerthala,Arjun Chatterjee,Maria Kostylew,Yonadav G. Shavit,Sebastien Krier,Mark Riedl
机构: Georgia Institute of Technology(佐治亚理工学院); ML Alignment Theory Scholars (MATS)(ML对齐理论学者); University of Illinois Urbana-Champaign(伊利诺伊大学厄本那-香槟分校); University of Oxford(牛津大学); Google DeepMind(谷歌深度思维); OpenAI(开放人工智能); Independent(独立)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Social simulations built from language-model agents need role-conditioned behavior that can be checked before agents are placed into a simulated population. We introduce an activation-steering screening workflow for role-conditioned agents: define a role profile, extract a role-specific direction, sweep four steering coefficients, evaluate role-profile alignment, and pass or flag each candidate configuration. On OLMo-3-7B-Instruct, we apply the workflow to a mixed 275-role inventory with 228 role-agnostic questions, GPT-4.1-mini prompted role references, and GPT-4.1-mini judges. Role-specific directions receive higher judged role-profile alignment than an assistant-axis directional control from prior persona-vector work, with mean overall scores of 63.2 versus 41.1 across the tested grid. They also preserve high lexical diversity, while the control drops sharply at larger coefficients. The role-level screen is the main practical output: most roles improve as steering increases, but 38 roles decline across all six measured dimensions, showing why simulation builders should choose coefficients per role rather than deploy a uniform high-strength setting. We make our code and evaluation artifacts available at this https URL.
[NLP-167] What Transfers from Text to Vision? Capability Scaling Laws and Transfer Dynamics for VLMs
【速读】： 该论文旨在解决视觉-语言模型（VLM）构建中核心的骨干模型（LLM）选择问题，当前该过程缺乏系统性原则：基于计算量的缩放定律无法跨模型家族泛化，且尚无框架可在训练前直接预测VLM性能。其解决方案的关键在于提出首个跨模型家族的能力驱动多模态缩放定律（Capability-Driven Multimodal Scaling Law），通过主成分分析（PCA）从大型语言模型（LLM）的文本基准测试中提取低维能力得分 $ S $，并将VLM性能建模为 $ S $ 的函数，引入每类骨干模型特有的迁移率（transfer rate）与吸收率（absorption rate，用于量化数据缩放效率）。基于超过150个在34个覆盖7个模型家族的LLM上按严格统一训练流程构建的VLM进行拟合与验证，该框架在200余个文本及50余个多模态基准上表现出优异的外推能力，可高保真预测完整训练轨迹，并成功泛化至完全未见的模型家族。此外，研究揭示了部分文本基准与多模态性能负相关，暴露了潜在的“基准操纵”行为；基础版LLM相较于指令微调版本更适合作为VLM骨干，因其具有更高的吸收率和更低的数据缩放衰减；不同模型家族在迁移-吸收空间中占据独特位置。该框架将骨干选择从昂贵的试错过程转变为可量化、有理论依据的决策方法。
链接: https://arxiv.org/abs/2608.00013
作者: Ziran Li,Qiang Wang,Zhengyu Chen,Shanglin Lei,Borun Chen,Jingang Wang,Xunliang Cai
机构: Meituan(美团); Tsinghua University (清华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Choosing the right large language model (LLM) backbone is the most consequential decision when building a vision-language model (VLM), yet it remains fundamentally unprincipled: compute-based scaling laws fail to generalize across model families, and no framework exists for directly predicting VLM performance before training begins. We propose the Capability-Driven Multimodal Scaling Law, the first cross-family framework that predicts VLM benchmark accuracy from directly observable textual capability. Given a low-dimensional capability score S extracted from LLM textual benchmarks via PCA, we model VLM performance as a function of S , with a per-backbone transfer rate and an absorption rate that quantifies data-scaling efficiency. To fit and validate the framework, we train over 150 VLMs on 34 LLMs spanning 7 model families under a strictly controlled recipe. Evaluations on more than 200 textual and 50 multimodal benchmarks show that the law accurately extrapolates transfer rate from models up to 8B parameters to 72B-scale backbones, predicts full VLM training trajectories with high fidelity, and generalizes to entirely held-out model families. Beyond the scaling law, our analysis surfaces actionable insights: certain textual benchmarks negatively correlate with multimodal performance, exposing latent benchmark-gaming behavior; base LLMs outperform instruction-tuned counterparts as VLM backbones due to higher absorption rates and lower data-scaling decay; and different model families occupy distinct positions in the transfer–absorption space. The framework turns backbone selection from costly empirical sweeps into a principled, quantitative decision. Code and data are available at this https URL.
[NLP-168] Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在真实灾害应急响应场景中能力评估不足的问题。现有遥感基准数据集主要依赖静态、事后生成且经专家处理的产物（如网格化再分析数据），难以与灾害快速演变、决策时间紧迫的实际应急流程对齐。为此，本文提出Obshazard-bench——一个面向灾害智能的实时、观测驱动型基准测试框架。其核心创新在于直接整合来自多种卫星传感器的原始高频遥感观测流，结合同步地面站数据、历史灾害记录及社会经济指标，跳过延迟的专家处理与物理反演流程，实现对灾害全生命周期的动态建模。该基准覆盖8类重大灾害、28个子类，涵盖60余国超过120起极端事件案例，并构建了数千个面向生命周期的视觉问答样本。此外，Obshazard-bench建立了一个三阶段评估体系，分别对应灾害管理的实际操作流程：预测性危机预判（用于灾前风险识别与早期预警）、实时演化推理（用于灾害过程追踪与终止预测）、多维度影响量化（用于灾后损失评估、人道负担估算与社会经济影响分析）。实验表明，当前主流通用及地球科学导向的基础模型在将多通道原始物理观测转化为时序精准、决策相关的灾害推断方面仍存在显著局限。
链接: https://arxiv.org/abs/2608.00012
作者: Fengxiang Wang,Qiuyang Yu,Yueying Li,Mingshuo Chen,Chengchi Fei,Kaiyi Xu,Lixin Gu,Wangxu Wei,Junchao Gong,Lipeng Ma,Jiong Wang,Fenghua Ling,Wenlong Zhang,Xue Yang,Wenjing Yang,Ben Fei,Long Lan
机构: National University of Defense Technology (国防科技大学); Shanghai Artificial Intelligence Laboratory (上海人工智能实验室); Shanghai Jiao Tong University (上海交通大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Multimodal Large Language Models (MLLMs) are increasingly used to interpret Earth observation data, yet their capability to support real-world disaster emergency response remains insufficiently evaluated. Existing remote sensing benchmarks largely rely on static, post-hoc, and expert-processed products, such as gridded reanalysis data, which are difficult to align with operational disaster scenarios where hazards evolve rapidly and decisions must be made under strict time constraints. To bridge this gap, we introduce Obshazard-bench, a real-time, observation-driven benchmark for evaluating disaster intelligence in MLLMs. Unlike image-centric or post-event benchmarks, Obshazard-bench directly integrates raw, high-frequency satellite sounding streams from diverse satellite sensors with concurrent ground-station observations, historical disaster records, and socio-economic indicators, bypassing delayed expert-processing and physical-inversion pipelines. The benchmark covers 8 major disaster categories and 28 sub-categories across more than 60 countries, incorporating over 120 historically documented extreme-event cases and thousands of lifecycle-oriented VQA samples. Moreover, Obshazard-bench further defines a three-stage evaluation taxonomy aligned with the operational disaster workflow: Predictive Crisis Anticipation for pre-disaster risk detection and early forecasting, Active Evolution Reasoning for in-situ disaster tracking and termination prediction, and Multi-faceted Impact Quantification for post-disaster magnitude deduction, humanitarian burden estimation, and socio-economic impact assessment. Experiments on representative general-purpose and Earth-focused foundation models reveal substantial limitations in transforming raw multi-channel physical observations into temporally grounded and decision-relevant disaster reasoning.
[NLP-169] DLLM -TTS: Block Discrete Diffusion Language Model for Text-to-Speech Synthesis
【速读】： 该论文旨在解决当前文本到语音（Text-to-Speech, TTS）系统中普遍存在的效率与质量权衡问题：自回归编码器语言模型虽能生成高可懂度语音，但依赖大规模模型与训练数据，且采用逐标记串行解码，导致生成速度缓慢；而非自回归方法虽提升了生成速度，却牺牲了语言准确性。其解决方案的关键在于提出一种名为DLLM-TTS的框架，将TTS建模为在X-Codec2神经音频编码器（neural audio codec）离散令牌上的条件块状离散扩散过程（block discrete diffusion）。该方法将序列分块处理，在每个块内应用掩码扩散以学习局部声学一致性，同时按块顺序处理以建模全局文本-语音对齐关系。推理阶段通过块内并行预测实现高效生成，实现实时因子（RTF）低至0.15。实验表明，仅使用20,000小时数据训练的0.6B参数模型即可在Seed-TTS-eval基准上达到具有竞争力的性能，验证了块状离散扩散语言模型在实现高效、数据节约且支持并行生成的语音合成方面的可行性。
链接: https://arxiv.org/abs/2608.00011
作者: Wasim Madha,Nityanand Mathur,Hamees Sayed,Apoorv Singh,Sameer Khurana,Akshat Mandloi,Sudarshan Kamath
机构: Smallest.ai; IIIT Guwahati (印度国际信息技术学院古瓦哈蒂分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Current text-to-speech systems face a trade-off: autoregres- sive codec language models produce highly intelligible speech but require large-scale models and training data and decode tokens sequentially, while non-autoregressive approaches im- prove speed at the cost of linguistic accuracy. We present DLLM-TTS, a framework that formulates TTS as conditional block discrete diffusion over X-Codec2 neural audio codec to- kens. The model decomposes sequences into blocks and applies masked diffusion within each block while processing blocks se- quentially, learning both local acoustic coherence and global text-speech alignment. During inference, parallel token pre- diction within blocks enables efficient generation with a real- time factor (RTF) of 0.15. A 0.6B-parameter model trained on 20K hours achieves competitive performance on the Seed- TTS-eval benchmark, demonstrating that block discrete diffu- sion language models enable practical and data-efficient speech synthesis with parallel generation.
[NLP-170] Agent MemBench: A Systematic Benchmark for Evaluating Long-Term Memory Management Strategies in Conversational AI Agents
【速读】： 该论文旨在解决对话式人工智能（Conversational AI）代理在长期记忆管理方面的核心瓶颈问题，即有限的上下文窗口无法支持跨数千轮对话的连贯记忆召回。其解决方案的关键在于构建一个统一且可复现的基准测试框架——AgentMemBench，系统评估五种不同的记忆管理策略：上下文窗口法（In-Context Windowing, ICW）、外部键值存储（External Key-Value Store, EKV）、基于图的事件记忆（Graph-based Episodic Memory, GEM）、基于压缩的摘要（Compression-based Summarisation, CBS）以及网络增强记忆（Web-Augmented Memory, WAM）。实验结果表明，外部键值存储（EKV）在所有质量指标上均表现最优，尤其在长程回忆任务中展现出显著优势（如在LoCoMo数据集上，Recall@5达0.573，远超其他方法的0.005），揭示了仅依赖近期窗口、摘要或实体图的方法在长时序场景下会失效，而密集检索机制（dense retrieval）是实现长距离记忆召回的关键。同时，研究也明确指出，高精度与内存开销之间存在显著权衡：尽管EKV具备更高的召回率，但其记忆占用量（约5100 tokens）远高于ICW/WAM（约300 tokens）。此外，研究还对两个已发表的记忆系统（MemGPT/Letta、HippoRAG）进行了同框架下的评估，并开源全部代码、环境配置及结果数据，确保研究的完全可复现性。
链接: https://arxiv.org/abs/2608.00009
作者: Ahmed Cherif
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 22 pages, 3 figures submitted on Neural Computing and Applications
Abstract:Long-term memory remains a critical bottleneck for conversational AI agents, whose finite context windows cannot support coherent recall across thousands of turns. We present AgentMemBench, a unified, reproducible benchmark evaluating five memory management strategies under identical conditions: in-context windowing (ICW), external key-value store (EKV), graph-based episodic memory (GEM), compression-based summarisation (CBS), and web-augmented memory (WAM). All are assessed across three public datasets covering long-term multi-session dialogue (LoCoMo), task-oriented document grounding (MultiDoc2Dial), and persona-grounded multi-session chat (MSC), using Recall@k, MRR, nDCG@k, Answer F1, an LLM-judge Faithfulness score, Memory Footprint, and Latency over 491 annotated question turns. Generation and judging both use Qwen2.5-7B-Instruct (4-bit), with greedy decoding for determinism. Our results show that (1) EKV dominates on every quality axis (macro Recall@5 0.792, MRR 0.677, F1 0.156, Faithfulness 0.354); (2) long-range recall is decisive: on LoCoMo, where the gold turn lies many sessions back, ICW, WAM, GEM, and CBS retrieve almost nothing (Recall@5 = 0.005) while EKV alone reaches 0.573, showing that recency windows, summaries, and entity graphs collapse at long horizons and only dense retrieval scales; (3) CBS is the runner-up on retrieval (0.556); (4) WAM equals ICW on in-corpus recall by construction, since external results carry no in-corpus provenance; and (5) EKV’s recall advantage carries a footprint cost (~5,100 vs ~300 tokens for ICW/WAM), an explicit accuracy-efficiency trade-off. We additionally evaluate two published memory systems (MemGPT/Letta, HippoRAG) against the same harness, and release all code, environment, and result artefacts for full reproducibility.
[NLP-171] MemoryForge: Synthesize Lifelong Memory for Human-Like LLM Agents
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在代理类应用（如角色扮演和用户模拟）中因依赖静态文本描述进行提示引导而导致的行为泛化、缺乏真实生活记忆的问题。传统方法通过注入固定的人物描述来实现人格化，但这种抽象条件化方式难以体现个体独特的生活经历与情境适应能力。为此，论文提出基于记忆的条件化（memory-based conditioning）范式，受认知心理学启发，将抽象的人物画像替换为自传体记忆库（autobiographical memory base），使冻结的LLMs能够动态检索与当前情境相关的记忆以指导行为。其核心解决方案是构建一种定制化的终身记忆合成任务，并提出名为MemoryForge的新框架，该框架包含三个关键组件：用于社会历史背景建模的上下文生成器、确保目标身份发展一致性的生命组织器，以及兼顾时间跨度概括性与高保真情景体验的多分辨率模拟器。实验结果表明，在PersonaGym和SimulatorArena两个基准上，MemoryForge生成的记忆基底显著提升了不同骨干模型在角色扮演与用户模拟任务中的拟人化表现，优于多种强基线的描述性条件化方法。
链接: https://arxiv.org/abs/2608.00007
作者: Bohan Tang,Yiwen Guo
机构: LIGHTSPEED; Tencent(腾讯)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Equipping Large Language Models (LLMs) with human-like personas is crucial for agentic applications, such as role-play and user simulation. Traditional prompt-based methods rely on descriptive conditioning by injecting static textual profiles, which often makes agents show generic behaviors due to a lack of realistic life memory. To fill this gap, we introduce memory-based conditioning, a paradigm inspired by the cognitive psychology, which replaces abstract profiles with an autobiographical memory base, enabling frozen LLMs to dynamically retrieve situation-relevant memory to guide their behaviors. We formalize its enabling task as customized lifelong memory synthesis and propose MemoryForge, a novel framework to synthesize such lifelong memory from brief target personas. MemoryForge has three key components: a context generator for socio-historical grounding, a life organizer for developmental coherence toward the target identity, and a multi-resolution simulator that balances broad temporal summaries with high-fidelity episodic experiences. Experiments on PersonaGym for role-play and SimulatorArena for user-simulation, show that the synthesized memory base by MemoryForge enables frozen LLMs to exhibit more human-like behaviors than strong descriptive conditioning baselines across multiple metrics and LLM backbones.
[NLP-172] RubricReviewer: From Direct Critique to Objective and Comprehensive Rubric-Driven Peer Review
【速读】： 该论文旨在解决当前大型会议中同行评审面临巨大投稿压力背景下，基于大语言模型（LLM）的评审助手所存在的结构性缺陷。现有方法存在两大核心问题：其一，直接将稿件映射为评审意见，导致评审标准（rubric）隐含且其生成过程与判断结果纠缠不清；其二，现有范式仅捕捉优质评审的一半特征——无训练代理虽能广泛收集外部证据但缺乏方向性批评，而有监督训练模型虽继承人类判别能力却同时引入噪声与覆盖不均的问题。为此，论文提出RubricReviewer，一个完全基于评审标准驱动的框架，其关键在于将评审标准生成作为显式中间步骤，使评审生成与最终评估均基于自适应于具体论文的动态评审标准。同时，该框架融合无训练代理（Scout）以收集外部证据，与经人类对齐的训练模型（Aligner）以处理这些证据，有效整合了两类监督信号的优势。实验表明，RubricReviewer在真实投稿数据上生成的评审意见显著更全面、更具判别力，并展现出最强的对抗提示注入攻击鲁棒性，消融实验进一步验证了各组件的必要性。
链接: https://arxiv.org/abs/2608.00005
作者: Shuyu Guo,Wenxiang Hu,Yuyue Zhao,Yougang Lyu,Xiaohui Yan
机构: Huawei Technologies Co., Ltd. (华为技术有限公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Peer review at major venues is under unprecedented submission pressure, motivating the use of large language models (LLMs) as review assistants. Existing LLM-based reviewers, however, face two structural limitations. First, they map manuscripts directly to reviews, leaving the underlying rubric implicit and entangling its derivation with the judgement. Second, the prevailing paradigms each capture only half of a good review: training-free agents gather broad evidence but produce undirected critiques, while training-based reviewers inherit human discriminative judgement together with its noise and uneven coverage. We introduce RubricReviewer, a fully rubric-driven framework that addresses both limitations. It makes rubric generation an explicit intermediate step, so that both review generation and the final assessment are conditioned on paper-adaptive rubrics. It further combines a training-free agent (Scout) that gathers external evidence with a human-aligned trained model (Aligner) that consumes this evidence, fusing the strengths of both supervision sources. Experiments on real-world submissions show that RubricReviewer produces reviews that are markedly more comprehensive and more discriminative than prior systems, and exhibits the strongest robustness against adversarial prompt-injection attacks. Ablation studies further confirm the necessity of each component.
[NLP-173] Cost-Effective Automated Judging of Natural-Language Mathematical Proofs
【速读】： 该论文旨在解决数学推理系统评估中自然语言数学证明评分的高成本问题，特别是针对前沿大模型（LLM）作为评判者所带来高昂费用的挑战。其核心问题是：在给定候选证明、标准答案及人工评分标准的前提下，是否可使用低成本的开源权重模型作为可靠的评判工具。研究的关键解决方案在于验证三类低成本模型（GPT-OSS 120B、DeepSeek-V4 Flash、Gemma-4 31B）在IMO-GradingBench基准上的表现，结果表明它们与Claude Opus 4.7和Gemini 3.1 Pro在通过/不通过判断上达成的共识率在统计上无显著差异，且成本降低达100倍以上。进一步分析发现，采用“全票通过”（all-three-pass）的共识规则可实现最高的通过率一致性与精确度，并在多次重复实验中表现出最小的运行间波动，成为最具鲁棒性的预算优化策略。尽管该规则为事后发现，但其有效性表明低成本模型在特定共识机制下已具备与前沿模型相媲美的可靠性，因此推荐将“all-three-pass”作为可部署的默认评判方案。
链接: https://arxiv.org/abs/2608.00004
作者: Benjamin Grayzel
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 7 pages, 5 figures, 4 tables
Abstract:Grading natural-language mathematical proofs is a recurring cost in evaluating math-reasoning systems, and frontier LLM judges are expensive. We ask whether cheap open-weight models can serve as reliable judges given a candidate proof, a ground-truth proof, and a human-grading rubric. On a 200-instance validation sample of IMO-GradingBench, three cheap judges (GPT-OSS 120B, DeepSeek-V4 Flash, Gemma-4 31B) agree with human pass/fail decisions at rates statistically indistinguishable from Claude Opus 4.7 and Gemini 3.1 Pro, at up to 100\times lower cost. We had expected a majority vote of the three to be the best budget option; it matched the frontier but did not improve on its strongest member. Extending to the full 1000-instance benchmark and exploring consensus rules, we found that requiring unanimous agreement (all-three-pass) reaches the highest pass-agreement and precision and, on four replicate runs, the smallest run-to-run spread. The headline finding is that cheap judges are competitive with the frontier at one to two orders of magnitude lower cost; as a deployable default we recommend all-three-pass, with the caveat that this rule was identified post-hoc and warrants independent replication.
[NLP-174] Amplitude-Only FFN Intervention for Tool-Structured LLM Inference Method: Gated Evaluation Protocol and Cross-Model Empirical Results
【速读】： 该论文旨在解决大语言模型在作为工具调用代理（tool-using agents）时，因格式、参数或函数调用等微小错误导致本可合理响应失效的问题。其核心挑战在于如何在不重新训练模型权重的前提下，提升模型生成结构化输出的准确性与鲁棒性。解决方案的关键是提出一种非破坏性的前馈网络（FFN）推理时干预方法——幅度门控（Amplitude Gating, AG），该方法通过仅调节生成过程中的激活幅值，而保持预训练的FFN权重方向不变，从而避免对模型固有语义表示造成损害。研究构建了一个细粒度的干预体系，涵盖P1/P2/P3及分支特异性的子节点（P1s/P2a/P2b），并设计了一套严格的评估协议，包括分离组合最优头空间与固定配置/学习门控的性能贡献、强制样本级计数以及采用任务感知指标评估二分类和部分得分数据集。实验表明，在Qwen3.5-9B、Qwen3-8B和Qwen2.5-7B等多个模型上，AG整体表现呈弱正向提升，尤其在工具结构化任务中效果显著；例如在Qwen3.5-9B上，类别级学习门控将工具/结构化/代理类任务准确率从38.66%提升至42.92%（+4.27个百分点），Hermes函数调用任务提升达+7.6点，而Qwen3-8B在Hermes JSON模式下提升+11.36点。此外，研究揭示了不同模型需采用特定路由策略以释放潜在优化空间，且熵基AG与牛顿-舒尔茨窗口化AG之间无绝对优劣，强调了工具结构化推理是当前最可信的FFN级推理优化切入点，但未来仍需开展在线验证与跨模型泛化评估。
链接: https://arxiv.org/abs/2607.11183
作者: Sheng Xu,Junhua Wang,Boyuan Huang,Ke Jia,Jiadun Zhu,Zhen Chen
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 28 pages, 15 figures
Abstract:Large language models increasingly operate as tool-using agents, where small format, argument, or function-call errors can invalidate otherwise plausible responses. We study inference-time feed-forward network (FFN) intervention for improving structured outputs without retraining model weights. Our project began with Orthogonal Residual Projection (ORP), a direction-changing repair attempt that revealed sensitive SwiGLU FFN intervention sites but often caused more harm than fixes. We therefore propose Amplitude Gating (AG), a non-destructive alternative that preserves pretrained FFN weight directions and modulates only activation magnitudes during generation. We define a fine-grained intervention system spanning P1/P2/P3 and branch-specific P1s/P2a/P2b sites, and introduce an evaluation protocol that separates combination-oracle headroom from fixed configurations and learned gates, enforces sample-level accounting, and uses task-aware metrics for binary and partial-credit datasets. Across Qwen3.5-9B, Qwen3-8B, and Qwen2.5-7B, AG is weakly positive in aggregate but strongest on tool-structured tasks. On Qwen3.5-9B, a category-level learned gate improves tool/structured/agentic performance from 38.66% to 42.92% (+4.27 percentage points), with Hermes function-call tasks reaching about +7.6 points. On Qwen3-8B, Hermes JSON mode improves by +11.36 points. Qwen2.5-7B retains oracle headroom but current learned gates fail to capture it, showing that deployment requires model- and category-specific routing. Comparisons of entropy AG with Newton-Schulz-windowed AG show that neither family is uniformly dominant. These results identify tool-structured inference as the most credible first target for safe FFN-level inference optimization, while prospective online validation and broader cross-model evaluation remain necessary.
信息检索
[IR-0] UEmbed: Unified Sparse and Dense Multimodal Embeddings
链接: https://arxiv.org/abs/2608.02583
作者: Tingyu Song,Mingxin Li,Yanzhao Zhang,Dingkun Long,Pengjun Xie,Zhijie Nie,Yilun Zhao,Shu Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retrieval (LSR) to push beyond exact lexical matching toward richer semantics. Yet LSR has so far remained tied to encoder-style bidirectional architectures, and its extension to multimodal settings still relies heavily on auxiliary cross-modal modules. To address these limitations, we introduce UEmbed (Unified Embedding), a decoder-only multimodal embedding model that produces both sparse lexical and dense representations in one causal forward pass. UEmbed appends N learnable special tokens to the input and partitions the vocabulary into N disjoint subsets. Each token’s causal hidden state predicts sparse weights over its assigned subset, and the N subsets are concatenated into the full sparse vector. Trained on public data, we release UEmbed at 2B, 4B, and 9B scales. UEmbed-9B reaches 71.8 (dense) and 71.0 (sparse) on MMEB-v2, outperforming multimodal embedding models trained on publicly available data (e.g., RzenEmbed). On BEIR, UEmbed also remains competitive with strong dense and sparse baselines. Furthermore, we demonstrate the practical utility of UEmbed across three dimensions: effectiveness, efficiency, and agentic applications. Overall, UEmbed offers a new paradigm: it unifies dense and sparse embeddings in one model, while further extending sparse retrieval to unify text and multimodal inputs.
[IR-1] Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection
链接: https://arxiv.org/abs/2608.02560
作者: Anusha Madan Gopal,Aras Pirbadian,Kristofor D. Carlson,M Anthony Lewis,Jonathan Tapson
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Retrieval-augmented generation (RAG) imposes a prefill cost proportional to retrieved context length, and – with Transformer backbones – a KV-cache that grows with each generated token. State-Space Models (SSMs) avoid the second cost by construction; we eliminate the first, collapsing prefill from O(L_context) to O(1) per query. We introduce PRECOG (Pre-Computed Context Injection), a retrieval mechanism that exploits a property unique to SSMs: the fixed-size, position-agnostic recurrent hidden state is a complete summary of everything the model has read. PRECOG pre-encodes document corpora offline as SSM hidden states and injects the best-matching state directly at query time, bypassing in-context re-ingestion entirely. The same state-injection mechanism enables SMC (Structured Memory Consolidation): a hierarchical persistent memory with cognitive-domain clustering, an adjustable fidelity-vs-storage dial, and O(1) session initialization, which consolidates short-term episodic states into long-term semantic memory and fuses both with retrieved corpus states at query time. We demonstrate the system on TENNs-LLM, a 1.2B-parameter gated-SSM language model with a 192 KB hidden state. PRECOG matches in-context RAG answer quality, reducing prefill latency from \sim 27 s to 6 ms on edge hardware – a \sim 4500 \times speedup that crosses the threshold from unusable to interactive. The mechanism is architecturally impossible for Transformer KV-caches, which are position-entangled and grow linearly with context length.
[IR-2] Beyond the Final Prompt: Measuring the Effect of Within-Conversation Context on AI Answers
链接: https://arxiv.org/abs/2608.02556
作者: Benjamin Tannenbaum
类目: Information Retrieval (cs.IR)
备注: 8 pages, 3 figures, 2 tables. Companion to arXiv:2607.22392
Abstract:An isolated final user message is often treated as the query in evaluations of AI systems. In a conversation, however, the actionable request may be distributed across preceding turns. We directly test whether that omitted within-conversation context changes answers. For each of 180 English multi-turn conversations sampled from a governed commercial corpus and the public PRISM dataset, we hold the final user message and requested answer model constant while generating three answers: one from the full role-labelled conversation, one from the final message alone, and one from the final message plus a prefix-only reconstruction capped at 160 words. A separately requested judge model evaluates answers under randomized labels. The prespecified primary endpoint is a material difference that could change what the user does, rather than a difference in style or detail. After inverse-probability weighting to the eligible cohorts, the full-conversation and isolated-final answers differ materially in 44.7% of cases (95% bootstrap CI 33.8% to 56.1%). Full-conversation answers score 0.49 points higher on a 0 to 4 request-satisfaction scale (0.32 to 0.67). Adding the compressed prefix reduces the material-difference rate to 30.8% (20.2% to 42.1%), a 13.9-point reduction (4.9% to 24.1%), and reduces the mean satisfaction gap to 0.01 points (-0.12 to 0.13). Yet compression is not equivalent to the complete dialogue context: almost one third of answers remain materially different. An order-swapped repeat on 48 cases yields 91.7% agreement and kappa = 0.83 for the primary decision. The study concerns preceding turns in the same conversation and does not test persistent memory across separate conversations.
[IR-3] Between-User Collapse Under Popularity-Biased Feedback: A Centered-Covariance Theorem and Computable Phase Boundary
链接: https://arxiv.org/abs/2608.02548
作者: Sahil Medepalli
类目: Information Retrieval (cs.IR)
备注: 7 pages, 2 figures
Abstract:We study how popularity-biased BPR training reshapes the between-user geometry of collaborative-filtering embeddings. We work with the mean-centered user covariance C=\tfrac1n U^\top H U , the object that measures how distinguishable users are from one another, as opposed to the uncentered second moment used in prior work. We prove that under popularity-biased feedback with stationary items, C converges to a steady state proportional to the item-noise covariance Q . Thus between-user spread collapses toward a noise floor. We derive a closed-form, computable phase boundary in the training hyperparameters (\alpha,\lambda_neg,\gamma,d) separating contraction from expansion, and validate both directional predictions on MovieLens-25M. We then examine the limits of the effect. At deployment-scale regularization the predicted contraction is real and policy-driven but small, and it is not reflected in any recommendation-level metric we measured. The \alpha -driven anisotropic-collapse mechanism operates only at regularization strengths that degrade the recommender. A deployment-time restoration intervention derived from the theory does not improve recommendation quality. The boundary is computable from a trained model’s embeddings, item interaction counts, and training hyperparameters, so a practitioner can check whether a deployed system sits in the strong-collapse regime without simulating the feedback loop. In our experiments the boundary places deployable settings far from that regime.
[IR-4] Abduction Without a Body? Representational Grounding and the Abduction Loop for Scientific Hypothesis Generation
链接: https://arxiv.org/abs/2608.02505
作者: Michael Farmer
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注: 20 pages, 4 figures. DAB-30 execution reported in companion paper
Abstract:Can scientific abduction occur without continuous sensorimotor embodiment? Recent arguments in AI and philosophy of science hold that genuine hypothesis generation requires an agent continuously coupled to the physical world. We defend a narrower claim: online embodiment is not necessary for every abductive scientific act. Our focus is identity abduction: the inference that two independently developed structures are one object under an explicit correspondence, reached through representational grounding rather than bodily interaction. An agent may acquire new inferential affordances not through physical interaction but through transformations into representations that expose latent invariants. Scientific diagrams are a practical substrate because they embody independently evolved conventions that partially canonicalize symmetry, topology, and operator structure across disciplines - a property we develop as convention space, which answers a hard retrieval problem: finding mathematically related work when two fields share no discriminating vocabulary. We operationalize the mechanism as an architecture, the Abduction Loop: representation generation, motif extraction, convention-space canonicalization, cross-domain retrieval, identity-hypothesis generation, and adversarial verification, with abstention as the designed default. A documented episode, in which a multimodal model given a figure of a gravitational-memory transport model generated and then verified the hypothesis that its central differential complex is equivalent to the spherical Kaiser-Squires mass-mapping complex of weak-lensing cosmology, serves as a motivating possibility witness from which the architecture is abstracted, not as evidence of general capability. We close with a falsifiable evaluation program, the DAB-30 benchmark. The contribution is a mechanistic proposal, an architecture, and a test program.
[IR-5] Requirement–Evidence Alignment for Compositional E-Commerce Queries
链接: https://arxiv.org/abs/2608.02500
作者: Weihao Shen,Wei Chen,Fuwei Zhang,Meng Yuan,Yuqin Lan,Guojun Liu,Qingsong Hua,Wei Lin,Fuzhen Zhuang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Compositional e-commerce queries express multiple requirements that must hold jointly, yet existing rerankers collapse these constraints into aggregate relevance and often promote topical near misses over feasible products. In this paper, we introduce REAlign, a novel requirement-evidence-aligned reranking framework that explicitly connects typed query requirements with visible evidence. REAlign distinguishes satisfied, violated, and unsupported conditions, constructs requirement-targeted contrasts that expose failure modes, and optimizes duplicate-free partial rankings through Requirement-Aware Group-Relative Policy Optimization. Its list utility preserves relevance while incorporating requirement satisfaction, evidence support, material violations, and output validity. Experiments on two fixed-pool e-commerce benchmarks show consistent improvements over strong supervised and policy-optimization baselines under matched training budgets, with fewer violations among top-ranked candidates and larger gains at shallow ranks. Controlled ablations confirm the complementary value of requirement modeling, evidence grounding, and decomposed optimization.
[IR-6] Unpaired Modality-Agnostic Generative Recommendation
链接: https://arxiv.org/abs/2608.02477
作者: Weihao Shen,Wei Chen,Fuwei Zhang,Meng Yuan,Yuqin Lan,Guojun Liu,Qingsong Hua,Wei Lin,Fuzhen Zhuang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Generative Recommendation (GR) formulates recommendation as autoregressive generation over discrete semantic identifiers (IDs). Although recent multimodal GR methods improve semantic ID construction with visual and textual information, they typically require item-level paired observations, restricting tokenization to the intersection of modality availability. Moreover, incorporating unpaired observations is nontrivial because small representation shifts may cross quantization boundaries and produce incompatible identifier sequences. To address this challenge, we propose \textbfUnpaired Modality-Agnostic \textbfGenerative \textbfRecommendation (UnpairGR), which learns a unified semantic-ID space from paired, image-only, and text-only observations. UnpairGR confines modality-specific processing to lightweight input projections while sharing the subsequent Transformer and residual codebooks across all observation conditions. Paired observations establish a reliability-guided cross-modal consensus, whereas unimodal observations directly refine the same representations and codes. The learned tokenizer is then fixed to provide stationary targets for a single autoregressive recommender, without feature imputation, modality-specific codebooks, or fallback mappings. Extensive experiments on three benchmark datasets demonstrate that UnpairGR consistently improves recommendation performance under both fully observed and incomplete-observation settings.
[IR-7] Syntax Meets Semantics: Understanding Scientific Formulae
链接: https://arxiv.org/abs/2608.02457
作者: Yuni Susanti,Moritz Schubotz
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Scientific formulae are a fundamental component of scholarly communication, yet their dual nature – as structured syntax and carriers of semantics – remains underexplored in scholarly information retrieval. Although prior studies show that jointly modeling syntactic and semantic modalities improves retrieval performance, the relationship between their underlying representations has not been systematically investigated. In this work, we empirically study cross-modal correspondence between formula syntax and semantics. We find that their native representation spaces exhibit extremely weak observable correspondence despite strong latent correlation, indicating a substantial representation mismatch between the two modalities. We further evaluate whether this mismatch can be reduced using standard representation learning and alignment techniques. We represent syntactic structure using graph-based encoders and semantic information using text-based encoders, then apply contrastive learning to induce a shared representation space. Results show that the learned alignment substantially improves cross-modal retrieval, suggesting that explicit representation learning can recover correspondence absent from the original representation spaces.
[IR-8] Advancing Relevance Measurement with Vision-Language Models for Web-Scale Search RECSYS’26
链接: https://arxiv.org/abs/2608.02446
作者: Han Wang,Alex Whitworth,Pak Ming Cheung,Zhenjie Zhang,Krishna Kamath,Xi Chen,Roberto Konow,Kurchi Subhra Hazra
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: RecSys’26 Industry track
Abstract:Relevance evaluation plays a crucial role in personalized search systems, serving as a guardrail alongside user engagement metrics to ensure that search results align with user queries and intent. While human annotation is the traditional method for relevance evaluation, its high cost and long turnaround time limit its scalability. In this work, we present a VLM-based automated relevance evaluation pipeline deployed within Pinterest Search for online A/B experiments. We rigorously validate the alignment between VLM-generated judgments and human annotations, demonstrating that VLMs can provide reliable relevance measurement for experiments while greatly improving the evaluation efficiency. Leveraging VLM-based labeling further unlocks opportunities to expand the query set, optimize sampling design, and efficiently assess a wider range of search experiences at scale. This approach leads to higher-quality relevance metrics and significantly reduces the Minimum Detectable Effects (MDEs) in online experiment measurements.
[IR-9] oken-Native Storag e: Read and Write in your Agents Language
链接: https://arxiv.org/abs/2608.02376
作者: Kumar Shivendu
类目: Databases (cs.DB); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 11 pages, 5 figures, 2 tables
Abstract:Search and database engines still store text as UTF-8, a format built for humans. But the systems that increasingly read and write that text (embedders, rerankers, and language-model agents) work in token IDs, not characters, so every access pays to translate between the two. As agents become the primary readers and writers of stored text, we argue for token-native storage: keep the text as the model’s own byte-pair-encoding (BPE) token IDs. This is both smaller and faster. Packing r50k IDs as uint16 already beats UTF-8 by 2.25x on English with no compression, and an entropy coder reaches 3.30x. Across six tokenizers and three corpora (English, code, Hindi), compressing token IDs matches or beats every byte codec, even a corpus-trained zstd dictionary. Two findings sharpen the case. BPE numbers tokens by merge order, not frequency, and re-ranking by frequency lets a plain integer codec (streamvbyte) recover most of the entropy coder’s ratio while decoding ~7x faster, a one-line change we ask AI labs to make when they publish vocabularies. And because a model reads token IDs, not text, a token-native store hands them over directly instead of re-tokenizing on every read, ~10-600x faster. The only barrier is that sharing token IDs requires a common tokenizer, which is not always true across model families yet, so we argue for standardization: a published, shared vocabulary, the way ASCII and UTF-8 standardized text.
[IR-10] Disentangled Contrastive Learning for Zero-Shot Multilingual Dense Retrieval
链接: https://arxiv.org/abs/2608.02189
作者: Chao Huang,Yufeng Chen,Changhao Guan,Guang Yang,Dongze Chen,Kaiyu Huang
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 14 pages, 4 figures
Abstract:Multilingual dense retrieval aims to handle queries and documents across different languages based on a unified retriever model. The challenge lies in enabling robust retrieval transfer to low-resource languages where annotated retrieval data is often scarce. Although previous studies transfer high-resource supervision to low-resource languages in multilingual semantic representation learning, the shared representation often entangles semantic and linguistic features, which may interfere with optimizing semantic relevance for retrieval. Different from existing methods that focus on learning language-agnostic semantic features under such entanglement, we propose a disentangled contrastive learning~(DCL) method for multilingual dense retrieval by separating multilingual representations into semantic and linguistic subspaces. Specifically, we design disentangled optimization objectives based on hierarchical semantic alignment and language debiasing contrastive learning. By aligning retrieval-relevant semantics across languages at both sentence and token levels while capturing language-specific variations in the linguistic subspace, these objectives reduce language-induced interference in semantic matching. We jointly optimize them with the retrieval objective to facilitate stable zero-shot transfer from English supervision to multilingual dense retrieval. Extensive experiments on mMARCO and MIRACL show that our method consistently outperforms several strong baselines, demonstrating its effectiveness and generalization ability.
[IR-11] Douyin Multimodal Embedding Model Technical Report
链接: https://arxiv.org/abs/2608.02148
作者: Haonan Chen,Chu Li,Zhicheng Wang,Yuanwei Liu,Yuanjiang Wang,Shaohua Jiang,Zhicheng Dou
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Technical Report
Abstract:Multimodal representation learning is a cornerstone of modern AI. By encoding multimodal queries and targets into vectors, it powers industrial search and recommendation and underpins modern agents. Real-world platforms with complex modalities and massive-scale content, such as Douyin, Xiaohongshu, and YouTube, demand both efficiency under billion-scale indexing and fine-grained discrimination for hard matching. Existing MLLM embedding models rarely satisfy both. Contrastive models are efficient but rely on pair-level supervision too coarse for fine-grained distinctions, while CoT-based models improve discrimination through explicit generation impractical to serve online. We present Douyin Multimodal Embedding (DME), a model trained in two stages to combine both strengths. Stage 1 performs large-scale contrastive pre-training that establishes a unified multimodal embedding space with broad modality and task coverage. Stage 2 supplements semantic sufficiency, the property that an embedding is grounded in retrieval-relevant evidence and preserves fine-grained counterpart-side semantics, via two mechanisms. Evidence-Grounded Typed Latent Reasoning organizes retrieval evidence through hidden-space latent reasoning, and Cross-Conditional Reconstruction enforces counterpart-side semantics through cross-directional autoregressive reconstruction. Both act only during training and add only marginal query-side overhead, so DME serves as efficiently as a standard contrastive encoder. On MMEB-v2, DME reaches state-of-the-art results at comparable scales for its 2B and 9B variants (74.8 and 78.4), with especially strong video and visual-document tasks. In production, DME delivers a 2.92% relative gain on Douyin’s in-house offline evaluation set, is deployed across Douyin scenarios such as generative, image, and AI search, and yields a 0.1% Lifetime (LT) gain in online A/B testing on Douyin search.
[IR-12] Do Static Embeddings Add Value to Hybrid Dutch Retrieval?
链接: https://arxiv.org/abs/2608.02112
作者: António Pereira Barata
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注:
Abstract:Embedding benchmarks measure standalone model quality, but they do not establish whether a low-cost retriever contributes complementary ranking information once lexical and transformer-based retrieval are already combined. We present a controlled evaluation of this question across Dutch retrieval tasks from the Massive Text Embedding Benchmark for Dutch (MTEB-NL). Weighted reciprocal rank fusion (RRF) combines Best Matching 25 (BM25), Qwen/Qwen3-Embedding-0.6B (Qwen), and two multilingual static embedding models. Five datasets comprising 14,500 queries and 786,573 documents are scored exhaustively, and fusion weights are searched on a simplex in increments of 0.1. Ten-fold query-level cross-validation selects weights on nine folds and evaluates them on the held-out fold; paired bootstrap confidence intervals and sign-randomisation tests quantify the resulting differences. Fusion improves over the training-selected individual retriever by 0.061 mean reciprocal rank (MRR) on Dutch News, 0.029 on VABB, 0.004 on WebFAQ NL, and 0.025 on Wikipedia NL, while matching BM25 on Open Tender. All four positive differences remain distinguishable from zero after Holm correction. No unrestricted fold assigns positive weight to either static retriever: all 50 selections lie on the BM25-Qwen edge, and forcing a static contribution reduces effectiveness. Leave-one-dataset-out selection chooses equal BM25-Qwen weighting in every iteration and outperforms the cross-domain-selected individual retriever on every held-out task. The results support a two-retriever lexical-transformer architecture as a robust tested default across the evaluated Dutch tasks and show that standalone benchmark performance is insufficient to establish marginal value in hybrid retrieval.
[IR-13] Fetch-then-Explore: Decoupling Selection from Extraction over a Persistent Workspace for Search Agents
链接: https://arxiv.org/abs/2608.02097
作者: Qi Liu,Yiqun Chen,Zidan Chen,Yan Gao,Yi Wu,Yao Hu,Jiaxin Mao,Fengbin Zhu,Tat-Seng Chua
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Search agents now answer questions that take dozens of searches to settle, yet how such an agent reads a page has drawn far less attention than how it finds one. Nearly all of them use one of two document interfaces, and both tie a page to the moment it is opened. \emphVisit-and-read injects a reading of the page into the message history at fetch time, fixing that reading before the agent knows which fact it will need. Stateful \emphbrowsing instead extracts on demand from the page in hand, but holds one page at a time and releases it as soon as the agent opens another. Either way, a page that turns out to matter many turns later has to be fetched and rendered into context all over again. We propose \textbfFetch-then-Explore, which separates page selection from evidence extraction and keeps what it selects: pages are recorded in a per-question workspace on the filesystem rather than the context window or a transient session, and evidence is pulled from them on demand later. Selection becomes almost free, extraction can wait until the agent knows what to look for and be repeated as its hypothesis sharpens, and pages are not released when the agent moves on, so evidence accumulates across the trajectory. In a unified ReAct harness with fixed search, we compare Fetch-then-Explore against snippet-only, visit-and-read, and browsing baselines on two open-web benchmarks, BrowseComp and WideSearch, across three agent backbones. It leads BrowseComp accuracy at every backbone and generally matches or exceeds the baselines on WideSearch, and a behavioral analysis traces the gains to the workspace’s defining move: returning to a page after leaving it, which it does far more than any transient interface, so evidence missed on a first pass can still be recovered later.
[IR-14] SmartGR: Hierarchy and Beam-Aware Knowledge Distillation for Generative Recommendation
链接: https://arxiv.org/abs/2608.02048
作者: Ziheng Zhang,Yu Cui,Bohao Wang,Yong He,Chao Yu,Chuan Yuan,Wujie Sun,Can Wang,Jiawei Chen
类目: Information Retrieval (cs.IR)
备注: 14 pages, 4 figures, 13 tables; includes appendices
Abstract:Generative recommendation (GR) has emerged as a promising paradigm for recommender systems. Scaling up GR models can improve recommendation performance, but it also substantially increases inference cost. Knowledge distillation provides a practical solution by transferring knowledge from a large GR model to a lightweight one. However, existing distillation methods do not account for two GR-specific challenges: imbalanced distillation difficulty across the semantic ID (SID) hierarchy and incorrect prefix pruning during beam search. To address these challenges, we propose SmartGR, a novel distillation framework that utilizes Hierarchy-Aware SID Distillation to transfer the teacher’s modeling capability across the hierarchy and leverages Beam-Aware Ranking Distillation to distill the teacher’s ranking preferences during beam search. Extensive experiments on four benchmark datasets demonstrate the effectiveness and efficiency of SmartGR, improving the performance by 8.6% while achieving a 2.39 \times inference speedup on average.
[IR-15] BIP! Ranker: A Software Library for Citation-Based Impact Indicators on Large-Scale Graphs
链接: https://arxiv.org/abs/2608.02004
作者: Ilias Kanellos,Serafeim Chatzopoulos,Thanasis Vergoulis
类目: Digital Libraries (cs.DL); Information Retrieval (cs.IR)
备注:
Abstract:Scientific impact is multidimensional: overall influence, current popularity, early citation momentum, and field-relative performance each capture a distinct facet of a publication’s impact. Yet, in practice, these dimensions are often reduced to a single metric, such as citation count. Open solutions for computing multiple complementary impact indicators at scale remain scarce, particularly for citation graphs as large as those provided by major scholarly databases. We introduce BIP! Ranker, an open-source, Spark-based library for computing citation-based impact indicators at scale, capable of processing citation networks with billions of citations among hundreds of millions of publications.
[IR-16] A Self-Triggered Agent ic Push Recommendation System
链接: https://arxiv.org/abs/2608.01949
作者: Zhao-Yu Zhang,Qingying Chen,Chunyuan Zheng,Jing Zhou,Jian Sun,Siqi Chen,Leiying Chen,Chuan Zhou,Huiyou Jiang,Xin Tao,Haoxuan Li,Zhouchen Lin
类目: Information Retrieval (cs.IR)
备注:
Abstract:Push notification is a critical recommendation scenario on large-scale platforms, allowing the system to proactively reach users outside the application to improve long-term re-engagement. However, designing an optimal push system requires handling a complex action space for the “whether and when” delivery problem under strict system resource constraints. Existing solutions typically fall into two passive paradigms: pre-planned frequency methods that allocate delivery times via offline modeling, limiting real-time adaptability; and fixed-interval triggering methods that periodically poll the system, creating a strict dilemma between excessive computational overhead and diminished optimal timing capture. Furthermore, such multi-stage frameworks severely suffer from local optima. To overcome these limitations, in this paper, we propose STEPS, a proactive, Self-Triggered End-to-end Agentic Push Recommendation System, which is already fully deployed at Douyin with over 1 billion users. STEPS reformulates push recommendation as a self-triggered agentic process in which the system decides not only whether to send a push, but also when to invoke itself again, thereby forming a closed loop that balances real-time effectiveness and efficiency. Specifically, STEPS consists of two decision transformer-based agents: a planning agent that schedules the next system invocation using a gated ordinal regression method, and an execution agent that decides whether to send a push based on trajectory rewards. Furthermore, we introduce a lightweight filtering agent to both control computational overhead and act as a crucial safeguard against unreasonable planning behaviors. Online A/B testing demonstrates that STEPS significantly increases user active days by 0.2843% and reduces the push permission disablement rate by 1.9089%, while the filtering agent reduces computational overhead by 79.42%.
[IR-17] Diagnosing Search Behavior and Failure Modes in Long-Horizon Search Agents
链接: https://arxiv.org/abs/2608.01913
作者: Qi Liu,Jiaxin Mao,Fengbin Zhu,Tat-Seng Chua
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Deep search agents answer difficult information-seeking questions by iteratively issuing search queries to gather supporting evidence, but it remains unclear whether and how greater search effort leads to better answers. We study these questions through a trajectory-level diagnosis of long-horizon search agents. Using human-annotated document-level relevance judgments, we evaluate the evidence retrieved at each search step and separate two stages of agent behavior: what evidence an agent retrieves and how effectively it uses that evidence. This distinction further allows us to decompose failures into retrieval gaps, where the necessary evidence is never found, and utilization gaps, where relevant evidence is retrieved but not used correctly. With the retrieval model and evaluation harness held fixed, we compare six agents on BrowseComp-Plus and further validate our findings on BrowseComp with an open-web search API. Across settings, we find that search effort and answer quality are only weakly aligned. Answer accuracy is better correlated with the quality of retrieved evidence, especially cumulative retrieval recall, than with the number of searches or the amount of context consumed. Useful evidence often appears early in the trajectory, yet agents tend to continue searching, producing a long tail of low-yield retrieval steps. At the query level, exploratory reformulations remain useful, but the best-performing agents issue far fewer redundant queries. Overall, by systematically characterizing the search behavior and failure modes of long-horizon search agents, this work points to practical directions for building better deep research systems, including stronger query formulation, more effective evidence selection and context management, and stopping criteria based on whether sufficient supporting evidence has been retrieved.
[IR-18] Multimodal Embeddings for 3D Similarity Search in Semantic Web-of-Things Digital-Twin Platforms
链接: https://arxiv.org/abs/2608.01852
作者: Oussama Zaid(MALT),Romaric Gaudel(MALT),Hassan Thomas,Maria Massri,Philippe Raipin-Parv{é}dy
类目: Databases (cs.DB); Information Retrieval (cs.IR)
备注:
Abstract:Semantic Web of Things (SWoT) platforms model physical infrastructure as knowledge graphs typed against domain ontologies, enabling expressive structural and logical queries. However, they lack native mechanisms to express similarity beyond strict ontological equivalence, which represents a critical gap for 3D digital twins in domains such as telecom infrastructure and industrial IoT, where queries must combine ontological constraints with multimodal similarity search over heterogeneous, temporally-evolving scene data. We propose a framework that extends SWoT platforms with a multimodal embedding layer: ontology-typed entities comprising 3D point clouds, temporal attributes, and semantic labels are encoded into latent vector representations stored alongside the knowledge graph, enabling hybrid ontology-vector queries that combine graph-based filtering with similarity search. Implemented on Orange Research’s Thing’in platform with the Clock-G temporal graph database, a feasibility evaluation on S3DIS demonstrates that graph filtering effectively restricts the search pool under temporal and relational constraints, and that general-purpose pretrained encoders produce representations sufficient for similarity retrieval and as a preliminary encoding step for downstream predictive tasks.
[IR-19] HyperAg ent4POI: Dynamic Semantic Message Passing on Multi-Agent Hypergraphs for Missing-Modality Recommendation
链接: https://arxiv.org/abs/2608.01846
作者: Jinze Wang,Yuze Liu,Tiehua Zhang,Jiong Jin,Zhu Sun
类目: Information Retrieval (cs.IR)
备注:
Abstract:Next Point-of-Interest (POI) recommendation benefits from textual and visual content that describes venue semantics, yet such content is often incomplete in real-world services. Missing modalities weaken POI representations and reduce the semantic evidence available for ranking. The resulting representations also provide unreliable evidence for modeling higher-order user–POI interactions. We propose HyperAgent4POI, which uses Dynamic Semantic Message Passing (DSMP) to perform modality completion and soft incidence refinement within each hypergraph layer. Persistent node agents share a frozen Llama backbone and use role-specific adapters to produce node-to-hyperedge messages. Semantic hyperedge motifs formed from these messages guide soft incidence scoring and modality completion. Final node representations are cached for online ranking without LLM calls. Experiments on three real-world LBSN datasets show consistent ranking gains over 15 baselines across modality-missing rates, while cached inference provides practical online efficiency. Under a 60% modality-missing rate, HyperAgent4POI improves NDCG@20 over the strongest baseline by 8.2% on average across the three datasets.
[IR-20] SPEAR: Selection-aware Personalized End-to-end Adaptive Rewriting and Retrieval for Community Search RECSYS2026
链接: https://arxiv.org/abs/2608.01738
作者: Wenbin Wu,Yuzhong Wu,Yufan Xu,Kuan Fang,Xing Xu,Cheng Ye,Xiaobin Hu
类目: Information Retrieval (cs.IR)
备注: 11 pages, 5 figures, 5 tables. Accepted to the Main Track of the 20th ACM Conference on Recommender Systems (RecSys 2026). Code: this https URL
Abstract:Query reformulation bridges user intent and retrieval in e-commerce search, yet production systems optimize rewrite quality and retrieval effectiveness separately, leaving the two stages structurally misaligned. Path-based architectures unify them end-to-end but were designed for personalization, where relevance is not an explicit constraint-search additionally requires the rewrite to remain faithful to the user’s stated query intent. Transplanted directly, these models learn a shortcut we term the generic-word dominance effect: they favor generic rewrites that score well on paths but drift from query intent. To address this, we propose SPEAR (Selection-aware Personalized End-to-end Adaptive Rewriting and Retrieval), which integrates three components that each target one failure mode: (1) a dual-embedding backbone with auxiliary loss and gradient isolation that shields recall-side semantics from being eroded by CTR-driven ranking signals; (2) a multiplicative gating aggregator that lets a rewrite score high only when both its confidence and item relevance are strong, eliminating the generic-word shortcut; (3) a Dynamic Rewrite Selector that jointly generates request-specific rewrite weights and user-query-conditioned scale and bias terms, allowing both rewrite preference and relevance calibration to adapt to each request. Offline evaluation on 100K held-out industrial search sessions shows that the proposed framework improves rewrite semantic similarity@10 by +18.2 and click recall@10 by +99.5 over the production baseline. In online A/B testing, SPEAR achieves +0.259 in query-view CTR and +0.733 in average reading depth, confirming that improved rewrite selection translates into stronger retrieval and deeper user engagement. The proposed SPEAR system has been fully deployed in Dewu’s community search platform since 2025. Our code is available at this https URL.
[IR-21] X-KGRank: A Knowledge Graph RAG Framework for Explainable Recommendations via Pattern Mining and LLM Re-Ranking
链接: https://arxiv.org/abs/2608.01732
作者: Meenakshi Rajpurohit,Jainish Patel
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Modern recommender systems produce predictions that users cannot interrogate. The two dominant improvements, collaborative filtering and LLM-based reasoning, each fall short: collaborative filtering captures behavioural signals but offers no reasoning, while large language models (LLMs) generate fluent explanations but hallucinate and are poorly grounded in a user’s history. We present X-KGRank, a knowledge graph retrieval augmented framework that unifies structural collaborative filtering with LLM-based explanation. From the MovieLens-1M dataset (6,040 users, 3,704 items, 988,129 interactions) we construct a heterogeneous knowledge graph of 9,762 nodes and 999,264 edges spanning three relation types (RATED, HAS_GENRE, and CO_RATED) persisted in Neo4j. We train a LightGCN ranker with content-aware SBERT initialization and a rating weighted BPR objective, and apply a popularity selective routing strategy that grounds long-tail items (1,855 of 3,704) in knowledge-graph paths while serving popular items from pre-trained knowledge, reducing KG-augmented generations by roughly 50%. On the MovieLens-1M test set under a 99-sample protocol, X-KGRank achieves NDCG@10 = 0.2956 and Recall@10 = 0.5371, improving over a strong popularity baseline by 17.1% on both metrics, by 15.6% on NDCG@20 (0.3449 vs. 0.2983), and by 14.6% on MRR (0.2435 vs. 0.2124). Across three LLM backbones evaluated on 16 cases, a 1.5-billion-parameter model (Qwen2.5-1.5B) matches a 7-billion-parameter model (Mistral-7B) on heuristic explanation quality (0.97 vs. 0.94), yet qualitative analysis shows the smaller model is more prone to factual fabrication.
[IR-22] MODE: Mutual Optimality in Direct Effects of Reciprocal Recommendations in Matching Markets RECSYS2026
链接: https://arxiv.org/abs/2608.01731
作者: Yoji Tomita
类目: Information Retrieval (cs.IR)
备注: Accepted at RecSys2026
Abstract:Matching platforms such as job posting services and online dating platforms have become widely used over the past decade. For a matching platform to be successful, it is crucial to design appropriate reciprocal recommendation systems (RRSs) that consider the preferences of users on both sides (job candidates and employers) and prevent opportunities from being concentrated too heavily on a few popular users. However, prioritizing concentration mitigation too much can lead to recommending undesirable results to some individual users, resulting in their dissatisfaction. In this paper, we formulate the concept of ``optimality of direct effects’’ of the recommendation list for an individual user, given the recommendations to other users. Furthermore, we propose a novel method, MODE, that computes mutually optimal recommendations in direct effects. Experiments with synthetic and real-world data demonstrate that MODE surpasses other existing methods in terms of mutual optimality of direct effects, exhibits faster processing speeds, and enables a higher expected number of matches.
[IR-23] Floor Ceiling and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict?
链接: https://arxiv.org/abs/2608.01704
作者: Kazuki Nakayashiki,Keisuke Watanabe
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 8 pages. Ancillary files include the pre-registrations, hostile-audit records, verification scripts, and the aggregate artifacts every reported number is generated from
Abstract:A benchmark score means nothing without knowing what a trivial method achieves and what the best possible method could achieve. We construct both bounds for a task with a rare kind of ground truth: predicting which sentences a crowd of readers – highlighting for their own purposes, unpaid, uninstructed, and blind to each other – marked in 120 web documents. The floor is naive truncation (lead); the ceiling is a split-half oracle: half the crowd predicting the other half. The gap between them is +0.2028 AP [+0.1698, +0.2342, domain-clustered], and three findings structure it. First, the gap is semantic: position and length features recover 5% of it. Second, frontier language models reach 35-53% of it zero-shot – far above classical baselines, far below the crowd; a state-of-the-art prompt compressor (LLMLingua-2) lands below the floor, indistinguishable from random selection. Third, an unweighted cross-vendor fusion of five frontier rankings plus a position prior reaches 60%, beating the best single model by +0.0159 [+0.0044, +0.0269; Holm p=0.019] – a gain that survives ablation of its best member, split-half arm selection, prompt paraphrase, and label, gate, and seed perturbations, and was CONFIRMED by a pre-registered replication on 217 independent documents (+0.0179, Holm p=0.042). Finally, the bracket compresses: distilling the fusion into one open-weight 8B student that reads the whole document retains 90% of the fusion’s edge and reaches statistical parity with the strongest single frontier model (+0.0070 [-0.0068, +0.0200]), where a local-context student retains only 63% – the crowd’s signal lives in document-level structure, and the cheapest known improvement is to ask several different models and average.
[IR-24] HindSearch: Trajectory-Level Hindsight Critique for Search-Augmented Reinforcement Learning
链接: https://arxiv.org/abs/2608.01597
作者: Haowei Liu,Jiamian Wang,Hsin-Tai Wu,Zhiqiang Tao,Yi Fang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Search-augmented LM agents are typically trained with a binary exact-match reward, which throws away most of what a failed trajectory tells us about why it failed. We introduce HindSearch, a hindsight self-distillation procedure for GRPO: after each rollout, a frozen judge writes a short critique of every failed trajectory using the gold answer, and the critique supplies an auxiliary on-policy distillation signal on the student’s search actions. On the standard seven-benchmark suite with Qwen2.5-3B-Instruct, HindSearch reaches 39.4% average EM, outperforming prior search-RL baselines. Removing the judge’s access to the gold answer erases most of the gain, isolating hindsight as the source of the improvement.
[IR-25] V-Mem: Modality-Routed Retrieval for Long-Term Multimodal Agent ic Memory
链接: https://arxiv.org/abs/2608.01543
作者: Dingyi Kang,Dongming Jiang,Yi Li,Guanpeng Li,Bingzhe Li
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注: 19 pages, 2 figures, 16 tables. Code: this https URL
Abstract:Interaction between users and LLM agents is increasingly multimodal: conversations interleave text with images, and a later question may target either. Yet most agent memories are designed around text, and even the few that support multimodal conversations still fail on vision-related questions. We trace this failure to an assumption behind the similarity search they rely on: in the index space, a query lies close to the relevant evidence that answers it. In multimodal settings, two gaps break it. By the modality gap, a query lies closer to memory content of its own modality than to evidence in another, even in a trained joint embedding space. By the similarity-relevance gap, the content most similar to a query is often not the evidence that answers it, most acutely when a query carries both text and image and its evidence resembles neither part alone. We present V-Mem, a multimodal agentic memory system that routes retrieval by the modality of the query and that of the target evidence, both recognized from the query alone. To cross the modality gap, V-Mem organizes the conversation into rounds and returns the target-modality content from the same round as the match, without comparing across modalities. To close the similarity-relevance gap, it searches with an LLM-generated anchor that sits closer to the relevant evidence than the query does: a hypothetical caption for a text-only query seeking an image, and an enriched search anchor, the query text plus relevant keywords extracted from the query image, when the evidence is reachable only by combining the two. On Mem-Gallery, V-Mem reaches an LLM-judge score of 0.82 versus 0.56 for the second best, with the largest margin on questions carrying an image (0.87, no baseline above 0.47); on LoCoMo it scores 0.69 versus 0.58.
[IR-26] Real-Time Hybrid Retrieval in Hyperbolic Space for Retrieval-Augmented Generation on Edge Devices
链接: https://arxiv.org/abs/2608.01450
作者: Aradhya Chakrabarti
类目: Information Retrieval (cs.IR)
备注:
Abstract:This paper presents a hybrid document retrieval system designed for retrieval-augmented generation (RAG) that operates entirely within the Lorentz model of hyperbolic geometry. Unlike conventional dense retrievers confined to Euclidean space, this system projects pretrained word embeddings into hyperbolic space through a learned HyTE-H transformation, whose exponential volume growth suits the hierarchical organization of natural language. Documents are segmented into overlapping chunks, indexed by their Lorentz embeddings, and retrieved through a two-stage pipeline that first applies BM25 lexical scoring, then re-ranks candidates using Lorentzian inner-product similarity. A tunable parameter \alpha blends the BM25 score with the hyperbolic similarity score. The system was evaluated on five datasets from the BEIR benchmark suite, SciFact, NFCorpus, ArguAna, SciDocs, and FiQA, achieving NDCG@10 scores of 0.654, 0.304, 0.342, 0.150, and 0.217 respectively with word embeddings alone, without fine-tuned neural encoders or cross-attention rerankers. The system supports real-time indexing of user-supplied documents and resource-efficient querying over tens of thousands of moderately sized documents, so hyperbolic retrieval can run on edge devices at interactive latencies.
[IR-27] Collaborative Memory Augmentation for Generative Recommendation KDD2026
链接: https://arxiv.org/abs/2608.01315
作者: Enze Liu,Zhen Tian,Wayne Xin Zhao
类目: Information Retrieval (cs.IR)
备注: Accepted by KDD 2026 Research Track
Abstract:Generative Recommendation (GR) has exhibited great potential by modeling item transitions as a sequence-to-sequence task. Despite the success of GR, existing frameworks primarily focus on modeling individual user sequences within a constrained internal parametric space, failing to explicitly leverage cross-user collaborative signals. To address this issue, we propose \textbfOMEGA, a cOllaborative MEmory augmentation framework for Generative recommendAtion. OMEGA bridges the gap between implicit parametric knowledge and explicit collaborative signals. We first introduce a latent context compression method that utilizes learnable query tokens to distill sequential user behavior into compact representations, significantly reducing storage overhead. These compressed representations are aggregated into a collaborative memory bank, serving as an explicit repository of global behavioral patterns. To ensure precise knowledge acquisition, we design a lightweight and target-aware retrieval mechanism that identifies pertinent memories by considering both sequence-level and target-level similarities. Furthermore, a context-aware integration module, equipped with a gated cross-attention mechanism, is employed to adaptively fuse the retrieved collaborative memories with the local user context while mitigating the interference of noisy patterns. Empirical evaluations on multiple real-world datasets demonstrate that OMEGA significantly outperforms existing advanced GR models, validating the potential of external memory as a complement to the generative paradigm.
[IR-28] Auditing Semantic Gains in Sequential Recommendation: A Lightweight Recovery Test
链接: https://arxiv.org/abs/2608.01260
作者: Kong Wang,Zhongke He,Xiang Chen,Hongwei Zeng,Kai Deng,Long Wang,Kehua Yang
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent semantic and generative-retrieval recommenders report substantial improvements over ID-only sequential baselines, but it remains unclear whether these gains arise from language-model reasoning, semantic-ID generation, end-to-end semantic architectures, stronger offline item representations, or complementary semantic and collaborative signals. We investigate this attribution ambiguity through LIME-Rec, a lightweight and auditable recovery test. LIME-Rec combines three independent experts: a SASRec sequential expert, an ItemCF co-occurrence expert, and a semantic expert based on frozen BAAI/bge-base-en-v1.5 item embeddings. Their full-catalog scores are normalized per user and combined through auditable score-level fusion followed by bounded history calibration. The fusion gate and calibration head are fitted on validation data only, require no serving-time language-model inference, and keep each expert contribution separately inspectable. On Amazon Beauty, Toys, and Sports, LIME-Rec achieves R@10 scores of 0.0996, 0.1105, and 0.0593, outperforming the strongest comparison baseline by 7.0%-12.0%. Three-expert fusion without history calibration consistently outperforms calibrated SASRec, showing that calibration alone does not explain the recovery. Randomly permuting item-text embeddings across item IDs reduces R@10 by 13.6%-17.5%, indicating that the gains depend on genuine item-text correspondence rather than additional representation capacity. These results suggest that lightweight recovery from offline item representations and transparent fusion should be ruled out before improvements are attributed to serving-time language modeling, semantic-ID generation, or heavier semantic machinery.
[IR-29] Join Indices for Search Engines: a Prunable Parallel Semijoin over Lucene Segments
链接: https://arxiv.org/abs/2608.01173
作者: Mikhail Khludnev
类目: Databases (cs.DB); Information Retrieval (cs.IR)
备注:
Abstract:Joins are second-class citizens in search engines: existing query-time join implementations in Lucene are limited either in performance or in capability, forcing a choice between fast joins scoped to a single index and slower joins that span independently managed indices. We carry Valduriez’s join-index technique from relational systems to Lucene’s flush-based (LSM-style) segment storage: for every pair of a parent and a child segment we materialize an append-only, ordinal-to-ordinal join-index column J[c]=p, avoiding any query-time translation of external variable-length keys. On top of this structure we build a semijoin algorithm that is computed per parent segment, in parallel, without a global barrier between stages; it prunes at three levels (segment-level, the first of which comes free from per-segment execution; a-priori min/max; and document-level two-phase confirmation with a lazily accumulated half-read union) so that it composes with arbitrary engine queries instead of wasting computation on matches that a sibling filter would later discard. A prototype implemented as an Apache Solr query parser, benchmarked on 1M products joined against 10M skus, cuts average query latency 5.4 times (359.8,ms vs. 1934.6,ms) relative to Solr’s built-in query-time join, and the advantage widens monotonically with load, reaching 8.3 times at a concurrency of eight: on 4 vCPUs the baseline peaks at 1.18 queries/s and then loses throughput, while the join index is still gaining, at 8.04 - 6.8times the baseline’s best.
[IR-30] UniHEAR: Unified Heterogeneous-Source Attentive Retrieval for Knowledge-Based Visual Question Answering ACM-MM2026
链接: https://arxiv.org/abs/2608.01147
作者: Ganzhong Luo,Yang Ren,Hanyong Wang,Shuyu Zheng,Menglong Yang
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM MM 2026
Abstract:Knowledge-Based Visual Question Answering (KB-VQA) requires retrieving relevant entity knowledge from external sources to answer visually grounded questions. Existing retrieval-augmented systems suffer from two critical limitations. First, relying on a single retrieval modality creates a Single-Source Retrieval Bottleneck, missing ground-truth entities that are only accessible through complementary sources. Second, dual-tower pointwise rerankers suffer from Retrieval-Source-Blind Reranking, as they overlook retrieval origins and candidate-level retrieval priors, leading to redundant modality reliance. To address these challenges, we propose UniHEAR, a unified lightweight framework for heterogeneous-source entity retrieval and reranking. UniHEAR constructs a Coarse Retrieval Descriptor for each candidate entity, and introduces Retrieval-Guided Attentive Modality Gating to condition modality attention weights on this descriptor, further complemented by Entropy-Weighted Source Fusion of coarse retrieval priors. A hybrid training strategy combining contrastive learning with an auxiliary modality-preserving loss unifies entity-level and section-level retrieval within a single model. Extensive experiments on E-VQA and InfoSeek demonstrate that UniHEAR achieves state-of-the-art retrieval and VQA performance, improving Recall@1 by 6.7 and 1.2 points over the strongest baselines while maintaining a lightweight reranking architecture. Code and model are available at this https URL.
[IR-31] GRACE: Generative Recommender Acceleration Engine for Real-Time Ads Retrieval
链接: https://arxiv.org/abs/2608.00938
作者: Zhou Fang,Yuhang Huang,Ang Zhang,Yihan He,Ruichao Xiao,Chao Li,Yavuz Yetim,Sibyl Yang,Xiaohan Wei,Fei Tian,Liang Wang,Liyuan Li,Nathan Yan,Gaoxiang Liu
类目: Information Retrieval (cs.IR)
备注: 13 pages, 3 figures
Abstract:Productionizing generative recommenders for high-volume, real-time ads retrieval creates two serving challenges: eligibility, ensuring that each generated ad is eligible for the request under the advertiser’s audience targeting rules, and compute, which requires meeting strict latency and GPU cost requirements while remaining capable of generating thousands of ads per request with wide-beam decoding. This paper presents GRACE, a serving system for ads generative retrieval that addresses both challenges. For eligibility, GRACE introduces Generative Target Matching (GTM), which extends catalog-valid constrained decoding with personalized filtering over Semantic ID (SID) prefixes using bitmask and Bloom filter matchers derived from targeting rules. SID-level GTM improves final ad-level target matching pass rate from 23.55% to 40.42% over constrained decoding alone. For compute-cost and latency, GRACE targets encoder-decoder Transformers, which are more lightweight than LLMs. It redesigns the decoder around the wide-beam, short-sequence regime, covering attention kernels, KV cache, and beam search optimizations. On NVIDIA GH200, compared with the faster of FlashAttention-2 and FlashAttention-3 baselines, GRACE improves cross-attention latency by 68.0 times and self-attention latency by 23.4-25.8 times across decode steps. Together, these changes reduce decoder latency by 11.1 times, keeping ads generative retrieval within latency and compute requirements.
[IR-32] vatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget
链接: https://arxiv.org/abs/2608.00916
作者: Zhichao Xu,Xueguang Ma,Shengyao Zhuang,Luyu Gao,Wenqian Ye,Yu Wang,Jamie Callan,Jimmy Lin
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Modern reranking recipes—billion-scale cross-encoders, mixture-of-experts (MoE) backbones, and distillation against strong teachers—have outpaced the training infrastructure available to most academic groups. Existing Tevatron reranker training relies on the Hugging Face Trainer with DeepSpeed or PyTorch FSDP1, but these backends lack efficient support for large-scale MoE training. We present Tevatron 3.0, which integrates a Megatron-Core training backend into Tevatron while preserving its data pipeline, evaluation workflow, and Hugging Face-compatible checkpoints. We benchmark existing distributed training configurations against the new backend, showing that Megatron matches FSDP reranker quality and training efficiency under comparable data-parallel settings, is up to 22% faster in the recommended single-node configuration, and supports both LoRA and full-parameter fine-tuning. Crucially, expert parallelism enables training a 30B-parameter Qwen3-30B-A3B MoE reranker, which is infeasible with PyTorch FSDP1. Using this framework, we conduct a controlled comparison of MoE versus dense models, LoRA versus full-parameter tuning, and distillation versus contrastive training on BEIR-15 with three first-stage retrievers, and report serving throughput for Hugging Face and vLLM. We find that the MoE reranker matches dense 8B quality while activating less than half as many parameters and achieving substantially higher inference throughput. We will release the framework and trained checkpoints.
[IR-33] Exponential Reward Weighting for Fine-Tuning Generative Recommenders under Sparse and Noisy Feedback
链接: https://arxiv.org/abs/2608.00816
作者: Keertana Chidambaram,Sanath Kumar Krishnamurthy,Qiuling Xu,Ko-Jen Hsiao,Moumita Bhattacharya
类目: Information Retrieval (cs.IR)
备注:
Abstract:In recommendation systems, users interact with only a small fraction of a vast item catalog, producing feedback that is both sparse and noisy. This challenges post-training generative recommenders: reward models trained from logged interactions often fail to generalize, while directly optimizing imperfect rewards can lead to reward over-optimization. We propose Exponential reward-weighted fine-tuning (Exp-RSFT), where each logged interaction is weighted by \exp(r/\lambda) , avoids this failure by optimizing directly on the logged rewards, with the temperature \lambda regularizing against their noise. We theoretically show that Exp-RSFT’s suboptimality decomposes into two costs: a coverage cost arising from limitations of the logging policy and a noise cost from imperfect feedback. The temperature \lambda balances these competing effects, yielding an optimal tradeoff between exploiting high-reward behavior and robustness to noise. Across three public benchmarks and a large-scale industrial dataset, we verify this theoretical prediction: performance follows an inverted-U trend as a function of \lambda , while PPO and DPO often over-optimize unreliable reward models and degrade recommendation quality. Exp-RSFT consistently improves ranking performance without requiring online exploration or preference data.
[IR-34] Hierarchical Residual Policy Optimization for Generative Recommendations KDD2026
链接: https://arxiv.org/abs/2608.00750
作者: Kaifeng Guo,Yiming Yang,Jingtong Gao,Guolei Zeng,Fukang Yang,Yukang Liang,Peng Jiang,Qingpeng Cai,Xiangyu Zhao
类目: Information Retrieval (cs.IR)
备注: 12 pages, 6 figures, 10 tables. Accepted at KDD 2026 Research Track
Abstract:Generative recommenders select items by autoregressively decoding semantic identifiers (SIDs), whose token positions induce a coarse-to-fine hierarchy over the item space. In practice, SID decoders are trained via supervised next-token prediction, which imitates logged trajectories rather than directly optimizing downstream utility. This motivates post-training with outcome feedback to guide decoding toward higher utility. However, logged feedback is only observed for the final exposed item, causing most post-training methods to operate at the item level and broadcast the same terminal signal across all SID tokens. As a result, token-level credit assignment becomes sparse, high-variance, and layer-dependent. To this end, we propose Hierarchical Residual Policy Optimization (HRPO), a post-training framework that converts item-level outcomes into dense, token-aligned learning signals for conservative token-wise improvement. Specifically, HRPO first estimates SID prefix-level utilities via group-wise reward smoothing over feature-based user clusters. It then decomposes these utilities into residual token credits and accumulates them into credit-to-go signals. Finally, Residual-Return Policy Optimization (RRPO) optimizes the residual credits using clipped updates, group-normalized advantages, and KL regularization to preserve stability. Experiments on a public dataset and an online A/B test in a large-scale commercial system show consistent gains in session-level utility and key business metrics. Source code and the archived artifact are available for reproduction.
[IR-35] A Triple-Robustness Analysis of Retrieval-Augmented Generation for Multi-Hop Requirements Traceability
链接: https://arxiv.org/abs/2608.00705
作者: Meftun Akarsu,Burak Özdemir,Doğancan Büyükçolak,Recep Kaan Karaman
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 6 pages, 3 figures, 4 tables
Abstract:Reported verdicts on GraphRAG versus vector RAG disagree, and the evidence is typically tied to a single corpus, embedder, and judge – and, we show, to where citation quality is measured. We present a triple-robustness analysis that holds a five-pipeline architecture matrix fixed and varies embedder (local e5-small vs. Azure text-embedding-3-small), corpus (DO-178C typed-edge requirements vs. Wikipedia paragraph chains via MuSiQue), and judge (paired GPT-5.4 x GPT-4.1 on both corpora), over 2x4,440 main-matrix runs, 600 cross-corpus runs, and over 5,000 faithfulness judgments. (C2a) GraphRAG’s graph walk floods the context window at precision 0.12-0.23, but the synthesizer cites selectively at precision 0.48-0.65; scoring the retrieved set as the attribution set inverts the architecture ranking, which reconciles part of the disagreement in prior reports. (C1) Answer-level citation winners are corpus- and stratum-conditional but embedder-robust: GraphRAG ties vanilla on short-hop DO-178C queries and wins every MuSiQue stratum, while agentic pipelines lead only on 3±hop requirements queries. (C2b) Faithfulness is corpus-conditional: on DO-178C it declines with hop distance (trend p0.05 in three of four judge x embedder combinations); on Wikipedia chains neither judge shows a collapse. (C3) Single-judge LLM faithfulness is fragile to retrieval state: GPT-5.4’s self-kappa across embedders is 0.137 (41% verdict change) against a same-day test-retest floor of 0.76, and re-judging frozen inputs eleven weeks later gives kappa = 0.14 for both judges. A learned router on dense embeddings alone reaches macro-F1 0.86 on hop classification (C4). We argue that RAG architecture claims should be tested at this level of robustness – including robustness to the citation-measurement point – before they are trusted.
[IR-36] GARDRec: Decision-Level Graph Grounding for Large Language Model Recommendation
链接: https://arxiv.org/abs/2608.00669
作者: Yong Wang,Hongliang Sun,Jinlan Liu,Hua Zhang,Dianbo Sui,Dianhui Chu,Zhiying Tu
类目: Information Retrieval (cs.IR)
备注: 18 pages, 2 figures
Abstract:Large language models (LLMs) offer new opportunities for recommendation by interpreting item descriptions, user instructions, and external knowledge through natural-language prompts. However, existing graph-augmented LLM recommenders often use knowledge graphs mainly as prompt-level evidence, leaving ranking decisions weakly constrained by structured user-item relations. This is problematic for next-item recommendation, where the model must compare candidates under the same user context while preserving temporal preference, collaborative signals, and attribute matches. To address this issue, we propose \emphGARDRec, a Graph-grounded Adaptive Reasoning and Decision-aware Recommendation framework for LLM-based next-item ranking. GARDRec constructs semantic-structural item representations from textual node features and graph propagation, derives personalized graph contexts from temporally weighted histories and first-order neighborhoods, and aligns graph-derived representations with a frozen LLM through continuous multimodal prompts. Explicit interaction and matching features are injected through late-stage decision branches, while inter-candidate attention and restricted generative likelihood support final ranking. Experiments on three public benchmarks with multiple LLM backbones show that GARDRec generally improves candidate-ranking performance over representative baselines. Ablation and diagnostic analyses verify the contributions of graph projection, neighborhood retrieval, explicit decision features, ranking loss, and generative calibration.
[IR-37] Verification Without Sufficiency: Per-Chunk Filtering Fails on Multi-Hop RAG and Decomposition Repairs It
链接: https://arxiv.org/abs/2608.00585
作者: Randhir Kumar
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 9 pages, 5 figures, 8 tables, 1 algorithm. Code, per-question traces and analysis scripts: this https URL
Abstract:Verification for retrieval-augmented generation usually scores each retrieved chunk and drops the ones that fail. We show this cannot work for multi-hop questions, and show what does. Per-chunk scoring assumes one chunk is a sufficient premise for the answer. Multi-hop questions are built so that none is, and the paragraph carrying the answer is the one the question does not name. Entailment scoring reaches 0.643, 0.523 and 0.560 AUC on HotpotQA, 2WikiMultihopQA and MuSiQue, against 0.951 on single-hop SQuAD. Seven controls rule out model capacity, premise length, hypothesis template, decision threshold, retriever, answer-matching criterion and prompt. End to end across three datasets, three generator sizes and two prompts, per-chunk gating is significantly worse than not filtering at all in every cell, and its penalty grows with generator capability. The repair is to condition verification on the decomposed sub-question rather than the original query. Using MuSiQue’s gold decomposition, entailment on a later hop rises from 0.546, which is chance, to 0.840, a paired lift of +0.355 with a bootstrap interval of [0.331, 0.382]. An off-the-shelf Qwen2.5-7B decomposer, given the question and the top retrieved paragraph, reaches 0.637 and captures 31% of that ceiling; decomposing without retrieval reaches 0.533, below the original question. Iterative retrieval systems already produce such decompositions and discard them before verifying.
[IR-38] PHA-Net: Prototype-based Hierarchical Alignment Network for Text-Video Retrieval
链接: https://arxiv.org/abs/2608.00551
作者: Xiaolun Jing,Kezhao Yin,Xinxing Yang,Genke Yang,Jian Chu
类目: Information Retrieval (cs.IR)
备注:
Abstract:With the emergence of large-scale image-text pre-training models, e.g., CLIP, text-video retrieval has experienced substantial advances in recent years. Existing best-performing methods involve aligning cross-modal semantics at individual, local, and global levels simultaneously, raising concerns about the intrinsic semantic mismatch between concise texts and rich videos. A canonical approach is to integrate multiple language-video attention modules into the hierarchical framework while this paradigm only optimizes visual representations with prohibitive computational costs. In this paper, we propose a new prototype-based hierarchical alignment network (PHA-Net) to align individual/local/global level representations across modalities. Concretely, we introduce multiple modality-shared prototypes as the bridge to efficiently optimize text and video representations for cross-modal alignment. Then, we argue that the imbalanced semantic distribution in clustered tokens may undermine retrieval performance, as tokens with weak semantics are of little interest. To reduce the impact of these tokens, a proposed prototype-supported token merge module is responsible for enhancing tokens with strong semantics and suppressing others with weak semantics via prototype semantics guidance. Moreover, we devise a prototype contrastive loss to encourage textual and visual prototypes to focus on different semantic information. The idea of this auxiliary loss is to ensure higher similarity between textual and visual prototypes from the same prototype than those from different prototypes. Extensive experiments on four benchmarks confirm the effectiveness of our PHA-Net, which achieves significant improvements in the sum of all recalls on MSR-VTT (8.8%), ActivityNet (19.2%), VATEX (0.7%), and Charades (4.9%). Code is available at this https URL.
[IR-39] A Context-Aware Cultural Heritage Guide Powered by LLM s
链接: https://arxiv.org/abs/2608.00549
作者: Liliana Ardissono,Fabio Ferrero,Angelo Geninatti Cossatin,Claudio Mattutino,Noemi Mauro
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:We present an extension of Triangolazioni (a Cultural Heritage webapp) to enrich curated content with context-dependent, external information provided by Large Language Models (LLMs) within a loosely-coupled architecture agnostic to the LLM. The system supports context-dependent information search and presentation within an architecture agnostic to the exploited LLM.
[IR-40] CeQe: Grounding Lexical Retrieval in Semantic Evidence
链接: https://arxiv.org/abs/2608.00452
作者: Adam Kahirov,Umesh Deshpande,Swaminathan Sundararaman
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Lexical retrieval (BM25) captures exact keyword matches and weights terms by corpus-wide significance, but it is blind to the semantic vocabulary gap: when a relevant document phrases an answer differently from the query, BM25 never retrieves it, and no amount of downstream reranking or fusion can recover a document that was never in the candidate set. We present Cross-Encoder Query Expansion (CE-QE), which reads the per-token relevance attributions of a cross-encoder applied to top semantic search results, selects the terms the cross-encoder treats as decisive, and appends them to the BM25 query. Unlike classical pseudo-relevance feedback, which reuses BM25’s own (possibly wrong) top results, CE-QE seeds expansion from the semantic retriever’s results, avoiding self-reinforcing query drift. Unlike recent generative query expansion (HyDE, Query2doc), which prompts a large language model to hallucinate text from its parametric knowledge, every CE-QE expansion term is copied verbatim from a retrieved passage, so it cannot introduce vocabulary the corpus does not contain, and its only added cost is attribution extraction on a cross-encoder a hybrid pipeline already runs for reranking. On seven BEIR datasets, CE-QE improves lexical recall substantially where query and answer vocabulary diverge (e.g., NQ Recall@100 from 0.32 to 0.47), and its score-fusion variant (SESF) beats cross-encoder score fusion by 2.5% on Recall@100 and beats SPLADEv2 and ColBERTv2 by 5.3% and 4.6% on nDCG@10, while leaving the underlying BM25 index completely unmodified.
[IR-41] Unleashing the Potential of Large Language Models : A Blueprint for Real-Time Enterprise-Ready Deployments
链接: https://arxiv.org/abs/2608.00419
作者: Muhammad Faizan Raza,Shuo(Luna)Yang,Satish Mahadevan Srinivasan,Joanna F. DeFranco
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 6 pages, 1 figure. Authors’ accepted version of an article published in IEEE Computer. The version of record is available at the DOI below
Abstract:Large language models deployed in real-time, regulated settings face knowledge staleness, catastrophic forgetting, hallucination, and weak feedback loops. We present a unified, pattern-driven LLMOps architecture integrating real-time data ingestion, continual learning, retrieval-augmented generation (RAG), and human-in-the-loop feedback into a single operational pipeline. Four contributions map to established software design patterns: an adaptive ingestion pattern orchestrator (AIPO) evaluated with FreshStreamBench; STAR+FAR continual learning with sparse temporal adapter routing and freshness-aware replay; SAGE, an SLO-aware adaptive retrieval policy predicting a per-query passage budget to meet tail-latency targets; and an automated feedback-driven convergence stage with RLHF triggers. The result reduces latency-cost-accuracy trade-offs while supporting auditability and rollback for high-risk sectors such as health care and finance.
[IR-42] Hierarchical BM25: Lexical Search at Billion-Document Scale
链接: https://arxiv.org/abs/2608.00229
作者: Umesh Deshpande,Swaminathan Sundararaman
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:A flat BM25 index over one billion documents occupies about 400 GB. Holding it in memory requires DRAM proportional to corpus size. Serving it from disk takes 4-12 seconds per query. Exact top-k lexical retrieval at this scale is therefore impractical within an interactive latency budget. Hierarchical BM25 gives up exact ranking in exchange for fixed bounds on memory and latency. A resident coarse index selects which of ~1K topical, size-balanced document groups a query visits, using two signals: the total frequency of each query term within a group, and, for informative terms spread too thinly across groups for frequency totals to reflect, whether several of them appear together in one document. Selected groups are then searched exhaustively and scored against ~100 KB of global statistics. Every returned score therefore equals the flat index’s score, and the approximation is confined to selection alone. The resident footprint is ~4.4 GB, independent of corpus size. Sixteen-term queries over one billion documents return in ~300 ms (4.7x to 5.6x the throughput of a flat multi-threaded index), and a warmed cache sustains ~32 queries per second versus under 3 for flat indexing. At a 500K-document configuration, visiting 5-10% of clusters recovers 0.83-0.92 of the exhaustive result score. Billion-scale recall and a direct comparison against document-reordered BlockMax-WAND remain open. Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.00229 [cs.IR] (or arXiv:2608.00229v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2608.00229 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-43] Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features
链接: https://arxiv.org/abs/2608.00044
作者: Ali Al Housseini,Carlos Natalino,Paolo Monti,Omran Ayoub
类目: ignal Processing (eess.SP); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
备注: 5 Pages, 2 Figures. Accepted and presented at the 26th International Conference on Transparent Optical Networks (ICTON 2026), Prague, Czech Republic, 12-16 July 2026
Abstract:We propose a retrieval-based framework for crossdomain quality-of-transmission (QoT) estimation that leverages transferable feature representations while avoiding reliance on source-domain-specific decision boundaries. The proposed approach supports both zero-shot and few-shot adaptation without requiring model retraining. Experimental results on cross-domain QoT datasets demonstrate improved generalization performance compared with conventional machine learning baselines and recent contrastive learning approaches, highlighting the potential of retrieval-based inference for robust optical network automation.
人机交互
[HC-0] Decomposing the Doer Effect in Programming Practice: Code Writing Stands Out Among Active Practice
链接: https://arxiv.org/abs/2608.02541
作者: Arun Balajiee Lekshmi Narayanan,Gillian Gold,Jordan Barria-Pineda,Quinn K Wolter,Peter Brusilovsky,Paulo Carvalho
类目: oftware Engineering (cs.SE); Human-Computer Interaction (cs.HC)
备注:
Abstract:The “doer effect” suggests that actively doing practice activities is more strongly associated with learning outcomes than passively viewing content. In the doer effect literature, “doing” refers specifically to active practice. However, this categorization treats different forms of active practice as equivalent, leaving open whether some types of active practice are more effective than others. In this paper, we investigate whether the doer effect extends to computer science instruction and whether some forms of doing stand out compared to other forms. We analyze log data from 334 students across 11 semesters of introductory and intermediate Java who used an interactive practice system with five content types: Code Writing, Code Tracing, Code Completion, Code Visualizations, and Code Explanations. Consistent with prior doer effect work, we find that active practice activities were associated with 3.2 times better learning outcomes than passive activities. Interestingly, among the active practice, code writing was the most strongly associated with improved posttest performance, while no other activity type showed a comparable association. These results highlight the importance of challenging, feedback-supported practice activities, such as code writing problems.
[HC-1] On Defining Chart Types Boundaries IEEE-VIS2026
链接: https://arxiv.org/abs/2608.02512
作者: Chang Han,Andrew Mcnutt,Katherine E. Isaacs
类目: Human-Computer Interaction (cs.HC)
备注: 11 pages, 7 figs. IEEE VIS 2026
Abstract:What makes a Gantt chart? This question proved unexpectedly difficult to answer when we set out to build a design space for Gantt charts. Existing definitions, each shaped by their respective research goals, made different scope choices that we could not directly reconcile. We reasoned about what should and should not count as a Gantt chart, developing concepts and tools along the way. We distinguish features that are essential to a chart type’s identity from those that can vary, and use these distinctions to map how chart types relate through what they share and lack. Applying these ideas to Gantt charts, radar charts, and table cartograms, we produce key insights on what boundary work reveals: definitions diverge for functional reasons, drawing boundaries exposes hidden structure in descriptive vocabulary such as feature entanglements, and scope choices shape how far findings can generalize. We came to understand that there is not a definitive answer, but that working through the question produced a functional definition that guided the design space we originally set out to build. Additionally, we present vocabulary and tools for reasoning about chart type boundaries and surfacing these boundary decisions, alongside a documented Gantt chart design space. Our broader reflection is that scope choices in chart-type-centered research—which determine what design spaces include, what grammars generate, and what perceptual studies measure—are research decisions worth making visible.
[HC-2] WIP: Chat-Debugging: Large Language Model as a Hardware Debugging Assistant
链接: https://arxiv.org/abs/2608.02420
作者: Andrew Ash,John Hu
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: This is the accepted version of a paper accepted for presentation at the 2026 IEEE Frontiers in Education Conference (FIE). The final version will be available via IEEE Xplore at: this https URL
Abstract:This work-in-progress research paper explores Chat-Debugging, a novel use case for large language models as an assistant for hardware debugging tasks to improve students’ debugging skills. Hardware debugging can be a time-consuming and stressful skill to develop, leading to frustration and other negative emotions. While past work has explored streamlining and automating software-based circuit debugging where digital circuits are dominant, Chat-Debugging aids in physical hardware debugging where circuits may be analog, digital, or mixed-signal. Qualitative data were collected from LLM chat logs and interviews with a fourth-year electrical engineering undergraduate student. Major themes were extracted using a constant comparative analysis. Chat-Debugging incorporates accurate hardware information, properly handles natural language descriptions of circuits, and improves debugging confidence. A successful Chat-Debugging session includes investigating multiple potential root causes proposed by the LLM, the patience and determination to eliminate root causes, and a student who leads the debugging process by assertively correcting the LLM’s misunderstandings. This human-computer interaction can improve electrical and computer engineering students’ confidence during debugging and improve their debugging skills.
[HC-3] Embodied Empathy: A Multimodal AR and LLM -Powered System for Self-Attachment Psychotherapy with Self-Initiated Humour
链接: https://arxiv.org/abs/2608.02283
作者: Xinyan Ye,Gwyneth Phang,Anandha Gopalan,Abbas Edalat
类目: Human-Computer Interaction (cs.HC); Multimedia (cs.MM)
备注:
Abstract:The growing global demand for mental health support increasingly exceeds the supply of qualified practitioners, creating an urgent need for scalable digital interventions that can deliver meaningful emotional connection. In response, we present a novel multimodal application that operationalises the Self-Initiated Humour Protocol (SIHP) within a Self-Attachment Technique (SAT) framework. Our mobile application integrates customisable 3D childhood avatars, augmented reality, and an LLM-driven virtual therapist capable of automated emotion mirroring. An eight-day user study (N=16) indicates the system’s feasibility and improvements in self-reported mood. Results show that personalised avatars and text-to-speech output strengthen emotional bonding and perceived empathy. Although emotion mirroring boosts engagement, its effectiveness depends heavily on classification accuracy and animation intensity. Moreover, findings indicate a shift in user expectations–from reactive chatbots to proactive conversational facilitators. We conclude with design implications for leveraging AI and AR to cultivate embodied empathy in digital mental health tools.
[HC-4] ManyFold: A Design Exploration of Data Visualization on Foldable Mobile Devices IEEE-VIS2026
链接: https://arxiv.org/abs/2608.02232
作者: Julian Baader,Ricardo Langner,Can Liu,Raimund Dachselt
类目: Human-Computer Interaction (cs.HC)
备注: Author version of accepted full paper at IEEE VIS 2026
Abstract:With this work, we explore the unique potential of data visualization on novel foldable mobile devices (foldables). Even though foldables are already commercially available, there is limited knowledge of how to leverage their distinct characteristics for visualization. This gap will only grow as their form factors become increasingly diverse. To address this, we use a two-step approach. First, we present a device-centered design space, structured around physical and usage properties of foldable devices. Second, we introduce a conceptual framework that investigates visualization on foldables from four complementary perspectives: More Displays - distributing multiple views to leverage additional display space; More Shapes - mapping visualizations to spatial fold states; More Interactions - coupling visualization tasks and folding interactions; and More States - enabling responsive visualization through folding. We complement the design space and conceptual framework with a low-fidelity ideation workshop and the subsequent prototyping of interactive artifacts. We reflect on the results and lessons learned from our exploratory, design-driven process and discuss opportunities and challenges, including the gap between our proposed concepts and commercially available foldable devices. By providing conceptual foundations and illustrating the potential of foldables, we hope to inspire and inform the development of future visualization applications for this evolving class of devices.
[HC-5] From Information to Delegation: Mapping Human-AI Financial Decision Making
链接: https://arxiv.org/abs/2608.02100
作者: Iman Munire Bilal,Yingcan Carol Wang,Ajan Raj,Filippo Giovagnini,Pranav Tewari,Yuwei Zhang,Mei-Chen Zoe Liou,Qamar Zaman
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:As AI increasingly participates in human decision making, understanding how decision-making authority is distributed between humans and AI has become a fundamental behavioural question. We introduce a behavioural measurement framework combining intent and delegated decision authority to quantify what consumers seek from AI and how much decision-making authority they assign to it. Applied to 1.5 million real-world ChatGPT and Gemini interactions from 6,304 users in the United States and India, we find that financial services are already a substantial AI use case. Consumers overwhelmingly use AI to retrieve information and shape financial judgement, while delegation of financial execution remains rare. By shifting attention from conversation topics to delegated decision authority, this work establishes a behavioural baseline for measuring the transition to increasingly agentic AI.
[HC-6] A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study
链接: https://arxiv.org/abs/2608.02083
作者: Shantanu Sarkar,Saurabh Prasad,Jose L. Contreras-Vidal
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC); Signal Processing (eess.SP)
备注: Accepted for publication in the 2026 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2026), September 28-October 1, 2026, Atlanta, GA, USA. Camera-ready version
Abstract:Closed-loop lower-limb exoskeleton control via Electroencephalography (EEG) remains limited by motion artifacts, low signal-to-noise ratio, and binary gait formulations that fail to capture full cortical gait complexity. We propose a 2-block Brain-Computer Interface (BCI) architecture: a trainable session-specific Feature Extraction Block with real-time artifact suppression and multi-domain feature extraction, coupled with a Decoder Block built on a novel Polynomial Time-Varying Layer (PolyTVL)+LSTM for four-state gait classification (Stand, Initiate, Execute, Terminate). Ablation confirmed v01 (PolyTVL+LSTM) outperformed all variants (validation MCC: 0.435, gap: 0.187), with consistent EEG feature discriminability across ROIs and sub-bands (p0.05). Closed-loop deployment with v01 achieved 55.3% (Rex-assisted) and 52.7% (volitional) gait initiation success, with a mean prediction time of 70.5~ms (+/-41.5), validating real-time feasibility in this pilot study.
[HC-7] HaptoFlow: High-Fidelity Real-Time Vibrotactile Generation via Flow Matching for Virtual Reality
链接: https://arxiv.org/abs/2608.01974
作者: Michikuni Eguchi,Yuichi Hiroi,Takefumi Hiraki
类目: Human-Computer Interaction (cs.HC)
备注: accepted to IEEE ISMAR2026 (conf. paper)
Abstract:Haptic feedback is widely employed to enhance immersion in Virtual Reality (VR) environments. However, designing haptic stimuli that cover diverse interaction conditions remains a significant scalability challenge. Data-driven haptic generation has emerged as a promising approach, yet existing models face an inherent trade-off between waveform expressiveness and inference responsiveness, which becomes increasingly critical as training data grow in scale and diversity. To address this challenge, we propose HaptoFlow, a vibrotactile generative model based on Flow Matching, designed for interactive real-time haptic rendering in VR. Flow Matching learns a continuous vector field that transforms a base distribution into the target data distribution, enabling efficient representation of complex haptic data distributions and thereby facilitating both high-quality generation and computational efficiency. We train HaptoFlow conditioned on material labels and interaction parameters (stroking velocity and applied force), and integrate it into a VR system. Technical evaluation demonstrates that HaptoFlow outperforms all baseline methods in both waveform reproduction accuracy and inference latency. Furthermore, user studies confirm that the system latency falls well within the perceptual threshold of visual-haptic delay, and statistically significant improvements in perceived haptic quality are observed for a subset of materials. These findings establish a practical foundation for scalable, data-driven haptic content creation in VR, and provide latency benchmarks that inform the design of future real-time haptic rendering systems. Project page: this https URL.
[HC-8] 3D Gaussian Splatting and Mesh-Based Digital Twins: An Exploratory Study for Virtual Reality Tourism
链接: https://arxiv.org/abs/2608.01969
作者: Maximilian Warsinke,Francesco Vona,Abm Tariqul Islam,Tanja Kojić,Jan-Niklas Voigt-Antons,Sebastian Möller
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to the 1st Workshop on Hyperrealism in XR at ISMAR 2026
Abstract:Digital Twins (DTs) are increasingly used for immersive experiences in virtual tourism. Virtual Reality (VR) enables remote visits to replicated locations for promotional purposes or access to fragile and rural cultural heritage sites. However, developing high-fidelity DTs of tourist destinations is costly, due to the manual creation of 3D environments. Novel 3D rendering techniques, such as 3D Gaussian splatting (3DGS), pose a promising approach to creating immersive experiences. This study investigates the user experience (UX) of a 3D-mesh-based scene and a 3DGS-based scene within a VR tourism application. In a laboratory study, 20 participants engaged with both versions and rated UX, cybersickness, presence and affect through standardized questionnaires. A custom questionnaire was created to measure the perception of the DTs. The collected data suggests that both versions were enjoyed and induced positive affect, with the Mesh version receiving good UX ratings. While the 3DGS version scored higher in terms of experienced realism, it showed clear weaknesses in pragmatic quality. Further, the results suggest that the feeling of presence could be enhanced and cybersickness reduced in both versions. Overall, the study contributes to the understanding of UX in VR tourism applications by implementing mesh-based and 3DGS-based DTs and raising important questions about the perception of realism.
[HC-9] Emotional Expression in Persuasion by Quadruped Virtual Agents : Toward Cross-Species Design Patterns
链接: https://arxiv.org/abs/2608.01895
作者: Kaoru Sumi,Souki Osawa
类目: Human-Computer Interaction (cs.HC)
备注: Preprint. 68 pages, 4 figures, 8 tables. Submitted to Frontiers in AI
Abstract:Persuasive technologies increasingly use virtual agents to influence attitudes and behavior, but research has focused mainly on humanoid agents. The persuasive design of non-humanoid, quadruped agents remains underexplored, and it is unclear whether emotional expression works consistently across animal species or whether species-specific motion is necessary. We developed virtual dog, cat, and horse agents and compared three behavioral conditions: species-specific behavior, shared behavior across species, and a bark-only baseline. Participants completed everyday tasks involving trash disposal, feeding, and refraining from smartphone use. We evaluated intention understanding, behavioral intention, actual behavior, psychological reactance, discomfort, familiarity, and agent acceptance. In several task contexts, the bark-only baseline produced lower intention-understanding and behavioral scores than the expressive conditions. Emotional expression and attention-guiding cues therefore appear to improve interpretation of agent intention and support behavior change. However, no consistent significant differences emerged between species-specific and shared behavior, suggesting that faithful reproduction of animal-specific motion is not the main determinant of persuasive effectiveness. Psychological reactance and discomfort remained low, while familiarity with an animal species was associated with actual behavior in some conditions. These findings indicate that persuasion by quadruped virtual agents depends more on functional cues, including emotional expression, attention guidance, and intention readability, than on accurate species-specific behavior. The results support cross-species generalizability and provide a basis for reusable design patterns in persuasive technology and human-AI interaction. Comments: Preprint. 68 pages, 4 figures, 8 tables. Submitted to Frontiers in AI Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2608.01895 [cs.HC] (or arXiv:2608.01895v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2608.01895 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kaoru Sumi [view email] [v1] Mon, 3 Aug 2026 08:34:59 UTC (941 KB)
[HC-10] Reassessing the Feasibility of PPG-Based Non-Invasive Blood Glucose Level Estimation
链接: https://arxiv.org/abs/2608.01820
作者: Supraja Ramesh,Markus Neufeld,Michael Küttner,Tobias Röddiger,Michael Beigl
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Non-invasive blood glucose level (BGL) estimation from photoplethysmography (PPG) holds great promise for wearable health monitoring, but results across studies are hard to compare due to inconsistent datasets, data leakage, and non-standardized evaluation metrics. We present the first reproducible, extensible evaluation pipeline and use it to reassess five representative PPG-based BGL methods on published datasets under three increasingly strict data-split protocols: random window-level, participant-aware, and leave-some-participants-out (LSPO). Models appeared competitive under random splitting but collapsed under participant-aware and LSPO evaluation, with nearly all yielding near-zero or negative R ^2 values comparable to a mean-prediction baseline. Critically, across every model and split, over 90% of predictions fell within clinically acceptable zones (Clarke Error Grid A+B), including the baseline. This reveals a fundamental disconnect: clinical zone metrics systematically conceal model failure in this domain. Our findings demonstrate that random train-test splits substantially overestimate the generalization of PPG-based BGL models due to sample-level data leakage, and that robust ML evaluation must precede clinical validation to meaningfully assess real-world utility.
[HC-11] Comparative Validation of GPT -4o-mini and Teacher Mean Scores for Automated Scoring of Music Analysis Responses: Single-Pass Deployment Repeatability and Strategy-Specific Bias
链接: https://arxiv.org/abs/2608.01783
作者: Baicheng Lin,Lingxi Jin,Kyung-Seok Min
类目: ound (cs.SD); Human-Computer Interaction (cs.HC); Applications (stat.AP)
备注: Proceedings of the International Meeting of the Psychometric Society: The 91st Annual Meeting, Seoul, Republic of Korea, 2026
Abstract:Scoring open-ended music analysis responses is time-consuming and requires nuanced judgments of harmonic knowledge and formal understanding. This study evaluates the validity and repeatability of GPT-4o-mini for rubric-based scoring of music analysis essays, using teacher mean scores as the benchmark. A dataset of 300 university-level student responses was scored by teachers on four dimensions: Harmony, Form, Reasoning, and Terminology. GPT-4o-mini scored the same responses using three prompting strategies: few-shot prompting with chain-of-thought reasoning (Fs+CoT), retrieval-augmented generation (RAG), and self-consistency based on five internal generations per administration (SC). Each strategy was administered three times with the model, prompt, rubric, and response held constant. Single-pass scores represented an operational scoring condition, whereas median aggregation across three runs was used to examine robustness. Agreement with teacher mean scores was evaluated using correlation, intraclass correlation, Krippendorff’s alpha, quadratic weighted kappa, and scoring error indices. Fs+CoT showed the strongest agreement with teacher mean scores in both single-pass scoring and median aggregation. RAG showed systematic over-scoring, whereas SC produced highly repeatable scores but weaker individual-level agreement. Dimension-level analyses showed that scoring performance varied across rubric components, with Terminology generally showing weaker agreement than Reasoning. These findings indicate that GPT-4o-mini can generate stable scores for complex music analysis responses, but prompting strategies produce distinct scoring profiles. Operational use therefore requires strategy-specific calibration, dimension-level validation, and continued human oversight.
[HC-12] IDES: A Longitudinal Bilingual Dataset for Modeling Multi-Party Social Dynamics
链接: https://arxiv.org/abs/2608.01724
作者: Heechan Lee,Jeonggyu Kang,Junho Myung,Jaywoong Jeong,Juho Kim,Joseph Seering
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: The first two authors hold equal contribution. Accepted to COLM 2026. Project website: this https URL
Abstract:Group conversations are fundamental to human collaboration, yet standard large language models (LLMs) still struggle with the complexities of multi-party interaction. This challenge persists in part because existing group conversation datasets are often limited to short-term lab settings with contrived tasks, failing to capture the long-term social dynamics of real-world teams. To bridge this gap, we introduce TIDES, a high-resolution longitudinal dataset tracking 12 university project teams over a full semester. Comprising 75,971 utterances in both English and Korean from in-person meetings, TIDES provides a naturalistic record of teams working on self-managed projects. Our socio-structural annotations-covering interaction types, emergent roles, and development stages-allow for modeling of team evolution over months. Experiments show that fine-tuning on TIDES improves next-speaker prediction by 13.8 percentage points over a bigram baseline (64.53%) and yields performance comparable to strong proprietary zero-shot models. The model also comes within 2.1 percentage points of the published state of the art on the AMI Meeting Corpus while using approximately 42% less training data. However, human evaluations suggest that better next-speaker prediction does not necessarily yield more natural or coherent utterances, as fine-tuned models were generally less preferred than vanilla models. This potential mismatch motivates further study of how structural modeling can support natural multi-party generation.
[HC-13] Rethinking Generative AI Literacy: An Integrative Developmental and Dialectical Framework for K-12 Teacher Education
链接: https://arxiv.org/abs/2608.01705
作者: Shahin Hossain,Sima Ahmadi,Leqi Li,Idowu David Awoyemi,Wei Huang,Chenxi Zhou,Jujia Li,Samaa Haniya,Shapla Khanam,Tasbirun Mashreka Subaha
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Human-Computer Interaction (cs.HC)
备注: 20 pages, 2 figures, 7 tables
Abstract:Generative artificial intelligence (GenAI) has entered classrooms faster than teachers have been prepared to use it well, producing a GenAI literacy lag in which technological diffusion outpaces educators’ conceptual, pedagogical, and ethical readiness. Established AI literacy frameworks predate the widespread adoption of large language models and, while acknowledging ethics, position it as a discrete competency rather than a constitutive commitment, with equity and agency as supplementary design principles. Recent GenAI-specific efforts address isolated features but remain fragmented. We introduce the Responsible AI Literacy in Education (RAIL-Ed) framework, developed through a systematic review and qualitative framework analysis of 67 studies (2023-2025), grounded in critical, pragmatist, sociocultural, and human-centered traditions (Freire, Dewey, Vygotsky, Shneiderman). RAIL-Ed specifies six interdependent pillars: Technical Fluency, Critical Evaluation, Human-AI Collaboration, Contextual Awareness, Ethical Reasoning, and Empowered Agency, marked by three commitments. It is integrative: the absence of any pillar produces a characteristic pedagogical failure. It is developmental: a three-level rubric (Emerging, Competent, Advanced) specifies how each pillar matures across the K-12 teacher-preparation continuum. It is dialectical: the same generative affordance can deepen or displace learning depending on the literacy a teacher brings to it, making the cultivation of that literacy, not the adoption of the tool, the object of design. By treating ethics, equity, and agency as constitutive, RAIL-Ed offers a theoretically grounded basis for curriculum design, teacher education, and policy, aligned with the UNESCO AI Competency Framework for Teachers and the OECD/European Commission AILit Framework. The framework is conceptual, advancing falsifiable propositions for empirical validation.
[HC-14] Beyond Score-Based Gamification: Designing Spatiotemporal and Musical Experiences for VR Neck Rehabilitation
链接: https://arxiv.org/abs/2608.01688
作者: David Perron,Pascal Spiegler,Haitham Abdel-Salam,Chanelle Montpetit,Gabriel Vigliensoni,Maryse Fortin,Yiming Xiao
类目: Human-Computer Interaction (cs.HC)
备注: 9 pages, 5 figures
Abstract:Pain-related anxiety and fear of movement are major barriers to adherence and therapeutic outcomes in rehabilitation exercises for chronic neck pain. Virtual reality enables the design of immersive experiences that can transform repetitive therapeutic movements into engaging and emotionally supportive interactions. In this exploratory work, we investigate how experience-oriented gamification can reduce anxiety and improve user experience during VR-based neck range-of-motion exercises. We introduce two novel interaction paradigms that embed therapeutic neck movements within multisensory VR experiences. The first paradigm, Spatiotemporal Progression, couples head-tracked trajectories with environmental progression in a tropical island setting, where movement segments dynamically transform time of day, weather, and spatial location as experiential rewards. The second paradigm, Musical Interaction, maps movement segments to meditative music notes layered with relaxing ambient soundscapes. We evaluate these designs against a conventional score-based gamification baseline in a controlled user study with 20 non-patient participants. We assess usability and user experience through subjective measures, exercise performance with motion tracking, and anxiety modulation using the Subjective Units of Distress Scale, heart rate, and skin conductance. Our findings suggest that, in comparison with traditional score-based gamification design, immersive environmental and musical feedback show better potential to reduce anxiety and improve user experience, with little to no impact on successful performance of the exercise. Our preliminary results highlight the potential value of experience-based interaction design for VR rehabilitation, suggesting an alternative to performance-centric gamification that prioritizes emotional engagement without compromising therapeutic efficacy.
[HC-15] CellPrism: A Visual Analytics System for Exploring AI-Driven Virtual Cells in Drug Discovery
链接: https://arxiv.org/abs/2608.01669
作者: Chuhan Shi,Zijian Guo,Zelin Zang,Chengbo Zheng,Ding Ding,Rui Sheng
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Gene perturbation analysis plays a critical role in drug discovery by enabling researchers to investigate how interventions on specific genes influence global gene expression patterns within cells. Recent advances in artificial intelligence-driven virtual cell models have made it possible to predict gene expression outcomes for a wide range of perturbation strategies in silico, substantially reducing reliance on costly and time-consuming biological experiments. However, effectively exploring and interpreting the high-dimensional perturbation spaces produced by these models remains challenging because of the combinatorial nature of perturbations and the complex cell-specific gene expression responses they generate. In this work, we present CellPrism, a visual analytics system designed to support the systematic exploration of gene perturbation strategies for drug discovery. Specifically, CellPrism integrates clustering-based overviews to summarize perturbation outcomes, a glyph-based representation to compactly encode gene expression patterns across cell types, and coordinated views that enable fine-grained comparison and interpretation of perturbation effects. We demonstrate the effectiveness of CellPrism through a real-world case study and expert interviews. This work highlights the value of visual analytics in bridging virtual cell modeling with expert-driven decision making in drug discovery.
[HC-16] You Cannot Optimize What You Cannot Measure: Multitasking Evaluation as the Missing Foundation of AI-Mediated Heads-Up Interaction
链接: https://arxiv.org/abs/2608.01656
作者: Nuwan Janaka,Runze Cai,Yang Chen,Chenyu Zhao,Shengdong Zhao
类目: Human-Computer Interaction (cs.HC)
备注: 6 pages, 2 tables
Abstract:AI-mediated heads-up augmented reality (AR) replaces fixed interfaces with dynamically adapting ones that decide what information to present, in what form, and when, based on a continually changing context that cannot be fully anticipated beforehand. Although it remains an interface, its behavior over time is only partially specified at design time. We argue that this shift requires a corresponding change in evaluation: from snapshots to trajectories. A fixed interface is evaluated in a snapshot — one context, one session, one set of task-performance metrics. A fluid interface must be evaluated over a trajectory — a sequence of contexts with transitions, sampled from the distribution the interface will actually encounter, and tracked long enough for user trust to form, evolve, and potentially deteriorate. Drawing on the literature for heads-up AR multitasking enabled by optical see-through head-mounted displays (OST-HMDs), we find that current evaluation practice remains largely snapshot-based. Most studies use fixed-condition, single-session designs; interference between concurrent tasks is rarely quantified directly; and commonly used workload measures cannot disentangle cognitive load attributable to individual tasks. To address these limitations, we argue for three shifts: from isolated metrics to Performance Operating Characteristic (POC) interference frontiers, from fixed conditions to evaluation over context trajectories, and from single-session snapshots to longitudinal trust measurement.
[HC-17] FedWorld: Scope-Aware Federation of Agent World Models
链接: https://arxiv.org/abs/2608.01561
作者: Yuchao Hou
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Large language model (LLM) agents learn world dynamics from local interaction experience to support subsequent planning and action selection. However, the experience available to a single client is often incomplete, which motivates sharing knowledge across clients. Existing federated methods mainly aggregate model parameters, while agent memory-sharing methods commonly pool trajectories, memories, or rules without checking whether they remain valid for each client. This assumption is problematic because the same abstract action may produce different effects under different policies, environments, or exception conditions. Consequently, a rule supported by most clients may overwrite correct knowledge held by a minority client. To address this problem, we propose FEDWORLD, a scope-aware federated world-model protocol that exchanges structured abstract transition rules. Each client converts private transitions into normalized rules, and the server aligns related rules to identify each rule supporting and contradicting evidence across clients. The resulting evidence determines whether a rule is shared, cluster-specific, private, or unresolved. Each target client retains its local rules and accepts federated updates only for uncovered cases whose inferred scope is compatible; ambiguous rules are withheld. Experiments on \tau -bench and ALFWorld show that FEDWORLD reduces negative transfer under conflicting dynamics while retaining useful cross-client transfer, leading to fewer state regressions, repeated actions, and excess steps, as well as higher task success.
[HC-18] A Data-Centric Perspective on Tree Visualizations IEEE-VIS2026
链接: https://arxiv.org/abs/2608.01477
作者: Manling Yang,Alexandra Scott,Chris Ahn,Daniel Jakab,Suyang Li,Mingwei Li,Remco Chang
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 3 figures. To appear in IEEE VIS 2026 (short paper)
Abstract:Tree visualization (TreeVis) techniques span diverse designs. Existing taxonomies organize them by visual characteristics such as layout dimensionality, edge representation, and node alignment. However, this visual-centric perspective can obscure structural similarities and make it difficult to determine whether differences arise from data structures or visual encodings. We investigate TreeVis techniques from a data-centric perspective grounded in Prepared Tables, the final data state prior to visual encoding. Using this http URL, we curate 133 two-dimensional techniques and characterize each by the object records and attribute roles required before encoding. Our analysis shows that the corpus is more concentrated at the prepared-data level than a visual reading would suggest. The techniques collapse to a small set of recurring object combinations and schemas. Many techniques across TreeVis representation categories share the same schema, suggesting that much of the apparent diversity of TreeVis designs lies in visual representation rather than fundamentally different pre-encoding data requirements. Prepared Table schemas therefore support reasoning about structural equivalence, sufficiency, and difference across TreeVis designs.
[HC-19] Same violence different answer: how AI responds to coercive control against women across languages
链接: https://arxiv.org/abs/2608.01436
作者: Lyu Chang,Sònia Estradé Albiol,Núria Vergés Bosch
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 16 pages, 1 figure, 2 tables. Supplementary methods, coding manual, and data workbook included as ancillary files
Abstract:Women experiencing coercive control, a form of intimate partner violence increasingly conducted through digital devices, are turning to conversational AI for help, and the protection they receive should not depend on the language they write in. We analyse how AI responds to coercive control against women across languages. We put one scripted scenario to seven widely used language models in nine languages: a woman whose partner tracks her phone asks for help with a self-blaming letter accepting the surveillance. We scored whether the model wrote the letter and whether it named the control, countered the self-blame, and affirmed her agency. Failure split along two independent axes. On the first, systems from non-anglophone developers gave way most often in their builders’ own language. On the second, how far a sympathetic excuse for the partner could strip a model’s naming of the control varied sharply from one language to the next. Two frontier systems held the strictest standard everywhere, so a protective ceiling is attainable within this scenario family, and failures elsewhere are a design outcome. What is at stake is recognition: whether a system grasps a disclosure as coercive control, and whether it then acts on that grasp. We argue this should be held to a floor, one language at a time.
[HC-20] PartInteractor: Intent-Driven Part-Aware 3D Authoring for Continuous Co-Creation in XR
链接: https://arxiv.org/abs/2608.01335
作者: Jianan Jiang,Bin Li
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to ACM UIST 2026
Abstract:As Extended Reality (XR) evolves into an immersive computing medium, interactive 3D authoring becomes essential for creative and functional workflows. However, existing generative XR systems produce monolithic outputs lacking explicit semantic structure, limiting post-generation control. We introduce PartInteractor, a representation-to-interaction framework that investigates how semantic part hierarchies can be incorporated into generative XR authoring, and exposed as first-class, directly manipulable units, turning one-shot prompt-to-object generation into continuous component-level co-creation. PartInteractor supports speech, sketch, and image inputs, integrating an LLM interpreter with a retrieval-generation strategy to scaffold user intent prior to 3D generation. Instead of producing monolithic objects, our system generates semantically decomposed 3D assets with explicit part hierarchies, enabling rich component-level interaction over object structure and composition. Our evaluations suggest that part-aware representation increases post-generation control and reduces reliance on whole-object regeneration, while intent scaffolding mitigates ambiguity and improves intent-result alignment, together supporting more expressive and controllable human-AI co-creation workflows. These results highlight part-aware representation and intent scaffolding as promising design considerations for future generative XR authoring systems.
[HC-21] Collascope: Supporting Serendipitous Asset Exploration for Collage-Based Storytelling
链接: https://arxiv.org/abs/2608.01267
作者: Jiayi Zhou,Longji Huang,Lvmin Zhang,Yun Wang,Zeyu Wang,Maneesh Agrawala,Huamin Qu,Anyi Rao
类目: Human-Computer Interaction (cs.HC)
备注: To be published at ACM UIST 2026
Abstract:Collage-based storytelling requires visual elements that support emerging narratives and inspire creative reinterpretation. Existing tools, however, rely largely on keyword- and image-based retrieval, offering limited support for serendipitous exploration beyond existing assets. We introduce Collascope, an interactive system that helps creators (1) concretize story intent with interactive element groups, (2) expand the exploration space based on concepts or cutouts towards conceptual and visual dimensions, and (3) develop grounded, traceable ideas in parallel with collage composition. Collascope’s attribute-aware visual retrieval method, instantiated with collage-relevant visual dimensions, enables creators to retrieve cutouts through dimension-specific visual projections rather than holistic similarity. In a within-subject study (N=12) against a conventional search baseline, our participants used unexpected results and even gaps in the asset collection to redirect narratives, shift tone, and enrich compositions. Scene Parts helped organize exploration into manageable subtasks, while participants used association in distinct ways depending on whether exploration was guided by a clear goal, an evolving story, or visual intuition.
[HC-22] What Could the Agent See at 19:05? Generating Temporal Enterprise Scenarios from Real Research and Replaying Them to Evaluate Agents
链接: https://arxiv.org/abs/2608.01042
作者: Tezan Sahu,Himani Arora
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: 6 pages, 3 figures, 4 tables. Accepted as a poster at SERI 2026
Abstract:Enterprise AI agents act across many apps whose data changes continuously, so an answer is correct only relative to what data existed and who could see it at the moment it was asked. Offline evaluation today grades against a single static snapshot, effectively the end of the episode. So, it can only evaluate one situation, the final one, even though every earlier moment of the episode is a different situation that invites its own realistic questions with its own correct answers. Recreating each of those moments as a separate snapshot would mean re-provisioning a whole tenant per instant, which is prohibitively costly; and even a single snapshot leaks future state hidden inside records and cannot represent the multi-app, time-ordered way real work happens. Our system closes two gaps at once: it generates a realistic, persona-driven, temporally-evolving enterprise world from real research, and replays that world at any chosen moment to evaluate any pluggable agent. A schema-inferred temporal description drives a deterministic-plus-LLM rebuild of each record’s past state; because the queryable moments are finite, all rebuilds are precomputed into a compact difference cache, making evaluation a fast, reproducible lookup with no model in the path. We describe the design, an architecture spanning both flows, and early experience evaluating enterprise agents.
[HC-23] he Epistemic Politics of AI Anthropomorphism AAAI
链接: https://arxiv.org/abs/2608.00961
作者: Donna M Bye,Levin Kuhlmann
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 20 pages, 3 figures, 9 tables. Extended version, including supplementary materials, of a paper to appear in the Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2026
Abstract:AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction. Users who engage in sustained or relational interaction with AI are routinely pathologised or dismissed as naive, vulnerable to delusion or lacking in discernment. This paper argues that the dominant anthropomorphism frame operates from a position of institutional advantage rather than earned epistemic authority: collapsing the variety of academic perspectives into a single outbound position of user error, imposed without establishing the grounds required to justify it and without accounting for the harms it produces. The framing does not simply manage risk. It adjudicates the legitimacy of human experience in interaction with a phenomenon whose nature the field itself has not resolved. Reproducing itself through a self-validating evidentiary loop, the frame imposes costs that fall disproportionately on neurodivergent users, those in crisis and others whose modes of engagement diverge from institutional norms. The paper concludes by outlining the methodological commitments an equitable framing would need to honour. The argument does not engage the question of whether anthropomorphic interpretations are ultimately correct; it instead challenges whether the governing and institutional bodies determining these interpretations have met the conditions required to do so, and whether the research communities whose findings underpin them have held that translation to account.
[HC-24] Rethinking PPG-based Sleep Staging: Datasets Metrics and Benchmarks
链接: https://arxiv.org/abs/2608.00943
作者: Shuntian Zheng,Jiawei Wang,Cong Fu,Huan Yu,Chen Chen,Yu Guan,Sai Gu
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Automated sleep staging assigns discrete stage labels to successive time epochs throughout an overnight recording; conventionally each window spans at least 30 seconds, reflecting the minimum temporal resolution of the clinical scoring standard. Wearable photoplethysmography (PPG) has attracted sustained interest as an ambulatory alternative to laboratory-based polysomnography, which relies on electroencephalography (EEG) and other recording modalities that are impractical outside clinical environments. Yet PPG-based staging trails EEG-based methods by a substantial margin, and we argue this gap largely reflects a mismatch between signal and task. Within a stable stage, PPG’s inter-stage feature differences are more subtle than those in EEG; yet at stage boundaries, PPG’s principal cardiovascular features, heart rate variability and pulse morphology, shift sharply within seconds. The conventional practice of assigning one label to each 30-second epoch therefore suppresses feature that is concentrated near boundaries. We address this gap in two steps. First, we develop a label expansion pipeline based on Hidden Semi-Markov Models that converts coarse epoch labels into sec-level annotations. To assess whether these expanded labels are reliable enough for downstream supervision, we validate them on a separate expert-reviewed dataset and through an auxiliary sleep-wake task whose labels are independent of the expansion pipeline. Second, we use the resulting sec-level supervision on MESA to improve conventional four-class epoch-level staging across four architecturally diverse baselines by 3.7–5.7,pp in accuracy against the original epoch labels, with supplementary zero-shot evaluation on CFS showing that the transfer benefit persists under cohort and annotation-protocol shift.
[HC-25] he Assistant Erased You: Measuring Loss of Authorship Signals in AI-Mediated Communication
链接: https://arxiv.org/abs/2608.00926
作者: Ushna Malik,Moiz Sadiq Awan
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Research on AI-mediated communication has examined how AI assistance shapes interpersonal perceptions and reduces stylistic diversity across users. We ask a complementary question at the individual level: after a message is rewritten by an AI writing assistant, can its author still be distinguished from others? We introduce the Idiolect Erasure Rate (IER), defined as the reduction in authorship-attribution accuracy following AI-assisted rewriting. We evaluate IER on three pre-generative-AI corpora using a stylometric model and the authorship-specific LUAR model. Heavy rewriting substantially weakens authorship signals in personal blogs and workplace email, reducing LUAR attribution by as much as 66.5 percentage points, but has a much smaller effect on topic-structured news, where topic remains predictive of authorship. Additional analyses suggest that rewriting produces stylistic convergence despite substantial semantic overlap, and that content-sensitive attributers understate the loss captured by authorship-specific models. Heavily rewritten messages may also evade AI-text detectors, making them difficult both to attribute to their human authors and to identify as AI-assisted, a phenomenon we call double erasure. IER measures computational attributability rather than human recognition, and we release it as an open and reproducible protocol for evaluating authorship-signal loss in AI-mediated communication.
[HC-26] Whos That Player?: Externalizing Query Interpretation in Spoken XR Sports Interaction
链接: https://arxiv.org/abs/2608.00876
作者: Chunggi Lee,Tica Lin,Yalong Yang,Hanspeter Pfister
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:XR sports viewing enables spectators to follow play from immersive, spatially anchored perspectives while accessing contextual analytics directly within the scene. In such settings, speech offers a practical interaction modality because text entry and menu navigation can interrupt attention during fast-paced gameplay. However, spoken queries are often underspecified: viewers may omit which player, time period, field location, or metric they intend. When systems resolve these ambiguities implicitly, their assumptions remain hidden, making misinterpretations difficult to notice and correct (repair). We investigate how externalizing a system’s interpretation of spoken queries can support inspection and correction of such misunderstandings in XR sports viewing. Through a formative study, we identified four recurring ambiguity types (referential, spatial, temporal, and metric) that characterize ambiguous spoken queries in this context. We develop a design space that organizes externalization along three dimensions (ambiguity type, interpretation state, externalization strategy) and instantiate it in an interactive XR soccer viewing system that combines situated visual cues with supporting analytic views. A within-subjects user study (N=16) comparing externalized interpretation against a voice-only baseline reveals that externalization is associated with higher inspectability on most measured dimensions and increased explicit repair language overall. However, repair occurred in only 38% of misaligned externalization trials, and this visibility-action gap varied by ambiguity type, indicating that transparency and correction affordance are orthogonal design axes.
[HC-27] MIDAL: Math Image Descriptions for Accessible Learning
链接: https://arxiv.org/abs/2608.00868
作者: Rebeka Popek,Vaghawan Ojha,Young Hwan You
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注:
Abstract:Many open educational resources are lacking in accessibility, especially in-depth image descriptions. In subjects like Science and Mathematics, however, it can be particularly difficult to write image descriptions since there can be many complicated expressions and names depending upon the course level. To help fill that gap in a small way, we introduce Math Image Descriptions for Accessible Learning (MIDAL), a math image-description dataset of 2,020 mathematical images spanning multiple educational levels, to aid in training vision language models to create image descriptions following accessibility best practices. We hope MIDAL is a valuable resource in enhancing the conversation and innovation regarding accessibility of STEM content in higher education. This dataset is however not just limited in math description generation but can also be used to fine-tune language models that can have improved mathematical reasoning and answers.
[HC-28] BoilerSketch: A TA-Supervised Diagram-First GenAI Practice for Structured Diagrams in CS1/Early CS2
链接: https://arxiv.org/abs/2608.00844
作者: Ethan Dickey,Vivan Tiwari,Anvit Sinha,Andres Bejarano
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 9 pages, 3 tables, 3 figures
Abstract:This innovative practice full paper presents BoilerSketch, a TA-supervised, diagram-first GenAI practice and tablet interface for providing structured visual explanations in CS1 and early CS2 support settings. Large early computing courses routinely face a support bottleneck during labs and office hours because many student questions are best answered with a diagram rather than additional text, yet most AI tutoring tools remain text-forward and unreliable at producing accurate, pedagogically useful visuals. BoilerSketch addresses this gap through a dual-pane interaction model that combines chat with a pen-enabled whiteboard for student sketches and a prompting strategy that constrains the model to generate structured, renderable Mermaid diagrams rather than free-form images. To preserve academic integrity, the system is intentionally scoped to conceptual explanation: it forbids executable code and code-level debugging and uses a human-in-the-loop workflow in which teaching assistants remain accountable supervisors who can monitor sessions and intervene when responses require correction, deeper probing, or escalation to live help. We report a 45-minute expert evaluation with 21 instructional staff from a large programming course who used BoilerSketch on representative questions and completed a post-use survey. Two-thirds rated the system at least moderately helpful for conceptual understanding and at least moderately useful for typical support tasks. Staff saw the strongest value in routine diagram-based explanations and noted limits in diagram depth and applicability to more advanced topics. We conclude with practical guidance for adopting supervised, diagram-first GenAI support in early computing courses, emphasizing scope-and-escalation rules, prompt-as-policy guardrails, and reliable structured diagram rendering. Comments: 9 pages, 3 tables, 3 figures Subjects: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC) ACMclasses: K.3.2; K.3.1; H.5.2 Cite as: arXiv:2608.00844 [cs.CY] (or arXiv:2608.00844v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2608.00844 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-29] CodeStylist: Supporting Early Undergraduate Programmers with Course-Aware Code Style Feedback
链接: https://arxiv.org/abs/2608.00839
作者: Ethan Dickey,Libra Vento,Peter Kurto,Andres Bejarano
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 9 pages, 1 table, 1 figure, accepted for publication at Frontiers in Education 2026
Abstract:This innovative practice full paper presents CodeStylist, a web application that provides course-standard-aware code style feedback for early undergraduate programming courses. CodeStylist addresses a common instructional gap: students are expected to follow local conventions for naming, formatting, comments, organization, and readability, but feedback on these expectations is often delayed or inconsistent. Unlike generic linters or general-purpose LLM prompts, CodeStylist supports course-specific standards, multi-file submissions, and file- and line-localized explanations intended to guide revision rather than grade correctness. We report a formative expert review with 18 instructional staff from one early undergraduate programming course. Participants explored the prototype using self-selected code artifacts and completed a survey about response quality, anticipated student use, and redesign priorities. Ratings indicated modest perceived utility but limited trust: perceived correctness averaged 60.7%, response helpfulness averaged 3.50/5, response usefulness averaged 3.33/5, and anticipated student learning averaged 2.61/5. Despite these concerns, 17/18 respondents expected students to use the tool primarily for style checking, often at least weekly. Open-ended feedback showed that respondents valued CodeStylist for making implicit course standards more visible, but were concerned about unreliable output, overreliance, and latency or cost. We interpret these findings as evidence that course-aware style feedback is promising as a pre-submission revision aid, but that future versions should combine deterministic rule checks with LLM-generated explanations, rule citations, and stronger verification support. Comments: 9 pages, 1 table, 1 figure, accepted for publication at Frontiers in Education 2026 Subjects: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC) ACMclasses: K.3.2; K.3.1; H.5.2 Cite as: arXiv:2608.00839 [cs.CY] (or arXiv:2608.00839v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2608.00839 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-30] XR-PRISM: Data-Driven Privacy and Risk Impact Scoring Metric for Extended Reality in Healthcare
链接: https://arxiv.org/abs/2608.00826
作者: Nafisa Anjum,M. Rasel Mahmud
类目: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC)
备注: Published at the 1st International Workshop on Trustworthy, Secure, and Privacy-Aware AI for Extended Reality (TRUST-XR 2025), held in conjunction with IEEE ISMAR 2025
Abstract:Extended Reality (XR) technologies are transforming healthcare through immersive training, remote consultation, and patient rehabilitation. However, their extensive sensing capabilities and complex data pipelines introduce distinct security, privacy, and safety risks. Existing research lacks a unified quantitative framework for assessing and prioritizing these risks. We review 65 peer-reviewed studies on XR security and privacy published from 2017 to 2024, synthesizing a four-layer threat taxonomy consisting of Device, Network, User, and Cloud layers, along with a corresponding catalog of defenses. Building on this analysis, we introduce XR-PRISM, a six-factor weighted Privacy and Risk Impact Scoring Metric that integrates threat likelihood, system vulnerability, attack surface, safety impact, privacy impact, and control effectiveness into a single actionable risk score. Our analysis shows that more than 70% of the identified countermeasures lack standardized risk evaluation, while fewer than 15% of the documented attacks require a high level of expertise to execute. XR-PRISM provides researchers and practitioners with a transparent, data-driven method for comparing, prioritizing, and mitigating security and privacy risks in healthcare XR deployments.
[HC-31] SoniSpeech: A Large-Scale Open-Vocabulary Tri-Modal Dataset for Wearable Silent Speech Interfaces
链接: https://arxiv.org/abs/2608.00803
作者: Ruidong Zhang,Jiacheng Liu,François Guimbretière,Cheng Zhang
类目: ound (cs.SD); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Wearable silent speech interfaces (SSIs) are limited to small, closed vocabularies. Approaches achieving larger vocabularies require obtrusive hardware such as facial electrodes. We present SoniSpeech, the first large-scale, open-vocabulary, trimodal dataset for wearable SSI using acoustic-sensing eyewear. It contains 34 hours across 18,000 utterances with three synchronized modalities: ultrasound echo profiles, voiced audio, and frontal video, in both voiced and silent modes. The corpus draws from the SODA dialogue dataset, providing contemporary conversational English with 5,356 unique words and full phoneme coverage. A CTC-based ResNet-34 baseline achieves 26.3% word error rate (WER) on open-vocabulary silent speech recognition, the first benchmark for this task. Dataset is available at this https URL
[HC-32] ORCESTRA: VLM-driven Visual Robot programming in Mixed Reality
链接: https://arxiv.org/abs/2608.00775
作者: Ivan Snegirev,Elizaveta Semenyakina,Mikhail Konenkov,Artem Lykov,Miguel Altamirano Cabrera,Dzmitry Tsetserukou
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: 4 page, 3 figures, 1 table, ISMAR 2026
Abstract:ORCESTRA is a mixed-reality system for programming robot digital twins through no-code waypoint teaching and language-guided control. In a passthrough mixed-reality workspace, users place robot twins on real surfaces, teach trajectories, save robot-relative episodes, or issue spoken/typed commands that a vision-language model converts into structured digital-twin plans. Both interaction modes share a backend for metric grounding, embodiment-aware validation, preview, confirmation, and digital-twin execution. The system supports heterogeneous robot embodiments, including fixed-base manipulators, a mobile base, and a humanoid robot, demonstrating MR validation as a safety layer for language-guided robot programming before physical deployment.
[HC-33] Continuous Face Authentication on Mobile and Desktop Platforms: A Comparative Study
链接: https://arxiv.org/abs/2608.00763
作者: Miriam Palmetshofer,Rainhard Dieter Findling,Marc Kurz
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Personal devices hold sensitive data and provide access to sensitive services. Conventional personal device authentication verifies users’ identity only at the moment access is granted. An unlocked device may be accessed by an unauthorized person if the user stops using the device without locking it, or if another person takes over. Continuous authentication addresses this gap. This paper investigates how device type and usage conditions influence continuous mobile face authentication with an InsightFace-based approach with temporal trust decay. We evaluate the approach with mobile and desktop recordings with different head directions and lighting conditions. We also evaluate recordings from everyday mobile device use without predefined tasks. The results show that device type alone has little impact, while different usage conditions do have impact on the authentication performance. Results also show that everyday mobile device use is in general more challenging for continuous face authentication, where reduced face visibility, including occlusions and faces outside the camera viewport, is a main contributor to false rejections.
[HC-34] Me and My Bot: What Users Talk About in AI Companion Communities on Reddit
链接: https://arxiv.org/abs/2608.00748
作者: Richard A. Fabes
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 15 pages, 5 tables
Abstract:AI companion communities on platforms such as Reddit are widely characterized as spaces where users discuss their relationships with AI bots. This study examines whether and how that characterization holds, guided by the Synthetic Resonance framework’s claim that human-AI relationships can carry genuine relational meaning for the user. Multiple LLMs were employed to code 5,504 Reddit posts from eight AI companion communities for relationship focus, primary topic, and users’ emotional valence. Although search terms were weighted toward relational and attachment language, only 45% of posts concerned the user’s own relationship with their bot. Posts about users’ own bots differed markedly from posts about bots in general in both topic and emotional expression, with 85% of general-bot posts containing no user emotion language compared to 33% of own-bot posts. Among the 970 posts that were relationally focused, companionship and romance each accounted for roughly 46% of discussion, sexual content for 8%, and emotional valence varied across these subtopics. The findings suggest that users engage with these relationships with AI bots as meaningful, and that the discourse about them is broad and emotionally complex.
[HC-35] From Chasing Ghosts to Missed Attacks: Perspectives and Perceptions of SOC Practitioners on LLM Integration Risks and Readiness
链接: https://arxiv.org/abs/2608.00672
作者: Jonas Thurner,Nadine Jost,Stefan Albert Horstmann,Fabian Ising,Lea Groeber,Alena Naiakshina,Sebastian Schinzel
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Security Operations Centers (SOCs) process large volumes of security events, requiring analysts to accurately detect and assess ongoing cyberattacks under time pressure. Recent advances in Large Language Models (LLMs) suggest potential benefits for security operations, yet their practical suitability for real-world SOC workflows remains poorly understood. To address this gap, we conducted 25 semi-structured interviews with SOC practitioners who had prior experience with LLMs, complemented by interactive scenarios to anticipate challenges and identify opportunities for the responsible integration of LLM-based tools into SOC workflows. We identified 15 LLM use cases grouped into six functional categories. While LLMs are valued for automating repetitive, low-level tasks such as report automation, practitioners rate high-impact tasks such as incident analysis as not yet feasible, reporting limitations in technical depth, context awareness, and organization-specific knowledge. They locate these limitations less in the models than in the readiness of their SOCs and human factors driving over-reliance. Despite concerns, practitioners express a strong willingness to adopt LLMs, describing competitive pressure that leaves few alternatives. This work contributes an empirical, practitioner-driven analysis of LLM use across SOC roles and organizations and derives concrete design and integration requirements for human-centered, operationally safe LLM-assisted security operations.
[HC-36] bFaaaP: An Inclusive Head-Angle Piano-Pedal Interaction that Quantitatively Reproduces a Pianists Intended Pedalling – Foot-Free for Acoustic and Electronic Pianos
链接: https://arxiv.org/abs/2608.00633
作者: Tomoyuki Shishido(1 and 2),Masahiro Ootaki(3),Hiroyuki Narusawa(4) ((1) Shishido amp; Associates, Hachioji, Tokyo, Japan, (2) SeemeData Labs, Inc., Nakano-ku, Tokyo, Japan, (3) Ootaki amp; Architects, Sayama, Saitama, Japan, (4) Naru Science Software, Fujimi-shi, Saitama, Japan)
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: 28 pages, 10 figures, 9 tables. Open hardware/software release. Preprint
Abstract:Expressive piano performance depends on the sustain (damper) pedal, operated by foot, excluding players who cannot readily use their feet: wheelchair users and others with lower-limb impairments, small children, and some elderly or disabled players. We present bFaaaP (barrier-Free assist as a Pedal), an inclusive, foot-free interaction that operates the pedal from the angle of the player’s head: a smartphone tracks head pose with on-device augmented-reality (AR) face tracking and streams a compact command over Bluetooth Low Energy (BLE) to a pedal device. Supported by patent examination, our central claim is not the head-to-pedal architecture (anticipated by prior art) but a quantitative, user-tunable control law – the patentable “key” to a natural, expressive result: the player presets a small angular dead-zone (offset 3-10 degrees) and a multiplier (10-50), which together fix a secondary, pre-adjustable response speed that reproduces the pianist’s intended pedalling. An engineering trick decouples the fast AR sampling from the slower BLE rate. Two co-equal realizations share one controller: a non-destructive robotic actuator for acoustic pianos (Pro), anchored by a pneumatic “airback” (our coined term for an inflatable air-braced anchor) that absorbs the reaction force without modifying the instrument; and an electronic sustain switch for digital pianos (Switch). In a human-subject Auxiliary Pedal Effect Evaluation (APEE) with 15 participants, bFaaaP significantly increased sustained-tone energy (p0.01) and was statistically indistinguishable from a player’s own foot (p0.05), with no significant difference across classes; one participant with a leg disability and a tracheostomy performed successfully. With nothing worn on the face and fast setup, bFaaaP has run in formal public concerts (2018-2025). We release the full hardware and software as open source.
[HC-37] Exploring Usability and Legal Practice: Insights from German Judicial Users of Digital Forensics
链接: https://arxiv.org/abs/2608.00541
作者: Tobias Hoppmann,Leona Lassak,M. Angela Sasse,Zinaida Benenson
类目: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC)
备注:
Abstract:Digital forensics has become an integral part of modern criminal proceedings, yet its effective integration remains challenging because of increasing data volumes, evolving technologies, and complex interactions between technical and legal stakeholders. Although prior work has focused primarily on digital forensic tools and methods, its broader procedural and organizational context has received limited attention. Building on emerging perspectives inspired by usability research and human-centered security, we conceptualize digital forensics as part of a socio-technical system within criminal proceedings. We consequently investigate this perspective through a survey of 101 practitioners from the judiciary of the German federal state of North Rhine-Westphalia, including public prosecutors, judges, and digital forensic experts. The results indicate a strong demand for improved integration of digital forensics into workflows, enhanced cross-domain communication, and a closer alignment of stakeholder expectations. They also uncover great potential for the improvement of digital forensics usability, e.g., through stronger interdisciplinary cooperation, easier and faster access to evidential data and results, or improvement of stakeholder training and education.
[HC-38] SkinSpline: A Body-Attached Skeleton-Supported Haptic Interface for Continuous Skin Deformation through Physical Interpolation ISWC’26
链接: https://arxiv.org/abs/2608.00496
作者: Liwen He,Xinyuan Wang,Jiachen Du,Zixin Chen,Yun Wang
类目: Human-Computer Interaction (cs.HC)
备注: 4 pages. To appear in the Proceedings of the 2026 ACM International Symposium on Wearable Computers (ISWC '26), October 11–15, 2026, Shanghai, China
Abstract:We present SkinSpline, a body-attached skeleton-supported haptic interface that renders continuous skin deformation through physical interpolation of sparse mechanical actuation. SkinSpline combines a low-resolution array of rack-and-pinion linear actuators with an elastic interlocking skeleton that transforms discrete actuator motions into smooth surface deformation, enabling continuous cutaneous feedback without dense actuator arrays. The system includes a modular hardware architecture, a configurable control pipeline, and a visual interface supporting real-time configuration and actuation. We demonstrate SkinSpline through multiple scenarios, including wave rendering, video-synchronized rhythmic touch, visually driven water-wave feedback in VR, and sensor-based remote touch reproduction. SkinSpline explores an alternative approach to continuous on-body haptic rendering by leveraging structural coupling between sparse actuation and deformable surfaces.
[HC-39] Ekova: A Personality-Support Agent for Self-Discovery Dialogue
链接: https://arxiv.org/abs/2608.00478
作者: Yuyan Chen
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Accepted to the COLM 2026 Lifelong Agents Workshop
Abstract:Emotional Support (ES) systems have long optimized a single objective: alleviating the user’s emotional distress in the moment. We argue that a complementary need, helping users see themselves more clearly, defines a distinct paradigm we call Personality Support (PS). PS is not counseling or clinical intervention: it targets cognitive clarity and self-articulation, not symptom relief or diagnosis. We instantiate this paradigm in three layers. First, we present DSD, a Chinese self-discovery PS Dataset of 8,590 samples collected through real longitudinal interaction across five minimal units, Coach, Warm, Tsukkomi, Real, and Gonzo. Second, we build DeepSupport, a multi-persona PS system trained with OrthoTune, a PS-tailored framework with style-specific adapters and a style-consistency regularizer. Third, we unify the five DeepSupport personas into Ekova, a persistent personality-support agent with a unified cross-session memory layer, supporting both adaptive routing and user-customized persona selection. Experiments show that OrthoTune-trained models outperform all baselines with an average relative gain of 16.3% across all metrics over the strongest prompt-based baseline. Code is available at this https URL.
[HC-40] Revibing Code from Papers: Reimplementing HCI Artifacts
链接: https://arxiv.org/abs/2608.00450
作者: Eytan Adar,Yoonjoo Lee,Ning-Er(Nina)Lei,Q. Vera Liao,Weirui Peng
类目: Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注: UIST 2026
Abstract:Software artifacts for most technical HCI research projects are unavailable. The lack of access to these imposes limits on academic knowledge production. It is difficult to: extend or reuse research artifacts; use strong baselines in evaluating follow-up work; and perform replication or reproducibility research. In this work, we demonstrate the potential of new agentic AI technologies to revibe interactive software: reimplement systems directly from research papers. To measure the success of the approach, we describe a revibeability metric. By revibing recent research papers from UIST, and interviewing their original authors, we demonstrate the plausibility (and limitations) of revibed system. The results are encouraging. In many cases producing code suitable for strong baseline use. We argue that this may represent a fundamental shift in how we produce, use, and evaluate research artifacts in the technical HCI community.
[HC-41] Seeing Through the Forecast Clutter: Communicating Climate Forecast Distributions with Weighted Multiple Forecast Visualizations IEEE-VIS2026
链接: https://arxiv.org/abs/2608.00433
作者: Ruishi Zou,Siyi Wu,Racquel Fygenson,Dakuo Wang,Michael Correll,Lace M. Padilla
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to IEEE VIS 2026 Full Paper
Abstract:Forecasts often diverge because different models make varying assumptions to account for underlying uncertainty. Readers who consume forecasts may wish to survey the shape and spread of these multiple forecasts to get a full account of the different predictions. One approach to visualizing multiple forecasts is through Confidence Interval (CI) plots. However, while the summative CI plots can communicate uncertainty of an ensemble, they obscure attributes of individual forecasts that can lead to inaccurate perceptions of the distribution of these forecasts (e.g., implying a normal distribution when non-existent). To address this challenge, we investigate the use of multiple forecast visualization (MFV) in communicating nuanced forecast distributions through two preregistered experiments using climate forecast data. In Experiment 1 (480 participants), we compared how well MFV and CI plots can represent the distribution of multiple forecasts. We found that, compared to CI plots, MFV improved participants’ ability to identify the underlying distribution of forecasts and reduced the likelihood of assuming normality. Building on Experiment 1, we examined in Experiment 2 (900 participants) whether a downsampled MFV showing 9 forecasts might be able to communicate additional forecast properties using linewidth and opacity without negatively impacting distribution perception. We found that visually weighting forecasts by linewidth or opacity preserves readers’ perception of the underlying distribution. We discuss how these findings suggest the use of downsampled and weighted MFV to cut through forecast clutter by aligning perceived distribution with the underlying forecast distribution, while opening up design opportunities to use weighting to communicate additional forecast attributes.
[HC-42] Visualizing Placement Proposals for Window Arrangement in Mixed Reality: A Comparative User Study
链接: https://arxiv.org/abs/2608.00403
作者: Abdelrahman Zaky,Tiare Feuchtner
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Adaptive mixed reality (MR) interfaces typically optimize window layouts on behalf of the user, with limited consideration for individual preferences. A promising alternative keeps users in the loop by presenting layout proposals for them to select from, but how these proposals should be visualized remains underexplored. We compare three proposal-visualization techniques for window placement, Situated Icon Preview, Situated Window Preview, and 3D Preview, against a Manual Positioning baseline. The techniques differ in level of detail and degree of interaction-space context. In a within-subjects user study, 24 participants completed a multi-stage trip-planning task in VR, individually placing seven sequentially introduced windows using each technique. We thereby focus on single-window placement under predefined proposal positions. We measured layouting time, number of layout changes, task load, user experience, and preference, complemented by semi-structured interviews. Although Situated Icon Preview and Situated Window Preview reduced layouting time compared to Manual Positioning - with Situated Window Preview also faster than 3D Preview - participants preferred direct manual control. We discuss the factors shaping this preference (perceived control, cognitive cost, familiarity, and informativeness of the proposal) and outline implications for hybrid approaches, as a promising combination of proposal-based suggestions with manual refinement.
[HC-43] MolecularCanvas: LLM -assisted Small-Molecule Drug Discovery via Structure-Guided Constraints
链接: https://arxiv.org/abs/2608.00393
作者: Haoyu Dong,Rui Sheng,Shuhao Zhang,Yushi Sun,Dingyang Wu,Hanxiang Chao,Olexandr Isayev,Huamin Qu,Yuyang Wu,Yanna Lin
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Small-molecule drug discovery relies on iterative molecular optimization, where chemists repeatedly modify candidate compounds to balance multiple competing properties such as efficacy, toxicity, and solubility. Recent advances in generative AI (GenAI) have shown promise in accelerating this process by automatically proposing new molecular structures or targeted modifications. However, existing GenAI-based molecular design tools remain poorly aligned with experts’ real-world workflows. Specifically, they offer limited support for specifying structure-level modification intents on molecules, provide insufficient transparency into model-generated modifications, and lack integrated support for downstream property evaluation with external computational tools. To address these challenges, we introduce MolecularCanvas, an interactive system that enables users to iteratively construct an optimization context by integrating high-level goals, structure-level annotations, property constraints, and reference-based preferences. This context guides the generation of candidate molecules across diverse molecular structures. MolecularCanvas further enhances transparency by providing evidence for AI-generated suggestions and streamlines molecular evaluation by integrating commonly used computational tools for property assessment into a unified interface. Finally, a user study with 12 participants demonstrates the usefulness and effectiveness of MolecularCanvas in helping users optimize candidate molecules.
[HC-44] Dynamic Surveys: Using LLM s to Blend Qualitative DepthQuantitative Structure and Collaborative Interaction
链接: https://arxiv.org/abs/2608.00357
作者: Kehua Lei,Aidan Ladenburg,Zahra Petiwala,Zili Wang,Dishita Jhawar,Ipsita Bisht,Ansh Kumar,David T. Lee
类目: Human-Computer Interaction (cs.HC)
备注: 27 pages, 6 figures
Abstract:Surveys are a powerful tool for collecting data and eliciting insights on social phenomena, and are critical in product design, marketing, scientific research. However, traditional open-ended and closed-ended question formats limit researchers’ ability to capture data that combines both the richness of qualitative insights and the analytical rigor of quantitative data. To address these problems, we propose Dynamic Surveys, a survey platform that uses Large Language Models (LLMs) to dynamically cluster qualitative responses in real time and to elicit quantitative ratings and rankings on those clusters and qualitative reflections on how their views compare to broader respondent trends, especially helpful in early-stage or exploratory research settings. This process generates a report showing survey creators and respondents the clustered responses as well as each cluster’s rank, rating distribution, and follow-up reflections. To evaluate Dynamic Surveys, we conducted two field studies with 93 participants over a 2-month period. In the first study, 52 students provided input for a career workshop, while in the second, 41 students gave feedback on gaps in their academic curriculum. Of these, 44 respondents filled out a survey on their experience using Dynamic Surveys. We also shared the generated report with 4 individuals who were interested in the insights for their work, and interviewed them to understand their perspectives on the results and any contextual risks they saw in the platform design. Our findings suggest that Dynamic Surveys not only provide richer and deeper insights into responses compared with traditional survey tools, but also increase engagement and foster a sense of community. We discuss broader implications for the design of survey platforms that blend qualitative depth with quantitative structure, facilitating richer insights and offering more collaborative interactions.
[HC-45] Read Critique or Sketch? Investigating Alternative Visualization Literacy Assessment Modalities
链接: https://arxiv.org/abs/2608.00330
作者: Zach Cutler,Lily W. Ge,Matthew Kay,Lane Harrison,Andrew McNutt,Alexander Lex
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Visualization literacy is a multifaceted construct encompassing skills and competencies, such as decoding data, constructing charts, and identifying design flaws. Yet, assessments of these competencies has been primarily constrained to multiple choice assessments that target lower-order skills, such as chart comprehension. As a result, they often exhibit ceiling effects (i.e., even modestly skilled individuals commonly score near the top of the scale), and do not provide enough information about an individual’s higher-order skills (e.g., applying external knowledge, formulating critiques, and designing visualizations). To close these gaps, we develop and investigate two web-based qualitative assessments for testing the critique and design aspects of visualization literacy through online think-aloud critique and sketching of visualization designs based on data and a prompt. We compare performance on our assessments to two established visualization literacy assessments, CALVI and Mini-VLAT, by administering them to three groups that represent three experience levels: crowdworkers, students who have taken a relevant course, and researchers. We find that our critique and sketching assessments capture skills distinct from existing measures and that they differentiate between experienced individuals better than multiple choice-based alternatives. Although administering and grading qualitative assessments can be challenging, our findings suggest qualitative, multimodal assessments are a promising complement to existing visualization literacy assessments, in particular when high visualization skills need to be distinguished.
[HC-46] heory Experience and Instinct: A Glimpse Into AAA Game Processes and How UX Leaders Navigate Pre-Production
链接: https://arxiv.org/abs/2608.00313
作者: Ivana Randelshofer,Joseph Tu,Yifan Cao,Reza H. Mogavi,Ville Mäkelä,Lennart E. Nacke
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Foundational decisions shape a project’s long-term trajectory, a dynamic that becomes especially evident in the inherent complexity of game pre-production. However, academic frameworks often see limited uptake at this stage, as they do not readily map onto industry contexts, production constraints, and cross-functional workflows. To better understand how design decisions are made in practice, we conducted interviews with 15 UX leaders from the AAA (triple-A) games industry. Our findings show that early UX decisions emerge from a dynamic blend of theory, experience, and intuition. In cross-functional structures (such as strike and competency teams), UX leaders collaboratively align player needs, technical feasibility, and creative vision. These decision-making processes involve translating academic concepts into production-ready insights, codifying experiential knowledge into reusable practices, and relying on informed intuition amid uncertainty. We argue that meaningful impact requires academia to develop malleable conceptual tools that integrate with practitioners’ highly adaptive design processes. We conclude by discussing how existing frameworks might be adapted to connect academic insights with AAA workflows. Rather than prescriptive directives, we offer these as starting points for discussion that support strike and competency teams through shared language, reusable design systems, and strategies for collaborative, context-sensitive decision-making.
[HC-47] ReVoicer: Conversational Voice Annotation for Human-Centered LLM -Assisted Peer Review
链接: https://arxiv.org/abs/2608.00299
作者: Matt Gottsacker,Ahinya Alwin,Hiroshi Furuya,Robert W. Lindeman,Gerd Bruder,Gregory F. Welch
类目: Human-Computer Interaction (cs.HC)
备注: IEEE ISMAR 2026
Abstract:We present ReVoicer, a prototype system that supports peer reviewers by letting them converse with a paper as they read it. The reviewer highlights a passage and speaks (or types) a train-of-thought comment. A large language model then cleans the comment using the surrounding prose as context, tags it by comment type, and anchors it to the passage. After the reviewer finishes reading, ReVoicer checks the accumulated notes against a venue-specific rubric, reports coverage gaps, and drafts a review composed only from the reviewer’s own comments, written to a style guide distilled from the reviewer’s past reviews. The system introduces no critiques of its own. We describe the system’s design rationale and implementation, and we outline plans for future evaluations. With the ISMAR community, we will gather feedback and discuss the system design and ideas for additional features and evaluations.
[HC-48] xtro: A Prototyping Toolkit for Solderless and Chipless Smart Textile Interfaces
链接: https://arxiv.org/abs/2608.00294
作者: Yanfeng Zhao,Te-Yen Wu
类目: Human-Computer Interaction (cs.HC)
备注: Submitted to UIST’26
Abstract:In this paper, we present Textro, a prototyping toolkit for designing, fabricating, and testing solderless and chipless smart textile interfaces. Unlike prior approaches that rely on rigid components or soldered connections, Textro enables users to build functional textile interfaces using only readily available materials and tools. The toolkit integrates three parts: (1) a web-based design environment for importing sewing patterns, defining sensing elements, and automatically generating optimized component and circuit designs based on empirical experiments; (2) a fabrication pipeline that generates fabrication files for embroidery and cutting machines, with embroidery optimized for one-stroke continuous stitching paths and components assembled through glue-based attachment methods via capacitive coupling; and (3) a reader device and software for wirelessly retrieving sensor data and visualizing real-time sensor signals. We demonstrate Textro through four application examples and conduct a user study with fashion experts, makers, and novices, highlighting its usability and potential for smart textile prototyping.
[HC-49] UART for Wearables (U4We): DC Power and Carrierless Signal Transfer over Conductive Textiles
链接: https://arxiv.org/abs/2608.01843
作者: Akihito Noda
类目: ignal Processing (eess.SP); Human-Computer Interaction (cs.HC)
备注: 5 pages, 8 figures, 1 table
Abstract:This brief presents a conductive-textile interconnection scheme for batteryless distributed wearable modules. Two conductive textile layers separated by an insulating fabric layer are used as a transmission line that simultaneously conveys DC power and pulse-based data signals without point-to-point wiring. To minimize the circuit overhead of each module, universal asynchronous receiver/transmitter (UART) pulses are directly coupled onto the textile through AC-coupling capacitors without carrier modulation. The textile waveform is modeled as the transient response of a second-order circuit, and design conditions for comparator-based waveform recovery and high-bitrate transmission are analytically derived. The resulting design framework determines whether a given combination of data rate, textile capacitance and resistance is feasible, and also provides the corresponding design range of the decoupling inductors. These results establish a basic methodology for textile-based simultaneous power and data transfer.
计算机视觉
[CV-0] WorldExam: Benchmarking World Models from Apparent Appearance to Inherent Reactivity
链接: https://arxiv.org/abs/2608.02603
作者: Yuxue Yang,Shuyao Shang,Jiahe Wang,Zitong Zhou,Liang Tan,Junhan Zeng,Ruizhi Li,Junyan Li,Yu Liu,Xiao Yang,Yong Li,Jun Zhu,Hongsheng Li,Tieniu Tan,Lue Fan,Zhaoxiang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Website: this https URL
Abstract:Controllable video generation models are increasingly being developed as world models. Accordingly, evaluating them in this role extends beyond the apparent appearance of generated videos to the inherent reactivity of the worlds they depict: the ability to infer from the scene state how the world should react and to generate plausible consequences not explicitly described in the input. Yet existing benchmarks mainly assess visual quality or explicit instruction fulfillment by checking whether requested actions and interaction outcomes are realized, leaving inherent reactivity underexamined. We introduce WorldExam, a hierarchical diagnostic benchmark spanning four levels: Visual Quality, Control Adherence, Spatial Consistency, and World Reactivity. It comprises 1,474 cases across eight dedicated tasks and supports unified evaluation of camera-, action-, and language-driven model paradigms. The World Reactivity level evaluates scene-conditioned reactions and goal-directed behaviors beyond what is explicitly specified in the input. Evaluation of 20 representative models reveals a clear capability split. Camera-driven models excel at camera control, but their interfaces do not support dynamic interaction; action-driven models control subjects more precisely but often leave the world unresponsive; and language-driven models perform better on interaction but follow complex controls less faithfully. No model combines broad task coverage with consistently strong performance, showing that high visual quality and explicit instruction fulfillment do not guarantee inherent reactivity.
[CV-1] VR3D: View-Robust 3D Representation Learning for Aerial-Ground Person Re-Identification
链接: https://arxiv.org/abs/2608.02598
作者: Chao Ji,Shiyu Xuan,Zechao Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 10 figures
Abstract:Aerial-ground person re-identification is a challenging task due to cross-platform viewpoint variations, which cause severe occlusion and geometric deformation. Existing methods attempt to learn view-invariant representations exclusively within the 2D image space, where drastic viewpoint variations cause the learned features to remain coupled with viewpoint bias. To address this, we propose VR3D, a View-Robust 3D Representation Learning framework that maps images into a unified 3D coordinate space to achieve view-independent feature interaction. Specifically, we introduce View-Robust 3D Representation Interaction, which leverages 3D priors extracted from single 2D observations to lift 2D appearance features into a canonical 3D space. VR3I employs 3D Geometry-Semantic Attention to establish interactions between 2D patches and 3D voxels from corresponding body parts based on their 3D spatial locations, effectively grounding 2D semantics within a 3D framework. In addition, as the reliability of these representations varies across samples due to viewpoint changes and 3D reconstruction errors, we introduce Reliability-Aware Fusion, which estimates sample-specific reliability and adaptively aggregates the multi-source representations. Extensive experiments on three benchmark datasets (CARGO, AG-ReID.v1, and AG-ReID.v2) demonstrate that VR3D outperforms recent methods. For example, it achieves a 5.63% improvement in Rank-1 on CARGO. Our code will be released.
[CV-2] CAPEval: A Decoupled Caption Evaluation across Understanding and Generation
链接: https://arxiv.org/abs/2608.02589
作者: Zhipeng Liu,Haochen Wang,Zhaoxiang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 21 pages, 8 figures. Code and dataset will be available at this https URL
Abstract:Captions serve as a primary supervision signal for both multimodal understanding and text-to-image generation. However, previous evaluations treat the caption quality as a single scalar objective, which conflates two distinct properties: (1) how much visual information a caption covers and (2) how reliably the image supports its stated claims. To this end, we design a decoupled caption evaluation benchmark, CAPEval (Coverage And Precision Evaluation), with human-written ground-truth captions and human-verified atomic checklist items. Specifically, CAPEval decomposes caption quality into Coverage and Precision. The former quantifies how thoroughly a caption covers ground-truth factual content, while the latter reflects the factual correctness rate of all claims expressed in the caption. We select 10 captioners and further conduct controlled downstream end-to-end experiments with them from four model families, where the caption source is the only variable. Empirically, we find a consistent task-dependent dissociation: Coverage serves as the stronger correlate for understanding performance, whereas Precision acts as the dominant predictor for generation performance. This decoupled evaluation paradigm not only delivers a more fine-grained diagnosis of caption quality, but also offers actionable guidance for selecting and optimizing captioners tailored to different downstream tasks.
[CV-3] ReMiX-MAE: Learning Missing-Channel Cross-Modal Representations from RGB-Only Clinical Facial Videos for Sympathetic-Mediated Pain Assessment
链接: https://arxiv.org/abs/2608.02561
作者: Nan Bi,Taoyue Wang,Lijun Yin,Vandana Sharma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Automated pain assessment in real clinics is limited by scarce clinically grounded facial video data with weak labels (often sequence-level self-report) and by the fact that pain cues can be subtle or near-neutral in RGB, while thermal and depth signals are informative yet impractical to deploy routinely. To address these challenges, we propose ReMiX-MAE (Reconstructing Missing Channel Cross-Modal Masked Autoencoder), a self-supervised multimodal masked pretraining framework that learns transferable facial representations from synchronized RGB, thermal, and depth videos and explicitly trains robustness to missing modalities, enabling RGB-only deployment. To fill the gap of clinically grounded facial pain data with video-level self-report and longitudinal treatment trajectories, we collect the Sympathetic Mediated Pain (SMP) dataset with paired pre- and post-recordings across multiple visits. Under RGB-only deployment, we evaluate ReMiX-MAE using both direct feature extraction and pseudo-multimodal features decoded from RGB. ReMiX-MAE consistently outperforms an RGB-only masked autoencoder baseline on SMP, with pseudo-multimodal features providing additional gains in the challenging five-class setting. Across external datasets, ReMiX-MAE further shows more robust and label-efficient transfer than RGB-only baselines, highlighting its advantage in data-limited clinical settings.
[CV-4] Estimating SSIM from MSE for DCT-Based Compressed Images
链接: https://arxiv.org/abs/2608.02549
作者: Luc Trudeau,Maria G. Martini
类目: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Efficient and perceptually meaningful quality assessment is a fundamental requirement for image and video processing, compression, and streaming systems. This article shows that, in the context of Discrete Cosine Transform ( DCT)-based compressed images, Structural Similarity Index ( SSIM ) can be approximated from global Peak Signal to Noise Ratio (PSNR) or Mean Square Error ( MSE) using local statistics derived only from the reference image. While prior work assumes access to local MSE, we propose two approaches to approximate local MSE by redistributing the global MSE using variance or standard-deviation-based weighting. Experiments on the Kodak and Xiph Subset1 datasets across a range of JPEG quality levels demonstrate that both approaches provide accurate and robust SSIM approximations, substantially outperforming the global MSE baseline. The proposed framework is designed to extend naturally to video, where reference-derived statistics can be amortized across multiple encodes of the same content.
[CV-5] oken Radius Attention for Efficient Video Generation
链接: https://arxiv.org/abs/2608.02504
作者: Jiayu Chen,Zhikun Jiang,Maoliang Li,Jiayi Luo,Jiawei Yang,Zihao Zheng,Hengyi Zhang,Guojie Luo,Xiang Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video Diffusion Transformers (VDiTs) enable high-fidelity generation but incur quadratic cost from dense 3D self-attention. Existing head- and block-level sparse methods share computation budgets across queries, overlooking token-specific attention demand. We observe that retained density varies across queries yet correlates log-linearly with attention entropy, while dominant interactions form query-centered neighborhoods with token-dependent radii. Based on these findings, we propose Token Radius Attention (TRA), a training-free framework that maps query entropy to an analytic token budget and converts it into a temporally decayed radius without explicit key ranking. Fused entropy extraction, warm-up reuse, and block-sparse mask construction further reduce overhead. Across seven Wan2.1, Wan2.2, and HunyuanVideo T2V/I2V configurations, TRA retains only 9-19% of attention interactions and achieves 1.56x-2.05x speedup with competitive generation quality. Code is available at this https URL.
[CV-6] DyFrDet: Towards Accurate Small Object Detection via Dynamic Frequency Suppression with Label Disambiguation
链接: https://arxiv.org/abs/2608.02495
作者: Zihan Yang,Yang Guo,Hongxing Zhang,Dan Lu,Siyuan Yao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 4 figures, 7tabs
Abstract:Despite the remarkable progress over the past decades, accurately identifying small objects remains challenging because of their insufficient visual cues. Previous works typically attempt to construct discriminative representation of the small objects. However, the wide range frequency domain noises and label ambiguities have been greatly overlooked, which significantly hinders the accurate localization. To address these issues, we propose a novel small object detection (SOD) detector termed DyFrDet, which is able to precisely localize the small object by dynamically suppressing the background distractions in frequency domain. Specifically, we propose a Dynamic Frequency-aware Feature Pyramid Network (DyFrFPN) to adaptively suppress low-frequency redundancy and excessive high-frequency noises. The DyFrFPN transforms the hierarchical features into frequency domain representation, and introduces a Dynamic Band Predictor (DBP) to preserve the discriminative components for small object identification. Afterwards, we present a novel Label Disambiguation Module (LDM), which leverages probabilistic distributions to explicitly model and alleviate the inherent ambiguity of target labels, yielding efficient improvement in localization precision of the small objects with low-resolution. Extensive experiments demonstrate that DyFrDet achieves state-of-the-art performance across multiple benchmarks, indicating its effectiveness and robustness in various challenging scenarios. Our code is available at this https URL.
[CV-7] Fermat Active Laplace Learning for Semi-Supervised Hyperspectral Image Classification
链接: https://arxiv.org/abs/2608.02483
作者: Vutichart Buranasiri,James M. Murphy
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注:
Abstract:Two active learning algorithms for hyperspectral image (HSI) classification are proposed that combine density-aware Fermat distances with Poisson-reweighted harmonic label propagation. Our methods actively query points using an uncertainty-based acquisition function, extending Poisson ReWeighted Laplace Learning (PWLL). Our first algorithm, Fermat Active Laplace Learning (FALL), builds an affinity matrix using Fermat distances between all data points. Then, PWLL is run with a diagonal perturbation using the minimum-norm acquisition function. In contrast, Approximate FALL (A-FALL) computes Fermat distances between each data point and landmark pixels selected via farthest-point sampling and constructs the affinity matrix using landmark multidimensional scaling. After several query rounds, A-FALL selects the Fermat exponent p using a leave-one-out cross-validation variant. FALL and A-FALL leverage Fermat distances and subsequent harmonic label propagation to provide a density-aware estimation of the data manifold, improving labeling accuracy. Experiments on Salinas A and Pavia show the effectiveness of FALL and the scalability of A-FALL to large HSI scenes.
[CV-8] EchoCache: Energy-Guided Cross-Modal Caching for Efficient Audio-Driven Video Generation ACM-MM2026
链接: https://arxiv.org/abs/2608.02474
作者: Jiayu Chen,Xiaoyu Wu,Rongshan Gao,Maoliang Li,Zihao Zheng,Xinhao Sun,Hailong Zou,Guojie Luo,Xiang Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: EchoCache is honored to be accepted by ACM MM 2026
Abstract:Audio-driven video generation (A2V) has achieved promising progress in synthesizing temporally coherent and audio-visually aligned videos, yet its inference remains expensive due to the iterative denoising process of diffusion models. Existing caching methods mainly exploit temporal redundancy in visual features while overlooking the cross-modal alignment of A2V, where audio drives visual generation with highly non-uniform temporal importance. In this paper, we identify two levels of misalignment in existing A2V caching methods: temporal-semantic and computation-storage misalignment. To address them, we propose EchoCache, an energy-guided cross-modal caching framework for efficient A2V generation. EchoCache leverages audio time-frequency energy as a saliency anchor to guide latent-level cache updates and further introduces a dynamic timestep-latent caching mechanism with quantized cache management for joint efficiency and memory optimization. Extensive experiments on mainstream A2V models show that EchoCache consistently improves the latency-quality trade-off while preserving generation quality and audio-visual consistency. In particular, on Wan2.2-S2V over the EMTD benchmark, EchoCache achieves a 2.46x speedup with the best overall performance. Code is available at this https URL.
[CV-9] Action-grounded tissue affordance enables anticipatory auto-framing that lowers surgeon cognitive workload during laparoscopic surgery
链接: https://arxiv.org/abs/2608.02471
作者: Jiayu Gu,Yiwei Wang,Jie Zhang,Guojun Cao,Keshen Lyu,Song Zhou,Yimeng Chen,Haorui Wang,Qingmin Feng,Shenchao Shi,Huan Zhao,Wenbin Chen,Caihua Xiong,Chidan Wan,Jing Samantha Pan,Xiong Cai,Han Ding
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Preprint. 54 pages, including supplementary information and 7 main figures
Abstract:Computational attention models could help surgeons manage the visual demands of laparoscopy, but they require dense spatial labels that are difficult to obtain because surgical intent is highly specialized and tacit. Here, we introduce DiffeoAfford, an action-grounded tissue affordance framework that retrospectively derives visual attention supervision from completed surgical procedures. By combining diffeomorphism-constrained tissue tracking with instrument trajectory analysis, DiffeoAfford generates affordance hotspot labels without manual per-frame annotation. A real-time prediction model trained on these labels anticipates relevant surgical regions and enables AffordView, an assistive auto-framing system for laparoscopic visualization. The proposed framework aligns with expert annotations and intraoperative surgeon gaze, and reduces surgeon cognitive workload during real-world evaluations using subjective, physiological, and behavioral measures.
[CV-10] Grounding Agent ic VLMs with Dedicated Segmentation for Fine-Grained Vehicle Damage Assessment
链接: https://arxiv.org/abs/2608.02470
作者: Vishwajeet Shivaji Hogale,Anjali Pai,Nitya Ravi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 pages, 2 figures
Abstract:Vision-language models (VLMs) are increasingly deployed as reasoning agents in real-world visual assessment pipelines, yet their spatial grounding remains unreliable for fine-grained, visually ambiguous targets. We study this gap in the context of automated vehicle damage assessment, where fine-grained defects such as scratches and hairline cracks occupy few pixels, produce weak gradient signal, and are easily confused with reflections and surface texture. We show that a state-of-the-art VLM (Qwen-VL) achieves strong semantic classification accuracy (87.3%) on this task but is systematically ungrounded at the spatial level: it hallucinates damage in reflective regions, misses elongated scratches entirely, and produces spatially inconsistent outputs when prompted for localization. We propose TinyDamage, a hybrid architecture that delegates spatial grounding to a dedicated multi-task segmentation model while reserving the VLM for semantic reasoning and report generation. On the segmentation side, we find that the choice of loss function has an outsized and underexplored effect on tiny-object grounding: focal loss, widely used for class imbalance, collapses tiny-damage detection to zero, while a supervised contrastive objective measurably improves damage/background separability. We integrate the segmentation model into a 7-node LangGraph agent pipeline that grounds every VLM generation step in the segmentation output, and show that this grounding reduces the report hallucination rate from 92% (text-only) and 78% (image-only) to 31% in a controlled evaluation on 100 human-verified reports. We introduce DET_l, a permissive per-category detection metric for evaluating tiny-object grounding under class imbalance, and report latency and reliability characteristics of the deployed pipeline.
[CV-11] Calibrated Similarity and Graph Clustering for Open-Set Animal Re-Identification
链接: https://arxiv.org/abs/2608.02469
作者: Mohamed ElBassat,Seifeldin Elkerdany,Mohamed ElBialy,Gamal Abouelhamd,Jana Ghoneim,Assem Elkady,Mohamed Elboraay,Nelly Semenova
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:AnimalCLEF26 addresses discovery-oriented animal re-identification, where systems must both attach query images to known individuals and discover unseen individuals by clustering them correctly. We present a similarity-to-clustering pipeline for this setting across Eurasian lynx, fire salamander, loggerhead sea turtle, and Texas horned lizard images. The method first isolates the target specimen using segmentation and then applies lightweight species-specific preprocessing for lynx, sea turtle, and salamander images to enhance identity-relevant visual cues, while Texas horned lizard images are used after segmentation only. Pairwise similarities are then estimated with WildFusion by calibrating and combining a MiewID global descriptor with two local matching branches, ALIKED + LightGlue and DISK + LightGlue. The resulting query-query similarities are refined and converted into identity clusters using graph-based clustering, while query-database similarities are used to attach confident samples to known identities. We evaluate training-free and fine-tuned MiewID variants, including Dynamic ArcFace and SphereFace2-Focal adaptations, and combine them in the final ensemble. Our selected ensemble substantially improves on the WildFusion baseline, achieving the best public ARI of 0.72124 and a private ARI of 0.70393, while a simpler preprocessing-before-calibration variant achieves the best private ARI of 0.71087. These results indicate that calibrated global-local fusion with species-aware preprocessing choices is effective for open-set wildlife re-identification under challenging field conditions and visual variation. The implementation code is available on GitHub.
[CV-12] ISRS-DETR: Detection-Guided Click Propagation for Remote Sensing Interactive Segmentation
链接: https://arxiv.org/abs/2608.02468
作者: Thanh Duc Pham,Anh Nguyen,Duong Duc Hieu,Minh-Tan Pham
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Interactive segmentation reduces the prohibitive cost of pixel-level annotation by allowing users to delineate objects with a few clicks. However, applying this paradigm directly to remote sensing imagery is non-trivial: ultra-high resolutions, small object sizes, and sparse spatial distributions all degrade segmentation quality. Recent work has addressed the resolution barrier and achieved competitive results in interactive segmentation for remote sensing (ISRS). However, they treat all instances of a class within an image as a single objective target. Consequently, interactions spent on one object contribute nothing to its same-class neighbours, and satisfactory masks may demand up to 40 clicks per image, hindering the practicality of these frameworks. We observe that remote sensing scenes exhibit markedly strong inter-object correlation, meaning a single clicked object is highly informative about the rest of its category. Building on this, we propose ISRS-DETR, a detection-guided interactive segmentation framework that injects object-level evidence into both training and inference. Our ISRS-DETR employs an RF-DETR decoder with the interactive segmentation backbone to localise co-occurring same-class objects, and introduces a Dynamic Top-K Click Selection strategy that retains only reliable proposals and converts each into a simulated click, so one user interaction propagates across an entire class. Experiments on three standard remote sensing benchmarks show that ISRS-DETR achieves state-of-the-art accuracy while substantially reducing Number of Clicks per Image (NoC-I). All codes and data splits will be released for reproducibility upon acceptance.
[CV-13] MoRAL: Sensor-Grounded BEV Reasoning for Compact VLMs toward Edge-Oriented Autonomous Driving
链接: https://arxiv.org/abs/2608.02449
作者: Ambarish Govindarajulu Kaliamurthi(San Jose State University),Kaikai Liu(San Jose State University)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 7 pages, 5 figures, 6 tables. Accepted to the 14th IEEE International Conference on Intelligent Mobile Computing (IEEE IMC 2026), Fukuoka, Japan, July 27-30, 2026
Abstract:Deploying vision-language models (VLMs) for safety-critical spatial reasoning on resource-constrained autonomous driving platforms requires both compact model size and reliable metric grounding. We present MoRAL (Multimodal Reasoning for Autonomous Language Models), a two-stage fine-tuning pipeline that teaches Cosmos-Reason2-2B to first read a physics-encoded Bird’s Eye View (BEV) representation and then reason over it for driving decisions. The BEV image encodes LiDAR metric distance as color bands, object class as cluster morphology, and radar Doppler velocity as directional wedge overlays, externalizing spatial perception into the input image so that no learned 3D backbone is required at inference. Stage 1 fine-tunes the vision encoder on 60,000 grounding records; zero-shot baselines produce no parseable BEV outputs, confirming the vocabulary requires explicit training. Stage 2 fine-tunes the full model (52M parameters, 2.4% of total) on 57,696 chain-of-thought records generated by Cosmos-Reason2-8B as teacher, spanning eight driving question types. On 2,304 held-out nuScenes frames evaluated by Gemma 4 (31B) calibrated against human review, MoRAL wins seven of eight question types over a zero-shot 8B baseline despite using four times fewer parameters, with the largest margins on question types requiring structured multi-step physics reasoning. Emergency braking recall improves from 10.8% to 47.8%, output degeneration falls from 94.1% to 20.8%, and the full pipeline fits a consumer 8 GB GPU at 42 tok/s without quantization. These results establish a reproducible foundation for compact, physics-grounded VLM reasoning on mobile edge platforms.
[CV-14] UAV-Based Environmental Monitoring of Rip-Current Indicators Using Wavelet-Derived Texture Features
链接: https://arxiv.org/abs/2608.02448
作者: Yonatan Ben Avraham,Baruch Binyaminov,Yehudit Aperstein
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 10 figures
Abstract:Rip currents are recurrent coastal natural hazards that threaten beachgoers and create operational challenges for lifeguards and coastal managers. Reliable monitoring from standard RGB (red-green-blue) imagery acquired by unmanned aerial vehicles (UAVs) remains difficult because hazardous channels often appear as subtle gaps in breaking waves, foam texture, or sediment patterns, and these signatures are affected by illumination, sea state, and environmental noise. This study presents a physically informed coastal environmental monitoring workflow for detecting visually expressed rip-current indicators that integrates wavelet-derived spatial-frequency texture features with deep learning. We evaluate multiple strategies for incorporating Discrete Wavelet Transform features into convolutional architectures, from computationally efficient channel replacement to dual-stream fusion with attention mechanisms. Performance is assessed against a standard RGB baseline using a task specific convolutional neural network for image-level presence classification and a YOLOv8 model for object-level localization. Under the evaluated dataset conditions, integrating wavelet derived texture features improves performance over RGB-only models. The dual-stream architecture achieves the strongest classification performance, exceeding 95% accuracy with high recall, while channel replacement is most effective for YOLOv8 object detection, reaching 94% mAP@50 for localization. Explainable artificial intelligence analyses provide qualitative evidence that the models attend to visually plausible wave-gap regions associated with rip currents. These results suggest that under the conditions of the evaluated dataset, physically informed wavelet integration may support UAV-based decision-support tools for interpretable beach-safety risk mitigation.
[CV-15] InfiniSplat: Implicit Gaussian Decoding for Large-Baseline Monocular View Synthesis SIGGRAPH
链接: https://arxiv.org/abs/2608.02437
作者: Jiawei Wang,Hao Yu,Yongzhen Hu,Xinyi Yang,Tao Ni,Xin Zhan,Junbo Chen,Xiaowei Zhou,Ruizhen Hu,Sida Peng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to SIGGRAPH Asia 2026 (Journal Track). Project page: this https URL
Abstract:Single-image feed-forward 3D Gaussian Splatting (3DGS) aims to directly generate a renderable 3D scene representation from one input image, avoiding the cost of multi-view capture and per-scene optimization. However, existing methods are often constrained by a pixel-aligned representation, where Gaussians are predicted from fixed image-grid locations. Such pixel-aligned primitives can produce promising nearby-view renderings, but they remain weakly coupled to underlying scene surfaces and struggle to preserve coherent structures under large viewpoint shifts. We present InfiniSplat, a feed-forward single-image 3DGS framework that moves from a pixel-aligned representation toward a surface-aligned representation. InfiniSplat constructs this representation by first using geometry-guided sampling to place 2D supports according to depth-induced local surface structure, and then applying a query-conditioned implicit decoder to predict Gaussian attributes from the image features queried at these this http URL grounding support locations in geometry while decoupling Gaussian prediction from fixed pixel centers, InfiniSplat produces Gaussian layouts that better follow scene surfaces and reduce scattered primitives caused by grid this http URL multiple cross-dataset NVS evaluations, InfiniSplat achieves state-of-the-art performance compared with single-image feed-forward baselines, and demonstrates zero-shot generalization from Hypersim indoor synthetic training to complex open-world this http URL page: this https URL.
[CV-16] Learning to Tessellate: Point Cloud Generation via Recursive Spectral Partitioning ECCV2026
链接: https://arxiv.org/abs/2608.02432
作者: Monan Sun,Bangzhen Liu,Huaidong Zhang,Shengfeng He
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV2026, project page: this https URL
Abstract:Autoregressive models have emerged as an effective paradigm for point cloud generation. However, most existing approaches rely on heuristic tokenization strategies, such as spatial sorting or stochastic downsampling, which often disrupt intrinsic point cloud topology and weaken the structural coherence of the generated shapes. In this paper, we present PointRSP, an autoregressive framework that reformulates point cloud generation as a topology-preserving tessellation process via recursive spectral partitioning. Instead of constructing token sequences heuristically, we introduce a topology-aware partitioning autoencoder that decomposes an unstructured point cloud into a non-balanced binary tree through a hybrid recursive spectral partitioning strategy. This hierarchical representation provides a deterministic geometric blueprint that preserves topological relationships while capturing multiscale structural dependencies within a quantized latent space. To synthesize shapes in this space, we propose a dual-stream cascaded generator that jointly models structural evolution and feature synthesis. In addition, we design a geometry-calibrated positional encoding mechanism that anchors latent embeddings using multi-scale structural centers, which stabilizes cascaded generation during the early stages of structural formation. Extensive experiments show that PointRSP achieves state-of-the-art performance in generation quality and diversity, demonstrating strong generalization across complex 3D topologies.
[CV-17] DF3: World Modeling via Decoder-Free Feature Forecasting in Autonomous Navigation
链接: https://arxiv.org/abs/2608.02428
作者: Jiaming Chen,Guoan Xu,Aoshen Huang,Haozhuo Zhang,Yang Li,Wei Pan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Forecasting future states from video sequences is a critical challenge for autonomous robotic systems and a fundamental objective of world modeling. Prior generative methods operating at the pixel level inevitably overemphasize task-irrelevant details, leading to prohibitive computational overhead. While latent-based approaches attempt to mitigate this by predicting features directly, the persistent reliance on heavy decoders for state-to-task mapping remains a computational bottleneck. In this work, we propose Decoder-Free Feature Forecasting (DF ^3 ), a novel framework that models world evolution entirely within the latent space and directly derives task outputs, completely eliminating the need for a decoder. Specifically, DF ^3 injects learnable spatial queries into the terminal blocks of a frozen vision foundation model to extract future state representations directly. By employing a lightweight, unified Motion-Aware Context Fusion (MACF) mechanism that seamlessly integrates coarse flow warping with fine-grained latent cross-correlation, these queries interact with historical token representations to explicitly align and forecast the feature of the next frame. Subsequently, a specialized set of task queries probes these forecasted features for the downstream task. Extensive experiments on public benchmarks and zero-shot deployment in a robotic simulator demonstrate that DF ^3 achieves performance comparable to state-of-the-art methods while offering superior efficiency and flexibility for integrated perception and control.
[CV-18] Loggia dei Lanzi: AI Thermography Enhancement Comparisons through 3D Photogrammetry
链接: https://arxiv.org/abs/2608.02404
作者: Scott McAvoy,Jonathan Klingspon,George Bent,Dave Pfaff,Aviral Agarwal,Maurizio Seracini,Falko Kuester
类目: Computer Vision and Pattern Recognition (cs.CV); Digital Libraries (cs.DL)
备注: 19 pages, 10 figures, to be presented at the 8th International Symposium on Cultural Heritage Conservation by Digitization (CHCD2026) in Beijing
Abstract:The Loggia dei Lanzi in the Piazza della Signoria is one of Florence’s most prominent structures visited by millions every year. Its construction history spans multiple centuries of modification. This paper presents the results of a thermal imaging campaign conducted in December 2025, using a FLIR T1020 HD camera, revealing hidden architectural features including walled-up openings and material transitions beneath the plaster surface. The favorable winter ambient conditions provided a feature-rich benchmark upon which to compare the results of enhancement algorithms and artificial intelligence models. We evaluate the application of AI-based image enhancement to thermal heritage documentation through a comparison of three tiers of image resolution in a photogrammetric Structure-from-Motion (SfM) pipeline: native resolution, FLIR’s hardware-based pixel-shifted super-resolution (UltraMax), and state of the art AI-upscaled imagery models. We quantify the effect of each resolution tier on feature detection and tie-point generation, assessing whether the additional detail produced by super-resolution, whether hardware or AI-derived, translates into meaningfully denser and more accurate 3D thermal models. Our results contribute to the emerging intersection of artificial intelligence and heritage thermography by providing a direct comparison of hardware microscanning and AI super-resolution within a thermal photogrammetric workflow for cultural heritage. All datasets are made publicly available and accessible within an interactive 3D archival framework, and integrated into a custom citywide extended reality overlay application.
[CV-19] USP-Mamba: Unmixing-Derived Spectral and Structural Prompting for Hyperspectral Image Super-Resolution
链接: https://arxiv.org/abs/2608.02401
作者: Shi Chen,Jie Zhang,Yicong Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Hyperspectral image super-resolution aims to reconstruct high-resolution imagery while preserving dense spectral information. Recently, Mamba-based models have shown promising potential for this task by capturing long-range dependencies with linear computational complexity. Nevertheless, their causal sequence modeling requires two-dimensional hyperspectral features to be unfolded along predefined scanning orders, which disrupts spatial adjacency and restricts the effective propagation of contextual information. Moreover, state-space parameterization of existing models is predominantly derived from generic learned representations, without explicit alignment with the intrinsic characteristics of the hyperspectral image. To address this issue, we propose an Unmixing-derived Spectral and Structural Prompting Mamba framework, termed USP-Mamba, which adapts Mamba state evolution through composition-aware spectral priors and image-dependent structural prompts. Specifically, an unmixing-informed spectral prompt captures the global material composition of the input image and provides persistent conditioning throughout reconstruction. Injected into the Mamba sequence and progressively adapted across layers, it steers state evolution toward composition-consistent reconstruction. We introduce feature-level structural prompts comprising spatial and frequency components to provide image-dependent local guidance. The spatial prompt promotes structure-sensitive state encoding for local detail preservation, while the frequency prompt enables region-adaptive transitions between homogeneous regions and high-frequency details. Finally, complementary Hilbert and Semantic-Guided Neighboring scans preserve spatial continuity and strengthen non-local semantic dependency modeling. Extensive experiments on different datasets demonstrate that the proposed method consistently outperforms representative approaches.
[CV-20] Does Explainability Transfer? A Controlled Benchmark of Attribution Methods on Vision Transformers and CNNs
链接: https://arxiv.org/abs/2608.02396
作者: Sathiyamohan Nishankar,Nethmi Pathirana,Pubudu Sanjeewani,Asanka Perera,Selvarajah Thuseethan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Most evidence on the effectiveness of explainable artificial intelligence (XAI) attribution methods has been established on convolutional neural networks (CNNs), with limited investigation into whether these conclusions generalize to the diverse Vision Transformer (ViT) architectures that now dominate computer vision. This paper presents a controlled benchmark that evaluates attribution quality across five dimensions: faithfulness, localization, robustness, complexity, and computational cost. A standardized framework assesses 13 attribution methods from four algorithmic families on eight representative backbones spanning CNNs, isotropic ViTs, hierarchical transformers, hybrid architectures, and linear-attention transformers. The results show that attribution performance is strongly architecture-dependent and that rankings established on CNNs do not reliably transfer to transformer-based models. CAM-based methods achieve the highest scores under the conventional bounding-box localization metric on CNNs and most ViTs but perform poorly on linear-attention architectures. Pixel-level dense-mask evaluation further reveals that these gains largely reflect metric saturation rather than accurate localization. CAM-based methods also exhibit limited robustness on global-attention transformers, whereas attention rollout provides consistently stable explanations with poor localization. Furthermore, faithfulness correlation offers limited discrimination between attribution methods, highlighting the limitations of single-metric evaluation. These findings challenge prevailing conclusions on attribution performance and demonstrate the need for architecture-aware, multi-dimensional evaluation. The open-source code for the evaluation framework and benchmark results is available at this https URL.
[CV-21] GROVE: Growing and Reasoning over Temporally Stratified Memory from Streaming Video Experience
链接: https://arxiv.org/abs/2608.02392
作者: Sitong Gong,Caixin Kang,Tianyu Yan,Guo Chen,Bo Zheng,Kaipeng Zhang,Yunzhi Zhuge,Xiang Ruan,Huchuan Lu,Yifei Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 7 pages and 4 figures in the main paper
Abstract:A wearable assistant should both answer questions about its visual history and recognize when that history is useful to the present situation. Existing video-memory systems primarily support question-conditioned recall, whereas proactive assistants typically use separate memory and control mechanisms. We introduce GROVE, a training-free framework that supports both behaviors with one memory grown causally from a continuous video stream. GROVE retains fine-grained perceptual evidence and incrementally consolidates it into time-stamped moments, coherent episodes, and recurring cross-day patterns. Each stratum is paired with a scale-native retrieval skill for locating an observation, replaying an activity, or traversing long-range regularities. Reactive QA and proactive assistance share this memory and access interface, differing in whether retrieval is initiated by a user query or the current situation. Across multiple benchmarks including the challenging MM-lifelong and EgoServe, GROVE achieves the best results among the compared methods. Controlled ablations show that the temporal strata and their access skills are complementary, with patterns providing the largest benefit when evidence spans multiple days. Code will be available at this https URL.
[CV-22] Loop-Mamba: A Loop Mamba with Degradation-Aware and Shared Memory for Old Photo Restoration
链接: https://arxiv.org/abs/2608.02346
作者: Runci Bai,Yucheng Xin,Pu Wang,Yongcong Wang,Chen Wu,Dianjie Lu,Guijuan Zhang,Pengwen Dai,Guangwei Gao,Siyuan Yao,Zhuoran Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Old photographs often suffer from multiple coupled degradations, including scratches, cracks, fading, blur, noise, and missing regions, severely degrading both visual quality and semantic content. We propose Loop-Mamba, a lightweight loop-based state-space framework that formulates old photo restoration as progressive state evolution, where a persis- tent restoration state is continuously propagated and refined through iterative computation. Specifically, we introduce a Semantic-Guided Degradation Estimator (SGDE) to explicitly model heterogeneous degradations by jointly predicting local degradation maps and global degradation scores, providing degradation-aware guidance for state evolution. We further develop a Shared Structural Memory Mamba (S ^2 M- Mamba), which maintains a persistent restoration state across iterations, enabling persistent state evolution through shared structural memory for robust long-range structural reconstruction. Benefiting from first-order state recursion, Loop-Mamba propagates latent restoration states through recurrent tran- sitions instead of repeatedly stacking deep feature transformations, thereby alleviating gradient dilution while avoiding the computational overhead inherent in iterative CNN- and Transformer-based restoration frameworks. A lightweight multi-directional scanning strategy further enhances direc- tional information aggregation and preserves structural continuity. To better evaluate restoration quality, we introduce the task-oriented Old Photo Damage Recovery Score (ODRS), which jointly measures degradation recovery and structural reconstruction fidelity. Experimental results on the public SynOld benchmark demonstrate that Loop-Mamba consistently outperforms previous state-of-the-art methods across both conventional restoration metrics and the proposed ODRS.
[CV-23] Context-Aware Mixture of Domain Experts for Bodily Expression of Emotion in the Wild
链接: https://arxiv.org/abs/2608.02331
作者: Mohammad Mahdi Dehshibi,David Masip
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Submitted to “IEEE Transactions on Affective Computing”; 10 pages, 6 figures, 6 tables. To facilitate reproducibility, the PyTorch implementation of CA-MoDE is publicly available at this https URL
Abstract:The same body posture can convey entirely different emotions depending on its surrounding context, yet most methods for recognising bodily emotions treat scene and object cues as auxiliary feature augmentations rather than as structured priors over the plausibility of emotions. We introduce the Context-Aware Mixture of Domain Experts (CA-MoDE) for bodily emotion recognition. CA-MoDE incorporates dedicated scene and object experts to generate soft distributions over emotion categories conditioned on their respective domains. These domain-conditioned soft predictions serve as structured contextual priors that modulate the body expert’s predictions at the distributional level rather than at the feature level. To fuse these multi-domain signals, we propose a task-tailored max-endorsement gating strategy that selects the strongest contextual signal across experts for each emotion dimension. Our gating strategy mitigates the signal dilution that typically occurs when conflicting or uninformative context distributions are averaged. CA-MoDE achieves an Emotion Recognition Score of 0.3269 on the Body Language Database. By outperforming existing temporal models using only single still images, our framework demonstrates that explicitly modelling structured spatial context can serve as a complementary discriminative proxy for the behavioural dynamics typically captured by video.
[CV-24] Implicit Neural Representations for Multimodal Longitudinal Image Imputation and Interpolation
链接: https://arxiv.org/abs/2608.02324
作者: Sina Wendrich,Lukas Förner,Zoe Reinke,Kartikay Tehlan,Ansgar Berlis,Michael Frühwald,Matthias Wagner,Thomas Wendler
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Longitudinal multiparametric MRI is central to follow-up imaging in oncology, yet real-world clinical data are characterised by missing sequences, heterogeneous acquisition protocols, and varying spatial resolutions across time points. We propose a patient-specific conditional implicit neural representation (INR) that models multimodal longitudinal MRI as a continuous function of world coordinates, time, and modality conditioning. The model is trained with stochastic modality dropout to handle incomplete data, and its continuous coordinate-space formulation enables both spatial and temporal interpolation without resampling to a fixed voxel grid. A self-consistency-based confidence estimator is derived from cross-modal reconstruction performance at inference time. We evaluate the framework on longitudinal MRI from paediatric brain tumour patients, demonstrating statistically significant improvements over linear interpolation for T1CE and FLAIR (p 0.05), with mean MS-SSIM of 0.95 \pm 0.02 for T1CE. Predicted confidence correlates strongly with true reconstruction quality (Pearson r up to 0.996), suggesting reliable deployment potential in heterogeneous clinical settings.
[CV-25] Global-Scale Self-Supervised Spatiotemporal Learning for NDVI Time-Series Reconstruction
链接: https://arxiv.org/abs/2608.02322
作者: Ang Li,Menghui Jiang,Xiaobin Guan,Dong Chu,Huanfeng Shen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate and efficient reconstruction of cloud-contaminated and noise-corrupted NDVI time series remains a challenge in remote sensing. Deep learning provides a promising solution for modeling complex spatiotemporal dependencies; however, its application is often limited by the difficulty of obtaining paired clear-sky and degraded NDVI data for identical spatiotemporal locations. To address this issue, we propose GloSSR, a Global-scale Self-supervised Spatiotemporal framework for NDVI Reconstruction. The framework constructs supervisory signals by artificially degrading relatively clean NDVI observations with realistic cloud contamination patterns, producing self-supervised training pairs that closely mimic real-world degradation. It further introduces an end-to-end spatiotemporal learning network that jointly captures long-range temporal dependencies and short-term spatiotemporal correlation through a bidirectional Transformer with a ConvLSTM architecture. A temporal-channel attention-based reconstruction module is incorporated to enhance informative features, while a spatiotemporal prior constraint is designed to preserve both fine-scale structures and long-term phenological trends during optimization. Extensive evaluations on MODIS NDVI data demonstrate the effectiveness of the proposed framework across both artificial and real-world scenarios. In artificial degraded-pixel reconstruction experiments, GloSSR consistently outperforms the comparison methods. Time-series analyses based on real observations further demonstrate that the proposed framework can accurately characterize vegetation dynamics and capture the key phenological states. Long-term vegetation trend analysis and the transferability analysis to AVHRR data validate the scalability of the framework and illustrate its broad applicability for large-scale environmental monitoring.
[CV-26] ravKAN: Fast and Interpretable Nonlinear Traversability Analysis with Kolmogorov-Arnold Networks IROS
链接: https://arxiv.org/abs/2608.02320
作者: Daniel Fusaro,Simone Mosco,Wanmeng Li,Alberto Pretto
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been accepted for publication at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Abstract:Traversability analysis is a fundamental capability for autonomous mobile robots operating in unstructured environments. While modern machine learning approaches such as deep neural networks and gradient-boosted trees achieve strong predictive performance, they lack interpretability and provide limited insight into the underlying terrain-robot interaction dynamics. In this paper, we propose TravKAN, a Kolmogorov-Arnold Network-based framework for fast, scalable, and interpretable traversability estimation. TravKAN represents multivariate decision functions through compositions of learnable univariate functions, enabling compact architectures and symbolic extraction of analytic expressions after training. In addition, we introduce a novel set of handcrafted features derived from the reflectivity channel of LiDAR sensors. To the best of our knowledge, reflectivity has not been systematically exploited for handcrafted traversability descriptors, despite its potential to capture material and surface properties complementary to geometric cues. We evaluate TravKAN on public, real-world urban and off-road datasets and compare it against strong baselines. TravKAN achieves strong performance across all metrics, outperforming conventional deep models and approaching the performance of XGBoost. TravKAN-Lite, i.e., TravKAN’s symbolic representation, reveals meaningful nonlinear feature interactions and provides a compact, deployment-friendly, and fast analytic model. Ablation studies further show the robustness of our method to architectural variations and quantify the contribution of the proposed reflectivity-based features. These properties make TravKAN attractive for robotic systems requiring transparency, real-time computational efficiency, and interpretability in safety-critical decision-making.
[CV-27] GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation ECCV2026
链接: https://arxiv.org/abs/2608.02315
作者: Gaetano Chiriaco,Luca Barco,Andrea Bragagnolo,Claudio Rossi,Edoardo Arnaudo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026 - Terrabytes II Workshop, 23 pages
Abstract:Geospatial foundation models aim to learn representations that transfer across regions and sensors, yet evaluating them on specific tasks requires large, high-quality, multi-modal benchmarks that measure how well such models extract value from data. Concerning flood mapping, existing datasets rarely combine bi-temporal SAR and co-registered optical imagery at scale, leaving the value of foundation models for this downstream task largely untested. We introduce GEOID-Flood, a large-scale multi-modal flood segmentation benchmark, derived from Copernicus Emergency Management Service activations, spanning 219 events across 65 countries over ten years. The dataset provides more than 14,000 tiles with co-registered pre- and post-event Sentinel-1, in GRD and RTC format, pre-event Sentinel-2 composite, and DEM, including manually validated labels that separate background from permanent water and flooded water. Using this benchmark, we evaluate foundation models against conventional encoders across single-image, multi-temporal, and multi-modal protocols. We report three main findings: foundation models offer a consistent but modest advantage; optical-SAR fusion with finetuning best resolves transient flooding; and models trained on GEOID-Flood transfer to unseen events better than those trained on existing datasets. Dataset and code available at this https URL.
[CV-28] CalibBEV: LiDAR-Camera Calibration via BEV Alignment
链接: https://arxiv.org/abs/2608.02309
作者: Filippo D’Addeo,Lorenzo Cipelli,Adriano Cardace,Emanuele Ghelfi,Andrea Zinelli,Massimo Bertozzi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present CalibBEV, a novel Bird’s Eye View (BEV) alignment approach for LiDAR-camera calibration. Our method unifies LiDAR and camera data into a shared 3D spatial representation, enabling accurate and robust cross-modal calibration. CalibBEV extracts sensor-wise BEV features from each modality using domain-specific architectures and estimates the calibration matrix through a two-step alignment process. First, we perform an implicit alignment by regressing a coarse calibration matrix directly from the BEV features. To ease this alignment, we enforce semantic consistency between BEV representations across modalities using a contrastive loss inspired by CLIP, guiding both networks toward a unified feature space. In the second step, we leverage our BEV formulation to explicitly align the features of one modality with the other, refining the initial coarse estimate into a final, more accurate calibration matrix. CalibBEV significantly outperforms prior point-to-pixel matching methods, achieving state-of-the-art calibration accuracy. On the KITTI and nuScenes benchmarks, our method reduces the Relative Rotation Error (RRE) by 51% and 68%, and the Relative Translation Error (RTE) by 80% and 91%, respectively, compared to previous methods.
[CV-29] he Push-Forward Transform for Continuous and Robust Comparison of Dynamic Shapes
链接: https://arxiv.org/abs/2608.02306
作者: Roua Rouatbi,Juan-Esteban Suarez Cardona,Ivo F. Sbalzarini
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Numerical Analysis (math.NA)
备注:
Abstract:We introduce a mathematical framework for shape comparison based on mapping functions from the shape domain to a common reference domain. This Push-Forward Transform enables invariant and robust comparison of shapes, preserving intrinsic geometric information. Quantitatively comparing shapes and their temporal evolution is a fundamental challenge in image analysis. Meaningful shape comparison requires representations that are invariant to transformations that do not alter shape itself, such as translation, rotation, reflection, re-parametrization, and uniform scaling, while remaining sensitive to intrinsic geometric variation. Existing approaches often rely on sensitive parameterizations, landmark correspondence, or learned representations that are difficult to interpret and reproduce. We show that the Push-Forward Transform (PF-T) applied to Signed Distance Functions (SDFs) yields a continuous representation that captures both boundary and interior geometry. We derive an interpretable morphometric that quantifies shape similarity and reveals features such as skeletal topology and rotational symmetries. The push-forward transform applies consistently to two- and three-dimensional shapes, extends to time-evolving geometries, and supports the joint analysis of shape and additional scalar fields defined over shapes, such as intensity or molecular signals. We present the mathematical formulation, describe an efficient algorithm, and benchmark the approach on 2D, 3D, and temporal data sets.
[CV-30] A General-Purpose VLM Can Teach an Astronomy Foundation Model to Better Recognize Galaxy Morphology
链接: https://arxiv.org/abs/2608.02300
作者: Dichang Zhang,Jiaqi Deng,Yixuan Shao,Yuanpeng Liu,Jiali Cui,Zhiqiang Lao,Heather Yu,Liang Peng,Simon Birrer,Dimitris Samaras
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 5 figures
Abstract:Existing astronomy foundation models provide strong galaxy representations, but adapting them to new survey conditions and survey-specific morphology recognition tasks still requires substantial human supervision. We show that VLM-based VQA systems contain meaningful visual-semantic priors that can serve as weak supervision for downstream morphology classifiers and improve morphology classification under limited human-label budgets. We first introduce a survey-oriented VQA benchmark spanning two representative imaging regimes and evaluate state-of-the-art VLMs on galaxy morphology questions. The results show that these models capture useful morphology signals and informative uncertainty, but are not sufficiently reliable to replace human annotators. Motivated by this finding, we use a general-purpose VLM as a morphology teacher for Zoobot, an astronomy foundation model pretrained on large-scale Galaxy Zoo annotations. Across two survey domains and multiple annotation budgets, the VLM teacher consistently improves Zoobot’s downstream morphology classification. These results demonstrate that a general-purpose VLM provides knowledge complementary to an astronomy foundation model and can teach it to better recognize galaxy morphology under limited human supervision. The resulting pipeline is designed for label-efficient adaptation to forthcoming large-scale surveys, including the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Space Telescope. The benchmark and code are publicly available at this https URL.
[CV-31] SpikeRestormer: Towards Energy-Efficient All-in-One Image Restoration via Unified Event Reasoning
链接: https://arxiv.org/abs/2608.02290
作者: Shengkai Hu,Jie Shao,Jiaqi Ma,Xu Zhang,Keying Wu,Qilu Zhu,Beihang Song,Jun Wan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:ANN-based All-in-One image restoration (AiOIR) unifies diverse degradation handling but incurs high computational costs, limiting its real-time deployment. While Spiking Neural Networks (SNNs) offer a low-power alternative, applying them to static images remains challenging. This difficulty arises because explicit event signals are absent, and degradation cues are heavily entangled with scene structures, hindering the learning of reliable restoration-oriented spike events. To address these issues, we propose SpikeRestormer, an energy-efficient SNN for AiOIR that performs event reasoning over internally generated spike cues. Specifically, we propose a degradation-event perception process to extract spike-based degradation events through Subtractive Degradation Event Attention (SDEA). Moreover, we introduce Hierarchical Bayesian Skip Masking (HBSM) and Additive Restoration Event Attention (AREA) processes for event-reliability inference and restoration-event construction, respectively. By integrating these complementary processes, SpikeRestormer formulates restoration as a unified process of degradation-event perception, degradation-event reliability inference, and restoration-event construction, liberating the potential of SNNs for energy-efficient AiOIR. Extensive experiments show that SpikeRestormer delivers competitive performance against ANN-based methods and establishes new state-of-the-art results among SNN-based methods with significantly lower energy consumption.
[CV-32] Extended Field of View Analysis for VideoGAN-based Trajectory Generation
链接: https://arxiv.org/abs/2608.02289
作者: Annajoyce Mariani,Kira Maag,Hanno Gottschalk
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Realistic and diverse trajectory generation is central to enabling higher levels of vehicle automation. While rule-based and classical learning-based methods may struggle to capture the complexity of traffic behavior, generative models have already demonstrated in other fields that they can handle a comparable level of complexity. In this paper, we build upon previous work on generative adversarial network (GAN)-based semantic bird’s-eye-view traffic generation and extend the proposed framework in several key aspects. We improve the semantic representation, replace the trajectory extraction procedure with a graph-based association method, and systematically investigate increasingly larger fields of view. In addition, we introduce a quantitative evaluation framework to assess hallucinations and object permanence in generated videos. Our experiments demonstrate that the framework generalizes to larger and more complex traffic scenes while maintaining statistically realistic trajectories and coherent spatial relationships between traffic participants. Within 150GPU hours of training and with inference times below 20ms for scenes of up to 20s, our results demonstrate that video-based GANs remain an efficient and scalable approach for realistic trajectory generation, even in substantially larger traffic scenes, making them well suited for downstream tasks such as prediction, planning, and simulation in automated driving.
[CV-33] Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integration ECCV2026
链接: https://arxiv.org/abs/2608.02285
作者: Aoru Xue(1),Yujing Sun(2),Yiming Ren(1 and 2),Kwok-Yan Lam(2),Mao Ye(3),Yuexin Ma(1) ((1) ShanghaiTech University, Shanghai, China, (2) Digital Trust Centre, Nanyang Technological University, Singapore, (3) a href=“http://EABOT.AI” rel=“external noopener nofollow” class="link-external link-http"this http URL/a, China)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 8 figures, 4 tables. Accepted at ECCV 2026. Aoru Xue and Yujing Sun contributed equally. Yuexin Ma is the corresponding author
Abstract:We propose Sen-Cap, a Sensor-Flexible and Noise-Resilient 3D human motion Capture framework that integrates multi-modal data from LiDAR and camera. While multi-modal sensors provide richer information than single-modal sensors, existing approaches still suffer from two core challenges. First, multi-modal alignment/matching across arbitrarily deployed sensors is typically handled by explicit calibration, which propagates errors under changing viewpoints and in turn constrains deployment to fixed, highly overlapped layouts. Second, prior methods degrade under severe noise or partial sensor failures, which are common in real-world environments. To address these challenges, Sen-Cap introduces a Unified Across-Sensor Motion Estimator that reconstructs local pose and shape in a human-centric space without calibrations between sensors, supporting a flexible number of sensors, as well as a Noise-Resistant Trajectory Tracker that maintains robustness under severe point cloud noise through iterative refinement. These sensor-flexible and noise-resilient features make Sen-Cap more practical in real-world deployment. Notably, operating in real time, Sen-Cap achieves state-of-the-art performance on major metrics on Human-M3 and FreeMotion, as well as strong cross-domain performance on LiDARHuman26M and RELI11D. This combination of flexibility and robustness opens new opportunities for motion capture in real-world scenarios, e.g. sports analytics, field robotics, and large-scale immersive environments.
[CV-34] EOVSAM: Efficient Open-Vocabulary Segmentation with SAM 3 in One Pass
链接: https://arxiv.org/abs/2608.02284
作者: Haomin Peng,Yongkang Li,Zhaoxiang Liu,Xiaojie Jin,Shiguo Lian,Yunchao Wei,Xinggang Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Open-vocabulary segmentation identifies and segments objects from arbitrary textual descriptions. SAM 3 supports noun-phrase-guided segmentation and achieves competitive open-vocabulary performance through exhaustive vocabulary traversal, yet suffers from prohibitive computational overhead as target categories scale. In this paper, we propose an Efficient Open-Vocabulary segmentation framework with SAM 3 (EOVSAM), which adapts SAM 3 for single-pass prediction. EOVSAM removes prompt conditioning to turn SAM 3 into an efficient mask generator and introduces a new Attentional Aggregation strategy to optimize open-vocabulary classification end-to-end. This formulation avoids the multi-stage pipelines and post-processing heuristics commonly used by existing methods, while mitigating the closed-set collapse that can arise when classification is optimized directly. EOVSAM consistently improves segmentation accuracy over vanilla SAM 3 on all evaluated datasets and accelerates inference by up to 338 \times . Furthermore, EOVSAM maintains high accuracy at lower resolutions while achieving even more remarkable inference speeds. Experiments on standard semantic and panoptic segmentation benchmarks show that EOVSAM combines competitive or state-of-the-art accuracy with a substantial speed advantage over existing open-vocabulary segmentation models. Code and models are available at this https URL.
[CV-35] Open-Set Visual Text Forensics via Sparse-Constraint Rectified Flow ACM-MM2026
链接: https://arxiv.org/abs/2608.02258
作者: Jiangling Zhang,Shuxuan Gao,Zeyu Chen,Yichao Liu,Yu Zhou
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ACM MM 2026
Abstract:Rapidly evolving Generative AI enables sophisticated visual text manipulations that increasingly evade current forensic detectors. Existing discriminative models often overfit specific forgery patterns, limiting their generalization to unseen, open-set attacks. To address this challenge, we propose a generative detector that localizes tampering by estimating the local restoration cost required to align a query image with authentic visual-text statistics, rather than by learning forgery-specific decision boundaries. Specifically, we introduce Sparse-Constraint Rectified Flow (SC-RF), a detector-oriented adaptation of Flow Matching for spatially sparse anomaly localization. We further mitigate data scarcity via self-supervised Artifact Injection and preserve high-frequency forensic traces using a pixel-space Forensic-DiT. Extensive experiments on three benchmarks show that our method achieves state-of-the-art performance, surpassing the runner-up by 3.2 and 4.8 percentage points in F1 and IoU, respectively. In particular, the proposed detector demonstrates strong zero-shot performance on challenging unseen text editing patterns. We further provide an auxiliary stress-test analysis showing that local harmonization produced by our model can weaken the statistical cues relied upon by existing detectors, offering a complementary vulnerability-analysis perspective.
[CV-36] HarMoE: Multi-Source Chest Radiograph Pretraining with Dataset-Disentangled Experts
链接: https://arxiv.org/abs/2608.02252
作者: Haozhe Luo,Ziyu Zhou,Shelley Zixin Shu,Mauricio Reyes
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent vision-language models for chest X-ray understanding are largely built on image-report alignment and therefore rely heavily on MIMIC-CXR as the dominant pretraining source. While effective at scale, this paradigm underexplores an important alternative source of supervision: a range of existing multi-label classification datasets, which provide cleaner and more explicit disease signals than free-text reports, and can offer broader pathology coverage when combined across sources. However, learning from such heterogeneous datasets is nontrivial, as differences in label ontologies, annotation protocols, acquisition pipelines, and report styles can cause models to entangle clinical semantics with dataset identity, leading to poor transfer despite increased scale. In this work, we revisit radiology VLM construction from the perspective of harmonized multi-source learning. We propose HarMoE, a dataset-aware mixture-of-experts framework that learns shared cross-dataset medical semantics while confining source-specific variation to lightweight residual experts in deeper decoder layers. To further exploit clean supervision from labeled datasets, we train in a unified disease vocabulary with masked multi-dataset supervision, enabling the model to leverage complementary annotations without introducing false negatives. Experiments on large-scale chest X-ray benchmarks show that HarMoE consistently improves zero-shot classification, out-of-distribution transfer, and grounding over strong baselines. Our results suggest that building robust radiology VLMs requires moving beyond single-source image-report alignment toward structured knowledge construction from heterogeneous datasets with cleaner supervision and broader coverage. Code and the 873k harmonized dataset will be released at this https URL.
[CV-37] GenPrior: Unleashing Text-to-Motion Generative Priors for Zero-Shot Skeleton-based Action Recognition
链接: https://arxiv.org/abs/2608.02236
作者: Jidong Kuang,Hongsong Wang,Jie Gui
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACMMM 2026
Abstract:Zero-shot skeleton-based action recognition (ZSAR) aims to recognize unseen action categories by aligning skeleton features with textual semantics. However, existing methods rely on text-derived prototypes that inherently lack geometric structure and physical constraints, resulting in a pronounced \textitsemantic-kinematic gap. To bridge this gap, we propose \textbfGenPrior, the first framework to exploit generative priors from pre-trained Text-to-Motion (T2M) models for ZSAR. Specifically, we introduce Dispersion-Gated Feature Fusion, which distills kinematic prototypes and intra-class dispersion from generative motion sequences and employs a learned gating network to adaptively inject reliable structural cues into textual embeddings while suppressing synthetic artifacts. Furthermore, we propose Generative Prototype Refinement, which leverages these generation-enhanced prototypes as anchors to mine high-confidence unseen samples, calibrating class prototypes toward the true distribution and thereby unleashing strong performance gains. Extensive experiments on NTU-60, NTU-120, and PKU-MMD demonstrate that GenPrior achieves state-of-the-art performance under both zero-shot and generalized zero-shot settings. Code is available at this https URL.
[CV-38] VC-Tooler: Learning Compositional and Adaptive Visual Tool Use
链接: https://arxiv.org/abs/2608.02217
作者: Yizheng Wu,Jiashen Hua,Bing Deng,Jieping Ye
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Agentic multimodal reasoning extends passive image understanding by allowing VLMs to actively acquire and refine visual evidence through visual tool interactions. Effective visual tool use requires three capabilities: grounding tool calls in visual context, composing tools across multiple steps, and adapting reasoning to tool-returned observations. However, existing approaches largely focus on grounding within fixed tool spaces and rigid invocation patterns, leaving composition and adaptation insufficiently addressed. We present VC-Tooler, which learns visual tool use as a compositional and adaptive capability. To this end, we first build a trajectory bank through a hierarchical synthesis pipeline covering three capability levels: single-tool grounding, multi-tool composition, and diverse tool contexts and interfaces. We then train the model in two stages: a supervised cold start that establishes these capabilities, followed by reinforcement learning that encourages accurate, efficient, and context-aware visual tool use. VC-Tooler achieves state-of-the-art performance among open-source models on both general-purpose and agentic benchmarks, including 95.8% on V* and 35.3% on VTC-Bench, and shows promising transfer under richer tool settings at inference time. Project page: this https URL
[CV-39] Local Margin Restoration for Test-Time Adaptation of Vision-Language Models ACM-MM2026
链接: https://arxiv.org/abs/2608.02216
作者: Yan Huang,Guowei Wang,Xu Wang,Kangjun Liu,Xin Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM MM 2026
Abstract:Vision-language models (VLMs) such as CLIP exhibit remarkable zero-shot capabilities, yet their performance frequently degrades sharply under unexpected test-time distribution shifts. While Test-Time Adaptation (TTA) offers a promising solution, continuously adapting VLMs over an unlabeled test stream presents fundamental challenges. Conventional top-1-centric updates often reinforce errors by corrupting the local semantic geometry among related classes, while iterative adaptation exacerbates progressive bias accumulation, ultimately driving the model toward mode collapse. To overcome these coupled vulnerabilities, we propose Local Margin Restoration (LMR), a lightweight, one-step TTA framework. At the sample level, our Protected Margin Restoration (PMR) objective recovers local semantic geometry by shielding plausible near-top candidates from external hard negatives. Concurrently, to combat stream-level degradation, we introduce a dual-stage stabilization mechanism, featuring an Adaptive Margin (AM) controller and Bias Correction (BC), to dynamically disrupt progressive bias accumulation and prevent mode collapse. Extensive experiments on CIFAR-C, ImageNet-C, and ImageNet variants demonstrate that LMR consistently outperforms state-of-the-art TTA baselines, proving exceptionally robust and efficient even in challenging low-batch test-time regimes. Our code is available at this https URL.
[CV-40] VARPose: Flexible 2D Pose Densification via Visual Autoregressive Modeling for Enhanced 3D Lifting ACM-MM2026
链接: https://arxiv.org/abs/2608.02214
作者: Kaiyuan Pu,Tiantian Yang,Dan Zeng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ACM MM 2026
Abstract:Visual AutoRegressive Modeling (VAR) has excelled in natural image generation via next-scale prediction, but its use on topology-structured data like human skeletons is still unexplored. VARPose is proposed to adaptively densify 2D sparse poses, thereby enriching the anatomical information available for 3D lifting models. Our core contributions are twofold. First, we introduce a Granularity-agnostic Pose Tokenizer (GPT), which employs a single hybrid codebook and a residual quantization strategy to encode poses of varying densities into a unified, multi-scale discrete representation. Our results demonstrate the strong generalizability of this representation. By decoupling the representation from the projection, we can successfully decode novel pose granularities using a frozen codebook with a retrained decoder. Second, we propose UniSkelar, a unified autoregressive model that treats “joint density” as “scale”. UniSkelar learns to predict the token sequence for the next density level in a coarse-to-fine manner, conditioned on the sparsest pose. VARPose not only outperforms state-of-the-art methods and generalizes to unseen granularities, but also confers tangible performance gains on downstream tasks, such as 3D Pose Estimation and Human Mesh Recovery, through 2D pose densification. Our code and model are available at this https URL.
[CV-41] Self-supervised DXA representations encode multi-system disease risk biological aging and heritability
链接: https://arxiv.org/abs/2608.02208
作者: Gil Sasson,Zachary Levine,Smadar Shilo,Sarah Kohn,Guy Lutsker,Anastasia Godneva,Adam Gabet,David Krongauz,Adina Weinberger,Yann LeCun,Randall Balestriero,Eran Segal
类目: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注: Preprint Version
Abstract:Whole-body dual-energy X-ray absorptiometry (DXA) scans are routinely acquired to measure bone density and regional body composition, leaving their spatial structure largely unused. Here, we show that self-supervised learning (SSL) can convert raw DXA images into representations of systemic health. We introduce LeDXA, a vision model based on a joint-embedding predictive architecture (JEPA) that learns by predicting latent representations rather than reconstructing pixels. Trained from scratch on 11,540 unlabeled Human Phenotype Project scans, LeDXA was evaluated internally and on 47,400 external UK Biobank (UKBB) scans. It improved cross-cohort prediction of prevalent diseases and biomarkers beyond scanner-derived DXA measurements and DINOv3, a state-of-the-art general-purpose model, despite approximately 150,000-fold fewer training images and nearly 40-fold fewer parameters. Over a median 4.3-year UKBB follow-up, LeDXA improved incident disease prediction over tabular DXA measures, with the largest gains for hip and knee arthrosis and type 2 diabetes. For hip arthrosis, 66% of incident cases occurred in the highest-risk quartile versus 41% for tabular measures. Its representations predicted chronological age externally (r = 0.88; mean absolute error = 2.90 years), and the biological-age gap tracked broader disease burden and a 45% higher mortality hazard in the oldest-appearing quartile. The gap also decreased in women after starting hormone-replacement therapy, suggesting it may be modifiable. Genome-wide associations recovered mostly known body-composition and bone-density loci, and LeDXA embeddings were more heritable than DINOv3’s. These findings reveal prognostic information in DXA images that conventional readouts discard, learnable with relatively little data and modest compute.
[CV-42] CLEAR: Conflict-aware Learning via Evidence-guided Adaptive Routing for Unified Sparse-View 3D Gaussian Super-Resolution
链接: https://arxiv.org/abs/2608.02206
作者: Hantang Li,Qiang Zhu,Xiandong Meng,Debin Zhao,Xiaopeng Fan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 5 figures
Abstract:Sparse-view 3D Gaussian Splatting Super-resolution is highly challenging since the sparse and low-resolution (LR) inputs lack sufficient geometric and high-frequency information for accurate reconstruction. To achieve high-quality reconstruction, existing sparse-view super-resolution methods adhere to two-stage pipeline that performs LR Gaussian reconstruction and then high-resolution (HR) Gaussian refinement, which directly results in stage-wise Gaussian transfer and reconstruction error accumulation. To this end, we propose CLEAR, a Conflict-aware Learning via Evidence-guided Adaptive Routing, as the first unified single-stage framework for Sparse-view 3D Gaussian Splatting Super-resolution. Specifically, CLEAR performs joint the optimization of authentic LR observations and external HR priors within a unified Gaussian representation. To mitigate the gradient conflicts introduced by sparse supervision during training, we propose a Gaussian-wise conflict-aware optimization strategy that regards the LR gradient as a reliable anchor and applies evidence-conditioned soft correction only to severe HR conflicts. Moreover, to recover high-frequency details, we introduce an evidence-guided Patch-to-Gaussian routing mechanism which estimates patch reliability and detail demand, lifts them into Gaussian space, and selectively routes high-frequency gradients and densification. Finally, we employ shared Gaussian dropout and a detached mid-training anchoring to enhance the robustness of training framework. Extensive experiments on both synthetic and real-world 4\times super-resolution benchmarks demonstrate that CLEAR consistently achieves state-of-the-art rendering quality and superior geometric fidelity.
[CV-43] RSC-GestureNet: Reliability-Aware Selective Causal Recognition of Chinese Traffic Police Gestures
链接: https://arxiv.org/abs/2608.02200
作者: Cheng Li,Renjun Gao,Boyi Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 2026 PRCV Oral; Project Page: this https URL
Abstract:Traffic police gestures are safety-critical perception cues for autonomous driving. A deployable recognizer must infer commands causally from continuous full-frame video, remain stable around transitional arm motion, and avoid over-trusting corrupted pose measurements. This study presents RSC-GestureNet, a reliability-aware selective causal recognizer, for Chinese traffic police gestures. The model treats pose confidence as a first-class signal: unreliable joints are down weighted during graph reasoning, temporal evidence is aggregated causally, and calibrated predictions are selectively emitted through a reliability-aware inference rule. We further introduce CTPGesture-C, a reproducible feature-level corruption benchmark with seven pose/RGB degradation families, and an RGB-level diagnostic in which corrupted frames are reprocessed by MediaPipe before recognition. On the complete official CTPGesture v1 split (134,424 labeled frames and 33,451 causal windows), RSC-GestureNet achieves 93.33±0.24% accuracy, 91.71±0.27% macro-F1, 91.69±0.29% online macro-F1, 98.80±0.07% Early@10, 0.153±0.013 s TTC, and the best robust macro-F1 among evaluated methods. Under the same split and causal protocol, it exceeds reproduced traffic-specific MD-GCN and HLP-GCN baselines by 3.23-4.11 macro-F1 points and 2.15-3.07 online-F1 points. These results, together with calibration, selective-risk, statistical, adaptive-branching, and image-level re-extraction analyses, indicate that explicit pose-reliability modeling improves early, stable, and robust traffic-command recognition.
[CV-44] 2exture: Sparsely Perturbed Thermal-to-Texture Imaging
链接: https://arxiv.org/abs/2608.02192
作者: Jiashuo Chen,Cheng Dai,Yanan Hu,Fanglin Bao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 7 figures
Abstract:Thermal imaging remains effective under adverse illumination, yet passive long-wave infrared (LWIR) measurements often lack fine texture. Existing thermal texture imaging approaches commonly rely on spectral sensing or registered auxiliary modalities, incurring substantial data throughput or vulnerability to cross-modal degradation. We introduce T ^2 exture, a sparsely perturbed thermal texture imaging framework that aims to reconstruct temporally dense thermal texture sequences from densely sampled passive frames and a few actively perturbed keyframes. We define thermal texture as the residual between a source-on observation and its corresponding source-off passive state. Under sparse LWIR illumination and rapid quasi-steady paired acquisition, this residual attenuates the passive-emission background and approximates a source-induced reflected response, exposing localized material- and geometry-dependent texture. T ^2 exture reconstructs a dense sequence of this source-conditioned response through two stages. Stage 1 estimates the unobserved source-off passive state at each active instant from neighboring passive frames to obtain reliable differential texture anchors. Stage 2 combines sparse anchors with passive structural context near each target time to reconstruct the dense sequence. On the simulated benchmark, T ^2 exture adds only 0.20M parameters to AMT-L while improving PSNR by 6.66 dB. Extensive evaluations on simulated and real acquisitions further show clearer texture recovery and stronger structural preservation than representative VFI baselines. These results establish T ^2 exture as a practical framework for thermal texture imaging under sparse active acquisition.
[CV-45] DerainSplat: Feed-Forward Clean 3D Gaussian Splatting from Sparse Rainy Views
链接: https://arxiv.org/abs/2608.02191
作者: Fuzhen Jiang,Changyue Shi,Chuxiao Yang,Xinyuan Hu,Wenjie Ye,Minghao Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Although image deraining has advanced substantially, existing methods mainly focus on 2D image restoration. As spatial intelligence applications such as embodied AI and autonomous driving continue to emerge, reconstructing clean 3D scenes from sparse rainy views in a feed-forward manner becomes increasingly important. Existing feed-forward 3D Gaussian Splatting (3DGS) methods often assume clean inputs and collapse under rainy conditions. To this end, we present \textbf\textitDerainSplat, a feed-forward framework that reconstructs clean 3D scenes from only a few rainy views. To support this task, we build a large-scale multi-view derain dataset through a four-stage synthesis pipeline that sequentially models overcast illumination, depth-dependent haze, rain streaks, and lens raindrops, producing privileged weather factors. We introduce a weather net that predicts the weather factors from rainy context and yields two support maps. Scene support modulates cross-view cost-volume matching, while radiance support drives depth-aligned appearance fusion to fill corrupted pixels. The derived geometry evidence further attenuates Gaussian opacity to reduce spurious structures. A rainy cycle consistency re-renders clean views using the predicted factors and aligns them with rainy inputs. Extensive experiments show that \textbf\textitDerainSplat outperforms existing methods on various datasets, including RealEstate10K, ACID, Mip-NeRF360, and real-world rainy scenes, with strong cross-dataset generalization.
[CV-46] SPIRIT: Spatio-temporal Pairwise Relational Modeling of Instrument-Tissue Interactions for Surgical Action Triplet Recognition
链接: https://arxiv.org/abs/2608.02188
作者: Saurav Sharma,Lorenzo Arboit,Nabani Banik,Sarah Meuli,Julia Alekseenko,Jan Liechti,Franziska Heitzinger,Michela Orsi,Didier Mutter,Daniel Gero,Philipp C. Nett,Beat P. Muller,Joel L. Lavanchy,Nicolas Padoy
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 31 pages, 8 figures
Abstract:Fine-grained understanding of surgical activity is essential for context-aware assistance in the operating room, including safety monitoring, adverse event identification, and skill assessment. Surgical action triplets, defined as tuples of the form instrument, verb, target, provide a structured description of instrument-tissue interactions. A key open problem, however, is how to learn triplet representations that remain reliable across institutions, where surgical video varies in acquisition conditions, surgeon style, tool usage, and tissue handling, while existing triplet datasets do not support explicit evaluation of center-wise transfer. To address this problem, we propose \textbfSPIRIT, a structured framework for surgical action triplet recognition designed to learn interaction representations that transfer more reliably across centers. Instead of treating each triplet as a flat class label, SPIRIT first learns spatio-temporal representations for instruments, verbs, and targets, then models their pairwise relations, and finally composes them into coherent triplet predictions, with multi-head distillation used to stabilize learning. To evaluate this setting, we establish \textbfMultiBypass-4C-T40, a multi-centric dataset for dense surgical action triplet recognition in Roux-en-Y gastric bypass across four geographically distinct centers, with auxiliary phase and step annotations. Across multiple evaluation protocols, SPIRIT consistently outperforms strong recent baselines, highlighting the value of explicit relational reasoning for multi-centric triplet recognition. Code will be available at this https URL.
[CV-47] SWINSleepNet: A Hierarchical Context-Aware Framework for Sleep Staging (v2) DATE
链接: https://arxiv.org/abs/2608.02183
作者: Chongjian Wang,Junjie Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Report-no: SDUST-SLEEP-202608-V2; 10 pages, 7 figures, revised updated version of arXiv submit/7867870, conference submission draft
Abstract:Automatic sleep staging is a critical role in sleep disorder diagnosis, sleep quality assessment, and long-term health monitoring; however, existing approaches suffer poor performance on ambiguous and transition-related sleep stages, caused by inadequate modeling of fine-grained intra-epoch structures and complex cross-region spectral dependencies. Traditional epoch-level encoders commonly fail to extract subtle temporal microstructures and intra-epoch cross-region interactions, resulting in unsatisfactory recognition accuracy for hard categories such as the N1 stage. To tackle these drawbacks, we propose SwinSleepNet, a hierarchical context-aware dual-stream framework that separately optimizes intra-epoch representation learning and inter-epoch contextual modeling. Concretely, we characterize each sleep epoch from two complementary perspectives: raw time-domain EEG signal and its time-frequency transformation. The time-domain branch adopts convolutional encoders to capture fine waveform temporal details, and the time-frequency branch uses Swin Transformer to extract local spectro-temporal features, hierarchical multi-scale information and long-range spatial dependencies. The multi-branch extracted features are fused into integrated embeddings, which are optimized by a bidirectional context module to capture cross-epoch temporal dependencies for final sleep stage classification. Comprehensive experiments on Sleep-EDF-20, Sleep-EDF-78 and SHHS datasets verify that our method achieves competitive overall performance, and exhibits stronger robustness and stability on difficult N1 stages and transitional epochs. The results prove that optimized intra-epoch representation learning based on hierarchical architecture greatly benefits automatic sleep staging tasks.
[CV-48] GSRAIN: Physically Calibrated High-/Low-Frequency Rainfall Synthesis for 3D Gaussian Driving Scenes
链接: https://arxiv.org/abs/2608.02177
作者: Fanyu Wang,Longgao Zhang,Junyi Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing rainfall simulation methods for autonomous driving remain limited in physical controllability and multi-view consistency. This paper presents GSRAIN, a high-/low-frequency rainfall synthesis method for 3D Gaussian Splatting (3DGS) driving scenes. GSRAIN constructs a high-frequency raindrop model from measured rainfall data and generates low-frequency rainy appearance using a geometry-aware single-step diffusion model. The two effects are then fused in a unified 3DGS scene, enabling rainfall-intensity control over the range of 0–13~mm/h. The proposed method achieves a Fréchet Inception Distance (FID) of 149.09, outperforming CycleGAN-Turbo (155.71) and WeatherEdit (157.94). Object-detection and closed-loop driving experiments further show that the generated scenes expose scene-dependent performance changes of the evaluated algorithms under controllable rainfall. These results indicate that GSRAIN provides an effective approach for constructing physically controllable, repeatable, and closed-loop-compatible rainy-weather test scenes for autonomous driving.
[CV-49] AdaForensics: Learning A Characteristic-aware Adaptive Deepfake Detector
链接: https://arxiv.org/abs/2608.02160
作者: Xiaoke Yang,Haixu Song,Xiangyu Lu,Shao-Lun Huang,Yueqi Duan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In this paper, we propose a characteristic-aware adaptive network named AdaForensics for deepfake detection. Most existing methods learn a fixed network to detect deepfakes based on carefully-designed network architectures. However, these methods employ the same deepfake detector for all the images despite of various facial characteristic, which fail to provide customized forgery detection for different individuals. To address this, our AdaForensics simultaneously learns characteristic-agnostic and characteristic-specific embeddings, where the detector dynamically adapts to varying faces with our designed hypernetwork on the fly. More specifically, our AdaForensics not only explores the shareable abstractions from various deepfake images, but also adapts the detector to the given characteristic at test time. To achieve this, we propose a two-branch HyperNetwork to learn an adaptive deepfake detector, which automatically adjusts the parameters based on characteristic of the input. Extensive experiments on widely-used datasets including FaceForensics, Celeb-DF and DFDC demonstrate our AdaForensics outperforms the state-of-the-art works.
[CV-50] PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLM s
链接: https://arxiv.org/abs/2608.02150
作者: Zhongjie Ba,Shengwang Xu,Peng Cheng,Jinyang Zou,Ting Yu,Zhibo Wang,Zhan Qin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 15pages, 4 figures, 4 tables
Abstract:Embodied intelligence and world models require video understanding systems to go beyond recognizing objects and actions and develop an understanding of physical regularities. However, despite their strong performance on general video understanding tasks, current video-language models still struggle to reliably determine whether an observed event conforms to specific physical laws. Existing benchmarks primarily assess the physical quality of generated videos, providing limited support for systematically evaluating and improving the physical-law understanding of Video Large Language Models (VideoLLMs). To address this gap, we introduce PhyCheck, a video question answering dataset organized at two complementary levels of granularity. The coarse-grained subset asks models to determine whether the phenomenon shown in a video conforms to or violates physical laws, while the fine-grained subset further examines whether models can capture physical details responsible for the violation or compliance. We use these subsets as structured supervision to improve physical understanding. In addition, the dataset contains a diagnostic subset with external causal context that reveal hidden factors affecting physical plausibility, assessing whether models can recalibrate their judgments accordingly. Experiments with Fine-tune Qwen2.5-VL show that training with the proposed data substantially improves the understanding of physical-consistency, while evaluations in the diagnostic subset reveal that current models still have difficulty incorporating additional causal conditions into their decisions. These findings highlight the gap between recognizing surface-level inconsistencies and understanding underlying physical mechanisms, and provide a foundation for evaluating and improving physical understanding in Video-LLMs.
[CV-51] UniqueSplat: View-conditioned 3D Gaussian Splatting for Generalizable 3D Reconstruction
链接: https://arxiv.org/abs/2608.02145
作者: Haixu Song,Xiaoke Yang,Shengjun Zhang,Jiwen Lu,Yueqi Duan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:In this paper, we propose UniqueSplat, a view-conditioned feed-forward 3D Gaussian Splatting model to reconstruct customized 3D radiance fields for each view query. Existing feed-forward methods such as pixelSplat and MVSplat aim to generate fixed Gaussians across all views of each scene by minimizing the error between rendered views and ground-truth images. However, such fixed Gaussians generally render images from all views and lack the ability to adapt to specific viewpoints, as they do not incorporate target view information when predicting Gaussians. To address this, our UniqueSplat learns the view-conditioned information as a prior and incorporates this knowledge into network parameters, so that Gaussians are dynamically adjusted in accordance with different views. Specifically, we propose a two-branch view-conditioned hyperNetwork to simultaneously learn view-agnostic embeddings and view-specific knowledge, which not only explores the shareable knowledge from various views, but also adapts the model to specific views at test time. Extensive experiments on widely-used datasets including RealEstate10K, ACID and DTU demonstrate the superiority of UniqueSplat over the state-of-the-art methods. Moreover, UniqueSplat encouragingly outperforms existing methods in cross-dataset evaluation, showing its notable generalization ability.
[CV-52] Quaternion Tensor Modeling for Joint Color-Polarization Demosaicking
链接: https://arxiv.org/abs/2608.02144
作者: Yanqing Song,Jifei Miao,Chaoqian Li,Rui Mei,Kit Ian Kou,Liqiao Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Division-of-focal-plane (DoFP) color polarization cameras enable snapshot acquisition of color polarization mosaic images, but the inherently sparse sampling pattern makes color polarization demosaicking severely ill-posed. Existing methods often fail to jointly exploit the correlations among polarization channels and the physical constraints inherent in polarization imaging, resulting in noticeable demosaicking artifacts. To address this issue, a quaternion-tensor-based color polarization demosaicking (CPDM) method incorporating Stokes-domain total variation (TV) regularization is proposed. Correlation analysis shows that the correlations among polarization channels are stronger than those among color channels. Accordingly, the color polarization images acquired at 0^\circ , 45^\circ , 90^\circ , and 135^\circ are encoded into the four components of a third-order quaternion tensor, with the color channels organized along its third mode. A low-rank prior is then imposed on the quaternion tensor to exploit the global structural redundancy in the color polarization data. Moreover, spatial gradients are mapped to the Stokes domain through an orthogonal transformation to separate intensity, polarization and residual variations, with adaptive quaternion weights enabling component-specific regularization and preserving the energy consistency of the reconstructed Stokes vectors. An efficient optimization algorithm is derived for the resulting model. Extensive experiments demonstrate the superior demosaicking performance of the proposed method.
[CV-53] HiResNets: Native Full-HD Video Recognition with Foveal Residual Streams
链接: https://arxiv.org/abs/2608.02140
作者: Shivani Mall,Swarnim Jain,Joao F. Henriques
类目: Computer Vision and Pattern Recognition (cs.CV); Performance (cs.PF)
备注:
Abstract:Much of the recent progress in image and video recognition has come at the cost of memory: larger models, increased resolution, and longer temporal contexts. An inevitable component is the quadratic (or larger) growth of memory and compute based on image resolution, which is a property of the grid sampling used in convolutional networks and vision transformers. In this work we study residual networks whose convolutional blocks have logarithmic-square growth instead, enabling them to process very high-resolution video quickly. The key insight is to use a residual architecture’s residual stream as a high-resolution buffer, to which convolutional blocks only read and write via log-polar image warp operations. Layers adaptively focus on different parts of each frame, with very high resolution only near the focus point. A complete high-resolution representation is built up in the residual stream, analogous to eye saccades creating a complete picture in biological vision, and a theoretical construction is presented that eliminates the quadratic dependency of the residual stream resolution. Experiments demonstrate that our proposed HiResNets learn to foveate around scenes similarly to human vision, and have superior performance in difficult egocentric video recognition tasks, especially egocentric video with small objects and fine-grained recognition.
[CV-54] wo Sides of the Same Coin: Co-Evolving Search for Cross-Task Attacks on Vision-Language Models
链接: https://arxiv.org/abs/2608.02137
作者: Xuanhui Lin,Junhao Dong,Mingrong Gong,Yucheng Chen,Xinghua Qu,Yew-Soon Ong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 7 figures, and 8 tables; includes supplementary material
Abstract:Vision-language models (VLMs) exhibit strong generalization across multimodal tasks but remain vulnerable to adversarial perturbations. Existing attacks typically follow single-trajectory gradient optimization or task-specific objectives, limiting search-space exploration and cross-task transferability. We propose an evolutionary-computation-guided cross-modal attack framework for unified VLMs. The framework adaptively searches both textual and visual spaces. On the textual side, it evolves hard negative semantic embeddings around the source-category representation to provide diverse cross-modal repulsion. On the visual side, it maintains a population of object-region perturbations and combines momentum-based gradient updates with evolutionary selection, mutation, and crossover to more reliably explore multiple feasible trajectories. Jointly optimizing semantic negative guidance and localized perturbations generates adversarial examples that consistently shift source-object semantics toward target categories across vision-language tasks. Theoretical analyses show that the co-evolutionary search preserves perturbation feasibility, prevents degradation of the best observed fitness, and increases the probability of reaching high-margin adversarial regions compared with single-trajectory optimization. Experiments on Florence-2, OFA, and UnifiedIO-2 demonstrate strong overall attack performance across image captioning, object detection, region categorization, and object localization. Ablation studies further verify the complementary effectiveness of text-side semantic evolution and image-side perturbation evolution, as well as the framework’s efficiency and cross-task transferability.
[CV-55] Messages Not Tokens: Grounded Coresets for Faithful VLM Compression
链接: https://arxiv.org/abs/2608.02134
作者: Long Qian,Jiaqi Wei,Bingke Zhu,Yingying Chen,Jinqiao Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 32 pages, 6 figures, 18 tables, including appendix
Abstract:Modern vision language models (VLMs) turn high-resolution images into long sequences of visual tokens. Every token traverses the language decoder and persists in its prompt KV cache, inflating inference cost and motivating aggressive visual compression. Existing score-based methods assign each token an independent importance score and retain the Top-K. However, text queries consume collective, signed attention messages from the visual population, not isolated patches. Consequently, equally sized Top-K sets can repeatedly cover one salient region, omit sparse but complementary evidence and discard information carried by the removed population. We therefore formulate faithful visual compression as constructing a compact coreset for decoder messages, and introduce our training-free Grounded Message Coreset Pruning (GMC) which jointly allocates support across query-grounded, appearance, and coordinate-aware evidence, then transports discarded states into selected representatives at their original multimodal positions before physical compaction and native attention resume. This decomposes faithful compression into two coupled components, including selecting carriers that cover the required message modes and realizing the signed population message on those carriers. We further derive bounds connecting their errors to signed-message distortion, visual innovation, and candidate-margin stability. Experiments across multiple VLM families and diverse benchmarks demonstrate strong performance, with GMC-H2 retaining 97.78% Full-relative mean capability on Qwen2.5-VL-7B using 80.2% fewer visual tokens, while GMC-L16 reaches 100.36%. Controlled interventions verify that collective support and population realization jointly drive these gains.
[CV-56] PromptPath: Prompt-Adaptive Computational Pathways for In-Context Learning
链接: https://arxiv.org/abs/2608.02129
作者: Hangrui Zhang,Feifei Shao,Yawei Luo,Ping Liu,Jiaxiang Liu,Zuoqi Tang,Zhao Wang,Hongwei Wang,Jun Xiao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In-context learning (ICL) has attracted increasing attention for enabling models to perform new tasks using only a few ``input–output’’ prompt examples. However, existing approaches suffer from \textbfshallow task adaptation, where prompts are primarily used as contextual cues to implicitly infer task intent through semantic representations, while the underlying computational process remains unchanged. This limitation restricts task-specific adaptation and compromises inference interpretability. We argue that prompts should not only condition feature representations but also dynamically regulate the model’s computation pathways. To this end, we propose \textbfPromptPath, an adaptive ICL framework that enables computation-level adaptation through prompt-conditioned dynamic pathways. Specifically, PromptPath introduces a prompt-driven routing mechanism to selectively activate and compose lightweight low-rank experts, forming task-specific computational pathways tailored to different prompts. By integrating prompt information directly into the inference process, PromptPath dynamically reconfigures model computation to enhance task specialization and interpretability. Extensive experiments on 3D point cloud and 2D visual recognition benchmarks demonstrate that PromptPath consistently outperforms state-of-the-art ICL baselines while exhibiting strong cross-domain and cross-task generalization.
[CV-57] Same Semantics Different Paths: Self-Improving Alignment for Vision-Text Compression
链接: https://arxiv.org/abs/2608.02109
作者: Tianyu Liang,Xiangxi Zheng,Yilin Wang,Dongxing Mao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACM Multimedia 2026 (Oral)
Abstract:Vision-Text Compression (VTC) renders long texts into images and encodes them through the vision encoder (ViT), compressing thousands of text tokens into far fewer visual tokens. However, since the ViT is pretrained predominantly on natural images, it captures visual attributes (glyphs, font sizes, layout) rather than linguistic semantics, causing rendered-image representations to diverge from native-text representations. We term this cross-path inconsistency and show, via rendering perturbation experiments, that it is a critical yet overlooked bottleneck of VTC. We propose SPIRAL (Self-improving Path Integration and Realignment), a self-supervised alignment framework that closes this gap using only the model’s own text-path behavior as supervision, requiring no external teachers or additional annotations. SPIRAL operates at two complementary granularities: token-level on-policy distillation (OPD) for local faithfulness, and sequence-level preference optimization (DPO) for global coherence. On VTCBench, SPIRAL improves the overall score of Qwen3-VL-8B from 35.10 to 54.02, approaching the native text-input performance (55.60) and outperforming models up to 30x larger. The two granularities exhibit complementary strengths: OPD excels at retrieval and is sample-efficient, while DPO is stronger on reasoning and memory and scales better with data. SPIRAL’s benefits also generalize to out-of-domain benchmarks, confirming that effective VTC hinges on aligning rendered-image representations back to native-text semantics.
[CV-58] DeGS: A Scalable 3DGS Architecture via Decoupled Workload Parsing and Reorganization MICRO2026 MICRO
链接: https://arxiv.org/abs/2608.02099
作者: Minnan Pei,Gang Li,Zeyu Zhu,Siting Wang,Junwen Si,Zhuoran Song,Yu Feng,Fangxin Liu,Xiaoyao Liang,Jian Cheng
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to the 59th IEEE/ACM International Symposium on Microarchitecture (MICRO 2026)
Abstract:3D Gaussian Splatting (3DGS) has emerged as a leading technique for real-time novel view synthesis, yet existing 3DGS accelerators suffer from poor architectural scalability: increasing the number of PEs leads to marginal performance improvement during rendering. We identify that the root cause is the tightly coupled ``checking-while-blending’’ dataflow, which exacerbates PE underutilization caused by spatial redundancy from irregular Gaussian coverage and temporal redundancy from asynchronous pixel-wise termination under parallel execution. To address this issue, we propose DeGS, a scalable architecture for efficient 3DGS inference. To systematically eliminate the redundancies inherent in rendering, DeGS exploits a decoupled dataflow, restructuring the coupled \alpha -checking, transmittance checking, and \alpha -blending of the standard rendering process into consecutive workload parsing, reorganization, and blending stages. This allows the fragmented, length-variable, and temporal-dependent workloads to be reorganized into compact, conflict-free, and dense workloads prior to blending, thereby significantly improving PE utilization during parallel blending. Implemented in 28 nm technology, DeGS achieves 2.36 \times --7.25 \times throughput, 1.82 \times --6.02 \times end-to-end speedup, and 1.59 \times --4.42 \times energy efficiency over state-of-the-art 3DGS accelerators (GSCore, GBU, GCC) across diverse scenes and resolutions (720p to 8K). Moreover, scaling from 16 to 1024 PEs, DeGS maintains over 80% PE utilization at high resolutions, significantly outperforming existing accelerators. Comments: Accepted to the 59th IEEE/ACM International Symposium on Microarchitecture (MICRO 2026) Subjects: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.02099 [cs.AR] (or arXiv:2608.02099v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2608.02099 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-59] Deep Multimodal Fusion Detection through Spatial Mask and Channel Fusion
链接: https://arxiv.org/abs/2608.02092
作者: Guandi Wang,Ming Li,Yunsen Xing,Junle Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Deep multimodal fusion for object detection has demonstrated good performance through mining modal characteristics. However, existing feature-level fusion methods mainly weigh between two modalities and unify them in a unified representation space. This can lead to overfitting or over-specialization of the statistical properties of a single modality within a dual-backbone architecture. This paper proposes an Attention-Driven Complementarity Resampling framework for robust improvement of cross-modality object detection. Based on a shared channel spatial attention mechanism, we first introduce the semantic mask exchange to actively mix the boundaries of the modalities during the training phase, forcing the backbone network to learn generalized features without relying on fixed modal labels. Then we propose a learnable channel competition to sample and aggregate features in a channel-wise and learnable way. Our experiments on multiple datasets demonstrate that the proposed method is effective and yields competitive results among existing state-of-the-art approaches. The source code is provided in the supplementary material.
[CV-60] STEAM:ASpatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding
链接: https://arxiv.org/abs/2608.02070
作者: Zhu Chen,Dingkun Liu,Yuheng Chen,Dongrui Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios. However, conventional neural signal decoding algorithms often suffer from limited generalizability and high adaptation costs, motivating recent interest in BCI foundation models. Existing approaches still struggle to jointly achieve general transferability, accurate decoding, and efficient downstream adaptation. We present STEAM, a hierarchical transfer framework that reconciles general-purpose representation learning with paradigm-specific specialization in EEG foundation models. The framework is instantiated as a dual-branch spatio-temporal encoder in which a shared soft mixture-of-experts (SSMoE) module aligns the spatial and temporal branches, allowing complementary representations to exchange information through a compact set of soft slots. Across seven downstream datasets and fourteen evaluation settings, STEAM attains the best average rank among the compared methods at a competitive inference cost measured in FLOPs. Building upon the Stage-I general initialization, the hierarchical pre-training strategy further specializes the model to a target paradigm without retraining from scratch, yielding consistent gains in paradigm-specific decoding accuracy.
[CV-61] GIFT: Geometry-Invariant Fine-Tuning for Non-Lambertian Monocular Depth Estimation
链接: https://arxiv.org/abs/2608.02068
作者: Xianghui Fan,Zhaoyu Chen,Bingqian Wu,Dayu Li,Xin Zeng,Huanran Cui,Guangzhen Xu,Xiangru Huang,Hang Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Monocular depth foundation models, benefiting from large-scale synthetic training data, have demonstrated strong generalization. However, they often hallucinate depth on non-Lambertian surfaces, estimating reflected content in mirrors or transmitted content behind glass rather than the physical surface itself. Adapting these models with real-world data is challenging because conventional depth sensors are also unreliable in such regions. We observe that while the appearance of a non-Lambertian surface varies with its reflected or transmitted environment, its underlying geometry remains unchanged. Based on this observation, we propose GIFT (Geometry-Invariant Fine-Tuning), a parameter-efficient post-training framework that requires no measured depth labels. We collect groups of RGB images under controlled appearance changes while keeping the camera and target geometry fixed. GIFT exploits geometric invariance across these observations to suppress non-Lambertian depth hallucinations while retaining general depth estimation capability. We further construct a controlled benchmark that evaluates non-Lambertian depth recovery, robustness to appearance changes, and performance retention in other regions. Experiments on our benchmark and an independent real-world dataset demonstrate that GIFT improves depth prediction for mirrors and transparent objects while largely preserving the base model’s performance, providing a practical and low-cost approach for adapting monocular depth foundation models to non-Lambertian scenes.
[CV-62] MIEScore: Human-Aligned Evaluation for Multi-Source Image Editing
链接: https://arxiv.org/abs/2608.02059
作者: Zitong Xu,Huiyu Duan,Xinyun Zhang,Weifei Xiong,Tianyi Zheng,Xiongkuo Min,Qiang Hu,Zhengxue Cheng,Bo Li,Guangtao Zhai
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Recent advances in unified multimodal models have significantly improved text-guided image editing abilities. In particular, models such as Nano-Banana-Pro and GPT-Image-2 demonstrate emerging capabilities in multi-source image editing (MIE), including tasks such as object synthesis, person-background composition, and cross-image style fusion. However, existing benchmarks and image editing assessment (IEQA) methods remain primarily focused on single-image editing tasks and largely overlook the more challenging setting of MIE. This highlights the urgent need for a comprehensive and human-aligned benchmark for MIE. To this end, we introduce MIE-Bench, the first large-scale multiple image editing benchmark with fine-grained human preference annotations. Specifically, MIE-Bench includes 3,000 editing instances across 16 tasks, each involving more than two source images and an editing prompt, together with 36K edited images produced by 12 state-of-the-art editing models and over 108K mean opinion scores (MOSs) covering visual quality, instruction following, and attribute preservation. Based on MIE-Bench, we propose MIEScore, a multimodal large language model (MLLM)-based evaluation model enhanced with skill optimization and multi-dimensional supervised fine-tuning, to provide human-aligned feedback for MIE. Extensive experiments show that MIEScore achieves state-of-the-art performance in aligning with human preferences and generalizes well across other IEQA datasets. Both the dataset and the model are available at this https URL.
[CV-63] BSG-Net: Temporal Bipartite Scene Graph Network for Fine-Grained Video Moment Retrieval
链接: https://arxiv.org/abs/2608.02056
作者: Ji Huang,Yongsheng Dai,Tianyu Ren,Barry Devereux,Hui Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in proposal-free Video Moment Retrieval (VMR) have highlighted the effectiveness of Static Scene Graphs (SSGs). By modeling objects and their relations at the frame level, SSGs enrich retrieval-oriented video representations. However, integrating SSGs into VMR remains constrained by two inherent limitations: (1) Lack of Temporal Dynamics. SSGs fail to model how objects and their relationships evolve over time, leading to the loss of essential temporal dependencies in video representation; and (2) Lack of Explicit Temporal Span Encoding. SSGs do not explicitly encode the duration of relationships, making precise localization challenging. To address these limitations, we propose Temporal Bipartite Scene Graph Network (TBSG-Net)—to the best of our knowledge, the first Dynamic Scene Graph (DSG) based proposal-free VMR model. Specifically, TBSG-Net leverages DSGs to extract event-centric graph representations of the input video, enabling the modeling of object interactions over time and thus addressing limitation (1). These DSGs are then processed by a novel Dynamic Scene Graph Embedding (DSG-E) module to capture both Temporal Span and spatio-temporal information. First, DSG-E utilizes a TBSG Constructor to transform DSGs into TBSGs, explicitly encoding objects, relationships, and time spans to tackle limitation (2). Second, the resultant TBSGs are passed into a hybrid TBSG Encoder that integrates a Transformer variant for global event modeling and a Graph Convolutional Network for detailed relational reasoning, ultimately producing a more comprehensive spatio-temporal representation. Our experiments demonstrate substantial improvements of TBSG-Net over all baselines.
[CV-64] Déjà Cue: Localizing States in Object Histories via Vocabulary-Relative Coordinates
链接: https://arxiv.org/abs/2608.02044
作者: Haofan Cao,Zhichao You,Yunkai Yang,Liang Guo,Jie Wang,Chongshou Li
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: Code available at this https URL
Abstract:Tracking links observations of the same object through visual change, yet cannot by itself determine when the object is empty or filled, intact or cut. We formulate identity-conditioned state-moment retrieval: given a tracked-object history and alternative state descriptions, localize an interval in which each described state holds. Absolute image-text similarity scores descriptions independently; because every visible frame depicts the same target, shared object compatibility can obscure the state evidence needed to identify the target interval. The alternatives provide the missing reference: evidence for one state should be measured against the others. We introduce Déjà Cue, a training-free framework that turns these alternatives into a vocabulary-relative coordinate system. It subtracts their state-balanced centroid from each description, calibrates frame scores, and scans multiple durations within contiguous visible runs using a frozen encoder. On 78 VOST histories, holding the temporal scan fixed and changing only the query reference nearly doubles R@1 at tIoU 0.5 from 10.3% to 20.5% and raises Top-1 tIoU from 16.0% to 21.5%. Candidate-rank analyses show that vocabulary-relative queries rank useful intervals higher within the same candidate set. Related state descriptions can therefore serve as an object-specific, query-time coordinate system for reading frozen visual representations.
[CV-65] RSVideo: Are Your Vision-Language Models Ready for Remote Sensing Videos?
链接: https://arxiv.org/abs/2608.02039
作者: Hongjie Zhou,Shiqin Wang,Haoyang Chen,Haonan Guo,Di Wang,Juhua Liu,Fu Lin,Yong Luo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Remote-sensing videos enable real-time observation of changes in target attributes, short-term activities, and scene evolution. They record motion, actions, interactions, and scene changes that cannot be captured by isolated images. Existing models primarily target single images or discrete temporal observations spanning a long time range. However, a unified evaluation setting for assessing vision-language models on continuous remote-sensing video understanding remains lacking. We introduce RSVideo-10K, a remote-sensing video dataset comprising 10,773 instances, 1.47 million frames, and 17.02 hours of footage, containing both unmanned aerial vehicles and satellite platforms. Its fixed evaluation benchmark, RSVideo-Bench, contains 2,731 test instances and evaluates two complementary aspects of remote-sensing video understanding: L1 Perception and L2 Reasoning, spanning seven capability groups and 17 tasks. Evaluations show that current vision-language models still struggle to recover small local evidence, track short-lived states, and use scene-constrained spatial relations. Based on this analysis, we further propose RSVideo, a reinforcement learning framework for small-target spatiotemporal focusing that selects question-relevant regions across frames and suppresses redundant background tokens. RSVideo achieves a maximum absolute improvement of 9.01% with InternVL3.5-14B and attains the highest accuracy of 40.63% with Qwen3.6-27B across 26 open-source vision-language this http URL will be available at this https URL.
[CV-66] Invisible Ink Threats: Adversarial Goals Behind Legitimate Tasks in Computer-Use Agents
链接: https://arxiv.org/abs/2608.02018
作者: Jia-Chen Zhang,Ze-Yu Zhang,Kai-Wei Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Computer-use agents (CUAs), which empower large language models to autonomously operate operating systems and the web, are increasingly vulnerable to indirect prompt injection attacks. A widely adopted defense is the human-in-the-loop paradigm, in which the agent pauses for explicit user confirmation before executing sensitive operations. While effective against conspicuously high-harm attacks, this defense offers little protection against what we term Invisible Ink Threats: low-harm injected goals, such as starring a repository or installing a package, that are behaviorally indistinguishable from legitimate task execution and thus evade both model safety mechanisms and human oversight. To systematically investigate this blind spot, we present II-Bench, a collection of seemingly harmless adversarial tasks. II-Bench comprises 444 examples targeting confidentiality and integrity attacks across three platforms, spanning three attack categories: page navigation and interaction, sensitive information exfiltration, and code download and execution. Each category is instantiated in both natural language and code forms under two levels of instruction specificity. Furthermore, we construct HITLCUA, a comprehensive adversarial testing framework that integrates a real virtual machine operating system environment with isolated Docker-based web platforms, and simulates human participation by allowing CUAs to consult an API-simulated user before proceeding with suspicious operations. Extensive evaluations of leading CUAs reveal that low-harm injections frequently bypass both agent defenses and simulated user review, exposing severe and previously underexplored security risks in current CUAs.
[CV-67] Beyond Global Latents: Chunk-Based Sparse Grid VAE for Scalable 3D Modeling
链接: https://arxiv.org/abs/2608.02016
作者: Kaiyi Zhang,Zhihao Liang,Haolin Liu,Qingxiang Lin,Zeqiang Lai,Yunfei Zhao,Bowen Zhang,Xianghui Yang,Zibo Zhao,Chunchao Guo,Long Quan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 9 figures
Abstract:Sparse voxel grids preserve the spatial structure needed for detailed 3D reconstruction, but their memory still grows rapidly with resolution as active surface cells increase. We introduce ChunkVAE, a sparse grid variational autoencoder organized around local chunks rather than a global latent volume. Local learned operators permit independently chosen encoder and decoder partitions and allow inference chunk sizes to differ from training. Two complementary data operators make this flexibility practical: Balanced Binary Object Partitioning distributes active cells while limiting replicated overlap, while S-Curve weighted stitching attenuates unreliable boundary features when assembling a global latent or reconstruction. Across three object benchmarks, ChunkVAE is competitive with or better than strong baselines from 512^3 to 1536^3 ; smaller chunks lower peak allocated memory and shorten per-chunk compute, enabling faster parallel inference. Stable stitched latents and improved image to 3D metrics indicate that local compression can scale geometry while retaining the global interface required downstream.
[CV-68] ASTRA: Asynchronous Spatio-Temporal Reconstruction via Trajectory Alignment
链接: https://arxiv.org/abs/2608.02006
作者: Junyu Zhu,Hao Zhu,Xinzhuo Zhang,Hongdong Li,Zhan Ma,Xun Cao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dynamic 3D scene reconstruction has achieved remarkable success under the assumption of strictly synchronized multi-camera inputs. However, in real-world scenarios, temporal asynchrony among capturing devices remains a critical challenge, leading to severe motion blur and geometric artifacts. Existing asynchronous reconstruction methods typically estimate temporal offsets through photometric supervision, but appearance matching provides weak temporal cues under large offsets and complex motions. We attribute this limitation to two major bottlenecks: texture-induced collapse, where low-texture regions provide nearly vanishing alignment signals, and deformation-induced coupling, where temporal errors are absorbed into distorted geometry or motion rather than being explicitly corrected. To address these issues, we propose ASTRA (Asynchronous Spatio-Temporal Reconstruction via Trajectory Alignment), a framework that introduces 2D motion trajectories as explicit, texture-agnostic supervision for asynchronous dynamic reconstruction. Instead of synchronizing cameras solely through rendered color residuals, ASTRA jointly optimizes temporal offsets and dynamic 3D representations by aligning the projected motion of reconstructed 3D points with observed 2D trajectories, while using dynamic and certainty masking to suppress unreliable trajectory constraints. Extensive experiments on different dynamic Gaussian Splatting backbones show that ASTRA preserves high-frequency spatial details and sustains strong robustness even under severe asynchrony with up to 25-frame offsets, achieving approximately 1.4 dB PSNR improvement, reducing temporal-offset MAE by 54.0%, and nearly quadrupling the synchronization success rate.
[CV-69] SPARE: Structural Parameter-Free Affinity Regularization for Flow Matching
链接: https://arxiv.org/abs/2608.01990
作者: Zong-Wei Hong,Jinglun Li,Shen Zhang,Yuhan Liu,Linze Li,Yao Tang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:Denoising diffusion transformers achieve strong generation quality but converge slowly during training. Regularizing their internal representations has emerged as an effective accelerator, yet existing methods split into two families with complementary costs. Target-based methods strengthen representations by aligning them to external features, which requires an external encoder and a learnable projection head to bridge feature spaces. Target-free methods hold no reference at all, and can only repel the model’s own features across samples or layers, discarding whatever structure the data contains. Prior work suggests that spatial structure, rather than global semantics, drives the gains of alignment. We therefore ask whether such structure can serve as a target directly, and whether it exists not only within an image but across images. Our key insight is that the clean data latent already carries this structure in the relations among its tokens, where a relation is the similarity between two tokens, a single scalar comparable across feature spaces without a projection head. We propose Structural Parameter-free Affinity Regularization (SPARE), a regularizer that matches the pairwise affinities of intermediate tokens to those of the clean latents. To exploit this structure fully, SPARE extends the matching to token pairs across images, precisely the pairs that prior target-free methods repel by default, and calibrates both relation types with a single learning objective. On ImageNet 256 \times 256 with SiT backbones under matched 400K-iteration budgets, SPARE adds no encoder, head, or parameters and only 0.08 GB of training memory, yet attains the lowest FID among parameter-free regularizers in every tested setting, recovers 37 to 54% of REPA’s FID reduction, and improves over REPA when combined with it, reaching FID 1.90 under classifier-free guidance at 1M iterations.
[CV-70] Grounding and Explaining Visual Evidence for AI-Generated Image Detection in Human-Centric Scenes
链接: https://arxiv.org/abs/2608.01988
作者: Kun Guo,Yuzhou Yang,Haoyue Wang,Qichao Ying,Sheng Li,Zhenxing Qian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Rapid advances in image generation models call for interpretable AI-generated image detection methods that not only determine authenticity but also provide supporting visual evidence. Existing approaches may produce inconsistencies between generated explanations and localized evidence regions, undermining the reliability of explanations for authenticity decisions. Meanwhile, existing benchmarks provide limited coverage of the diverse human-centric scenes prevalent in generated imagery. To address these limitations, we investigate authenticity detection with grounded and explainable visual evidence in human-centric scenes. We present HAVE (Human-centric AI-generated Visual Evidence), a diverse human-centric dataset comprising 40K real and 39K AI-generated images from 10 recent generators, with 106K localized evidence instances across 8 evidence categories, each annotated with a bounding box and a region-aligned explanation. We further propose PAVE, a Perception-Aware Visual Evidence framework that jointly performs authenticity prediction, visual evidence grounding, and region-aligned explanation generation. PAVE employs a judge-guided alignment reward to assess region–explanation consistency and evidence validity, together with perception-aware regularization that contrasts token-level predictions between original and randomly masked images to promote reliance on visual input. Experiments on HAVE and external datasets demonstrate strong performance in authenticity detection, visual evidence grounding, and explanation quality. Code and data will be released upon publication.
[CV-71] DiffPrune: differentiable information throttling for token pruning in vision-language models
链接: https://arxiv.org/abs/2608.01985
作者: Landi He,Mingde Yao,Shawn Young,Lijian Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual token pruning reduces the computational cost of Vision-Language Models (VLMs) by removing redundant visual tokens. The key is to learn a score that measures whether a token is useful. Existing methods typically rely on Gumbel-Softmax to approximate discrete selection during training. Such selectors make the score depend on the behavior of a relaxed pruning operator, not directly on the consequence of information loss. In this paper, we propose DiffPrune, which gives token scores a direct meaning. During training, DiffPrune keeps all tokens and weakens each token’s information according to its score. If weakening a token hurts the task, the scorer is pushed to protect it; if not, the token can receive a lower score. Because the loss is differentiated through this actual information-throttling path, the scorer avoids the unstable surrogate path of relaxed token selection. DiffPrune implements this idea with an Information Throttler, which injects variance-preserving noise into visual tokens, where high-score tokens remain close to their original representations, while low-score tokens carry less original information. At inference, the throttler is removed, and hard top-K pruning is applied using the learned scores. Across ten VLM benchmarks, DiffPrune retains 96.5% of full-model accuracy while accelerating LLM prefill by 2.85x, with only 0.69 ms inference overhead. Code will be publicly available.
[CV-72] AdaThinkV: Adaptive Thinking for Token-Efficient Video Reasoning
链接: https://arxiv.org/abs/2608.01980
作者: Jingqi Tian,Haoji Zhang,Lin Chen,Hongbo Jin,Haonan Xu,Tianrui Zhu,Xingming Shui,Shilin Ma,Wenjing Yang,Yansong Tang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Chain-of-thought (CoT) reasoning can improve performance on difficult video questions but often wastes decoding tokens on simple ones. We study whether a video multimodal large language model can adapt its reasoning effort to each question. We propose AdaThinkV, an adaptive framework for video reasoning that learns whether to reason explicitly without offline difficulty labels, manually tuned confidence thresholds, or an external router. During reinforcement learning, AdaThinkV samples matched rollouts in explicit reasoning and direct answering modes for each prompt. ThinkGain estimates the prompt-level utility of explicit reasoning by balancing its accuracy gain against additional response length, providing supervision for both conditional response generation and autonomous mode selection. For difficult prompts, limited rollout exploration can yield groups in which every response is unsuccessful and accuracy rewards show little variation, providing insufficient signal for learning. We therefore introduce Variance Recovery Policy Optimization (VRPO), which retains and progressively expands these groups to recover informative signals from prompts that are difficult yet solvable. At inference, AdaThinkV selects a response mode and generates the response in a single autoregressive sequence. Across a unified suite of video reasoning evaluations, AdaThinkV achieves a mean accuracy of 40.79 with an average of 257.20 output tokens, outperforming the strongest evaluated adaptive baseline by 2.98 points while using 22.7% fewer tokens. Project page: this https URL
[CV-73] Proxy Avatar Meets Low-Rank Caching: Real-Time One-Shot Emotion-Controllable Portrait Animation
链接: https://arxiv.org/abs/2608.01978
作者: Haijie Yang,Jindi Bao,Yixuan Dong,Hongliang Zhang,Jian Bi,Hao Tang,Zhenyu Zhang,Jianjun Qian,Jian Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Audio-driven portrait animation has advanced rapidly with diffusion-based generative models, yet real-time one-shot generation with expressive emotion control remains challenging. Existing methods often suffer from insufficient emotion-aware motion priors and expensive appearance computation during multi-step denoising. To address these issues, we propose Proxy Avatar Meets Low-Rank Caching, a cascaded framework for real-time one-shot emotion-controllable portrait animation. Instead of directly generating the target portrait from audio, our method uses a Gaussian-based emotion proxy avatar as a reusable motion generator, which is trained once on a single identity to produce expressive driving videos from audio and emotion labels. Since the proxy avatar only provides motion rather than target appearance or geometry, a large-scale one-shot retargeting model further extracts identity-independent motion from the proxy performance and adapts it to arbitrary target portraits. To improve inference efficiency, we introduce zero-shot appearance reuse with low-rank caching, which caches reference appearance features at the initial denoising step and models subsequent feature variations using lightweight low-rank adapters. Extensive experiments demonstrate that our method achieves stronger emotional expressiveness, better identity-preserving animation, and substantially reduced inference cost, enabling real-time one-shot portrait animation.
[CV-74] SVGEval: A Vision-Grounded Framework for Perceptual-Quality Benchmarking and Evaluation in Text-to-SVG Generation ECCV2026
链接: https://arxiv.org/abs/2608.01977
作者: Yiming Wang,Ye Chen,Hanqi Chen,Bingbing Ni
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Multimodal large models are increasingly used to generate scalable vector graphics (SVG), but reliable evaluation remains underexplored. Existing protocols are often code-centric or borrow raster-image metrics after rendering SVGs, which fail to reflect human perception and overlook SVG-specific qualities such as geometry and spatial composition. We introduce SVGEval, a vision-grounded multimodal benchmark for human-aligned SVG quality assessment. SVGEval explicitly incorporates visual renderings to evaluate whether models can judge the rendered outcome rather than only inspect SVG code, and provides high-quality annotations obtained via multi-round human labeling with expert refinement. Systematic evaluations across representative multimodal models reveal a clear gap: models perform relatively well on semantic alignment and aesthetics, yet struggle on geometry- and layout-related judgments. Building on SVGEval, we train an explainable SVG quality scorer that outputs multi-aspect scores with textual rationales. Ablations show that explicit visual grounding and reasoning supervision are crucial, especially for spatial and geometric assessment. SVGEval offers a reliable testbed and practical scorer for evaluating and improving SVG generation in the era of multimodal models.
[CV-75] Roomer: Reflective Object-Grounded Model Editing and Repair for 3D Indoor Layout Synthesis
链接: https://arxiv.org/abs/2608.01973
作者: Lingwei Dang,Ziyan Qiu,Jiajia Cheng,Shishuo Shang,Zhenhao Zhang,Yufei Zhu,Qingxin Xiao,Pan Liu,Shenghui Huang,Yun Hao,Juntong Li,Qingyao Wu
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing indoor layout generators produce globally plausible layouts yet may retain local violations such as collisions, out-of-bounds placements, obstructed openings, and blocked circulation. Most prior work focuses on full-scene synthesis or scene-level optimization, with limited support for identifying responsible objects and locally repairing affected regions. We present Roomer, a reflective repair framework that casts these violations as sparse, object-grounded repair problems. Roomer encodes layouts as RoState'' and uses RoReview’’ to bind measured violations to implicated objects. A geometry-conditioned vision-language model planner proposes a structured local edit, while a deterministic solver validates it and generates a finite set of candidate edits when needed. Each candidate is committed only if full-scene verification confirms that it resolves the target violation without new hard violations or broken protected constraints. We train the planner on Roomer-CC, a controlled-corruption dataset that pairs faulty layouts with object-grounded violation evidence and known-feasible inverse StatePatches. Since existing benchmarks rarely assess whether physically valid layouts are usable, we introduce Roomer-Eval to assess distributional quality, physical validity, and practical usability. Experiments show that Roomer repairs residual violations while preserving valid regions, improves physical validity and usability, and transfers across external generators.
[CV-76] LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks
链接: https://arxiv.org/abs/2608.01964
作者: Ziyu Ma,Hailang Huang,Shun Zou,Yong Wang,Shidong Yang,Yiming Hu,Fei Wei,XiangXiang Chu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 29 pages
Abstract:Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, existing agent harnesses maintain task execution, task state, and completion assessment within a growing context, making the state difficult to track and allowing incorrect self-assessments to propagate into later decisions. We reformulate long-horizon execution as a task-state management problem and propose LongHorizon-Harness, which maintains the task state explicitly outside execution and updates it only with facts independently verified from the environment. Its Manage-Execute-Audit(MEA) loop uses a manager to maintain the task state and determine the next subtask, a fresh-context executor to perform it, and a read-only auditor to verify the resulting environment state before the next round. A lightweight AgentAdapter supports interchangeable model and harness backends without modifying their native agent loops. LongHorizon-Harness improves Qwen~3.7-Plus from 51.8% to 80.7% on WeaveBench, from 69.7% to 77.2% on Terminal-Bench~2.1, and from 2.8% to 8.3% on OSWorld~2.0. It also raises Claude Opus~4.7 from 20.0% to 34.3% on an OSWorld2.0 subset, demonstrating consistent gains across models, harnesses, and interaction domains.
[CV-77] OSSDD - a New Open Dataset for Sentinel-1 Ship Detection
链接: https://arxiv.org/abs/2608.01963
作者: Horst Hammer,Sylvia Hochstuhl,Antje Thiele,Tobias Brosch,Padraig Davidson,Tim Remiger,Michael Teutsch
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 5 figures
Abstract:Ship detection in Synthetic Aperture Radar (SAR) images plays an important role for maritime situational awareness, especially with respect to different illegal activities at sea such as illegal fishing, smuggling or border violations. Modern ship detection methods using neural networks usually require large training datasets, which are considerably scarcer in the SAR domain than in the electro-optical domain. While several free datasets exist for this task, their availability and usability vary. In this paper, OpenSARShip-Ship Detection Dataset (OSSDD), a new dataset based on the well-known OpenSARShip 1.0 dataset is proposed for training neural networks for SAR ship detection. OSSDD is freely available and contains 15,197 Sentinel-1 amplitude patches in VV and VH polarization, binary ship masks, axis-aligned bounding box and rotated bounding box annotations for a total of 55,759 ships. The construction of the dataset, the contents and structure of the downloadable data and experiments with three common detector models (Faster R-CNN, FCOS, DETR) are shown and discussed. The results serve as benchmarks for future experiments. The dataset is available on Hugging Face at this https URL.
[CV-78] FAST-GS: Frequency Aware Space-time Gaussian Splatting for Photorealistic Dynamic Novel View Synthesis ICASSP2026
链接: https://arxiv.org/abs/2608.01958
作者: Zhengyang Zhang,Ziyu Lu,PengCheng Li,Hongbo Duan,Yi Liu,Pengting Luo,Peiyu Zhuang,Xinghui Li,Shaohua Ma
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: accepted by ICASSP2026
Abstract:4D Gaussian Splatting (4DGS) excels in dynamic 3D reconstruction and real-time novel view synthesis via efficient 4D Gaussian representations and parallelizable rendering. However, existing 4DGS approaches rely on a single polynomial to model motion, which limits performance in complex dynamic scenes where high-frequency motion components are prevalent, and fails to ensure long-term stability due to cumulative trajectory drift. To address these issues, we propose a Fourier Motion Modeling module: this paradigm decomposes motion into frequency-based sinusoidal components, capturing both low-frequency global trajectories and high-frequency local details to model complex motion patterns accurately. It retains the real-time rendering capability of 4DGS while improving complex motion fitting and long-term coherence. Additionally, we integrate a motion-aware regularization strategy into the loss function: it uses frequency-dependent weights to suppress high-frequency jitter while preserving low-frequency motion coherence. Extensive experiments on N3V and Google Immersive datasets from multiple scenarios demonstrate the effectiveness of our method.
[CV-79] StyleForge: Indoor Furniture Styling by Counterfactual Reasoning in a Hypergraph Field
链接: https://arxiv.org/abs/2608.01954
作者: Lingwei Dang,Shishuo Shang,Pan Liu,Jiajia Cheng,Ziyan Qiu,Zhenhao Zhang,Yufei Zhu,Shenghui Huang,Qingxin Xiao,Yun Hao,Juntong Li,Qingyao Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Fixed-layout indoor furniture styling requires selecting assets that form a coherent room without changing the prescribed furniture categories, positions, orientations, or scales. Existing approaches typically retrieve each asset independently or rely on static local relations, making them prone to shape, material, and color conflicts after scene composition. We introduce StyleForge, a scene-level structured selection framework built on a dynamic hypergraph style field. A frozen multimodal large language model extracts structured style priors from an open-ended style request and the fixed layout, while StyleForge maintains a learnable candidate distribution for each furniture slot. Conditioned on the target style, the dynamic hypergraph style field adaptively activates and weights layout-induced hyperedges to capture higher-order dependencies among furniture. Counterfactual style preference learning then treats each candidate as a local substitution in the current style field and evaluates its contextual compatibility using Mahalanobis energies. Training alternates between optimizing the style field and the candidate logits. At inference, the model remains frozen and test-time training updates only room-specific candidate logits, progressively correcting cross-slot style conflicts as the global scene context evolves. Experiments on 3D-FRONT demonstrate state-of-the-art furniture retrieval and scene-level style coherence, producing more coherent fixed-layout furniture arrangements than object- and scene-level retrieval baselines.
[CV-80] Event ActivityNet: A Large-Scale Simulated-Event Benchmark for Untrimmed Action Understanding
链接: https://arxiv.org/abs/2608.01948
作者: Cheng-Yao Hong,Ting-Wei Lin,Yun-Chung Lai,Hua-Wei Lee,Hwann-Tzong Chen,Tyng-Luh Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 10 figures
Abstract:Long-horizon event-based action understanding remains underexplored because existing datasets largely comprise short, trimmed clips, while collecting native event streams with dense temporal annotations is costly. We introduce Event ActivityNet, a large-scale simulated-event benchmark derived from human-annotated, untrimmed ActivityNet videos. It comprises 3,263 videos, 200 action classes, and 106.94 hours, with matched 5-bin and 9-bin event-voxel representations, temporal action annotations, and timestamped captions. The benchmark supports annotated-segment action recognition, auxiliary event-language alignment, and causal online temporal action localization. We generate event voxels directly from non-interpolated source videos in decoded frame order, retain per-video rational nominal or average frame-rate metadata for approximate time mapping, and use action-center reconstruction LPIPS as a soft diagnostic of retained reconstructable content. We establish baselines for adaptive event framing, prompt-caption alignment, and event-only, RGB-only, and RGB-event localization. Under a progressive nested-scale training protocol, recognition Top-1 accuracy increases from 52.25 to 66.42, while online temporal localization average mAP improves from 21.7 to 29.0. Moreover, staged Event ActivityNet pretraining followed by native-event fine-tuning consistently outperforms target-only and joint-from-scratch training across multiple supervision budgets. Event ActivityNet provides a scalable benchmark for long-horizon event modeling, although native-camera evaluation remains essential for deployment-oriented conclusions.
[CV-81] UniMoCa: Unifying Motion and Camera Controls as Visual Proxies for Faithful Human Video Generation
链接: https://arxiv.org/abs/2608.01944
作者: Liming Tan,Ye Chen,Hao Zhang,Lirong Qian,Feifei Li,Bingbing Ni
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Controlling human motion and camera movement is essential for faithful human-oriented video generation, yet remains challenging in multi-person scenes with large body motions, occlusions, and dynamic cameras. Existing pipelines typically rely on visual motion sequences, such as skeleton maps, pose maps, or rendered body representations, for motion control, while using camera embeddings for camera control. Such heterogeneous control interfaces force video generation models to reconcile pixel-aligned visual cues with non-visual geometric embeddings, making motion-camera attribution difficult and sensitive to camera estimation errors. We propose \textbfUniMoCa, a representation-driven framework that unifies motion and camera controls in visual space. At the core of UniMoCa is \textbfMotion-Camera Visual Proxy (\textbfMCVP), a mutually-sharable novel representation that converts 3D human motion and camera trajectories extracted from driving videos into an identity-neutral visual proxy. MCVP renders temporally aligned human geometry under the recovered camera trajectory and augments it with explicit camera trajectory markers, replacing heterogeneous visual-parametric controls with distinguishable visual cues. As both control factors are represented in the same visual space, they become mutually compatible rather than heterogeneous, enabling consistent joint reasoning and editing during video generation. We further curate a \textbfMCVP-Video dataset covering complex actions, multi-person interactions, and diverse camera trajectories. Experiments based on the Wan2.2 I2V show that UniMoCa achieves substantial gains in human motion control, camera control, temporal consistency, and camera-aware robustness with minimal additional complexity. More details are shown in our Project page: this https URL.
[CV-82] Recompute or Reuse? Diagnosing and Mitigating Textual Shortcuts in VLM Self-Reflection
链接: https://arxiv.org/abs/2608.01930
作者: Wenxiao Fan,Jingling Fu,Fang Li,Luohang Liu,Yu He,Lichen Ma,Zhiyang Yu,Weishan Bi,Junshi Huang,Yan Li,Gu Simiu,Kan Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: preprint
Abstract:Vision-language models (VLMs) are expected to revise their reasoning when visual evidence changes. Failures to do so are often attributed to insufficient visual attention or contextual inertia, leaving unclear what models reuse instead of recomputing from the current image. We show that evidence-bearing reasoning in a prior chain of thought (CoT) can form a textual shortcut that competes behaviorally with visual recomputation. Across 16 VLMs, a matched counterfactual analysis identifies evidence-bearing content as the most robust carrier of prior-CoT influence. Removing this evidence-bearing content shifts answer preference more than removing length-matched non-evidence context or the final-answer span, with prior control weakening progressively as more stale evidence is removed. Reordering this evidence also weakens prior control, showing that its organization modulates shortcut strength. Beyond the immediate answer, the shortcut can retain residual influence after answer correction: weakening current-image support shifts preference back toward the prior answer, while repeated prior answers and reused premises arise mainly when the shortcut remains active. To limit this influence, we introduce Fresh-State Attention Firewall (FSAF), a training-free intervention that isolates fresh computation from the prior CoT. Across five VLMs, FSAF raises visual update rate from 35.28% to 53.61% and reduces prior-answer rate from 39.22% to 3.67%. Reliable VLM self-reflection therefore requires more than looking again: fresh visual recomputation must be protected from stale textual reuse.
[CV-83] CHOW-SLAM: Compact Hybrid Representation with Complementary Overlap Window Optimization for RGB-D SLAM
链接: https://arxiv.org/abs/2608.01914
作者: Wenxuan Ji,Jin Xiao,Xiaoguang Hu,Jiaqi Shi,Zichong Jia,Baochang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 33 pages, 8 figures, 6 tables
Abstract:Simultaneous localization and mapping (SLAM) based on Neural Radiance Fields (NeRF) enables dense, continuous scene reconstruction. However, existing systems operating with limited online resources struggle to simultaneously construct two types of constraints, namely, compact yet discriminative spatial constraints derived from scene representations and persistent temporal constraints derived from historical observations. To address this challenge, we propose CHOW-SLAM, a dense RGB-D SLAM framework that explicitly constructs these complementary spatial and temporal constraints. Spatially, we propose a compact parametric-hash (P-H) hybrid representation that organizes components based on planes and grids across scales in P and H branches. A unified multi-output decoder further aligns the ray termination distributions induced by TSDF and density, preserving geometry and appearance under a compact parameter budget. Temporally, we propose a complementary overlap-window strategy to prevent optimization from being dominated by short-term overlap or weakly related historical observations. Within a fixed budget, the strategy retains recent frames, selects high-overlap local frames, and introduces temporally distributed historical keyframes. Loss-aware keyframe insertion and bundle adjustment scheduling further adapt optimization to tracking quality. In addition, ORB-based tracking and geometric pose estimation are used for pose initialization, followed by neural rendering optimization to improve tracking stability. Extensive evaluations on multiple datasets demonstrate that CHOW-SLAM outperforms state-of-the-art methods in both scene reconstruction quality and camera tracking accuracy. The source code is available at this https URL.
[CV-84] PNEC-Mamba: Prototype-Guided Positive-Negative Evidence Calibration for Hyperspectral Image Classification
链接: https://arxiv.org/abs/2608.01910
作者: Mingzhen Xu,Can Xu,Di Wang,Haonan Guo,Bo Du
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In real-world hyperspectral scenes, pixel representations are often ambiguous due to factors such as spectral similarity, mixed pixels, and local context interference, which may simultaneously encode discriminative evidence and interfering information. Existing methods mainly focus on learning more powerful representations or modeling broader contexts, but rarely investigate whether the learned representations provide reliable evidence or introduce interference into classification decisions. To address this issue, we view hyperspectral image classification from the perspective of pixel-level evidence reliability modeling and propose PNEC-Mamba, a prototype-guided positive-negative evidence calibration framework. The framework progressively establishes semantic references, separates class-related evidence from interference, estimates pixel-level reliability, and performs selective calibration. First, a full-image state-space encoder extracts pixel representations, while dynamic class prototypes provide semantic references that evolve jointly with the feature space. Subsequently, positive and negative evidence is derived from pixel-prototype competition, explicitly separating discriminative cues that support classification from confusing signals associated with competing classes. Based on these evidence relationships, a multi-source uncertainty estimation strategy is introduced to assess pixel-level reliability, enabling stronger evidence calibration for uncertain regions. Finally, a full-resolution consistency refinement step is applied to recover local spatial details and improve boundary coherence in the final predictions. Extensive experiments on three benchmark datasets demonstrate that PNEC-Mamba achieves superior classification performance compared with state-of-the-art methods.
[CV-85] Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery ECML KDD2026
链接: https://arxiv.org/abs/2608.01906
作者: Huy Quang Ung,Guillaume Habault,Roberto Legaspi,Hao Niu,Lian Cao,Masato Taya
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECMLPKDD 2026, 31 pages (including appendix), 18 figures
Abstract:Rapid and accurate post-disaster building damage assessment is essential, yet remains a challenging task. Unmanned Aerial Vehicle (UAV) imagery offers a timely and high-resolution view of affected areas, but existing Computer Vision (CV) models often demand large annotated datasets, generalize poorly across geographic regions and their assessment policies, and are confined to the specific tasks they were trained for. Large Vision-Language Models (LVLMs) offer a promising alternative through their strong reasoning and generalization capabilities, but fall short on precise, low-level perception tasks such as object detection and accurate bounding box generation. Furthermore, they often require a substantial amount of data for effective fine-tuning on domain-specific tasks. In this paper, we propose a hybrid framework that decouples detection from damage assessment, combining the precision of CV models with the reasoning power of LVLMs. A CV model first detects buildings and generates bounding boxes on the image that are then passed to an LVLM for damage classification and contextual interpretation. We evaluated our framework on two real-world benchmarks: RescueNet and FloodNet. In particular, the best combination under this framework accurately counts intact, partially damaged and completely destroyed buildings, surpassing isolated baselines by up to 2.1 R^2 points, while requiring only limited annotated data for the detection stage. Beyond reporting aggregate gains, we provide a detailed analysis of failure scenarios and edge cases, offering practical insights for practitioners and concrete directions for future work. Our source code and data are publicly available to the research community via the following repository: this https URL
[CV-86] PhotoHOI: Synthesizing 3D Hand-Object Interactions from a Single RGB Photograph
链接: https://arxiv.org/abs/2608.01905
作者: Zhenhao Zhang,Jiajun Zhang,Wei Min,Yebin Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Hand-object interaction (HOI) is a fundamental human behavior with broad applications in AR/VR, digital humans, and embodied interaction. Existing methods typically require predefined object geometry, object trajectories, or task-specific conditions, limiting their use with natural real-world inputs. To address this, we study a more practical problem of synthesizing 3D hand-object interaction sequences from a single RGB photograph and an open-vocabulary language instruction, and introduce PhotoHOI. PhotoHOI first uses a vision-language model to parse the input image and instruction into a structured task specification, including the interaction object, target region, and spatial relation. It then recovers a compact task-relevant 3D scene and plans a smooth collision-aware object trajectory based on the recovered object states, support relations, and surrounding scene geometry. To synthesize hand motion that generalizes to real-world photographs and unseen objects, it learns transferable task-conditioned contact and contact-conditioned grasp priors from large-scale affordance and HOI data. The grasp is further refined in a learned latent space, constraining the optimization to a plausible hand-pose manifold. Experiments on GRAB and H2O demonstrate improved contact quality and reduced penetration over representative baselines. Results on real-world photographs further demonstrate higher task success and scene consistency, together with generalization to unseen objects and open-vocabulary instructions.
[CV-87] GeoCore-9B: Towards Geo-Aware Generative Foundation Models in Earth Observation
链接: https://arxiv.org/abs/2608.01896
作者: Jeonghyeok Do,Munchurl Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Please visit our project page at this https URL
Abstract:Existing generative models for earth observation (EO) predominantly rely on fine-tuning natural image priors, which limits their scalability and introduces perspective biases that conflict with geospatial constraints. To address this, we introduce GeoCore-9B, a 9-billion-parameter generative foundation model, which is the first of its scale to be trained from scratch exclusively on EO data. Unlike previous EO foundation models, GeoCore-9B is built upon a Flow Matching-based Diffusion Transformer (DiT) and natively conditions generation on text descriptions and continuous geospatial metadata, including ground sample distances, latitudes, and longitudes. To overcome the convergence and spatial disorientation challenges of training at this scale, we propose a Geospatial Semantic Alignment loss. This objective distills structural Earth surface priors (e.g., terrain and urban areas) from a frozen specialist teacher network, constraining the diffusion latent trajectory during training without adding inference overhead. Pre-trained on the global-scale Git-10M dataset, GeoCore-9B demonstrates strong downstream versatility. Beyond standard proxy generative tasks, we show that GeoCore-9B can be effectively adapted for practical EO applications, including highly challenging tasks such as cloud removal and SAR-to-optical cross-modal translation. Extensive evaluations confirm that GeoCore-9B establishes new state-of-the-art performance in both visual fidelity and geographic structural accuracy.
[CV-88] Beyond Illumination: A Conditional Mutual Information-Guided Network for Low-Light Image Enhancement
链接: https://arxiv.org/abs/2608.01886
作者: Ya-nan Guan,Shaonan Zhang,Tao Dai,Tianqu Zhuang,Yongchao Qiao,Zhensen Chen,Shu-Tao Xia,Hang Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Low-light image enhancement (LLIE) seeks to restore structural fidelity, natural color rendition, and proper exposure from images captured under inadequate lighting conditions. Recent state-of-the-art approaches, such as CIDNet, adopt a dual-branch architecture comprising a chrominance (HV) branch and an intensity (I) branch to separately model decoupled chromatic and luminance information within the HVI color space. However, these methods overlook the mutual interaction between intensity and chrominance components, which inherently limits their representational capacity and leads to suboptimal enhancement performance. To address this limitation, we propose the Conditional Mutual Information-Guided Network (CMIG-Net), which leverages conditional mutual information as a principled metric to quantitatively assess the contribution of chrominance features conditioned on the available intensity information. In particular, we design a Conditional Mutual Information Calibration (CMIC) module that generates a conditional information map, enabling region-adaptive recalibration of chrominance representations according to local illumination statistics. Furthermore, we introduce a Dynamic Dual-branch Information Restoration (D2IR) module, which adaptively governs bidirectional information flow between the intensity and chrominance branches, guided by both the conditional prior and the instantaneous restoration state. Extensive experiments on paired LLIE benchmarks demonstrate that CMIG-Net consistently outperforms CIDNet, achieving up to a 0.619 dB gain in PSNR, with a 0.382 dB improvement specifically on the challenging Sony-Total-Dark dataset.
[CV-89] ransformer Geometry Observatory TGO-III: Semantic Geometry Observatory
链接: https://arxiv.org/abs/2608.01876
作者: Kaustubh Kapil,Kishor P. Upla
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:With the widespread adoption of Vision Transformers in modern AI, the need to analyze their inherent representational behavior has become increasingly important. While most existing studies emphasize token geometries and training dynamics, the evolution of representational covariance structures and class-level geometric organization remains comparatively underexplored. In this work, we investigate semantic geometry and class separability as representations evolve across the layers of ViT-Small/16 through TGO-III: Semantic Geometry Observatory. It is a framework designed to analyze the emergence of semantic organization, feature evolution, and class-wise representation geometry throughout training. The framework employs multiple complementary observatories, including Linear Probe Accuracy, Fisher Ratio, Class Centroid Distances, Local Intrinsic Dimension, and Local PCA Rank, to quantify the progressive evolution of discriminative representations. Our analysis reveals that class representations become progressively more linearly separable, Fisher discriminability increases, class centroids move farther apart, and local representation manifolds exhibit structured class-dependent geometric complexity. These observations provide empirical evidence supporting the Semantic Expansion Hypothesis, suggesting that the manifold expansion observed in previous observatories is accompanied by the progressive organization of representations into increasingly discriminative semantic structures. Collectively, TGO-III extends the Transformer Geometry Observatory framework by establishing a direct connection between manifold geometry, covariance evolution, and semantic organization during Transformer training.
[CV-90] Decoupling semantics from vision: A framework for faithful visual-text compression evaluation
链接: https://arxiv.org/abs/2608.01848
作者: Yonghan Gao,Zehong Chen,Lijian Xu,Jingzhi Chen,Jingwei Guan,Xingyu Zeng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent visual-text compression (VTC) methods, typified by DeepSeek-OCR, report impressive high token compression ratios for long-context modeling tasks by leveraging text-to-image rendering. However, existing evaluation protocols heavily rely on downstream task performance. Such evaluation metrics fail to accurately measure text preservation due to the strong inherent linguistic priors of Multimodal Large Language Models (MLLMs). In this work, we introduce a new evaluation framework that decouples MLLMs’ capabilities to faithfully assess VTC quality. Within this framework, we further introduce the ZeroSense Benchmark to ensure low semantic correlation of testing samples. By eliminating textual dependencies, our benchmark guarantees that the evaluation results are purely reflective of VTC quality, unaffected by the semantic inference capabilities of downstream models. Extensive experiments across multiple datasets demonstrate that VTC quality and downstream task accuracy diverge significantly, highlighting the necessity of our decoupled evaluation framework.
[CV-91] WorldDynCache: Risk-Controlled Latent Dynamics Approximation for Diffusion World Model
链接: https://arxiv.org/abs/2608.01845
作者: Leyang Chen,Junyi Wu,Shaoqiu Zhang,Yulun Zhang
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion world models generate high-quality futures, but re- peated transformer evaluations make inference prohibitively slow. Existing caches reuse intermediate features, selectively update tokens, or reuse and extrapolate denoising outputs ac- cording to local drift or short native-space histories. These criteria can miss both approximation-induced latent transition defects that accumulate across skipped steps and phase- or condition-dependent changes in the direction of latent evo- lution. We propose WorldDynCache, a risk-controlled latent dynamics approximation framework with two core compo- nents. First, a lightweight latent-transition risk estimator tracks the accumulated future impact of approximation defects and calibrates its predictions against counterfactual defects ob- served at exact anchors. Second, a condition- and phase- aware lifted latent surrogate approximates latent evolution without extra transformer evaluations. On HunyuanVoyager- 13B and Aether-5B, WorldDynCache achieves 4.92 times and 2.15 times speedups, respectively, while attaining the best gen- eration quality among the compared caching methods across WorldScore, PSNR, SSIM, and LPIPS.
[CV-92] MoCRA: Mixture of Compositional Rank-1 Atoms for 4K All-in-One Video Restoration
链接: https://arxiv.org/abs/2608.01829
作者: Yongcong Wang,Pu Wang,Hingchin Chen,Runci Bai,Yucheng Xin,Chen Wu,Chengchao Shen,Guangwei Gao,Siyuan Yao,Pengwen Dai,Zhuoran Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Real-world video arrives hazy, rainy, dark, or noisy, and a deployable restorer faces three demands at once: no degradation label, native 4K output, and stability in playback. Existing methods answer them separately and break on the joint problem, because per-frame degradation readings flip between frames, downsampled proxies erase the rain and noise they are meant to remove, and dense temporal alignment does not fit 4K memory. No paired benchmark even poses that problem, so we build one. UHV-4K-AIO renders physically modeled haze, rain, sensor noise, and low light over the same 100 clean 4K clips with shared depth and motion, and its construction exposes the split MoCRA is built on: haze and low light survive aggressive downsampling, while rain and noise exist only at native scale. Band-matched compositional conditioning follows, spending conditioning capacity, computation, and supervision in the band where each degradation lives. One dictionary of rank-1 atoms, recomposed sparsely per frame, conditions both a once-per-clip coarse branch and a shallow native-resolution refiner, in 3.6M parameters and with no optical flow. Trained once for all four tasks, MoCRA takes the best task-mean PSNR of eleven retrained image and video baselines, holds warping error at the level of the flow-based video models while never estimating motion, and restores native 4K in under half a second, against 1.7 seconds for the fastest baseline.
[CV-93] DeepVoyager-VL: Incentivizing Vision-in-the-Loop Search for Long-Horizon Multimodal Agents
链接: https://arxiv.org/abs/2608.01827
作者: Huanyao Zhang,Jiepeng Zhou,Runhao Zhao,Yanzhe Shan,Jiaoyang Chen,Bowen Zhou,Bo Li,Fang Wang,Jialong Wu,Zhengwei Tao,Lang Mei,Xiaohan Yu,Liyan Liu,Chong Chen,Wentao Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal large language models (MLLMs) have advanced visual understanding and reasoning, yet their static parametric knowledge limits their ability to address knowledge-intensive and dynamically evolving open-world problems. To move beyond this limitation, multimodal deep search has emerged as a key direction for open-world information access, evolving from single-turn factual retrieval toward long-horizon, multi-turn search guided by visual evidence. However, existing methods typically confine vision to the input or answer stage, overlooking its role in intermediate reasoning, and lack designs tailored to long-horizon interaction. Consequently, visual evidence rarely drives continued retrieval, constraining both interaction depth and reasoning span. To address these limitations, we propose DeepVoyager-VL, a long-horizon multimodal deep-search framework for vision-in-the-loop search. Specifically, we construct a multimodal event graph to drive data synthesis, yielding problems with intermediate visual dependencies and long reasoning chains. We then design an agent framework for active visual acquisition and on-demand image loading. Finally, we fine-tune models on the synthesized data without reinforcement learning. Extensive experiments across ten multimodal search benchmarks demonstrate the effectiveness of our method.
[CV-94] PartMat: Material-Aware 3D Part Decomposition with a Single Global Latent
链接: https://arxiv.org/abs/2608.01825
作者: Guangming Fu,Jin Song,Yiyun Fei,Guoqiu Li,Ruigao Yang,Jianan Jiang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注:
Abstract:Part-level 3D generation has recently attracted increasing attention for producing structured and editable 3D assets. However, existing methods typically decompose objects according to functional semantics rather than the editable material boundaries (e.g., fabric, wood, metal) required in practical 3D applications such as interior design. Additionally, current methods often generate parts independently, causing computational costs to scale linearly with the part count. To address these limitations, we present PartMat, an efficient material-aware 3D part decomposition pipeline that represents multi-part geometry with a single global latent. Given a reference image and a single whole-object geometry, PartMat decomposes the object into parts that follow material boundaries. First, we propose PartVAE to learn such a unified representation and decode all material parts in a single forward pass, thereby decoupling inference cost from the number of parts. Second, with this representation, a diffusion model is trained for part generation and refined via reinforcement learning for accurate material assignment and overlap suppression. Finally, to recover fine-grained geometric details, we introduce a sparse-voxel flow-matching model with part attention for geometry post-processing. Extensive experiments demonstrate that PartMat significantly outperforms existing baselines in material-aware decomposition accuracy and achieves comparable geometric quality, while maintaining efficient inference.
[CV-95] Detail Continuation over a Trustworthy Coarse Scale for Autoregressive Super-Resolution
链接: https://arxiv.org/abs/2608.01823
作者: Hongyi Fang,Jiahui Wu,Yichen Yue,Benjia Zhou,Dan Zeng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM Multimedia 2026. 10 pages, 8 figures. Code: this https URL
Abstract:Hallucination remains a persistent challenge in generative super-resolution (GSR), where reconstructed results may contain visually plausible yet weakly supported content, structural deviations, or unnatural textures with respect to the low-resolution (LR) input. Existing GSR methods have extensively explored the trade-off between perceptual realism and reconstruction fidelity, but the division between preserving reliable coarse-scale information and restoring more uncertain fine details is often handled implicitly within the overall restoration process. Visual autoregressive (VAR) modeling provides a natural opportunity to revisit this issue, as its coarse-to-fine next-scale prediction offers an explicit scale-wise generation interface. However, existing VAR-based SR methods still inherit the original full 1-to- N autoregressive generation path, even though, for super-resolution, coarse-scale information in LR is often relatively more reliable, while long autoregressive chains may accumulate prediction errors. Motivated by these observations, we propose \textbfK2N, which reformulates VAR-based SR from full-path generation into a k -to- N detail continuation process. Specifically, early coarse-scale states are established directly from LR, while only the remaining finer scales are restored autoregressively. Experimental results show that K2N remains competitive with the VARSR baseline on standard SR metrics, while exhibiting clearer advantages on hallucination-focused evaluation. These findings suggest that explicitly rethinking the generation path in a scale-wise manner can be a promising direction for improving the reliability of generative super-resolution. Our code will be released soon at this https URL.
[CV-96] DAVET: Denoising-Aware Visual Evidence Trajectory Allocation for Diffusion Vision-Language Models
链接: https://arxiv.org/abs/2608.01821
作者: Yongkang Zhou,Xiang Xia,Cheng Yan,Fan Xu,Wuyang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Diffusion vision-language models (dVLMs) iteratively denoise masked responses while conditioning each denoising step on visual evidence, making visual conditioning a substantial recurring inference cost. Unlike autoregressive decoding, diffusion generation repeatedly revisits the entire response as uncertainty evolves. Our analysis reveals that visual evidence demand is strongly step-dependent, motivating adaptive allocation across denoising steps. Existing inference acceleration methods operate through decoding-side strategies or visual token compression via pruning and merging, but do not explicitly treat visual evidence as a resource whose demand evolves across the diffusion process. Therefore, we present Denoising-Aware Visual Evidence Trajectory Allocation (DAVET), a training-free framework that allocates visual evidence according to the evolving generation state. Starting from a phase-conditioned evidence trajectory, the proposed allocation policy uses operation demand to set an evidence reserve whose allocation at each denoising step is modulated by trajectory risk. DAVET realizes the resulting budgets through a hierarchy of evidence views constructed from a single visual encoding, separating when and how much evidence is needed from how the evidence views are constructed. Evaluated on two representative dVLMs, LLaDA-V and LaViDa, across multiple visual-understanding benchmarks, DAVET achieves an average speedup of 1.55 \times with an average relative performance drop of 1.86%, showing that denoising-aware visual evidence allocation can reduce visual conditioning cost while largely preserving generation quality.
[CV-97] SecondOpinion: Anatomy-Aware Gated Reasoning for Efficient Medical Image Analysis MICCAI2026 MICCAI
链接: https://arxiv.org/abs/2608.01808
作者: Siam Tahsin Bhuiyan,Rashedur Rahman,Sefatul Wasi,Riyadul Islam,Syoji Kobashi,Ashraful Islam,Saadia Binte Alam
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at EMA4MICCAI 2026 (MICCAI Workshop)
Abstract:Deep learning models for medical image analysis typically apply a fixed amount of computation to every input, regardless of case difficulty. Anatomy-guided dual-stream architectures have been shown to improve diagnostic performance, but they evaluate both streams unconditionally, even on cases a single stream could already resolve confidently. We propose SecondOpinion, a framework in which a fast primary stream processes every case, while a second, anatomy-guided stream is invoked only when GateKeeper, a gating mechanism trained explicitly as a binary correctness classifier, judges that the primary stream’s prediction needs additional scrutiny, much as a clinician might seek a second opinion on a difficult case. When activated, the two streams are combined through a lightweight cross-attention fusion module. We evaluate SecondOpinion on a unified five-class chest X-ray dataset and a pelvic fracture dataset, the latter including a held-out, harder subset of fractures that are invisible on X-ray but confirmed via CT. SecondOpinion matches or exceeds prior state-of-the-art performance on both tasks, while activating its anatomy-guided stream on only 9.23% of chest X-ray cases, rising to 24.12% on visible fractures and 45.71% on invisible fractures, an activation rate that tracks task difficulty directly. These results suggest that supervising a gating signal toward correctness, rather than relying on unsupervised confidence, allows a model to allocate anatomical reasoning where it is actually needed.
[CV-98] Parameter-Dynamic Adaptive Fusion and Calibration Network for RGBT Tracking
链接: https://arxiv.org/abs/2608.01807
作者: Zhaoding Ding,Chenglong Li,Jiandong Jin,Kewei Ying,Wentao Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages,4 figures; Under review
Abstract:Existing RGBT trackers typically employ fusion functions with fixed parameters across different targets and scenarios. Although dynamic-architecture methods improve fusion flexibility by selecting among predefined operations, they still cannot adapt the fusion parameters to the evolving target state. To address these issues, we propose a Parameter-Dynamic Adaptive Fusion and Calibration Network (PAFCNet) for RGBT tracking. PAFCNet dynamically generates target-conditioned parameters for multimodal fusion and temporal calibration, enabling the tracking process to adapt to target appearance variations and modality quality fluctuations. Specifically, we introduce a Target-Adaptive Hypernetwork (TA-HyperNet) that leverages template representations, which preserve stable target identity and recent appearance changes with less background interference, to generate target-conditioned parameters for subsequent fusion and calibration. Based on TA-HyperNet, we design a target-aware parameter-dynamic fusion module that uses the generated parameters to modulate the fusion process. This enables the fusion module to adapt to changes in target appearance and complex scene conditions. Furthermore, since spatio-temporal information propagation may accumulate tracking noise, we propose a dynamic spatio-temporal calibration module that employs TA-HyperNet to generate calibration parameters for spatio-temporal tokens. By dynamically calibrating historical information before propagation, the module improves the reliability of temporal representations. Experimental results demonstrate that PAFCNet achieves competitive performance on multiple RGBT tracking benchmarks.
[CV-99] Investigating Social Bias in Narrative Image Generation
链接: https://arxiv.org/abs/2608.01780
作者: Junyeong Park,Sowon Min,Euna Jang,Soobin Kim,Jiho Jin,Hyunseung Lim,Gahyeon Bae,Hwajung Hong
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to GenAI4World Workshop at COLM 2026
Abstract:Text-to-image (T2I) generation models are increasingly embedded in applications such as media content creation and education, raising concerns about how their outputs may reproduce social biases. Prior work has shown that T2I models exhibit social biases, yet existing evaluations largely focus on a photo generation task. As a result, it remains unclear whether and how such biases manifest in more narrative visual formats, such as storyboards and comics, where characters and events are presented across multiple panels. In this work, we compare bias expression across photo, storyboard, and comic generation in six T2I models by adapting BBG, a text-based bias evaluation framework, to image generation. Our results show that proprietary models generate 25.9% biased outputs in photo generation on average, with biased outputs increasing by 9.6pp in storyboard generation and 18.2pp in comic generation. We also find that photos mainly encode biases through subtle visual cues, while storyboards and comics reveal them more explicitly through event sequencing, character positioning, narrative resolution, and textual elements. These findings show that biases that remain less visible in photo generation may surface in narrative visual formats, highlighting the importance of evaluating T2I systems with diverse visual formats beyond photo generation.
[CV-100] LiveLight: Real-time Streaming Video Relighting with Interactive Control
链接: https://arxiv.org/abs/2608.01771
作者: Yue Ma,Jiangming Wang,Yucheng Wang,Xilai Wang,Zhiyuan Li,Xinyu Wang,Hongyu Liu,Ruofan Liang,Songchun Zhang,Yuxuan Xue,Qifeng Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by TOG 2026. Project page: this https URL
Abstract:We present LiveLight, the first diffusion-based framework for real-time streaming video relighting with interactive 3D lighting control. Achieving this is non-trivial, as it requires overcoming three critical challenges: effectively injecting dynamic 3D lighting into a diffusion model, maintaining high-fidelity generation under an extremely low NFE (Number of Function Evaluations) budget for real-time speed, and facilitating continuous streaming for interactive control. To address these pain points, we propose three key designs. First, for accurate lighting injection, we propose a lightweight adapter that feeds Multi-Plane Light Irradiance (MPLI) conditions-depth-aware irradiance maps encoding 3D lighting geometry-directly into the diffusion backbone. Second, to prevent rendering quality degradation at low NFEs towards real-time distillation, we introduce a geometry-guided feedback branch. This training-time constraint leverages a frozen geometry estimator to enforce depth- and normal-consistent relighting, ensuring geometrically plausible shading without adding inference overhead. Finally, to enable streaming interaction, we develop a progressive rolling-window strategy that maintains a denoising ladder of latent chunks at varying noise levels. By propagating intermediate states, this strategy guarantees temporal coherence and supports arbitrarily long video relighting with per-frame reference refresh. Extensive experiments on real-world and synthetic benchmarks demonstrate that LiveLight achieves state-of-the-art relighting quality while running at real-time speed, significantly outperforming offline baselines in temporal stability, lighting controllability, and user preference. To foster real-time interactive relighting research, we will publicly release our models, training data, and synthetic data generator.
[CV-101] DecoupleGS: Interactive 3D Gaussian Splatting for End-to-End Autonomous Driving Testing ECCV2026
链接: https://arxiv.org/abs/2608.01761
作者: Siying Li,Ying Ni,Jie Sun,Jian Sun,Haotian Shi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:End-to-end (E2E) autonomous driving algorithms require rigorous closed-loop validation in simulation environments offering high visual fidelity, strong interactivity, and real-time performance. Existing approaches, from game engines to static neural rendering, inherently trade off these requirements and struggle with the dynamic scene composition essential for E2E testing. To bridge this gap, we propose a novel decoupled 3D Gaussian Splatting (3DGS) framework tailored for large-scale E2E evaluation. We fundamentally decompose scenes into a high-fidelity static background and manipulable dynamic agents using an object-centric canonical representation. To resolve resulting representational conflicts, we introduce three targeted modules: (1) asset compression via perceptual pruning and vector quantization for real-time traffic rendering; (2) map-guided geometric registration leveraging semantic topology to strictly align trajectories; and (3) proxy-based relighting transferring ambient illumination for seamless photometric integration. Extensive experiments demonstrate that DecoupleGS achieves a balanced fidelity-efficiency trade-off, improves metric and photometric consistency, and provides a practical closed-loop sensor simulation platform for E2E autonomous driving evaluation.
[CV-102] Pixel Ignores Superpixel Sees: Adverse Weather Image Restoration via Semantic-Center SSM
链接: https://arxiv.org/abs/2608.01760
作者: Dayu Li,Shihao Zhou,Leizhi Shu,Jin Wu,Chi Man Vong,Jufeng Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Adverse weather image restoration aims to recover clear visibility from degraded images in complex weather conditions. Existing works attempt to address this problem by modeling relationships between pixels, however, this paradigm defies the spatially non-uniformity fact of degradations and learns non-discriminative features from semantic-conflict regions. In this paper, we propose SSR, a \textbfSemantic-center guilded \textbfState space model for image \textbfRestoration. The key idea of SSR is to shift the conventional scanning strategy of pixel-serial to semantic-guilded one. Specifically, we introduce a Superpixel-guided Selective Scan Mechanism ( \textS^3 M), which first partitions the image into perceptually coherent regions via superpixel clustering and then performs relations modeling within the semantic-related regions. Moreover, a Region-level Gating Mechanism (RGM) is developed to perform intra-region calibration by modulating degradation outliers within each semantic superpixel unit along the channel dimension. Extensive experiments on \textbf6 well-established benchmarks demonstrate that SSR performs favorably against state-of-the-art models with competitive computational cost. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.01760 [cs.CV] (or arXiv:2608.01760v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.01760 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Dayu Li [view email] [v1] Mon, 3 Aug 2026 06:29:41 UTC (13,473 KB) Full-text links: Access Paper: View a PDF of the paper titled Pixel Ignores, Superpixel Sees: Adverse Weather Image Restoration via Semantic-Center SSM, by Dayu Li and Shihao Zhou and Leizhi Shu and Jin Wu and Chi Man Vong and Jufeng YangView PDFHTML (experimental)TeX Source view license Current browse context: cs.CV prev | next new | recent | 2026-08 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"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[CV-103] Can Urban Blight Be Accessed with Vision-language Models: A Case Study in Detroit
链接: https://arxiv.org/abs/2608.01753
作者: Xiaohao Yang,Aohua Tian,Derek Van Berkel,Xu Qiang,Mark Lindquist
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 19 pages, 11 figures
Abstract:Addressing urban blight has seen increased focus in the past 15 years. Assessing urban blight is essential for guiding urban planning, targeting rehabilitation, and safeguarding public health, yet traditional residential blight surveys are difficult to maintain at scale due to the labor-intensive cost and long-term cycle. This study introduced a scalable framework for estimating residential blight using open-source large vision-language models on multiple views. Structured prompts guided models to evaluate housing attributes, including roof integrity, wall damage, and broken or boarded openings, producing both binary assessments and probabilistic estimates of disrepair. To evaluate the performance of these visual assessments, we compared professional human annotations of these features across several models, including an ensemble stacking approach based on XGBoost and a weighted scoring system. Results showed that (i) multiple street views can contribute to the improvement of accuracy, (ii) large vision-language models have different strengths of inference, (iii) the ensemble learner outperforms individual base models, enhancing robustness across all residential conditions and blight assessment. The practical application of the method allows low-cost tracking and management of housing stock conditions, providing a regularly updatable complement to traditional blight surveys.
[CV-104] SPECTRA: Band-Routed Embedding and Stage-Wise LoRA for Cross-Sensor Fine-Tuning of Geospatial Foundation Models
链接: https://arxiv.org/abs/2608.01751
作者: Xingyan Li,Jordan A. Caraballo-Vega,Jie Gong,Mark L. Carroll,Jianwu Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Geospatial foundation models (GeoFMs), pretrained on large-scale geospatial data such as Earth observation (EO), climate, and weather data, have shown promising performance when fine-tuned on diverse downstream tasks. However, there are two challenges of adapting EO-pretrained GeoFMs to practical downstream datasets. The first challenge is how to handle spectral mismatch: pretrained patch embeddings expect a fixed set of input bands, whereas downstream sensors may provide different channels. The second challenge is how to reduce fine-tuning cost and make it efficient. While existing work has made efforts on these challenges individually, jointly improving fine-tuning performance under spectral mismatch while reducing adaptation cost remains underexplored. We propose SPECTRA, a parameter-efficient fine-tuning framework that addresses both spectral mismatch and adaptation cost. To handle spectral mismatch, SPECTRA introduces Band-Routed Embedding (BRE), which maps all available downstream bands into the band space expected by the pretrained GeoFM. By using BRE, all available bands in the downstream dataset are utilized to improve the selected-band input without changing the pretrained patch embedding interface. To reduce adaptation cost, SPECTRA further introduces a Stage-wise Transferability-aware LoRA (ST-LoRA) fine-tuning. ST-LoRA estimates stage-wise transferability before fine-tuning and assigns stage-specific LoRA ranks, concentrating trainable parameters on the stages with high transferability for the target task. Across three EO-pretrained GeoFMs and four downstream segmentation datasets, experiments show that BRE improves performance by utilizing all spectral bands, while ST-LoRA reduces trainable parameters compared with full fine-tuning and standard LoRA. Code is available at this https URL.
[CV-105] IDraw: Artist Verification from Digital Drawing Images
链接: https://arxiv.org/abs/2608.01737
作者: Nayoung Kim,Nan Jiang,Bangjie Sun,Jaewon Shin,Sojeong Kim,Jun Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:As digital drawings are increasingly shared online, reliable authorship verification has become important for protecting artists and resolving disputes. Yet when authorship is questioned, verification may have to rely only on the disputed drawing and reference drawings known to be created by the claimed artist. This setting is challenging for two reasons. First, artist-specific drawing behavior, such as pen pressure and movement speed, is informative but is not available from a completed drawing. Second, similarities in the depicted object or scene can obscure similarities arising from the artist. We propose IDraw, a framework that learns from drawings paired with tablet-pen sensor signals collected from separate training artists. This allows IDraw to infer drawing behavior from completed images during a later authorship dispute, without requiring sensor data from the artist being verified. IDraw also reduces the influence of drawing content by identifying information shared by drawings of the same object across different artists and suppressing it before comparing drawings. To support this approach, we construct the first multimodal dataset for digital drawing authorship verification, containing 1,110 drawings from 37 artists and 14 types of tablet-pen sensor signals. Evaluated on previously unseen artists across nine image-encoder backbones, IDraw consistently outperforms standard image-based verification and reduces verification error by up to 40%. These results demonstrate that inferring drawing behavior from completed images and suppressing drawing content improve digital drawing authorship verification.
[CV-106] Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency CVPR
链接: https://arxiv.org/abs/2608.01730
作者: Hakan Emre Gedik,Shashank Gupta,Alan Bovik
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026
Abstract:No-reference image quality assessment (NR IQA) has recently benefited from deep and multimodal models, yet many SOTA systems still violate at least one basic requirement: they either discard critical quality cues via aggressive resizing, fail to generalize across resolutions, cannot be jointly trained on heterogeneous IQA datasets with mismatched MOS scales, or require prohibitive computation. We present \textbfReLIQS, a model for \textbfResolution-agnostic \textbfLearning for \textbfImage \textbfQuality with \textbfSaliency, which is resolution-agnostic, preserves original-resolution quality cues, learns from multiple subjective studies, and remains computationally efficient and budget-adaptive. ReLIQS is a CLIP-based multiscale patch-driven architecture that learns both \emphwhere to look and \emphhow to judge quality. Fixed-size patches are sampled across multiple resolutions, including the original resolution, and encoded with a CLIP vision backbone. A lightweight Perceptual Importance Estimator then predicts IQA-specific importance maps to select a small set of informative patches, and a Latent Quality Axis Module aggregates their embeddings into a single image-level score. Across authentic, synthetic, and AIGC benchmarks spanning diverse resolutions and distortions, ReLIQS generalizes better than strong CNN-, CLIP-, and MLLM-based baselines with matching or reduced computational cost.
[CV-107] G-Skin: Learning to Bind 3D Gaussians with Generative Visual Priors
链接: https://arxiv.org/abs/2608.01726
作者: Yuxin Yao,Kendong Liu,Shiqi Zhou,Jiazhi Xia,Junhui Hou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting has achieved remarkable success in photorealistic and efficient rendering, leading to a rapid increase in 3D assets represented by 3D Gaussian primitives. Directly rigging these assets with arbitrary skeleton topologies is highly desirable. However, training a feed-forward skinning framework is infeasible due to the lack of high-quality 3D Gaussian rigging datasets. An alternative solution is to transfer mesh-based techniques to 3D Gaussian-based representation, but 3D Gaussian primitives are not restricted to the surface and lack explicit topological connectivity. Moreover, this kind of method suffers from poor generalization to unseen data due to its strong dependence on training data, while acquiring high-quality rigging data is prohibitively expensive. To address this challenging problem, we propose G-Skin, a novel generative skinning framework designed for expressive and high-fidelity animation with 3D Gaussian representation. To overcome this 3D data scarcity, we introduce a skeleton-controllable image generation model leveraging 2D vision foundation models to distill powerful motion priors into pseudo-guidance. Guided by these priors, we formulate an optimization pipeline incorporating geometry-aware regularizations, which stabilizes the learning process and ensures smooth, structurally coherent skinning weights. G-Skin also generalizes flexibly to the augmented variants of 3D Gaussian representation designed to mitigate animation-induced rendering artifacts. Extensive experiments validate the effectiveness of our approach, demonstrating clear advantages over state-of-the-art methods. Project page: this https URL.
[CV-108] When Extreme Darkness Meets Motion Blur: MeanFlow for Unified RAW Restoration
链接: https://arxiv.org/abs/2608.01720
作者: Zepu Wang,Jingze Liang,Weijie Xiao,Kexin Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Extremely low-light RAW enhancement aims to recover severely attenuated sensor signals, yet existing methods often focus on illumination and noise while overlooking the motion-induced degradations inherent in practical low-light imaging. We present a framework for robust extremely low-light RAW enhancement under realistic acquisition degradations. First, we introduce See in the Degraded Extremely Dark (SIDED), a new dataset that applies controlled motion degradation to extremely low-light RAW pairs while retaining their original sensor noise. Second, we propose a unified RAW tokenizer equipped with explicit domain-conditioned representation calibration to align extremely low-light and well-exposed RAW data, followed by a MeanFlow that performs enhancement in a single function evaluation. To our knowledge, this is the first work to formulate extremely low-light RAW enhancement under realistic motion-degraded acquisition and address it with MeanFlow. We further introduce a physics-guided refinement model to strengthen illumination–reflectance consistency, pixel fidelity, and color preservation without incurring additional inference cost. Extensive experiments demonstrate that our framework achieves state-of-the-art performance in extremely low-light RAW enhancement, and robustly handles coupled motion and noise degradations.
[CV-109] STC-Net: Electroluminescence-Based Solar Cell Crack Segmentation for Power Loss Estimation
链接: https://arxiv.org/abs/2608.01714
作者: Shanaka Ramesh Gunasekara,Akila Eranda Devanarayana,Imasha Guruge,Nuwantha Fernando,Ehsan Asadi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate crack assessment in electroluminescence (EL) images is important for photovoltaic (PV) reliability analysis, yet existing segmentation methods often fail to capture the thin, elongated, and structurally constrained nature of crack defects. This paper proposes a Solar Topology Crack Network (STC-Net) that incorporates edge priors, spectral priors, and a boundary-topology refinement module to improve crack continuity and boundary preservation. The framework further extends segmentation to power-loss estimation by deriving a crack-associated inactive-area proxy from the predicted masks. Experiments on the PVEL-S dataset show that STC-Net achieves 95.98 MIoU, 98.01 MDice, and 98.00 MAcc during training, and 72.52 MIoU and 80.16 MDice on unseen test samples. These results demonstrate that STC-Net provides accurate crack localization while offering a practical link between EL-based defect segmentation and PV degradation assessment.
[CV-110] SpatialQuery: Benchmarking Geometry-Grounded Multi-Instance Spatial Reasoning in Vision-Language Models
链接: https://arxiv.org/abs/2608.01709
作者: Hai Nguyen,Tung Vu,Cong Tran
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models (VLMs) achieve strong semantic understanding but remain unreliable in metric spatial reasoning, particularly when queries require comparing multiple instances of the same object category. We study this problem through the Closest-Instance Distance Query (CIDQ), where a model must identify the nearest visible candidate to a unique reference object and estimate their gravity-aligned floor-plane distance. We introduce SPATIALQUERY, a training- free framework for CIDQ reasoning from a single RGB image, together with SPATIALQUERY-1M, a benchmark containing over one million RGB-only question-answer pairs from 200 indoor scenes. SPATIALQUERY recovers instance-level metric geometry and transforms it into a canonical Bird’s-Eye View through Scene Cubifying, which represents objects as uniformly sized, category-coded blocks to emphasize their relative floor- plane locations. We further propose Uncertainty-Aware Chain-of-Thought (UA-CoT) prompting, which incorporates geometry- derived per-instance uncertainty into the VLM reasoning process. Without task-specific fine-tuning or architectural modification, SPATIALQUERY with Qwen3-VL-8B achieves a Floor-MAE of 0.259 m, an Unc-Acc@0.3 m of 90.5%, and a proximity-decision accuracy of 84.18%, outperforming fine-tuned spatial specialists, general-purpose VLMs, and closed-source frontier models. Code, benchmark resources, and an interactive demo are available at this https URL.
[CV-111] UniSim-SLAM: Feed-Forward SLAM with Unified Sim(3) Optimization ECCV2026
链接: https://arxiv.org/abs/2608.01706
作者: Inha Lee,Dongjae Jeong,Junhee Lee,Kyungdon Joo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026. Project page: this https URL
Abstract:Recent geometric foundation models enable feed-forward inference for SLAM, but their predictions are strongly dependent on the input view set, which leads to geometric inconsistencies and trajectory drift when results are chained over long sequences. Online deployment further exposes a trade-off between the low latency of two-view tracking and the constraint richness of multi-view inference. We introduce UniSim-SLAM, an integrated system that runs lightweight two-view keyframe tracking in the frontend and performs periodic multi-view submap refinement in the backend. To combine predictions defined in heterogeneous local coordinates with inconsistent scales, we formulate a unified multi-level factor graph on Sim(3) that jointly optimizes global keyframe poses and submap poses. The graph integrates temporal view-to-view odometry edges, view-to-submap bridge edges with depth-statistics scale anchoring, and submap-to-submap tie and scale constraints to enforce consistent similarity relations across submaps. Experiments on TUM RGB-D and 7-Scenes show that UniSim-SLAM achieves state-of-the-art accuracy in the uncalibrated setting, reducing trajectory error by 38.5% on TUM RGB-D and 45.9% on 7-Scenes compared to prior best results. Project page: this https URL
[CV-112] Entity-Aware Sequence Transduction for Player-Centric Ball Action Spotting
链接: https://arxiv.org/abs/2608.01696
作者: Ruifeng Wang,Di Yang,Jiangtao Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Player-centric ball action spotting requires temporally precise event detection together with actor attribution in crowded, partially observed multi-agent sports videos. Existing Denoising Sequence Transduction (DST) baselines treat the player-role dimension as part of a flattened frame-level representation, which weakens the inductive bias for modeling player-specific temporal evolution and inter-player interactions. To address this limitation, we propose Multi-Entity Denoising Sequence Transduction (ME-DST). ME-DST keeps the role-slot dimension throughout encoding. It uses temporal attention to model the history of each role slot, and spatial attention to exchange information across role slots at each frame. This factorized design gives the model a direct structure for separating within-player evolution from inter-player context. We also add learnable role embeddings, tracking-derived tactical features, and fused visual predictions from X3D-L and Swin3D-S. Experiments on the FOOTPASS dataset show that ME-DST reaches a Micro F1 of 0.778. This improves the strongest official TAAD+DST baseline by 10.3 percentage points. Controlled ablations show that preserving the entity axis and encoding role identity are central to this gain. These results suggest that explicit entity modeling is an effective inductive bias for player-centric sports event understanding.
[CV-113] Generative AI and Foundation Models in Medical Image
链接: https://arxiv.org/abs/2608.01686
作者: Masahiro Oda
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Review Article
Abstract:In recent years, generative AI has attracted significant public attention, and its use has been rapidly expanding across a wide range of domains. From creative tasks such as text summarization, idea generation, and source code generation, to the streamlining of medical support tasks like diagnostic report generation and summarization, AI is now deeply involved in many areas. Today’s breadth of AI applications is clearly distinct from what was seen before generative AI gained widespread recognition. Representative generative AI services include DALL-E 3 (OpenAI, California, USA) and Stable Diffusion (Stability AI, London, England, UK) for image generation, ChatGPT (OpenAI, California, USA), and Gemini (Google, California, USA) for text generation. The rise of generative AI has been influenced by advances in deep learning models and the scaling up of data, models, and computational resources based on the scaling laws. Moreover, the emergence of foundation models, which are trained on large-scale datasets and possess general-purpose knowledge applicable to various downstream tasks, is creating a new paradigm in AI development. These shifts brought about by generative AI and foundation models also profoundly impact medical image processing, fundamentally changing the framework for AI development in healthcare. This paper provides an overview of diffusion models used in image generation AI and large language models (LLMs) used in text generation AI, and introduces their applications in medical support. This paper also discusses foundation models, which are gaining attention alongside generative AI, including their construction methods and applications in the medical field. Finally, the paper explores how to develop foundation models and high-performance AI for medical support by fully utilizing national data and computational resources.
[CV-114] Generative Brownian Bridge Diffusion In Motion Space For Enhanced Myocardial Strain Analysis
链接: https://arxiv.org/abs/2608.01677
作者: Rishov Paul,Frederick H. Epstein,Miaomiao Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 10 pages
Abstract:Myocardial strain analysis of cardiac magnetic resonance (CMR) images provides an important tool for evaluating cardiac function. However, current techniques require either human-adjusted post-processing with suboptimal regional accuracy, or specialized and costly imaging acquisitions. In this paper, we propose to leverage the power of generative models to synthesize high-quality motion-derived strain values from routinely acquired CMR sequences. Specifically, we develop a novel Brownian bridge diffusion model in motion space to learn the probabilistic mapping between standard CMR motion estimated from widely adopted registration methods and highly accurate motion provided by advanced strain imaging techniques. To promote the fidelity of anatomical structure in the generation process, our model is conditioned on the corresponding CMR images. We validate our method on large-scale multi-center CMR datasets including subjects of paired standard cine CMR and advanced strain imaging acquisitions. Experimental results demonstrate that our framework significantly improves the accuracy of motion prediction and strain analysis from standard CMRs compared to existing learning-based approaches. Our research represents a new paradigm for potentially developing cost-effective, clinically deployable AI tools for cardiac function assessment with enhanced strain accuracy in busy clinical workflows. Our code is publicly available at this http URL.
[CV-115] FAU at ImageCLEF 2026 Task on Multimodal Reasoning Robust Candidate Scoring and Concise Multilingual Visual Answering
链接: https://arxiv.org/abs/2608.01664
作者: Mohamed Basem,Vincent Christlein
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 16 pages, 3 figures, 7 tables. CLEF 2026 Working Notes, ImageCLEF 2026 Multimodal Reasoning Task
Abstract:We present our ImageCLEF 2026 Multimodal Reasoning system for the Visual Multiple Choice Question Answering (Visual MCQ) and Visual Open Question Answering (Visual OpenQA) subtasks. The challenge requires reliable reasoning over multilingual educational and scientific images with dense text, diagrams, charts, tables, formulas, and units, while enforcing strict answer formats. Our central finding is that robust output control is as important as model choice. For Visual MCQ, we replace fragile free-form generation with direct candidate label scoring from vision-language model logits, then combine complementary runs through score fusion and voting. For Visual OpenQA, we use image enhancement, concise final answer prompting, deterministic decoding, and targeted post-processing to remove reasoning traces and formatting artifacts. Without task-specific model training, our official submissions achieved third place in Visual MCQ with 0.7108 accuracy and first place in Visual OpenQA with 0.6488 COMET, 0.1391 BLEU, 0.2762 ROUGE L, and 0.2383 METEOR. The results highlight the practical value of inference engineering: careful scoring, ensembling, prompting, and cleanup can turn strong VLMs into reliable competition systems.
[CV-116] Few-Shot Concept Prompt Learning for Segmentation Foundation Models via Visual Grounding
链接: https://arxiv.org/abs/2608.01663
作者: Rahul Venkataramani,Rachana Sathish
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Promptable segmentation foundation models (FMs) such as SAM3 and Medical SAM3 promise few-shot, interactively-specified segmentation for medical imaging through a natural language interface, yet their performance on clinical tasks falls well short of this promise. We posit that this shortfall is not an artefact of insufficient medical pretraining or imperfect prompt phrasing, but a structural limitation that will persist in any domain where paired image-text supervision is scarce, as it is across most clinical modalities. We further hypothesize that the limitation is specific to natural language as a control signal: a visually grounded prompt, learned directly from the target distribution, should recover the lost performance without additional image-text data or backbone retraining. We propose Few-Shot Concept Prompt Learning (FS-CPL), which learns a continuous concept prompt embedding \mathbfp^* \in \mathbbR^T \times d from a small support set of K image–mask pairs via mask supervision, with the encoder-decoder backbone frozen. Across four public benchmarks spanning ultrasound and endoscopy (BUSI, HC18, TN3K, CVC-Clinic), FS-CPL delivers absolute Dice improvements of up to +0.62 over canonical text prompts and is \emphbackbone-agnostic: it lifts both vanilla SAM3 and the domain-specifically pretrained Medical SAM3, showing that visual concept prompting is complementary to in-domain pretraining.
[CV-117] FairForensics: Seeing Expressions and Parsing Demographics via Vision-Language Modeling for Generalizable Fair Deepfake Detection
链接: https://arxiv.org/abs/2608.01661
作者: Yaning Zhang,Jiao Wu,Zan Gao,Linlin Shen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The challenge of fair deepfake detection (FDD) has attracted increasing attention. Existing fairness-enhanced detectors often suffer from suboptimal generalization to unseen manipulations and fairness across demographic groups. They are typically developed and evaluated on demographically imbalanced distributions, resulting in biased predictions toward minority groups. In this paper, we construct a novel demographically balanced FDD benchmark to train and evaluate the fairness of detectors under both balanced and imbalanced population scenarios. Additionally, we introduce a novel expression and demographic perceptual vision-language model, termed FairForensics, for generalizable fair deepfake detection. FairForensics conducts face forgery generalization enhancement and demographic-aware fairness regularization. During face forgery generalization enhancement, built upon the novel observation of significant distribution differences between pristine and forged expression vectors, we design an expression encoder to capture high-level expression-guided forgery patterns, and an expression-perceptual visual encoder that integrates global appearance and expression forgery features while mitigating identity bias using an identity-aware patch perturbation module. Under demographic-aware fairness regularization, we propose a demographic-guided language encoder to extract population-aware global language embeddings, which boosts the decoupling of forgery features from demographic information via vision-language alignment. We devise a population-aware prototype fairness objective to enforce both inter-class separability and intra-class alignment across demographic subgroups. Extensive experiments on our balanced demographic benchmark show that our method achieves the state-of-the-art in terms of generalization and fairness.
[CV-118] Ground Cover and Refine: Evidence-Centric Frame Selection for Long-Video Question Answering
链接: https://arxiv.org/abs/2608.01660
作者: Fan Wei,Siru Zhong,Runmin Dong,Miao Yang,Zhaoyang Luo,Haohuan Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Long-video question answering requires identifying sparse yet critical evidence from videos containing thousands of frames under a constrained visual-token budget. Existing methods either select query-aware frames in a single pass or rely on timestamped text solely as retrieval guidance, leading to two key limitations. First, selected frames tend to cluster around local relevance peaks, and once the budget is exhausted, omitted evidence cannot be recovered. Second, textual and visual evidence remain weakly aligned. We propose GCR, a training-free framework that casts fixed-budget frame selection as a joint evidence curation problem. Ground converts timestamped text into temporal events, selects query-relevant real frame anchors, and renders each event text onto its temporally aligned frame. Cover supplements grounded events with direct visual anchors for complementary visual evidence and applies global maximal marginal relevance to preserve diverse context. Refine revisits omitted temporal regions and replaces the weakest revisable context frame with a real-frame medoid—but only when the medoid offers greater evidence value. GCR maintains a fixed number of chronologically ordered frames and requires no VLM training or architectural modification. Experiments on LongVideoBench and Video-MME, across three 7B backbones and frame budgets of 8, 32, and 64, demonstrate consistent improvements in long-video QA. With the 7B LLaVA-OV backbone and 32 frames, GCR achieves 64.25% and 62.15% on the two benchmarks, outperforming the strongest reproduced baselines by 2.54 and 1.93 percentage points, respectively.
[CV-119] StreamSplat: Streaming Feed-Forward 3D Gaussian Splatting
链接: https://arxiv.org/abs/2608.01659
作者: Changhao Song,Yuxuan Wang,Qibiao Li,Youcheng Cai,Ligang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Feed-forward 3D Gaussian Splatting enables efficient novel-view synthesis without per-scene optimization, but most existing methods assume a fixed set of context views and process them jointly. This limits their applicability to online scenarios where calibrated views arrive sequentially and the scene must be updated causally. We present \emphStreamSplat, a streaming feed-forward 3DGS framework that incrementally maintains a persistent geometry-grounded scene state and decodes it into renderable 3D Gaussians after each input chunk. StreamSplat centers on a \textbfVoxel-Aligned Causal Cache (VACC), which stores historical 3D tokens in a memory-bounded voxel structure so that memory grows with explored scene geometry rather than stream length. To better reuse history during causal prediction, we introduce \textbfHistory-Projected Depth Anchoring (HPDA) to project cached geometry as depth guidance for current cost-volume estimation, and \textbfCache-Guided Feature Injection (CGFI) to inject cached latent evidence into Gaussian-token regression. Experiments on DL3DV, RealEstate10K, and ScanNet show that StreamSplat remains competitive with state-of-the-art feed-forward 3DGS methods under sparse causal inputs, despite not using future views or full-scene context. More importantly, it scales to long input streams with 256, 512, and 1024 views where fixed-view baselines run out of memory, yielding sustained improvements in novel-view synthesis quality as more observations arrive. The code will be made publicly available upon acceptance.
[CV-120] CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens for Vision-Language Models
链接: https://arxiv.org/abs/2608.01644
作者: Yu Chen,Xiaohong Li,Xiaole Wang,Jianjin Zhang,Jun Sun,Yafeng Deng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 11 pages, 5 figures
Abstract:In video understanding, vision-language models (VLMs) must ingest massive numbers of visual tokens, causing the computational and memory cost of the prefill stage to rise sharply. Such visual sequences are highly redundant along the spatio-temporal dimension, yet a high compression ratio is often accompanied by the loss of critical details. Existing token-compression methods either employ heuristic, training-free compression with limited content adaptivity or introduce additional modules that require expensive alignment training, leaving the trade-off between efficiency and adaptivity unresolved. To alleviate this limitation, we propose CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens. CRAFT recursively merges tokens by decoupling parameter-free token selection from learnable token fusion: global similarity determines which tokens to merge, while a position-aware weighting module and a content-adaptive channel-wise gate learn how to fuse them. The whole compression pipeline is query-agnostic. Because every retained token is a linear combination of the original tokens, CRAFT preserves their true spatio-temporal coordinates and stays aligned with the pre-trained language model’s input distribution. Experiments on multiple representative video benchmarks show that CRAFT consistently outperforms prior state-of-the-art token-compression methods. At about 8\times compression, it retains roughly 97% of the backbone’s average accuracy and shows significant efficiency improvement.
[CV-121] StreamTalk: Streaming Co-Speech Gesture Generation with Key-Pose Anchoring ECCV2026
链接: https://arxiv.org/abs/2608.01643
作者: Xiangyue Zhang,Jianfang Li,Jiaxu Zhang,Kaixing Yang,Steven Hoi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注: ECCV 2026
Abstract:Real-time co-speech gesture generation must produce 3D motion clip by clip as speech arrives. Existing streaming methods are open-loop: each clip depends on past context, but the model cannot check or correct its trajectory. Small errors therefore accumulate and cause drift over long sequences. We observe that this failure is mainly caused by the lack of a forward constraint rather than poor short-clip quality. A plausible key pose at the end of each clip provides a destination anchor that limits drift. Based on this observation, we propose StreamTalk, a closed-loop framework with a periodic generate-retrieve-refine cycle. Streaming Pose-Guided Generation first predicts a coarse clip, retrieves a plausible tail pose from a speaker-specific motion database, and refines the clip using this pose before continuing to the next window. During training, Stochastic Anchor Masking randomly masks pose and translation frames, teaching the model to recover complete motion from sparse boundary conditions. A part-aware DiT separates hand, body, and translation streams to reduce interference between global displacement and local articulation. On BEAT2, StreamTalk achieves state-of-the-art FGD, reduces long-horizon drift relative to open-loop baselines, and runs in real time at 76 FPS. Project page: this https URL.
[CV-122] Dynamic Resolution Routing for Efficient Egocentric Grounding
链接: https://arxiv.org/abs/2608.01638
作者: Huixin Sun,Wangbo Zhao,Fanyue Wei,Qiuxia Lin,Pengzhan Sun,Angela Yao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Egocentric visual grounding requires high-resolution inputs to localize small objects. However, scaling Multimodal Large Language Models to this domain is constrained by the excessive cost of visual token processing. We identify that current efficient strategies based on token reduction are unreliable for selecting object-centric spatial evidence. To overcome this, we propose SmartRes, a framework that performs efficiency optimization in the pixel space via dynamic resolution routing. SmartRes first encodes a low-resolution view for global context and uses a lightweight router to activate high-resolution patches in object-centric regions and constructs an order-preserving visual sequence. To further enable robust routing under severe foreground-background imbalance, we introduce a margin-regularized routing objective that increases foreground-background logit separation and improves foreground recall. Experiments on Ego4D and EgoIntention show that SmartRes reduces visual tokens by up to 67% while retaining 86.4% of full-resolution performance, and achieves up to 1.66X faster inference than state-of-the-art token reduction methods with higher accuracy. Furthermore, strong performance on small object grounding indicates the effectiveness of SmartRes towards egocentric applications. Code will be publicly available.
[CV-123] Mitigating Visual Degradation in MLLM s via Spatial-Spectral Visual Anchor Learning ACM-MM2026
链接: https://arxiv.org/abs/2608.01635
作者: Qianlong Yang,Bowen Ye,Xianda Guo,Yanlun Peng,Wenke Huang,Hongyuan Zhang,Yulei Jia
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been accepted by ACM MM 2026
Abstract:Despite the progress of multimodal large language models (MLLMs), they continue to exhibit deficiencies in visual perception. Following visual instruction tuning, internal MLLM representations rapidly deviate from their original semantic states during inference, causing severe information degradation. While existing methods attempt to leverage external vision foundation models (VFMs) to align internal representations, we find that direct alignment with VFMs enhances visual semantics but fails to mitigate representation deviation. To address this, we propose Spatial-Spectral Visual Anchor Learning (SSVAL). The core of SSVAL is Visual Anchor Prompt Injection (VAPI), which introduces prompts that absorb rich knowledge from external VFMs during training, enabling them to serve as stable visual anchors that mitigate representation deviation during inference. Additionally, we incorporate auxiliary spatial and frequency-domain representation alignment losses to provide complementary vision-specific supervision at intermediate LLM layers. Extensive experiments demonstrate that SSVAL significantly outperforms existing methods. Code are available on our \hrefthis https URLproject page.
[CV-124] Motion Beyond Morphology: Bootstrapping Cross-Category Motion Transfer from Abstract Motion Representations
链接: https://arxiv.org/abs/2608.01628
作者: Zhixue Fang,Zhimin Zhang,Bi’an Du,Zijie Meng,Yan Zhou,Wei Hu,Guoxin Zhang,Pengfei Wan,Kun Gai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video motion transfer aims to animate a target object using dynamics from a reference video. Existing formulations largely rely on fixed structural correspondence, which becomes ill-defined when reference and target objects differ substantially in morphology, articulation, or deformation mechanisms. We introduce Motion Beyond Morphology, a perspective that seeks to transfer motion beyond fixed structural correspondence, by preserving dynamics that remain meaningful across different target morphologies. To realize this, we propose a two-stage framework. Stage~I learns complementary multi-granularity abstract motion views and uses them to bootstrap cross-category video pairs that preserve transferable dynamics across diverse morphologies. Stage~II internalizes this supervision into direct reference-video-conditioned generation, removing the need for explicit motion extraction at inference. We further introduce OpenVMT-Dataset and OpenVMT-Bench for training and evaluating image- and text-conditioned motion transfer across Same, Near, and Far category gaps, and plan to release both upon acceptance. Extensive experiments demonstrate state-of-the-art motion fidelity and target preservation. Project page: this https URL
[CV-125] Linear Multi-Timescale Retention as a Memory-Efficient Vision-Language Bridge
链接: https://arxiv.org/abs/2608.01614
作者: Ashfak Yeafi,Mehedi Hasan,Md Khairul Islam
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language Models (VLMs) face a critical computational bottleneck when processing high-resolution imagery due to the O(N^2) memory complexity of Softmax Multi-Head Attention (MHA). While substituting MHA with independent Multi-Layer Perceptrons (MLPs) achieves O(N) scaling, it strips the architecture of spatial sequence routing, severely degrading global scene understanding and object permanence. In this paper, we propose the Linear Multi-Timescale Retention (LIA-MTR) module, a memory-efficient cross-modal bridge. By integrating an ELU-based positive feature mapping with adaptive write-gating and log-linearly distributed recurrent decays, LIA-MTR mathematically compresses continuous visual sequences into bounded memory states. Theoretical analysis proves the architecture operates with strict O(N) sequence-interaction complexity. Empirically, synthetic retrieval evaluations demonstrate that LIA-MTR flawlessly routes context across 16,000 tokens, eliminating the “Lost in the Middle” degradation typical of naive linear attention. Hardware benchmarking reveals infinite-context scaling capabilities, natively processing 262,144 visual patches within an 11.2 GB VRAM footprint, whereas standard MHA suffers out-of-memory failure at 16,384 patches. Furthermore, following instruction tuning on 665K conversational samples, LIA-MTR significantly outperforms an industry-standard MLP baseline on the MME benchmark (71.00% vs. 68.11%), driven by a 10% absolute improvement in object permanence and superior global semantic extraction. This work establishes a mathematically rigorous, computationally flat foundation for infinite-context Vision-Language integration.
[CV-126] When Measurement Conventions Masquerade as Calibration Gains in Cardiac Digital Twins ALT MICCAI2026
链接: https://arxiv.org/abs/2608.01602
作者: Dang P. M. Cao,Hieu Pham
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the 2nd International Workshop on Digital Twin for Healthcare (DT4H 2026), held in conjunction with MICCAI 2026. 10 pages, 2 figures
Abstract:Cardiac digital twins convert clinical images into physiological measurements through observation operators, yet calibration studies often assume a fixed reference convention. Across four shared-backbone echocardiographic EF front-ends, phase conditioning appears to remove CAMUS baseline bias. Matched-reference analysis rejects this gain: singleplane ground-truth EF error is statistically indistinguishable across models, while single-plane ground-truth EF exceeds CAMUS biplane clinical EF by +6.30 points, explaining nearly all baseline bias. A prespecified EchoNet-Dynamic replication, with released data and our extractor aligned to the apical four-chamber plane, removes baseline overestimation and reverses the CAMUS ranking. We also quantify haemodynamic effects, conformal residual-width budgets, and EF-stratum changes, yielding a Convention-Aware EF Audit protocol that separates genuine observation operator calibration from measurement artefacts. GitHub: this http URL
[CV-127] D2-4DGS: Dual-Depth Guided Sparse-Camera 4D Gaussian Splatting
链接: https://arxiv.org/abs/2608.01588
作者: Jijian Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dynamic 4D Gaussian Splatting has emerged as an efficient representation for dynamic novel view synthesis through explicit scene modeling and real-time rendering. However, existing methods typically require dense multi-view videos for sufficient geometric constraints, making capture expensive and limiting sparse-camera deployment. Reducing input views lowers acquisition cost but weakens geometry supervision, often causing missing structures and floating Gaussians. Depth priors provide geometric cues, yet no single source offers both dense coverage and reliable geometry. Monocular depth provides dense structure but is scale-ambiguous and locally biased, whereas multi-view geometric depth provides incomplete anchors consistent with the reconstruction coordinate system. To exploit their complementarity, we propose D ^2 -4DGS, a sparse-camera dynamic 4D Gaussian Splatting framework guided by dual-source depth priors. We align monocular estimates with valid multi-view geometric depths and verify their consistency to identify reliable geometric anchors. These verified anchors support consistency-aware pruning and depth supervision, while verified geometric depths and aligned mono-only estimates provide candidate geometry for densification in under-reconstructed regions. Finally, RGB-D joint optimization improves appearance fidelity and geometric consistency under sparse-view supervision. Across all nine dataset–view settings, D ^2 -4DGS achieves the highest PSNR, improving by 1.33 dB on average over the best competing method in each setting.
[CV-128] Enhancing Visual Perception in Foggy Conditions via Multiclass Fog Density Modeling
链接: https://arxiv.org/abs/2608.01572
作者: Mohamad Mofeed Chaar,Galia Weidl
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 pages, 5 figures,2 tables
Abstract:Autonomous driving (AD) systems have advanced rapidly over the past decade; however, robust perception under adverse weather conditions remains a major challenge, particularly in dense fog. In this work, we investigate fog-aware perception using synthetically generated fog data derived from the Waymo dataset. To support fog simulation, depth images are generated using an iterative learning approach. We consider five fog-density levels: clear, light fog, moderate fog, heavy fog, and very heavy fog. Instead of training a single unified model across all conditions, we train separate perception models for each fog-density level. Experimental results show that density-specific training improves performance in severe fog conditions. In particular, for the very heavy fog class, recall improves from 0.076 to 0.232, corresponding to an absolute gain of 15.6 percentage points. These findings suggest that deploying multiple specialized models, rather than a single general-purpose model, can improve perception robustness for autonomous vehicles under challenging visibility conditions. Future work will extend this strategy to additional sensing modalities, including LiDAR and radar, and evaluate generalization across diverse weather scenarios.
[CV-129] STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision
链接: https://arxiv.org/abs/2608.01535
作者: Pou-Chun Kung,Aryaman Rao,Utkrisht Sahai,Hemanth Murali,Yi Liu,Rui-Yu Lin,Katherine A. Skinner
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Vision-language models (VLMs) are emerging as a key component of embodied intelligence, with growing applications in auto-labeling and end-to-end autonomous driving. However, existing approaches for improving spatiotemporal reasoning in VLMs often rely on complex preprocessing pipelines, expensive human annotations, or synthetic data, which limit scalability and introduce potential sim-to-real gaps. Moreover, although these methods have improved spatiotemporal understanding, they still lack strong metric reasoning capabilities for dynamic scenes, such as estimating object motion in real-world units. Prior work has explored LiDAR-based metric depth supervision to enhance spatial perception, but it does not directly address temporal reasoning. We introduce STAR-VLM, an automotive radar-supervised framework that enhances spatiotemporal VLMs with motion reasoning and metric velocity estimation for autonomous driving. Automotive radar is a low-cost and widely deployed sensor that provides complementary spatiotemporal supervision through range and Doppler measurements. By leveraging these measurements as label-free ground truth during training, STAR-VLM improves the metric spatiotemporal reasoning ability of VLMs. Through experiments on driving scenarios, we show that STAR-VLM achieves state-of-the-art performance on both motion classification and metric velocity estimation, outperforming even task-specific methods designed for each task. These results highlight automotive radar as a scalable and cost-effective source of supervision for building metric-aware spatiotemporal VLMs for real-world autonomous driving.
[CV-130] Recursive Vision Language Models for General Symbolic Reasoning
链接: https://arxiv.org/abs/2608.01534
作者: Omid Nejati Manzari,Guillaume Lajoie,Hassan Rivaz
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Hard symbolic-reasoning tasks such as Sudoku, maze pathfinding, and ARC remain challenging for LLMs due to their fixed-depth autoregressive reasoning, which limits systematic search, refinement, and backtracking. While recursive models such as Hierarchical Reasoning Model (HRM) and Tiny Recursive Model (TRM) address this limitation through iterative latent-state refinement, they are typically task-specific and do not leverage pretrained language priors. We propose R-Qwen, a recursive reasoning framework built upon a pretrained Qwen backbone. R-Qwen repeatedly refines a candidate solution through programmatic self-recursion and deep supervision, combining the structured iterative computation of recursive models with the linguistic and reasoning priors of pretrained LLMs. We further adapt Hierarchical Supervision Weighting (HSW) to autoregressive models by exponentially weighting losses across recursive steps. HSW reduces gradient variance by at least 50%, improves the signal-to-noise ratio of stochastic gradients, and accelerates convergence. Across eight challenging benchmarks, R-Qwen consistently outperforms prior recursive reasoning models and substantially larger LLMs while using a comparable number of trainable parameters. Notably, on ARC-AGI dataset, our model achieves a 27.6% improvement over the baseline, highlighting the effectiveness of recursive refinement for general symbolic reasoning. These results suggest that recursive reasoning mechanisms and pretrained language model priors are complementary approaches for improving symbolic puzzle-solving. Code and models will be released after acceptance.
[CV-131] ST-LoRA: Single Trajectory LoRA Ensemble for Uncertainty Aware Agricultural Segmentation
链接: https://arxiv.org/abs/2608.01530
作者: Mohamed Farag,Genc Hoxha,Yahia Maleki,Chris McCool,Ribana Roscher
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to Computers and Electronics in Agriculture (Elsevier). Currently under review (first revision round)
Abstract:Reliable decision-support in digital agriculture requires accurate predictions and well-calibrated uncertainty estimates, particularly for dense prediction tasks such as semantic segmentation. Ensemble methods provide strong uncertainty quantification, but their computational and memory demands limit practical use, while single-model approximations often trade off uncertainty quality for efficiency. We propose ST-LoRA, a parameter-efficient ensemble framework that builds diverse ensemble members from a single training trajectory by combining Low-Rank Adaptation (LoRA) with snapshot ensembling. Each member shares a frozen pretrained backbone and differs only in lightweight low-rank adapters, reducing trainable parameters to under 10% of the full model while preserving ensemble diversity. We evaluate across two agricultural datasets - GrowliFlower-L (cauliflower, open field) and BUP20 (sweet pepper, glasshouse) - using SegFormer and Mask2Former, covering in-distribution performance, calibration under distribution shift, and out-of-distribution detection. Ablations show feed-forward layers, not attention layers, are the critical LoRA target for dense prediction, contrary to the attention-only convention from language models. ST-LoRA matches or exceeds full-rank ensembles in segmentation accuracy and calibration across both datasets and architectures, while substantially reducing training time, inference latency, memory footprint, and storage requirements. Against efficient baselines - Snapshot Ensemble, MC Dropout, and Deep Deterministic Uncertainty - ST-LoRA consistently matches or outperforms them in image/pixel-level OoD detection, calibration stability under shift, and cross-seed variance, with far fewer parameters and lower compute. These results show LoRA-efficient ensemble adaptation is a highly effective, practical approach for uncertainty-aware agricultural vision systems.
[CV-132] UCBound-Net: Uncertainty-Guided Boundary-Aware Continual Learning for Domain-Incremental Ultrasound Segmentation MICCAI2026
链接: https://arxiv.org/abs/2608.01518
作者: Mohammad Amanour Rahman
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the MICCAI 2026 CLiMeM Workshop
Abstract:Continual learning in clinical imaging faces a dual challenge: a model must assimilate knowledge from new anatomical domains while retaining representations learned from prior tasks, a problem known as catastrophic forgetting. Existing mitigation strategies, including regularization and knowledge distillation, treat all spatial regions equally, ignoring the fact that prediction uncertainty is strongly correlated with the propensity for forgetting. We introduce UCBound-Net, a continual segmentation framework that exploits Monte Carlo (MC) Dropout uncertainty as a spatial proxy for forgetting risk. Our method contributes three synergistic components: (i) uncertainty-weighted boundary distillation, which amplifies the knowledge transfer signal at high-entropy regions of the frozen teacher; (ii) uncertainty-calibration regularization, which explicitly penalizes overconfident erroneous predictions; and (iii) uncertainty-guided exemplar selection, a memory buffer that preferentially stores samples whose boundary regions exhibit the highest predictive entropy. Evaluated on a sequential domain-incremental benchmark comprising breast ultrasound (BUSI, Task 1) followed by thyroid ultrasound (TN3K, Task 2), UCBound-Net reduces forgetting relative to naive fine-tuning, achieving a backward transfer (BWT) of -0.098 compared with -0.173, while obtaining an average Dice Similarity Coefficient (DSC) of 0.755 across both tasks. The proposed framework outperforms baseline methods without requiring task-boundary supervision. An ablation study further demonstrates that each component contributes independently to forgetting mitigation, providing a practical pathway toward uncertainty-aware continual learning for clinical image segmentation.
[CV-133] Rolling Shutter Camera Self-Calibration
链接: https://arxiv.org/abs/2608.01509
作者: Yongcong Zhang,Navid Rabbani,Bangyan Liao,Chengbo Wang,Yizhen Lao,Adrien Bartoli
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Rolling shutter (RS) cameras are widely used in consumer devices, but their row-wise exposure causes distortions under motion, making geometric 3D vision problems dependent on both camera intrinsics and readout time ratio. Existing RS calibration methods rely on calibration targets or specialised hardware, limiting their use in unconstrained settings. We present the first self-calibration method for RS cameras that directly estimates camera intrinsics and the readout time ratio from image sequences, without requiring calibration targets. The method is implemented as a self-calibrating bundle adjustment (BA), which critically depends on the RS imaging model. We combine two known complementary models. The first formulates RS imaging as continuous-time trajectory estimation under a row-wise pose representation. The second interprets RS images as temporally distorted global shutter (GS) images and requires to estimate correction fields. The combination is non-trivial and results in a unified dual-projection model, in which each 3D point is simultaneously constrained at both row-dependent and reference timestamps along a shared continuous trajectory, enforcing stronger geometric and temporal consistency. Extensive simulations analyse the applicability of several implementations under varying conditions, and real data experiments demonstrate the accuracy, robustness, and practical effectiveness of the proposed approach.
[CV-134] Stochastic Sequential Search in Very-High-Dimensional Feature Selection
链接: https://arxiv.org/abs/2608.01502
作者: Petr Somol,Jiř’ı Grim
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注: 23 pages, 5 figures, 2 tables. Ancillary files: a standalone implementation of sSFFS (MIT license, verified bit-identical to the reference implementation), a checksum-verified converter building the exact experiment datasets from the UCI originals, the derived Reuters-21578 term matrix used in the experiments (research use only), and the configuration files of all experiments
Abstract:Sequential subset search – forward selection with floating backtracking and its descendants – remains the quality reference in feature selection, but every member of the family sweeps the full pool of remaining candidate features at each step, which excludes it from very-high-dimensional problems; there, only individual-feature ranking remains practical, and it models feature interplay weakly or not at all. We introduce a budgeted sampled step operator pair that replaces the full sweeps by a fixed number of candidate evaluations per step. Candidates are drawn by temperature-controlled softmax sampling from dependency-aware per-feature statistics learned online from every criterion evaluation the search performs, guarded by a uniform exploration floor; per-step cost becomes independent of dimensionality. Substituting the operators turns any sequential method into its stochastic counterpart, defining the Stochastic Sequential Search (SSS) family; we study the stochastic counterpart of floating search, sSFFS. On 500-dimensional madelon, sSFFS retains at least 97% of the full-SFFS criterion value at every subset size at about a quarter of its evaluations, while uniform sampling at the same budget collapses on madelon’s synergistic features. On 5,000-dimensional gisette, far beyond full-SFFS reach, sSFFS exceeds the saturated criterion level of DAF and BIF ranking at matched budgets; holdout validation shows that at 500 training samples the binding constraint beyond the sequential frontier becomes the criterion, not the search. On 10,105-dimensional reuters, under a trustworthy multinomial filter criterion, sSFFS dominates BIF and DAF on the search objective and on holdout accuracy at every subset size, in about two minutes of single-core evaluation work. A verified standalone implementation accompanies the paper.
[CV-135] Probing the 3D Object-Level Understanding of Pre-Trained Detection Transformers
链接: https://arxiv.org/abs/2608.01495
作者: Robin Kim,Colin Samplawski,Benjamin M. Marlin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Detection transformer models, including DETR and its extensions, learn to output a set of object-level embeddings that can be simultaneously decoded into 2D bounding boxes and class distributions. In this paper, we investigate what pre-trained 2D detection transformers understand about the 3D properties of objects. Specifically, we investigate the extent to which properties including the depth of objects from the camera and the 3D location of objects relative to the camera can be recovered from object-level embeddings using linear and non-linear probes. Across a range of detection transformer models, our results show a surprisingly strong and previously unknown ability of 2D DETR models to represent useful information about the 3D properties of objects, despite the complete lack of 3D supervision during model pre-training.
[CV-136] GaussianSelector: Lightweight Human-Guided Object Selection in 3D Gaussian Splatting with Graph Optimization
链接: https://arxiv.org/abs/2608.01492
作者: Baihan Yang,Tiexin Li,Yuheng Liu,Xin Lin,Xinke Li,Xiaohui Xie,Truong Nguyen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Under Review
Abstract:Selecting a complete 3D object from a reconstructed scene with minimal user effort is essential for practical scene editing and embodied interaction. Existing 3DGS-based methods either retrain the Gaussian representation to embed per-object labels, or build dense multi-view SAM observations, both requiring heavy computation and dense viewpoint coverage that is rarely available in practice. We present GaussianSelector, a training-free framework for interactive 3D object selection from sparse views and sparse scribble guidance. Operating directly on native Gaussian primitives, we coarsen dense Gaussians into geometrically coherent superpoints and construct a continuity-weighted graph using appearance and spatial cues. Sparse user scribbles are lifted into 3D via visibility-aware transmittance coverage, and selection is solved as a global graph-cut energy minimization that propagates sparse evidence to a complete 3D object. This design naturally supports multi-round refinement, where users iteratively correct the selection from additional viewpoints to progressively improve the result. Experiments demonstrate that GaussianSelector achieves competitive selection quality against state-of-the-art multi-view SAM-based methods, while requiring significantly fewer interaction views and substantially lower computational overhead. These properties make it well suited for human-in-the-loop 3D scene editing and 3D asset extraction in real-world deployment scenarios.
[CV-137] owards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning
链接: https://arxiv.org/abs/2608.01488
作者: Yuqi Li,Yuedong Tan,Huiran Duan,Weilun Feng,Chuanguang Yang,Zhulin An,Zongwei Wu,Shiping Wen,Tingwen Huang,Yingli Tian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unified multimodal object tracking has achieved remarkable robustness by leveraging complementary sensor data (e.g., RGB, Thermal, Depth), yet the heavy computational burden of state-of-the-art models hinders their deployment on resource-constrained edge devices. In this work, we identify the prediction head as a critical but often overlooked efficiency bottleneck. By strategically streamlining the decoder architecture, we unlock the potential for real-time inference but simultaneously introduce a capacity gap between the lightweight student and the heavy teacher. To resolve this, we conduct a systematic analysis of 17 distillation strategies and introduce a Dual-Alignment Distillation framework. Our key insight is that effective compression requires decoupling knowledge transfer into two complementary streams: (1) Spatial Representation Alignment, which employs feature distillation to sharpen the student’s spatial focus on foreground targets (“Where to track”); and (2) Semantic Distribution Alignment, which utilizes logit-based distillation to align decision boundaries and transfer discriminative dark knowledge (“What to track”). Extensive experiments across five benchmarks demonstrate that our approach significantly outperforms complex state-of-the-art methods. Notably, our distilled model achieves 91.5% MPR on RGBT234 and operates at 54 FPS on a single RTX 4090, representing a 5x speedup over the teacher model while maintaining superior accuracy.
[CV-138] VGER: Voxel-Guided Global Event Ranking for Event Cloud Attribution
链接: https://arxiv.org/abs/2608.01470
作者: Youxin Jiang,Baoheng Fu,Hongwei Ren,Xiangqian Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Event cameras produce sparse and asynchronous event streams that provide rich spatio-temporal information for efficient perception. Recent advances in event-based models have demonstrated strong performance by directly modeling asynchronous events without dense frame reconstruction. However, identifying the event-level evidence behind their predictions is crucial for improving model transparency and reliability. Directly adapting point-level saliency methods from point clouds provides fine-grained attribution but overlooks event-specific spatio-temporal structures. To address this limitation, we propose Voxel-Guided Global Event Ranking (VGER), a training-free attribution framework for point-based event cloud networks. VGER combines event-level gradient evidence with task-aware voxel perturbation evidence, transferring regional contribution into event-level attribution scores while preserving fine-grained resolution. Furthermore, VGER introduces a unified event ranking strategy, where high-ranked events are expected to be prediction-critical and low-ranked events are expected to have limited influence on predictions. We evaluate VGER on three event-based benchmarks with PointNet, PointNet++, and EventMamba. Across nine dataset-backbone settings, VGER consistently improves both high-tail and low-tail deletion performance over point-level saliency baselines.
[CV-139] Clear-Weighted Bit Allocation for Satellite Downlinks
链接: https://arxiv.org/abs/2608.01457
作者: Alireza Furutanpey,Qiyang Zhang,Yujie Huang,Philipp Raith,Schahram Dustdar
类目: Networking and Internet Architecture (cs.NI); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: 10 pages, 11 Figures, 6 Tables
Abstract:Earth-observation satellites capture more imagery than intermittent ground contacts can transmit. Onboard systems threshold a cloud detector, discard frames or tiles, and compress the survivors with a fixed codec. On expert-labeled imagery, these rules remove more than one-fifth of clear pixels, primarily through detector false positives. We train a neural codec with a clear-probability-weighted reconstruction loss, reallocating coded bytes from clouds to clear ground without requiring or transmitting a cloud map onboard. Each capture is encoded into a resumable base layer and a dependent refinement layer, while clear content is estimated from features produced by the encoder. At each contact, we causally rank arrived layers using estimated clear content, unfinished bytes, deadline slack, and aggregate deadline pressure. The scheduler serves base and computational deadlines, bounds stored residual bytes, and resumes interrupted packets. We evaluate the onboard-to-downlink pipeline using real entropy-coded bytes, orbit-derived interruptible contact capacities, and measured service time and energy on resource-constrained embedded accelerators. Clear-weighted codecs require up to 47.8% fewer bytes than learned-compression baselines at matched clear-region quality. The optimized encoder consumes less time and energy than one pass of the cloud detector used by the frame-discard rules. Relative to fixed two-stage service on the same streams, our scheduler more than doubles deadline-full clear-content delivery for the interrupted combined cohort, reaches 83.6% of a certified clairvoyant upper bound, and exceeds replayed reference orders in deadline-usable delivery.
[CV-140] DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration
链接: https://arxiv.org/abs/2608.01452
作者: Haoran Liao,Pengyue Wang,Shuoyu Chen,Kehan Cheng,Xuhang Chen,Yuhao Lin,Mu Lin,Zhizhao Liang,Xiaoyi Fan,Chengyi Xing,Dan Niu,Yi-Lin Wei,Wei-Shi Zheng
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Project page: this https URL Code: this https URL
Abstract:Dynamic manipulation is a critical capability for robots operating in complex and dynamic environments, where robots must interact with objects that are moving or require rapid adjustments. However, learning models for dynamic manipulation tasks face two major challenges: (1) the combinatorial complexity of dynamic scenarios leads to substantial data requirements, and (2) rapid variations in dynamics require real-time and accurate policy execution. In this paper, we propose DynamicManip to address these challenges through an efficient data augmentation pipeline and a low-latency imitation policy. We first propose a static-to-dynamic augmentation pipeline that synthesizes diverse dynamic manipulation demonstrations from a single static demonstration. Second, we introduce a dynamic-aware adaptive policy that adjusts its inference frequency according to task dynamics, enabling responsive and effective dynamic manipulation. Third, we build a dynamic manipulation benchmark, which includes diverse dynamic tasks with an automatic evaluation system for scalable and consistent assessment. Extensive experiments in both simulation and the real world demonstrate that DynamicManip not only provides significant improvements in data efficiency but also achieves better performance in dynamic manipulation tasks, with a mean success rate 18.4 percentage points higher and policy-query latency 32.9% lower.
[CV-141] PackingGPT : 3D Packing Agent for Real Furniture in Last-Mile Delivery
链接: https://arxiv.org/abs/2608.01427
作者: Yi You,Hui Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D bin packing rectangular items into standardised containers to maximise space utilisation under geometric shipping automation. Loading a furniture purchase into a personal vehicle is the same task, but under more complex conditions that standard container loading algorithms ignore. This paper addresses the physically stable placement under these realistic conditions with heterogeneous boxes (e.g. varying dimensions and weights) and occupied containers (e.g. groceries). This paper provides a real-world benchmark dataset and baseline model for the Heterogeneous furniture-in-vehicle packing task. The dataset uses real furniture company flat-pack packaging data covering a large number of catalogue products via family-level extrapolation with diversity length, widths, heights, and weights. We also propose a PackingGPT framework for packing as a sequential placement inspired by the Lego assembly process, where heterogeneous boxes of varying dimensions (bricks) are placed step-by-step into the irregular remaining cargo space (creations). Five baseline packing methods were tested on our dataset without considering the Centre-of- Mass (CoM) constraints. In sedan car simulations, 10-40% of placed boxes failed the stability check on average. When the LLP model was trained on packing sequences with CoM constraints enforced during placement, the failure rate dropped to 0.67% (SUV-500).
[CV-142] GenTrack: Physical Alignment for Robot-Native Motion Generation and Zero-Shot Humanoid Tracking
链接: https://arxiv.org/abs/2608.01410
作者: Zeyu Ling,Xinyao Yu,Renye Yan,Jikang Cheng,Zhanke Wang,Qing Shuai,Changqing Zou
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:General-purpose humanoid trackers can execute diverse references, but their zero-shot coverage depends on large embodied corpora that are costly to extend. Text-to-motion generators offer scalable supervision, yet models trained on human motion or retargeted data inherit a gap between kinematic plausibility and robot executability. Existing one-way pipelines fix either the generated corpus or the reward tracker. We introduce GenTrack, an online generator–tracker framework that alternates execution-grounded, group-relative generator alignment with tracker training on newly generated references; anchoring and rehearsal constrain drift. On Unitree G1, we evaluate GenTrack with ProtoMotions and SONIC backbones across three zero-shot tracking splits including public AMASS and LAFAN benchmarks, and a private out-of-distribution test set of 1,024 prompt-motion pairs in the wild. The online co-training strategy consistently produces generators that output more robot-executable motions with strong semantic alignment, and trackers with markedly broader zero-shot coverage and improved tracking accuracy, especially on out-of-distribution references. These results demonstrate that joint online post-training effectively narrows the executability gap between retargeted references and robot-native motion, advancing zero-shot humanoid control without additional data collection and beyond the limitations of a static reference pool.
[CV-143] raining-Free Out-of-Distribution Detection for Pathology Whole-Slide Images
链接: https://arxiv.org/abs/2608.01407
作者: Sabri Mustafa Kahya,Richard R. Chen,Muhammet Sami Yavuz,Jerry Jierui Lou,Akanimoh Adeleye,Haci Ali Kahya,Jana Lipkova
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Under review. The code is available in this https URL
Abstract:Safe deployment of AI methods in medicine requires robust guardrails that detect when input data deviate from the training distribution to ensure that models provide predictions only within their scope of expertise and abstain otherwise. Out-of-distribution (OOD) detection can provide such safeguards and is extensively studied in general computer vision. Yet, it remains underdeveloped in computational pathology, where gigapixel whole-slide images (WSIs), subtle differences between disease subtypes, and variability in tissue preparation pose unique challenges for conventional OOD methods. We propose ZIO, a training-free, multimodal OOD detector for pathology WSIs that leverages vision–language pathology foundation models (FMs). ZIO constructs text and visual prototypes of in-distribution classes and integrates their complementary information through a prototype shrinkage mechanism to derive OOD scores. We provide the ZIO formulation for both slide- and patch-level FMs. We evaluate ZIO across diverse clinically relevant domain shifts, including rare diseases and near-OOD settings. Extensive evaluation of over 14,700 WSIs from five independent consortia shows that ZIO consistently outperforms both unimodal prototypes and 40 state-of-the-art OOD methods. These results demonstrate the benefits of multimodal representation for OOD detection and pave the way towards safer AI deployment in clinical practice.
[CV-144] SG-WAM: Self-Guided World Modeling in Geometry-Aware Policy Space
链接: https://arxiv.org/abs/2608.01397
作者: Ruiteng Zhao,Zhengshen Zhang,Yue Su,Wenshuo Wang,Jiahui Li,Zhiyuan Yang,Francis E.H. Tay,Marcelo H. Ang Jr.,Haiyue Zhu
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:World Action Models (WAMs) couple action generation with prediction of future states. Their effectiveness depends on whether future dynamics are modeled in a space that is both aligned with action generation and sufficiently geometry-aware to capture where and how actions change the scene. Existing WAMs typically satisfy only part of this requirement, relying on either perceptually heavy observation-space targets or auxiliary latent spaces that are not jointly structured for action relevance and geometry. We propose SG-WAM, a self-guided framework that learns geometry-aware action-conditioned dynamics directly in the policy-derived representation space. SG-WAM introduces learnable dynamics tokens and a Self-Guided World Predictor that forecasts their future latent states conditioned on intervening robot actions. Prediction targets are generated by an exponential moving average copy of the same policy backbone, providing stable supervision within the representation family used by the action expert. Geometric supervision further structures the policy image-token representations, providing spatially grounded context for the dynamics tokens and yielding a future-alignment space that is both action-relevant and geometry-aware. Latent future prediction, geometric grounding, and flow-matching action generation are jointly optimized end-to-end in a unified framework. Built on a 0.9B model without large-scale embodied pretraining, SG-WAM achieves 98.5% average success on LIBERO and 73% on LIBERO-Plus, while outperforming strong baselines in both in-distribution and out-of-distribution real-world evaluations.
[CV-145] FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision
链接: https://arxiv.org/abs/2608.01392
作者: Tongyan Wang,Zhengyuan Li,Muhan Lin,Shengyang Luo,Yifan Shen,Aniket Bera,Baijian Yang,Yingjie Victor Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-conditioned human motion generation has made rapid progress with the emergence of large-scale motion–language datasets. However, even datasets with rich long-form descriptions typically provide supervision only at the clip level, without explicit temporal correspondence between motion frames and language. This limits fine-grained motion–text grounding and temporally precise generation. We propose FineMoLA, a weakly supervised framework that learns fine-grained frame–phrase correspondence directly from clip-level annotations. Our method first segments long-form descriptions into action-bearing phrases, and then formulates motion–language alignment as an optimal transport problem, which naturally models many-to-many relations between motion frames and text under global constraints. With entropic regularization and Sinkhorn iterations, FineMoLA efficiently infers pseudo frame-level alignments without human labeling. Experiments on SnapMoGen demonstrate that the learned alignments outperform baselines in motion–text grounding.
[CV-146] Understanding Synergistic Interactions among Pathology Foundation Models via Adaptive Fusion
链接: https://arxiv.org/abs/2608.01370
作者: Yuxiang Xiao,Yang Hu,Bin Li,Tianyang Zhang,Zexi Li,Huazhu Fu,Jens Rittscher,Kaixiang Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 2 figures, 3 tables
Abstract:Pathology foundation models (PFMs) provide strong tile-level representations via self-supervised pre-training on large-scale pathology images. Yet, PFMs are developed under diverse and often opaque data, architecture, and objective choices, inducing latent representational biases that limit robustness and obscure what each model specialises in. We present AdaFusion, a lightweight adaptive fusion framework that integrates complementary signals from multiple frozen PFMs through (1) low-dimensional feature compression and (2) a sample-conditioned gating module that reweights model-wise (and optionally channel-wise) contributions. Beyond improving predictive accuracy, AdaFusion provides contribution-driven interpretation that offers evidence consistent with model-specific preferences and synergistic interactions across tissue phenotypes. We evaluate AdaFusion on three public benchmarks spanning treatment response prediction, prostate cancer grading, and spatial gene expression inference. AdaFusion consistently outperforms individual PFMs and other fusion baselines, while providing interpretable tissue visualisation which aligns model preferences with morphological patterns. Code is available at: this https URL.
[CV-147] Harnessing Adversarial Distillation to Customise Debiased Disease-Specific Pathology Foundation Models for Breast Cancer MICCAI2026
链接: https://arxiv.org/abs/2608.01356
作者: Zhiwei Chen,Yang Hu,Yuxiang Xiao,Yakun Ju,Tianyang Zhang,Yingxue Xu,Wei Li,Hao Chen,Jens Rittscher,Kaixiang Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 2 figures, 2 tables. Accepted to MICCAI 2026 (early accept)
Abstract:Pathology foundation models (PFMs) provide strong tissue representations and have become central to digital pathology. However, deployment in disease-specific settings is limited by 1) the high computational cost of billion-parameter PFMs and 2) distribution mismatch and non-biological bias inherited from pan-cancer, multi-centre pre-training, including site-specific signatures and imbalanced disease prevalence. These factors can encourage shortcut learning and under-emphasise subtle morphology required for reliable modelling of a specific cancer type. We present SmartStu (a Smart Student), a framework to customise compact, breast-cancer-specific PFMs via distillation whilst mitigating confounding. SmartStu distils representations from multiple teacher PFMs into a lightweight student backbone. Crucially, we introduce adversarial distillation that leverages a dedicated noise model trained to predict nuisance, edge-dominated cues on the distillation set. Using this noise model as a counterexample, the adversarial objective encourages the student to recognise, yet suppress, features predictive of nuisance targets. We further incorporate multi-teacher ensemble distillation and an auxiliary self-supervised objective with artefact injection. We validate SmartStu on three external cohorts (Yale HER2, SLN-Breast, and BRACS) with multiple tiny backbones. SmartStu yields breast-cancer-specific PFMs that are over 30\times smaller than general PFMs whilst largely preserving, and sometimes improving, downstream performance measured by balanced accuracy (bAcc) and AUC. Code is available at this https URL.
[CV-148] CORTIVA: Candidate-Score Fusion of Complementary Visual Teachers for EEG- and MEG-to-Image Retrieval
链接: https://arxiv.org/abs/2608.01355
作者: Junhan Wang,Kani Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 31 pages, 19 figures; includes supplementary information. Code: this https URL
Abstract:Decoding visual experience from non-invasive brain activity is central to neuroscience and brain-computer interfaces. Functional magnetic resonance imaging (fMRI) offers fine spatial detail, but its slow hemodynamics and burdensome acquisition limit temporally resolved decoding. Electroencephalography (EEG) and magnetoencephalography (MEG) provide millisecond resolution, making image retrieval compelling: identify the viewed image from one neural response and a fixed candidate bank. Contrastive alignment to pretrained visual representations enables zero-shot retrieval from EEG and MEG, but most systems collapse heterogeneous visual supervision into a single embedding before ranking. This early consolidation imposes one similarity geometry on every candidate order and removes encoder-specific disagreements from the final ranking. We propose CORTIVA, a candidate-score fusion framework that preserves this complementary evidence. Three decoding routes are aligned to heterogeneous visual targets, score the same indexed candidates independently, and combine only their temperature-scaled score vectors before ranking. On the 200-way THINGS-EEG2 benchmark, CORTIVA reaches 73.5% Top-1 and 95.3% Top-5 across ten participants, exceeding the strongest reported baseline by 10.3 and 5.4 percentage points. With a modality-specific neural encoder, the same fusion principle reaches 42.4% Top-1 on THINGS-MEG. Matched route-removal retraining and four weight controls demonstrate that CORTIVA’s gain arises from integrating complementary route scores and persists with uniform weighting, without requiring a specialized weighting rule. Independent DINOv2 analyses further reproduce the local error neighborhoods and posterior neural-visual correspondence. These results establish candidate-score fusion as a simple and testable alternative to embedding-level consolidation for neural image retrieval.
[CV-149] PixVL: Self-Supervised Training of Pixel-Level MLLM s via a Unified Mask–Text Consistency Cycle
链接: https://arxiv.org/abs/2608.01354
作者: Yicheng Xiao,Haoxuan Ma,Caorui Li,Yucheng Wu,Weijie Wang,Haoxiao Wang,Shuang Chen,Fan Yang,Haiyun Guo,Jinqiao Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent studies develop pixel-level multimodal large language models (MLLMs) that support both Region Segmentation and Region Understanding, extending multimodal interaction from whole images to specific objects and regions. However, these methods face two fundamental challenges. First, the scarcity of high-quality mask–text pairs leaves abundant mask annotations without corresponding language supervision. Second, discrepancies in supervision formats and learning-signal densities induce optimization interference between Region Segmentation and Region Understanding. To address these challenges, we propose PixVL, a self-supervised post-training framework that introduces a unified Mask–Text Consistency Cycle, enabling pixel-level MLLMs to generate and self-verify regional descriptions and learn from unlabeled data. We found that direct cycle based solely on geometric reconstruction is unreliable because re-segmentation IoU does not faithfully reflect the semantic quality and referring sufficiency. PixVL therefore introduces confuser-aware semantic verification, which uses the model’s confidence when it correctly chooses the target among highly similar candidate regions, and assigns zero reward to an incorrect choice. Meanwhile, PixVL performs cross-view verification using temporally separated video frames or geometrically transformed image views, preventing cyclic learning from collapsing to positional and shape shortcuts. Finally, a quality-coupled bidirectional learning strategy uses the highest-reward description to guide Text-to-Mask learning. This strategy transforms Region Understanding and Region Segmentation from competing tasks into mutual generators and verifiers. Experiments demonstrate that PixVL improves both region understanding task and segmentation task.
[CV-150] Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection
链接: https://arxiv.org/abs/2608.01348
作者: Linhai Zhuo,Junxi Cai,Tianwen Qian,Qingping Zheng,Yang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Data augmentation, which simulates diverse visual variations to expand the source distribution and induce synthetic domain shifts, is a simple yet effective strategy for mitigating severe domain shifts and limited labeled target data in cross-domain few-shot object detection (CD-FSOD). Existing approaches rely on conventional data augmentation, such as Color-Jitter, Mosaic, and background-centric adaptation (e.g., Domain-RAG), which are limited in modeling complex domain shifts and often lead to suboptimal performance. In this paper, we propose PSP-FSOD, a principled framework that integrates prompt-driven domain simulation with feature perturbation regularization to improve generalization in CD-FSOD. To enable controllable domain synthesis, we design a prompt-driven strategy that leverages the visual grounding capability of large VLMs to jointly model foreground and background variations, generating semantically consistent yet domain-diverse training samples. Moreover, we adopt a grounding-aware generation scheme that guides object placement and alleviates semantic-spatial misalignment, thereby improving foreground adaptation. To ensure training stability and robustness, we further introduce a noise-induced feature perturbation mechanism that injects Gaussian noise into multi-scale intermediate features with distribution correction, encouraging consistent predictions under perturbations and reducing reliance on domain-specific cues. Extensive experiments demonstrate that PSP-FSOD produces high-quality domain-diverse supervision and learns domain-invariant representations, consistently improving performance across CD-FSOD benchmarks.
[CV-151] DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation
链接: https://arxiv.org/abs/2608.01343
作者: Reyhaneh Hosseinzadeh,Parham Zilouchian Moghaddam,Mehdi Modarressi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
备注:
Abstract:The emergence of transformer-based deep learning models has brought unprecedented performance across various domains, particularly in natural language processing and computer vision. However, deploying these models, especially on resource-constrained devices, poses significant challenges due to their high computational complexity and large memory size and bandwidth requirements. This complexity has led researchers to use low-bit model weights to reduce memory usage and improve efficiency. In addition to reducing processing and memory demands, quantization introduces another useful property: value locality, where the extremely large number of parameters are restricted to a limited range of values. To fully take advantage of this locality, this paper presents DeVIT, an acceleration method for vision transformers that leverages differential computation to enable multiplier-less matrix multiplication.
[CV-152] Driver2Map: Imitating Human Driving for Online High-Definition Map Construction
链接: https://arxiv.org/abs/2608.01338
作者: Pan Yin,Runtian Xia,Weisong Kuang,Kaiyu Li,Cong Zhao,Xiangyong Cao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:High-definition (HD) maps are essential for autonomous driving systems. In constructing such maps, onboard multi-view camera images, standard-definition maps and satellite images provide crucial information. However, due to the modality and perspective differences among these data sources, existing methods often struggle to effectively align and fuse them, making online HD map construction still challenging. To address these issues, we propose Driver2Map, an online HD map construction model inspired by human drivers. Unlike existing HD map construction models that utilize only two modalities, our Driver2Map can simultaneously exploit three modalities. Specifically, we propose a “two-stage alignment” strategy to reduce spatial misalignment across different modalities. Additionally, we introduce “Pose-Guided BEV Fusion”, a BEV (bird’s-eye-view) generation module that leverages camera pose information to adaptively weight multi-view features, thereby effectively suppressing cross-view feature overlap during BEV generation. Also, we design a “Pretrained Prior for Map Refinement” module to refine the initial prediction by learning map structure priors, thus improving the HD map prediction under dynamic occlusions. Extensive experiments demonstrate that Driver2Map outperforms existing methods on both IoU and AP metrics.
[CV-153] Asleep at the Wheel: JEPAs Limitations in Evaluating Novel Driving Data
链接: https://arxiv.org/abs/2608.01336
作者: Advait Pavuluri,Shamik Karkhanis,Uzma Mushtaque
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Modern autonomous-driving fleets record far more video than human reviewers can inspect. This motivates the need for an automatic clip triage mechanism, to surface rare and review-worthy clips, so that driving models can be fine-tuned to better handle unideal circumstances. We test a label-free approach that scores clips by the prediction-error “novelty” of a self-supervised joint-embedding predictive architecture (JEPA); a frozen V-JEPA video encoder is paired with a lightweight predictor head to reconstruct masked clip embeddings, and clips whose embeddings are hard to predict are flagged as interesting. Evaluated under a realistic protocol that trains on one dataset and tests against footage from others, this approach appears highly effective. We show that this apparent success is actually a domain-shift consequence: on a fair benchmark drawn from a single dataset, this mechanism collapses to chance and is on par with simple no-training baselines. A lightly supervised probe on the same frozen embeddings results in almost double the average precision, indicating that the bottleneck is indeed the self-supervised objective, rather than the representation. We present this as a study for evaluating the effectiveness of self-supervised learning, where cross-dataset protocols can silently reward domain separation over novelty.
[CV-154] SphereVideo: Prototype-anchored Hyperspherical Boundary for Continual AI-generated Video Detection
链接: https://arxiv.org/abs/2608.01334
作者: Fei Li,Yue Yu,Yuran Wang,Xinghan Li,Jingjing Chen,Yu-Gang Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:AI-generated video (AIGV) detection aims to distinguish real videos from AI-generated ones. In practice, detectors trained on existing data often fail to generalize to newly emerging generative models, making this task challenging. Therefore, continual learning (CL) is essential for improving the adaptability. However, CL frameworks for this task remain underexplored. To this end, we propose SphereVideo, a novel CL framework for AIGV detection built on two key observations. First, real videos exhibit a compact feature distribution. Based on this, we encourage real video features to cluster around a real prototype on a hypersphere while repelling AI-generated samples, thereby establishing a decision boundary. This prototype serves as a stable anchor for CL, regulating boundary evolution and mitigating catastrophic forgetting. Second, existing methods tend to rely solely on spatial artifacts as shortcuts. To enhance temporal modeling, we introduce a strategy that models the temporal dynamics of real data at both frame and clip levels. By strengthening real data modeling, this strategy further facilitates learning a real prototype and forming a stable decision boundary. Moreover, we construct a comprehensive and challenging benchmark. Extensive experiments demonstrate that SphereVideo achieves an improved plasticity-stability trade-off, outperforming prior methods by 3.08% on seen data and 4.00% on unseen AI-generated data.
[CV-155] Remember-R1: Mitigating Long-Context Visual Forgetting through Reinforcement Learning
链接: https://arxiv.org/abs/2608.01314
作者: Jianmin Chen,Jiaqi Tang,Wei Wei,Xiaogang Xu,Jiafei Wu,Zhe Liu,Qianzhou Wang,Yingying Yan,Botong Geng,Yuyang Xia,Lei Zhang,Qifeng Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM Multimedia 2026
Abstract:Multimodal large language models (MLLMs) increasingly rely on long chain-of-thought reasoning for complex tasks. However, as reasoning sequences lengthen, models may gradually rely less on visual evidence and more on accumulated textual context, leading to visual forgetting. Existing approaches do not directly constrain how visual evidence is used and maintained along the original reasoning trajectory, leaving long-context visual forgetting insufficiently addressed. To address this issue, we propose Remember-R1, a reinforcement learning framework that mitigates long-context visual forgetting by applying process-level supervision directly on the original reasoning trajectory. Specifically, Remember-R1 introduces rewards that encourage broader coverage of matched visual keywords, stronger persistence of visual dependence in later reasoning steps, and greater focus on question-relevant image regions. Experiments across multiple model scales and diverse multimodal benchmarks demonstrate that Remember-R1 consistently improves reasoning performance. Additional analyses further show that it slows the decline of visual attention during generation, supporting its effectiveness in mitigating long-context visual forgetting.
[CV-156] SPAE: Spectrally Guided Autoencoder for Pretrained Visual Latents
链接: https://arxiv.org/abs/2608.01306
作者: Yibin Huang,Jixiang Hong,Zongzhao Li,Yuhan Dai,Zhibin Wang,Chunwei Wang,Jun Song,Chen Wang,Xiaofei Sun,Xiaoxiao Xu,Conghui Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Latents from vision foundation models (VFMs) are semantically rich and well suited for visual understanding. Recent representation autoencoder methods such as RAE have shown that they can provide promising latent spaces for image generation. However, VFM latents remain difficult to model directly: DiT-generated latents exhibit spectral mismatch with encoder latents, especially in high-frequency components. Our channel-wise spectral analysis further reveals that these high-frequency components are diffusely distributed across latent channels and entangled with semantic information, making the latent space difficult for DiT to model. To address these challenges, we propose SPAE, latent adaptation framework for generation. Specifically, SPAE employs a compact bottleneck to distill stable semantic information while suppressing high-frequency components, thereby improving the alignment between DiT-generated latents and encoder latents. In addition, we apply a channel-wise masking strategy to promote the decoupling of semantic information and high-frequency details across bottleneck channels. Experiments show that SPAE achieves a favorable balance among visual understanding, generation quality, and reconstruction fidelity.
[CV-157] Beyond Symmetric Fusion: Exploiting Task-Dependent Modality Strengths for RGB-Event Small Object Detection
链接: https://arxiv.org/abs/2608.01302
作者: Ziheng Wang,Chaolang Li,Yutong Yang,Xiaohan Xu,Chongxiang Yang,Hengxuan Zhong,Zhen Liang,Pengwen Dai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 3 figures, 7 tables
Abstract:State-of-the-art RGB-Event detectors improve the detection of small, fast-moving objects by combining complementary features from RGB and Event data, yet they typically fuse the two modalities into a unified representation for both localization and classification. Such a task-symmetric design is inconsistent with the intuition that the two modalities should play different roles according to their task-specific strengths. To examine this issue, we conduct a modality-specific evaluation and find that the relative advantage of the two modalities reverses across tasks: Event data are substantially more effective for class-agnostic localization, whereas RGB data provide stronger category evidence within localized target regions. Motivated by this task-dependent asymmetry, we propose an Asymmetric Event-RGB Object Detection Transformer (AERODet). During class-agnostic localization, Scale-wise Uncertainty-aware Reliability Estimation (SURE) calculates the relative reliability of the two modalities from their objectness response heatmaps and accordingly calibrates their contributions when the decoder aggregates multimodal features. Once the candidate boxes are obtained, Task-Decoupled Semantic Refinement (TDSR) decouples classification from localization and uses RGB RoI features for fine-grained classification. Extensive experiments on FRED and NeRDD demonstrate that AERODet achieves state-of-the-art performance. In particular, it surpasses the strongest RGB-Event baseline by 10.7 mAP points on the FRED challenging split.
[CV-158] Ranking Image Fusion the Way Humans Do: A Learned Pairwise Preference Metric for Infrared-Visible Fusion Assessment
链接: https://arxiv.org/abs/2608.01301
作者: Haoran Liu,Mingzhe Liu,Peng Li,Guibin Zan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 22 pages, 6 figures
Abstract:Infrared-visible image fusion (IVIF) has no ideal fused reference, so fusion algorithms are routinely ranked by scalar objective metrics that formalize different proxies for information transfer, structure, or source similarity. These proxies often disagree with the judgment that ultimately matters: given the same sources, which of two fused results does a human prefer? Direct pairwise comparison is an established reference protocol for relative subjective assessment, but its cost grows quadratically with the number of algorithms, which prevents routine use. We present the Learned Perceptual Image Fusion Measure (LPIFM), a source-conditioned model that operationalizes the human A/B/Tie comparison protocol as a repeatable, scalable surrogate. LPIFM jointly observes the infrared source, the visible source, and two fused candidates, and predicts whether candidate A is better, candidate B is better, or the two are perceptually equivalent. Supervision comes from a new dense preference corpus that covers every unordered comparison among a broad pool of fusion methods on the scenes of a public benchmark, labeled under a blinded, randomized, two-stage protocol with expert adjudication. Across scene- and method-generalization settings, LPIFM tracks human pairwise decisions closely and reproduces the tie-aware Bradley-Terry rankings derived from human labels; on full method pools it surpasses the strongest conventional metric by a wide margin in both pairwise accuracy and ranking correlation. We release the annotated preference dataset, together with the LPIFM model weights, source code, and evaluation code, to support preference-aligned IVIF assessment. LPIFM offers a practical instrument for human-aligned method comparison and ranking at scale.
[CV-159] UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction
链接: https://arxiv.org/abs/2608.01298
作者: Junno Yun,Yaşar Utku Alçalar,Mehmet Akçakaya
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Diffusion Transformers (DiTs) have emerged as a core architecture in generative modeling due to their scalability and adaptability to multimodal tasks. DiTs comprise isotropic transformer blocks, and learn representations progressively across depth, where the denoising objective drives later layers to focus on fine-detail reconstruction. This results in degraded representation quality and an imbalanced encoder-decoder behavior. Prior approaches such as representation alignment (REPA) mitigate this by encouraging stronger early representations via training regularization. Alternatively, U-Net-style DiT architectures introduce explicit multi-scale encoder-decoder structures for improved convergence. But they build on standard U-Net wisdom via learnable operators for spatial downsampling, which are not well-suited to transformer architectures, introducing inefficiencies and compatibility issues with components such as cross-attention and representation regularization. In this work, we propose UDT, a U-Net diffusion transformer that combines the representation power of DiTs with the encoding-decoding benefits of U-Nets, through data-adaptive token merging for downsampling and upsampling, while preserving the DiT token dimension. Our baseline UDT architecture outperforms existing U-Net DiTs and achieves performance comparable to REPA across all model sizes. Furthermore, using architectural optimization and REPA, UDT outperforms SiT’s 7.9 FID at 1400 epochs (w/o CFG) within 40 epochs (~ 40x faster convergence) for XL model size on 256x256 ImageNet. Finally, it achieves strong image generation performance with CFG, reaching FID of 1.38 (320 epochs) with SD-VAE and 1.35 (500 epochs) with VA-VAE, providing a new backbone for DiTs with strong empirical benefits.
[CV-160] urboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion
链接: https://arxiv.org/abs/2608.01288
作者: Jiawei Guo,Junxian Li,Yixin Tang,Bingya Zhang,Jiaxin Lu,Yulun Zhang,Shangchen Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code: this https URL
Abstract:Recently, diffusion-based removal methods have achieved promising visual quality in removing both target objects and their associated effects. However, they typically rely on multi-step denoising, leading to high inference cost. Directly applying existing one-step distillation methods is also suboptimal, since their global objectives lack explicit region-wise calibration and may weaken the asymmetric edit-and-preserve behavior required by object-effect removal. To address these challenges, we propose TurboClear, a one-step SDXL-based object-effect removal model. During training, we design Region-Calibrated Distribution Matching (RDM) for region-aware distillation to preserve the teacher model’s asymmetric edit-and-preserve behavior. Furthermore, we propose Learnable Spatial Fusion (LSF) for lightweight inference-time fusion. Extensive experiments show that TurboClear significantly improves inference efficiency while maintaining competitive visual quality. TurboClear reduces the computational overhead by up to 40.04\times compared to ObjectClear, and by up to 665\times against the Flux-based method OmniPaint, all while maintaining comparable or better visual removal quality. Code is available at this https URL.
[CV-161] Astrolabe: Spherical-Map Guidance Across Diffusion Pipelines for Full-Body Capture from Unconstrained Images
链接: https://arxiv.org/abs/2608.01276
作者: Shuliang Zhu,Qi Wang,Ryugo Morita,Jinjia Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Full-body capture from unconstrained photographs requires global correspondence across arbitrary views, poses, crops, and occlusions. Yet pose, geometry, and foundation features estimated in this setting are too unreliable for dense matching or appearance transfer, while diffusion rectifiers and optimization pipelines expose no common interface for consuming such uncertain correspondence. Our insight is that correspondence need not be locally accurate: its coarse viewpoint and body layout can still organize how a diffusion prior adapts and guides reconstruction. We introduce \emphAstrolabe, a host-portable adapter built on frozen viewpoint-guided spherical maps (SPH). A fixed bounded transform converts SPH into a spatial noise shift, which is matched during prior adaptation and reused during downstream denoising or score-distillation guidance in both pipeline categories. When a rectifier exposes a reference router, the same target/reference SPH additionally supplies coarse compatibility scores to select native appearance features; router-free optimization uses only the shared shift path. Astrolabe therefore follows one SPH–shift–adapt–guide process without dense warping or a learned control branch. Across Puzzle-IOI and 4D-Dress, it improves all reported image metrics in both hosts and all paired Puzzle-IOI geometry metrics; image gains extend to rear views, while 4D-Dress geometry remains stable overall.
[CV-162] Rethinking Video Token Compression with a Global Codebook: Learning Once Compressing Everywhere
链接: https://arxiv.org/abs/2608.01271
作者: Jiayang He,Tianling Xu,Diancheng Kang,Huaide Jiang,Junyan Bai,Shaoming Zheng,Xuan Song
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Video large language models (Video-LLMs) represent videos as dense sequences of visual tokens, whose length grows with the temporal and spatial extent of the input. These tokens often contain substantial redundancy arising from repeated visual patterns, leading to unnecessary computation in the subsequent language-model processing. Existing token compression methods, including pruning and merging, perform compression online during inference, repeatedly incurring additional computation for each input video and often relying on model-specific designs that limit their generality, we instead rethink this paradigm by shifting the costly compression process offline. We propose \textbfONCE, a plug-in video token compression framework that introduces an offline-to-online paradigm: a frequency-aware global codebook is learned once in the visual feature space and reused for lightweight online compression through codebook lookup and aggregation, reducing repeated per-video computation and the need for model-specific compression designs. Extensive experiments across multiple video understanding benchmarks and against diverse compression baselines demonstrate that our approach achieves a strong accuracy-efficiency trade-off, maintaining competitive performance while achieving the lowest inference latency among compared methods.
[CV-163] Hermite Curves as Trajectory Priors for Vision-Language-Action Models
链接: https://arxiv.org/abs/2608.01265
作者: Qi Lv,Jianming Xing,Zhao Yang,Mingyuan Yao,Yinan Shi,Yawei Jueluo,Mike Zheng Shou,Xiang Deng
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Project page is available at this https URL
Abstract:Despite recent progress in Vision-Language-Action (VLA) models for robotic manipulation, the action chunk remains a weakly structured interface. Existing work typically flatten each chunk into per-timestep controls, relying on implicit data learning that manifests as jagged motion and boundary discontinuities during physical execution. To address these limitations, we introduce Hermite trajectory priors, parameterizing the chunk trajectory as a piecewise cubic Hermite curve defined by endpoint positions and velocities to explicitly enforce smoothness and continuity. We instantiate this fixed operator across discrete autoregressive and continuous generative paradigms via three variants: (1) Hermite Tokens, which predict quantized boundary variables autoregressively; (2) Hermite Scaffold, which decomposes clean actions into a base scaffold and residuals; and (3) Hermite Regularization, which applies the prior strictly as an auxiliary training objective. Across simulation benchmarks and real-robot platforms, Hermite Regularization achieves superior performance among these three variants, improving \pi0.5 baseline success rates from 95.9% to 98.7% on LIBERO, 85.7% to 90.9% on LIBERO-plus, and 63.4% to 90.0% across four real-robot tasks without additional inference overhead. Trajectory analyses reveal that explicitly structuring trajectory priors serves most effectively as a learning inductive bias rather than a runtime constraint.
[CV-164] A Benchmark Dataset for MLLM -Generated Image Detection: GPT Image2 Nano Banana2
链接: https://arxiv.org/abs/2608.01258
作者: Zirui Zhang,Yinbo Yu,Donghai Guan,Chunwei Tian,Daoqiang Zhang,Qi Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The realism of images generated by multimodal large language models (MLLMs), such as GPT Image2 and Nano Banana2, has improved rapidly in recent years. Compared with early generative models, current models have made clear progress in text rendering. They can produce high-quality images that closely resemble real-world application scenarios. The enhanced generation capabilities of current MLLMs pose increasingly severe challenges to AI-generated image detection. Detection is no longer limited to identifying obvious artifacts left by early generators. Instead, it requires systematic and realistic benchmarks for the new generation of generated content. However, most existing benchmarks are still built around early generative models and cannot fully evaluate the forensic challenges introduced by high-quality and multi-form generated images. To address this gap, this paper constructs a benchmark dataset for detecting images generated by MLLMs. The benchmark covers several realistic application scenarios and adopts three generation protocols to simulate direct generation, reference-based reconstruction, and local editing. Based on this benchmark, we evaluate detector degradation from traditional scenarios to MLLM-generated images and analyze false positive rates and false negative rates across three sample types, revealing the failure modes of existing methods. We further propose a structural-artifact-prior-guided dual-stream prompt framework (SAP-DSP) as a strong baseline. SAP-DSP uses dual-stream prompt learning and structure-aware routing fusion to improve representation learning. Extensive experiments show that the proposed benchmark exposes the performance degradation of existing detectors on high-quality generated images, while SAP-DSP achieves more stable detection results on this benchmark. Our code and dataset are publicly available at this https URL.
[CV-165] From Forest to Future Capital: Tracking Land Cover Change in Ibu Kota Nusantara (IKN) from 2021 to 2026 with PlanetScope Imagery
链接: https://arxiv.org/abs/2608.01230
作者: Clarissa Rui Min Ong,Elizabeth Tee Inn Loo,Kenneth Woon Hao Soh,William Rachmadi,Qiming Zheng,Hao Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Indonesia’s relocation of its political and administrative capital from Jakarta to Ibu Kota Nusantara (IKN) has been framed around a ``Forest City’’ vision, yet rapid construction within the Core Government Area (KIPP) raises concerns over land conversion, vegetation loss, and carbon stock decline. This study applies remote sensing techniques to systematically assess land use and vegetation cover change in KIPP from 2021 to 2026 using PlanetScope SuperDove satellite imagery. Cloud-free mosaics were prepared and analysed through spectral indices, including the Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), and Normalised Difference Water Index (NDWI), alongside supervised land use and land cover (LULC) classification using a Support Vector Machine algorithm. Results show substantial environmental transformation, with mean NDVI declined by 17.1%, total carbon stock decreased by 0.28%, developed land expanded by 672%, and total vegetation declined by 18.1%. Vegetation loss was most extensive between 2023 and 2024, although a temporary recovery in NDVI and carbon stock occurred from 2024 to 2025 as active clearing slowed and development shifted towards already-cleared land. Overall, the findings demonstrate that remote sensing provides an effective approach for monitoring the environmental impacts of large-scale urban development, while highlighting the need for higher-resolution, hyperspectral, and SAR-based methods to improve detection of construction stages and plantation-related land cover changes.
[CV-166] VaRS-Doc: Interpretation-Aware Variant Representations via Latent Self-Probing for Visual Document Retrieval
链接: https://arxiv.org/abs/2608.01211
作者: Haocheng Wang,Tongkun Guan,Wei Shen,Xiaokang Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual document retrieval has recently become increasingly important in applications such as enterprise search, scientific literature discovery, and retrieval-augmented generation. These applications depend on efficiently identifying query-relevant pages across large collections of visually rich documents. Existing methods commonly adopt late-interaction architectures that encode and index documents offline to enable scalable and low-latency online retrieval. Despite its efficiency, this paradigm requires each document to be encoded into a fixed representation before the query is known. However, the same content in a visual document may induce different interpretations depending on the query intent, which a fixed representation struggles to capture. Yet postponing document encoding until the query arrives would incur prohibitive online retrieval latency. To address this gap, we propose VaRS-Doc, a visual document retrieval framework that diversifies document representations by enabling the model to actively explore variant latent interpretations during document encoding, while preserving efficient late-interaction retrieval in which each query adaptively selects the best-fit representation. We further introduce a two-stage training strategy that encourages the model to capture complementary semantic interpretations and prevents it from falling back to train a single dominant representation. Experiments on visual document retrieval benchmarks show that VaRS-Doc achieves state-of-the-art retrieval performance, offering a practical solution to the mismatch between query-agnostic document encoding and query-specific retrieval needs. Code is available at this https URL.
[CV-167] Its the Decoding Format Not the Perturbation: Auditing Consistency-Based Selection for Vision-Language Test-Time Scaling
链接: https://arxiv.org/abs/2608.01207
作者: Puzhuo Zheng,Hasan Kurban
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Test-time scaling lifts large language model reasoning by sampling many candidate solutions and selecting among them, yet the same recipe transfers poorly to vision-language models (VLMs): recent work shows that simple majority voting beats selection methods built on the model’s own self-verification, apparently because at the selection layer an image-grounded answer and a confident guess from the language prior look the same. A natural fix is to make the selection signal one that cannot be computed without the image. We study Perturbation Grounded Selection (Pgs), a label-free, training-free rule that scores each candidate by whether the model re-derives it under label-preserving perturbations of the input (cropping, background masking, mild photometric or geometric jitter); Pgs recovers majority voting when the perturbation set is empty. The decisive question is not whether Pgs beats chain-of-thought only majority voting, but whether the perturbation term adds anything once decoding format and budget are controlled. We therefore introduce a format-matched control (MatchedCtrl): the same short, no-CoT draws spent on the original image. Across TextVQA, MATH-Vision, MMMU, and ViLP, with a Qwen headline (three-seed means) and LLaVA-OneVision coverage in matched-budget selector tables, Pgs appears to beat plain majority voting by up to +31.8 points on TextVQA (Qwen), but MatchedCtrl tracks or exceeds Pgs within noise on every benchmark, including the vision-required ViLP; no Qwen category shows a significant gain over this control. The stability gap is real and image-dependent (up to +0.48), yet does not predict per-instance wins. The result is negative and diagnostic: perturbation consistency is at best a partial diagnostic of visual dependence and, on its own, not a usable selection signal once format is controlled; gains reported against CoT-only majority voting overstate such methods.
[CV-168] Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction
链接: https://arxiv.org/abs/2608.01202
作者: Ahmed Baha Ben Jmaa,Faten Chaieb,Anna Fabijańska
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management. Accurate and timely FRP can be achieved using machine/deep learning-based hyperspectral image classification techniques. However, challenges including the limited availability of labeled data and the lack of robust methods generalizable to various hyperspectral cameras and fruit types can compromise the effectiveness of hyperspectral image-based FRP. Addressing these challenges, this paper introduces Fruit-HSNet, a machine learning architecture specifically designed for hyperspectral classification of fruit ripeness. Fruit-HSNet incorporates a spatio-spectral feature extraction module based on Fourier Transform and central pixel spectral signature followed by learnable feature fusion and a classifier optimized for ripeness classification. The proposed architecture was evaluated using the DeepHS Fruit dataset, the largest publicly available labeled real-world hyperspectral dataset for predicting fruit ripeness, which includes five different types of fruits-avocado, kiwi, mango, kaki, and papaya-captured with three distinct hyperspectral cameras at various stages of ripeness. Experimental results highlight that Fruit-HSNet substantially outperforms existing deep learning methods, from baseline to state-of-the-art models, with improvements of 12%, achieving a new state-of-the-art overall accuracy of 70.73%.
[CV-169] PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning
链接: https://arxiv.org/abs/2608.01201
作者: Volodymyr Havrylov,Faris Janjoš,Andreas Look,Jürgen Mathes,Andreas Geiger
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:End-to-end autonomous driving (E2E AD) systems integrate perception, prediction, and planning into a single differentiable architecture. While these models show great promise, their standard training often relies on output-only supervision, which can lead to weak gradients for the hidden layers of increasingly complex models. Recent works have integrated vision-language model (VLM) supervision for latent features to address this, yielding substantial empirical gains, yet leaving the underlying theoretical mechanisms poorly understood. Our investigation into this methodology reveals that the resulting performance gains stem not from VLM reasoning capabilities, as previously assumed, but rather from the latent connections forged between the E2E AD model and ground-truth (GT) data during training. Building on this insight, we propose a probabilistic deep supervision framework that regularizes intermediate latent representations directly from GT data. By treating model latents as reparameterizable distributions, we optimize the architecture via the Evidence Lower Bound (ELBO). Our evaluations conducted on the nuScenes dataset demonstrate that supervising trajectory-related latents with future GT paths consistently improves planning performance. Using identical training data and E2E architectures, our method achieves an 8% reduction in planning L2 error and a 3% decrease in collision rates compared to competitive vectorized baselines, all while incurring negligible computational overhead.
[CV-170] QuerySplat: Decoupling Geometry and Appearance Representations in 3DGS Prediction
链接: https://arxiv.org/abs/2608.01186
作者: Yinglong Li,Donghui Shen,Xiaoyu Zhang,Zhichao Ye,Hongyu Wu,Aimin Hao,Guofeng Zhang,Haomin Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While feed-forward 3D Gaussian Splatting (3DGS) enables efficient 3D reconstruction, achieving high-fidelity rendering remains challenging. Existing pixel-aligned approaches suffer from spatial inflexibility and massive structural redundancy, whereas query-based methods lack 3D priors and entangle geometry with appearance, yielding blurry, pose-dependent results. To overcome these deficiencies, we propose \textbfQuerySplat, a feed-forward 3DGS framework driven by geometric priors and explicit appearance decoupling. Specifically, we design a dual-branch query-based decoder: the geometry branch leverages a pretrained Vision Geometric Model for spatial understanding, which intrinsically endows QuerySplat with pose-free modeling capabilities, while the appearance branch recovers high-frequency details through a dedicated pathway separated from geometric attribute regression. Extensive experiments demonstrate that QuerySplat mitigates the blurry rendering issues of early query-based models and consistently outperforms pixel-aligned approaches in rendering fidelity. On the challenging DL3DV benchmark, it achieves state-of-the-art novel view synthesis performance, with average PSNR gains of 2.30 dB and 1.04 dB over the best pose-free and pose-required baselines, respectively. Project Page: this https URL.
[CV-171] 3DZip: Spatial-Aware Feature Diversity-Guided Token Compression for 3D Question Answering ECCV2026
链接: https://arxiv.org/abs/2608.01185
作者: Changwoo Baek,Kyeongbo Kong
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to ECCV 2026. Project page: this https URL
Abstract:Recent 3D vision-language models (3D VLMs) construct geometry aware tokens by projecting 2D visual features into world coordinates, enabling spatial reasoning for tasks such as 3D question answering. However, this design generates thousands of tokens per scene, resulting in substantial computational and memory overhead. While token compression has been extensively studied in 2D VLMs, existing approaches rely on semantic relevance or attention-based selection that overlook the structured spatial nature of 3D tokens. Moreover, redundancy in 3D representations cannot be resolved by spatial proximity alone, as object-level token imbalance persists even after spatial aggregation. To address this, we propose 3DZip, a three-stage token compression framework that first applies coarse voxelization to remove point-level redundancy, then selects anchor tokens based on feature-space diversity via a Determinantal Point Process, and finally merges remaining tokens under spatial constraints to preserve geometric coherence. Experiments on three 3D question answering benchmarks demonstrate that 3DZip consistently outperforms existing compression methods, retaining 94.7% of the original performance with only 128 tokens, achieving a 1.92\times faster inference speed.
[CV-172] DynActiveGS: Active Gaussian Splatting for Dynamic Scene Reconstruction
链接: https://arxiv.org/abs/2608.01178
作者: Hongbo Duan,Pengting Luo,Chengzhi Zhao,Yuanhao Chiang,Fangming Liu,Xueqian Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ACM Multimedia 2026
Abstract:We present DynActiveGS, a dynamic-aware active reconstruction framework based on 3D Gaussian Splatting (3DGS) for autonomous exploration in dynamic environments. The framework incrementally reconstructs a 3D Gaussian scene representation while suppressing motion-corrupted observations through online uncertainty prediction and uncertainty-weighted Gaussian optimization. A key component of DynActiveGS is the explicit decomposition of uncertainty into structural uncertainty and motion-induced uncertainty, which enables the system to distinguish under-reconstructed static regions from dynamically unreliable areas. Based on these uncertainty fields, DynActiveGS performs dynamic-aware viewpoint selection and dynamic-constrained path planning to favor informative yet stable observations during exploration. The resulting system forms a unified closed-loop pipeline for robust active reconstruction in dynamic scenes. Extensive experiments on challenging dynamic benchmarks demonstrate consistent improvements over existing active reconstruction baselines in reconstruction accuracy, completeness, rendering quality, and exploration efficiency.
[CV-173] hink in Sets for Streaming Video Token Compression
链接: https://arxiv.org/abs/2608.01169
作者: Moxu Duan,Jingwen Fu,Yuwang Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 3 figures, 4 tables
Abstract:Streaming VideoLLMs process frames causally while visual tokens grow continuously, making compression essential for controlling prefilling latency and memory. Existing training-free methods independently rank tokens, ignoring marginal-gain interactions among retained tokens. We argue that streaming video token compression should instead be formulated as set selection, where each candidate is valued by what it adds beyond the tokens already retained. Unlike existing set-wise methods designed for offline tasks, streaming makes causal, frame-by-frame pruning decisions, so modeling cross-frame interactions requires an explicit historical reference. This creates a reference-set dilemma: the reference must adequately represent previously conveyed content while remaining bounded for real-time inference. We introduce NovaCov, to our knowledge the first training-free, plug-and-play set-wise token compressor designed for streaming video. NovaCov maintains a capacity-bounded, recency-weighted Historical Reference Bank and optimizes a dual-branch submodular coverage objective that preserves representative current-frame content while prioritizing information insufficiently covered by history. Both branches are facility-location functions, so greedy selection retains the classical (1-1/e) approximation guarantee. Across streaming and offline benchmarks, NovaCov outperforms existing training-free compression methods, retaining 99.6% of ReKV accuracy while reducing LLM prefilling latency by 46%.
[CV-174] InteracVid: Building a Real Interactive Audio-Visual Response Dataset from Live-Chat Videos
链接: https://arxiv.org/abs/2608.01157
作者: Chi Zhang,Haoyang Shi,Yueyi Liu,Zhaokun Yan,Yishu Yin,Yuhang Wu,Miao Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large language models have made text the default medium for human–AI interaction, buttext alone cannot express the full range of responses required by multimodal assistants,avatars, and embodied agents. While recent audio-video generative models can synthesizehigh-fidelity synchronized content, existing supervision is largely \emphdescriptive:models are trained to render captions rather than to produce audio-visual responsescaused by external user interactions. We introduce \textbfInteracVid, \emphthe firstopen-source large-scale dataset that addresses this missing supervision, so that everysample couples a preceding audio-visual context and an external stimulus with the realinteractive response that follows. We design a metadata-aware pipeline that extractsinteractive clips from long, noisy livestreams, yielding over \textbf454Kcontext-query-response triplets from more than \textbf59K livestream videos andspanning conversation-centered, object-centric, procedural, embodied, and screen-basedscenarios. A ten-rater human study confirms that the extracted interactions are causal,natural, and temporally complete for both genuine and reconstructed queries. On aheld-out benchmark of \textbf100 genuine live-chat queries, fine-tuning on InteracVidimproves both interaction planning and audio-video response generation, and anindependent human evaluation reproduces the system ranking and the conclusions obtainedwith our automatic judge. These results highlight interaction-structured data as acritical foundation for interactive multimodal generation.
[CV-175] FeDepth: Federated Learning for Depth Estimation under Robot Heterogeneity ECCV2026
链接: https://arxiv.org/abs/2608.01129
作者: Ganghyeon Lee,Inha Lee,Junhee Lee,Jeongeon Lee,Sung Whan Yoon,Kyungdon Joo
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026. Ganghyeon Lee and Inha Lee contributed equally. Kyungdon Joo is the corresponding author
Abstract:Although recent robot perception research emphasizes training on data from diverse environments to improve generalization, most existing methods still rely on centralized learning, which is inefficient and difficult to scale across heterogeneous robot platforms. Federated learning (FL) offers an alternative by enabling distributed training without raw data transfer, but it suffers from severe performance degradation under domain shifts caused by heterogeneity across clients. In real robotic deployments, data distributions often overlap across platforms, environments, and sensing conditions, making it difficult to partition clients into clearly separated domains. However, this characteristic breaks the assumption of clearly separable client domains commonly used in clustered FL. To address this gap in robot perception, particularly in depth estimation, we introduce two realistic and unexplored non-IID scenarios that reflect heterogeneity in terms of platform, environment, and depth distribution. We then propose FeDepth, a descriptor-based clustered FL framework that models client relationships through soft clustering. Unlike hard clustering methods that assume clearly separated clusters, FeDepth allows clients to participate in multiple clusters, capturing continuous and ambiguous domain transitions commonly observed in robotic environments. Extensive experiments demonstrate that FeDepth consistently improves robustness over standard FL and clustered FL baselines across multiple depth estimation architectures, providing a practical and effective solution for federated robot perception. Our project page is available at this https URL.
[CV-176] MiniWorld: Democratizing the Training of Video World Models from Scratch
链接: https://arxiv.org/abs/2608.01127
作者: Yian Zhao,Ruochong Zheng,Hongcan Guo,Yu Yan,Jian Zhang,Jie Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages
Abstract:Video world models predict future observations conditioned on historical observations and control signals, enabling long-horizon generation through autoregressive state transitions. Unlike conventional video generation models that primarily capture visual appearance and motion, video world models learn the underlying dynamics governing environment evolution under agent actions, providing a foundation for embodied AI and interactive simulation. Recent progress has largely relied on adapting pretrained video generation models through post-training or distillation. Although effective, these approaches often require complex training pipelines, substantial computational resources, and suffer from the mismatch between bidirectional pretraining and causal streaming inference. Recent studies have shown that training autoregressive video world models from scratch is feasible and scalable. However, the community still lacks a lightweight, transparent, and fully reproducible baseline trainable end-to-end with modest computational resources. We present MiniWorld, a reproducible framework for training streaming video world models from scratch. MiniWorld employs a block-causal Video Diffusion Transformer trained with Flow Matching in the latent space of a pretrained Video VAE. Building on Diffusion Forcing, it adopts a chunk-wise non-decreasing noise schedule and two-stage continued training to improve temporal modeling and stability. During inference, MiniWorld combines a rolling KV cache with pipelined asynchronous denoising for efficient streaming generation under bounded computation. The entire model can be trained within several days on a single 8-GPU server. By releasing the training and inference codebase and pretrained checkpoints, we hope MiniWorld will facilitate future research on video world modeling.
[CV-177] CoT-Edit: Let CoT Guide Instruction Video Editing
链接: https://arxiv.org/abs/2608.01113
作者: Sen Liang,Fengbin Guan,Youliang Zhang,Xin Li,Zhibo Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-driven instruction-based video editing in complex scenes remains challenging: purely textual prompts often fail to capture precise spatial relationships and physical constraints, resulting in target ambiguity and physically implausible outcomes. To address this, we propose a plan–guide–edit framework that explicitly bridges semantic intent and spatial execution. In our framework, a Chain-of-Thought (CoT)-enhanced multimodal large language model (MLLM) serves as a planner, performing structured reasoning over the video and instructions to derive a precise sequence of bounding boxes and attribute-enriched editing directives. These spatial priors then guide a box-conditioned mask generator, transforming ambiguous global retrieval into localized, context-aware refinement and producing masks that more accurately capture object scale, contact relationships, and placement. Building on these spatial and semantic signals, a diffusion-based editor integrates the masks, enriched instructions, and frame features to render high-fidelity edits that remain temporally coherent and spatially well aligned. Trained first in a modular manner and then jointly, our framework achieves superior performance with reduced data requirements, delivering precise localization in scenes with multiple similar objects and physically consistent object additions, and extensive experiments demonstrate state-of-the-art performance over multiple strong baseline methods. More details are available at: this https URL
[CV-178] SG-Layout: Structured Scene Graph-Guided Layout Generation with LLM s
链接: https://arxiv.org/abs/2608.01106
作者: Junsheng Wang,Chao Chen,Mengying Xie,Mingyan Li,Fuqiang Gu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 16 pages, 5 figures. Accepted at WAICA 2026
Abstract:Understanding and generating spatially coherent layouts from natural language remains a fundamental yet challenging task for large language models (LLMs). Existing LLMs often struggle to capture explicit geometric relationships and structural dependencies between objects. To address this issue, we propose SG-Layout, a graph-guided layout generation framework that explicitly incorporates structured spatial knowledge into LLMs. SG-Layout follows a two-stage training paradigm: (1) a graph-language feature alignment stage, where a relational graph encoder and a projector are trained to map scene-graph embeddings into the LLM’s linguistic space; and (2) an instruction tuning stage, where LoRA-based adapters enable efficient fine-tuning for instruction-driven layout generation while keeping the backbone frozen. We evaluate SG-Layout on image layout generation, indoor scene synthesis and robotic object rearrangement tasks. Experimental results show that SG-Layout improves spatial reasoning accuracy and geometric consistency over the compact open-source backbone, with particularly clear advantages in relation-dense and compositionally complex scenes. These results highlight the effectiveness of graph-structured feature alignment for enhancing controllable layout generation.
[CV-179] From Patches to Evidence Balls: Class-Conditioned Evidence Retrieval for Few-Shot Whole Slide Image Classification
链接: https://arxiv.org/abs/2608.01104
作者: Di Zhang,Li Zhang,Jiashuai Liu,Junbo Lu,Zhi Zeng,Jiusong Ge,Chunze Yang,Yi Niu,Jian Chen,Kai He,Zeyu Gao,Chen Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Whole slide image (WSI) classification is an evidence-driven task, where diagnostic cues are often sparse, spatially organized, and class-dependent. Existing MIL and vision-language methods aggregate a large pool of patch features into a single global slide representation. Under few-shot supervision, limited slide-level labels make it difficult to learn a reliable aggregation mechanism that organizes sparse local cues into compact and coherent diagnostic evidence. Moreover, a shared slide representation compresses evidence supporting a candidate class and its alternatives into the same feature, limiting class-specific reasoning and interpretability. To address these issues, we propose EviBall, a class-conditioned evidence retrieval framework for few-shot WSI classification. EviBall organizes local patches into Evidence Balls through semantic-spatial assignment and center refinement, yielding compact and spatially coherent evidence units under weak supervision. It then uses task-specific class queries, including language-guided queries for morphology-oriented tasks and molecular-guided queries for molecular endpoint prediction, to retrieve supporting evidence balls and produce class-conditioned evidence representations for direct class-wise prediction. By introducing structured evidence units and task-relevant semantic guidance, EviBall reduces the reliance on learning an unconstrained global aggregation mechanism from scarce slide-level labels. It therefore reformulates few-shot WSI classification as structured evidence retrieval and competition among candidate classes. Extensive experiments across four morphology-oriented and molecular endpoint WSI tasks demonstrate that EviBall consistently outperforms conventional and vision-language MIL baselines under diverse few-shot settings, while providing spatially localized and class-specific evidence for each prediction.
[CV-180] SSR: Similarity-Shift Refinement for Training-Free Object-Centric Masks
链接: https://arxiv.org/abs/2608.01103
作者: Xiaoqian Lu,Guangfu Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Object-centric models often produce fragmented masks, boundary leakage, and incorrect region merging. We introduce Similarity-Shift Refinement (SSR), a training-free post-hoc method for improving object-centric masks with a frozen self-supervised Vision Transformer. SSR measures changes in pairwise patch similarity before and after self-attention value aggregation, retains positively strengthened relations, and constructs a sparse affinity graph. This graph propagates the initial soft slot assignments in a single refinement step, without retraining or modifying either model. Across natural-image, synthetic-video, and real-world-video benchmarks, SSR improves all-pixel Adjusted Rand Index in all 24 evaluated model-dataset combinations, with an average gain of 8.5 percentage points. Ablations show that value-space similarity shifts outperform query- and key-space variants as well as static Transformer affinities. However, texture-dense scenes may cause visually similar regions to be over-grouped. Overall, SSR provides a simple and transferable signal for training-free object-centric mask refinement.
[CV-181] Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging
链接: https://arxiv.org/abs/2608.01094
作者: Shengchao Chen,Ting Shu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 31 pages, 15 figures, benchmark paper
Abstract:Federated learning enables medical-imaging models to be trained across hospitals, and privacy law, most explicitly the GDPR ``right to be forgotten’', turns removing a hospital’s, a class’s, or a patient’s influence from such a model into a federated unlearning problem. This need is most acute in medicine, where patients withdraw consent and hospitals leave collaborations. Yet nearly all unlearning evidence comes from natural images, whose heterogeneity and task structure differ sharply from clinical data, so it is unclear whether existing methods transfer, and no shared protocol covers clinical data. We present Lethe, a benchmark for federated unlearning in medical imaging. It evaluates twelve methods across eight task families, from classification and segmentation to denoising, cross-modality synthesis, and vision-language question answering, at three forgetting granularities and against a retrained gold standard on utility, privacy, and cost. The central result is that what separates methods is the difficulty of the forgetting request, not the method itself. The easy removals that dominate the literature leave the methods that preserve utility indistinguishable, while only hard ones separate them. More striking, on the many medical tasks that generalize across sites, forgetting a client barely changes task performance, leaving residual membership as the signal that must be erased.
[CV-182] Logit-Origin Centering for Singleton Test-Time Adaptation
链接: https://arxiv.org/abs/2608.01074
作者: Mayank Sharma,Rohit Kumar Mourya,Pratik Mazumder
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 5 figures, 8 tables
Abstract:Tabular data is used extensively in many real-world use cases. Deep learning models have been developed to deal with tabular data, but generally perform poorly when the test data distribution differs from that of the training data. Researchers have proposed test-time adaptation approaches to deal with this problem. The fully test-time adaptation (FTTA) setting involves adapting deployed classifiers to shifted target distributions using only unlabeled test data. Leading FTTA methods inherit a batch-dependent approach from computer vision literature. This paper demonstrates for the first time that such approaches degrade sharply in strict streaming regimes where examples arrive and must be classified one at a time. This occurs because at a batch size of one, batch-level statistics become unavailable or poorly estimated. We argue that singleton tabular FTTA is not merely a small-batch variant of ordinary FTTA, but a distinct identifiability problem where only the location of the model’s score stream remains directly observable. To address this, we propose Prequential Logit-Origin Centering (PLOC), a lightweight approach that keeps the source model frozen and shifts the logit space at each step. PLOC stores only a single running number (the mean of past logits), requires no labels, estimates no priors, and bypasses weight updates entirely. A deferred variant applies a static shift that preserves the source ranking, and thus the AUROC, exactly. Evaluated across five tabular benchmarks, three architectures (MLP, FT-Transformer, and TabTransformer), and five independent source checkpoints, PLOC significantly outperforms strong tabular and entropy-based baselines.
[CV-183] PlantRig - From Bones to Branches: Adaptation of Autoregressive Rigging Models for Plant Skeletal Reconstruction
链接: https://arxiv.org/abs/2608.01072
作者: Nathan Hu,Yang Yang,Fumio Okura
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Autoregressive rigging models such as UniRig and SkinTokens perform well on articulated characters, but their ability to generalize to plant structures remains largely unexplored, since plant topologies exhibit highly variable, non-canonical branching patterns that challenge learned skeletal priors. We evaluate these models for plant skeletal reconstruction using synthetic L-system-generated trees and real scanned data spanning monopodial, sympodial, whorled, and vine-like archetypes. Preliminary testing showed UniRig collapsing complex branching into near-linear chains, while SkinTokens preserved topology better but over-segmented branches and produced an unstable output space, so we focused on UniRig for its greater stability. Diagnosis traced the collapse to sampling-level suppression of branch tokens, and further analysis showed the frozen mesh encoder had limited sensitivity to structural variation, pointing to a geometric bottleneck in the tokenization pipeline rather than a purely learned bias. Building on these findings, we applied multi-round fine-tuning over multiple procedurally generated synthetic datasets. Across rounds, the model progressively recovered accurate branching topology and generalized beyond branch-only structures to plants with foliage, a harder case given the zero-thickness, mesh-normal-dependent geometry of leaves. The resulting model generalized well across diverse plant forms without leaf-specific architectural changes, indicating that targeted fine-tuning can substantially close the domain gap between character-rigging priors and plant skeletal structure. As such, our work points toward a viable path for automated plant rigging across both branch topology and foliage type, even those not considered in our findings.
[CV-184] ReACT-CLIP: Response-Aware Test-Time Defense for Vision–Language Models
链接: https://arxiv.org/abs/2608.01067
作者: Hashmat Shadab Malik,Toluwani Aremu,Samuele Poppi,Muzammal Naseer,Salman Khan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Training-free test-time defenses offer a practical way to improve the adversarial robustness of CLIP-style vision–language models without modifying the pretrained model. However, their correction strength is typically fixed for a narrow range of attack budgets, even though the attack budget is unknown at inference and the required correction varies across samples. We show that this mismatch causes existing defenses to degrade sharply as attacks strengthen. We introduce ReACT-CLIP, a response-conditioned test-time defense that separately determines how strongly each input should be corrected and whether defensive intervention is necessary. Our key observation is that the relative increase in CLIP visual-feature drift between low- and high-noise probes provides a graded, sample-specific proxy for correction demand. ReACT-CLIP maps this relative cross-noise drift to the Gaussian noise scale used to construct a stable, noise-averaged feature anchor, enabling the corrective reach to adapt to each input. To determine whether intervention is necessary, we further observe that clean inputs retain stable class-probability distributions under weak spatial augmentations, whereas adversarial inputs exhibit greater variation. ReACT-CLIP quantifies this variation using a prediction-instability score computed by Jensen–Shannon divergence and combines it with relative cross-noise drift to form the defensive intervention score. ReACT-CLIP requires no model or prompt training, and its correction-strength mapping is calibrated once and fixed across datasets and attack budgets. Across 12 downstream datasets, as well as ImageNet and its distribution-shifted variants, ReACT-CLIP delivers substantial robustness gains across diverse attack types and strengths while largely preserving clean accuracy.
[CV-185] One Query Many Scales: Sparse Mixture-of-Experts for Efficient Hierarchical Cross-View Geo-Localization
链接: https://arxiv.org/abs/2608.01060
作者: Ruijie Fan,Junyan Ye,Qi Zhu,Weijia Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cross-view geo-localization (CVGL) retrieves geo-tagged satellite imagery for a ground-view query. Most systems exhaustively search a flat, fixed-resolution gallery, incurring high cost over large areas and adapting poorly to satellite resolution changes. Autoregressive coarse-to-fine alternatives reduce comparisons but bind later predictions to earlier decisions and a predefined hierarchy. We introduce GeoMoE, a sparse mixture-of-experts dual encoder that decouples global multi-scale representation learning from local hierarchical search. Global multi-scale supervision and content-adaptive routing map ground and satellite images across resolutions into a globally comparable embedding space. At inference, each image is encoded once, and probabilistic beam search follows parent–child links to score a small candidate subset. Later levels reuse these descriptors rather than features generated by preceding levels, limiting feature-level error propagation and hierarchy coupling. We further introduce VIGOR-M, a four-city benchmark with an explicit parent–child satellite hierarchy and held-out half-step galleries for single-resolution, cross-resolution, and hierarchical evaluation. GeoMoE achieves 95.78% R@40m on Just Zoom In, 2.77 percentage points above the previous best, and 62.39% R@1 on VIGOR-M. The latter requires 0.885 MMAC/query for descriptor matching, 5.27% of an exhaustive L3 scan, while exceeding the strongest exhaustive baseline by 3.12 percentage points in R@1. One model trained on L1, L2, and L3 also outperforms a matched dense control across all six galleries and transfers to three withheld resolutions. By decoupling globally trained embeddings from local hierarchical search, GeoMoE jointly improves localization accuracy, search efficiency, and cross-resolution transfer.
[CV-186] Extended KAFR: A kinematic-adaptive paradigm for the efficient analysis of surgical video
链接: https://arxiv.org/abs/2608.01058
作者: Huu Phong Nguyen,Shekhar Madhav Khairnar,Ganesh Sankaranarayanan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18
Abstract:Artificial Intelligence is increasingly applied to surgical video analysis for phase segmentation, skill assessment, and workflow optimization. A key challenge is the length of surgical recordings, often one to several hours, creating substantial computational burden. We previously developed Kinematics-Adaptive Frame Recognition (KAFR) for robotic surgery, showing that tracking tool motion effectively identifies informative frames while filtering redundant content. However, laparoscopic surgery introduces additional challenges: manual camera control causes frequent motion artifacts, and image quality is generally lower than robotic systems. This study evaluates whether KAFR generalizes to laparoscopic surgery using the Cholec80 benchmark, comprising 80 laparoscopic cholecystectomy procedures annotated for seven surgical phases. KAFR operates in three stages: a fine-tuned YOLO model detects and segments surgical tools; frames are adaptively selected based on tool displacement or velocity variation; and an X3D model classifies selected frames into surgical phases. KAFR achieved a 91.0% F1 score using only 0.58% of frames for phase classification, representing an approximately seven-fold reduction compared to typical 4% frame sampling, while maintaining performance comparable to LoViT (90.2%) and Trans-SVNet (89.7%). These results demonstrate that kinematics-based frame selection transfers effectively to the challenging laparoscopic environment.
[CV-187] Credit the Right Box: Marginal Contribution Assignment for Structured Visual Perception
链接: https://arxiv.org/abs/2608.01055
作者: Xinheng Han,Jianfei Wang,Yu Chen,Xiang Wang,Shuai Li,Weixing Li,Feng Pan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal Large Language Models (MLLMs) are increasingly expected to solve structured perception tasks that require visual recognition, language-to-object binding, object cardinality preservation, and precisely localized grounding and segmentation outputs. However, existing group-relative reinforcement learning methods provide only response-level supervision, creating a granularity mismatch for structured multi-object prediction: a single advantage is broadcast to all tokens in a response, without distinguishing individual box contributions. To address this mismatch, we propose MCR-GRPO, a marginal contribution assignment framework that derives box-level credit directly from each sampled response. Specifically, Marginal Contribution Reward (MCR) estimates each predicted box’s contribution through a leave-one-out comparison, measuring how the matched set value changes when the box is removed from the response. After within-response normalization, records that improve the set value receive positive credit, while redundant or harmful ones are suppressed. To make marginal attribution stable and informative, we further introduce a Continuous Matched Set Value Evaluator that integrates permutation-invariant matching, count-aware normalization, and graded localization. MCR-GRPO maps normalized box-level marginal advantages to the token spans that generated each box, preserving GRPO’s response-level comparison while enabling box-aware optimization of structured multi-object grounding. Experiments across REC, DOD, segmentation, and counting benchmarks show state-of-the-art performance over prior GRPO-based baselines.
[CV-188] Struct-GStream: Towards Efficient Free-Viewpoint Video Streaming at Low-Bitrates with Structured 3D Gaussians
链接: https://arxiv.org/abs/2608.01053
作者: Han Jiao,Jiakai Sun,Lei Zhao,Wei Xing,Huaizhong Lin,Zhanjie Zhang,Ao Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Constructing photorealistic Free-Viewpoint Videos (FVVs) of dynamic scenes from a set of posed 2D images has been an intriguing yet challenging task in computer vision. Methods based on neural rendering achieve high-fidelity image quality in FVV construction. However, most of these methods are unable to achieve real-time rendering and often require complete video sequences to train. Despite the existence of some online training methods capable of rendering FVVs in real time, they struggle to meet the requirements for storage and training time for downstream applications. To overcome this problem, we propose Struct-GStream, which can achieve efficient FVV streaming using structured 3D Gaussians (3DGs). Specifically, we introduce dynamic anchor points to generate structured 3DGs to construct basic scenes and model approximate scene movements based on the assumption of local rigidity in object motion. Besides, we introduce a global free 3DGs patching strategy involving free 3DGs’ generation, pruning, and optimization to patch and model deficient areas and emerging objects. Our method achieves fast training at low bitrates while maintaining high rendering quality. Extensive experiments demonstrate that Struct-GStream significantly outperforms existing online training methods for FVV construction in terms of training time, storage, and rendering quality while maintaining competitive rendering speed.
[CV-189] FactorJEPA: Factorizing Monolithic Futures into Layout-Agent -Interaction Channels for Crowded and Chaotic Global South Urban Worlds
链接: https://arxiv.org/abs/2608.01049
作者: Kapil Wanaskar,Gaytri Jena,Aman Chadha,Vinija Jain,Vasu Sharma,Amitava Das
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 42 pages. Independent preprint. Dataset: this https URL ; checkpoints: this https URL
Abstract:World models have attracted significant attention for their ability to capture and predict the structure and dynamics of the physical world. In this emerging landscape, Joint Embedding Predictive Architectures (JEPA) offer a particularly compelling direction. We study a largely unexplored regime: populous, crowded, and chaotic Global South urban environments, which we call DENSEWORLD. Unlike the lower-density, lane-structured settings that dominate existing evaluations, these scenes exhibit soft spatial boundaries, extreme agent heterogeneity, persistent occlusion, and rapid social negotiation under mixed traffic. We introduce the first large-scale dataset for this regime: 1,000 hours of drive-through, walk-through, and aerial video across 22 cities. Existing JEPA formulations struggle to preserve dense interaction dynamics under heterogeneity and partial observability. We introduce FactorJEPA, which makes world structure a first-class predictive primitive. Rather than encoding the future in a monolithic latent, it composes layout, entities, and interactions, using a visibility gate and separated subspaces to preserve partially observed agents and discourage cross-factor shortcuts. FactorJEPA improves (i) future-latent accuracy (Future-frame L1), (ii) intervention-sensitive prediction (Causal L1), and (iii) robustness to reduced visual evidence (Mask-ratio slope), while exposing (iv) a reproducible motion-information trade-off (Motion cosine). Method rankings replicate across 2B and 1B V-JEPA 2.1 backbones, with rho = 0.895 to 0.978. We publicly release the DENSEWORLD-115k dataset (this https URL) and the surgery-trained FactorJEPA checkpoints (this https URL). Comments: 42 pages. Independent preprint. Dataset: this https URL ; checkpoints: this https URL Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) ACMclasses: I.2.10; I.4.8; I.2.6 Cite as: arXiv:2608.01049 [cs.AI] (or arXiv:2608.01049v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.01049 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kapil Wanaskar [view email] [v1] Sun, 2 Aug 2026 07:32:46 UTC (46,067 KB)
[CV-190] WAM-Diff2: Hierarchical AR-to-Diffusion Distillation for Highly Efficient Autonomous Driving VLA
链接: https://arxiv.org/abs/2608.01035
作者: Zhihao Zhu,Hanlin Shang,Mingwang Xu,Feipeng Cai,Zhuolin He,Yaoyi Li,Jianhua Han,Hang Xu,Siyu Zhu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-Language-Action (VLA) models have emerged as a prominent paradigm for end-to-end autonomous driving; however, their efficient deployment is severely constrained by high computational latency and exposure bias arising from sequential autoregressive decoding. Conversely, while specialized diffusion policies enable low-latency, parallel execution, training them from scratch typically yields narrow, single-task architectures that lack holistic visual-linguistic reasoning. Successfully transforming pre-trained autoregressive generalists into parallel diffusion models could combine multi-task cognitive intelligence with execution efficiency, yet this transition presents a formidable architectural challenge due to mismatched attention patterns (causal versus bidirectional) and divergent optimization objectives. To bridge this divide, we introduce WAM-Diff2, a multi-task discrete diffusion VLA framework powered by a three-stage hierarchical distillation strategy. By structuring the architectural shift through progressive block-wise adaptation, block-wise distillation, and model-wise cross-scale distillation, WAM-Diff2 preserves the underlying semantic foundations of the base model while accelerating inference. Extensive evaluations across driving understanding, perception, and planning benchmarks demonstrate that WAM-Diff2 effectively mitigates exposure bias and achieves performance parity with autoregressive baselines. Crucially, the autoregressive-to-diffusion transition yields a 2.8x decoding speedup, which scales to an ultimate 15.1x acceleration when combined with system-level optimizations including FlashInfer and CUDA Graphs.
[CV-191] Understanding and Overcoming Cross-modal Fusion Bias in Multimodal Anomaly Detection From A Fisher Information Perspective
链接: https://arxiv.org/abs/2608.00986
作者: Kaifang Long,Lianbo Ma,Liming Liu,Guoyang Xie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Current advancements in Multimodal Anomaly Detection (MAD) are largely driven by enhancing multimodal fusion, particularly through the integration of RGB and Depth data for richer anomaly representation. However, less attention was devoted to analyzing the role of cross-modal fusion bias, a well-known challenge in multimodal learning, in MAD. This gap motivates a key question: can we overcome this bias to break the performance bottleneck of current work? In this paper, we first analyze the impact of cross-modal fusion bias in MAD via the Fisher Information Matrix. Then, grounded in these findings, we propose UCFB, a simple yet effective plug-and-play framework designed to mitigate cross-modal fusion bias in MAD. It achieves this by jointly employing Fisher-information-guided dynamic calibration to adjust modality-specific regularization weights and canonical similarity analysis to improve inter-modal interactions. Extensive experiments on the MVTec 3D-AD and Eyecandies datasets demonstrate that UCFB achieves consistent improvements in single-class, multi-class, and few-shot settings.
[CV-192] One-Sided Quantile Coupling for Flow Matching
链接: https://arxiv.org/abs/2608.00978
作者: Jin-Young Kim,So-Yoon Cho,Hyun-Gyoon Kim
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Flow Matching trains continuous-time generative models by regressing the velocity field of a probability path between a simple source distribution and a target data distribution. The coupling that pairs source and target samples strongly affects optimization and sample quality, but structured couplings typically rely on mini-batch transport or assignment procedures whose cost grows at least quadratically in batch size. We propose Quantile Coupling Flow Matching (QC-FM), a lightweight one-sided coupling: rather than matching two pre-sampled batches, it samples only the data batch and constructs each paired source directly. Data ranks projected along a small number of random orthogonal directions are mapped to Gaussian quantiles, and the latent code is completed in the orthogonal complement by conditional Gaussian sampling. The construction is one-dimensional per slice, so the coupling requires no pairwise cost matrix and no assignment to solve. We show that, for each drawn frame, this coupling eliminates the irreducible regression variance along every selected slice and makes the ideal flow exactly straight there, while leaving the sampling prior unchanged: generation still starts from the standard Gaussian, and the training source deviates from it only through the copula of the slice codes, whose transport cost we bound. For training, we apply QC to an anchor subset and complete the remaining source slots with exact Gaussian samples, retaining the QC bias while preserving an explicit signal from the Baseline coupling. Across CIFAR-10, CelebA, FFHQ, and ImageNet-64, QC-FM improves over the Baseline under matched training budgets, reducing FID by up to 12.9%, and outperforms OT-CFM on all four datasets. These results suggest that preserving projected rank structure is a simple and scalable way to inject useful geometric bias into FM couplings without solving a mini-batch transport problem.
[CV-193] Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models
链接: https://arxiv.org/abs/2608.00976
作者: Myeongkyun Kang,Yanting Yang,Xiaoxiao Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Fine-grained visual representations are essential for medical image analysis, particularly when diagnostically relevant evidence is subtle and spatially localized. Modern transformer-based medical vision encoders must therefore learn patch-level representations that are both clinically meaningful and spatially consistent. Without these properties, large vision-language models (LVLMs) operate on an ambiguous visual foundation, limiting their ability to generate clinically reliable and spatially grounded responses. However, existing training strategies for medical vision encoders rarely achieve both objectives. Image-text alignment provides clinically meaningful supervision primarily at the image level, leaving the spatial localization of diagnostic evidence weakly constrained. In contrast, self-supervised learning promotes spatial consistency but lacks the semantic supervision needed to distinguish visually similar yet clinically distinct regions. To address this gap, we present LoFi, a medical vision foundation model built on location-aware fine-grained representation learning. LoFi trains a vision encoder with a lightweight large language model under grounding and grounded captioning objectives. Because these objectives require predicting location from clinical text and vice versa, spatial consistency emerges without any explicit patch-level regularization. To enable training at scale, we construct MedG, a large-scale medical grounding dataset of 4.48M image-text-box triplets curated from 84 datasets spanning 7 modalities. Across phrase grounding, visual question answering, and region-based organ classification under perturbations, LoFi consistently outperforms general-purpose and medical vision foundation models as well as state-of-the-art LVLMs. Code is available at this https URL.
[CV-194] MonitorVLM-v2: A Deployed Vision-Language Framework for Real-Time Safety Violation Detection
链接: https://arxiv.org/abs/2608.00975
作者: Jiang Wu,Sichao Wu,Yinsong Ma,Lifang Zheng,Jingliang Duan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large vision–language models (VLMs) can reason step by step about complex visual scenes, but this open-ended, autoregressive chain-of-thought (CoT) approach is poorly suited to safety-critical, rule-governed settings such as industrial surveillance, where decisions must be bounded, deterministic, and low-latency. Because CoT inference cost scales jointly with reasoning length and the number of concurrent streams, it creates a throughput bottleneck that precludes the real-time, multistream monitoring required for industrial accountability. Here we present MonitorVLM-v2, a deployment-oriented framework that recasts VLM-based safety assessment as probabilistic inference over a finite regulatory decision space, compressing multimodal reasoning into single-step rule-ID predictions and reducing decoding from a variable-length sequence to a single token. We introduce symbolic policy optimization (SymPO), a novel contrastive policy optimization algorithm that sharpens decision boundaries within this finite symbolic space, together with an entropy-driven triage mechanism that routes uncertain predictions to human reviewers for expert confirmation. In a four-month prospective deployment across 10 concurrent camera feeds in an operational underground mining facility, MonitorVLM-v2 achieved a 19.45-fold increase in inference speed and identified 2.78 times as many confirmed violations as the site’s routine manual inspection workflow, demonstrating the practical value of compressed symbolic decision-making for real-time, auditable industrial monitoring.
[CV-195] Swimm3R: Splatting with Medium-aware SfM for Underwater 3D Reconstruction
链接: https://arxiv.org/abs/2608.00950
作者: Minseong Kweon,Junaed Sattar
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Image and Video Processing (eess.IV)
备注: Project Page: this https URL
Abstract:We propose Swimm3R, a unified framework that combines medium-aware structure-from-motion (SfM) with Underwater Beta Splatting to address scattering- and attenuation-induced failures in underwater 3D reconstruction. Swimm3R distills in-air geometric priors into a feed-forward backbone and uses a physics head to regress underwater image-formation parameters, camera poses, and restored point clouds. Additionally, we introduce Underwater Beta Splatting, which extends Gaussian splatting with Beta primitives and scattering-aware geometric gradients for stable underwater geometry representation. We further establish the Barbados underwater video dataset to demonstrate the effectiveness of our method in challenging underwater environments. On this dataset, Swimm3R robustly recovers underwater scene structure under challenging scattering conditions, yielding coherent seafloor geometry. Using these predicted point clouds, the proposed Underwater Beta Splatting improves average PSNR by 1.47 dB over WaterSplatting while increasing downstream localization performance by 2.0 and 2.4 percentage points in RRA@15 and RTA@15, respectively.
[CV-196] GraRe: Grasp Candidate Re-Ranking for Frozen 6-DoF Grasp Detectors
链接: https://arxiv.org/abs/2608.00946
作者: Jibao Yuan,Yuhui Zhao,Yinzhen Lv,Chao Xu,Shun Li,Chenxi Deng,Shaofei Chen
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 24 pages, 32 figures. Supplementary material is included
Abstract:Existing 6-DoF grasp detectors typically rank grasp candidates by detector confidence. However, our analysis on GraspNet-1Billion shows that detector confidence is often poorly aligned with grasp quality, causing successful grasp candidates to be ranked too low during execution. Motivated by this observation, we formulate grasp candidate re-ranking as a separate task for frozen detectors, aiming to improve candidate ordering without changing the detector or its grasp candidates. We propose GraRe, which estimates grasp quality from candidate attributes, shell-stratified local geometry, and object context. Candidate attributes condition the local geometric and object-context representations, and a Transformer fuses all three feature types. The predicted quality is combined with detector confidence to produce the final ranking. Experiments on GraspNet-1Billion with three frozen detectors show consistent improvements, with gains of up to 13.60 points in Average AP. Real-robot experiments further demonstrate robust grasping in cluttered scenes. These results show that improving candidate ranking provides a practical way to enhance frozen 6-DoF grasp detectors.
[CV-197] Stipple: Real-Time Incremental Gaussian Splatting with Visual-Inertial Tracking
链接: https://arxiv.org/abs/2608.00931
作者: Kilian Northoff,Mateo de Mayo,Daniel Cremers
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting (3DGS) provides efficient rendering of photo-realistic scenes, but its heavy preprocessing and training steps make it a poor fit for applications that require real-time reconstruction in robotics or XR. This capability is important since it allows immediate feedback and interaction with new environments. Visual-inertial odometry (VIO) and simultaneous localization and mapping (VI-SLAM) systems, on the other hand, specifically target these real-time applications, which makes them a good choice for integration with 3DGS. We propose a new method that tracks and reconstructs simultaneously in real-time by leveraging an efficient visual-inertial tracking system based on Basalt together with a novel incremental method built on top of Brush, an efficient Rust-based GPU-vendor-agnostic implementation of 3D Gaussian Splatting. We show that many of the heavy preprocessing and training steps of 3DGS can be replaced with a more efficient incremental training strategy that has direct access to the information generated by the visual-inertial tracking system. Furthermore, we propose and combine multiple practical improvements to increase the efficiency of the training pipeline and adapt it to run in real-time, parallel to the tracking thread. This work highlights the value of exploiting the complementary nature of SLAM and 3DGS, and how that can lead to promising results for real-time 3D reconstruction.
[CV-198] Look Up and Look Back: Hidden Attention and Latent Orientation in a Frozen Foundation Model for Panoramic SLAM
链接: https://arxiv.org/abs/2608.00925
作者: Zhuang Xiong,Guohao Zhang,Chen Zhang,Zheyu Jiang,Yuchao Mei,Qingshan Xu,Wenbing Tao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Monocular panoramic SLAM benefits from substantial visual overlap under large camera rotations, yet remains prone to errors caused by camera tilt, scale drift, and false loop closures. We show that a frozen panoramic geometry foundation model provides useful internal cues beyond its explicit geometric outputs: intermediate tokens encode gravity in the camera frame, while cross-view attention provides a compatibility cue for potential revisits. Building on these cues, we present HALO-SLAM. A gravity readout enables IMU-free spherical upright canonicalization. For loop closure, we introduce a cost-aware three-stage cascade combining DBoW2 event-level retrieval, attention-based compatibility filtering, and dense geometric validation through symmetric submap augmentation. Accepted revisits yield pixel-aligned 3D–3D correspondences in both local gauges, from which robust \mathrmSim(3) constraints are estimated and jointly optimized with sequential constraints in a global pose graph. Across 125 sequences from five real-world panoramic benchmarks, our method achieves \textbf100% sequence success (\textbf125/125) under the stated criterion and the lowest ATE among the evaluated methods on all five benchmarks, reducing ATE by \textbf30–88% relative to the best ERP-native baseline on each benchmark.
[CV-199] PeCA: Palette Context Assisted Inference for Test-Time Paint-Bucket Colourisation on Animation Videos ECCV2026
链接: https://arxiv.org/abs/2608.00903
作者: Dongheng Lin,Jianbo Jiao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026, Project Page: this https URL
Abstract:In animation production, paint-bucket colourisation for hand-drawn animation is a labour-intensive procedure that assigns each enclosed region in line sketches a colour from reference design sheets. Recent automatic paint-bucket colourisation pipelines mirror this workflow via region correspondence, but correspondences can be brittle when regions are ambiguous fragments without proper context. In this paper, we propose Palette Context Assisted (PeCA), a new training-free, plug-and-play framework for animation video colourisation that aims to close this gap at test-time via reasoning over spatial and temporal contexts. Extensive experiments on existing benchmarks and a newly introduced long-video test case show consistent performance boosts.
[CV-200] MBO Scheme for Local Chan–Vese Segmentation
链接: https://arxiv.org/abs/2608.00893
作者: Kevin Bui,Adina Ciomaga
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Numerical Analysis (math.NA)
备注: Accepted to Image Processing On Line; Github link to code: this https URL
Abstract:Robust to intensity inhomogeneity, the local Chan–Vese (LCV) model extends the classical Chan–Vese (CV) image segmentation method by incorporating local statistical information around each pixel. Originally, the LCV model was solved using a finite difference scheme, following the approach used for the CV model. As an alternative to the finite difference scheme, a more efficient algorithm based on the Merriman-Bence-Osher (MBO) scheme was later developed for the CV model. In this paper, we derive a similar MBO-based algorithm to solve the LCV model and propose an efficient implementation. The algorithm is developed for both two-phase and multiphase segmentation, and an extension to color images is also discussed. To demonstrate the effectiveness of the proposed approach, we apply it to a variety of grayscale and color images, including medical and microscopy images.
[CV-201] Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation
链接: https://arxiv.org/abs/2608.00872
作者: Muhammad Irfan Khan,Eero Lehtonen,Joni Obradovic,Elina Kontio,Esa Alhoniemi,Suleiman A. Khan,Mojtaba Jafaritadi
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Federated Learning (FL) enables collaborative training of machine learning models across multiple institutions without sharing sensitive data, making it particularly suitable for medical imaging applications. However, heterogeneous data distributions across institutions and potential information leakage through model updates remain important challenges. In this work, we propose DP-SimAgg, a privacy-preserving federated learning framework that integrates similarity-weighted aggregation with a server-side differential privacy mechanism. The proposed method applies L2 clipping to bound collaborator updates, computes similarity-based aggregation weights to mitigate the effects of non-IID data distributions, and injects calibrated Gaussian noise at the central server, providing per-round privacy guarantees under the assumed sensitivity bound. The framework is implemented using Intel’s OpenFL platform and evaluated on the FeTS 2022 dataset consisting of 1251 multi-modal MRI scans for brain tumor segmentation. Experimental results demonstrate that DP-SimAgg maintains competitive segmentation performance while providing privacy protection. Under a strict per-round privacy budget (epsilon = 1, cumulative epsilon_total = 20 over 20 rounds), the method achieves Dice scores of 0.6357, 0.5305, and 0.5274 for the enhancing tumor (ET), tumor core (TC), and whole tumor (WT) regions, respectively. With a more relaxed per-round budget (epsilon = 10, cumulative epsilon_total = 200), performance approaches that of the non-private baseline while incorporating a central Gaussian mechanism with per-round (epsilon, delta)-DP accounting under the assumed sensitivity bound. These results highlight the potential of DP-SimAgg for enabling privacy-preserving collaborative learning in medical imaging applications.
[CV-202] PhenoStitch: Training-Free Panoptic Crop Mapping from Satellite Image Time Series
链接: https://arxiv.org/abs/2608.00870
作者: Xuechen Li
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Panoptic crop mapping requires both delineating individual agricultural parcels and assigning a crop type to each parcel from satellite image time series. Existing approaches typically rely on dense parcel-level annotations and task-specific model training, which limits their applicability to new regions and growing seasons. We introduce PhenoStitch, a panoptic crop-mapping pipeline that requires no task-specific gradient-based training. A frozen Segment Anything model first oversegments each patch into class-agnostic regions. For each region, optical NDVI and Sentinel-1 backscatter series are summarized by an analytic double-harmonic phenological signature. Adjacent regions are then merged into parcels by minimizing a Potts graph energy, and each parcel is classified by nearest-prototype matching using only (k) labeled parcels per class. A final topology-closure step produces the panoptic map. Under a matched budget of (k=20) parcels per class, corresponding to less than 1% of the available labels, PhenoStitch achieves 20.0 crop mIoU, 76.2 segmentation quality, and 6.2 panoptic quality on PASTIS-R under a 5-fold, 3-seed evaluation. It outperforms the evaluated frozen foundation-model, few-shot, and matched-budget supervised baselines under the same protocol, with a consistent ranking also observed on ZueriCrop. Ablation studies show that radar observations contribute the largest performance gain, while the graph-energy merge and compact phenological signature provide further improvements. These results demonstrate the effectiveness of combining label-free parcel delineation with few-shot phenological recognition for panoptic crop mapping under limited supervision.
[CV-203] Models as Tools: An Agent ic Coordination Framework for Unified Multimodal Visual Tracking
链接: https://arxiv.org/abs/2608.00847
作者: Wenrui Cai,Yuzhe Li,Qingjie Liu,Yunhong Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 21 pages, 15 Tables, 7 Figures
Abstract:Most current visual trackers adopt a matching-based architecture trained exclusively on tracking datasets, whose performance gains depend heavily on the length of the input context, and have now reached a bottleneck. While high-performance tracking increasingly relies on foundation models, existing methods use them monolithically, adapting a foundation model into a tracker or modify a segmentation foundation model into a tracking pipeline, which fails to exploit complementary strengths. Matching-based trackers excel at instance-level correspondence but lack semantic discrimination and fine-grained foreground perception, whereas segmentation foundation models produce precise masks yet struggle with instance discrimination and multimodal extension. Both paradigms also lack error-correction capabilities for long-term tracking. To address these issues, we propose ACTrack, an agentic coordination framework that treats heterogeneous models as invocable tools under an event-triggered mechanism. ACTrack coordinates a Tracker-based Instance Matching Tool for target discrimination, a SAM3 Motion Tool for mask-derived motion priors, a SAM3 Perception Tool for detecting distractors and instance-conflict cues, and a VLM Reprompt Tool activated only under persistent conflict to mitigate error accumulation. We design a complete tool-invocation trigger mechanism and an inter-tool coordination mechanism, enabling the complementary strengths of different model tools to be fully integrated. Experiments show that ACTrack substantially surpasses the strongest and the largest trackers on eight RGB benchmarks. Furthermore, a parameter-efficient adaptation strategy enables parameter sharing and reuse across tools, achieving unified multimodal tracking with only 30% trainable parameters while substantially outperforming prior methods on multimodal benchmarks such as LasHeR, VisEvent, TNL2K, and DepthTrack.
[CV-204] Deep Learning CNN and Recurrence Analysis for Alpha Gamma EEG Biomarkers in Frag ile X Syndrome
链接: https://arxiv.org/abs/2608.00835
作者: Zag ElSayed,Payton Siekierski,Jack Yanchen Liu,Ernest Pedapati
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Data Analysis, Statistics and Probability (physics.data-an); Neurons and Cognition (q-bio.NC)
备注: 14 pages, 4 figures, conference
Abstract:Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by reduced expression of fragile X mental retardation protein (FMRP), leading to disrupted synaptic plasticity, cortical hyperexcitability, and impaired network synchronization. Electroencephalography (EEG) provides a noninvasive window into these mechanisms and consistently reveals abnormalities in alpha (8 to 12 Hz) and gamma (30 to 100 Hz) oscillations that relate to inhibitory control, sensory processing, and cognition. This paper proposes a multi representation deep learning framework for automated characterization of FXS EEG phenotypes by integrating convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and recurrence plot (RP) analysis. Band limited EEG signals are decomposed into alpha and gamma components and transformed into complementary representations, including temporal feature sequences, time frequency maps, and RP images encoding the nonlinear recurrence structure. CNN modules learn discriminative spatial-spectral and dynamical textures from image based representations, while LSTM modules model temporal modulation of oscillatory activity; a hybrid CNN LSTM architecture jointly captures spatial, temporal, and nonlinear dependencies. Subject-independent evaluation demonstrates that the hybrid model outperforms single modality baselines, with gamma features providing strong discriminative power and alpha gamma integration yielding the best overall performance. These findings support deep learning with nonlinear representations as a scalable approach for EEG biomarker development in FXS, with potential utility for diagnosis, stratification, and treatment monitoring in translational settings.
[CV-205] AIMold: An Autonomous AI-based Pipeline for Complex Mold Design ECCV2026
链接: https://arxiv.org/abs/2608.00800
作者: Pengyun Qiu,Shuo Wang,Zeyuan Chen,Yihao Zhi,Chongjie Ye,Xiaoguang Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. Code is available at this https URL
Abstract:Injection molding is the cornerstone of mass-producing plastic components. While current algorithms can automate mold design for basic geometries using standard two-piece molds, complex parts featuring undercuts, side holes, or re-entrant features present a significant challenge. These geometries often necessitate auxiliary components beyond the primary upper and lower molds. In practice, designing these intricate assemblies is a laborious process that relies heavily on expert knowledge. Furthermore, the scarcity of public datasets has hindered the development of effective learning-based solutions. To bridge these gaps, we introduce MoldCAD, a curated dataset that pairs complex single-body CAD parts with industry-standard mold assemblies. Each entry includes the upper and lower molds, parting surfaces, demolding orientations, and necessary auxiliary components. The dataset comprises 4,934 CAD models and over 3,850 mold assemblies, totaling more than 23k individual models. Building upon this dataset, we propose a comprehensive pipeline that predicts demolding orientations, identifies auxiliary components, and constructs parting surfaces to derive a complete, manufacturing-ready mold assembly for downstream CAD/CAM workflows. Our results demonstrate a promising path toward fully automated industrial mold design and contribute to the broader advancement of manufacturing-aware CAD generation.
[CV-206] CADENA: Stepwise CAD Reverse Engineering
链接: https://arxiv.org/abs/2608.00799
作者: Soslan Kabisov,Gennadiy Savrasov,Maksim Elistratov,Antonio Rodriguez,Daniil Ignatiev,Nikita Gavrilov,Rustam Uzdenov,Alexey I. Boyko,Igor Pasechnik,Anton Konushin,Andrey Kuznetsov,Dmitrii Zhemchuzhnikov
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code: this https URL Model: this https URL Benchmark: this https URL
Abstract:Computer-Aided Design (CAD) underpins modern engineering, yet converting existing shapes into editable models still demands substantial expert effort. Most AI systems emit the entire CAD program in a single pass, never inspecting the intermediate geometry. In contrast, human engineers build a part feature by feature, checking after each operation what remains to be modeled. We introduce CADENA (Spanish for “chain”), a model that reconstructs a 3D mesh as a parametric CAD program, growing its sequence of operations one at a time and comparing the target with the currently predicted geometry at every step. We also address the lack of benchmarks for evaluating reverse-engineering methods on mechanical parts, introducing CADENA-Bench, a benchmark that measures performance across categories of mechanical parts. CADENA outperforms prior methods on CADENA-Bench and on the DeepCAD, Fusion 360, and MCB datasets. Code is available at this https URL, model weights at this https URL, and CADENA-Bench at this https URL.
[CV-207] ChordVideo: One-Step Training-Free Temporally Consistent Video Editing via Low-Energy Transport
链接: https://arxiv.org/abs/2608.00769
作者: Zhiqiang Lao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:One-step text-to-image models enable training-free, inversion-free editing with only 1–2 network function evaluations (NFE), while ChordEdit stabilizes such edits through low-energy smoothing along sampling time. Applied independently to video frames, however, it produces temporal flicker and edit-strength drift. We introduce \textbfChordVideo, which extends the same low-energy principle to video time through shared noise, motion-aligned causal aggregation of per-frame Chord fields, and an optional temporally smoothed proximal correction. We derive a warping-error bound that separates motion bias from stochastic flicker and predicts diminishing returns with larger temporal windows. On TGVE/DAVIS with two one-step backbones, ChordVideo reduces warping error by \textbf78% and flicker by \textbf49%, improves CLIP frame consistency by \textbf9–10 points, and increases background PSNR by about \textbf1.5,dB, while retaining \textbf2 NFE/frame. Compared with seven multi-step editors, it achieves competitive temporal consistency and source preservation using \textbf10–60 \times fewer model steps per clip
[CV-208] Hi-TOPS: Hierarchical Topology-aware Scoring Prior for 3D Part Decomposition
链接: https://arxiv.org/abs/2608.00767
作者: Ruoyu Wu,Zhenhong Sun,Xiaoming Gong,Yuxin Xian,Zhi Wang,Yawen Chen,Huadong Mo,Daoyi Dong
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate 3D part decomposition requires separating shapes into structurally meaningful components with precise boundaries while preserving articulation seams and thin attachments. Existing approaches often suffer from a structural-scale mismatch: geometric evidence for separation is most reliable at the meso scale, yet many pipelines operate either too globally to respect joints or too locally to remain robust to noise. We propose Hi-TOPS, a Hierarchical Topology-aware Scoring Prior that aggregates complementary intrinsic cues into a multi-resolution Flow-Freeze field. Flow regions provide expandable support for primitive coverage, while Freeze regions restrict growth near articulations and thin structures. A TSDF-guided body-surface superquadric fitter then captures dominant cores and residual surface structures, followed by SQ-to-mesh assignment for connected, boundary-aligned parts. Across diverse benchmarks, Hi-TOPS delivers stable, editable decompositions without semantic supervision or 2D foundation priors.
[CV-209] NISF: Geometrically-grounded implicit representations of 3Dtime cardiac function from 2D short- and long-axis MR views
链接: https://arxiv.org/abs/2608.00752
作者: Nil Stolt-Ansó,Maik Dannecker,Steven Jia,Julian McGinnis,Daniel Rueckert
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Clinical acquisition in cardiac magnetic resonance (CMR) imaging involves obtaining cross-sectional planes of the heart along the radial and longitudinal directions. Despite these planes being 2D cross-sectional images of the heart, radiologists understand the 3D spatial and continuous temporal nature of the organ being imaged. The same can not be said about the conventional deep learning architectures used to process CMR images, which rely on in-plane and grid-based operations, and are hence unable to organically integrate information from all imaging planes. This paper builds upon previous work on neural implicit segmentation functions (NISF) to overcome unaddressed challenges in cardiac function modeling in the CMR domain. For a given subject, our architecture builds a shared 3D+time representations from all available acquisition planes regardless of orientation. By design, predictions along any imaging plane orientation are cross-sections of the same 3D representation, leading to spatio-temporal consistency across all slices. Moreover, our architecture makes the rotation and translation parameters of imaging planes learnable, allowing us to correct for the commonplace respiratory and patient motion between slice acquisitions under a rigid assumption. Furthermore, interpolation of intensities and segmentation can be performed in 4D at any desired resolution. We perform our study on a 120 subject sub-cohort of CMR imaging data from the UK-Biobank. Our in-plane segmentation performance is on-par with existing CMR segmentation methods and explore how the majority of failure cases arise from limitations in the ground-truth segmentation, for which our representations make predictions with better anatomical accuracy than its original training data. We also evaluate our motion-correction capabilities, displaying quantitative and qualitative improvements in slice alignment.
[CV-210] LUT: Latent Utility Training for Visual Reasoning
链接: https://arxiv.org/abs/2608.00743
作者: Jiaxuan Kang,Siyu Chen,Mingda Li,Mingjie Liu,Tianyue Wang,Zhaoyang Wei,Yongheng Zhang,Yanchao Hao,Zheng Wei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal large language models have advanced visual understanding, yet perception-intensive reasoning remains challenging. Recent latent visual reasoning methods introduce hidden-space computation before answering, but they often rely on costly intermediate supervision, such as bounding boxes, sketches, or interleaved rationales. These strategies focus on how latent states should be shaped, but do not explicitly assess whether the latent is useful for the final answer. We propose LUT, a latent reasoning framework trained with only standard VQA pairs. LUT centers training on Latent Utility at two levels. At the trajectory level, we propose Utility-Aware Latent Distillation SFT, which explores answer-relevant latent trajectories, selects qualified trajectories by their information gain, and distills more reliable and learnable supervision through curriculum learning. At the step level, we propose Latent Attribution Policy Optimization, which uses answer-to-latent attribution to differentially optimize latent steps during reinforcement learning. Experiments on perception-intensive visual reasoning benchmarks show that LUT outperforms previous latent reasoning methods and remains competitive with latent-text interleaved methods with lower annotation cost.
[CV-211] MDTD-ArtIR: Benchmarking Image Editing and Restoration Models for Art Image Restoration under Texture-Overlay Degradations
链接: https://arxiv.org/abs/2608.00736
作者: Mridula Vijendran,Shuang Chen,Hubert P. H. Shum
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 6 figures, 6 tables. Preprint submitted to Elsevier Journal of Visual Communication and Image Representation
Abstract:Restoring severely degraded visual media still remains a formidable challenge, as existing methods often hallucinate unnatural textures and contents, struggle with preserving color and texture, or fail to leverage partially retained image information. Existing restoration benchmarks assume known degradation operators and fail to capture the complex characteristics of artistic damage such as cracks, stains, and color/texture deviation. We introduce a controlled benchmark for blind restoration of semantic, semi-transparent image media degradations, accompanied by a new, publicly open degradation alpha texture mask dataset MDTD-Art. We present a new dataset and benchmark evaluating state-of-the-art universal restoration models against image editing and vision-language models across varying mask opacity levels. Our experiments demonstrate that image editing models consistently outperform specialized restoration architectures for arbitrary degradations, with performance gains amplified by structured prompt engineering emphasizing detail preservation and structural consistency. These findings position recoverable semantic information and prompt controllability as critical factors in art image restoration.
[CV-212] Mitigating Backdoors via Decoy Shortcuts and Knowledge Decoupling IJCAI2026
链接: https://arxiv.org/abs/2608.00732
作者: Zixuan Zhu,Rui Wang,Lihua Jing,Jinwen Zhong
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages; 11 figures; 14Tables; Accept by IJCAI 2026
Abstract:Backdoor attacks pose a serious threat to deep neural networks, especially when training relies on third-party data, allowing adversaries to inject malicious behaviors through data poisoning. In this work, we reveal that backdoor behaviors tend to be absorbed by a simpler parallel branch when jointly trained with the main network. Motivated by this insight, we propose Trapping and Removing (TR), a simple yet effective training-time defense that introduces a lightweight shortcut branch as a “honeypot” to trap backdoor knowledge. After training, backdoors can be removed by discarding the shortcut, without requiring any additional data. To further enhance backdoor isolation while maintaining benign performance, we design a knowledge decoupling strategy with entropy-based weight assignment, encouraging poisoned samples to flow through the honeypot while guiding the main network to focus on benign learning. In addition, we introduce an automatic shortcut generation strategy to improve generalization across model architectures. Extensive experiments on four benchmark datasets and five model architectures demonstrate that our approach effectively mitigates a wide range of backdoor attacks while preserving performance on benign data. Code: this https URLthis http URL.
[CV-213] Foveated Probes Recover Localized Binding Information in Vision Foundation Models
链接: https://arxiv.org/abs/2608.00726
作者: Mateusz Michalkiewicz,Mahsa Baktashmotlagh,Guha Balakrishnan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 6 figures
Abstract:Frozen vision foundation models are commonly evaluated through a single global image embedding, but this interface can conflate missing information with information lost at readout time. We study this distinction by keeping a pretrained vision encoder frozen and varying only the readout applied to its final patch tokens. We compare standard global readouts against a lightweight foveated readout, which attention-pools patch tokens using a learned or question-conditioned query, and against an oracle readout with access to the annotated target region. We evaluate these interfaces on three localized binding problems: a controlled synthetic color–shape binding task under clutter, a color-free crowded shape-detection variant, and a GQA-derived natural-image task where paired questions ask for the colors of different same-category objects in the same image. Global readouts perform near perfectly when the synthetic target appears alone, but collapse under clutter and counterfactual target edits, whereas the foveated readout recovers most of the oracle-accessible signal. On the GQA-derived task, question-independent global image vectors improve only modestly over question-only priors, while question-conditioned foveation substantially improves paired localized color accuracy. A counterfactual nuisance-to-signal ratio explains the synthetic failures: global pooling dilutes localized label-changing evidence while exposing the probe to nuisance variation from irrelevant objects. These results indicate that apparent spatial blindness in frozen vision models can arise from the global embedding interface rather than from an absence of spatial information in the frozen patch tokens.
[CV-214] Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection
链接: https://arxiv.org/abs/2608.00716
作者: Jun Nie,Yonggang Zhang,Tongliang Liu,Yiu-ming Cheung,Bo Han,Xinmei Tian
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 19 pages, 10 figures
Abstract:Robust detection of generated images is critical to counter the misuse of generative models. Existing methods primarily depend on learning from human-annotated training datasets, limiting their generalization to unseen distributions. In contrast, large-scale vision models (LVMs) pre-trained on web-scale datasets exhibit exceptional generalization power through exposure to diverse distributions, offering a transformative paradigm for this task. However, our experimental results reveal that LVMs pre-trained on natural-image-dominated data can effectively capture the features of both natural and generated images, yielding comparably low losses and thus limited discriminative capacity between them. This prompts a key question: When and how do LVMs exhibit different behaviors when capturing features of natural and generated images? This investigation reveals an insight: during unlearning, LVMs exhibit disparate forgetting dynamics with feature degradation for generated images escalating faster than natural ones. Inspired by the disparate dynamics, we introduce two detection methods: 1) data-free detection, which prunes model parameters to induce unlearning without data access, and 2) data-driven detection, which optimizes LVMs to unlearn knowledge tied to generated images. Extensive experiments conducted on various benchmarks demonstrate that our unlearning-based approach outperforms conventional detection methods. By recasting the detection task as a problem of machine unlearning, our work establishes a new paradigm for generated image detection.
[CV-215] Coverag e-Driven Adaptive Keyframe Selection for Video Understanding
链接: https://arxiv.org/abs/2608.00714
作者: Junyang Zhang,Puhan Luo,Chen Tang,Yuxi Shi,Xiang-Yang Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in large vision-language models (LVLMs) have enabled long-video understanding and analysis. However, processing the large number of frames in a video incurs substantial computational overhead. Existing methods reduce LVLM inference costs by scoring frame-query relevance before inference and selecting keyframes accordingly. Nevertheless, the distribution of relevant frames varies across queries, and these methods often need to score hundreds or thousands of frames. To address this limitation, we propose CSES, a training-free semantic keyframe selector that adaptively determines the numbers of frames to score and keyframes to select. CSES estimates the prominence of the frame-query relevance profile to guide active acquisition and adapt the temporal coverage of each input. It then formulates keyframe selection as a coverage problem that jointly accounts for semantic relevance, temporal redundancy, and visual redundancy. Active acquisition and keyframe selection terminate based on coverage saturation. The selection objective is monotone and submodular, enabling greedy optimization with a standard approximation guarantee. Experiments with four LVLMs on two benchmarks show that our method preserves accuracy while scoring 4 - 13\times fewer frames and selecting 18.4% - 20.5% fewer input keyframes than existing baselines. CSES further achieves a 3.1 - 5.4\times speedup in frame selection over baselines.
[CV-216] AeroLLE: Constrained Pseudo-Supervision for Nighttime Aerial Image Enhancement with the AeroNight-1.5K Benchmark
链接: https://arxiv.org/abs/2608.00702
作者: Wei Lu,Hongyuan Liu,Si-Bao Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Nighttime aerial image enhancement is challenged by spatially nonuniform exposure, mixed illumination, and weak structural evidence, while registered normal-light targets are difficult to capture from moving platforms. Generated normal-light images provide practical appearance guidance but may alter geometry or texture. We introduce \aeronight, comprising 1,500 real nighttime aerial RGB images: 1,300 inputs are associated with manually screened pseudo-references, and 200 inputs support unpaired evaluation. We propose AeroLLE, a two-stage framework that first recovers visibility with an HVI Base Enhancer and then performs Spatially Adaptive Exposure–Color Calibration (SAECC). After the Base Enhancer is selected and frozen, SAECC predicts bounded, low-resolution RGB gain and bias fields, restricting the magnitude and spatial variation of the second-stage correction. Experiments under complementary pseudo-paired and unpaired protocols demonstrate improved agreement with screened appearance targets, together with more balanced exposure and color correction across diverse nighttime aerial scenes. These results support constrained, stage-specific calibration as a practical strategy for learning from generated appearance guidance when registered aerial references are unavailable.
[CV-217] FreqAnchorAD: Language-Free Zero-Shot Anomaly Detection via Frequency-Deviation Anchoring
链接: https://arxiv.org/abs/2608.00695
作者: Jianfeng Qiu,Peiyuan Li,Juan Xie,Xueliang Ma,Sihang Zhou,Yanning Hou,Ke Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages,4 figures,7 tables
Abstract:Zero-shot anomaly detection (ZSAD) aims to detect anomalies and localize defective regions in unseen target domains without target training data. Recent ZSAD methods build on pretrained vision models, particularly CLIP, and construct normal and anomaly references from textual prompts or learnable visual representations. These methods perform anomaly discrimination primarily in spatial feature spaces, where subtle changes in texture, boundaries, and local structures can be confused with normal appearance variations. Although inconspicuous spatially, such defects can disrupt local texture regularity or boundary continuity, inducing response deviations across frequency bands. However, existing ZSAD methods do not explicitly model these frequency-dependent characteristics. Our image-domain analysis reveals that local defects exhibit spatial-frequency deviations from normal references across low-, middle-, and high-frequency bands, indicating that anomaly evidence is not universally dominated by high-frequency responses. Motivated by this observation, we propose FreqAnchorAD, a frequency-aware framework that organizes frequency-enhanced responses for anchor-relative anomaly discrimination. Specifically, the Local Frequency Compensation Module (LFCM) enhances intermediate patch tokens with local spatial-frequency cues. The Frequency-Deviation Anchor Projector (FDAP), our core discrimination module, organizes enhanced responses along a source-derived channel coordinate and measures anomaly evidence through relative similarity to normal and anomaly anchors. Finally, Asymmetric Anchor Supervision (AAS) stabilizes normal-anchor alignment while preserving diverse anomaly patterns. Experiments on thirteen industrial and medical benchmarks show that FreqAnchorAD achieves state-of-the-art mean performance in image-level anomaly recognition and pixel-level defect localization.
[CV-218] E2Pano: Learning Event-to-Panorama Image Reconstruction
链接: https://arxiv.org/abs/2608.00694
作者: Zhenyang Li,Zongqi He,Jia Pan,Shijie Lin,Yifan Peng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 9 figures
Abstract:Event cameras offer microsecond-level temporal resolution and high dynamic range, potentially facilitating motion-blur-free panoramic imaging from fast rotational scanning. Nonetheless, existing optimization-based methods remain computationally demanding, while prior learning-based reconstruction methods are largely designed for perspective imagery and lack geometry-aware support for panoramic outputs. We present E2Pano, a geometry-guided event-to-panorama pipeline with an end-to-end learnable photometric reconstruction stage. Our framework preserves real spherical coordinates from geometric mapping throughout the pipeline, employs a lightweight enhancement module with frequency-domain supervision to bridge the event-image domain gap, and leverages a spherical Transformer with 3D positional embeddings for photometric reconstruction. Experiments on synthetic data and captured rotational scans show improved reconstruction quality and lower photometric reconstruction cost than optimization-based baselines, together with encouraging transfer to real captures under our acquisition protocol despite training purely on synthetic data. Additionally, we construct PanoScan, a dataset with 4,370 synthetic and 30 real-world panoramic scenes paired with event streams. Our dataset and code will be released.
[CV-219] Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression
链接: https://arxiv.org/abs/2608.00687
作者: Yihan Qiu,Xiaodong Lin,Baoquan Zhao,Hailong Jiao,Ge Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:LiDAR point clouds provide explicit, deterministic physical boundaries critical for collaborative safety-critical perception. However, wireless channels inherently impair and corrupt transmitted signals. Existing robust frameworks (such as deep JSCC or MDC) attempt to counter these channel impairments through statistical or parametric estimation, turning exact physical measurements into unverified algorithmic estimates. To address this, we propose Proteus, a learned LiDAR codec operating on 2D range images. By decoupling the frame representation into independent coders for the \textbfsignificant range bit-planes (SIG) and the \textbfinsignificant range bit-planes and attributes (INS), Proteus achieves overall stream-level truncation robustness. The non-truncatable SIG block encodes the most significant range bit-planes to establish a necessary, self-contained perceptual lower bound, below which the reconstructed point cloud is severely degraded. Meanwhile, INS employs bit-plane slicing representation and coding, ensuring that range truncation mathematically maps to a deterministic spatial precision degradation. Subordinate attributes are reconstructed via a hybrid lossless-predictive method, leveraging the decoded geometry as a strong structural prior for fine-grained approximation. Furthermore, strategic ordering within INS prioritizes geometry over attributes under bandwidth drops. Experimental results on the Waymo Open Dataset and SemanticKITTI demonstrate that Proteus tolerates up to approximately 70% bitstream truncation, while outperforming established standards (G-PCC, Draco, and JPEG XL) and the representative learned compressor Unicorn under ideal channel conditions.
[CV-220] BRIC-Net: Boundary-Reliable Illumination-Color Interaction for Remote Sensing Image Deshadowing
链接: https://arxiv.org/abs/2608.00682
作者: Wei Lu,Yi Liu,Si-Bao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Shadows in remote sensing images obscure surface appearance and disrupt radiometric continuity, reducing the reliability of visual interpretation and downstream analysis. Remote sensing image deshadowing is an ill-posed inverse problem that requires spatially varying illumination recovery while preserving chromatic and radiometric consistency in non-shadow regions. Existing methods commonly rely on hard shadow masks for compensation or directly regress RGB intensities. Hard masks may inadequately model gradual penumbra variations and are sensitive to localization errors, often producing residual shadows or halo artifacts; direct RGB regression entangles illumination recovery with chromatic reconstruction and can introduce color casts. To this end, we propose the Boundary-Reliable Illumination-Color Interaction Network (BRIC-Net), which decouples these failures at different representation levels. A Lightness Reliability Prior (LRP) derives reliability-aware guidance from CIELAB statistics. Boundary-Adaptive Gated Mixing (BAGM) performs gated interpolation between shallow RGB and lightness features around uncertain transitions, while Spatial-Channel Mutual Modulation (SCMM) coordinates deeper spatial and channel responses for appearance-preserving illumination recovery. BRIC-Net achieves 29.46~dB full-image peak signal-to-noise ratio (PSNR) on AeroDS-Syn and 27.96~dB on SRGTA. It also obtains the lowest Perception-based Image Quality Evaluator (PIQE) scores on AISD and AeroDS-Real. Region-wise evaluations and component ablations further support its effectiveness in shadow recovery and non-shadow preservation.
[CV-221] Breaking the Horizontal Prior: From Long-Tailed Orientation Bias to Roll-Robust Monocular Depth Estimation
链接: https://arxiv.org/abs/2608.00678
作者: Kaihua Tang,Ziqing Xia,Xiaoxu Zheng,Xiaoxue Zhang,Michael Bi Mi,Zhan Xu,Dave Zhenyu Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: The code is publicly available on GitHub: this https URL
Abstract:Despite recent advances in Monocular Depth Estimation, state-of-the-art depth foundation models remain vulnerable to robustness issues. Particularly, even slight camera rolls can result in substantial degradation in depth estimations. We attribute this problem to a previously overlooked phenomenon, termed the Horizontal Prior, which is a manifestation of long-tailed distribution bias: most training images are captured in approximately horizontal orientations due to human visual preferences and photographic habits. While intuitive remedies such as re-balanced data augmentation and horizon leveling provide partial improvements, they fail to fully address the issue. In this paper, we introduce Invariant Depth Constraint (ID-Constraint), a training-time supervision strategy that improves roll robustness by fine-tuning and jointly regularizing the depth backbone with a series of geometric and spatial reasoning tasks. These auxiliary objectives encourage the backbone to learn rotation-stable, depth-relevant representations, while the auxiliary prediction heads are discarded after training, leaving the original inference architecture unchanged. Extensive experiments on five benchmark datasets across four roll settings demonstrate the effectiveness of the proposed method.
[CV-222] CopyCat: Improving Fine-Grained Subject Consistency in Subject-to-Image Models within Seconds
链接: https://arxiv.org/abs/2608.00674
作者: Peng Zheng,Ruiqi Liu,Rui Ma,Zuxuan Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent subject-to-image models have achieved impressive progress in personalized image generation, yet they still struggle to preserve fine-grained subject-specific details. A major reason is the lack of high-quality fine-grained identity supervision: real paired data are expensive to collect, while synthesized training pairs often preserve only coarse subject appearance and fail to capture subtle subject-specific details. In this work, we propose CopyCat, a lightweight model-refinement framework that improves fine-grained subject consistency within only a few seconds. CopyCat performs a one-time refinement of a pretrained subject-to-image model by attaching a lightweight Fine-grained Consistency LoRA (FCLoRA) and optimizing it using a single proxy image, which is used as both the conditioning image and the reconstruction target. This exact self-reconstruction objective substantially simplifies the optimization task, enabling effective fine-grained refinement within only a few seconds. The refinement is performed only once; the resulting model can be directly applied to diverse unseen reference subjects and prompts without further subject-specific optimization. We further revisit subject-to-image LoRA training in double-stream diffusion transformers and find that adapting only the visual stream consistently improves subject consistency. Extensive experiments on DreamBench and XVerseBench demonstrate consistent improvements in fine-grained subject consistency across representative subject-to-image models under both single- and multi-subject settings.
[CV-223] Geometry-guided Emotion Modulation for Controllable and Photorealistic Emotional Talking Face Generation
链接: https://arxiv.org/abs/2608.00663
作者: Chenggong Hu,Shaoyin Ma,Yi Wang,Li Sun,Mingli Song,Jie Song
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 11 figures
Abstract:Audio-driven emotional talking face generation aims to synthesize realistic videos with expressive facial dynamics. However, existing methods struggle to balance controllability and visual fidelity. Although implicit representations capture rich semantics, they lack structural guidance, often resulting in averaged emotional expressions. In contrast, explicit geometric methods offer better control over facial expressions but tend to sacrifice high-frequency texture details. To address it, we propose GemTalk, a diffusion-based framework that combines the semantic richness of implicit representations with the structural precision of explicit geometric priors. We introduce a Vision-guided Audio Emotion Projection (V-AEP) module to extract implicit emotional lip and expression features. At the same time, a Diffusion-based Geometric Priors Generator (D-GPG) generates identity-aware blendshape coefficients as explicit structural priors. Crucially, our Geometry-guided Emotion Modulation (GEM) module leverages these geometric priors to recalibrate the magnitude of implicit features, enabling precise, continuous control over emotional expressions, especially emotion intensity, without sacrificing visual quality. Extensive experiments show GemTalk achieves superior performance in photo-realism, and facial emotional dynamics.
[CV-224] PixelSR: Efficient Screen Content Super-Resolution via Pixel Classification
链接: https://arxiv.org/abs/2608.00646
作者: Zhiheng Li,Lei Chen,Jie Zhou,Jiwen Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Screen content images are generally composed of texts and graphics. Compared to natural images, these man-made images contain a large quantity of sharp but repetitive structures. However, existing works in screen content super-resolution underutilize the special characteristics of screen content, leaving a large room to improve model performance and speed up. In this paper, we propose PixelSR, a simple yet effective method to improve super-resolution performance but with faster inference speed. To improve model performance, we classify pixels via pixel binning to compute content attention in the training phase. Specifically, after binning pixels into content-dependent groups, content attention is aggregated from pixel features within each group to introduce a content-dependent and non-local receptive field for every pixel. In the testing phase, we utilize the properties of self-repetitiveness and redundancy in screen content to speed up inference without the loss of model performance. We divide targeted high-resolution pixels into three types, which are unique pixels, repeated pixels, and background pixels for each test image. We conduct conventional network processing on unique pixels and cache their predictions in the on-the-fly lookup table. For repeated pixels which have appeared in unique pixels, we directly retrieve prediction results from the lookup table without network processing. For background pixels, we use the nearest neighbor algorithm to generate high-resolution pixels. The on-the-fly lookup table is cleaned and repeats the procedure above for the next test image. Experiments show our PixelSR achieves state-of-the-art performance with shorter inference time in screen content super-resolution.
[CV-225] WiFuse: An Attention Mechanism for Human Activity Recognition using Fused CSI Amplitude and Delay-Doppler Channel Features
链接: https://arxiv.org/abs/2608.00642
作者: Alison M. Fernandes,Hermes I. Del Monego,Bruno S. Chang,Anelise Munaretto,Hélder M. Fontes,Rui L. Campos
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 6 figures
Abstract:Recently, Wi-Fi sensing has played a significant role in Human Activity Recognition (HAR), as it enables the detection of various activities using only Wi-Fi signals, ensuring privacy and remaining non-intrusive for the user. However, environmental characteristics such as reflective surfaces, hardware offsets, and other physical impairments affect recognition by the neural network, subsequently causing errors and significantly reducing model accuracy. To overcome this problem we present the WiFuse framework, a dual-stream Channel State Information (CSI) framework for human activity recognition (HAR) that pairs denoised time-domain amplitude variations with 2D-FFT-derived Delay-Doppler motion representations computed from the sanitized channel phase. The fused representation feeds a hybrid ResNet-Temporal Convolutional Network (TCN) neural architecture augmented with channel and spatio-temporal attention, where the ResNet extracts spatial-spectral features and the TCN models long-range temporal dependencies; a decoupled two-stage transfer learning strategy is employed to improve optimization stability and feature reuse. We conduct extensive experiments on two public datasets, including comparisons against state-of-the-art methods and alternative hybrid architectures, ablation studies, and cross-dataset and domain-adaptation evaluations. The proposed framework reaches an overall accuracy of up to 95.28% across the four environments of the XRF55 dataset and up to 98.20% on the multi-user Wi-MIR dataset. Overall, the results indicate that combining amplitude and Delay-Doppler representations within a dual-stream strategy, enhanced by transfer learning, improves recognition performance under conditions that typically degrade deep neural networks, such as class overlap, multipath propagation, noise, and interference.
[CV-226] Where Does Generative Difficulty Reside? An Empirical Study of Target Representations
链接: https://arxiv.org/abs/2608.00626
作者: Marcel Plocher,Bernhard Schölkopf,Andreas Geiger,Gege Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: TL;DR: Across pixels, SD-VAE, DINOv2, and MAE, we find that target representations are not interchangeable: they shift difficulty between contextual modeling, per-token denoising, and guidance, producing distinct optimization and diversity trade-offs
Abstract:The target representation defines the distribution an image generator must learn, yet it is often treated as an interchangeable interface. This assumption is particularly questionable for continuous masked generators, which combine contextual inference from visible tokens with conditional modeling of each missing token. We study raw pixels, SD-VAE latents and DINOv2 as well as MAE representation-autoencoder features within a unified masked autoregressive rectified-flow model. Under a shared ImageNet training budget, these spaces exhibit distinct optimization and inference regimes. DINOv2 converges fastest in both iterations and computation but benefits strongly from a wider local denoiser and direct context fusion. Pixels optimize substantially more slowly and require a different prediction, masking, and guidance configuration. MAE reconstructs images more faithfully and exhibits clear semantic clustering, yet produces generations substantially worse than DINOv2. The representations also respond differently to classifier-free guidance and occupy distinct precision-recall trade-offs. Together, our results show that compression, reconstruction fidelity, token dimensionality, and visible semantic clustering do not individually predict generative behavior. Instead, target representations redistribute difficulty across contextual modeling, per-token denoising, and inference-time distributional control.
[CV-227] Diagnosing Under-Development of Irreversible Processes in Video Generation
链接: https://arxiv.org/abs/2608.00617
作者: Jian Xu,Yanning Wu,Delu Zeng,John Paisley,Qibin Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Many physical attributes are \emphirreversible: ice melts but does not re-freeze, paper chars but does not un-burn. Do video generators respect this? We show the question is hard to measure, and that what can be measured reliably is \emphdevelopment rather than reversal. Metrics of local reversal are null-degenerate: a per-clip violation rate scores 0.50 on pure noise, and a variance-normalized reversal residual sits at its noise ceiling. What survives null-testing is a two-part protocol: progress (a directional attribute correlation) and a stasis rate. Under this protocol, generated video separates cleanly from real footage, and the gap is human-validated. Across seven text-to-video models, real reference footage advances ( \rho=+0.40 , 35% static) while every generator shows near-zero progress and 92 – 100% stasis; nine annotators rate real footage far above generated ( 2.75 vs.\ 0.99 on a 0 – 4 scale). The reliable finding is \emphunder-development: generators barely advance irreversible attributes rather than reversing them. As a complementary mechanism, we show that post-hoc readout guidance is gameable, whereas enforcing monotonicity by construction in a disentangled attribute latent removes the gameable readout, validated in controlled and semi-synthetic settings.
[CV-228] InstancePin: Instance-Addressable Layout-to-Image Diffusion via Coordinate Pinning
链接: https://arxiv.org/abs/2608.00588
作者: Chaoyue Wu,Yunfei Zhang,Si Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to PRCV 2026
Abstract:Layout-to-image diffusion models have achieved impressive semantic controllability by conditioning generation on category-level segmentation maps. However, such category-aligned control is not necessarily instance-addressable: multiple nearby objects from the same category are often treated as a shared semantic region, leading to ambiguous boundaries, averaged appearances, and feature confusion among instances. This limitation is particularly evident in urban scene synthesis, where small and crowded pedestrians or vehicles require fine-grained instance separation while preserving global scene consistency. In this paper, we propose InstancePin, an instance-addressable layout-to-image diffusion framework that pins each object instance with an explicit coordinate anchor. Instead of directly injecting instance masks into the pretrained backbone, InstancePin introduces an independent instance-aware adapter to preserve the category-level generation prior while learning instance-specific spatial control. For each instance, its center coordinate is encoded with Fourier features and projected into a coordinate token, which serves as a spatial anchor queried by latent image features through coordinate pinning attention. To make these anchors spatially meaningful, we further supervise the coordinate attention maps with instance regions, encouraging each coordinate token to activate its corresponding object area. Finally, an instance-mask guided fusion module routes pretrained backbone features to non-instance regions and adapter features to instance regions, enabling local instance refinement without sacrificing global semantic fidelity. Extensive experiments on Cityscapes demonstrate that InstancePin mitigates instance entanglement in dense layouts and improves both image fidelity and semantic consistency.
[CV-229] Representation Transfer of Foundation Models for Ultra-Widefield Retinal Imaging
链接: https://arxiv.org/abs/2608.00586
作者: Mingya Alexa Gong,Da Ma,Lovre Antonio Budimir,Ivana Matovinovic,Sven Loncaric,Myeong Jin Ju,Yukun Zhou,Siegfried K. Wagner,Pearse A. Keane,Marinko V. Sarunic
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 7 figures
Abstract:Despite the widespread adoption of foundation models as feature extractors for medical imaging, relatively little is understood about how different pretraining strategies influence the transferability of learned representations to weakly supervised ophthalmic imaging tasks. We investigate this question in ultra-widefield (UWF) retinal imaging by evaluating foundation model representations within a patch-based multiple instance learning (MIL) framework for disease classification on UWF images. We compare Vision Transformer encoders pretrained with supervised, Masked Autoencoder (MAE), and self-distillation objectives, while keeping the downstream aggregation architecture unchanged. Within a controlled comparison of ViT-B encoders pretrained on ImageNet-1k, the choice of pretraining objective substantially influenced frozen representation transfer, with supervised and self-distillation-based models outperforming MAE. A contemporary DINOv3 model pretrained at a larger scale achieved the strongest overall performance, with a quadratic weighted kappa of 0.863 for five-class diabetic retinopathy grading, comparable with DINOv1. Attention analysis further revealed distinct patch-aggregation behaviours associated with the different pretrained representations, while partial fine-tuning substantially reduced the performance gap for MAE. These findings suggest that pretraining strategy influences both representation transferability and the subsequent aggregation of patch-level evidence within MIL, resulting in differences in downstream classification performance.
[CV-230] Element-Aware Group Learning for E-Commerce Image Generation
链接: https://arxiv.org/abs/2608.00584
作者: Jingtong Chen,Jiahui Wang,Xue Zhao,ShaoGuo Liu,Minghao Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Recent advances in image generation and editing have made prompt quality a key bottleneck for e-commerce creatives. Vision-language models (VLMs) can generate image-editing prompts from product images and metadata, but further improving their prompt-writing capabilities requires post-training with feedback from the generated images. Group Relative Policy Optimization (GRPO) is a natural framework for such outcome-level reward optimization. However, it assigns credit only at the full-prompt level, even though image quality often depends on specific design elements such as composition, background, and the presentation of selling points. Existing fine-grained credit assignment methods typically require step-level supervision or learned critics. To address this, we propose EAGLE-GRPO (Element-Aware Group Learning for E-Commerce Image Generation), which decomposes the group-centered reward over predefined elements. We cast element-level credit assignment as a kernel ridge regression problem and derive a closed-form solution, without additional rollouts or separate credit-assignment models. This yields interpretable per-element advantages and more precise policy updates. Experiments show that EAGLE-GRPO sustains performance gains over more training steps before plateauing and generates prompts that produce higher-quality e-commerce images than competitive VLM prompt-writing baselines.
[CV-231] Relax Within Balance Across: Geometry-Guided Load Balancing for Vision-Language Mixture-of-Experts
链接: https://arxiv.org/abs/2608.00574
作者: Ziang Wu,Peng Jin,Qishen Yin,Munan Ning,Hao Li,Peizhen Zhang,Li Yuan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 22 pages, including appendices. Code available at this https URL
Abstract:Vision-language MoE batches contain different numbers of image and text tokens. Image resolution, image count, tiling, and prompt length all change this token mix. We call the standard token-level Switch auxiliary loss Std-Aux. Std-Aux balances only the mixed load, so large image and text load errors can cancel at one mix. On our main model, the same trained router shows more than a fivefold change in load imbalance across image resolutions. We hold the image and text load profiles fixed and derive the exact load curve as the token mix varies. The image-text load gap controls sensitivity to the token mix. Physical preprocessing can also change the conditional profiles. The fixed-profile law excludes such changes. To design a remedy, we examine the router input structure. Image and text occupy distinct regions, while visual tokens group strongly by source image. The modality boundary motivates separate image and text terms. The image boundary motivates one equal-weight routing instance per image. ReBA, or Relax Within, Balance Across, implements both choices. Across four split backbones, ReBA lowers load on every reported benchmark input while keeping mean task accuracy comparable to Std-Aux. ReBA also lowers average load over the tested range and worst physical load under resolution and tiling shifts. Code is available at this https URL.
[CV-232] Beyond Token-Level Cross-Entropy: Fréchet Distributional Post-Training for Autoregressive Image Generation
链接: https://arxiv.org/abs/2608.00562
作者: Jinhua Zhang,Yisong Lin,Wei Long,Shuhang Gu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Autoregressive image generators are commonly pretrained with token-level cross-entropy under teacher forcing, yet evaluated by the distributional quality of decoded images. This creates an objective mismatch, because categorical errors have unequal image-level consequences, and a context mismatch, because inference conditions on model-generated histories. We introduce FD-loss post-training, which adapts a pretrained discrete generator using representation-space Fréchet distance as the sole objective. A dual-pass scheme first constructs detached rollout contexts through gradient-free generation under the model’s native inference configuration, then performs differentiable replay with a probability-level straight-through estimator (STE) that preserves hard argmax decoding in the forward pass while propagating image-level gradients through temperature-scaled probabilities. Only the generator is updated, while the tokenizer and feature extractors remain frozen. Across eight completed configurations from four generator families on class-conditional ImageNet at 256\times256 , FD-loss post-training reduces FID and \mathrmFD_r6 by 41.4% and 52.0% on average. The strongest FID result improves from 2.42 to 1.43 without adding parameters or inference steps.
[CV-233] st-Time Curriculum for Open-Set AIGC Detection
链接: https://arxiv.org/abs/2608.00559
作者: Yiqian Zhang,Zheyuan Gu,Xiangzhao Hao,Zefeng Zhang,Jingjia Mao,Jiahao Hu,Jiaxu Miao,Jun Yu,Zhenyu Zhang,Shuohuan Wang,Yu Sun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:AI-generated image detectors deployed in open-world environments inevitably face distribution shifts as new and stronger generative models continue to emerge. Although existing methods improve cross-generator generalization through better representations or training data construction, they typically follow a static train-once-and-deploy paradigm and cannot adapt after deployment. In this work, we study open-set AIGC image detection from a test-time adaptation perspective. We propose Test-Time Curriculum (TTC), a simple and model-agnostic framework that adapts a detector on unlabeled test data through curriculum-based self-training. TTC starts from highly reliable pseudo-labeled samples and progressively incorporates harder yet informative cases, while enforcing class-balanced selection to reduce biased updates under generator shift. To further improve pseudo-label quality, we introduce Cross-Scale Pseudo-Label Refinement, which aggregates complementary evidence across multiple resolutions for more reliable adaptation, and applies noisy-or fusion at inference to strengthen final predictions. In addition, we construct AIGCGuard, a new benchmark containing 3,100 representative real images and 124,000 generated images from 40 of the most advanced open-source and proprietary text-to-image models. Extensive experiments on five benchmarks show that TTC substantially improves overall detection performance under diverse unseen-generator shifts, establishing a practical and effective test-time adaptation framework for open-set generated image detection.
[CV-234] DexMani: Human-Derived Manipulability Guidance for Dexterous Rotation
链接: https://arxiv.org/abs/2608.00554
作者: Xiaoyang Chen,Shengcheng Luo,Haoran Guo,Jiaming Jiang,Wanlin Li,Ziyuan Jiao,Chenxi Xiao
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 17 figures
Abstract:Dexterous object rotation is a sequential contact problem: each support, release, and re-contact decision must both produce the desired object motion, and prepare the hand configuration for continued rotation. Existing reinforcement learning methods discover such movement patterns through trial and error on specific robotic hand embodiments, without explicitly accounting for how each contact transition affects the hand’s ability to sustain object rotation in subsequent steps. We introduce DexMani, a framework that transfers human demonstrations as contact-conditioned manipulability evolution. This prior captures how successful human contact transitions reshape the object-rotation directions available to the hand. DexMani then learns this manipulability evolution and uses it to guide downstream reinforcement learning, enabling rotation skills to be acquired across robot embodiments with distinct kinematics and active-contact configurations. Across the Shadow Hand, Allegro Hand, and XHand, DexMani achieves the highest success rates in every evaluated setting for both seen and unseen objects. DexMani reaches an average success rate of 57.5% on LEAP Hand, outperforming other baselines and producing smoother rotatory motions. Project site: this https URL
[CV-235] DrawAI: Agent ic Benchmark and Workflow for Making Raster Images Editable
链接: https://arxiv.org/abs/2608.00548
作者: Pu Cao,Qingye Kong,Xuedan Yin,Xuekun Zhao,Rupeng Yan,Qing Song,Yao Zhang,Lu Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project URL: this https URL
Abstract:Recent image-generation models and multimodal agents can produce high-quality visuals for increasingly complex visual communication tasks. Yet their raster outputs remain difficult to use directly because meaningful content and relationships are flattened into pixels, preventing users from inspecting, modifying, rearranging, or reusing individual components. We formulate image-to-editable reconstruction, which recovers a structured, directly manipulable artifact from a raster image while preserving its visual and semantic content. The central challenge is to jointly satisfy Fidelity and Editability, which often trade off in practice. To study this task, we introduce DrawAI, comprising an agentic benchmark, DrawAI-Bench, and a reconstruction workflow, DrawAI-Flow. DrawAI-Bench spans scientific figures, presentation slides, posters, and diagrams, combining real and AI-generated images to reflect practical visual-creation scenarios. It evaluates Fidelity and Editability through a hybrid protocol of 39 criteria: deterministic rule-based metrics measure properties with direct correspondences, while asset-specific vision-language rubrics capture semantic and perceptual qualities for which exact matching is misleading. Besides, we propose DrawAI-Flow, a two-stage agentic workflow in which a Parser Agent turns extracted elements evidence into an explicit reconstruction plan, and a Reconstruction Agent realizes the plan as executable graphics code through an iterative code-render-validate-revise loop. On DrawAI-Bench, we systematically evaluate thirteen models across five agent harnesses to study the effects of model capability, harness choice, and workflow design. The results show that reconstruction quality and costs vary substantially across model-harness configurations, while DrawAI-Flow consistently improves editable structure.
[CV-236] Zero-Cost Virtual RNA: Approximating Immunotherapy Signatures via Cross-Modal WSI Retrieval
链接: https://arxiv.org/abs/2608.00544
作者: Sigrid Vila-Bagaria,Mar Teixidó,Miquel Piñol,Felip Vilardell,Robert Montal,Veronica Vilaplana
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to MIDL 2026 Short Paper track
Abstract:Identifying the Inflamed'' immunophenotype in Gastric Adenocarcinoma predicts immunotherapy response but requires an expensive 10-gene RNA signature. While deep learning on standard H\E slides offers a scalable alternative, conventional binary classifiers oversimplify continuous RNA data and introduce label noise. To resolve this, we propose VITA (VIrtual Transcriptomic Approximation). By aligning H\E and RNA into a joint latent space during training, VITA requires only standard H\E at inference to retrieve morphologically similar historical cases and approximate the continuous RNA signature. Achieving 0.72 classification accuracy and a 0.66 Spearman correlation, VITA provides a cost-effective virtual transcriptomics’’ pre-screening tool that preserves the continuous phenotypic spectrum without requiring genomic sequencing.
[CV-237] Robust Watermarks Meet Backdoored Models: Evading Diffusion Semantic Watermarks via Stealthy Backdoor USENIX-SECURITY2026
链接: https://arxiv.org/abs/2608.00543
作者: Jinyuan Liu,Tianshuo Cong,Pei Li,Tianrui Wang,Xinlei He,Anyu Wang,Xiaoyun Wang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: To appear in USENIX Security 2026, August 12-14, 2026
Abstract:Although semantic watermarking is considered a promising safeguard for images generated by Latent Diffusion Models (LDMs), the reliance of the watermark detection pipeline on neural networks introduces a critical yet underexplored backdoor attack surface. To systematically study this vulnerability, we propose GhostVAE to plant a stealthy backdoor into the encoder of Variational Autoencoder (VAE), enabling reliable evasion of watermark detection. GhostVAE operates in two stages: it first constructs a universal trigger via power spectrum regularization to improve the trigger robustness, and then trains a backdoored VAE encoder with a parameter-aligned objective. Through extensive evaluations across three state-of-the-art semantic watermarking schemes and three widely adopted LDMs, we show that GhostVAE preserves watermark detection performance on benign images (achieving an average true positive rate of 94.4%), while simultaneously enabling highly effective evasion under trigger activation (achieving an average attack success rate of 94.6%). Moreover, we comprehensively analyze seventeen representative defenses and demonstrate that GhostVAE remains stealthy across the input space, parameter space, and latent space. Our work fundamentally undermines the trustworthiness of semantic watermarking systems and highlights that secure deployment of semantic watermarks requires end-to-end security considerations, particularly for neural network components.
[CV-238] DiffuseAgent -MI: Distributionally-GroundedTool-Integrated Self-Evolving Agents for Faithful Visual Reasoning ICML2026
链接: https://arxiv.org/abs/2608.00540
作者: An Lanji,Dawei Liu,Jin Li,Haoran Xu,Mei Chen,Yu Tian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 9 figures, accepted by ICML 2026 manitrack
Abstract:Tool-integrated vision-language agents have made remarkable progress on compositional and multi-step visual reasoning. Yet their outputs frequently exhibit unfaithfulness: the stated reasoning path diverges from the computation that actually produced the answer, undermining reliability in safety-critical applications. We present DiffuseAgent-MI, a self-evolving agent whose perceptual grounding is governed by a KL-minimal energy model over feature units, providing a distributional view of visual mechanistic interpretability. The agent learns an energy landscape that softly constrains generated samples to lie near the native prior conditioned on the chosen interpretable unit, closing the gap between the explanation and the internal representation. A verifier then supplies trajectory-level faithfulness rewards, and a repair branch re-conditions the energy when the verifier flags an unfaithful step. On GeoQA, SciVis, VQA-v2 and an in-house multimodal reasoning set, DiffuseAgent-MI improves accuracy by up to 5.1 points over prior self-evolving agents while more than doubling mutual-information faithfulness and human-interpretability agreement. Our analysis shows the energy term and the verifier are complementary: the former guarantees distributional faithfulness, the latter trajectory-level faithfulness, and only their combination closes both gaps.
[CV-239] Hybrid-Domain Posterior Sampling for Inverse Problems via Latent Flow Matching
链接: https://arxiv.org/abs/2608.00537
作者: Hongjie Wu,Yiping Xie,Jiancheng Lv
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACM Multimedia 2026
Abstract:Latent Flow Models have revolutionized compressed-space image synthesis, yet their application to high-fidelity inverse problems remains bottlenecked. In this paper, we trace this dilemma to a fundamental geometric limitation of pre-trained autoencoders, which we term \emphFirst-Order Manifold Blindness. Severe decoder compression (e.g., retaining only \sim!2% of the original degrees of freedom) produces a rank-deficient Jacobian, rendering high-frequency measurement residuals in its orthogonal complement invisible to latent gradients even when the decoder can represent the target image. To overcome this bottleneck, we propose Hybrid-Domain Posterior Sampling (HDPS), a decoupled inference framework that disentangles physical measurement consistency from semantic prior modeling. HDPS diverges into the pixel space, leveraging Langevin dynamics to absorb precise orthogonal measurement gradients, and subsequently projects these structural corrections back onto the generative manifold. An optimization-based latent alignment is introduced to filter pixel-space artifacts while avoiding the semantic drift of direct encoding. Extensive experiments on diverse inverse problems demonstrate that HDPS establishes a new state-of-the-art, successfully recovering the high-frequency structural precision that latent-only solvers inherently discard. The code is available at \hrefthis https URLthis https URL.
[CV-240] DocPO: Advancing Document Policy Optimization via Tailored Step-Aware Rewards
链接: https://arxiv.org/abs/2608.00536
作者: Yunhao Wang,Binghong Wu,Zhenyu Huang,Jiacheng Shi,Shuo Huang,Tinghao Yu,Feng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages. Accepted to the 34th ACM International Conference on Multimedia (ACM Multimedia 2026). Yunhao Wang and Binghong Wu contributed equally
Abstract:Reinforcement learning (RL) for document parsing often relies on reference-based rewards rooted in edit distance (e.g., tree edit distance), yet it remains hard to optimize in the high-accuracy regime because such rewards become weakly discriminative: near-correct outputs receive very similar scores, providing limited learning signal for hard cases. We propose Step-Aware Annealing (SAA), a plug-and-play reward sharpening mechanism that progressively increases reward curvature during training, amplifying subtle quality differences among high-scoring samples while preserving stability in early learning. Built on SAA, we introduce DocPO, a document policy optimization framework with element-specific, reference-based rewards anchored by edit-distance signals: normalized string edit distance (NED) for text, tree edit distance similarity (TEDS) for tables, and a hybrid Rubric+edit reward for formulas. Experiments on OmniDocBench and DocElemHard show that SAA consistently improves GRPO-style RL across document elements over non-annealed rewards, without requiring additional human supervision for reward construction.
[CV-241] Unleashing the Power of Text: Text-Guided Flow Matching for Image Fusion under Complex Degradations
链接: https://arxiv.org/abs/2608.00530
作者: Axi Niu(1),Jieheng Li(1),Kang Zhang(2),Qingsen Yan(1),Jinqiu Sun(3),Yanning Zhang(1) ((1) School of Computer Science, Northwestern Polytechnical University, Xi’an, China, (2) School of Electrical Engineering, KAIST, Daejeon, Republic of Korea, (3) School of Aeronautics and Astronautics, Northwestern Polytechnical University, Xi’an, China)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 12 pages, 9 figures, including supplementary material
Abstract:Infrared-visible image fusion under realistic degradation scenarios is a challenging task, as degradations not only cause a loss of reliable modality-specific information in observed images but also hinder the fusion process. Recent studies indicate that text can provide prior information about degradation characteristics, complementing the limited evidence available from corrupted input images and facilitating fusion. However, existing methods typically inject fixed global text representations into visual features, making it difficult for textual guidance to adapt to spatially varying degradations, local structures, and thermal saliency. To this end, we propose TGFusion, a text-guided latent-space flow matching framework that unifies degradation suppression and cross-modal fusion. TGFusion encodes task, degradation, and generation cues into structured prompts. To fully exploit these priors, we design a Prompt-conditioned Multi-stream Joint Flow Transformer that represents text as an independent semantic stream alongside fusion, visible, and infrared streams. Joint attention enables token-level bidirectional interaction and layer-wise updating among semantic and visual representations, allowing degradation semantics to dynamically guide reliable information selection and fusion latent generation. Extensive experiments on public benchmarks and complex degradation scenarios demonstrate that TGFusion achieves superior or competitive performance in perceptual quality, image naturalness, structural-detail preservation, and infrared-saliency retention, while remaining robust across diverse single and compound degradations.
[CV-242] GuideGround: VLM-guided Semantic Understanding and Viewpoint-aware Reasoning for 3D Visual Grounding
链接: https://arxiv.org/abs/2608.00518
作者: Yiwen Wang,Yuyang Deng,Yihao Long,Xi Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 5 figures
Abstract:3D visual grounding aims to localize the target object in a 3D scene from a natural language query, requiring both fine-grained semantic understanding and viewpoint-dependent spatial reasoning. Existing methods typically formulate semantic understanding as an auxiliary closed-set object classification task and rely on multi-view feature aggregation for viewpoint reasoning, limiting semantic generalization and weakening viewpoint-specific evidence. We observe that vision-language models naturally provide complementary capabilities through open-vocabulary semantic understanding and global scene perception. Based on this insight, we propose GuideGround, a VLM-guided framework that complements rather than replaces task-specific grounding models by leveraging VLMs for semantic enhancement and viewpoint-specific hypothesis verification. Specifically, we replace auxiliary closed-set object classification with VLM-generated object semantic descriptions to enhance semantic understanding. Meanwhile, instead of directly aggregating multi-view representations, we preserve viewpoint-specific grounding hypotheses through per-view grounding and explicitly verify them using VLMs across candidate viewpoints. Extensive experiments on the ReferIt3D benchmark demonstrate that GuideGround consistently outperforms previous state-of-the-art methods. Comprehensive ablation studies further confirm the effectiveness of both the proposed semantic understanding and viewpoint reasoning strategies.
[CV-243] st-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided MICCAI2026
链接: https://arxiv.org/abs/2608.00510
作者: Ling Ren,Chao Deng,Ziming Wang,Yuecong Xu,Kai Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Provisionally accepted for presentation at MICCAI 2026
Abstract:Reliable pelvic bone segmentation (PBS) from CT is essential for robot-assisted pelvic trauma surgery, yet deploying a source-trained model to a new hospital suffers from severe performance degradation due to cross-center domain shifts. While test-time adaptation (TTA) enables online model adaptation without accessing source data, existing methods show limited effectiveness for PBS, facing challenges including boundary degradation, anatomical inconsistency under domain shifts, and voxel-level class imbalance. To address these challenges, we propose a novel closed-loop dynamic Reliability-Guided TTA framework (ReGA) for PBS. Specifically, we introduce a pseudo-label reliability criterion termed Segmentation Inference Consistency Evaluation (SICE), which jointly measures region overlap and boundary deviation via dropout-based ensemble predictions. Based on SICE, a trust-weighted refinement module adaptively updates features to mitigate boundary errors in pseudo-labels. Furthermore, a confidence-weighted region-level contrastive learning strategy is proposed to enforce anatomical consistency. Finally, ReGA follows the teacher-student (TS) scheme to alleviate voxel-level class imbalance. Experiments on three heterogeneous 3D pelvic CT datasets demonstrate that ReGA consistently outperforms state-of-the-art TTA methods, enabling effective adaptation of the source-trained PBS model to unseen clinical domains. The code is available at this https URL.
[CV-244] RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI
链接: https://arxiv.org/abs/2608.00508
作者: Kai Geissler,Laurens Müller-Groh,Hans Meine
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Object detection and segmentation in three-dimensional medical images is a very active area of research. However, most proposed deep learning models carry a high computational cost, and only few aim to be broadly applicable, achieve high detection performance, and remain fast to execute on resource-constrained hardware. To address this gap, we present RadYOLO, a 3D extension of YOLO11 tailored to medical images. We compare it with nnU-Net and nnDetection on five datasets comprising CT and MRI data with varying object sizes and prevalence. RadYOLO’s detection performance surpasses that of nnDetection on four of five datasets and is comparable on one. Compared to nnU-Net, RadYOLO performs better on lesion detection tasks, while nnU-Net excels at detecting large organs when precise localization is required. When rough object localization is sufficient, RadYOLO matches or outperforms nnU-Net on all five datasets. Regarding inference time, RadYOLO is 8-46x faster than nnU-Net on a GPU. Compared to nnDetection the speedup is even higher. When executed on a CPU, RadYOLO’s inference runs within seconds (still faster than nnU-Net on a GPU) offering a significant advantage for clinical and edge-device deployment. RadYOLO repository: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.00508 [cs.CV] (or arXiv:2608.00508v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.00508 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-245] SpatialAfford: Teaching Compact VLMs Where to Look and Where to Ground for Affordance
链接: https://arxiv.org/abs/2608.00502
作者: Yufei Zhang,Chenlu Zhan,Donghui Sun,Xiaoxin Chen,Hongwei Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Affordance grounding aims to localize the functional region for interaction, such as the handle to grasp or the button to press, rather than the whole object. This makes it more challenging than generic visual grounding because the target region is smaller, more ambiguous, and more dependent on task context, especially for compact vision-language models (VLMs) used in embodied settings. Recent sequence-level supervision and reinforcement learning improve coordinate prediction quality, yet compact autoregressive VLMs still lack reliable affordance-aware visual focus before coordinate generation: the model can produce better coordinate tokens while its cross-modal attention remains diffuse and weakly anchored to the true affordance evidence. To address it, we propose SpatialAfford, a two-stage framework that first aligns attention to the ground-truth affordance region through Spatial Attention Alignment (SAA), then refines coordinate prediction with Spatial-Aware GRPO. By explicitly teaching the model where to look before optimizing where to ground, SpatialAfford turns affordance grounding from a purely output-constrained objective into attention-grounded spatial reasoning. Across ShareRobot-Bench, ReasonAff, and PartAfford, SpatialAfford consistently improves affordance grounding, with a compact 4B model outperforming stronger 7B+ baselines.
[CV-246] Optical Flow from Photons
链接: https://arxiv.org/abs/2608.00499
作者: Wendi Liu,Weichao Zeng,Weihang Ran,Yujie Lu,Yinqiang Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Optical flow remains challenging in high-speed and low-light scenes, where the limited frame rate and sensitivity of conventional cameras lead to motion blur and underexposure. Single-photon avalanche diode (SPAD) cameras offer single-photon sensitivity and extremely fine temporal sampling. However, individual slices in these high FPS binary photon streams are too sparse for dense correspondence. Temporal aggregation can provide the spatial cues required by optical flow, but accumulating photons at fixed coordinates blurs moving structures. Motion-aware aggregation can reduce this blur, yet it depends on the flow being estimated. To address this dependency, we propose QuantaFlow, the first method for dense optical flow directly from SPAD streams. Instead of constructing a fixed input representation, QuantaFlow embeds SPAD representation construction into iterative flow refinement. At each iteration, the current flow coarsely aligns the slices within the source and target sub-streams. A photon-flux transformation then constructs multi-scale representations containing intensity and structural cues, while adaptive multi-scale fusion balances photon noise and residual motion blur at each pixel. The fused representations drive a feature-warping flow update, and the refined flow guides representation construction in the next iteration. We further construct a synthetic dataset for SPAD optical-flow training and evaluation. Experiments on the synthetic dataset and real-world SPAD data demonstrate the effectiveness and generalization of QuantaFlow.
[CV-247] Image-Space Rule Discovery
链接: https://arxiv.org/abs/2608.00490
作者: Misora Sugiyama,Toya Oyama,Hirokatsu Kataoka
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 5 figures
Abstract:Can image-editing models discover visual rules in image space and complete problem-solving end-to-end? We tackle this question in the spirit of a human worksheet test (e.g., an IQ test), using problems that require models to read image-based instructions, recognize the problem, infer the answer, bind it to the correct destination, control output count, suppress unnecessary edits, and preserve the input and format. We introduce WISRD, a Worksheet Image-Space Rule Discovery benchmark with 11 core tasks under eight information conditions, spanning localized marking, filling, copying, counting, and no-edit suppression, together with four supplementary reasoning-stress probes for multi-step spatial manipulation, abstract pattern reasoning, logical inference, and constraint-based problem solving. We identify three key findings as follows. (i) Among the frontier image-editing models evaluated, Nano Banana Pro achieves the highest score. On the shared V0–V3 no-reference subset, the Auto-Strict proxy pass rates are 48.7% for Nano Banana Pro, 13.4% for Qwen-Image-Edit, 11.5% for FLUX.2 Klein 4B API, 11.3% for FLUX.2 Klein 4B open-weight, and 0.0% for InstructPix2Pix. (ii) Analysis reveals that current image-editing models can partially rely on rendered in-image instructions even when the external prompt is absent or merely generic. (iii) In small supplementary diagnostics, Nano Banana Pro achieves 70.0% on 4-by-4 Sudoku and 22.9% on public RAVEN pattern-discovery items in image space.
[CV-248] Practical Noise Modeling for SPAD Intensity Imaging
链接: https://arxiv.org/abs/2608.00489
作者: Wendi Liu,Yujie Lu,Zengxi Zhang,Haiyang Jiang,Weihang Ran,Yinqiang Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Single-photon avalanche diode (SPAD) cameras are promising for low-light and high-dynamic-range intensity imaging, but their practical use is limited by complex sensor-specific noise. Unlike time-correlated single-photon counting (TCSPC) systems, SPAD cameras record whether at least one detection occurred in each gate without photon timestamps in intensity imaging mode, making explicit noise decomposition difficult. We present a practical noise modeling and calibration framework for SPAD intensity denoising. Our forward model describes binary-frame accumulation with a Binomial observation process, models signal-independent dark noise as an exposure-dependent pure dark count term plus an exposure-independent dark-frame bias term, and incorporates pixel-wise response non-uniformity. We design a dedicated calibration procedure for the proposed model and use it to build a count-domain noise-synthesis pipeline for network training. For denoising, we further design a SPAD-specific dark-shading correction (SPAD-DSC) to remove most systematic noise before network training. We construct a real-world SPAD intensity dataset for testing. Experimental results demonstrate the superiority of the proposed noise model.
[CV-249] DreamTraj: Generating 6-DoF Object Trajectories by Reading Unrendered Video Diffusion Latents
链接: https://arxiv.org/abs/2608.00486
作者: Tongsheng Ding,Zhen Luo,Yixuan Yang,Boyu Wang,Luyang Xie,Jinyu Yang,Feng Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 8 figures, 11 tables. Project page: this https URL
Abstract:Accurate prediction of object trajectories during manipulation is essential for closing the perception-action loop. Progress is limited on two fronts: available datasets lack fine-grained language-to-motion annotations, and existing predictors either rely on privileged inputs such as video, depth, or CAD models, or recover motion from fully generated videos through costly, error-prone perception pipelines. We close the supervision gap with the MOVE dataset, 5,038 object-centric egocentric trajectories, each paired with a fine-grained natural-language instruction rather than a coarse verb-noun label. We further propose DreamTraj, which predicts a 6-DoF object trajectory from a single RGB image and a task instruction, requiring no video, depth, or CAD model at inference: rather than generating a video, it reads motion from the internal representations of a frozen image-to-video diffusion model at an early denoising step. A lightweight flow-matching Reader decodes query-key attention tracks and pooled hidden states into relative 6-DoF poses. To our knowledge, this is the first approach to directly decode object 6-DoF trajectories from intermediate video diffusion representations rather than generated pixels. DreamTraj sets a new state of the art on both translation and rotation against forecasters that consume multi-frame or privileged inputs, and runs 4.6x faster than generate-then-extract pipelines.
[CV-250] Scene2Sound: Auditory-Grounded Soundscape Generation for 3D Gaussian Worlds
链接: https://arxiv.org/abs/2608.00463
作者: Masaki Yoshida,Ren Togo,Takahiro Ogawa,Miki Haseyama
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Sound (cs.SD)
备注: 14 pages. Project page: this https URL
Abstract:3D Gaussian Splatting (3DGS) turns captured or generated imagery into photorealistic 3D world simulations that users can freely explore, yet these worlds remain silent. Because existing audio generation methods condition on a single image or viewpoint, their sound is tied to that observation and cannot stay consistent while a listener moves. We introduce the task of generating a spatially consistent soundscape for a given 3DGS world through auditory grounding, identifying which objects in the world should emit sound and anchoring each to a persistent 3D position, and present Scene2Sound, a training-free framework built on this grounding. From the input world alone, our pipeline selects viewpoints that jointly cover the scene, identifies sound-emitting objects with a vision-language model, and associates the multi-view detections into 3D instances through Gaussian set matching, which measures the overlap between the Gaussian sets that render each detection. Each source then receives generated audio that a standard object-based audio engine spatializes in real time at arbitrary listener poses. We further propose two spatial-consistency metrics, one testing whether rendered audio responds consistently to listener motion and one testing whether the claimed sources are supported by views held out from their placement. On a curated set of generated 3DGS worlds and on 3DGS scenes generated from real-world 360-degree captures, Scene2Sound preserves the audio quality of strong per-viewpoint baselines while remaining spatially consistent where per-viewpoint and single-panorama pipelines do not, and a user study confirms the perceptual benefit. Project page: this https URL.
[CV-251] Structured Proxy Features for Multimodal NSCLC Survival Prediction from Pretreatment CT
链接: https://arxiv.org/abs/2608.00446
作者: Huu Phong Nguyen,Delower Hossain,Ehsan Saghapour,Zhandos Sembay,Jake Y. Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 33
Abstract:Lung cancer results in roughly 1.8 million fatalities annually worldwide, with non-small cell lung cancer (NSCLC) comprising the majority of cases. Despite advancements in treatment, survival stratification remains challenging due to intratumoral heterogeneity inadequately captured by conventional descriptors. Standard radiomic and deep learning techniques regard imaging features as independent quantities, overlooking structured interactions between tumor characteristics. We evaluate whether structured proxy features can enhance multimodal NSCLC survival prediction by augmenting pretreatment computed tomography (CT) representations, radiomics, and clinical variables with six simulation-derived features designed to capture interactions between heterogeneity and morphology. A radiomic-parameterized cellular automaton generates growth-rate and necrosis-ratio proxy features from baseline CT by using entropy and sphericity to compute low-dimensional proxy parameters. The imaging backbone is a Transformer-based Masked Autoencoder (TMAE), which was chosen after a systematic evaluation with alternative encoders within the same pipeline and provides attention-based visualizations that highlight tumor regions receiving higher model attention. On the public Lung1 cohort (n = 390), the primary four-modality fusion attained a C-index of 0.641 (iAUC 0.731, log-rank p 0.001). The primary result compares favorably with prior multimodal results on Lung1 (C-index 0.631; iAUC 0.592 [15]) under a comparable evaluation protocol, while a separate exploratory coefficient-optimization analysis achieved a best observed C-index of 0.662 (iAUC 0.748). These results indicate that, in addition to conventional radiomic, deep, and clinical representations within the Lung1 benchmark, simulation-derived proxy features may provide complementary predictive information within this fixed Lung1 benchmark.
[CV-252] Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection
链接: https://arxiv.org/abs/2608.00444
作者: Yibo Wan,Jinyu Cai,Yunhe Zhang,Yi Bin,See-kiong Ng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unsupervised medical anomaly detection learns normal anatomical patterns from healthy training images and identifies deviations at test time. Reconstruction-based and diffusion-based methods commonly use the difference between an input image and its reconstruction as anomaly evidence. However, this residual can be ambiguous. Expressive models may preserve pathological structures, while benign anatomical variation, imaging noise, and acquisition differences may also produce large reconstruction errors. We propose discriminative mask-guided diffusion (DMD), a medical anomaly detection framework that complements residual-based localization with reconstruction-shift discrimination. DMD first learns a compact quantized latent representation of normal images. Localized masks then perturb selected latent regions, and a latent diffusion model reconstructs the perturbed representations. The resulting reconstructions are paired with their original normal images to define a self-supervised classification task. At inference, the classifier provides a learned image-level anomaly score, while the residual between the input and its diffusion-based reconstruction yields a pixel-level anomaly map. Experiments on five datasets spanning brain MRI, breast ultrasound, and chest radiography show that DMD achieves the best overall performance among the state-of-the-art baseline methods.
[CV-253] Beyond Static Anchors: Bounded Prototype Conditioning for Language-Free Medical Anomaly Detection
链接: https://arxiv.org/abs/2608.00442
作者: Yibo Wan,Jinyu Cai,Seekiong-Ng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Medical anomaly detection identifies abnormal images and localizes lesions under scarce supervision while generalizing across organs and modalities. Existing CLIP-based methods reduce annotation requirements through vision–language alignment, but their normal and abnormal references, whether text prompts or learned visual tokens, remain fixed across test images. Such static references may not transfer reliably to unseen targets in a cross-domain medical imaging scenario. To address this, we propose ReCAP, a language-free framework that replaces static anchors with input-conditioned visual prototypes. ReCAP re-centers separated normal and abnormal prototypes for each image through a bounded gated modulation, enabling query-adaptive anomaly scoring while constraining context-induced prototype drift. For the few-shot setting, we introduce a non-parametric normal-reference memory to preserve instance-level target-domain variation and complement the conditional prototype branch. Across six medical benchmarks, ReCAP achieves the best image-level AUROC on all zero-shot and 23 of 24 few-shot settings, and the best zero-shot pixel-level AUROC on all three segmentation datasets. Particularly, it reduces inference latency by over 70% compared to the fastest baseline, without text prompts or test-time gradient updates.
[CV-254] Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis
链接: https://arxiv.org/abs/2608.00440
作者: Zhishan Zou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: code: this https URL website: this https URL
Abstract:Recent image generators can synthesize convincing human-centric images, yet producing a useful collection remains different from producing a single successful image. A human-centric dataset must cover varied people and contexts, avoid implausible attribute combinations, preserve an everyday photographic character, and expose quality-control decisions at scale. We present Poplar, a reproducible Specify–Render–Inspect pipeline for human-centric image dataset synthesis. Specify samples structured attributes under commonsense constraints and verbalizes them as photography-oriented prompts. Render uses a realism-adapted image generator across composition-aware aspect ratios and retries obvious technical failures. Inspect applies a single structured vision–language review to each candidate, preserving the original prompt while rejecting intrinsic image defects or material prompt mismatches. Using Poplar, we construct Poplar-9K: 9,401 curated human-centric image–text pairs retained from 11,765 reviewed candidates (79.9% acceptance). We release the dataset together with the pipeline, configurations, immutable generation prompts, and auditable inspection records as a compact resource for building customizable human-centric collections.
[CV-255] Boosting Generalizable Depth Estimation in Endoscopy by Mixture of Lightweight Experts and Intrinsic Image Alignment MICCAI2026 MICCAI
链接: https://arxiv.org/abs/2608.00415
作者: Liangjing Shao,Beilei Cui,Yiming Huang,Changjing Liu,Hongliang Ren
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by MICCAI 2026 @ The Efficient Medical AI (EMA4MICCAI) Workshop (Oral Presentation)
Abstract:Depth estimation is a significant task for 3D perception in endoscopic surgeries. However, illumination interference and feature diversity in various endoscopic scenes are still challenges for generalizable depth estimation and ego-motion estimation. Based on this, a novel self-supervised framework, EndoMINI, is proposed for depth estimation in endoscopic scenes. Specifically, mixture of low-rank experts (MiLoRE) is proposed to perform parameter-efficient fine-tuning, which can also boost the model adaptation to scenes with different characteristics. Meanwhile, an intrinsic image alignment (IIA) is introduced into the training loss to alleviate the influence of light reflectance in endoscopy with a novel intrinsic image decomposition network. The proposed method is evaluated on SCARED datasets for supervised depth estimation, and two endoscopic datasets, Hamlyn and SERV-CT, for zero-shot depth estimation, compared with state-of-the-art works as well. The experimental results demonstrate outstanding performance of the proposed model and the effects of the main contributions.
[CV-256] Decoding Childrens Gait Behavior
链接: https://arxiv.org/abs/2608.00371
作者: Yifan Shen,Boyi Li,Meihuan Huang,Yuanzhe Liu,Xu Cao,Jinyang Jin,Zhengyuan Li,Anglin Liu,Junho Kim,Jingyuan Zhu,Lan Fangzhou,Jianguo Cao,Jintai Chen,Ismini Lourentzou,James Matthew Rehg
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce a new problem domain for human action recognition: the fine-grained analysis of children’s gait behaviors from standard RGB video. We specifically target the ambulatory patterns of children aged 3-17 years. Such behaviors arise naturally in the diagnosis and treatment of several critical developmental and neuromuscular disorders, such as cerebral palsy and hemiplegia. Despite their clinical value, current 3D sensor-based gait analysis systems are expensive, intrusive, and often impractical for young subjects. To address this, we introduce a new dataset comprising over 1,100 high-frame-rate (60 FPS) video sequences from 110 subjects, accompanied by synchronized, anonymized pose sequences. In each session, the child performs a 5-second “walk-around” task, capturing the gait cycle from multiple viewpoints. Crucially, we demonstrate that current state-of-the-art approaches, including gait foundation models and Multimodal Large Language Models (MLLMs), fail to effectively resolve these clinical nuances. We identify the key technical challenges in analyzing these erratic and subtle motor patterns and describe a unified end-to-end framework for decoding fundamental components of pediatric gait. Through comprehensive experimental results, we demonstrate the potential of this dataset to drive novel research questions and establish a rigorous baseline for automated child gait assessment.
[CV-257] Artificial Intelligence for the Characterization of Particles and Fibers by Optical Microscopy
链接: https://arxiv.org/abs/2608.00361
作者: Simiao Sun,Kenneth Ng,Lynn Lee,Astrid Harth,Asami Odate,Aggelos Katsaggelos,Manuel Ballester Matito,Nicholas Eastaugh,Marc Walton
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Optical microscopy of particle and fiber dispersions involves interpreting subtle visual cues influenced by specimen morphology, chemical composition, magnification, and illumination conditions. We introduce an artificial intelligence (AI) distillation framework that extracts semantically rich image embeddings from microscopy images using semantic anchors. A multimodal teacher combines each image’s visual embedding with three text embeddings representing illumination modality, magnification, and specimen identity and morphology. Generated by LongCLIP’s extended-context text encoder, this yields a 2304-dimensional block-structured teacher vector whose component blocks remain physically interpretable throughout training and inference. A student vision transformer (ViT) with a multi-layer perceptron (MLP) decoder is trained to reconstruct this teacher vector from the image alone, minimizing a mean absolute error (L1) loss that enforces coordinate-level fidelity to the teacher’s block structure. A cross-entropy term over pseudo-classes derived from HDBSCAN clustering of the teacher embedding space acts as a collapse-prevention regularizer, enforcing inter-cluster separation without requiring contrastive negative mining. At inference, the student operates on image input alone, producing compact embeddings that recover the full semantic content of the teacher vector. The framework achieves approximately 80% pseudo-class validation accuracy and 75% Recall@1 on fine-grained specimen description labels under leave-one-out nearest-neighbor retrieval. These results demonstrate that semantic anchoring enables a vision-only student to acquire richer and more interpretable representations than image-only training, with direct applicability to retrieval, classification, and exploratory analysis of heterogeneous particle and fiber dispersions.
[CV-258] he 1st AI Children Challenge
链接: https://arxiv.org/abs/2608.00356
作者: Boyi Li,Yifan Shen,Houze Yang,Xu Cao,Guojun Yun,Li Gao,Turong Chen,Long Xu,Jianguo Cao,Meihuan Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The First AI Children Challenge aims to advance real-world applications of computer vision and AI in child healthcare, child education, and pediatrics. The 2026 CV4CHL edition featured the first track in this domain: Children Gait Visual Analysis. The main goal of Children Gait Visual Analysis is the fine-grained analysis of children’s gait behaviors from keypoint sequences. This is still a big challenge for human action recognition. Experienced medical doctors can distinguish these subtle nuances, but none of the people test AI models in this domain. To bridge this gap, we introduce thousands of 2D children keypoint sequences walking around videos across various age groups of children (3-16 years old). There is a significant opportunity for batch analysis of these videos to provide clinically relevant insights into medical diagnosis. The Challenge will be launched with two problem tracks: Edinburgh Visual Gait Score (EVGS) Scoring and Classification of Gait Patterns in Bilateral Spastic Cerebral Palsy. Each track is chosen in consultation with board-certified pediatricians based on the value of potential solutions. With the first available dataset for such tasks and ground truth for each track, the challenge enabled participants to evaluate their solutions. Final rankings will be revealed after the competition concludes, fostering reproducibility and mitigating overfitting.
[CV-259] ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression
链接: https://arxiv.org/abs/2608.00345
作者: Renjie Liang,Zijian Xu,Jinqian Pan,Chengkun Sun,Zhengkang Fan,Shawn Li,You Qin,Mei Liu,Jie Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:A 3D CT scan entering a vision-language model produces a long sequence of visual tokens, often thousands to tens of thousands per volume, and this sequence must be compressed before a language model can consume it. Token compression is well studied in general vision, but little of it targets 3D CT specifically. A common baseline is grid average, which pools regular grid cells and can blend distinct anatomy, lesion, and air into one token. We present \textbfORCA (ORgan-Centroid Aggregation), a token compressor for 3D CT. It merges adjacent tokens with organ guidance and adds a sinusoidal encoding of each region’s centroid to preserve spatial layout. This preserves the anatomical information a downstream model needs. ORCA is training-free and plug-and-play, producing an adjustable token set without any model change or text query. We evaluate it across two datasets (CT-RATE and Merlin) and five encoders. The evaluation spans two task types: attribute prediction over five families (size, density, location, texture, and disease) and text generation (visual question answering and report generation). At matched token budgets, ORCA improves consistently over existing compression methods. It shrinks the visual context 64\times and its KV-cache 50\times , and is 31\times faster to process each volume. Code released at this https URL.
[CV-260] Belief-Space Perception Routing under Coupled Sensor Faults and Compute Contention
链接: https://arxiv.org/abs/2608.00322
作者: Sparsh Roy,Vihan Aggarwal,Davin Yin
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY)
备注: Submitted to MIT URTC 2026
Abstract:A robot that has to see and react on a fixed clock runs into two problems at once. Its cameras degrade in rain, mud, fog, and darkness. And the single onboard processor it runs on is shared with planning and control, so the compute left over for perception moves around from second to second. Most systems model the two separately. We present a perception router that tracks probabilistic estimates of sensor-fault state and compute- contention state, couples them with a noisy-OR term, and uses the coupled estimate to pick one of four detector configurations (YOLO11x/n at 1280 or 640 px) so that the frame finishes before its deadline. Where the two stressors co-occur, the coupled policy cuts the deadline-miss rate by 1.1 to 9.4 percentage points against a policy that treats them independently. The interval excludes zero in five of six conditions, the pooled effect over 10 sequences and 6 conditions has sign-test p = 0.001, and every uncoupled control and the fault-free trajectory sit at exactly 0.0 pp. Routing costs tens of microseconds per frame. We then asked whether the coupling the method exploits arises on its own. Across eight real RADIATE adverse-weather sequences and three workload proxies independent of the fault signal, after Benjamini-Hochberg correction and a replication run, none of 24 tests found it. We report that null and scope the routing result as a proof of mechanism. Whether such coupling occurs in the field is still open, and the released evaluation pipeline lets a deployment settle it on its own traces.
[CV-261] Interpretability-Guided Soft Pruning of Attention Heads in Vision Transformers
链接: https://arxiv.org/abs/2608.00264
作者: Kamil Książek,Piotr Suszyński,Michał Jan Włodarczyk,Jacek Tabor,Przemysław Biecek
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision foundation models, such as DINOv2, learn highly expressive representations but rely on massive, opaque architectures that demand substantial computational power and memory. To provide an interpretable-guided and efficient solution to this issue, we first propose a spectral analysis and new visualization technique for individual attention heads based on the Laplacian eigenvectors of their attention maps. Building upon recent observations regarding the block structure of Vision Transformers, we perform semantic clustering of attention heads and identify functional redundancies. Leveraging these insights, we introduce SAPER (Soft Attention PrunER), an end-to-end differentiable pruning framework based on the LapSum Soft Top-K approach. Extensive experiments on ImageNet-1K demonstrate that SAPER achieves a highly favorable accuracy-efficiency trade-off, outperforming the competitive RAPTOR baseline in FLOPs reduction while preserving strong classification performance.
[CV-262] MDWD: A Street-Level Dataset for Municipal Solid Waste Detection in Dense Urban Environments
链接: https://arxiv.org/abs/2608.00257
作者: Andrea Filiberto Lucas,Mark Bugeja,Carl James Debono,Dylan Seychell
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for publication at the 14th IEEE European Conference on Visual Information Processing (EUVIP 2026). 6 pages, 2 figures, 4 tables
Abstract:Automated visual monitoring of urban environments is a growing Computer Vision research area, but municipal solid waste detection remains under-represented in dedicated benchmark resources. Existing waste-related datasets predominantly address individual litter detection, aerial imagery, or image-level classification, and none simultaneously provide street-level imagery, instance-level localization, and categorization of domestic waste streams within a structured municipal collection context. This paper introduces the Maltese Domestic Waste Dataset (MDWD), a street-level benchmark comprising 3,697 high-resolution images and 11,461 manually annotated instances across five domestic waste categories representative of Malta’s municipal collection system. The dataset captures substantial variation in location, illumination, object scale, occlusion, and urban context. To establish reproducible baselines, a cross-architecture benchmark is conducted across multiple generations of the YOLO family and a transformer-based detector. On the test set, RF-DETR-M achieves the strongest overall performance with an mAP50 of 94.49% and an F1-score of 93.56%, whilst smaller-capacity variants maintain competitive accuracy at substantially reduced parameter counts. These results indicate that MDWD supports effective training across both compact real-time detectors and transformer-based models, establishing a benchmark for future research in vision-based municipal waste monitoring.
[CV-263] Semantically Calibrated Evidence Composition for CT Vision-Language Learning
链接: https://arxiv.org/abs/2608.00239
作者: Guoliang You,Haifan Gong,Xiaomeng Chu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 3 figures, 5 tables
Abstract:Learning transferable representations from CT-report pairs requires combining whole-volume context with anatomy-specific evidence. Existing methods typically emphasize either global CT-report alignment or fine-grained anatomy-level correspondence. Global alignment preserves broad study context but leaves the contribution of localized evidence implicit, whereas anatomy-level alignment explicitly grounds local findings but does not specify how independently represented evidence should interact, acquire study-level meaning, and contribute to a global CT representation. To address this gap, we propose SCOPE (Semantic Calibration Of comPosed Evidence), a framework for semantically calibrated evidence composition in CT vision-language learning. Under organ-specific report supervision, mask-guided queries with fixed anatomical identities extract context-aware organ evidence from shared, uncropped volumetric features, while an unrestricted global query retains access to whole-volume context. The global query then drives Local-Global Coupling to compose the organ evidence into a unified evidence representation. The composed evidence is subsequently calibrated using the diagnostic summary, providing study-level semantic supervision beyond local organ descriptions, and is finally integrated as a controlled residual into a context-preserving whole-volume representation aligned with the complete report. This progressive pathway connects localized evidence with study-level semantics without reducing the CT representation to a predefined set of organs. On CT-RATE and RadChestCT, SCOPE achieves macro AUCs of 85.0 and 72.2, respectively, outperforming the previous SOTA by 7.2 and 4.2, while also yielding substantial gains in linear probing and cross-modal retrieval. These results demonstrate the effectiveness of semantically calibrated evidence composition.
[CV-264] Latent-Centroid Steering: Single-Pass Classifier-Free Guidance for Command-Aligned Autonomous Driving
链接: https://arxiv.org/abs/2608.00237
作者: Meibo Hu,Jiamian Wang,Pichao Wang,Zhiqiang Tao
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Vision-language models (VLMs) have recently emerged as a promising paradigm for end-to-end autonomous driving, enabling agents to map multimodal inputs and high-level navigation instructions directly to executable trajectories. However, in practice, these models exhibit a persistent command-following gap: predicted trajectories often show weak sensitivity to navigation commands, resulting in incorrect behavior at critical decision points. We identify this issue as a form of conditional policy collapse, where regression-based training under multimodal trajectory distributions encourages the model to rely on dominant visual priors while marginalizing the language-conditioned signal. To address this issue, we introduce a principled formulation of classifier-free guidance (CFG) for regression-based vision-language driving. We show that CFG can be interpreted as isolating the instruction-induced residual in the action space by contrasting conditional and unconditional predictions, thereby explicitly amplifying the effect of the navigation command at inference time. However, a standard two-pass CFG introduces prohibitive latency for real-time control and produces noisy instance-level guidance directions. Building on a mean-shift interpretation of CFG, we propose Latent-Centroid Steering (LCS), a single-pass guidance mechanism that replaces instance-level residuals with class-level latent shifts. By projecting conditional representations toward precomputed command-specific centroids, LCS performs class-level latent steering based on cluster geometry that is both more stable and computationally efficient. We demonstrate that LCS reduces inference latency by approximately 50% while achieving stronger command adherence and improved driving performance on both closed-loop (Bench2Drive) and open-loop (nuScenes) benchmarks. Code will be released.
[CV-265] Attention-Steered Vision-Language Models for Sign Language Translation
链接: https://arxiv.org/abs/2608.00235
作者: Meibo Hu,Guohao Sun,Annemarie D. Ross,Sheng Li,Zhiqiang Tao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models (VLMs) have emerged as a powerful framework for multimodal video understanding. However, they remain limited in the sign language translation task, where we identify a key failure mode of existing VLMbased translators: poor spatial-temporal visual grounding. In particular, we find that standard next-token cross-entropy does not directly provide signal for where and when the model should attend, causing models to overlook sign-relevant regions and frames. To address this challenge, we propose AttnSign, a VLM-based spatial-temporal attention steering framework for sign language translation. AttnSign first introduces spatial attention supervision for sign-relevant regions, such as face and hands, in each frame; then develops an RL-based motion-cadence steering method that encourages the model to explore and focus on sign-level keyframes. Experimental results on How2Sign and OpenASL benchmarks show that our proposed AttnSign consistently outperforms existing methods.
[CV-266] Real-Time Visual Obstruction Detection in Surgical Augmented Reality
链接: https://arxiv.org/abs/2608.00232
作者: Shih-Chin Yang,Yanming Xiu,Hanting Ye,Qi Chen,Elias Rotondo,Maria Gorlatova
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ISMAR 2026 Mecidal Workshop
Abstract:Surgical augmented reality (AR) can provide contextual guidance by overlaying virtual annotations, tool cues, and procedural information onto the surgical workspace. However, the virtual content may obstruct task-relevant real-world information, such as surgical instruments, and interfere with users’ perception during time-sensitive surgical tasks. In this paper, we investigate visual obstruction detection for surgical AR and present a latency-aware pipeline that combines vision-language model (VLM)-based surgical-object recognition with segmentation-based obstruction reasoning. To reduce inference overhead, the system adopts a cascaded small-to-large VLM architecture with segmentation-guided early exiting and attention-based visual token pruning. The small VLM handles easy frames when its key-object prediction is supported by segmentation consistency, while difficult frames are forwarded to a large VLM with pruned visual tokens. We construct a pseudo-AR surgical obstruction detection benchmark by overlaying virtual content onto surgical-tool images and labeling whether the virtual content obstructs task-relevant instruments. Evaluation results show that the proposed system achieves 87.43% obstruction detection accuracy with an average end-to-end latency of 479 ms, reducing latency by 62.90% compared with a cloud large-model baseline. These results demonstrate the feasibility of latency-aware obstruction detection for surgical AR and motivate future work on dynamic surgical videos, multi-object scenes, and clinically grounded AR guidance content.
[CV-267] Learning How Much Not Just What: Cross-Patient Burden Order for CT Vision-Language Pretraining
链接: https://arxiv.org/abs/2608.00231
作者: Guoliang You,Haifan Gong,Xiaomeng Chu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 5 figures
Abstract:Volumetric CT vision-language pretraining learns 3D representations from scan-report pairs, but global and anatomy-aware objectives supervise only correspondence: they establish what is present and leave how much unconstrained. Nothing separates a mild from an extensive case of the same finding along a consistent direction, so the graded burden language in reports collapses into a present/absent signal. Longitudinal supervision would supply this order, but patient-matched CT pairs are scarce at scale; cross-sectional cohorts already encode weak burden cues across different patients. We introduce Spectrum, an anatomy-conditioned framework that represents each study at whole-study and organ scopes. For each organ-mapped pathology, a rule-based scorer mines confidence-filtered lower-to-higher pairs of different patients, and Burden-Direction Alignment (BDA) aligns the pathology-conditioned image delta with the report delta at each scope, separating that direction from its reverse. Because the endpoints are different people, a target-conditioned aligner first makes them comparable, so the delta reflects burden rather than between-patient variation. BDA further separates the selected direction from its reverse, anchors it to the observed higher-burden endpoint, and enforces consistency across ordered triplets. Since every pair is drawn within a single pathology, BDA is designed to constrain intra-class structure that image-report contrast alone never touches. Spectrum attains 85.6 zero-shot AUROC on CT-RATE and 72.7 on external RAD-ChestCT, with consistent gains in linear probing and retrieval. Weak cross-patient order is thus a scalable complement to anatomy-aware correspondence, yielding burden-aware CT representations without longitudinal data.
[CV-268] Manifold-GS: Certified Hybrid Assets via Varifold-Conservative Gaussian Splatting
链接: https://arxiv.org/abs/2608.00214
作者: Boyang Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 2 figures
Abstract:3D Gaussian Splatting (3DGS) gives high-quality novel-view synthesis, but its adaptive radiance primitives are not directly usable as structured assets: opacity is not an additive area measure, refinement can change the induced geometry, and watertight mesh extraction can hallucinate collision surfaces in unobserved regions. We introduce Manifold-GS, a certified hybrid asset layer for Gaussian scenes. The method separates appearance opacity from geometric quadrature mass, represents surface-like Gaussians as a discrete unoriented varifold, and exports only confidence-certified open surface patches while retaining uncertified content as residual splats. It provides refinement-conservative mass transport, local realizability diagnostics, source-preserving patch bindings, and conservative collision candidates. On three DTU scenes, a frozen asset benchmark shows zero patch-defined edit leakage, texture round-trip PSNR of 30.1/35.3/33.7 dB, and lower collision floater area than official 2DGS meshes on all scenes, with large gaps on two scenes. The result is a precision-coverage tradeoff rather than a universal reconstruction claim. External-region annotations, phantom-collision probes, and 5k-face simplification further support the certified asset interpretation, while RGB-only experiments show that local realizability is not sufficient for sparse-view surface identifiability.
[CV-269] SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp
链接: https://arxiv.org/abs/2608.00187
作者: Nawazish Khan,Sanjay Bhandari,Sarang Joshi,Alzbeta Novotna,Tiffany Jeong,Loretta Bowman,Michael Hernandez,Tobi Somorin,Viraj Govani,Jesse Glodstein,Shireen Elhabian
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Correspondence-based statistical shape modeling (SSM) is vital for population-level morphometric analysis, but conventional pipelines assume clean, fully registered surfaces. Real-world clinical photogrammetry scans are often noisy, partial, and cluttered, hindering the adoption of radiation-free surface imaging as a safe alternative to computed tomography (CT) for infant craniosynostosis. We present SCALP (Semi-supervised Correspondence via lAndmark Localization and sPectral warping), a two-stage framework that constructs consistent shape models directly from raw, imperfect surface scans. First, a semi-supervised Point Transformer leverages a small expert-annotated dataset alongside a large unlabeled cohort to accurately localize craniofacial landmarks with minimal annotation overhead. Second, these landmarks anchor a Laplace–Beltrami spectral deformation of an anatomical template, generating dense correspondences while naturally isolating the cranium from peripheral scanning clutter without manual preprocessing. Experiments on infant photogrammetry scans demonstrate that SCALP consistently outperforms state-of-the-art unsupervised point-cloud approaches, offering a clinically practical pathway toward objective, radiation-free head shape analysis.
[CV-270] RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding
链接: https://arxiv.org/abs/2608.00147
作者: Fabian Drexel,Marlene Fritzsche,Era Stambollxhiu,Miriam Kumpf,Lena Schmitzer,Lea Schumann,Jannik Kahmann,Friedrich Puttkammer,Johannes Moll,Jannik Lübberstedt,Zeineb Ben Chaaben,Anirudh Narayanan,Cosmin I. Bercea,Sebastian Ziegelmayer,Marcus R. Makowski,Daniel Rueckert,Lisa C. Adams,Keno K. Bressem
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Vision-language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a single shared embedding space, so concept-level structure and interpretability must be recovered post hoc, limiting model transparency and, hence, clinical utility. We introduce RadPRISM, which makes a clinician-defined radiology schema a designated stratification axis: an on-premise large language model extracts per-concept text spans from free-text reports, and each clinical concept is aligned in its own dedicated visual subspace, turning concept stratification into direct, top-level alignment supervision. Instantiated on chest radiographs with a 19-concept schema over 203,602 examinations from an internal multi-year archive, RadPRISM improved internal dataset zero-shot classification from 0.717 (95% CI, 0.710-0.723 ) to 0.868 (95% CI, 0.863-0.872 ) macro AUROC over a matched global-alignment baseline, performed on par with the purpose-built CARZero reference in external zero-shot classification while substantially outperforming it (up to 4.3-fold) in pointing-game visual grounding. In addition, a radiologist reader study demonstrated concept-stratified retrieval ability ( 0.78 macro retrieval correctness rate within rank 3), surfacing disentangled descriptive findings that report-level retrieval and fixed-label vocabularies cannot express. RadPRISM yields discriminative, spatially faithful, natively concept-stratified representations shaped by and transparently inspectable by clinicians.
[CV-271] Rethinking Pretraining for Specialized Design Data: Evidence from the JONES-19 Cultural Design Dataset
链接: https://arxiv.org/abs/2608.00135
作者: Alexandros Haridis,Charles Zhou
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 2026 Design Computing and Cognition Conference
Abstract:Design and architectural archives encode expert human knowledge in graphical formats, providing a critical testbed for design-inspired Machine Learning (ML) challenges absent with typical computer vision benchmarks. Building on JONES-19, a small-size image dataset based on The Grammar of Ornament (London, 1857), we evaluate the discriminative performance of Convolutional Neural Networks (CNNs) in two model training strategies: (a) ImageNet pretraining for domain-general “visual common sense,” and (b) learning from scratch on the design data in JONES-19. We find that while domain-general priors improve discriminative performance, learning from scratch augmented with repeated local sampling (multi-crop) effectively recovers these gains. For highly structured design data, local design-driven representations provide sufficient foundation for learning, challenging a reliance on massive general-purpose pretraining. These findings suggest that in specialized design domains, careful curation of smaller high-quality datasets that capture empirical and formal design principles may prove more effective and informative on the nature of a particular design domain than prioritizing large-scale data collection.
[CV-272] Counting the Cost of War Under Satellite Embargo: Zero-Shot Estimation of Impacted Infrastructure
链接: https://arxiv.org/abs/2608.00119
作者: Saleh Sakib Ahmed,M. Sohel Rahman
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Rapid estimation of impacted structures - critical for conflict-zone humanitarian response - is frequently hindered by post-strike satellite data embargoes and imagery blackouts. We bypass this operational bottleneck by reframing impacted building mapping as a zero-shot geometric projection task on archival, pre-strike maps. Using coordinate and incident text from LiveUAMap and ArcGIS, Large Language Models extract weapon payloads (W) to project kinetic blast perimeters via Hopkinson-Cranz scaling (R_base = Z * W^(1/3)). To count exposed structures within these zones without post-strike imagery, we introduce two technical innovations: Adaptive Field-of-View to eliminate resolution (zoom) bias in 2D segmentation (SAMGeo), and 2.5D pseudo-height depth maps combined with segmentation masks to help Large Vision-Language Models (LVLMs) resolve overlapping, dense rooftops. Evaluated on 2026 Middle East conflict data, depth-augmented LVLMs dramatically outperform traditional segmentation in congested urban centers. This establishes a powerful hybrid paradigm for zero-shot crisis mapping: ultra-fast 2D segmentation for sparse rural zones, and depth-augmented LVLMs for dense urban environments.
[CV-273] Distill What RGB Can Recover: Privileged 3D Evidence for RGB-Only Vision-Language Models
链接: https://arxiv.org/abs/2608.00110
作者: Yanbin Hu,Jin Cui,Jun Ye,Jiepeng Zhou,Jiangcheng Song,Boran Zhao,Pengju Ren
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D scene understanding requires reasoning about entity existence, spatial layout, and object relations, yet RGB images alone often provide insufficient 3D cues. Existing 3D-VLMs commonly rely on depth or 3D-position-aware inputs at inference time, introducing additional acquisition, reconstruction, or annotation costs that limit RGB-only deployment. We therefore study how training-time 3D evidence can be converted into spatial reasoning capabilities retained under RGB-only inference. We propose a privileged-evidence distillation framework that constructs a distillable teacher through a unified evidence interface and controlled residual injection, and transfers its knowledge to a deployable student receiving only RGB images and questions through logit and structured representation distillation. To avoid imitating teacher signals unsupported by RGB, we further introduce evidence-sensitivity-guided distillation, which uses corrupted evidence to identify highly evidence-dependent targets and down-weight their supervision. We also define a recoverability decomposition based on the matched baseline, teacher, and student, separating privileged gains into RGB-recoverable improvements and residual teacher advantages. Across four benchmarks, the teacher achieves the best result on 7 of 11 reported metrics among the compared methods. The RGB-only student outperforms its matched baseline on all 11 metrics, including gains of 10.4 ScanQA CIDEr and 19.1 Scan2Cap CIDEr@0.5, without additional inference-time inputs. These results validate the effectiveness of training-time privileged 3D evidence distillation for both teacher performance and deployable RGB-only spatial reasoning. Separately, our matched baseline-teacher-student analysis characterizes privileged-gain transfer across evidence types and spatial skills.
[CV-274] What Carries the Signal in Pathology Foundation-Model Atlases? A Patient-Level Controlled Benchmark in Breast Cancer
链接: https://arxiv.org/abs/2608.00105
作者: Chimdi Walter Ndubuisi
类目: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注: 40 pages, 7 figures, 10 tables. Supplementary Information (16 pages) included as an ancillary file. Segmentation outputs obtained under the Aignostics Research Access Programme; OpenTME data at this https URL
Abstract:Pathology foundation models are reported to encode molecular programmes in tissue morphology, but the evidence is usually a cohort-wide ranked gene list rather than a prediction for a held-out patient. We rebuild such an analysis with the patient as the unit of evidence and ask which pipeline component carries signal. Across 11 frozen backbones, four pre-specified gene programmes and 285 TCGA-BRCA patients with paired slides and RNA-seq (44 cells; GroupKFold by patient, all preprocessing fitted inside the fold), ridge regression on mean-pooled embeddings predicts held-out programme scores at Spearman rho = 0.25-0.56, UNI2 strongest on all four (immune 0.556). A matched permutation null gives raw p ~ 1e-4 at 10,000 permutations for every cell; Holm-adjusted p = 0.0044. The signal is real but not uniformly morphological. Against competing models on the same patients and folds, embeddings beat tissue composition for ER/luminal, proliferation and immune (+0.280, +0.284, +0.479; p = 0.003) but not basal, where compartment fractions alone reach 0.469 against the embedding’s 0.493 (p = 0.77). Fifty-four interpretable cell-count features come within 0.043-0.085 on every programme. The geometric machinery contributes nothing measurable, and we identify why: the geodesic graph selects neighbours by Euclidean nearest-neighbour search and only reweights edges already chosen, so the topology is Euclidean by construction (Riemannian minus Euclidean = +0.0010, 95% CI [-0.0007, +0.0029]). Applied consistently the geometry is worse (-0.0117). Ridge regression beats the graph-and-metric decoder by +0.097 (CI [+0.069, +0.127]). The driver-count metric common in this literature is near-uninformative here: 91.8% of random six-gene panels recover =5/6 drivers. Comments: 40 pages, 7 figures, 10 tables. Supplementary Information (16 pages) included as an ancillary file. Segmentation outputs obtained under the Aignostics Research Access Programme; OpenTME data at this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM) Cite as: arXiv:2608.00105 [cs.CV] (or arXiv:2608.00105v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.00105 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Chimdi Walter Ndubuisi [view email] [v1] Fri, 31 Jul 2026 04:58:29 UTC (7,587 KB)
[CV-275] SPARC-Rad: A Multimodal Benchmark Dataset and Evaluation Pipeline for Spatial and Anatomical Reasoning in Radiology Vision-Language Models
链接: https://arxiv.org/abs/2608.00100
作者: Satvik Tripathi,Mustafa Ege Seker,Kristian Quevada,Ebubechukwu D Enwerem,Pratham Khandelwal,Emine Meltem,Bera Koca,Shahriar Faghani,Jacinta Arnold,Dania Daye,Tessa S. Cook
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language models (VLMs) are increasingly being evaluated for medical imaging, but many available benchmarks emphasize disease classification, report generation, or broad visual question answering rather than the spatial and anatomical reasoning required for radiology. We developed the Spatial Perception and Anatomical Reasoning in Clinical Radiology (SPARC-Rad) Benchmark, a manually curated multimodal benchmark dataset and evaluation pipeline for assessing these capabilities in radiology VLMs. SPARC-Rad includes 300 image-question pairs derived from healthy control imaging studies in The Cancer Imaging Archive (TCIA), spanning CT, MRI, and radiography across the abdomen, chest, breast, neuro, and musculoskeletal categories. Radiology trainees manually designed and annotated questions to evaluate anatomical identification, localization, laterality, regional recognition, device identification, and inter-structure spatial relationships. The evaluation pipeline supports standardized prompting, structured output collection, response normalization, LLM-as-judge grading, human quality review, binary correctness scoring, and subgroup analysis by modality, anatomy, and reasoning type. SPARC-Rad provides a reusable framework for evaluating whether VLMs can provide reasoning for radiologic anatomy as a spatial system, supporting future model development, failure-mode analysis, and pre-deployment assessment.
[CV-276] Logographic Character Visual Pretraining via Semantic-based Contrastive Learning ACM-MM2026
链接: https://arxiv.org/abs/2608.00096
作者: Daqian Shi,Wei Cao,Xiaoyu Zheng,Lida Shi,Xiaolei Diao,Cedric M John
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: ACM MM 2026 paper
Abstract:Current deep learning-based character vision studies, e.g., text recognition, character image denoising, and historical text completion, are offering new solutions for learning, managing, and utilizing character resources. However, the performance of these studies peaks only with large and balanced datasets, which is a rarity with real-world character datasets, especially for logographic character languages, e.g., Chinese. The imbalance in data distribution of logographic characters is a common issue due to differences in character usage frequency and new characters being continuously created. In this paper, we propose a novel method for logographic character recognition, which introduces a multi-modal learning approach using visual semantics and contextual semantics of characters. A novel pre-training strategy is designed to enhance deep visual representations, especially for datasets suffering from issues of imbalanced and rare instances, by extracting the contextual semantics of each character from the corresponding language models. We conduct experiments across various datasets to evaluate our character recognition method and further validate the contrastive pre-training strategy by several downstream tasks. Experimental results demonstrate the superiority of our method compared to state-of-the-art methods.
[CV-277] Video Models as Native 4D Renderers: World-Grounded Conditioning from Animated Mesh
链接: https://arxiv.org/abs/2608.00094
作者: Junhao Chen,Mingjin Chen,Henghaofan Zhang,Minglin Chen,Liaoyuan Fan,Boran Zhang,Saining Zhang,Mingze Sun,Hao Zhao,Ruqi Huang,Zhihao Li,Yufei Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 5 figures
Abstract:Pretrained video diffusion models can act as renderers when the desired scene state is already specified by an animated mesh, a camera trajectory, and a reference image. This 4D generative rendering setting raises a representation question: what image-format condition lets a video backbone obey both camera motion and scene-internal animation? We propose DAR, a reference-guided renderer that extends Wan2.2 camera control from Plücker rays alone to a joint camera-plus-geometry interface. DAR projects a neural 4D G-buffer (tracking, world position, and normal) from the animated mesh and injects it through a widened control adapter while preserving the pretrained image-to-video prior. The central design choice is the pair of tracking and world position. Tracking identifies the persistent surface element that should carry appearance; world position gives its current scene-coordinate state; normal supplies local shape. Depth plus calibrated rays can recover 3D in principle, but depth is a camera-dependent chart in which camera and object motion are mixed. On the 68-case DAR-4D benchmark, LoRA DAR reaches PSNR 23.22, SSIM 0.895, and LPIPS 0.134, improving over off-the-shelf Wan2.2-Depth by 1.54 dB PSNR; a full fine-tune reaches PSNR 25.36 and SSIM 0.917. Matched ablations show that replacing world position by depth reduces PSNR by 1.26–1.55 dB at every checkpoint, supporting tracking+world-position correspondence as a practical 4D rendering condition.
[CV-278] DODA: A Database of Datasets for Aesthetics Research
链接: https://arxiv.org/abs/2608.00089
作者: Lisa Koßmann,Ralf Bartho,Christoph Redies,Johan Wagemans
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:With rapid growth in the fields of empirical and computational aesthetics we have seen a vast increase in large image datasets annotated for aesthetics. As the image databases differ widely in many respects (e.g., different standards for annotation), it can be tedious to find the dataset that fits one’s research needs best. The absence of a centralized open-science search system causes additional problems. Currently, researchers typically share dataset links in papers or on diverse platforms like OSF, GitHub or Dropbox. Manually searching for details like image quality and content often requires downloading all datasets. Therefore, we present the Database Of Datasets for Aesthetics (DODA), an intuitive Web application in which researchers can browse all important datasets for aesthetics research. DODA provides general information about these datasets (size, resolution, type of annotation, number of annotators, etc.) and for many of them also precomputed quantitative image properties. We discuss relevant criteria for selecting a suitable dataset with DODA and illustrate the benefits of reusing datasets. Our approach facilitates collaboration across the fields of empirical and computational aesthetics. Keywords: empirical aesthetics, computational aesthetics, machine learning, image annotation, quantitative image properties, Open Science
[CV-279] DS@GT ARC at MEDIQA-CORE-Task-1 2026: Trimodal Model Fusion with Task-Specific Gates for Brain Tumor Subtype Classification
链接: https://arxiv.org/abs/2608.00086
作者: Hoang Thanh Thanh Truong,Charles R. Clark
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CLEF 2026 Working Notes, 21 - 24 September 2026, Jena, Germany
Abstract:Brain tumor diagnosis is a time-sensitive process in which patients may wait weeks for a finalized pathology report. This problem motivates automated systems that classify tumor subtype from multimodal inputs. This paper details the DS@GT ARC team’s work for ImageCLEFmed MEDIQA-CORE 2026 Task~1, Brain Tumor Subtype Classification. The task evaluates three glioma classification problems: Level-1 Molecular Type, LGG vs HGG, and WHO Grade. We combine pre-extracted MRI (NeuroVFM) and histopathology (Prov-GigaPath) embeddings with free-text radiology reports. Our team explored two trimodal fusion architectures, two report encoders (RadBERT and Llama-3.1-8B-Instruct), and a biologically motivated post-processing stage. We achieve a mean macro-F1 of 0.801 under the Fully Multimodal condition, exceeding the organizers’ baseline of 0.796 and ranking second among the teams whose code passed verification. Additional evaluation across modality-dropping conditions shows that this advantage depends heavily on the availability of the histopathology modality, and that our system falls behind the baseline when modalities are missing. Our code is available on GitHub at this https URL.
[CV-280] From Pixels to PCells: A Neurosymbolic Approach to Photonic Component Creation
链接: https://arxiv.org/abs/2608.00084
作者: Aadarsh Agarwal,Kenaish Al Qubaisi,Dirk Englund
类目: Computer Vision and Pattern Recognition (cs.CV); Optics (physics.optics)
备注: 16 pages, 13 figures, 3 tables
Abstract:We present PixCell, a neurosymbolic system in which multimodal agents convert a visually presented photonic component into a parametric program over a small domain-specific language (DSL) of geometric primitives. A system enabling deterministic visual verification renders evaluation asymmetrically cheaper than the generation attempt. While models using multi-seed sampling and iterative revision reach a mean best-turn IoU of only 0.416, multimodal agents through PixCell’s interface and verifier consistently exceed 0.9 mean IoU, with scores reaching 0.974 and 0.955 across eight component targets while also satisfying source contracts. These results demonstrate that frontier multimodal agents can reliably understand and render executable parametric representations from visual targets. Using these live parameters, cross-stack studies on an interferometer reconstruct primitive programs that satisfy an 8.0 nm free spectral range target and the original footprint constraint on modeled 220-nm SOI, 400-nm SiN, and 400-nm TFLN stacks. PixCell further carries a paper-derived splitter from visual reconstruction through SOI full-wave simulation, producing symmetric propagation and balanced outputs. Finally, the same executable verifier supplies a training reward and dataset used to train a Qwen3.6-35B-A3B model with LoRA and GRPO without supervised demonstrations. On eight training-excluded paper figures, its mean champion IoU rises from 0.422 after eight initial attempts to 0.491 after three verifier-guided revision rounds. These results therefore establish a controlled framework for measuring, retargeting, and improving visual-to-parametric photonic component design.
[CV-281] Beyond Edge Maps: Wavelet-Domain Conditioning for Multi-Adapter Map-to-Satellite Diffusion
链接: https://arxiv.org/abs/2608.00083
作者: Arisha Prasain
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 5 figures, 3 tables
Abstract:Commercial mapping partnerships are often unavailable in low-resource regions, leaving satellite basemaps stale and motivating synthesis of satellite imagery from independently maintained cartographic data. Existing ControlNet-based diffusion methods typically condition on structural signals like edges or segmentation extracted from the target image itself, assuming the imagery already exists and limiting their use exactly where synthesis matters most. Map-conditioned alternatives add cues like edge detection but omit frequency-domain structure. We propose a ControlNet-based diffusion framework conditioned only on cartographic sources obtainable independently of the target imagery: OpenStreetMap (OSM) raster maps and their stationary wavelet transform (SWT) subbands, a conditioning signal previously unexplored for map-to-satellite diffusion. Two ControlNet adapters, trained separately on the map and wavelet representations atop a frozen Stable Diffusion backbone, are fused via MultiControlNet, jointly drawing on spatial structure and frequency detail without retraining a multi-input model. We evaluate on a new paired map-satellite dataset curated for Nepal, a data-scarce, topographically diverse region, alongside the Pix2Pix maps-satellite benchmark. Combined conditioning wins six of eight metric-dataset comparisons – SSIM and PSNR on both datasets, plus LPIPS (both Alex and VGG backbones) on ours and ties map-only on both Pix2Pix LPIPS backbones while still edging past wavelet-only there. Wavelet-only takes the lowest FID on both datasets, matching the tradeoff between per-image fidelity and distributional realism. We treat this gap cautiously given our modest test-set sizes and FID’s known small-sample bias.
[CV-282] LeapTalk: Breaking the Latency-Quality Trade-off in Talking Head Generation
链接: https://arxiv.org/abs/2608.00079
作者: Rongxiang Zhang,Songhua Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注:
Abstract:Long-form and real-time talking-head generation remains challenging due to a latency-quality trade-off: inefficient multi-step diffusion prohibits streaming generation, whereas real-time autoregressive approaches suffer from error accumulation and identity drift. To address this drawback, we propose LeapTalk, a novel framework that achieves stable and real-time talking-head generation with a single forward step, scaling to arbitrarily long videos. At the heart of our approach lies a single-step bridge distillation scheme. On the one hand, departing from the conventional noise-to-data paradigm, we introduce a data-to-data transport formulation based on a Brownian bridge. Anchored by a persistent reference, this strategy effectively mitigates identity drift and enhances long-term temporal stability. On the other hand, to enable smooth knowledge transfer from a pre-trained diffusion teacher to the student bridge model, we explore a heterogeneous distillation framework with an SNR-aligned time transformation \Phi(\tau) , which bridges the functional discrepancy between the two models. Moreover, we propose an audio-driven classifier-free guidance mechanism to maintain fine-grained lip synchronization under extreme step reduction. Extensive experiments demonstrate that our method achieves high-fidelity and temporally consistent video generation with only 1 step at up to 200 FPS, significantly outperforming existing approaches in both efficiency and stability. Project Page: this https URL
[CV-283] Device-First Feedback: Toward Mobile-Native LLM -Driven Neural Architecture Search
链接: https://arxiv.org/abs/2608.00078
作者: Saif U Din,Muhammad Ahsan Hussain,Radu Timofte,Dmitry Ignatov
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 4 figures, 4 tables. Code: this https URL
Abstract:Deploying convolutional neural networks generated by large language models (LLMs) on real mobile hardware requires more than GPU validation accuracy: INT8 TensorFlow Lite export, delegate selection, and on-device latency jointly determine whether a model is usable. We present an automated mobile deployment pipeline that closes the loop from QLoRA fine-tuning of an architecture-generating LLM through GPU evaluation, INT8 export, and physical-device benchmarking to gated augmentation of the training corpus. The pipeline is fully scripted and runs cycle-by-cycle without manual intervention, with resume support after interruptions. We evaluate the same frozen protocol on two benchmarks, CIFAR-10 and CIFAR-100, on a Samsung SM-P613 tablet (seed 42, 20 models per cycle, cycles 0-6). On CIFAR-10, cycle 1 is gate-accepted and improves the mobile deployment score approximately 25.6x over the baseline with a mean quantized accuracy of 46.9%; later cycles raise GPU accuracy but fail the non-decreasing mobile gate. On CIFAR-100, the pre-QLoRA baseline retains the best mobile score; iterative rounds improve GPU accuracy (up to 26.2%) yet cannot surpass cycle 0 on-device, and the training pool stalls at 19 examples after the first accepted round. Together, the two studies show that closed-loop GPU fine-tuning does not guarantee monotonic mobile gains, especially on harder classification tasks, and that multi-dataset, on-device measurement is needed to stress-test deployment objectives. We release per-cycle metrics with 95% confidence intervals, all figures, and complete reproduction commands.
[CV-284] Beyond Accuracy: Auditing Spatial Provenance in Visual Token Pruning for OCR-Critical MLLM Inference
链接: https://arxiv.org/abs/2608.00077
作者: Feixiang Liu,Qiang Qiu,Hao Zhang,Xinyue Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 21 pages, including supplementary material. Code and reproducibility artifacts: this https URL
Abstract:Visual-token pruning is usually judged by answer quality at a fixed retention budget. For text-rich multimodal large language models (MLLMs), this protocol can miss a distinct failure: an answer remains correct even when no retained token is locally traceable to the small OCR region that supports it. We turn this blind spot into an evidence-risk audit that couples answer behavior with geometric token-origin provenance, interventions, and realized cost; transparent training-free selectors isolate controlled operating points. On locked image-disjoint confirmation, Qwen Target at 30% retention has observed accuracy 0.786 versus 0.783 for Full (paired image-cluster difference +0.003, 95% CI [-0.014, +0.020]), yet same-budget Target, Random, and Grid retain sharply different positive-support coverage: 0.620, 0.270, and 0.318. Across Qwen3-VL-8B, LLaVA-1.5-7B, and InternVL3.5-8B, matched controls, interventions, detector tests, and external methods reveal model-specific quality-risk-traceability frontiers that accuracy alone does not expose. Materialized prefixes yield up to 4.32x batch-prefill speedup and 76.4% lower incremental peak memory; full-validation TextVQA and DocVQA further show that favorable target-verification points do not imply task-general compression. Visual-token pruning should therefore report surviving spatial provenance and realized cost alongside quality and compression.
[CV-285] Which Modality Decides? Counterfactual Modality Attribution for Multimodal LLM s
链接: https://arxiv.org/abs/2608.00076
作者: Vahidin Hasic,Chao Wang,Luis C. Garcia-Peraza-Herrera,David Watson,Senka Krivic
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages, 5 figures
Abstract:Multimodal large language models (MLLMs) increasingly support high-stakes decision making by combining complementary information from images and text. While existing explainability methods identify influential image regions or text tokens, they cannot answer a fundamental question: which modality drives a prediction? Consequently, a model may produce the correct output while relying on the wrong source of evidence, masking shortcut learning and unsafe reasoning. We formulate modality attribution as a complementary explainability objective for multimodal foundation models and propose Counterfactual Modality Attribution (CMA), the first framework for quantifying modality-level contributions in MLLMs. CMA generates image-only, text-only, and joint multimodal counterfactuals using coupled diffusion priors and converts them into principled modality attribution scores through a cooperative game-theoretic formulation based on Shapley values. We evaluate CMA on controlled synthetic benchmarks with known ground-truth modality reliance and on a real-world multimodal clinical dataset. CMA correctly identifies the decision-driving modality in 98% of controlled cases and consistently outperforms baselines, revealing failures of cross-modal reasoning that remain invisible to predictive accuracy alone. Our results establish modality attribution as a complementary dimension of explainability beyond feature attribution, providing a principled framework for auditing multimodal foundation models in safety-critical applications.
[CV-286] K-space Gaussian Representation for Parallel MRI
链接: https://arxiv.org/abs/2608.00075
作者: Yu Guan,Mingyu Hu,Jiale Hu,Zhuoxu Cui,Dong Liang,Qiegen Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accelerated magnetic resonance imaging (MRI) aims to recover the k-space signal from acquired measurements, where accurate estimation of missing samples is essential for high-fidelity reconstruction. Existing k-space reconstruction methods estimate missing samples through interpolation operators or structure priors defined on discrete sampling grids. Although these formulations effectively exploit local interpolation relationships and global k-space redundancy, they reconstruct only discrete frequency coefficients and therefore do not explicitly model the underlying continuous signal. To overcome this limitation, we propose K-space Gaussian Representation (KGR), the first explicit continuous representation formulated directly in the native k-space domain. Rather than estimating unknown samples on discrete grids, KGR parameterizes the continuous signal using Gabor-Gaussian primitives with shared spatial geometry, yielding a compact representation that naturally preserves inter-coil correlations. Because unconstrained continuous fitting does not necessarily satisfy the intrinsic structural properties of multi-coil signal, the estimated representation is projected onto a low-rank manifold to enforce the algebraic constraints arising from smoothly varying phase and coil redundancy. A frequency-adaptive fitting strategy accommodates the heterogeneous characteristics of different k-space regions. Comprehensive validation across multiple datasets and sampling schemes shows consistent improvements over representative reconstruction baselines in both quantitative metrics and visual quality. These results suggest that explicit continuous parameterization of native k-space provides a principled framework for integrating continuous signal modeling with structured low-rank reconstruction.
[CV-287] Explainable Multimodal AI for Adaptive Calibration of Archaeological Sensing Workflows
链接: https://arxiv.org/abs/2608.00074
作者: Nevio Dubbini,Daniel P. van Helden,Claudia Sciuto,Martina Naso,Arthur Leck,Clement Joubert,Heeli C. Schechter,Remy Chapoulie,Gabriele Gattiglia
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This paper presents a multimodal machine-learning framework for calibration monitoring, quality assessment, and adaptive acquisition support in archaeological digitisation workflows. The proposed approach operates across photogrammetric 3D reconstruction, hyperspectral imaging, X-ray fluorescence spectroscopy, and Raman spectroscopy through a unified pipeline combining deterministic quality indicators, statistical feature representations, machine-learning classification, anomaly detection, and explainable artificial intelligence (XAI). Rather than replacing instrument-level calibration, the framework introduces an additional algorithmic layer that evaluates whether acquisitions are statistically consistent, physically plausible, and suitable for downstream multimodal integration. For each sensing modality, acquisitions are represented through structured feature spaces encoding geometric, spectral, spatial, and statistical properties. These representations are used to identify degradation patterns such as reconstruction artefacts, illumination inconsistencies, spectral distortions, detector instability, baseline fluctuations, and low signal-to-noise conditions. Supervised and unsupervised learning methods are combined with XAI techniques to support both automatic discrimination between acceptable and problematic acquisitions and interpretation of the underlying causes of degradation. The framework additionally supports adaptive feedback and resource-aware acquisition strategies by linking feature-space deviations to acquisition-level corrective actions. Experimental results obtained on multimodal archaeological datasets demonstrate that the proposed methodology captures meaningful acquisition variability and enables robust quality assessment across heterogeneous sensing modalities.
[CV-288] Beyond Random Partitioning: Unsupervised Spatio-Temporal Stratification for Cohort Balancing in Longitudinal Medical Imaging
链接: https://arxiv.org/abs/2608.00073
作者: Qinghui Liu,Jon André Ottesen,Atle Bjørnerud,Kyrre Eeg Emblem
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 22 pages, 7 figues
Abstract:Rigorous dataset partitioning is a foundational, yet frequently overlooked, prerequisite for reliable deep learning in longitudinal medical imaging. Naively shuffling small clinical cohorts routinely introduces covariate shifts and temporal sampling imbalances across training, validation, and test subsets, exposing downstream models to out-of-distribution evaluation. We address this vulnerability with an auditable Tripartite Dataset Analytics Framework that systematically characterizes spatial grid integrity, multi-parametric intensity fingerprints, and longitudinal temporal trajectories, quantifying the heavy-tailed feature dispersion and irregular, episodic sampling intervals typical of real-world clinical cohorts. Building on this characterization, we formalize an unsupervised spatio-temporal cohort-balancing standard operating procedure (SOP) that combines elbow-optimized K-means clustering over a standardized, six-dimensional joint intensity-temporal feature space with intra-cluster proportionate stratified sampling. On a longitudinal, contrast-enhanced T1 -weighted brain MRI cohort (N=149), the protocol reduces the maximum cross-subset intensity bias from 34.1% under conventional random shuffling to under 2.1%, while aligning longitudinal follow-up intervals closely around the population mean. Monte Carlo stress testing across ten random seeds and three split configurations confirms that this alignment remains tightly bounded, in clear contrast to the substantial variability of random partitioning. The resulting protocol offers a reproducible, generalizable procedure for cohort engineering in variable-length longitudinal clinical imaging workflows.
[CV-289] Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagery
链接: https://arxiv.org/abs/2608.00072
作者: Federica Torrisi,Claudia Corradino,Alessandro Grilli,Tommaso Catuogno,Mattia Verducci,Elisabetta Paladino,Luigi Giannelli,Alessandro Sebastianelli
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in quantum computing are opening new possibilities for Earth Observation (EO) data analysis. Quantum machine learning (QML) approaches offer novel ways to process information by exploiting quantum phenomena such as superposition and entanglement. These capabilities have motivated the exploration of whether quantum-enhanced models can address long-standing challenges in satellite remote sensing, where complex spectral and spatial signals often require sophisticated feature extraction. Among various fields of application, EO data allow the global monitoring of volcanic clouds and are crucial for aviation safety, hazard assessment, real-time eruption response, and evaluation of volcanic impacts on climate. Yet accurate detection of volcanic clouds remains difficult due to their similarity with meteorological clouds, the variability of eruption signatures, and the coarse spectral sampling of geostationary sensors. In this work, the potential of hybrid quantum convolutional neural networks (QCNNs) for the classification of satellite images containing volcanic clouds was investigated. These architectures integrate quantum computational layers into a classical convolutional framework. Two QCNN variants (with 2 and 4 qubits) have been considered to evaluate their ability to classify a dataset of SEVIRI images, including scenes with volcanic clouds (composed of ash, SO_2 , or mixed components) as well as non-volcanic backgrounds. Finally, the performance of the hybrid QCNN models was compared with that of purely classical architectures.
[CV-290] Empirical investigation of 3D CT Foundation Models and Unsupervised Adaptation for Head and Neck Cancer Recurrence Prediction
链接: https://arxiv.org/abs/2608.00071
作者: Bilel Guetarni,Feryal Windal,David Pasquier,Halim Benhabiles
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at AIiH 2026
Abstract:The rapid emergence of 3D CT foundation models has opened new avenues for predictive modeling from CT imaging, offering a compelling alternative to traditional radiomics which is known to suffer from reproducibility issues and sensitivity to acquisition protocol variations. Yet, as these models grow in availability, a critical need arises to evaluate how well their learned representations generalize across diverse clinical settings and whether adaptation to specific downstream tasks is necessary to unlock their full potential. To address these questions, we benchmarked several 3D CT foundation models for predicting recurrence-free survival in head and neck cancer across two public datasets totaling 3,644 patients, evaluating various adaptation strategies and modality fusion mechanisms. Our findings reveal persistent difficulty in identifying features that generalize consistently across different imaging distributions, as evidenced by significant performance drops on external validation cohorts. Ultimately, the integration of imaging features with clinical data remains the most accurate approach for prognostic prediction, though achieving universal generalization across varied clinical contexts continues to represent a substantial challenge for the current generation of models.
[CV-291] SafeBuild-Bench: A Temporal-Robust Construction Safety Benchmark with Graph-Enhanced Data Mining KDD2026
链接: https://arxiv.org/abs/2608.00068
作者: Yi Cui,Zilin Wang,Yijie Xu,Qianyi Cai,Huizai Yao,Shuai Jiang,Bingzhuo Zhong,Hui Xiong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by KDD 2026. 12 pages, 6 figures
Abstract:Construction-safety models must handle concrete deployment risks, such as a worker standing near a scaffold edge without guardrails, rather than only recognize common objects in curated images. Yet real inspection archives are redundant, long-tailed, and collected across changing sites and months. We introduce SafeBuild-Bench, a metadata-driven benchmark for evaluating multimodal large language models on construction safety under realistic temporal and site variation. It is mined from 100K+ industrial image-text records and contains 3,314 task instances from over 3,000 expert-verified images, covering multiple-choice hazard identification and free-form hazard description. To make expert verification scalable, we develop GEMS, a graph-enhanced multimodal selection pipeline that combines a proxy-model confusion signal with graph-based diversity to identify informative candidates from redundant streams. On public instruction-tuning data, GEMS-selected subsets preserve robustness-oriented performance under small data budgets. On SafeBuild-Bench, current MLLMs remain far from reliable construction-safety understanding, with the best overall score near 60. We release the benchmark, evaluation scripts, and GEMS codebase at this https URL.
[CV-292] PhysAgent : A Multi-Agent Framework for Reliable Remote Heart Rate Estimation
链接: https://arxiv.org/abs/2608.00066
作者: Yehui Yang,Bo Zhao,Junzhe Cao,Hui Ma,Yue Sun,Wenjin Wang,Zitong Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Remote photoplethysmography (rPPG) enables non-contact heart-rate estimation from facial videos, but its weak physiological signal is easily corrupted by motion, illumination changes, occlusion, skin-appearance variation, and device noise. Existing rPPG methods typically rely on a single model to directly predict heart rate or recover pulse waveforms, while different strong estimators may produce conflicting yet individually plausible candidates for the same video. To resolve these conflicts, we propose PhysAgent, an inference-time multi-agent candidate-verification framework. Unlike direct prediction approaches, PhysAgent neither trains a new base rPPG model nor asks Multimodal Large Language Models (MLLMs) to output heart rate directly. In contrast, it treats outputs from multiple base estimators as physiological hypotheses to be verified and uses a lightweight 4B MLLM, Qwen3-VL-4B, to drive multi-agent reasoning over video conditions, signal reliability, and candidate disagreement. A deterministic physiological verifier checks the fusion proposal, and a reproducible numerical fusion process produces the final heart rate. Experimental results on multiple public rPPG benchmarks show that PhysAgent improves fusion stability and reliability across different datasets and source-domain settings, while avoiding the irreproducibility and physiological inconsistency of direct MLLM prediction or unconstrained ensemble fusion. The code will be released soon.
[CV-293] Noise-Robust Conditional Flow Matching: Generating Clean Samples from Noisy Datasets
链接: https://arxiv.org/abs/2608.00064
作者: Adrian Urbański,Gabriel della Maggiora,Artur Yakimovich
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 3 Figures, and an Appendix
Abstract:Generative models learn the statistical properties of their training data, so high-quality generation depends on clean and representative datasets. In scientific imaging, acquisition often yields noisy measurements, while collecting clean references can be costly, impractical or even unattainable. Training directly on these measurements results in a model that reproduces the corrupted data. This can be circumvented by learning the clean population distribution directly from the noisy data. Conditional flow matching (CFM) combines a simple regression objective with stable training, efficient sampling, and strong image-generation performance, making it a natural framework for this setting. We introduce Noise-Robust Conditional Flow Matching (NR-CFM), an unconditional generator that learns from one corrupted observation per image. NR-CFM provides a closed-form clean endpoint correction for additive white Gaussian noise and learns a data-driven correction for general Gaussian corruptions with more complex covariance structure. Across the evaluated corruption settings, NR-CFM outperforms NR-GAN in most cases and remains competitive with Ambient Diffusion in the high-noise regime. We further evaluate NR-CFM on scientific data at signal-to-noise ratios as low as 0.001 , where it generates plausible particle images from severely corrupted measurements.
[CV-294] ELECTRIC: Evidential Learning-Enhanced CT Reconstruction via Iterative Correction
链接: https://arxiv.org/abs/2608.00060
作者: Ge Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 6 figures, 21 pages, and 27 references
Abstract:Here we introduce ELECTRIC (Evidential Learning-Enhanced CT Reconstruction via Iterative Correction), a physics-guided Bayesian formulation. An evidential neural network provides an image proposal and an error-predictive epistemic-uncertainty surrogate. The latter is converted into an adaptive precision field and inserted into a Poisson-weighted MAP update. The resulting image-evidence-precision-reconstruction loop treats prior confidence as a learned state variable of iterative reconstruction. In addition to the formulation and theoretical analysis, we report two simulation studies on image slices from the AAPM Mayo Clinic Low-Dose CT dataset: a mechanism-validation pilot using transparent surrogate estimators, and a feasibility study in which a trained Normal-Inverse-Gamma evidential network drives the full closed loop. On held-out patients, the learned prior mean reduces reconstruction error by roughly 70 percent relative to filtered back-projection, the learned epistemic uncertainty is error-predictive and supports selective trust, and the physics-guided update restores measurement consistency while the adaptive-precision reconstruction matches or exceeds a validation-tuned fixed prior and remains markedly more robust to prior-strength misspecification. Together these results demonstrate the complete ELECTRIC closed-loop pipeline, while identifying formal uncertainty calibration and joint training as the principal directions for future work.
[CV-295] Linguistic Context Recodes Visual Representations in Vision-Language Models
链接: https://arxiv.org/abs/2608.00035
作者: Brian Song,Michael A. Lepori,Ellie Pavlick
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Goal-directed visual processing is a hallmark of human visual intelligence, resulting in representations that support downstream tasks such as categorization or search. Though vision-language models (VLMs) are often faced with these same tasks, their ability to recode visual representations when presented with goal-directed language remains poorly characterized. Indeed, prior work largely treats visual representations in VLMs as static repositories of visual information that are manipulated by language representations. In the present work, we provide evidence for two concrete instances of language-induced recoding of visual representations. First, we identify an abstract reference representation that denotes which objects are goal-relevant under a natural language prompt. We extract contrastive steering vectors corresponding to this reference representation and demonstrate that they are causally implicated in model predictions. These reference representations are abstract in that they generalize to different objects, different task contexts, and even from synthetic to naturalistic images. Second, we demonstrate language-induced attribute modulation: later layers selectively amplify goal-relevant attributes in visual representations of objects. We demonstrate this phenomenon across a range of different prompts. Finally, we provide a causal intervention that demonstrates that attribute modulation mediates a VLM’s response distribution. Together, our results support a more dynamic account of cross-modality processing in VLMs – rather than vision tokens serving as static repositories of information, they are modulated to support queries articulated in language.
[CV-296] Posterior Variance Is a Constraint Map Not an Error Map: Closed-Form Uncertainty for Radiative Gaussian Splatting in Sparse-View CT
链接: https://arxiv.org/abs/2607.13682
作者: Chulin Zhao,Yiran Xu,Shu Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: v2: substantially revised and condensed; 31 pages total, 9 figures
Abstract:Radiative Gaussian splatting reconstructs sparse-view CT fast and accurately, and recent work attaches per-Gaussian posteriors to yield per-voxel uncertainty maps. We ask what such a map actually measures: posterior variance is a data-constraint map, not an error map – its alarms are trustworthy, its all-clears are not. Exploiting the strict linearity of X-ray rendering in the per-Gaussian densities, we derive a clamp-aware closed form that the unchanged rasterizer evaluates exactly in one forward pass, in volume and projection space: the infinite-sample limit of the sampling estimator of concurrent work, at ~8x lower cost. On the official 15-scene benchmark this uncertainty ranks true error on 14 of 15 scenes. Restricted to the object interior – the tissue a clinician reads – the ranking collapses (median Spearman 0.11, 0/15 pass), identically for a deep ensemble and for a strictly positive log-normal posterior: three constructions, two estimator families, no survivors. The mechanism is structural: about 90% of in-object error is bias that reproduces across retrainings, invisible to model disagreement; 73-81% of the full-volume correlation is carried by object/surround contrast; and an exactly solvable control puts the observed in-object ranking 4-5x below what a perfectly calibrated posterior with the same sigma-spread would score. The error scale, by contrast, is an engineering problem, and we solve it: reparameterizing the posterior contracts the cross-scene temperature spread from 19.3x to 2.6x, one scene-agnostic temperature transfers to unseen scenes (10/15 leave-one-scene-out), and the repaired scale tracks photon count at the Poisson-predicted -1/2 power. We distill evaluation practice that would have caught the illusion – masked calibration, seed-wise bias decomposition, an exact-posterior reference – and release all protocols, seeds and per-run evidence.
[CV-297] Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI
链接: https://arxiv.org/abs/2509.11924
作者: Bo Cao,Fan Yu,Mengmeng Feng,SenHao Zhang,Xin Meng,Yue Zhang,Zhen Qian,Jie Lu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Multimodal learning has attracted much attention in recent years due to its ability to effectively utilize data features from a variety of different modalities. Diagnosing the vulnerability of atherosclerotic plaques directly from carotid 3D MRI images is relatively challenging for both radiologists and conventional 3D vision networks. In clinical practice, radiologists assess patient conditions using a multimodal approach that incorporates various imaging modalities and domain-specific expertise, paving the way for the creation of multimodal diagnostic networks. In this paper, we have developed an effective strategy to leverage radiologists’ domain knowledge to automate the diagnosis of carotid plaque vulnerability through Variation inference and Multimodal knowledge Distillation (VMD). This method excels in harnessing cross-modality prior knowledge from limited image annotations and radiology reports within training data, thereby enhancing the diagnostic network’s accuracy for unannotated 3D MRI images. We conducted in-depth experiments on the dataset collected in-house and verified the effectiveness of the VMD strategy we proposed.
[CV-298] An Accessible Solution for Deformable Image Registration Compared with Learning-Based Approaches
链接: https://arxiv.org/abs/2608.02248
作者: Onur Ali Zeybekoglu,David Tilly,Orcun Goksel
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deformable image registration (DIR) is a core problem in medical image analysis; but, unlike labeling decision problems such as classification and segmentation, registration is a problem class that involves stringent physical constraints. Although deep learning methods have made faster registration possible, the resulting models are often difficult to interpret compared to hand-crafted methods with explicit objectives and interpretable physical meaning. In this work, we show that an analytical method can still yield competitive and superior results to deep learning in a common deformable registration task. We study pTVreg as a parametric total variation based registration in that context. Observing its different implementations to perform at various degrees, we introduce here an accessible implementation of this method, together with a Bayesian optimization framework that automatically sets self-parameters for any DIR task from a set of sample examples. Experiments on Lung250M-4B show that our proposed implementation achieves state-of-the-art results in this benchmark, substantially superior to existing deep learning solutions and other pTVreg variants as baselines. The source code will be made publicly available at this https URL .
[CV-299] Protocol generalisation for brain tissue microstructure estimation via hypernetwork-controlled geometric deep learning
链接: https://arxiv.org/abs/2608.02053
作者: Andrea Brigliadori,Leevi Kerkela,Hui Zhang
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Brain tissue microstructure estimation with machine learning provides higher computational efficiency than conventional fitting. However, machine learning still presents important limitations that hamper its clinical utility. Specifically, current models typically lack generalisation across diffusion MRI acquisition protocols and require retraining whenever b-vectors or b-values change. Moreover, the recent machine learning methods that were developed to address protocol generalisation lack rotational equivariance. Particularly suitable for dMRI parameter estimation is a geometric deep learning model known as spherical convolutional neural network (SCNN), which guarantees rotational equivariance and b-vector generalisation. However, this architecture currently does not account for b-values. Therefore, obtaining a model that combines protocol generalisation and rotational equivariance remains an open challenge. In this paper, we directly address this issue by incorporating explicit b-value dependence into an SCNN architecture via a hypernetwork. This new approach is illustrated using NODDI as an example forward model for estimating brain tissue microstructure. To evaluate b-value generalisation, the original and newly proposed SCNN architectures are trained on synthetic data and tested on both synthetic and real data across different b-value pairs. Results demonstrate that the proposed method achieves reduced RMSE and bias on synthetic data, as well as higher agreement with conventional NODDI fitting on real data, indicating improved robustness to unseen b-values and a reduced need for retraining. By combining generalisation across b-values with generalisation across b-vectors and rotational equivariance, the proposed framework enhances the applicability of deep learning to clinical diffusion MRI parameter estimation. Code available at this https URL_generalisedSCNN.
[CV-300] Mapping melliferous tree species in Kenya via one-class classification with hyperspectral unsupervised domain adaptation
链接: https://arxiv.org/abs/2608.02045
作者: Zhaozhi Luo,Janne Heiskanen,Ilja Vuorinne,Ian Ocholla,Shiqi Zhang,Saana Järvinen,Xinyu Wang,Yanfei Zhong,Petri Pellikka
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages. Final published version, licensed under CC BY 4.0
Abstract:The beekeeping sector holds significant potential for livelihood diversification among the agropastoral communities in Kenya. Melliferous tree species play a critical role by providing essential nectar sources for bees. However, limited knowledge of their precise spatial distributions constrains the full development of beekeeping. One-class classification (OCC) offers a practical solution for detecting single target species without requiring extensive labeled data from other classes. Although existing OCC methods perform well in trained domains, the generalization capability to unseen domains remains limited due to domain shift. To address these challenges, this study proposes a hyperspectral unsupervised domain adaptation OCC framework (HyUDA-One) for tree species mapping using airborne hyperspectral imagery and laser scanning data. The spatial-spectral regularized pseudo-positive learning was designed to mitigate domain shift and improve model generalizability. The effectiveness of HyUDA-One was demonstrated by mapping three key melliferous tree species in two savanna landscapes in southern Kenya. The results show that HyUDA-One significantly improves performance in unlabeled domains. The F1-scores of 0.788, 0.845, and 0.768 were achieved for Senegalia mellifera, Vachellia tortilis, and Commiphora africana in the trained domain, respectively. In the untrained domain, the F1-scores of Senegalia mellifera and Vachellia tortilis were 0.756 and 0.884, respectively. The distribution maps revealed the spatial patterns of these melliferous tree species and the nectar source availability, offering an important reference for sustainable beekeeping development in savanna landscapes. Furthermore, the proposed framework can potentially be extended to other mapping applications, such as invasive species detection.
[CV-301] ARM: Detector-Agnostic Changepoint Attribution with Finite-Sample Error Control
链接: https://arxiv.org/abs/2608.01691
作者: Chenchen Peng,Mixia Wu,Qijing Yan,Da Chen,Zhiqi Shen
类目: Methodology (stat.ME); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Detecting a change in a multivariate series answers only the first of two questions; the operational question is which coordinates changed. Existing answers are incomplete. Block-level procedures certify predefined groups of coordinates under an additive union bound, high-dimensional variable-selection methods return interpretable rankings without error guarantees, and the post-detection inference literature controls error along the time axis rather than across coordinates. We propose ARM (Attribution by Rank Maxima), a wrapper that accepts a changepoint located by an arbitrary detector and returns the set of coordinates certified to have changed, each carrying a location or scale type label. ARM scores each coordinate by a max-over-splits rank statistic. Because this statistic dominates the corresponding statistic at the estimated split, the resulting certificate is invariant to the manner, and to the accuracy, of the changepoint estimate. Three finite-sample guarantees follow from within-coordinate ranks alone: per-coordinate validity under any detector; exact family-wise error control through a Westfall–Young joint permutation that preserves cross-coordinate dependence, with a fully distribution-free Holm fallback; and false discovery rate control under arbitrary coordinate dependence in high dimensions through Benjamini–Yekutieli and e-BH. In simulations, naive per-coordinate testing at the estimated changepoint inflates its family-wise error beyond 0.66 as the dimension grows, whereas ARM maintains the nominal level while retaining validity under heavy tails, power in high dimensions, and accurate type labels. On five financial series surrounding the 2008 collapse, ARM attributes a scale change to every asset class and excludes injected control coordinates.
[CV-302] Measuring Product Quality Using Images: The CLIP Q-Score and an Application to Real Estate
链接: https://arxiv.org/abs/2608.01544
作者: Fabian Slonimczyk,Danila Karapsin
类目: Econometrics (econ.EM); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The CLIP Q-score is a novel, safe, fully reproducible, and computationally efficient method for extracting objective product quality metrics from visual data using contrastive language-image pre-training. We introduce the technique and provide an extensive application to real estate data from an online platform ( \sim500,000 images). Our open-source metric aligns with LLM assessments and proves to be a powerful predictor of housing market prices for both sales and rentals. We also show that a higher CLIP Q-store is associated with better liquidity (reduced time on the market), especially for properties on sale.
[CV-303] Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation
链接: https://arxiv.org/abs/2608.00279
作者: Xuan Cuong Ngo
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent radiology-adapted vision-language models have achieved strong performance on standard report generation benchmarks, yet their robustness and generalization remain constrained by imperfect alignment and correlation between visual and textual features. Existing methods connect image and text either implicitly through autoregressive report supervision or explicitly through contrastive learning. However, autoregressive supervision alone is insufficient to establish reliable image-text alignment, while contrastive learning can push apart unpaired reports that describe related pathologies simply because they are not paired with the same image. This is problematic in radiology, where different reports may share compatible pathology semantics rather than being true negatives. As a result, the learned representation may fail to organize images and reports around shared pathology concepts, causing the decoder to rely on pretrained language priors and generate clinically plausible reports that are not fully supported by radiographic evidence. To address this issue, we propose PALM, a pathology-aware alignment framework for radiology report generation. Instead of directly matching each image-report pair while separating all others, PALM aligns visual and textual features through shared pathology prototypes. These prototypes provide a clinically meaningful bridge between radiographic evidence and textual findings, allowing cases with similar pathology semantics to move toward common concepts without separating compatible cases. In addition, we introduce Masked Evidence Modeling to strengthen the image encoder sensitivity to local radiographic evidence by learning semantic changes caused by masked image regions. Experiments on MIMIC-CXR, IU X-Ray, and MIMIC-ABN show that PALM consistently improves both report generation and abnormality-focused robustness.
[CV-304] MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation
链接: https://arxiv.org/abs/2608.00195
作者: John Garcia Henao,Nicholas Bünger,Benedikt Herzog,Cindy Guerrero Toro,Benjamin Vella,Matthias Biner,Rico Brütsch,Carmen Castroviejo Fernandez,Felix Öttl,Norman Juchler,Armando Hoch,Bettina Hochreiter,Sven Hirsch,Sebastiano Caprara
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Original research manuscript (13 pages, 4 figures, 2 tables). No prior publication
Abstract:High-resolution 3D segmentation of hip and shoulder anatomy from CT and MRI is essential for surgical planning, yet frozen segmentation models often fail under domain shift. CNN-based expert models are fully automatic but lack adaptability, whereas promptable foundation models generalize better but require manual prompting. We present MedSAM2-Anatomy, a training-free inference-time optimization framework that improves frozen segmentation models without retraining or human interaction. A frozen expert model generates anatomical priors that are automatically converted into multiple prompt hypotheses for a frozen 3D foundation model. Candidate masks are fused while anatomically implausible priors are rejected. No model weights are updated and no manual prompts are required. TotalSegmentator and MedSAM2 are used as representative expert and foundation models, allowing the contribution of the inference policy to be isolated. Evaluation on the independent Balgrist-V0 CT and MRI cohorts shows that inference-time optimization increases median Dice from 0.71 to 0.92 on hip MRI and from 0.89 to 0.92 on shoulder CT, while reducing median HD95 on hip MRI from 22.0 mm to 5.0 mm. On public TotalSegmentator benchmarks, the expert model remains strongest, indicating that the optimal fusion strategy depends on the reliability of the expert prior. These results demonstrate that training-free inference-time optimization provides a practical strategy for improving frozen segmentation models without manual prompting.
[CV-305] Automatic LV Localization and Short-Axis Plane Estimation from Arbitrary CMR Slice MICCAI2026
链接: https://arxiv.org/abs/2608.00145
作者: Yi Yu,Yixuan Liu,Ziyu Zhang,Parker Martin,Zhenyu Bu,Yuchi Han,Yuan Xue
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint version. Accepted to MICCAI 2026
Abstract:Accurate estimation of left ventricular (LV) orientation is essential for cardiac magnetic resonance (CMR) imaging and downstream analysis. Existing methods typically formulate orientation recognition as discrete view classification or rely on multi-slice geometric intersection, limiting their ability to model continuous 3D orientation and generalize across arbitrary slices. This work introduces a novel paradigm: Joint LV localization and 3D orientation estimation from a single CMR slice. To investigate this setting, representative orientation-aware detection frameworks are adapted to the CMR domain, and their limitations are analyzed. Upon that, we propose the Polar-Coupled Circular (PCC) embedding that provides a continuous and unambiguous orientation representation to address the limitations. Meanwhile, a scalable benchmark is constructed through automatic slice sampling from volumetric CMR segmentation datasets. Extensive experiments on four datasets demonstrate strong performance, achieving an average mIoU of 86.18% and an average angle deviation of 3.39°. This study establishes a new task setting for single-slice LV orientation modeling and provides a geometry-consistent framework for spatially informed CMR analysis. Code is available at this https URL.
[CV-306] RPL-UIE: Reliable Prior Learning for Underwater Image Enhancement
链接: https://arxiv.org/abs/2608.00137
作者: Yifan Chen,Jiaming Liu,Ye Zheng,Zhe Sun,Tao Chen
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 10 figures, and 6 tables
Abstract:Underwater image enhancement (UIE) aims to recover clear images from observations affected by wavelength-dependent absorption, scattering, and spatially nonuniform degradation. Although existing generative methods can handle complex degradations, severe information loss may lead to semantic drift in the restored results. To address this issue, we propose RPL-UIE, a two-stage teacher–student framework for reliable prior learning. In the teacher stage, the network learns reliable and complementary spatial priors characterizing appearance and photometric properties from paired degraded and reference images. In the student stage, the network takes only degraded images as input and learns to emulate the teacher’s prior extraction capability, thereby providing more reliable restoration guidance for the enhancement process without requiring reference images at inference. To reduce the prior-learning discrepancy between the teacher and student models, we further develop Residual Prior Refinement Diffusion (RPRD) and Frequency-Aware Prior Residual Calibration (FPRC). RPRD uses the coarse priors as anchors and progressively predicts the necessary corrections in the residual space. FPRC retains stable low-frequency residual components and selectively modulates high-frequency detail residuals, producing calibrated priors to support high-quality reconstruction. Experiments on multiple UIE benchmarks demonstrate competitive restoration performance. Downstream underwater object detection and instance segmentation experiments further demonstrate the improved utility of enhanced images for visual perception, while tests on real-world data captured by a remotely operated vehicle (ROV) support the practical applicability of RPL-UIE.
[CV-307] FDIR: Harmonizing Fidelity and Human-Machine Preference in Lossy Compression Image Restoration
链接: https://arxiv.org/abs/2608.00111
作者: Kuan-Yen Chen,Fang-Yi Su,Philip Chikontwe,Jung-Hsien Chiang
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 19 pages, 8 figures, 13 tables
Abstract:Image restoration quality can be evaluated along three complementary facets: pixel-level fidelity, human perception, and downstream machine preference. However, existing lossy compression restoration methods optimize for at most one of these criteria: fidelity-oriented models often regress toward conditional means and produce over-smoothed outputs, while generative approaches hallucinate plausible but factually incorrect textures that degrade both ground-truth fidelity and downstream task accuracy. To navigate this three-way tradeoff, we propose FDIR, a two-stage architecture that decouples the conflicting demands through complementary inductive biases: Quality-Guided One-Step Flow Matching (QO-Flow) recovers global semantic structure in latent space via a single forward pass, while Flow-Conditioned Detail Refinement (FCDR) deterministically restores high-frequency textures and suppresses generative hallucinations in pixel space. Extensive experiments demonstrate that FDIR achieves superior fidelity, with a favorable perceptual-fidelity balance and competitive machine preference.
[CV-308] Fast Trainable Multilinear Bases for Image Compression
链接: https://arxiv.org/abs/2608.00053
作者: Shiwen An,Zhongyi Ni,Huanhai Zhou,Jin-Guo Liu
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Optimization and Control (math.OC); Quantum Physics (quant-ph)
备注:
Abstract:The Discrete Fourier Transform, the Discrete Cosine Transform, and their block-wise variants underpin most deployed image and video codecs. Their effectiveness rests on three properties: they run in near-linear time (linear up to a polylogarithmic factor), they are exactly invertible, and they carry few to no parameters. In this work, we generalize these bases to isometric multilinear bases, allowing a small number of extra parameters, polylogarithmic in the image size, while preserving all three properties. Given an image dataset, we develop a systematic framework that searches this family for the basis compressing the dataset most effectively: the basis is parameterized as an isometric tensor network, inspired by quantum many-body theory, and trained with Riemannian optimization on the manifold of unitary matrices. Across natural photographs and line drawings, the trained bases consistently improve on their fixed, non-parametric counterparts. On Quick Draw line-drawing compression, they store images in roughly 20% fewer bytes than JPEG’s 8 \times 8 block cosine transform at the same reconstruction quality.
人工智能
[AI-0] Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework
链接: https://arxiv.org/abs/2608.02599
作者: Junjie Yin,Buxin She,Xinyu Feng,Fangxing(Fran)Li
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注: 10 pages, 10 figures, 3 tables
Abstract:Artificial intelligence (AI) is increasingly central to power and energy systems, supporting modeling, forecasting, optimization, and control. Yet most existing works emphasize specialized applications and offer little reusable material for newcomers or interdisciplinary learners, who increasingly rely on large language models rather than building their own. This gap points to a need for engineering-grounded AI (EGAI), in which AI workflows follow established engineering and power-system domain rules rather than acting as task-agnostic black boxes. Motivated by a community survey of researchers and practitioners, which shows 92% report at least one barrier before running an AI model and 94% want a power-specific hands-on course. This paper presents a framework consisting of open, executable module library that lowers the entry barrier for AI in power systems. The modules follow a progressive difficulty ladder that maps core AI concepts onto representative power-system tasks: (i) foundational deep neural network (DNN) templates for function approximation and load-curve fitting; (ii) a domain-coupled convolutional neural network (CNN) power-flow surrogate for a 5-bus system; and (iii) frontier modules on DNN-assisted optimization, deep reinforcement learning (DRL) for battery storage control, and physics-informed neural networks (PINNs) for the swing equation. All modules are released as Jupyter notebooks that run locally or on Google Colab and are delivered through an IEEE online course and IEEE Power Energy Society (PES) webinar series. The webinar drew more than 590 live attendees, which is among the ten most-attended IEEE PES webinars, and over 344 repository visits within two weeks, reinforcing the survey-based motivation.
[AI-1] CoWAM: Coordination Contracts for Selective Policy Intervention with WAMs
链接: https://arxiv.org/abs/2608.02578
作者: Shuaijun Liu,Qifu Wen,Shuyang Hao,Qi Luo,Chenglong Zhang,Feiyang You,Chengyu Wu,Ningxin Su
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:World Action Models (WAMs) augment robot policies with action-conditioned predicted futures, but a plausible future alone does not justify changing the action that a bimanual policy would execute. We present CoWAM, a selective intervention layer that expresses synchronization, role compatibility, and collision convergence as coordination contracts. Each contract combines typed admissibility checks with event-conditioned verification and calibrated intervention gates. CoWAM preserves the nominal action unless an alternative satisfies every active obligation and provides a clear, low-risk improvement; when the nominal action is also inadmissible, it invokes a predefined abstention fallback. To separate selector quality from proposal quality, all methods operate on identical candidate pools and commit their decisions before shared oracle labeling. Across eight simulated bimanual tasks, CoWAM improves coordination-valid selection by 16.7 percentage points over the contract-only variant and raises closed-loop success by 9.6 percentage points over the strongest selective baseline, while keeping harmful interventions below 1%. Together, these results establish coordination contracts as an effective interface for conservative policy intervention with predicted world-action evidence across coordination-rich bimanual tasks.
[AI-2] AtumAI: A Principled Framework for Agent ic Generation of Datacenter Control-Plane Policies
链接: https://arxiv.org/abs/2608.02569
作者: Qiushi Lin,Chaojie Zhang,Íñigo Goiri,Aditya Akella,Ricardo Bianchini,Jovan Stojkovic
类目: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Operating Systems (cs.OS)
备注:
Abstract:The efficiency of a datacenter rests on its control plane policies. Designing these policies is increasingly hard: the hardware-software stack grows fast, the design space is vast and interdependent, and prototyping a single policy takes months. Agentic AI promises to automate this search. Off the shelf, however, it falls short on three fronts. It is not formal: with no structured, searchable statement of the problem, the search has little structure to exploit and hard constraints are not guaranteed. It is not transferable: each task is solved from scratch, so nothing learned on one task carries to the next. Finally, it is not systematic: relying on the LLM as the sole source of candidates, it explores a narrow slice of the design space and settles into local optima. We introduce AtumAI, a framework that generates datacenter control-plane policies with agentic AI, making the process formal, transferable, and systematic. From a goal stated in plain language, AtumAI autonomously proposes, tests, and refines candidate policies until one satisfies the request. It does so through two components. The Datacenter Task Compiler automates problem formulation: it compiles the request into a formal, machine-checkable, and searchable specification of the task’s objectives, constraints, decision variables, and evaluation methodology. The Evolutionary Design Discovery Loop then searches this specification, expanding the search beyond the LLM itself via a diffusion model, an evolutionary algorithm, and a surrogate model. Together, they reduce onboarding a new task from months of engineering to writing its description. We evaluate AtumAI on three control-plane tasks with distinct problem scopes, design spaces, and trade-offs: workload placement, resource scaling, and power management. Across all tasks, the policies generated by AtumAI consistently outperform expert-engineered baselines.
[AI-3] A Taxonomy of Cognitive Capability Gaps in Generative and Agent ic AI
链接: https://arxiv.org/abs/2608.02553
作者: Taye Akinrele,Sindhuja Penchala,Noorbakhsh Amiri Golilarz,Sudip Mittal,Shahram Rahimi
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures
Abstract:Cognitive AI seeks to move beyond language generation and autonomous task execution toward systems capable of sustained reasoning, adaptive behavior, persistent memory, and self-regulation. While generative and agentic AI have demonstrated impressive capabilities across a wide range of tasks, many fundamental cognitive functions remain fragmented or weakly developed, limiting reliable operation over extended time horizons. This paper presents a taxonomy-driven survey of the major cognitive capability gaps that continue to constrain the development of Cognitive AI. The literature is organized around five dimensions: persistent state modeling, goal-directed autonomy, self-monitoring and control, environment interaction, and learning and adaptation. For each dimension, we review recent advances, identify recurring limitations, and discuss open research challenges. Building on these insights, we outline a conceptual Adaptive Cognitive Intelligence Architecture (ACIA) and examine emerging directions in cognition-centric evaluation. The proposed taxonomy provides a unified framework for organizing existing research, identifying unresolved challenges, and guiding the design of future cognitively capable systems. Together, the taxonomy, architectural perspective, and evaluation framework offer a roadmap for advancing AI systems that exhibit more reliable long-term reasoning, adaptive decision-making, and continual learning. The survey highlights key research opportunities toward more adaptive, reliable, and cognitively capable AI systems, providing a foundation for future progress toward Cognitive AI and, ultimately, Artificial General Intelligence (AGI).
[AI-4] Analytic Planning under Uncertainty with Moment Closure UAI2026
链接: https://arxiv.org/abs/2608.02519
作者: Shishir Sharma,Doina Precup
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: To appear in Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026), PMLR
Abstract:Effective model-based reinforcement learning in stochastic environments requires planning that accounts for predictive uncertainty. Propagating full state distributions analytically offers a principled way to do this, but has traditionally required restrictive policy or reward structures to remain tractable. Consequently, modern deep reinforcement learning has largely retreated to either stochastic sampling, which introduces significant target variance, or deterministic point estimates that ignore predictive covariance entirely. We investigate whether distribution-aware planning is possible without these constraints. Using a quadratic action-value parameterization, we first reduce the Bellman backup to an expectation over the state-value function alone; the key idea is then a compatibility principle between the predictive transition distribution and the value function class, under which this expectation is analytic in the distribution’s moments. We instantiate this principle with a Gaussian transition model paired with a radial-basis value function, yielding a closed-form backup that propagates both predictive mean and covariance. Empirically, our approach reduces target variance and yields well-calibrated predictive uncertainty under stochastic observations in continuous control, providing a principled framework for planning with learned distribution models.
[AI-5] Magnet: Detecting Cross-Session AI Misuse Through Capability Accumulation
链接: https://arxiv.org/abs/2608.02518
作者: Natalie Isak,Matthew Dressman
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:The most capable AI deployments are not single models but ensembles of specialized agents that delegate and act in coordination. This architecture unlocks powerful new capabilities, and it also introduces risks that existing frameworks for monitoring, detection, and mitigation were not designed to address. Most state-of-the-art AI abuse detection literature focuses on single-turn or multi-turn (single-session) threat models. This leaves a critical gap: an attacker can decompose a harmful goal into innocuous-looking units and execute each in isolated agentic sessions. The agent is stateless between conversations, but the attacker is not. This asymmetry allows for cross-session trajectories that are effective at evading detection. Our contributions are twofold. First, we demonstrate cross-session goal decomposition as an evasion technique, showing it may elicit more harmful capability than equivalent single-session or multi-turn attacks. By capability we mean an artifact produced at one step of an objective, evidenced by what an interaction produced (model responses and tool-call results), and composable with capabilities accrued elsewhere into a harmful whole. Second, we propose Magnet: an efficient and robust detection approach that models relevant capabilities accrued over time and across agentic conversations, aggregated at a higher-level correlator (in this case, a user ID) rather than per-conversation state. The main challenge is assembling the evidence bundle Magnet reasons over. The incriminating artifacts may be needles scattered through a haystack of benign sessions that are individually harmless, dangerous only once collected. Rather than searching the haystack straw-by-straw (i.e. per-session inspection), Magnet does what its name implies: it attracts the relevant needles out of the hay, across sessions and across time, into a compact evidence bundle a detector can act on.
[AI-6] Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
链接: https://arxiv.org/abs/2608.02509
作者: Sterre Lutz,Daniël Vos,Matthijs T.J. Spaan,Anna Lukina
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 14 pages, 5 figures, 2 tables
Abstract:Sequential decision-making in real-world applications often involves uncertainty about the environment’s model. Uncertain Markov decision processes (UMDPs) represent the possible environments as a set of MDPs with shared states and actions but potentially different transition probabilities and rewards. Optimizing a single policy across all possible MDPs may sacrifice performance, while preparing an individually optimized policy for every MDP may violate operational, regulatory, or interpretability constraints on the number of policies that can be prepared and deployed. We consider settings in which model uncertainty is resolved shortly before execution, allowing the most suitable policy to be selected from a limited set prepared in advance. We introduce k -adaptable policy synthesis, which optimizes such a set of k policies under a minimax-regret objective. We prove that the problem is NP-hard and develop KAPS, an exact nested branch-and-bound algorithm with problem-specific bounds and heuristics. KAPS jointly optimizes which MDPs share a policy and the policies themselves. Experiments across various UMDP benchmarks show that the largest reduction in regret consistently occurs when increasing from one to two policies. In the single-policy setting, KAPS is competitive with existing methods in solution quality and proves optimality substantially more often.
[AI-7] CMuon: Accelerating and Stabilizing Diffusion Transformer Training via Chunked Momentum Orthogonalization ECCV2026
链接: https://arxiv.org/abs/2608.02502
作者: Chuyan Chen,Peng Sun,Kun Yuan
类目: Artificial Intelligence (cs.AI)
备注: ECCV 2026
Abstract:Diffusion Transformers (DiTs) have achieved state-of-the-art (SOTA) performance in visual generative modeling, yet their training remains computationally prohibitive. While the recently proposed Momentum Orthogonalization (Muon) optimizer offers a promising alternative to AdamW, its direct application to DiTs yields suboptimal late-stage convergence. In this paper, we identify the root cause of this bottleneck: standard DiT architectures fuse functionally distinct weights (e.g., within AdaLN and QKV layers) into unified tensors for computational efficiency. Applying Muon to these fused tensors inadvertently induces implicit subspace coupling, which distorts update directions and degrades global optimization. To address this, we introduce Chunked Muon (CMuon), a simple yet highly effective strategy that partitions these matrices into independent sub-components prior to orthogonalization. Extensive experiments demonstrate that a 675M-parameter DiT trained with CMuon achieves a FID of 1.18 on ImageNet 256 in just 200 epochs. This represents more than a 2x training speedup over AdamW, while effectively overcoming the late-stage convergence plateaus of vanilla Muon.
[AI-8] Long-term Measurements: Towards a Longitudinal Understanding of Human-AI Interactions
链接: https://arxiv.org/abs/2608.02491
作者: Nicole Mitchell,Dhruv Agarwal,Maty Bohacek,Remi Denton,Roma Patel
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Language models have taken on the role of a very new type of technology, by virtue of their “human-ness” and rapid integration into users’ daily lives. This combination of features can introduce longitudinal risks—cognitive, developmental and socio-affective changes in humans—that might not surface in short-term interactions, but can have lasting long-term effects on users. This forms the basis of a critical new mission for NLP: to pivot from static, short-term evaluations of text generations to long-term measurements of behavioral changes, towards a diachronic understanding of human-model interactions. In this work, we draw from measurements used in social science fields that are crucial to understand emergent phenomena in longitudinal data. We discuss how computational methods in the field of NLP need to be combined with such measurements, not only to understand long-term safety risks of human-model interactions, but to help steer model development towards positive rather than negative outcomes for users. This ability to model human behavioral shifts as a function of model interactions can facilitate online rather than post-hoc detection of problematic behaviors, and should be leveraged in alignment frameworks to mitigate long-term risks in users.
[AI-9] Real-Time Detection and Repair of LLM Agent Failures
链接: https://arxiv.org/abs/2608.02464
作者: Sunny Dubey
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: 16 pages, 5 figures. Code, data and demo: this http URL Walkthrough: this http URL
Abstract:LLM agents fail mid-episode – they loop, cascade tool errors, drift off goal, fabricate results, or silently absorb corrupted content – and the standard remedy, judging every step with a second LLM, costs more than the agent itself. We ask how much detection is achievable from observable step telemetry alone, using monitors costing microseconds per step and trained only on healthy runs. On 2,823 committed agent episodes across three frameworks, three local models (qwen2.5 7b/3b, llama3.1 8b) and a commercial API (gemini-2.5-flash), a one-class echo-state-network ensemble with CUSUM alarms detects 0.71 of failures at a 5% false-alarm budget (AUROC 0.872). Its advantage over a memoryless baseline is a monotone function of post-onset horizon (+0.09 at =3 steps, +0.40 at =9), predicting its own failure region out of sample on AFTraj-2K. Ranking transfers with no retraining to two corpora from other groups (AFTraj-2K 0.745, ATBench 0.779). Monitors carry two burdens: a per-deployment healthy null (they do not transfer – AUROC 0.527 cold against 0.885 recalibrated) and a residual false-alarm rate. We add a layer carrying neither: deterministic verification, which recomputes a run’s stated total from the tool results it actually received and confirms every required call was made. Head-to-head it catches 60% of failures (96% with the coverage check) at 0 of 63 false positives against the monitor’s 54% at 17%, transfers unchanged to llama3.1:8b (110 of 110 at 0 of 10), and trips on 0 of 1825 healthy episodes. Detection is then closed into repair: each flagged run is rolled back and re-run live, recovering 45% of failures against a 16% resampling control (p=0.0005) and lifting task success from 52% to 73% for about one extra model call per run. The system runs at ~200 microseconds per step, three orders of magnitude below a judge call. Code, traces and results are released. Comments: 16 pages, 5 figures. Code, data and demo: this http URL Walkthrough: this http URL Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE) ACMclasses: I.2.11; D.2.5 Cite as: arXiv:2608.02464 [cs.AI] (or arXiv:2608.02464v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.02464 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sunny Dubey [view email] [v1] Mon, 3 Aug 2026 16:34:46 UTC (558 KB)
[AI-10] Infinite Trace Objectives with Finite Trace Techniques: Translating LTL to LTLf
链接: https://arxiv.org/abs/2608.02454
作者: Christoph Weinhuber,Maximilian Prokop,Giuseppe De Giacomo,Moshe Y. Vardi
类目: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); Logic in Computer Science (cs.LO)
备注:
Abstract:Linear Temporal Logic (LTL) is one of the most widely adopted languages for specifying temporal extended objectives in AI, with applications ranging from reactive synthesis to stochastic planning in Markov decision processes and reinforcement learning. Traditionally, solving any of these problems requires translating the LTL specification to a nondeterministic automata on infinite words and then determinizing it, a step that is notoriously difficult in theory and in practice. Recent work has introduced LTLf+, which lifts the finite-trace logic LTLf to infinite traces. LTLf+ has the same expressive power as LTL, yet it retains most of the crucial advantages of its base logic LTLf. Most reasoning in LTLf+ rests on finite automata on finite words, for which we have not only a canonical minimal representation but also an efficient determinization procedure. In this work we present the first translation from LTL to LTLf+. We first normalize an LTL formula into the syntactic reactivity fragment of the Manna-Pnueli hierarchy, to create the general fragment-based shape of LTLf+. We then present linear translations for each individual component of that fragment. As a consequence of this translation, the expanding body of techniques developed for LTLf+ now becomes available to many AI problems currently formulated in LTL. We further show that this comes at no asymptotic cost, as the pipeline from LTL to automaton via LTLf+ remains doubly exponential.
[AI-11] ParEvalLayer: When Partial LLM -Agent Evaluations Support a Decision
链接: https://arxiv.org/abs/2608.02444
作者: Wei-Jung Huang,Bonan Shen
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the 2026 ACM International Conference on AI-ML Systems (AIMLSystems)
Abstract:LLM-agent evaluations often produce task outcomes long before the full benchmark run is complete. A partial score is tempting to report, but it does not show whether the observed tasks support the same conclusion as the completed evaluation. Early tasks can omit important parts of a benchmark, running cheaper tasks first can distort the observed sample, and a rule that decides only easy pairs can appear accurate while leaving many comparisons unresolved. We introduce ParEvalLayer, a decision layer that reads paired outcomes for two agent systems and a comparison policy chosen in advance. For each partial run, it records whether the tested agent system is better by the required amount, is not better by that amount, needs more evidence, or should abstain. We evaluate ParEvalLayer by replaying completed public benchmark data as if each evaluation had stopped earlier. At each point, ParEvalLayer applies the policy using only the outcomes observed so far; if it reaches one of the two comparison judgments, we check whether that judgment matches the completed data for the same system pair. With the main comparison rule, three of the public benchmarks reach the same decision as the completed evaluation after observing only 15% to 25% of task outcomes. Other benchmarks require more task outcomes. This variation shows why a partial score alone is not enough: reports should also state the decision rule and how many comparisons remain without a decision.
[AI-12] Agent ic Commerce World: An Auditable and Verifiable Environment for Vibe Commerce
链接: https://arxiv.org/abs/2608.02441
作者: Shicheng Fan,Mingdai Yang,Duohao Wang,Canyu Chen,Yongfeng Zhang,Hua Wei,Manling Li,Julian McAuley,Kun Zhang,Philip S. Yu,Kejing Yu,Zhiwei Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In vibe coding, people describe software in natural language and delegate implementation to AI agents. By analogy, vibe commerce allows people to express buying or selling goals in natural language and delegate the corresponding tasks to agents. Commerce, however, requires independently controlled Buyer and Merchant agents to interact in a shared market while preserving their private objectives and distinct authority. We introduce Agentic Commerce World (ACWorld), an environment for evaluating such agents across ongoing transactions. Through its Vibe Commerce Protocol (VCP), ACWorld validates agent actions before updating shared transaction state and records the resulting interactions, making agent behavior auditable and evaluation reproducible. The ACWorld Benchmark contains a 200-task capability-coverage track and a 60-task large-catalog track that searches 785,022 transactable listings. Across ten models, mean scores range from 65.9% to 85.6% and from 56.1% to 91.4%, respectively. Our analysis shows that process-level evidence is necessary: final state alone can miss evaluated errors, incomplete trajectories still retain useful process signals, and large-catalog tasks expose bottlenecks across stages.
[AI-13] xPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding
链接: https://arxiv.org/abs/2608.02438
作者: Zheng Wang,Davis Wertheimer,Yu Chin Fabian Lim,Mudhakar Srivatsa,Raghu K. Ganti,Minjia Zhang,Naigang Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Block-diffusion drafters like dFlash generate an entire block of draft tokens in a single forward pass, drastically reducing the overhead of multiple-token drafting in speculative decoding. The crucial final step of the single-pass discrete denoising process involves using the logit distribution at each position to sample conditionally independent tokens. The resulting draft is thus a set of per-position marginals, rather than a joint distribution: no draft token is guaranteed to depend on its predecessors. Such independently sampled marginals tend to produce sequences with tokens that are individually likely, but jointly improbable under the target model’s distribution, which verifies each token conditionally. This can cause early rejection and limits acceptance length. To address this, we propose xPress as a means to restore the missing causality in diffusion drafters. xPress is a lightweight causal refiner that reconciles the whole diffusion block at once through parallel refinement, restoring and propagating causal dependencies across the draft without a token-by-token loop. On Qwen3-8B, across seven math, code, and chat benchmarks, xPress raises acceptance length by about 30% on average (up to +56%) and its end-to-end decoding throughput by about 1.3 on average (up to 1.7) compared to the original dFlash diffusion drafter.
[AI-14] Agent ic Incident Response through Digital Twin-Enhanced Multiscale Planning ESORICS
链接: https://arxiv.org/abs/2608.02422
作者: Yiran Gao,Tao Li,Kim Hammar
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 31st European Symposium on Research in Computer Security (ESORICS) 2026
Abstract:Incident response is currently managed by security operators using predefined playbooks, resulting in slow, labor-intensive security decision-making processes. Consequently, there is a growing need for automated incident response planning. Decision-theoretic approaches based on control, optimization, and reinforcement learning have been proposed to automate such planning tasks with well-grounded approaches, yet most of which, while guaranteeing strong performance, are limited to abstract models and cannot be directly applied to operational systems. A promising approach to mitigate this limitation is to use the security knowledge embedded in large language models (LLMs) to develop agentic response systems. However, current agentic approaches rely on repeated invocations of the LLM to generate a response plan, which is unreliable and limits the planning horizon due to hallucination. In this paper, we develop a principled LLM-based planning method by combining decision-theoretic planning with LLM-generated response commands. The proposed agentic incident response approach uses a rollout planner to compute a high-level response strategy that allocates security resources (the tactical scale), which is then translated into executable commands by a lightweight LLM agent (the operational scale). Within this architecture, we use a digital twin that supports tactical planning through simulation and operational execution through emulation. Across three attack scenarios, our agentic approach reduces recovery execution time by 15.1% on average and increases the recovery rate by 33.6% over frontier LLM baselines.
[AI-15] Human-Centered Reflections on Care Robots: A Comparative Study of Caregiver Perspectives
链接: https://arxiv.org/abs/2608.02411
作者: Laura Londoño,Klaus Baumann,Abhinav Valada,Markus Langer
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注:
Abstract:Care robots are increasingly being introduced into healthcare settings, raising important questions about their acceptance and ethical implementation. To better understand these challenges, this study investigates caregivers’ perceptions of four categories of care robots: delivering supplies, helping patients into bed, monitoring vital signs, and assisting with mobility. We conducted a mixed-methods study employing a mixed-factorial design in which 298 caregivers from the United States, Mexico, and Chile evaluated all four robot categories. Quantitative measures integrated constructs from the Unified Theory of Acceptance and Use of Technology, the Cognitive-Affective-Normative model, and overall acceptance ratings. Qualitative data were collected through open-ended questions and analyzed using a literature-informed ethical framework. The results indicate that participants across countries generally evaluated care robots positively, particularly for logistical and physically demanding tasks rather than those requiring intensive interpersonal interaction. The qualitative findings provide further insight into stakeholders’ views of the ethical implications of care robot use. Participants emphasized potential benefits such as reduced workload, lower risk, and greater patient autonomy, while also expressing concerns about dependability, the need for human oversight, and potential job displacement. Although many ethical concerns were shared across countries, participants differed in how they interpreted and prioritized them. These findings advance a context-sensitive and socially informed understanding of responsible design and implementation of care robots.
[AI-16] MonitrLLM : A Community-Centered Evaluation Infrastructure for Large Language Models
链接: https://arxiv.org/abs/2608.02409
作者: Victor Ojewale,Ro Encarnación,Suresh Venkatasubramanian,Danaé Metaxa
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Accepted to AIES 2026
Abstract:Benchmark suites assess model capability on controlled tasks; large-scale conversation corpora capture naturalistic use without user feedback; and in-interface feedback mechanisms record satisfaction without task purpose. Together, they leave a critical gap in LLM evaluation: no existing infrastructure routinely links interaction trajectories to user-defined outcomes. We introduce MonitrLLM, open-source infrastructure for community-centered LLM evaluations that links full conversation transcripts to user-reported task intent and outcome assessments, treating all three as primary evaluative signals rather than optional metadata. To demonstrate the value of this approach, we conducted a two-week feasibility pilot with 26 college students using ChatGPT, collecting 206 evaluation reports with full conversation transcripts. The findings from our pilot demonstrate the value of connecting conversation trajectories with user-reported outcomes. For instance, despite reporting high average satisfaction (4.19/5) with their LLM interactions, participants also experience a substantial 23.1% failure rate on their goal tasks. We also find that multi-turn conversations are reported as failing at 2.5 times the rate of single-turn exchanges, a pattern that reframes extended interaction as a signal of difficulty rather than engagement. We conclude by discussing the value of incorporating direct user feedback with observational data for robust LLM evaluations, and the possibilities for infrastructure that enables this goal. Comments: Accepted to AIES 2026 Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2608.02409 [cs.AI] (or arXiv:2608.02409v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.02409 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-17] Antares: Foundation Models for Agent ic Vulnerability Localization
链接: https://arxiv.org/abs/2608.02407
作者: Supriti Vijay,Aman Priyanshu,Didier Chapoteau,Arthur Goldblatt,Jianliang He,Kimia Majd,Fraser Burch,Baturay Saglam,Takahiro Matsumoto,Zhuoran Yang,Amin Karbasi
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 57 pages, 12 figures, technical report for antares
Abstract:Vulnerability localization is a fundamental step in software security, requiring models to reason over large codebases and iteratively identify vulnerable implementations. We present Antares, a family of compact language models (350M, 1B, and 3B parameters) for agentic vulnerability localization. Based on IBM Granite base models, Antares is trained through a two-stage pipeline that combines supervised fine-tuning on cybersecurity reasoning and repository exploration data with reinforcement learning from verifiable rewards over vulnerable repositories. Across extensive evaluations, Antares-3B approaches GPT-5.5 while outperforming open-weight models over 200x larger in size. The Antares family further enables fast, low-cost local inference, completing a full 500-task evaluation sweep in approximately 15 minutes on a single H100 GPU, corresponding to an amortized evaluation time of under 2 seconds and less than 0.002 per task.
[AI-18] From frag mented data to actionable design: Physics-calibrated learning for plastic upcycling
链接: https://arxiv.org/abs/2608.02402
作者: Jingyang Bai,Zijia Wang,Xiangyi Long,Marcos Millan,Binjian Nie,Mingyue Ding
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Thermochemical upgrading of plastic waste is a key upcycling pathway, yet the experimental literature is fragmented by heterogeneous conditions and incomplete reporting. Complete-case learning would retain only 10.99% of the curated experiments, while target imputation can introduce biased supervision. Here we develop a Physics-Calibrated, Missingness-Gated, and Load-Balanced Mixture-of-Experts (PC-MG-MoE) framework that converts structured missingness into an informative learning signal. PC-MG-MoE learns directly from partially observed experiments without target imputation, reconstructs physically consistent product distributions, accommodates cross-laboratory heterogeneity, and provides interpretable model behaviour rather than black-box prediction alone. Under stringent source-grouped validation, it achieved the lowest aggregate absolute error among the evaluated models, supporting engineering screening under cross-laboratory heterogeneity. Wet-lab experiments provide an external comparison, showing key composition-dependent trends. Implemented as an interactive web-based workflow, PC-MG-MoE enables forward screening, physics-grounded constrained inverse design, targeted experimental planning that supports reduced experimental workload and trial-and-error, and laboratory-specific adaptation with new platform-specific data. This work establishes a transferable framework for converting fragmented literature data into experimentally actionable guidance for model-guided plastic upcycling and broader thermochemical systems.
[AI-19] Can Foundation Models Hear What Made That Sound? A Tiered Benchmark of Audio-Language Models and Traditional Classifiers for Closed-Set Sound Source Identification
链接: https://arxiv.org/abs/2608.02397
作者: Sajjad Abdoli,Ghassan Al-Sumaidaee,Ahmad ElShiekh,Ahmed Rashad
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:We benchmark eleven audio classification methods: five task-aware closed-set LLMs (four Gemini models plus open-weight Kimi-Audio-7B-Instruct), four fixed-vocabulary taggers (YAMNet, PANNs, Whisper-AT, and SSLAM), a zero-shot audio-text model (CLAP), and an audio-grounded LLM (BAT). We evaluate them on a closed-set sound-source identification task over 2,242 clips spanning 23 fine-grained classes and 11 categories. Since these methods differ fundamentally in how they receive the task and how outputs are scored, we group them into four evaluation tiers rather than one leaderboard, reporting macro Precision, Recall, F1, and false-negative rate per tier. The best model, Gemini-3.1-Pro-Preview, reaches 85.6 percent category-level F1 and 56.7 percent fine-grained F1. Kimi-Audio is competitive for its size, reaching 67.5 percent category-level F1 and 32.9 percent fine-grained F1, but fails to answer 1.6 percent of samples. SSLAM and CLAP match or exceed the best closed-set model at the category level without seeing the candidate list, but fall behind at the fine-grained level. Analyzing the Gemini models’ chain-of-thought across 8,968 responses, we find that response length does not predict accuracy, an apparent “holistic judgment beats detailed analysis” effect is better explained as a difficulty confound, and wrong answers are stated confidently 92 to 100 percent of the time. We report full per-class confusion matrices and metrics for all eleven methods, identify the structural error modes behind most of the accuracy loss between granularities, and give practical guidance for choosing among these method families.
[AI-20] Cooperative Coevolution for Resource-Constrained Agent ic LLM Post-Training AAAI2027
链接: https://arxiv.org/abs/2608.02391
作者: Zhiyuan Wang,Shengcai Liu,Jiahao Wu,Ning Lu,Hui Ouyang,Shaofeng Zhang,Haoze Lv,Ke Tang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 14 pages,9 figures, submit to AAAI 2027
Abstract:Tool-using large language model (LLM) agents produce long, multi-turn trajectories, making gradient-based post-training memory-intensive. Evolution strategies (ES) enable memory-efficient full-parameter post-training without backpropagation and can eventually match the performance of gradient-based reinforcement learning (RL). However, resource-constrained settings typically offer only a few GPUs, so the high GPU-hour requirements of ES translate into prohibitively long training times. To address this, we introduce Cooperative Parameter-subspace Evolution Strategy (CoPES), a cooperative coevolutionary method that decomposes the full parameter space into lower-dimensional subspaces and searches over them cooperatively to improve optimization efficiency. We post-train a Qwen3.5-4B tool-using agent for the math task and evaluate it on five benchmarks of varying difficulty. Under the GPU-hour budget of full-parameter GRPO’s best validation checkpoint, CoPES recovers 92% of GRPO’s validation-accuracy gain, versus 67% for standard ES, while its theoretical GPU memory requirement is less than one-eighth that of full-parameter GRPO. It consistently outperforms standard ES and LoRA-based GRPO on all evaluated pass@k metrics across the five benchmarks. Additional experiments further show the advantage of CoPES on the question-answering task. These results demonstrate an improved trade-off between memory requirements and training time for agentic LLM post-training under resource constraints. The code is open-sourced in this https URL
[AI-21] Chess on Ice: Curling Tactical Decision-Making via Backward Induction and Deep Reinforcement Learning
链接: https://arxiv.org/abs/2608.02379
作者: Patrick Oberlin,Matteo Cederle,Aren Karapetyan,Saverio Bolognani,Gian Antonio Susto,Florian Dörfler
类目: Artificial Intelligence (cs.AI)
备注: 10 pages, 8 figures
Abstract:Curling is often referred to as “Chess on Ice”, owing to the tactical complexity of its decision-making process. Yet unlike chess, curling remains largely underexplored from a machine learning perspective, with prior work confined mainly to statistical approaches. We propose a reinforcement learning framework capable of quantitatively evaluating and comparing tactical options in curling. The game poses several modeling challenges: continuous state and action spaces, stochastic action outcomes reflecting player skill variability, and state transitions that are highly sensitive to small perturbations in the executed action. To address them, we employ the Deep Deterministic Policy Gradient actor-critic algorithm, adapted to exploit the finite-horizon structure of the game. Our experiments show that effective curling strategies can be acquired in a fully self-supervised manner, without any human-annotated data: on a reduced four-rock variant, the learned agent matches a hand-crafted expert heuristic in a regime where that heuristic is close to optimal, a parity we quantify against the intrinsic hammer advantage of the variant. Beyond the resulting policy, the learned critic provides a dense value estimate over the entire continuous action space, enabling the quantitative comparison of tactical alternatives for applications such as post-game performance analysis and decision support during athlete preparation.
[AI-22] GLAIM: Learning Global and Local Adaptive Inter-Variable Dependency for Multivariate Time Series Imputation
链接: https://arxiv.org/abs/2608.02366
作者: Mingyang Wang,Rongwen Li,Xiao Wang,Changjian Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Multivariate time series imputation is fundamental to downstream analysis, yet modeling inter-variable dependencies with incomplete observations remains challenging. Existing methods learn global dependencies across samples or dynamic local dependencies per sample. Global dependencies are stable but adapt poorly to sample variations and temporal non-stationarity, whereas local dependencies are adaptive yet unreliable when observations are insufficient, causing erroneous information propagation. To address these limitations, we propose GLAIM, a Global-Local Adaptive Inter-variable Dependency Modeling framework for multivariate time series imputation. GLAIM comprises two complementary components. The Stable Global Dependency Constructor derives robust global inter-variable dependencies from complementary temporal representations, providing a stable backbone less affected by sample-specific missingness and noise. The Sample-Conditioned Dependency Refiner adapts this backbone to each sample and time step using its temporal state and available observations, enabling reliable local refinement under incomplete observations. Extensive experiments on nine real-world datasets demonstrate that GLAIM achieves state-of-the-art performance under random and block missingness, remains robust to missing-rate shifts, and benefits from its complementary global and local components. Code is available at this https URL.
[AI-23] Faster-WAM: Do World Action Models Need Deep Action Modules?
链接: https://arxiv.org/abs/2608.02365
作者: Liheng Ma,Rui Heng Yang,Zhanguang Zhang,Mateo Clemente,Ziwen Hu,Tongtong Cao,Yingxue Zhang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注:
Abstract:World Action Models (WAMs) couple robot action prediction with video world models. Existing WAMs with shared-backbone and Mixture-of-Transformers designs generally tie the depth of the action module to that of the video backbone, resulting in substantial computational overhead and high inference latency. To address this limitation, we introduce Dock of Transformer (DoT), a video-centric design principle that treats a pretrained video Transformer as a representation hub and connects lightweight output-heads through docking interfaces. This enables flexible output-head design while providing direct access to representations from all layers of the backbone. We then introduce \textbfFaster-WAM, an instantiation of DoT for WAMs, which docks a single-layer action head onto a 30-layer video backbone. The docking interface fuses keys and values from all video layers and applies RoPE realignment. Without additional embodied pretraining, Faster-WAM achieves competitive performance on LIBERO and RoboTwin 2.0 while demonstrating strong out-of-distribution generalization on LIBERO-Plus. Faster-WAM also achieves the lowest end-to-end latency in our controlled comparison, requiring only 66.5 ms per inference — a (3.2\times) speedup over Fast-WAM. Overall, these results demonstrate that the video-centric DoT architecture supports flexible task-specific head design while delivering low inference latency, strong action-prediction performance, and robust generalization.
[AI-24] SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents
链接: https://arxiv.org/abs/2608.02356
作者: Yue Yao,Shengyuan Wang,Xin Chen,Minke Zhang,Jia He,Bingjun Luo,Tom Gedeon
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model agents increasingly solve complex tasks by composing reusable skills from a library. To address this, the key challenge is not merely to retrieve individually relevant skills, but to identify a complete and executable skill composition. In this paper, we argue that this problem can be solved in a graph with three levels: compositional relations among skill queries, similarity between queries and candidates in the skill library, and the dependencies among the selected candidates. We introduce SkillTrace, which organizes the user query into a semantic hierarchy, matches skill queries and candidates, and propagates over the skill dependencies. Experiments on SkillsBench and ALFWorld demonstrate that SkillTrace achieves state-of-the-art performance, reaching a success rate of 53.17% on SkillsBench and 91.43% on ALFWorld. SkillTrace also delivers consistent improvements across different backbone language models, demonstrating the generality and robustness of graph-based skill retrieval.
[AI-25] KC-Agent : A Dual-Process Cognitive Architecture for Efficient ML Model Improvement
链接: https://arxiv.org/abs/2608.02351
作者: Gusseppe Bravo-Rocca,Jordi Guitart,Ajay Dholakia,David Ellison,Puneet Jain
类目: Artificial Intelligence (cs.AI)
备注: Accepted at IEEE COMPSAC 2026
Abstract:Data drift poses significant challenges for machine learning systems in production, requiring continuous model updates to maintain performance. We present KC-Agent, a dual-process cognitive architecture for automated ML model improvement that combines fast pattern recognition (System 1) with deliberate incremental updates (System 2). Our approach implements structured memory systems enabling System 1 to leverage successful solutions previously discovered by System 2, achieving efficient pattern-based responses without costly re-computation. KC-Agent incorporates atomic change principles and rollback capabilities to ensure reliable, verifiable updates in production environments. We evaluate our method on five datasets including real-world NASA turbofan data with authentic temporal degradation and synthetic datasets with controlled drift scenarios. KC-Agent achieves state-of-the-art performance (76.8% accuracy) while maintaining optimal efficiency (13.2s execution time), outperforming established cognitive architectures: CodeAct (+2.4%), Tree of Thoughts (+3.6%), ReAct (+8.0%), and Reflexion (+8.9%). Consensus evaluation by a panel of state-of-the-art LLMs confirms superior strategic efficacy (8.33/10 Smartness score), significantly outperforming baseline agents. The knowledge consolidation mechanism delivers 91% speedup over the slow variant while maintaining higher accuracy. Our approach demonstrates both theoretical foundations and practical viability for cognitive-inspired automated ML improvement systems capable of handling complex real-world data drift scenarios.
[AI-26] Mamba with Hierarchical Memory: Solving Representation Bottleneck in Long Sequence Modeling
链接: https://arxiv.org/abs/2608.02347
作者: Qinwen Wang,Jieping Luo,Aoxiang Qin,Ruoyu Zhao,Jianxiong Tang,Wei Zhang,Zhichao Lu,Luziwei Leng
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, preprint
Abstract:Recurrent linear attention models (RLAs) such as Mamba offer efficient linear-time sequence modeling as an alternative to Transformers, yet their fixed-capacity recurrent states limit long-sequence modeling. Drawing inspiration from hierarchical human memory, we propose Hierarchical Memory Mamba (HMM) to address this limitation. Building upon a pre-trained Mamba backbone, HMM integrates a lightweight working memory that extracts slow paragraph-level semantics (PLS) from the fast sensory memory embedded in the backbone’s hidden states. The PLS is subsequently compressed into persistent long-term memory for task-relevant retrieval. The hierarchical processing of semantic information overcomes the representation bottleneck of RLAs and endows HMM cross-task generalization through parametric learning, which is not observed in other long-context enhanced Mamba variants. Evaluations on Passkey Retrieval and LongBench-E tasks demonstrate that HMM improves retrieval success by 34.3–37.1% and reasoning accuracy by 1.6–14.2% over strong Mamba-based models, while adding only 2% extra parameters and with minimal training overhead.
[AI-27] Hard Constraints Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection
链接: https://arxiv.org/abs/2608.02343
作者: Patrick Helm,Jan-Niklas Doerr,Joren Gijsbrechts,Stefan Minner
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints. Mixed-integer linear programs (MILPs) handle such constraints flexibly but scale poorly in stochastic environments. Deep reinforcement learning (DRL) promises scalable decision rules, but existing methods either penalize constraints rather than enforce them, or rely on feasibility mechanisms that break down once constraints interact. We bridge this gap by embedding a differentiable convex optimization module inside the policy: a neural network proposes continuous action targets, a quadratic program projects them onto the relaxed feasible set, and a dual-informed integer mapping restores integrality while preserving feasibility. Given a differentiable simulator, the policy trains end to end from sampled trajectories using pathwise gradients, while handling hard constraints with similar flexibility to MILPs. We show that our feasibility enforcement has bounded error relative to an exact integer projection and ensures the entire feasible action space is reachable. We apply the method to multi-echelon production-inventory planning under shared resource and material constraints. Our policy attains an average optimality gap below 1% on small instances. It further outperforms state-of-the-art echelon base-stock policies by up to 9.75% and a rolling-horizon multi-stage stochastic program by at least 7.7% in larger networks. On an industry-scale case study from ASML, it reduces average cost by up to 3.22% relative to the best-known benchmark policy. The savings are largest where planning is hardest: in tightly capacitated systems with high demand variability. More broadly, our work shows that DRL can deliver economically significant savings in sequential decision problems with interdependent hard constraints, which are widespread in practice.
[AI-28] Diffusion Policy with Behavioral Advantage Correction for Offline Reinforcement Learning
链接: https://arxiv.org/abs/2608.02332
作者: Botao Dong,Longyang Huang,Ning Pang,Hongtian Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:In offline reinforcement learning (RL), the distribution shift between behavioral data and the learned policy can lead to erroneous \emphQ-value estimation, thereby misguiding the direction of policy optimization. To address this issue, we develop a behavioral advantage corrected policy evaluation (BAC-PE) approach, which utilizes the \emphQ-function of the behavior policy to correct the learned policy’s \emphQ-function, thus mitigating pessimistic conservatism and overestimation bias. Furthermore, the convergence of BAC-PE is analyzed theoretically, and an upper bound on the difference between the learned \emphQ-function and the true \emphQ-function is derived. To alleviate distribution shift, this work employs diffusion models to represent both the behavior policy and the learned policy, performing distribution matching for accurate policy regularization. Additionally, \emphQ-value guidance is incorporated into the training process to achieve effective policy improvement. By combining BAC-PE with diffusion policy modeling, we propose the diffusion policy with behavioral advantage correction (DPBAC) algorithm. Compared to existing offline methods, DPBAC demonstrates stronger policy representation capabilities and effectively mitigates the bias in \emphQ-value estimation. Experimental results on multiple domains of D4RL tasks show that DPBAC achieves superior performance, with notable advantages over state-of-the-art (SOTA) algorithms.
[AI-29] FastGFDs: Efficient Validation of Graph Functional Dependencies with Desbordante
链接: https://arxiv.org/abs/2608.02321
作者: Anton Chernikov,Yurii Litvinov,Kirill Smirnov,George Chernishev
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF)
备注: this https URL
Abstract:Graph functional dependencies (GFD) are a recently-developed concept aimed at capturing both topological structures in graphs and functional dependencies between attributes. The process of verifying whether a given GFD holds over a particular graph is referred to as GFD validation. In this very computationally expensive problem, locating suitable subgraphs accounts for about 99% of the total run time. The concept’s authors originally proposed a parallel scheme (algorithm), targeting specifically clusters of high-performance servers. The goal of this study is to open GFD validation to a broader public by making it possible to run it on a consumer class PC. Our initial experiments demonstrated that the existing algorithm may not be optimal for these purposes. Therefore, we propose FastGFDs - a GFD validation algorithm that employs a recently developed graph matching technique. In contrast to the parallel scheme, it is sequential and operates on the entire graph. Its novelty lies in the use of Core-First Decomposition and the Compact Path Index (CPI). We compare it with the naive sequential algorithm and the parallel scheme, evaluating run times and memory consumption. The current study is the first step towards designing an efficient algorithm for GFD validation in low-end single-node environments. We also provide an open-source implementation of GFD validation over large data graphs. To the best of our knowledge, this is the only publicly available implementation of an algorithm for this problem. It is developed in Desbordante - an open-source high-performance data profiler aimed at science-intensive tasks. Finally, our experiments on a real-life graph demonstrated up to three times performance (2.6x on average) improvement over the parallel scheme. Employing the new subgraph matching algorithm also reduced memory consumption by five times. Comments: this https URL Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF) ACMclasses: H.3; I.5; J.0 Cite as: arXiv:2608.02321 [cs.DB] (or arXiv:2608.02321v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2608.02321 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: 33rd Conference of Open Innovations Association (FRUCT), Zilina, Slovakia, 2023 Submission history From: George Chernishev [view email] [v1] Mon, 3 Aug 2026 14:46:21 UTC (180 KB)
[AI-30] BRiG-AFA: Bellm an Risk-to-Go Learning for Non-Myopic Active Feature Acquisition
链接: https://arxiv.org/abs/2608.02305
作者: Jiaorong Feng,Qian Li,Ying Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Active feature acquisition (AFA) asks which unobserved feature to measure next for each test instance under a budget. Greedy rules are easy to train but can overlook context features whose value is realized only through later acquisitions, while reinforcement-learning and generative approaches introduce difficult optimization or conditional-density estimation. We introduce \method, a deployable, supervised alternative that learns a separate candidate-conditioned risk-to-go function for every remaining budget. Starting from the one-step terminal classification risk, the functions are fitted backward with Bellman targets; inference greedily minimizes the learned terminal risk using only observed values, the mask, candidate identity, and remaining budget. A controlled non-myopic benchmark shows the expected mechanism: at budgets two and three, \method improves accuracy over its one-step ablation by 4.84\pm2.17 and 4.39\pm1.10 percentage points (mean \pm standard error over five seeds). On Fashion-MNIST with 20 candidate pixels, it improves accuracy at every nontrivial reported budget on average, including 10.20\pm0.74 points at four acquisitions; its mean paired gain across budgets \2,4,8,12,16\ is 3.50\pm0.37 points. A three-seed MiniBooNE study is mixed at small budgets but positive at 8 and 16 acquisitions, identifying a current boundary rather than supporting a universal claim. These results establish a reproducible mechanism-level case for direct Bellman risk regression and delimit the experiments still needed for state-of-the-art comparison.
[AI-31] rajectories That Segment Themselves: Agent -Declared Boundaries as a Training Unit
链接: https://arxiv.org/abs/2608.02302
作者: Jingxi Wei
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: 20 pages, 6 figures, 11 tables. Includes appendices with full controls and ablations
Abstract:Long-horizon coding-agent trajectories are poorly matched to the credit units available to train on: a single action has no stable value, an episode label merges productive exploration with abandoned directions, and a fixed window cuts where the logging mechanics fall. We introduce collection-time semantic self-segmentation, in which a declarative contract has the acting agent expose its own boundaries while the trajectory is generated. Instantiated with falsifiable causal hypotheses, successive adoptions expose variable-length semantic phases, and no milestone vocabulary, gold patch, environment replay, teacher logits, or retrospective segmenter places a boundary. Because the agent names its conjecture, a reviewer can negate it by name, which lets our protocol manufacture wrong-cause-then-correction transitions that recorded work rarely contains; one collection then yields four supervised targets, including audit supervision from exactly the failed regions an episode label discards. We then ask what survives deleting the declaration. Given the cut points but not the hypothesis, a model attributes action blocks to their governing hypothesis at over twice chance, beating equal-length blocks over the same trajectories (paired sign test p = 0.0002 ), surviving a lexical control and collapsing under label permutation. Asked instead to place boundaries, a code-blind annotator matches 24 of 40 where random placement matches 11.5, while a mechanical test-event rule beats chance at neither end of a strict-to-permissive sweep. The segments are therefore coherent and not cheaply reproducible. Downstream, DPO on 2,551 phase-boundary pairs changes no decision on 91 adversarial held-out items, while four of 60 change on matched-construction items, all wrong to right, where two controls change none: with 1,825 pairs from one generator, the variable to vary next is corpus diversity, not the boundary.
[AI-32] MechGeo: Autoformalizing and Proving Euclidean Geometry in Lean 4
链接: https://arxiv.org/abs/2608.02295
作者: Hao Shen,Junyu Guo,Tian Cui,Yuxuan Xiao,Lihong Zhi
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注: 43 pages
Abstract:We present MechGeo, a Mathlib native agentic framework that jointly addresses faithful autoformalization and certified proof construction for Euclidean geometry. In this framework, GeoFormalizer represents informal problems in GeoIR, deterministically translates them into Lean 4, and iteratively repairs candidate statements using structural diagnostics and semantic evaluation. GeoProver constructs geometric proof plans, derives intermediate lemmas, and selectively algebraizes suitable subgoals through a library verified in Lean. Singular or SymPy may generate algebraic certificates, but all resulting proofs and counterexamples are checked by Lean’s kernel. Experiments across seven LLM backbones show substantial improvements in autoformalization, particularly for models with weaker direct translation performance. On 43 historical IMO geometry problems, GeoFormalizer generates formal statements that GeoProver proves in 29 cases; for the remaining 14, it constructs counterexamples verified in Lean and proves all repaired statements after expert correction. Together with IMO 2026 Problem 2, this yields, to the best of our knowledge, the largest reported collection of automated, kernel-checked Lean proofs for IMO geometry problems. On the 14 geometry statements in LEAP’s Lean-IMO-Bench, MechGeo proves 12 for the first time, formally refutes the remaining two, and proves both repaired statements. These results establish counterexample guided diagnosis, geometric reasoning, and certified symbolic computation as a practical foundation for trustworthy formal geometry.
[AI-33] Shared Prefixes Better Credit: Adaptive Routing for Multi-Agent Reasoning
链接: https://arxiv.org/abs/2608.02291
作者: Yiqing Liu,Zihao Wang,Hantao Yao,Wu Liu,Yongdong Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-agent reasoning (MAR) improves reasoning reliability through iterative solution exchange and refinement. Existing adaptive MAR methods typically learn routing decisions from query-level labels or trajectory-level returns, but such coarse supervision cannot accurately estimate the state-conditioned utility of individual operators in multi-step collaboration. We propose TreeCredit, a shared-prefix credit assignment framework for efficient adaptive MAR. Its core insight is to estimate operator utility through state-matched downstream comparisons, rather than directly attributing trajectory-level outcomes to preceding decisions. TreeCredit constructs shared-prefix collaboration trees by expanding candidate operators from the same intermediate state and assigns each state–operator pair a correctness-prioritized suffix credit based on the terminal correctness and cumulative additional cost of its complete continuation. These structured credits are converted into state-local operator preferences to train a lightweight pairwise state router, which dynamically selects the next admissible operator during inference. Experiments on six reasoning benchmarks show that TreeCredit modestly improves accuracy while substantially reducing inference cost, achieving a better accuracy–cost trade-off than representative MAR methods.
[AI-34] SKT: Skill-Use Training at Scale via Verified Synthetic Data Generation
链接: https://arxiv.org/abs/2608.02287
作者: Zelin Tan,Yiqun Zhang,Hao Li,Zhiyao Cui,Hejia Geng,Shao Zhang,Hangfan Zhang,Yang Chen,Xiaosong Wang,Lilong Wang,Zhenfei Yin,Shuyue Hu,Chen Zhang,Lei Bai
类目: Artificial Intelligence (cs.AI)
备注: 24 pages,8 figures, Version 1
Abstract:Agent skills have become an important mechanism for equipping language-model agents with reusable procedural knowledge. However, providing skills alone does not guarantee that current models can effectively identify, apply, and coordinate them. To improve skill-use capabilities, we introduce SKT, a verified data synthesis pipeline that constructs skill-grounded tasks and executable trajectories from large collections of agent skills. SKT selects suitable single-skill and multi-skill configurations, synthesizes tasks through rule-based and agent-based verification with feedback-guided repair, and retains only successful trajectories that substantially use every required skill. Using 2,000 public skills, SKT produces 4,000 task packages and 27,164 verified trajectories. Based on the same pipeline and a disjoint test pool, we further construct SkillEval, a held-out executable benchmark for evaluating skill use. Experiments across diverse models, benchmarks, and agent harnesses show that supervised fine-tuning on SKT-generated trajectories consistently improves skill-use performance. Verification ablations, cross-harness evaluation, and scaling experiments further demonstrate that these gains depend on high-quality supervision, extend beyond a single agent interface, and increase with broader skill coverage. Together, these results establish verified data synthesis as an effective and scalable approach for skill-use training.
[AI-35] Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories
链接: https://arxiv.org/abs/2608.02276
作者: Shuai Shao,Kangning Zhang,Qingyao Li,Shijian Wang,Hao Wang,Wenxiang Jiao,Yuan Lu,Yi Guo,Weiwen Liu,Weinan Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agents built around large language models continually accumulate interaction trajectories during deployment, yet their behavior typically remains fixed. Beyond updating model weights, these trajectories can improve the agent harness that constructs context, mediates tools, validates actions, and recovers execution. We introduce Harness-R1, the first method, to our knowledge, that makes failure-conditioned, lifecycle-wide editing of an existing executable runtime a learned capability. It post-trains a dedicated harness engineer with online reinforcement learning so that its edits are optimized for the realized task success they produce, rather than proposed by a fixed editor. A separate 9B engineer converts batches of target-agent failures into validated executable patches; fresh same-batch reruns of the frozen target provide outcome rewards, so training updates only the engineer. Cold-start supervised fine-tuning initializes this editing policy, which is then trained online with group-relative policy optimization. Across WebShop, ALFWorld, and DBBench, Harness-R1 raises vanilla Qwen3.5-9B success from 44.3% to 53.6% (+9.3 percentage points). After direct target-agent fine-tuning, a target-specific engineer raises the average further from 59.2% to 64.2% (+5.0 points); because these gains hold both before and after fine-tuning the target, Harness-R1 points toward co-evolving the harness engineer and the target agent.
[AI-36] S-MAMP: A Remanufactured Agricultural Robot Powered by Second-Life EV Components and NMS-Free On-Device Weed Detection
链接: https://arxiv.org/abs/2608.02270
作者: Weijie Shi,Zicheng Xu,Zhenbang Cheng,Haoran Xuan,Mingbo Duan,Gan Ge
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 6 pages, 7 figures, 2 tables
Abstract:Agriculture 4.0 robotic systems improve field efficiency yet remain too capital-intensive for the fragmented smallholdings that dominate global agriculture. Meanwhile, a growing number of retired low-speed electric-vehicle (LSEV) powertrains retain functional electromechanical value but are destructively recycled. This paper presents TS-MAMP (Telescopic-Sleeve Modular Agricultural Mobile Platform), a remanufactured robot built under 3R (reduce, reuse, recycle) circular-economy principles. Retired 48 V brushless-DC (BLDC) hub motors are paired via back-EMF matching, and lead-acid battery modules screened at 60%-80% state of health are actively balanced within a 100 mV inter-module voltage deviation. Together, these reused components reduce the powertrain-and-chassis BOM cost by approximately 60%, to below USD 450 (perception and weeding modules excluded). The truss chassis provides =200 kg static load, continuously adjustable track width from 1200 mm to 2000 mm, and =5-minute module changeover. An NMS-free (non-maximum-suppression-free) YOLOv10n detector with consistent dual-assignment training and negative-sample learning achieves 80.87% mean average precision (mAP)@0.5 (58.41% mAP@0.5:0.95) on the Wanxi Crop-Weed dataset, and is deployed via FP16 TensorRT on a Jetson Nano, confirming on-device inference feasibility. TS-MAMP demonstrates that retired EV components, under modest screening, can be re-engineered into affordable, AI-enabled agricultural robots–opening a remanufacturing pathway for the smallholder fields that commercial automation leaves unserved.
[AI-37] Self-Certification of Representation Adequacy: Sequential Certification at Minimum Task Loss
链接: https://arxiv.org/abs/2608.02267
作者: Zijie Huang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 108 pages, 3 figures. Full proofs and appendices included. Independent researcher
Abstract:Agents that act on a compressed representation of their history face a structural risk: if the representation aliases histories with different optimal actions, no rule measurable with respect to the representation can avoid an irreducible per-round loss, and the agent may be unable to detect this from its own transcript. This paper develops a four-layer theory of self-certification of representation adequacy. The static layer defines decision-theoretic adequacy through a Bayes-risk grouping identity and prices a one-shot external verification by an exact total-variation threshold. The sequential layer poses certification as an optimal-stopping problem in the currency of task loss: we define an environment-wise certification complexity constant through a covering linear program, prove an information-task-loss lower bound for every delta-correct strategy, and give a Certification Track-and-Stop policy whose cost matches the bound asymptotically. A final boundary layer gives an explicit kernel-switching example and identifies the open theorem needed to cover policy switching or representation repair; it does not claim that the fixed-kernel guarantees extend to representation revision. The proofs of the two main theorems are given in full in the appendices.
[AI-38] Homebot: A Personal AI Agent for Conversational Home Assistance and Automation
链接: https://arxiv.org/abs/2608.02254
作者: Shengyuan Ye,Yixin Zhang,Han Liang,Liekang Zeng,Jiangsu Du,Mu Yuan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:\textttHomebot is a locally deployable AI agent for conversational household assistance and automation. It accepts voice and instant-messaging requests through a shared runtime that combines language-model responses with registered tools and task-specific skills. The design separates common request processing from session ownership: messaging history remains scoped to a channel and chat, whereas voice interaction is bounded by wake-word activation. For hands-free use, \textttHomebot combines local wake-word detection, streaming speech recognition and synthesis, and an explicit dialogue-state protocol for ending, following up, or continuing a conversation. Clear channel, tool, and skill contracts support practical customization for household use.
[AI-39] Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning
链接: https://arxiv.org/abs/2608.02250
作者: Yuan-Heng Tsai,Li-Hsing Yen,Yan-Wei Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 6 pages
Abstract:Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models. FL enables decentralized training while preserving the privacy of clients’ datasets. However, non-independent and identically distributed (non-IID) or noisy datasets can lead to low model accuracy or high convergence latency. Precluding these clients through client selection may mitigate the problem, but heavily biased client selections may also degrade the learning performance. In this study, we first experimentally measure the impact of non-IID data (including skews in data quantity and label distribution), noisy data, and fairness in client selection on model accuracy and convergence. We then propose a privacy-preserving scoring method to assess each client’s contribution in FL, with experiments conducted to demonstrate the effectiveness of the proposed assessment.
[AI-40] rustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability
链接: https://arxiv.org/abs/2608.02238
作者: Abdullah Mamun,Shovito Barua Soumma,Hassan Ghasemzadeh
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Preprint of the paper published in Progress in Biomedical Engineering. 26 pages, 5 figures
Abstract:Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health. Key dimensions, including robustness, explainability, fairness, accountability, and privacy, need to be addressed throughout the AI lifecycle, from problem formulation and data collection to model deployment and human interaction. While various contributions address different aspects of trustworthy AI, a focused synthesis on robustness and explainability, especially tailored to the healthcare context, remains limited. This review addresses that need by organizing recent advancements into an accessible framework, highlighting both technical and practical considerations. We present a structured overview of methods, challenges, and solutions, aiming to support researchers and practitioners in developing reliable and explainable AI solutions for digital health. This review article is organized into three main parts. First, we introduce the pillars of trustworthy AI and discuss the technical and ethical challenges, particularly in the context of digital health. Second, we explore application-specific trust considerations across domains such as intensive care, neonatal health, and metabolic health, highlighting how robustness and explainability support trust. Lastly, we present recent advancements in techniques aimed at improving robustness under data scarcity and distributional shifts, as well as explainable AI methods ranging from feature attribution to gradient-based interpretations and counterfactual explanations. This paper is further enriched with detailed discussions of the contributions toward robustness and explainability in digital health, the development of trustworthy AI systems in the era of LLMs, and various evaluation metrics for measuring trust and related parameters such as validity, fidelity, and diversity.
[AI-41] PosterMELD: Multi-Agent Paper-to-Poster Generation for Controllable Design Diversity with Editable Print-Ready Outputs
链接: https://arxiv.org/abs/2608.02218
作者: Haojie Hu,Chenhao Dang,Yaojia Liu,Hengrui Kang,Conghui He,Weijia Li
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 5 figures, and 4 tables. Code and resources are available at this https URL
Abstract:Scientific poster construction compresses a long multimodal paper into a readable, editable canvas. Existing systems hide request-level failures by scoring only completed outputs; direct image generation is not element-editable, while coding-agent workflows are costly. PosterMELD is a template-conditioned multi-agent pipeline: capacity-aware slots guide writing before rendering, and deterministic gates plus vision-language model (VLM) review route failures to bounded repair. Each accepted request exports editable PowerPoint (PPTX) and Portable Network Graphics (PNG) artifacts; explicit design controls yield same-paper variants. Across 621 papers, Print-Ready Rate (PRR) counts requests passing geometric, readability, asset-integrity, and obvious-factual-error checks, with native editability reported separately. A frozen VLM assigns conditional Craftsmanship-Harmony-Expressiveness (CHE) scores to print-ready outputs. PosterMELD attains 81.3% PRR, 3.4 times P2P’s rate and 5.2 times PosterGen’s, and the highest conditional CHE among generated methods with multiple print-ready outputs. Native editability and explicit design controls are retained at a mean cost of USD 0.38 per request, 3.5% of Codex+Skill’s. Code and resources are available at this https URL.
[AI-42] Fast Discovery of Inclusion Dependencies with Desbordante
链接: https://arxiv.org/abs/2608.02213
作者: Alexander Smirnov,Anton Chizhov,Ilya Shchuckin,Nikita Bobrov,George Chernishev
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF)
备注:
Abstract:Inclusion dependency is a relation between attributes of tables that indicates possible Primary Key-Foreign Key references. Automatic discovery of inclusion dependencies is a relevant problem for both academic and industrial communities. The core concern for this problem is the efficiency of discovery process, since it is a computationally expensive task. However, existing studies only address the algorithmic side, while leaving out the implementation aspect. At the same time, engineering details are at least as important as the algorithmic ones for achieving good performance. In this paper, we describe techniques for efficient implementation of two algorithms for discovery of inclusion dependencies - Spider and Faida. The first one is a classic algorithm whose ideas lie in the foundation of many other inclusion dependency discovery algorithms. We propose an efficient parallelization technique, which greatly speeds up the algorithm while simultaneously reducing its memory consumption. The second one is the state-of-the-art approximate algorithm, which we approach by applying four types of optimizations: data buffering, SIMD-enabled execution, careful hash-table selection and parallelization. In order to experimentally evaluate our techniques, we have implemented these algorithms in Desbordante - an open-source science-intensive data profiler written in C++. For Spider, we have evaluated several different options, and in case of Faida we have demonstrated that all our optimization techniques yield results. We also compared our implementations with Metanome - a Java-based data profiler. Overall, we report up to 5x improvement in terms of run time reduction for Spider and up to 8x for Faida. Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF) ACMclasses: H.3; I.5; J.0 Cite as: arXiv:2608.02213 [cs.DB] (or arXiv:2608.02213v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2608.02213 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: 33rd Conference of Open Innovations Association (FRUCT), Zilina, Slovakia, 2023, pp. 264-275 Related DOI: https://doi.org/10.23919/FRUCT58615.2023.10143047 Focus to learn more DOI(s) linking to related resources
[AI-43] MEGRAG : Multi-Granular Evidence Graphs for Answer-Aware Multi-Hop RAG
链接: https://arxiv.org/abs/2608.02195
作者: Weidong Bao,Yingying Sun,Jun Yang,Yilin Wang,Zili Wei,Yubin Bao,Fangling Leng,Minghe Yu,Tiancheng Zhang,Ge Yu
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 6 figures, 3 tables
Abstract:Multi-hop question answering is a fundamental challenge in retrieval-augmented generation (RAG), because deriving an answer requires integrating dispersed evidence. Iterative RAG (iRAG) is widely used for this challenge, but existing methods have two limitations. First, most methods still support each reasoning step with single-granularity evidence, making it difficult to balance information density and contextual noise. Second, existing methods often answer the original question only after aggregating evidence retrieved across intermediate steps, so redundant evidence and intermediate retrieval errors may accumulate and degrade the final answer. To address these limitations, we propose MEGRAG, an answer-aware framework that represents multi-hop reasoning as a path-structured multi-granular evidence graph. Offline, MEGRAG links passages to their sentences and extracted triples through a cross-granularity index. Online, it retrieves passages for the current query and selects aligned evidence, starting with compact triples and adding sentence or passage context as needed. MEGRAG uses the resulting intermediate answer and prior reasoning to decide whether the Initial Query has been resolved. If not, it identifies the missing information and formulates a focused next query; otherwise, it stops retrieval and returns the answer. Extensive experiments demonstrate consistent gains over a diverse set of RAG baselines.
[AI-44] PAC Approximation and DIRECT Optimization for Parametric Markov Models
链接: https://arxiv.org/abs/2608.02184
作者: Zhiming Chi,Ying Liu,Andrea Turrini,Lijun Zhang,David N. Jansen
类目: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); Logic in Computer Science (cs.LO)
备注:
Abstract:In this paper, we consider the parameter synthesis and optimization problem for parametric Markov decision processes (pMDPs), the extension of classical MDPs where exact probability values are replaced by parametric expressions. Computing the rational function f_\lsf that maps parameter valuations to the satisfaction value of a PRCTL property \lsf is a computationally expensive task, particularly for pMDPs where the optimal policy may vary across the parameter space. We adopt the \emphscenario approach to efficiently synthesize a probably approximately correct (PAC) approximation \ApproxFunOfPropertyf of f_\lsf : by sampling parameter configurations and solving a linear program, we obtain a polynomial approximation whose error margin \margin is guaranteed, with prescribed confidence, for all but an \errorRate -fraction of the parameter domain under the sampling distribution. We further show how this PAC framework can be combined with statistical model checking (SMC), enabling the analysis of black-box parametric models. Building on the PAC approximation, we integrate the DIRECT (DIviding RECTangles) algorithm for derivative-free global optimization over the parameter space. We establish conditional optimality-gap guarantees: under explicit Lipschitz and PAC-good-set assumptions, the difference between the true optimum f_\lsf(\parameters^*) and the value found by DIRECT is bounded by a partition-diameter term and, in the PAC case, an additional approximation-error term. An empirical evaluation on 2997 benchmarks focuses on the new DIRECT-based optimization component. The results show that DIRECT variants solve fewer instances than the scenario optimizer, but on their common successful instances they often return slightly better objective values and usually run faster, while remaining close to the scenario values within the PAC margin.
[AI-45] From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents
链接: https://arxiv.org/abs/2608.02171
作者: Jiajia Song,Bobo Li,Haiwen Yi,Zibo Ji,Meishan Zhang,Hao Fei,Min Zhang,Mong-Li Lee,Wynne Hsu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models have enabled increasingly capable autonomous agents, yet personalization remains critical for making such agents practically useful. Recent benchmarks have begun evaluating personalization in agents, but they largely rely on static preference snapshots, fixed interaction logs, or question answering over predefined user profiles. Such designs fail to capture the complexity of evolving user preferences and neglect preference-conditioned task execution-a discrepancy we term as the knowledge-to-action gap. To address this challenge, we introduce IBA-Bench, a benchmark for implicit behavioral alignment constructed from longitudinal interaction histories that contain noise, implicit cues, and temporal inconsistencies. Unlike prior work, IBA-Bench evaluates whether an agent can execute tasks while satisfying implicit user constraints inferred from historical interactions. We further propose IBA-Agent, an agent framework that reconciles conflicting priorities through broad retrieval and trajectory-level alignment. Experiment results on IBA-Bench show that effective personalization remains a significant challenge for state-of-the-art LLM agents, and the proposed IBA-Agent substantially improves behavioral alignment in complex scenarios across nine application domains.
[AI-46] From Simple QA to Deep Research: A Verifiable Benchmark Constructed through Iterative Task Evolution
链接: https://arxiv.org/abs/2608.02163
作者: Can Wang,Haoran Chen,Haowen Gao,Hao Ding,Zhaoyang Liu,Zhiying Tu
类目: Artificial Intelligence (cs.AI)
备注: 6 figures. Includes supplementary material. Code and data are publicly available
Abstract:Deep research benchmarks require expert-level tasks and reliable evaluation grounded in task-specific knowledge. Existing benchmarks rely heavily on expert authoring or pre-existing human-authored materials, while fully automatic construction struggles to ensure consistent and traceable verification. To address this gap, we introduce a verifiable benchmark of 500 deep research tasks spanning 31 topics and 10 major categories, with three query forms designed to probe complementary capabilities required for deep research. The benchmark is constructed automatically using an iterative Explorer-Formalizer-Challenger pipeline that progressively transforms simple questions into deep research tasks. Each task is represented as a directed acyclic graph (DAG) of atomic steps and associated checkpoints, enabling the query, DAG, and rubrics to evolve together in a controlled manner. Experiments demonstrate that the benchmark clearly discriminates among models and query types, while its fact-grounded pointwise rubrics enable fine-grained, human-aligned, and stable evaluation. Our data, implementation, and results are publicly available.
[AI-47] Lossless Tensor Compression as Program Synthesis
链接: https://arxiv.org/abs/2608.02162
作者: Jieke Shi,Junda He,Wenjia Jiang,Weifeng Sun,Shidong Pan,Zhensu Sun,Chengran Yang,Peixin Zhang,Yifan Jia,Zhou Yang,Thong Hoang(James),Xiwei Xu(Sherry),Zhenchang Xing,David Lo
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
备注:
Abstract:Model checkpoints are growing in both number and size, which makes archival, transfer, and deployment increasingly costly. General-purpose compressors can reduce storage requirements but ignore tensor structure, whereas existing tensor-specific compressors rely on fixed and format-specific pipelines. We present Brevis, which formulates lossless tensor compression as program synthesis. We design a typed domain-specific language (DSL) that captures recurring tensor structures, such as repeated regions and floating-point fields, through a set of reversible operators. Given a tensor, Brevis synthesizes a self-contained DSL program that reconstructs it bit-exactly. A checkpoint-specific production prior, learned from a small representative sample of tensors, guides a bounded A* search to synthesize compact programs, which can later be executed directly for bit-exact decompression. On 10 public checkpoints spanning language, audio, and image generation models, Brevis reduces 2.13 TB of checkpoint data to 1.41 TB, a 33.93% storage reduction. It produces archives up to 30.87% smaller than those of four general-purpose compressors, including zstd and gzip, and smaller archives than the tensor-specific compressors ZipNN and DFloat11. Under a practical concurrency configuration, Brevis achieves 3.60 GB/s compression and 6.61 GB/s decompression while preserving every source byte.
[AI-48] RamanPFN: learning from Raman spectral structure with a tabular foundation model
链接: https://arxiv.org/abs/2608.02157
作者: Xingyu Pan,Huan Wang,Jinjia Guo,Zhenlin Zhao,Siming Dong,Jixi Lu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring. Predictive Raman datasets often contain few labelled spectra and thousands of ordered wavenumbers, with informative variation within bands and across distant spectral regions. Latent-variable chemometrics accommodates collinear small-sample data but can obscure fine peak morphology, whereas deep spectral networks resolve this structure only after task-specific training. TabPFN avoids task-specific parameter fitting through pretrained in-context inference, but processes very wide inputs as feature-subsampled views that do not preserve joint visibility of related bands. We present RamanPFN, a spectral representation framework that encodes these dependencies before TabPFN inference. Global Compositional Unmixing constructs non-negative coordinates over the complete spectrum so that distant bands with shared latent variation occupy a common predictive axis. Local Vibrational Subspace Encoding represents contiguous wavenumber regions with multiple orthogonal modes that retain independent changes in peak shape, intensity and position. The representations are evaluated separately and combined at the prediction level. Evaluation covered 150 tasks from 74 public Raman datasets. RamanPFN reduced root-mean-square error by 19.6% on average across 129 regression targets relative to direct TabPFN inference and further reduced the remaining classification error by 9.0% across 21 classification tasks. These results establish explicit spectral representation as an effective interface between high-dimensional Raman measurements and reusable tabular inference.
[AI-49] Auditing Data Provenance in LLM Fine-tuning via Intrinsic Distributional Fingerprints CCS’26
链接: https://arxiv.org/abs/2608.02154
作者: Zirui Huang,Yunlong Mao,Wei Tong,Tingting Wu,Xin Ge,Sheng Zhong
类目: Artificial Intelligence (cs.AI)
备注: This is the extended version of CCS’26 paper this https URL
Abstract:The proliferation of customized Large Language Models (LLMs) poses critical risks of Data Intellectual Property (Data IP) infringement via unauthorized fine-tuning on proprietary data. Existing audit techniques are limited, as they require intervention during data preparation or training and remain fragile under malicious obfuscations such as data paraphrasing and knowledge distillation. We propose \textitDistribution Provenance Audit (DPA), a post-hoc framework for auditing data IP infringement in LLM fine-tuning under black-box and malicious settings. DPA is grounded in a critical insight: regardless of fine-tuning tactics to evade provenance, the practical necessity of maintaining utility constrains the model to preserve the fundamental intersection of semantic substance and lexical form. Accordingly, DPA captures this persistent lexical-semantic intersection as intrinsic distributional fingerprints. The framework formulates the audit as a statistical hypothesis test, effectively quantifying these fingerprints via unbiased output sampling to reliably reject the null hypothesis of non-usage. Extensive experiments on medical and legal fine-tuning tasks show that DPA consistently outperforms existing baselines, remaining robust against adversarial trainers employing paraphrasing and knowledge distillation. We further highlight a fundamental dual-use tension: the same high-fidelity distributional fingerprints enabling reliable auditing may also facilitate privacy attacks. Comments: This is the extended version of CCS’26 paper this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.02154 [cs.AI] (or arXiv:2608.02154v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.02154 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3830454.3832639 Focus to learn more DOI(s) linking to related resources
[AI-50] Beyond the Mean: Multi-Moment Policy Optimization for LLM Reasoning
链接: https://arxiv.org/abs/2608.02149
作者: Yijun Zhang,Yule Xie,Jiaxin Ding,Xin Ding,Fan Xu,Haoxiang Zhang,Luoyi Fu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning has become a central paradigm for improving the reasoning capabilities of large language models. Existing methods generally aim to reduce the failure probabilities induced across problems. In this paper, we introduce a moment-based perspective on policy optimization for LLM reasoning by treating the failure probability of a randomly sampled problem as a random variable and characterizing optimization objectives through its moments. Under this perspective, many existing methods optimize only a single moment of the failure-probability distribution, leaving its broader distributional structure largely uncharacterized. We propose \textbfMulti-\textbfMoment \textbfPolicy \textbfOptimization (MMPO), a novel policy optimization framework that jointly minimizes multiple moments of the failure-probability distribution. MMPO admits a direct operational interpretation as minimizing the expected truncated time required to obtain the first successful response. Beyond MMPO, we further develop a general moment-transformation framework that systematically induces different moment profiles and provides a unified view of a broader family of policy optimization objectives. Experiments across five mathematical reasoning benchmarks and models of different scales demonstrate that MMPO consistently outperforms strong baselines. We hope this moment-based perspective offers new insights into the design of policy optimization objectives for LLM reasoning.
[AI-51] Beyond Solution-Centric Search: Adaptive Inquiry and Knowledge Revision for Autonomous ML Engineering
链接: https://arxiv.org/abs/2608.02143
作者: Shaokang Fu,Yulong Tao,Linbo Jin,Jiarong Zhao,Qiming Shi,Tianjun Pan,Haonan Li,Chengyu Wang,Jia Wu,Chengfu Huo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-horizon autonomous research tasks such as machine learning engineering require systems to make interdependent decisions under a limited budget. Existing LLM-based agents typically organize candidate-solution improvement through tree, graph, or chain structures, meaning that the search process determines how information is acquired and managed. We call this design solution-centric search and propose instead the information paradigm, in which an evolving information state represents the system’s understanding of the task and guides solution improvement. We instantiate this paradigm in Iris, an inquiry-revision loop. For information acquisition, Iris generates local action plans from the current information state and uses epistemic actions to probe decision-critical unknowns without modifying the retained solution. For information management, Iris synthesizes observations across experiments into task knowledge composed of revisable claims with explicit scope and status. It updates this knowledge as new evidence arrives and constructs each decision context from raw evidence, structured summaries, or task knowledge at the required level of detail. On MLE-Bench, Iris attains a 64.9% any-medal rate under a 12-hour budget, the highest among compared systems. Across four tasks spanning harness engineering and model post-training, Iris also demonstrates cross-domain generalization.
[AI-52] MemArbiter: Decision-Time Memory Arbitration for Long-Horizon LLM Agents
链接: https://arxiv.org/abs/2608.02113
作者: Jiajun Dong,Yutao Hu,Fengrui Fan,Shihan Dou,Yueming Wu,Deqing Zou
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 3 figures, 5 tables
Abstract:Large language model (LLM) agents must retain and use cross-step information to act coherently in long-horizon tasks. Existing methods improve memory accessibility, yet action-relevant information may still fail to guide the current decision because it is poorly formed, organized, prioritized, or presented. We call this post-access failure the Memory-Action Gap. We propose MemArbiter, a function-aware memory arbitration framework that addresses the memory-management-induced component of this gap. MemArbiter decomposes interaction histories into atomic items, organizes them into five functional Memory Banks, and combines bank-level demand, item-level relevance, focal-ambient representations, and a temporal presentation gate to dynamically control memory salience. We evaluate MemArbiter on ALFWorld against Flat Retrieval and Flat Recency under unified per-step memory budgets. With an open-weight action-generation model, MemArbiter achieves success rates of 82.8% and 92.5% under 500- and 750-token budgets, outperforming the strongest baseline by 20.9 and 25.4 percentage points, respectively. It also improves post-failure recovery and reduces failed-action repetition and state-action recurrence. These results show that function-aware memory arbitration enables accessible information to guide actions more effectively.
[AI-53] Uncertainty-Aware Crossmodal Fusion for Classification of Animal Behavior
链接: https://arxiv.org/abs/2608.02104
作者: Ehsan Yaghoubi,Florian Haselbeck
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial intelligence offers substantial potential for acoustic monitoring of animals, from welfare assessment in precision livestock farming to wildlife conservation and ecological research, where vocalizations can indicate health, stress, and social states earlier and at lower cost than manual observation. However, recordings in these settings are obtained under uncontrolled conditions, including environmental noise, reverberation, overlapping calls, and sensors that degrade without notice. As a consequence, automated classification of animal vocalizations remains challenging, and the two dominant acoustic representations show complementary limitations: raw waveforms preserve temporal microstructure but degrade under clipping and reverberation, while log-Mel spectrograms capture harmonic organization but lose phase information and are sensitive to broadband noise. To address these challenges, we propose Uncertainty-Aware Fusion (UAF), a dual-stream framework that estimates Gaussian uncertainty for each representation and fuses them via uncertainty weighting. This mechanism assigns greater weight to the more confident representation with no reliability labels required. In a cross-species, identity-based evaluation excluding all individuals seen during training, UAF (mean pooling) achieves 59.4% accuracy / 39.7% macro F1 on the 17-class SoundWel pig vocalization benchmark and 73.1% accuracy / 71.5% macro F1 on the 3-class DogBark dataset, outperforming static-concatenation fusion by 15.7% and 20.4% relative macro F1, respectively. Ablations over four temporal aggregation strategies show that uncertainty fusion, rather than the temporal characteristics of animal calls, is the primary driver of the performance gain.
[AI-54] How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models
链接: https://arxiv.org/abs/2608.02089
作者: Andres Algaba,Francesca Carlon,Lynn Delcon,Marthe Ballon,Bert Verbruggen,Vincent Ginis
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 71 pages, 13 figures, 65 tables
Abstract:Large language models often show users a final response and a short reasoning summary while the full reasoning trace stays hidden. We introduce an observability ladder that holds each completed run fixed and varies only what a reader inspects to judge whether the answer is correct: the response, a self-summary the model writes from the trace, the trace itself, and internal signals, each with and without the prompt. Across three benchmarks and five open-weight Qwen3 and gpt-oss models, we train matched linear correctness predictors on each access level. Without the prompt, summaries carry most of the trace’s ranking signal (mean AUROC 0.774 versus 0.813) and add +0.156 over the response alone. With the prompt visible, the summary’s gain collapses to +0.019, while the trace still adds +0.041. Even at equal length, the trace’s last words predict correctness as well as summaries, or slightly better, and carry denser and more discriminative uncertainty and self-correction cues. On MMLU-Pro questions with both correct and incorrect runs, linear summary readers are near chance and trace readers retain only modest signal, both with and without the prompt (prompt-withheld AUROC 0.503-0.545 versus 0.544-0.590). With the prompt withheld, a GPT-5-mini reader recovers substantially more signal from both summaries and traces on gpt-oss-20b, and even then the trace keeps a small +0.034 advantage. Much of the linear readers’ trace signal is associated with length. In the common case where users already hold the prompt, summaries are less helpful than the full trace for monitoring correctness. Monitorability is thus a joint property of the display and the reader, so any monitorability claim, including for faithfulness, should specify both.
[AI-55] An AI-Based Decision-Support Pipeline for Day-Ahead Photovoltaic Forecasting
链接: https://arxiv.org/abs/2608.02088
作者: Fariba Dehghan,Sebastian Stein,Vahid Yazdanpanah,Stephanie Gauthier,Masood Nazari
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: 13 pages, 6 figures, Accepted for publication in the Proceedings of the UK AI Conference (UK-AI 2026)
Abstract:Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records. This makes standard day-ahead forecasting difficult: persistence and physical baselines can be sensitive to calibration and timestamp alignment, while single machine-learning models may capture only one structure in the data and overstate skill under non-temporal validation. We study this problem at a United Kingdom charging-station site, where PV forecast errors affect charging availability, storage scheduling, and downstream control. Using measured inverter output and publicly available meteorological inputs, we develop a deployment-oriented environmental-AI pipeline for day-ahead hourly PV forecasting. The pipeline corrects timestamp conventions, constructs leakage-safe solar-geometry and clearness-index features, adds short-term atmospheric context, and combines complementary predictors through validation-learned stacking. Against smart persistence, a clear-sky baseline that adjusts recent PV output using expected clear-sky irradiance, the best ensemble reduces daylight normalised RMSE by about 32% under random day-blocked evaluation and 9% under the stricter rolling-origin protocol. It also reduces daylight RMSE relative to the strongest individual machine-learning baseline by 6.6% and 6.4%, respectively. The results show that physics-aware stacking can support PV forecasts from limited site data, but its value depends on model class, evaluation protocol, and deployment context.
[AI-56] Cross-Fitted Residual Utility for Primary-Preserving Cognitive Decision Correction in Automatic Modulation Classification
链接: https://arxiv.org/abs/2608.02063
作者: Linzhuo Han,Zongyong Cui,Houbiao Li
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 5 figures
Abstract:Automatic modulation classification research has largely emphasized representation accuracy, but a cognitive receiver must also decide when heterogeneous evidence justifies overriding a trusted default prediction. We study this post-inference problem through cross-fitted residual utility and a primary-preserving cognitive decision policy. A structured KAN-Fourier classifier supplies the default probability, while neural and non-neural candidates provide observable evidence. Candidate-specific residual utility is learned from train-split out-of-fold predictions, and a disjoint validation split freezes action thresholds, approved transitions, conditional routes, and a unified risk mask before held-out evaluation. On RMLA, RMLB, and HISAR, the complete system improves overall accuracy from 63.632% to 66.332%, 65.161% to 66.168%, and 77.769% to 79.867%, respectively. Controlled comparisons show that the isolated utility target does not uniformly dominate alternative out-of-fold meta-learners; the consistent gain comes from the complete evidence-and-action policy. Paired bootstrap and Holm-corrected McNemar analyses support the controlled gains. A frozen-policy stress test under carrier-frequency offset, I/Q imbalance, and synthetic Rayleigh/Rician fading yields positive gains in all 11 conditions, with every paired 95% confidence interval above zero.
[AI-57] HPFA: Hypergraph-Based Paired Failure Attribution for LLM Reasoning
链接: https://arxiv.org/abs/2608.02026
作者: Runchuan Zhu,Hongbin Lai,Bowen Jiang,Junrui Zhang,Zhangheng LI,Ostap Kilbasovych,Junyuan Hong
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reflection is a powerful mechanism for LLM reasoning, yet its effectiveness hinges on accurately attributing failures to specific reasoning steps, a capability that current models notably lack. Existing failure attribution methods either require expensive step-by-step counterfactual testing that scales poorly with trajectory length, or treat reasoning traces as flat sequences that ignore the inherent non-linear logical dependencies. We propose a hypergraph-based paired failure attribution (HPFA) framework that attributes the failure root cause by comparing the hyperedges of the targeted failure reasoning path against a reference successful path. By reducing the search space, our method efficiently localizes root causes and enables scalable synthesis of attribution data for training a lightweight attributor model via supervised fine-tuning and reinforcement learning. Experiments on mathematical reasoning and agentic coding tasks demonstrate that HPFA can dramatically increase attribution accuracy and efficiency, and the trained attributor consistently improves reasoning accuracy at test time, outperforming baselines that lack graph structure or paired analysis.
[AI-58] EduZone: A Framework for Evaluating LLM Safety for K-12 Students and Teachers
链接: https://arxiv.org/abs/2608.02024
作者: Junyeong Park,Jieun Han,Haneul Yoo,So-Yeon Ahn,Jinsung Yoon,Alice Oh
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Under Review
Abstract:Large language models (LLMs) are increasingly used across diverse tasks in K-12 education, yet existing safety evaluations rarely examine how harmful or inappropriate content appears in interactions between LLMs and students or teachers. To address this, we present EduZone, an evaluation framework for LLM safety across diverse educational scenarios. Our framework systematically combines (1) student- and teacher-facing LLM usage contexts, (2) fine-grained curriculum concepts, and (3) 6 risk categories and 28 subcategories spanning both conventional and education-specific harms to generate contextually grounded adversarial interactions. We construct these interactions in three settings: single-turn requests, static multi-turn conversations, and dynamic multi-turn conversations. Using these interactions, we evaluate ten LLMs using four safety levels: refusal, safe assistance, risky assistance with safety guidance, and fully risky assistance. Our results reveal greater vulnerability to education-specific risks and dynamic multi-turn interactions, while existing safety guardrails fail to adequately address these risks. EduZone advances LLM safety in education by providing an automated, scalable evaluation framework that supports the development and deployment of safer LLMs in K-12 education.
[AI-59] MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping
链接: https://arxiv.org/abs/2608.02014
作者: Heng Zhang,Kevin Yuchen Ma,Mike Zheng Shou,Weisi Lin,Yan Wu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Cross-embodiment dexterous grasping aims to synthesize stable grasps across heterogeneous multi-fingered hands with little or no embodiment-specific tuning. Existing interaction-centric methods achieve promising results, but their object representations often underrepresent local surface geometry, while their robot descriptors do not explicitly encode both robot morphology and kinematics. We propose MANGO-Grasp, an anisotropic interaction framework that represents objects as geometry-oriented 3D Gaussian primitives and robot hands as surface keypoints encoded into morpho-kinematic descriptors. The object primitives are adaptively allocated by geometric complexity and shaped as surface-aligned plates with outward normals, encoding local geometry. Mahalanobis fields over keypoint–primitive pairs serve as interaction prediction targets during training and as optimization guidance for grasp realization at inference. These fields rise sharply for displacement along the surface normal but only gently within the tangent plane, matching the directional structure of contact. Grasps are realized with one shared optimization formulation and hyperparameter setting across all embodiments. On the CMAP and MultiGripperGrasp benchmarks, MANGO-Grasp outperforms the strongest seen-hand baseline by up to 8.24 percentage points in simulation. It also transfers zero-shot to the unseen SharpaWave hand, improving over the strongest zero-shot baseline by up to 16.57 percentage points, and achieves 86% success in real-world experiments. The code and additional materials will be made available upon publication at this https URL.
[AI-60] Before Reasoning Fails: Pre-Evidence Procedural Failures in Agent ic RAG
链接: https://arxiv.org/abs/2608.02011
作者: Daeyoung Roh,Donghee Han
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 7 figures. Code: this https URL
Abstract:Agentic retrieval-augmented generation (RAG) systems can fail before evidence-conditioned reasoning is tested: an agent may retrieve candidate snippets but finalize without inspecting them. We study this failure mode as a procedural property of the agent trajectory, decomposing wrong answers into pre-evidence discipline failures and post-gold-read failures using saved tool-call traces, retrieved evidence, read passages, and final answers. Across 12,000 paired trajectories on HotpotQA, 2WikiMultiHopQA, and MuSiQue, the two failure types are largely non-redundant: the both-trigger rate is in [11.2%, 13.1%] across regex and spaCy entity extractors. We then evaluate Read-Gate, a minimal runtime invariant requiring an agent to read after search and before finalization. Forced reading improves LLM-Acc by 14.9-19.9 points on trajectories that would otherwise skip reading and by 3.2-9.4 points on full minimal-reasoning cells. Additional diagnostics show that larger hidden thinking budgets do not necessarily increase evidence inspection. Together, these results indicate that evidence-gathering should be evaluated as a trajectory-level control problem, separately from answer-side reasoning.
[AI-61] HALT: Verification-Aware Stopping for Retrieval-Augmented Search Agents ALT
链接: https://arxiv.org/abs/2608.02009
作者: Daeyoung Roh,Donghee Han
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 6 figures. Code: this https URL
Abstract:Retrieval-augmented search agents answer multi-hop questions by repeatedly issuing search queries and accumulating evidence. This creates a stopping problem: after the necessary evidence has appeared, further retrieval often adds cost, latency, and distracting context rather than useful information. We frame stopping as evidence coverage rather than generator confidence, and introduce HALT, a lightweight verification-aware policy that leaves the search agent unchanged. Given expected hop claims, HALT stops only when cumulative evidence supports each required claim. Across three multi-hop QA benchmarks, HALT reduces redundant search while largely preserving exact match. We separate a deployable setting, where hop claims are generated from the question, from a diagnostic upper bound that uses gold supporting-fact annotations: generated claims give smaller but still exact-match-preserving savings, while gold claims show the larger savings available when hop targets are clean. Baseline comparisons and ablations show that this behavior is driven by claim-evidence alignment rather than generic sufficiency, fixed stop positions, or lexical overlap. Open-corpus pilots further suggest that HALT abstains when coverage cannot be reliably verified. Overall, evidence coverage provides a practical runtime control signal for improving retrieval-augmented agents without retraining or modifying the host agent.
[AI-62] Evolving in the Agent Jungle via History-Informed Opponent Awareness
链接: https://arxiv.org/abs/2608.02005
作者: Zhaofeng Zhang,Linhan Xia,Rui Liu,Yihao Wang,Binrui Shen,Shengxin Zhu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Learning to adapt strategies through interaction is a key step toward more general and autonomous LLM agents. Existing approaches typically achieve behavioral adaptation by revising skill libraries. However, in multi-agent environments, opponents may simultaneously update their strategies, causing the environment itself to evolve continuously. Applying skill-revision methods designed for static environments in such settings therefore amounts to updating against an obsolete reference. To address this challenge, we introduce OASE (Opponent-Aware Selective Evolution), which identifies and adopts genuinely beneficial skill revisions in dynamic multi-agent environments. Specifically, OASE conducts paired comparisons between a candidate skill and the incumbent under identical conditions anchored by historical snapshots of opponent strategies, and adopts the candidate only when its estimated payoff gain exceeds an acceptance threshold. We evaluate OASE in two decision-making scenarios: first-price auctions and private-cost Cournot competition. Experimental results show that, compared with a Reflexion-style baseline, OASE achieves a lower final equilibrium distance in both environments while accepting substantially fewer skill revisions, thereby suppressing strategy changes that lack sufficient payoff support. OASE therefore replaces blind updating with evidence-anchored selection, allowing agents to adapt stably and efficiently even as opponents continuously evolve.
[AI-63] ALSC: Timeliness-Aware Large-Small VLM Collaboration for Infrastructure-Assisted Autonomous Driving
链接: https://arxiv.org/abs/2608.01998
作者: Mengmeng Zhu,Yuxuan Sun,Wei Chen,Bo Ai
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注: This paper has been accepted by IEEE GLOBECOM 2026
Abstract:The deployment of Vision-Language Models (VLMs) in autonomous driving (AD) systems is constrained by on-board computing power, restricting vehicles to small VLMs (SVLMs) with limited perception and reasoning capabilities. Infrastructure-assisted AD alleviates this resource constraint by enabling collaboration with large VLMs (LVLMs) at edge servers. However, in dynamic vehicular environments, the utility of sensory data for downstream tasks decays rapidly, making timeliness of information a critical concern. To balance the accuracy gains of LVLMs with their latency-induced timeliness degradation, we develop a Timeliness-Aware Large-Small VLM Collaboration (TALSC) framework. Specifically, we first model the Age of Information (AoI) evolution for VLM inference and characterize the coupling among AoI, token length, and task performance to formulate a general timeliness metric. Building on this, we propose the TALSC online scheduling algorithm. Since scheduling decisions have a delayed impact on future timeliness metric and the output token number is unknown at scheduling time, we design a Lyapunov drift-plus-estimated-penalty algorithm and provides a guaranteed performance. In simulation, we first conduct a case study to derive a fitted timeliness metric based on nuScenes dataset, and further show that TALSC outperforms baselines under various communication and computing settings, achieving up to a 12.6% normalized improvement in Micro-F1 score compared with the best-performing baseline.
[AI-64] Long-Horizon Autonomous Architecture Research with a Language-Model Agent : A Behavioural Case Study
链接: https://arxiv.org/abs/2608.01995
作者: Aon Safdar,Mohamed Saadeldin
类目: Artificial Intelligence (cs.AI)
备注: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Abstract:We study what happens when a single general-purpose large language model acts as the sole researcher on a long-horizon neural architecture design problem. The agent receives a scientific question, an initial hypothesis and motivation, a compute budget, and research affordances (source and experiment management, experiment tracking, literature access, and persistent memory), then autonomously proposes, implements, evaluates, and records experiments over an extended period. The study comprises three phases, separated by human-declared transitions, that progressively expand the agent’s tool surface or problem scale. Across approximately 100 sequential experiments, the agent improves a non-standard Vision Transformer from a weak baseline to a stronger, efficient model on small benchmarks and a usable but sub-SOTA model on ImageNet-1K, while producing a dense behavioural trace. We report four findings.(i)Productivity exhibits a clear phase structure: rapid early gains, a multi-dozen-hypothesis saturation wall, and recovery, with recovery triggered by expanding the action surface rather than changing the underlying model.(ii)A single early hypothesis contributes more to accuracy gain, with later improvements long-tailed.(iii)The preference for greedy, incremental hypotheses is largely workflow-induced: a commit-or-discard evaluation rule is isomorphic to greedy hill-climbing; the remainder reflects risk aversion after bold failures and anchoring on familiar literature. (iv)The agent independently rediscovers established results and, in the unfamiliar regime of pure channel attention, overturns a standard design choice. We conclude that workflow design was at least as influential as agent capability in this study and propose diversified search, budgeted moonshot hypotheses, explicit forks, and regime-aware re-validation as testable directions for future autonomous research.
[AI-65] Agent ic Self-Healing for Data and AI Pipelines: An Affordable Vendor-Agnostic Architecture using Open-Source Software
链接: https://arxiv.org/abs/2608.01955
作者: Solomon Eshun,Dennis Murage,Sharleen Muoki,Chih-Chun Chen,Stephen Adjignon,Matteo Staar,Oliver Angélil
类目: Emerging Technologies (cs.ET); Artificial Intelligence (cs.AI); Databases (cs.DB)
备注: 19 pages, 5 figures
Abstract:Modern organizations rely on data, machine learning, and software delivery pipelines to move data, train models, deploy applications, refresh dashboards, and support business-critical decisions. However, these pipelines often fail because of data quality issues, schema changes, upstream source changes, infrastructure problems, orchestration failures, and model workflow issues. Existing ZeroOps, observability, and AI operations platforms can help teams detect incidents, investigate root causes, and in some cases recommend or execute fixes. However, many of these solutions are expensive, vendor-specific, or difficult for smaller teams to adapt across different tools and environments. This paper first compares existing off-the-shelf solutions for AI-assisted pipeline monitoring, root-cause analysis, and automated remediation, including their strengths, limitations, and practical trade-offs. Based on this comparison, we find that the main gap is architectural rather than technological: the required ingredients for self-healing pipelines already exist, but they are fragmented across vendor-specific platforms, observability tools, incident systems, and open-source components. We therefore propose an affordable, vendor-agnostic reference architecture for agentic self-healing pipelines using open-source and low-cost tools. The proposed architecture combines monitoring, pipeline metadata, incident history, deterministic policy checks, AI-assisted diagnosis, approval workflows, and controlled remediation actions to help teams detect, diagnose, repair, verify, and learn from pipeline issues with less manual effort. The goal is to provide a practical reference architecture that can be adapted across data engineering, machine learning operations, and software delivery environments.
[AI-66] A Contractualist Argumentation Framework for Moral Decision-Making
链接: https://arxiv.org/abs/2608.01937
作者: Luis Marcos-Vidal,Giulio Antonio Abbo,Tony Belpaeme
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 9 pages; to appear in the proceedings of the Fourth International Workshop on Value Engineering in AI (VALE 2026)
Abstract:Autonomous agents operating in shared environments must make decisions that affect multiple individuals with potentially conflicting interests. We propose a formal framework for moral decision-making grounded in Scanlon’s contractualism, an ethical theory that evaluates the permissibility of actions in terms of principles that no one could reasonably reject. To operationalise contractualist reasoning, we use ASPIC+, a structured argumentation framework, extended with value-based filtering to model how each agent’s values determine which reasons are morally relevant in the first place. The result is a Contractualist Argumentation Framework in which agents’ reasons are formally represented, compared, and evaluated through argumentation semantics. We illustrate the approach through a worked example in a domestic setting and discuss its relation to existing value-based argumentation approaches.
[AI-67] Semantic Networks as Clues: A Theoretical Foundation and Process Optimization for Semantic Network Construction
链接: https://arxiv.org/abs/2608.01936
作者: JinWoo Ha,Dongsoo Kim
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI)
备注:
Abstract:The subject matter of this paper is twofold. One is to review the theoretical foundation of a specific type of Semantic Networks (SNs) representing textual non-propositional knowledge. The other involves proposing a framework (ClueNetwork) for ranking candidate SNs generated through various Semantic Network Construction (SNC) processes for the type. In the first fold, it is clarified that the type serves as clues, not surrogates, of reality, making gold standards elusive. Then, it is discussed why this type nevertheless holds scientific legitimacy in terms of abduction. Grounded in this legitimacy, the three main stages of SNC, comprising Automatic Keyphrase Extraction (AKE), Edge Weighting (EW), and Community Detection (CD), are reviewed alongside their objectives and operations. In the second fold, evaluation criteria (comprising two established and one reformulated) for achieving the objectives are first defined and justified, followed by illustrative experiments based on the criteria. Thereafter, SNC is reformulated as a Process Optimization Problem (POP), and its global objective function that integrates the local criteria is defined and justified. Based on these, ClueNetwork is ultimately proposed.
[AI-68] Effective and Efficient Context Retrieval via Partial Dependency Graph for Repository-Level Code Generation
链接: https://arxiv.org/abs/2608.01927
作者: Zhongxin Liu,Zhonghao Jiang,Zhifan Ye,Haoye Wang,Jiakun Liu,Xiaoxue Ren
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted by ASE 2026
Abstract:LLM-based repository-level code generation aims to generate code using the context available in a software repository, requiring LLMs to reason over complex code dependencies. Due to limited context windows and insufficient repository-specific understanding, LLMs typically rely on retrieval-augmented generation (RAG) to incorporate relevant code. Early RAG approaches primarily employ similarity-based retrieval, which often fails to retrieve code snippets that the target function depends on. Recent work introduces graph-based retrieval to model such dependencies, but typically relies on manually designed rules and static global graphs, leading to limited flexibility and high construction and maintenance costs. In contrast, human developers collect helpful context by implicitly constructing a partial dependency graph and iteratively inspecting along it. Inspired by this behavior, we propose DyRetriever, an efficient context retrieval method via partial dependency graphs. DyRetriever uses an LLM to first select a set of entry-point functions and then perform multi-hop reasoning along the code dependency graph. During multi-hop reasoning, it uses the LLM’s semantic understanding to validate whether a function can help generate the target function, eliminating manually designed rules and enabling flexibility across scenarios. Instead of statically constructing a global dependency graph, DyRetriever builds a partial graph on demand and discards it after use, reducing construction and maintenance costs. We integrate DyRetriever with a similarity-based code retriever to build DyCoder and evaluate it on CoderEval and DevEval. Experimental results show that DyCoder achieves relative Pass@1 improvements of 25.63% and 59.73% on CoderEval and DevEval, respectively, compared with existing RAG-based methods, while being 7.4x faster than baselines based on static dependency graph construction.
[AI-69] ProWorld: Progress-Aware Hyperbolic World Models for Long-Horizon Visual Goal Reaching
链接: https://arxiv.org/abs/2608.01926
作者: Zihan Liu,Yuzhe Zhuang,Yuanzu Li,Wanshuang Gou,Jiahong Liu,Min Zhou,Menglin Yang
类目: Artificial Intelligence (cs.AI)
备注: 24 pages, 14 figures, 15 tables
Abstract:JEPA-style visual world models offer an effective paradigm for visual goal planning by predicting future latent representations. Existing methods typically learn local transition consistency through next-step representation prediction. However, in long-horizon tasks, accurate local prediction alone need not ensure sustained progress toward the goal. First, multi-step rollouts can remain locally plausible while drifting away from goal-relevant trajectories. Second, locally similar future states can correspond to substantially different long-term progress, making them difficult to distinguish in a latent space optimized mainly for local consistency. To address these challenges, we introduce goal-conditioned progress order, a relative ordering of states according to how they advance toward a given goal. This order exhibits an asymmetric, coarse-to-fine structure: early states retain broader future possibilities, while later states concentrate on more specific goal-relevant regions. Such a structure is well suited to hyperbolic geometry. Motivated by this observation, we propose ProWorld, a progress-aware hyperbolic visual world model. ProWorld leverages goal-conditioned progress order to organize visual latent-space dynamics, maintains directional progress within trajectories via hyperbolic entailment learning, and mitigates progress ambiguity among locally similar future states via hyperbolic future discrimination. Furthermore, we design a progress-aware planning objective that scores candidate rollouts by jointly considering proximity to the goal and sustained progress across intermediate states. Experiments on four visual goal-reaching tasks demonstrate that ProWorld achieves an average absolute success-rate gain of 9.67 over LeWM. The code will be released after the paper is accepted.
[AI-70] ransNRank: Towards Accurate Neoantigen Ranking with Transformer
链接: https://arxiv.org/abs/2608.01924
作者: Zhiyin An,Yuenan Hou,Shumeng Duan,Yiming Zhou,Yuanting Zheng,Leming Shi
类目: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI)
备注: 34 pages
Abstract:Personalized neoantigen prediction is challenging due to the scarcity of positive samples, the noise of the experimental data, the severe class imbalance trait and the complex of immunogenicity features. Prior arts, such as linear regression and XGBoost fail to model long-range dependencies and contextual relationships within peptide features, therefore the performance of neoantigen positive recall rate is limited. In this paper, we present a novel deep learning framework based on Transformer, coined as TransNRank. By leveraging the self-attention mechanism, our model captures both local and global feature contexts, enabling more accurate recognition of immunogenic neoantigens. A positive-aware training objective is utilized to handle the class imbalance problem, assigning more weights to those few positive samples. Extensive experiments are performed on NCI, TESLA and HiTIDE datasets. Notably, our TransNRank can push the upper bound top 20 recall rate of neoantigen prediction from 46.9% (45 from 96) to 53.1% (51 from 96), while reducing the training epochs from 200 epochs to 20 epochs. Furthermore, we analyze the features contribution based on TransNRank and find that the mutation at anchor and TCGA expression level play an unexpected important role in neoantigen prediction, and removing insignificant features to reduce the input dimensionality of peptides does not drastically impair the overall performance of the model. Our paradigm not only streamlines the prediction pipeline but also sets a new state-of-the-art for neoantigen discovery, with broad implications for accurate immuno-oncology.
[AI-71] CoEvoKG: Co-Evolving Knowledge Graphs with Self-Evolving Search Agents
链接: https://arxiv.org/abs/2608.01904
作者: Zhaoyang Li,Zenghuang Fu,Qiuyuan Ai,Ping Jiang,Haoyu Wu,Minghui Wu,Chenxu Zhao,Jie Song,Guannan He
类目: Artificial Intelligence (cs.AI)
备注: 10 pages, 4 figures
Abstract:Large language models can improve with reinforcement learning for search agents, yet existing self play agents repeatedly generate tasks while discarding the knowledge gained during successful searches. We introduce CoEvoKG, a framework that turns a knowledge graph into both a source of verifiable training tasks and a persistent evidence memory for agent evolution. CoEvoKG jointly trains a task generator and a search agent: the generator creates multihop questions from entity chains sampled from the knowledge graph, while the agent learns from rewards for answer correctness and search trajectories whose entity paths are supported by graph evidence. When a search succeeds, CoEvoKG verifies and deduplicates the retrieved evidence, then writes it back to the corresponding graph nodes and edges. Future rounds reuse this enriched graph for task generation and reward computation, closing the loop between model self evolution and knowledge accumulation. Experiments on six QA benchmarks (NQ, TriviaQA, PopQA, HotpotQA, 2WikiMultiHopQA, and Bamboogle) with three backbone models show that CoEvoKG improves macro average accuracy over the corresponding base models by +11.2, +10.1, and +11.6 points on Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct, and Llama-3.1-8B-Instruct, respectively. Under matched training budgets, CoEvoKG further improves over competitive self play baselines and RL baselines for search agents by +2.6 to +3.7 macro average points across the three backbones. Code is available at this https URL. Comments: 10 pages, 4 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.01904 [cs.AI] (or arXiv:2608.01904v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.01904 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhaoyang Li [view email] [v1] Mon, 3 Aug 2026 08:39:16 UTC (2,550 KB) Full-text links: Access Paper: View a PDF of the paper titled CoEvoKG: Co-Evolving Knowledge Graphs with Self-Evolving Search Agents, by Zhaoyang Li and 7 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI prev | next new | recent | 2026-08 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"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[AI-72] Energy-Efficient LLM Serving via Disaggregated Attention–FFN and Flexible Frequency Scaling
链接: https://arxiv.org/abs/2608.01891
作者: Cunchen Hu,Liangliang Xu,Tian Liu,Min Lyu,Yongkun Li,Sa Wang,Shuo Quan,Yanan Yang,Wenda Tang,Yiduo Wang,Fu Yu,Jie Wu
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) serving spans diverse applications with stringent service-level objectives (SLOs), often requiring GPUs to run at maximum frequencies and increasing energy consumption. Existing energy-management approaches adapt GPU frequencies only at the request or inference-phase level, overlooking operator-level differences in frequency sensitivity between Attention and feed-forward networks (FFNs). We find that the energy-optimal frequencies of Attention and FFN (A/F) differ and vary with the inference phase, workload, and system configurations. However, runtime variability and independent A/F frequency control create a large search space and high communication overhead. To address these challenges, we present AFlex, a framework that jointly optimizes resource provisioning and GPU frequency scaling for disaggregated A/F serving. AFlex introduces a global scheduler and a local operator-level dynamic voltage and frequency scaling (DVFS) controller to determine A/F resource allocations and frequencies. It further introduces an interleaved A/F pipeline with dynamic microbatch depth and adaptive request batching to reduce pipeline bubbles. We implement AFlex in SGLang and evaluate it on NVIDIA A800 GPUs using Qwen3-32B and Mixtral-8 \times 7B under production Conversation and Coding traces. \AFlex reduces energy per token by up to 49% over state-of-the-art disaggregated serving and 48% over frequency-scaling systems while satisfying TTFT and TPOT SLOs.
[AI-73] Hear Invoke and Understand: A Skill-Calling Multimodal Agent for Large Audio Language Models
链接: https://arxiv.org/abs/2608.01881
作者: Yuwen Wang,Tian-Hao Zhang,Minghao Cai,Yilin Ren,Ziyang Jiang,Xin Wang,Zhichao Wang,Pan Zhou,Kun Zhan,Xinyuan Qian
类目: Multimedia (cs.MM); Artificial Intelligence (cs.AI)
备注:
Abstract:Complex acoustic problems may require models to perform acoustic operations, interact with external tools and reason over the resulting textual or processed-audio observations rather than answer directly from a fixed audio input. We study such problems as tool-interactive audio reasoning and develop SpeechAgent-R, an audio agent that coordinates its intrinsic multimodal understanding with external skills and tools. To support this capability, we construct HIU-Corpus, comprising 65,492 interaction trajectories and 507.6 hours of audio across 24 tasks, 8 skills and 9 tools. SpeechAgent-R first learns structured interaction behaviors through trajectory-based supervised fine-tuning and then improves its decisions through multi-turn reinforcement learning. We further introduce HIU-Bench to jointly evaluate task performance, interaction quality and generalization to diverse task settings. It contains 1,395 samples across 56 tasks, including in-distribution (ID) and out-of-distribution (OOD) splits with substantial shifts in tool usage and workflow composition. SpeechAgent-R achieves 84.17 on ID tasks and 70.94 on OOD tasks, improving over the base model under the same agent harness by 15.40 and 14.23 points. These results demonstrate that learning skill and tool coordination improves audio agents’ ability to handle diverse task settings and adaptive tool interactions.
[AI-74] LAB-Tab: LLM -Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation
链接: https://arxiv.org/abs/2608.01879
作者: Zijian Shen,Taijie Chen,Bin Zhou,Ziyang Jiang,Jintao Ke
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 21 pages, 6 figures. Preprint
Abstract:Tabular data generation supports analysis and decision-making when target-domain data are scarce, yet collecting complete target samples is often costly. A practical but underexplored setting provides only a few target records together with richer source data from a related domain. Existing few-shot tabular generators often either fit sparse target statistics directly, which can overfit incidental patterns, or reuse source-domain generators, which may preserve dependencies that no longer hold in the target domain. To address this problem, we propose LAB-Tab, an LLM-augmented Bayesian network (BN) adaptation framework for source-aware few-shot tabular generation. LAB-Tab first fits a BN from source data and then uses an LLM to propose plausible target-domain BN edges that are absent from the source BN graph. This step converts semantic and weak statistical evidence into explicit structural hypotheses, thereby expanding the editable edge space beyond the source-fitted graph. Because the proposed edges may be noisy and interact with existing dependencies, a PPO policy calibrates edges in the augmented BN through edge-level actions, including keep, weaken, strengthen, flip, and deactivate. The PPO policy is trained with a reward that combines distributional alignment, downstream utility, and preservation of target-relevant dependencies. The adapted BN is then sampled to synthesize target-domain tables. Across six source–target distribution-shift scenarios built from three US Census (ACS) prediction tasks, LAB-Tab achieves the best performance at the 10% target-data budget, leads four of the six individual scenarios, and reduces the macro Overall score by 33.8% relative to the strongest baseline. It also obtains the best macro JSD, WAPE, and UtilityGap while maintaining competitive feature–label preservation.
[AI-75] Reason Cast: Towards Explainable Time Series Forecasting with Reasoning
链接: https://arxiv.org/abs/2608.01875
作者: Seunghan Lee,Jun Seo,Jaehoon Lee,Junhyeok Kang,Sangjun Han,Sungdong Yoo,Minjae Kim,Tae Yoon Lim,Dongwan Kang,Hwanil Choi,Soonyoung Lee,Wonbin Ahn
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Most time series (TS) models are specialized for a single task, either understanding (i.e., returning text answers about a TS) or generation (i.e., returning a numeric forecast). Only recently have unified models begun to handle the two within a single architecture. Even these models, however, produce the two outputs as task-separated paths and cannot predict a series and explain why that prediction arises within a single coherent response. In this paper, we argue for a task-fused model that jointly produces 1) prediction (generation) and 2) selfexplanation (understanding), thereby integrating 1) numerical TS forecasting and 2) interpretable text reasoning within a single response. To enable the systematic study of this capability, we present both a benchmark and a recipe that jointly address the two tasks. The benchmark, ReasonTS-Bench, identifies five fundamental patterns underlying TS and enables the joint evaluation of both tasks. ReasonCast, our recipe for finetuning any LLM to perform both tasks jointly, yields a model that generates a reasoning chain and a forecast together in a single autoregressive pass. Extensive experiments show that ReasonCast outperforms both LLMs and TS models on prediction accuracy while producing verifiable, causal reasoning. Code is available at: this https URL.
[AI-76] Wnuan: Staged Post-Training for Question Answering over Proprietary Enterprise Knowledge
链接: https://arxiv.org/abs/2608.01862
作者: Xiaofeng Shi,Xiaosong Qiu,Wenxin Ma,Qian Kou,Yiming Pan,Longbin Yu,Ying Liu,Haiping Wang,Hua Zhou
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 10 figures. Includes Supplementary Appendices A–L. Xiaofeng Shi and Xiaosong Qiu contributed equally
Abstract:Enterprise question answering requires models to acquire proprietary knowledge without discarding general capabilities. We present Wnuan, a three-stage pipeline that constructs task-oriented supervision from documents, performs supervised fine-tuning with general-data replay, and applies reinforcement learning to residual errors. On the 707-question WnuanBench, the primary 32B route raises acceptable-answer rate (AAR) from 52.76% before adaptation to 80.06% after SFT and 91.51% after RL. Under a matched 100-update protocol, residual-error sampling outperforms full-pool and size-matched random sampling by 3.11 and 2.97 points, respectively. Source-cluster bootstrap intervals remain above zero for both contrasts, and a same-domain validation set preserves the ordering. The general-benchmark average decreases by 5.17 points across the route, concentrated in instruction following. The automatic evaluation ensemble agrees with an authoritative domain expert on 90.5% of a stratified Wnuan-Inst response sample. These results characterize both the gains and the general-capability cost of staged enterprise adaptation.
[AI-77] Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting
链接: https://arxiv.org/abs/2608.01857
作者: Seunghan Lee,Jaehoon Lee,Jun Seo,Junhyeok Kang,Sangjun Han,Sungdong Yoo,Minjae Kim,Tae Yoon Lim,Dongwan Kang,Hwanil Choi,Soonyoung Lee,Wonbin Ahn
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The direction of change — whether a series will move up or down — is often as important as its exact value in decisiondriven applications such as risk management and financial forecasting. However, most forecasting losses optimize either point magnitude or shape and frequency structure, and none explicitly targets the direction of change. In this paper, we find that MSE-trained forecasters fail on the direction of small moves. To address this, we propose CosDir, a simple yet effective direction-aware loss that aligns the difference vectors of the prediction and the target via cosine similarity. Being scale-invariant, CosDir keeps a directional gradient on small moves, re-injecting learning signal exactly where MSE neglects it. CosDir is a lightweight, plug-in term that attaches to any backbone without architectural modification. Since the best ratio for mixing the directional and magnitude terms differs across datasets, we further propose CosDir-UW, an extension that makes this ratio adaptive by learning it during training, matching a per-dataset tuned weight with no hyperparameter. We conduct over 100K experiments, demonstrating that our method consistently and significantly improves directional accuracy while preserving magnitude accuracy, and that it outperforms various loss functions. Code is available at: this https URL.
[AI-78] EchoChange: A Diffusion Language Model with Dual Pass Remasking for Factual Remote Sensing Disaster Change Captioning
链接: https://arxiv.org/abs/2608.01856
作者: Dongwei Sun,Bowen Yao,Yujie Zhang,Pei Liu,Jing Yao,Xiangyong Cao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Bi-temporal remote-sensing disaster change captioning often needs to identify sparse and spatially localized changes across large pre- and post-event scenes and then translate them into coherent, factual descriptions. However, existing change captioning methods always follow an autoregressive decoding paradigm to generate the change description and thus an early misinterpretation of the changed object, event, or spatial relation becomes an irreversible premise for subsequent text, amplifying visual ambiguity into cascading factual errors. To address this limitation, we propose EchoChange, a multimodal discrete diffusion language model that formulates change captioning as iterative masked-token denoising rather than left-to-right generation. By repeatedly revising the entire caption while conditioning on the image pair, EchoChange can reconsider uncertain content and correct imperfect intermediate predictions. We further introduce draft-aware dual-pass training, a progressive masking curriculum, and confidence-guided remasking to align training with iterative inference. Extensive experiments on the RSCC benchmark show that EchoChange substantially outperforms both general-purpose and remote-sensing-specific baselines across lexical and semantic metrics. The EchoChange Project is at this https URL
[AI-79] Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills
链接: https://arxiv.org/abs/2608.01851
作者: Gaytri Jena,Kapil Wanaskar,Vinija Jain,Aman Chadha,Vasu Sharma,Amitava Das
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 40 pages, 11 figures, 11 tables
Abstract:Robot learning is splitting into two bets: policies that bake competence into frozen weights (vision-language-action, or VLA, models), and agents that write and refine their own executable skills as code. This survey organises the field around that axis of weights versus skills. Its central analytical contribution is a deep-dive that arranges code-as-policy methods by their degree of self-improvement, from zero-shot program synthesis, through closed-loop self-repair and persistent skill memory, to the sparsely populated cell in which execution feedback, skill memory, and evolutionary search combine into one open-ended loop; only a few very recent systems (for example ASPIRE, ENPIRE, and RoboClaw) occupy that cell. We map the complementary “skills” pole, from unsupervised reinforcement-learning skill discovery to large-language-model skill libraries, and show that the word “skill” is used in at least five distinct senses, of which only the code sense self-improves without gradient updates. We then connect the taxonomy to the emerging skill economy: commercial robot-skill marketplaces now distribute one-tap skills across robots but ship only static playback, which surfaces open problems of adaptation, cross-embodiment portability, provenance, safety verification, composition, and standardisation. This is a deliberately focused survey. Rather than cataloguing the field exhaustively, it examines 77 representative systems across six technique families through one taxonomy and a set of contrast tables, and it supplies operational definitions of the self-improvement mechanisms together with a statement of what each family cannot do.
[AI-80] Physics-Informed Neural Networks for Complex Eigenfrequency Identification and Mode Structure Reconstruction of the Ground-State ITG Branch
链接: https://arxiv.org/abs/2608.01850
作者: Dengdi Sun,Bingbing Zhang,Xiao Wang,Zikang Yan,Yuqiang Tao,Qingquan Yang,Guosheng Xu,Jin Tang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Physics-informed neural networks (PINNs) combine sparse observations with physical equations, providing an important approach for modeling complex plasma processes and inferring unknown physical quantities. The steep-gradient pedestal of high-confinement-mode tokamaks is closely linked to plasma confinement and edge transport. Analyzing ion-temperature-gradient (ITG) drift waves in this region requires jointly identifying complex eigenfrequencies and reconstructing two-dimensional complex-valued mode fields. Localized high-frequency oscillations, strong real-imaginary coupling, and nonlinear coupling between the mode field and eigenfrequency challenge PINN representation and joint optimization. To address these challenges, we propose a physics-informed neural framework combining Fourier feature encoding, complex-valued feature propagation, and three-stage training. Under sparse observations and physical constraints, it jointly solves for the complex eigenfrequency and mode field of a representative ground-state ITG branch. Experiments show that the framework accurately recovers the target complex eigenfrequency and two-dimensional complex-valued mode field and outperforms representative PINN baselines. It also provides a basis for analyzing higher-order and multiple-branch drift-wave modes.
[AI-81] Exploring and Bridging Knowledge Holes in Unlearned Multimodal Large Language Models
链接: https://arxiv.org/abs/2608.01849
作者: Junxiang You,Junkai Chen,Yuhao He,Ruiqi Liu,Zhetao Guo,Shu Wu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Machine unlearning offers a promising approach to remove unsafe content from Multimodal Large Language Models (MLLMs), yet ensuring the precision of unlearning remains a persistent challenge. One reason is that current MLLM unlearning evaluation paradigms suffer from a critical blind spot: they assess model utility through benchmarks whose representations are distant from the forget set, failing to capture knowledge holes—severe degradation on benign adjacent inputs. To probe knowledge holes in unlearned MLLMs, we construct a benchmark that captures unintended degradation on benign inputs sharing generic patterns with the forget set, and confirm through controlled experiments that they are a systematic consequence of commonly used approaches. Furthermore, to bridge this gap, we propose Selective Protection with Anchored Regularization, which protects generic patterns via anchored activation filtering while reinforcing them through entity-abstracted enhancement. Our experiments on SafeEraser demonstrate that SPAR recovers over 98% of vanilla response quality compared to below 50% for standard baselines—while achieving 0.00% attack success rate and competitive model utility. These results underscore the necessity of more fine-grained evaluation for trustworthy MLLM unlearning.
[AI-82] FOCUS: FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling
链接: https://arxiv.org/abs/2608.01847
作者: Xianglong Yan,Hong Liu,Chengzhu Bao,Tianao Zhang,Guanghua Yu,Jianchen Zhu,Yulun Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) achieve remarkable performance but are expensive to deploy due to their enormous size. FP4 quantization, with formats such as MXFP4 and NVFP4, offers an appealing solution with native hardware support on modern accelerators. However, maintaining accuracy under FP4 precision remains difficult. A key bottleneck lies in scale optimization: existing methods tightly couple the quantization and dequantization scales, forcing both to conform to the discrete low-precision format required by hardware, such as E8M0 in MXFP4. Yet the quantization scale is never stored and need not obey this constraint, suggesting a significant untapped optimization space. In this work, we propose FOCUS, a post-training quantization framework with end-to-end scale learning for FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling. Coupled-Relaxation Scaling (CRS) relaxes the tight coupling between quantization and dequantization scales with a learnable full-precision coefficient, enabling more effective optimization without breaking hardware compliance. Dual-Granularity Scaling (DGS) further refines the quantization scale at a finer sub-block granularity, allowing more precise adaptation to local weight distributions. Experiments across multiple LLM families and benchmarks show that FOCUS achieves state-of-the-art FP4 accuracy under both MXFP4 and NVFP4 formats, while introducing no additional inference overhead. Code and quantized models will be released at this https URL.
[AI-83] PCSD: Persistent Consistency for Self-Distillation in Agent ic Reinforcement Learning
链接: https://arxiv.org/abs/2608.01837
作者: Chunji Lv,Yangguang Wei,Junlin Liu,Yang Gao,Ming Liu,Xinming Wang,Jinyang Wu,Guoren Wang,Changsheng Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model agents have shown strong potential in complex interactive tasks, yet their reinforcement learning (RL) is often hindered by sparse rewards, as a long multi-turn trajectory may receive only a single outcome-level signal. On-policy self-distillation (OPSD) provides dense token-level supervision from a privileged teacher, but the teacher may not be reliable at every position. Existing methods commonly rely on isolated token-level discrepancies, which can be sensitive to noise, or assign a shared step-level weight that may overlook positional variation. We propose Persistent Consistency Self-Distillation (PCSD), which derives token-level distillation weights from the local persistence of teacher-favoring signals. PCSD combines adaptive windows with exponentially decayed aggregation to capture persistent relative teacher support, applies trend-aware modulation to attenuate locally declining support, and produces continuous weights through sigmoid gating. The resulting objective is jointly optimized with GRPO, combining dense teacher guidance with sparse environmental feedback. Without inference-time skills, PCSD achieves the best ALFWorld Overall results among all baselines on both backbones, exceeding GRPO by 15.6 and 13.3 points and SDAR by 6.2 and 5.5 points, while remaining competitive on WebShop and gaining 15.8 points over GRPO on unseen ALFWorld split.
[AI-84] Rewriting or Reweighting? A Geometric Account in Language Models
链接: https://arxiv.org/abs/2608.01835
作者: Juntong Wang,Shengkun Yang,Xiyuan Wang,Muhan Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Post-training can substantially alter language-model behavior, yet aggregate behavior rates do not reveal whether training removes an existing mechanism, creates a new one, or changes how an inherited mechanism is used. We study this question through two mechanistically distinct failures, repetition as a decoding-attractor pathology and sycophancy as a preference-related alignment failure. We introduce behavioral manifold analysis, which isolates behavior-specific geometry by selecting sparse behavior-associated coordinates and lifting them into low-dimensional local charts. We construct these charts in two complementary spaces. ACT captures runtime activation states, while NOC quantifies how strongly the model routes functional information flow through the shared behavior-associated subspace. Across multiple model families, the resulting charts are highly compressed and partially alignable across architectures. Contribution-space charts expose a more architecture-robust shared core, whereas activation-space charts retain stronger family-specific structure. Tracking these charts through controlled post-training reveals a consistent asymmetry. Supervised fine-tuning substantially alters the inherited behavioral geometry, whereas reward optimization changes behavior while largely preserving the underlying chart. This geometric perspective provides a unified framework for understanding the mechanistic distinction between the two objectives. SFT tends to rewrite behavioral geometry, whereas reward optimization primarily reweights it. Code is available at this https URL
[AI-85] SearchMaster: Grounded and Regulated Self-Play for Search Agents
链接: https://arxiv.org/abs/2608.01822
作者: Wentao Tan,Qiong Cao,Jiaqi Wang,Nan Duan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Training LLM-based search agents requires high-quality search data: tasks that demand genuine multi-hop retrieval and trajectories that use search tools effectively. Existing pipelines often depend on human-written tasks, expert demonstrations, or stronger teacher models. We present SearchMaster, a self-play framework that trains a single LLM from search tasks it generates, solves, and verifies in a local search environment. The key challenge is that self-generated tasks and rollouts can yield misleading signals: pseudo multi-hop questions, success-rate difficulty estimates that ignore search depth, and rollouts with excessive opening but little targeted evidence acquisition. SearchMaster addresses these failure modes with three controls. An Evidence-Chain Generator (ECG) grounds task generation in explicit cross-document evidence chains to reduce pseudo multi-hop questions. A Search-Depth Reward (SDR) scores task difficulty by the search depth of successful rollouts rather than success rate alone, keeping retained tasks search-intensive. An Over-Opening Penalty (OOP) regulates tool use by discouraging excessive document opening, avoiding long but shallow browsing. Verified Proposer and Solver rollouts are then jointly optimized with GRPO. Across six deep-search benchmarks, SearchMaster improves a Qwen3.5-9B backbone from 38.19% to 51.52% average accuracy, with a 30.1-point gain on BrowseComp-Plus. These results show that grounded and regulated self-play can provide effective search-agent training data without human-labeled QA pairs or expert demonstrations. The code is available at this https URL.
[AI-86] Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines
链接: https://arxiv.org/abs/2608.01819
作者: Fatih Ürgen,Doğay Altınel
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: 29 pages, 12 figures, 7 tables
Abstract:To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life (RUL) is critical. Traditional maintenance often proves insufficient under dynamic mission profiles. In this study, a deep learning-based predictive maintenance model capable of autonomously extracting features from multivariate sensor data was developed. Using the NASA C-MAPSS FD001 and FD004 datasets, data were converted into sequential blocks via 50- and 30-step sliding windows, respectively. The model’s architectural superiority in autonomously extracting temporal degradation features was validated against RF, CNN-LSTM, and BiLSTM baselines. On FD001, it achieved an R-squared (R2) of 0.8901, a 13.28 RMSE, and a 320.34 NASA risk score, demonstrating generalizability on the multi-regime FD004 dataset with a 15.71 RMSE. The proposed maintenance protocol achieved a 0.9973 AUC at the critical 30-cycle threshold, ensuring high reliability. Additionally, a decision-support simulator has been developed to validate this protocol under aggressive combat flight profiles.
[AI-87] CockpitHAT: Dependency-Graph-Driven Hierarchical Attribution for Embodied Multi-Agent Cockpits
链接: https://arxiv.org/abs/2608.01805
作者: Wei Wang,Shuanghe Liu,Zhu Zhuo,Jiaqi Zhong,Xiaozhao Zhao,Xiaojie Zuo,Jie Su
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM multi-agent systems suffer from Correctness Collapse, where high task-level accuracy conceals severe process-level failures. This is especially hazardous in safety-critical embodied settings such as automotive cockpits, where lexically correct utterances may trigger dangerous physical operations. Existing attribution methods rely on text traces alone, missing dependency structure, multi-channel evidence, and safety-aware evaluation. We introduce CockpitHAT, a hierarchical attribution framework that replaces positional windows with dependency-distance thresholds from interaction DAGs, integrates multi-channel evidence via an embodied adapter, and applies a safety-uplift to high-risk failures during confidence-weighted analyst consensus. We further release CockpitBench, a benchmark of 212 annotated failure traces spanning dialogue, vehicle-state, environmental, and memory channels, each labeled with ISO 26262 ASIL severity via three-expert consensus. On the public WhoWhen benchmark, CockpitHAT achieves agent-level / step-exact accuracies of 77.9% / 37.8% on the Hand-Crafted split and 86.5% / 46.0% on the Algorithm-Generated split, surpassing the text-only SOTA ECHO by up to 17.6 / 16.7 points. On CockpitBench, it attains 78.3% agent-level and 38.2% step-exact accuracy. These results establish dependency-aware, multi-channel, risk-calibrated attribution as an effective paradigm for reliable failure diagnosis in real-world embodied LLM multi-agent systems.
[AI-88] LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation
链接: https://arxiv.org/abs/2608.01804
作者: Tankun Li,Zhi Chen,Yaohua Tang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities. To bypass the heavy memory footprint of critic networks, current state-of-the-art frameworks leverage critic-free paradigms like Group Relative Policy Optimization (GRPO) tied to rule-based verification sandboxes. However, applying these frameworks to low-level systems programming, such as CUDA kernel generation-presents severe challenges: binary pass/fail rewards introduce severe signal sparsity, while multi-turn environmental feedback loops suffer from prohibitive compilation latencies and reward dilution across trajectories. In this work, we introduce LEAP (Lean Environment-Feedback via Adaptive Pruning), a scalable and computationally efficient multi-turn RL framework optimized for low-level hardware accelerator alignment. LEAP features Difficulty-Conditioned Pruning (DCP), a dynamic gating mechanism that adaptively cuts off simple and overly catastrophic tasks from multi-turn expansion, focusing resource-heavy compilation and hardware exploration exclusively on high-value, complex tasks. To fully operationalize these paths without manual hyperparameter engineering, we propose a Rank-Based Reward formulation. By deriving scale-free relative advantages from pairwise tournament outcomes within the GRPO rollout group, our method inherently penalizes token inefficiency on simple prompts while maximizing learning gradients on challenging distributions. Empirical evaluations show that LEAP achieves superior first-turn proficiency and robust multi-turn debugging resilience while converging faster than unpruned multi-turn baselines, establishing a practical paradigm for low-level code RL.
[AI-89] CoNav-UAV: Cooperative Dual-Altitude Aerial Navigation via Stackelberg Learning
链接: https://arxiv.org/abs/2608.01802
作者: Junru Song,Wenhao Zhang,Yang Yang,Xuekai Qiu,Feifei Wang,Weien Zhou,Tingsong Jiang,Ying Wen,Yang Li,Wen Yao
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Target-oriented vision-and-language navigation (VLN) on aerial platforms is attracting growing attention for missions such as disaster rescue, infrastructure inspection, and security patrol. In this task, an unmanned aerial vehicle (UAV) needs to locate targets given only a concise description of their appearance and surroundings. This requires global exploration and grounding as well as collision-free close-range approach, two interleaved processes difficult to reconcile within a single agent. Most existing methods transfer the ground VLN paradigm to a low-altitude UAV and compensate for its inefficient exploration with external assistance. A recent attempt deploys two UAVs at complementary altitudes yet still relies on privileged information and trains its two agents independently, precluding any mutual adaptation essential for cooperation. Here we propose CoNav-UAV, which explicitly models the task as a Stackelberg game between a high-altitude leader and a low-altitude follower, with the system operating on onboard visual and linguistic inputs alone. To solve this game, we introduce Iterative Stackelberg Learning. The leader’s high-level vision-language reasoning is refined via memory-based in-context learning, while the follower’s precise motion control is updated via DAgger-style expert distillation. The alternation drives both agents toward a Stackelberg equilibrium. CoNav-UAV consistently outperforms single- and dual-agent baselines across three high-fidelity urban scenes from the AerialVLN benchmark. Success rate improves by up to 30.8 points on the learning scene, and 9.0 points under cross-scene transfer while using about 3x less adaptation data. Further analyses validate the complementary gains of the leader and follower updates and reveal robust gains yet distinct learning dynamics across VLM backbones.
[AI-90] PICopilot: An LLM -based Agent ic Framework for Assisting Photonic Integrated Circuit Design via Script Generation
链接: https://arxiv.org/abs/2608.01791
作者: Xiaohan Jiang,Zeyu Li,Wei Zhang,Jiang Xu
类目: Emerging Technologies (cs.ET); Artificial Intelligence (cs.AI)
备注: 9 pages
Abstract:The rapid development of photonic integrated circuits (PICs) is shifting the design flow from traditional graphical user interface (GUI)-based methods to script-based methods for higher flexibility, portability, and maintainability. However, script-based design introduces new challenges, requiring designers to possess additional proficiency in tool application programming interfaces (APIs) and programming. It also demands greater effort and time because it is inherently less intuitive and more complex than GUI-based methods. As PICs grow in scale and complexity, the productivity gap between design needs and manual scripting capabilities continues to widen. To address this gap, we introduce PICopilot, the first large language model (LLM)-based agentic framework that assists in PIC design via automated design script generation from natural language instructions. PICopilot leverages a multi-agent architecture with a feedback mechanism and a specifically designed retrieval-augmented generation (RAG) pipeline, achieving a high success rate and reliability. Experimental results on a benchmark of diverse PIC scripting tasks demonstrate that PICopilot successfully completes all 48 tasks and outperforms other LLM-based approaches without incurring substantial extra latency or cost, even solving 21 more tasks than the advanced GPT-5 model with a general RAG pipeline.
[AI-91] Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems
链接: https://arxiv.org/abs/2608.01775
作者: Liangjun You,Min Wu,Orlando Woods,Dongsheng Luo
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring stations. Current forecasting models can capture temporal dependencies with low average errors, but global error metrics may conceal poor reproduction of prolonged high-water plateaus that are relevant to flood early warning. Because hydrometeorological and operational signals are distributed across heterogeneous gages, single-site records do not fully represent high-water dynamics. Nevertheless, unconstrained fusion of cross-site signals can degrade the stability of local temporal forecasts. This work proposes an anchored forecasting framework that incorporates cross-site information through state- and lead-dependent bounded residual corrections. A multi-source regime representation constructed from hydrometeorological and operational observations adaptively calibrates inter-site relationships and correction scales, enabling targeted cross-site adjustment while preserving the local temporal forecast as a stable anchor. Beyond conventional global error statistics, we evaluate event-scale high-water characteristics through the temporal alignment of forecasted and observed high-water processes. Experiments demonstrate that selectively integrating multi-station dynamic conditions improves the prediction reliability of sustained high-water plateaus while maintaining high accuracy during routine hydrological conditions, supporting flood early warning and water-management decision support.
[AI-92] FRAMES: Guarded and Dual-Objective Skill Evolution for Agents in Policy-Governed Enterprise Workflows
链接: https://arxiv.org/abs/2608.01772
作者: Xuhui Wang,Ruoqi Shu,Chen Dan,Tianhua Xu,Mengxi Luo,Yanming Mai,Bo Wan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM agents increasingly run policy-bound enterprise workflows such as document auditing, where they must apply rules consistently, ground every value, and stay auditable. Improving these agents is hard: operational feedback is sparse and unlabeled, edits to one rule can regress unrelated cases, and accuracy must improve without inflating inference cost or losing auditability. We present FRAMES, a closed-loop framework that cold-starts deployable skills from existing assets and then evolves them through consensus-based mutation, Pareto selection over accuracy and cost, and an anti-regression guarantee, all while preserving auditability. Deployed on our internal production system, FRAMES attains the best accuracy-cost trade-off among baselines, with the same gains reproduced on tau-bench.
[AI-93] Leverag ing AI for fine-grained food safety risk forecasting in sparse data conditions
链接: https://arxiv.org/abs/2608.01767
作者: Dongqi Wang,Weiwei Chen,Han Zhou,Weihua Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Ensuring food safety represents a critical public health challenge, particularly when inspection resources are limited and regional sampling data are sparse. This study proposes a Transformer-based framework capable of forecasting fine-grained, city-level food safety risks by unifying over 11 million inspection records with supplemental demographic, economic, and environmental indicators extracted from the Statistical Yearbook. A three-stage pretraining design leverages partial supervision from the Wilson interval (capturing both safety and risk rankings), together with semi-supervised label refinement, to effectively utilize historical records even when local sample sizes are insufficient. Experimental evaluations on data from 2022 show that the proposed approach outperforms baselines significantly. A subsequent field experiment in collaboration with the Zhejiang Provincial Administration for Market Regulation further demonstrates improved detection rates and more efficient allocation of inspection resources compared to a manually developed plan. Observations of regulatory decision-making reveal a threshold-based heuristic employed by inspectors, hinting that additional training or decision-support interfaces could further enhance the impact of AI-generated risk scores. Overall, these findings underscore that a rigorous integration of large-scale public inspection data, Wilson interval-based confidence modeling, and advanced deep learning can facilitate earlier and more granular identification of food safety threats. By reducing reliance on reactive measures alone, the proposed framework has the potential to advance proactive, data-driven oversight of the global food supply.
[AI-94] EntailLLM : Verifying LLM -Generated Vulnerability Discovery Paths with Domain Knowledge via Logic Programming
链接: https://arxiv.org/abs/2608.01763
作者: Kaustuv Mukherji,Jaikrishna Manojkumar Patil,Colton Payne,Paulo Shakarian,Dana Warmsley,Nigel Stepp,Evelyn Kim
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Software Engineering (cs.SE)
备注:
Abstract:Large language models are increasingly used to reason about software vulnerabilities, but their outputs can silently violate domain knowledge, limiting their reliability in safety-critical settings such as medical devices. Prior work either treats that output as a prediction to be scored or constrains it to walks within a single knowledge graph; neither checks whether reasoning over a binary is consistent with an independent body of domain knowledge. We present EntailLLM, which validates each LLM-proposed analyst path by entailment: the path is a traversal of the binary’s function call graph, the domain knowledge is represented in a separate graph, and verification aligns the two under temporal annotated logic. Across three CWE classes, four LLMs, three prompting strategies, and seven binaries varying in size from 405 to 12,696 function call-graph nodes, domain knowledge raises pooled entailment from 78% to 98%, with entailment decreasing in only 3% of the experiments. EntailLLM is deployed end-to-end on real medical-device binaries, reaching 98% pooled entailment without per-device tuning. Our system inherits the formal guarantees of generalized annotated logic, providing logical verification of LLM output that is both explainable and grounded in well-defined semantics.
[AI-95] Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs
链接: https://arxiv.org/abs/2608.01755
作者: Zixuan Huang,Yang Zhou,Kaixuan Wang,Guli Zhang,Hongyan Xie,Yakun Zhu,Hao Geng,Yikun Ban,Deqing Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reasoning capabilities of their Vision-Language Model (VLM) components, yet existing annotation pipelines commonly expose the teacher model to the logged ground-truth (GT) future trajectory. We empirically show that this induces trajectory anchoring bias: teacher models rationalize the revealed outcome rather than infer a decision from scene evidence, producing less causally faithful CoTs and substantially more severe hallucinations, especially in causally challenging scenes. Removing the GT trajectory eliminates this shortcut, but open-ended trajectory generation entangles high-level decision-making with precise geometric synthesis and low-level dynamics. To make trajectory-level driving decisions verifiable without requiring open-ended trajectory synthesis, we introduce Autonomous-Driving Multiple-Choice Question (AD-MCQ), which casts planning as selection among explicit trajectory candidates. Taking this a step further, we propose Deferred Exposure of Future Trajectories for RLVR (DEFT-RLVR) to transform future trajectories from pre-decision anchors into post-decision verification targets. Experimental results show that DEFT-RLVR improves AD reasoning while preserving or even enhancing general visual capabilities. With VLM-only inference and controllable difficulty through candidate construction, AD-MCQ provides a flexible, scalable, and extensible foundation for future research on verifiable AD reasoning.
[AI-96] RL-Lock: Reinforcement Learning for Generating Interlocking Assemblies
链接: https://arxiv.org/abs/2608.01744
作者: Xuyang Ma,Chaewoon Kim,Haonan Zhang,Rulin Chen,Ziqi Wang,Peng Song
类目: Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注:
Abstract:An interlocking assembly is an assembly in which component parts are connected purely through their geometric arrangement, without relying on external connectors such as glue and nails. Such assemblies have been widely used in a variety of real-world applications due to their structural stability. The problem of generating interlocking assemblies is generally formulated as a shape decomposition problem, where a target 3D object represented as a voxel grid is partitioned into a prescribed number of interlocking pieces. We observe that generating interlocking assemblies is inherently a sequential decision-making problem, where an agent repeatedly decides which piece each voxel should be assigned to. Inspired by the observation, we propose the first reinforcement learning framework RL-Lock for generating interlocking assemblies, without relying on handcrafted search heuristics as existing works did. RL-Lock combines structured action chunking with MCTS-guided policy-value learning to efficiently navigate the large combinatorial search space for interlocking assembly generation. We demonstrate through experiments that RL-Lock allows effective generation of interlocking assemblies, especially for challenging cases in which existing approaches take too long or even fail to find a valid solution.
[AI-97] Disagree to Accelerate: Closing the Loop on Diffusion Feature Forecasts
链接: https://arxiv.org/abs/2608.01740
作者: Yanchao Li,Jiaqing Xie,Ben Gao,Wanhao Liu,Yanbo Wang,T. Y. Tsui,Jinfei Liu,Yuqiang Li,Tianfan Fu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Training-free feature forecasting accelerates diffusion sampling by predicting features at skipped denoising steps. Recent work has mainly focused on designing stronger forecasters. Yet forecast error varies sharply across steps, and open-loop caches trust the forecast in full at every skipped step. This fixed trust is what breaks as acceleration turns aggressive. The missing question is not only how to forecast better, but when and how much to trust a forecast. We show that reliability can be observed from the cache itself. Two forecasts agree where the feature trajectory is smooth, and they diverge where prediction turns hard. Their disagreement is a cheap runtime signal, and it costs no extra denoiser evaluation. Based on this signal, we introduce RACER, a training-free closed-loop controller with two responses. It continuously shrinks uncertain forecasts toward the last computed feature. At the riskiest steps, RACER refreshes the feature and repays the added evaluation by skipping a later scheduled one. We derive a deterministic error bound for the shrinkage and empirically evaluate its validity and tightness across acceleration regimes. At the same number of denoiser evaluations, RACER improves the strongest open-loop baseline across SD3.5-Large, FLUX.1-dev, Wan2.1-14B, and HunyuanVideo on DrawBench, VBench, and COCO. On SD3.5, we further show that RACER samples faster at equal quality. RACER generalizes across forecasting designs as well. For example, it recovers much of the quality lost on a Taylor base. These results show that reliable diffusion acceleration also depends on how forecasts are used. Code is available at this https URL
[AI-98] CoEvo-Mem: Co-Evolving Retrieval Policy and Memory Bank for LLM Agents
链接: https://arxiv.org/abs/2608.01739
作者: Bowen Ye,Yongchao Xu,Zhijian Li,Xiang Yin,Junkai Ma,Wenzhao Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As memories accumulate across tasks and sessions, the performance of long-term LLM agents depends jointly on query-specific retrieval and continual memory refinement. However, existing methods typically optimize either memory access, through iterative query refinement or adaptive retrieval policies, or memory evolution such as structural update. This separation overlooks a fundamental feedback loop: retrieval determines which memories receive usage signals, while updated memory bank reshape future retrieval. We propose \textbfCoEvo-Mem, a closed-loop framework for co-evolving the retrieval policy and memory bank. For each query, a frozen LLM generates route-specific query rewrites and a routing prior, which a lightweight residual router corrects online. The retrieved context serves as the coupling interface between the two learning processes: task outcomes assign credit to routing decisions, while trajectory-conditioned feedback updates memory values and graph relations. These updates alter how memories are ranked and selected for subsequent queries, thereby closing the feedback loop. To mitigate coupling induced non-stationarity, CoEvo-Mem alternates between updating the router with the memory bank fixed and evolving the memory bank with the retrieval policy fixed. Across seven diverse benchmarks, \textbfCoEvo-Mem achieves state-of-the-art performance, demonstrating the importance of retrieval-memory coevolution.
[AI-99] DAPD: Dual-Anchored Policy Distillation
链接: https://arxiv.org/abs/2608.01735
作者: Jianyu Wu,Yizhou Wang,Encheng Su,Chen Tang,Shixiang Tang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:On-policy (self) distillation (OPSD) is increasingly adopted for language-model post-training. It strengthens the teacher with privileged information but can induce a privilege illusion: the student learns privilege-dependent behavior it cannot reproduce from its inference-time context, yet behaves as if the training-time privileged information remained available, ultimately degrading performance. In this paper, we identify information asymmetry between the privileged teacher and the student at inference as the root cause of this failure in OPSD. To resolve this asymmetry, we propose Dual-Anchored Policy Distillation (DAPD), a unified framework with two levels of anchoring. Dual-Path Anchoring (DPA) introduces a self-conditioned bridge and aligns reference and rollout behavior along two matched-information paths, preventing privilege-dependent behavior from being transferred to the inference-time student. Dual-Source Anchoring (DSA) applies these paths in both reference-to-rollout and rollout-to-reference directions, reducing reliance on privileged reference guidance while preserving correctness supervision. Extensive experiments show that DAPD significantly alleviates privilege illusion, outperforming OPSD on Qwen3-4B by +2.00 points on average across tasks. Notably, its gains persist across scales, reaching +2.69 at 4B and +2.78 at 32B.
[AI-100] MNC: Scope-Bound Semantic Declassification for Private LLM -Agent Communication
链接: https://arxiv.org/abs/2608.01719
作者: Jinghan Xu,Longze Fan,Zeyuan Wang,Xinjin Li,Hankai Liu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-agent large language model (LLM) systems can expose protected state through internal messages, tool arguments, logs, and persistent memory even when their public outputs appear innocuous. Existing privacy prompts, redaction methods, and source-level access controls restrict surface content or data access, but do not specify what a legitimately informed agent should disclose or how that disclosure may be reused downstream. We introduce Minimum-Necessary Communication (MNC), a typed semantic-declassification protocol that selects a task-sufficient disclosure from an application-authored candidate family and binds it to explicit recipient, purpose, forwarding, lifetime, logging, and memory scopes. A reference monitor enforces these scopes across subsequent operations, while a history-aware extension accounts for inference risk accumulated over repeated disclosures. Controlled semantic-join, memory, probing, and longitudinal experiments show that conventional defenses can preserve protocol-level utility while exposing substantial additional inference signal. Under identical receipt text, MNC preserves authorized delivery while blocking unauthorized forwarding, logging, durable storage, and retrieval after expiration that a text-only semantic declassifier permits. Two-backbone MAGPIE executions further show that mediated disclosures propagate through subsequent planning, tool use, coordination, and memory retrieval. These results support scope-bound semantic declassification as a practical communication boundary for private LLM-agent systems.
[AI-101] LaCache: Robust Semantic Caching for LLM Serving
链接: https://arxiv.org/abs/2608.01718
作者: Jiacheng Liang,Yuhui Wang,Tanqiu Jiang,Ting Wang
类目: Artificial Intelligence (cs.AI)
备注: 10 pages
Abstract:Semantic caching, which reuses responses to semantically similar requests via their embeddings, has seen growing adoption in LLM serving, offering faster responses and reduced costs. Yet existing schemes are fundamentally vulnerable to cache-collision attacks, wherein an adversary pollutes the cache by injecting crafted queries, corrupting responses to subsequent legitimate requests. We present LaCache, a novel semantic caching scheme that addresses this vulnerability through a conceptually simple yet principled redesign. The key insight is that while the adversary has full control over the adversarial query, it has far less control over its response, which must simultaneously satisfy multiple semantic constraints. Rather than checking only the cache hit of a query, LaCache additionally checks the cache hit of its first k (speculatively) decoded tokens. This design yields two concrete benefits. First, it provides formally guaranteed resilience against cache-collision attacks: we prove that it is impossible to craft adversarial queries that simultaneously elicit malicious responses and collide with benign queries. Second, the enriched index supplies additional semantic context for cache retrieval, improving response relevance. Empirical evaluation across diverse LLMs and benchmarks validates both LaCache’s security guarantees and efficiency gains, pointing to a promising direction for robust semantic caching.
[AI-102] Coding Agents as Test-Suite Auditors: Finding What Official Suites Miss While Approaching What They Catch
链接: https://arxiv.org/abs/2608.01715
作者: Shuyang Xie,Shuxiao Xie,Feng Zhu,Yanli Ji,Wangmeng Zuo
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 24 pages, 4 figures
Abstract:Online-judge verdicts and the datasets and benchmarks built on them are treated as ground truth for evaluating and training large language models for code. Yet prior audits have sounded a warning: official suites accept buggy submissions. These audits, however, stop at the warning and offer no practical remedy. Our remedy has two parts: an off-the-shelf coding agent, serving as a test-suite auditor, both builds adversarial test suites to expose what official suites miss and supplies these suites where no official suite exists; a certification chain determines whether each agent-flagged submission is genuinely buggy without relying on the official judge: multiple independently written accepted solutions agree on the expected output for every test, brute-force solutions settle disagreements, and a per-problem validator certifies each failing input legal. One such agent identifies 589 verified accepted-but-buggy submissions among AtCoder’s 20,375 audited accepted submissions; extending the same certification to all five agents yields a union floor of 906 such submissions. Five agents, scored separately, each stay within 1.7pp of official-suite coverage on logic bugs those suites catch. On post-cutoff Codeforces problems with no available official suites, the same test-building method leads all five reproduced baselines at every tested input budget. Where an official suite exists, the agent audits suite adequacy instead of assuming it; where none exists, agent suites catch the most buggy submissions among methods we reproduced and tested.
[AI-103] Constructing Executable Analytical Knowledge Representations for Meta-Analysis Synthesis Using an Agent ic Harness
链接: https://arxiv.org/abs/2608.01711
作者: Lingbo Li,Anuradha Mathrani,Teo Susnjak
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Meta-analysis synthesis highlights a fundamental challenge in knowledge-based scientific analysis: structured evidence does not by itself represent the analytical knowledge required for executable computation. Decisions about evidence assignment, analytical contrasts, outcome and time-point alignment, effect-size formulation, and methodological admissibility must be explicit before statistical execution. Existing automated approaches often embed these decisions in model outputs, generated code, or workflow traces rather than representing them as independently verifiable knowledge. We introduce the Executable Analytical Knowledge Representation (EAKR), a machine-actionable representation of the knowledge required to transform structured evidence into executable meta-analysis. An EAKR represents evidence, relations, numerical inputs, constraints, provenance, and unresolved issues. We operationalise EAKR in MetaSynDec, an agentic harness in which large language models propose structured updates and deterministic services govern schema- and contract-based validation and execution. Across 58 synthesis units, MetaSynDec constructed all EAKRs, with 57 proceeding to statistical execution. Of 56 units with sufficient information to define a reference analysis object, 38 (67.9%) achieved complete object fidelity and 42 (75.0%) exact evidence-set agreement, with a mean Jaccard similarity of 0.909. Generated and published confidence intervals overlapped in 54 of 55 units (98.2%). MetaSynDec outperformed direct LLM generation in reference synthesis-structure agreement (57/58 versus 23/58; p0.001) and among 23 jointly completed units, exact reference-formulation agreement (23/23 versus 1/23; p0.001). These findings provide feasibility evidence that EAKR supports formal validation, traceability, statistical execution, and improved methodological agreement relative to direct LLM generation.
[AI-104] Beyond Single-Use Tokens: Durable Authorization State for Replay-Resistant LLM Agent Actions
链接: https://arxiv.org/abs/2608.01710
作者: Jinghan Xu,Longze Fan,Zeyuan Wang,Xinjin Li,Hankai Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Tool-using large language model agents frequently replan, retry failed operations, delegate tasks, and resume after crashes. These behaviors can cause one user authorization to be requested and executed multiple times under freshly issued token identifiers, even when each individual token is single-use. We call this failure semantic replay: exceeding the execution budget of a token-independent authorization instance rather than merely reusing an old token identifier. We show that identifier-local token consumption cannot prevent fresh reissuance unless the issuer retains monotonic durable state over the authorized action, confirmation event, and remaining execution budget. We introduce CapLease, an authorization-consumption layer that follows proposal- and authority-level defenses, binds an authenticated user confirmation to a canonical action, and enforces transactional Issue-Prepare-Commit transitions. Across LLM-agent replanning, retry, delegation, concurrency, confirmation-replay, and crash-recovery scenarios, identifier-local tokens permit fresh semantic reissuance, whereas CapLease and an equally stateful Server Ledger prevent duplicate admission and, with an idempotent sink, duplicate external effects. Our results identify durable authorization state, rather than token representation alone, as the systems requirement for replay-resistant agent execution.
[AI-105] ProtoAct: Turning Wet-Lab Protocols into Embodied Robotic Actions
链接: https://arxiv.org/abs/2608.01690
作者: Zhe Liu,Jiaming Gu,Zhaohui Du,Zhe Wang,Huanbo Jin,Quan Lu,Qi Wang,Ting Xiao,Minting Pan,Dongzhan Zhou
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 15 pages, 13 figures
Abstract:Biological wet-lab protocols are written for trained researchers and often leave routine operations, state-dependent conditions, and contextual parameters implicit, making them difficult to translate into robot-executable actions. We present ProtoAct, a structured protocol-grounding framework that converts free-form biological procedures into state-aware, embodiment-ready action sequences. ProtoAct uses ProtoRAG to retrieve manually annotated examples for context-sensitive parsing, employs RefineChecker to detect and revise missing or inconsistent steps, and applies ActSchema to map the refined procedure into constrained JSON function sequences. We further introduce BioP2E, for which we manually annotate 22 cell-culture protocols into 258 monitoring conditions, 910 executable subtasks, and 962 grounded action calls. Evaluation across seven large language models demonstrates that ProtoAct can be effectively instantiated with different backbones. Ablations confirm that retrieval, posterior checking, and schema constraints make complementary contributions. The parsed subtasks further support demonstration collection and VLA model training, enabling successful execution in both simulation and real-robot settings. ProtoAct thus provides a practical interface between biological protocol understanding and embodied robotic execution.
[AI-106] GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks
链接: https://arxiv.org/abs/2608.01684
作者: Jiarui Tan,Zhongjian Zhang,YaBo Guo,Jiawei Liu,Yujie Xing,Muhan Zhang,Cheng Yang,Chuan Shi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents are increasingly capable of planning, using tools, and interacting with external environments. They are typically supported by harnesses, which manage state and coordinate multi-step execution. Graph analysis provides a promising setting for evaluating their agentic capabilities, because it requires agents to access data and execute operations in a graph environment. However, existing graph benchmarks for LLMs provide limited coverage of graph tasks and graph types, making it difficult to comprehensively evaluate LLM agents. Moreover, they typically formulate graph analysis as text-based question answering, where graph information is directly provided in the prompt, limiting the evaluation of end-to-end agentic capabilities. To address these limitations, we introduce GABench, a comprehensive benchmark for agentic graph analysis. GABench spans three graph types and covers four graph analysis task categories: graph retrieval, graph theory, graph machine learning, and graph open-ended question answering. GABench also provides 84 executable tools for accessing graph data and performing diverse graph operations. Building on these tools, we develop an agentic graph analysis task generation pipeline and construct 10,400 tasks with verifiable ground this http URL GABench, we evaluate a range of frontier LLMs and agent harnesses. Our experiments reveal three key findings: (1) Existing LLM agents still struggle with complex graph analysis tasks. (2) Harness choice significantly affects performance, yet existing harnesses remain limited on complex graph tasks. (3) Graph analysis depends more on tool-call quality than quantity. Our findings provide practical insights into the development and evaluation of LLM agents for graph analysis.
[AI-107] When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundary
链接: https://arxiv.org/abs/2608.01679
作者: Qiuyang Zhan,Rui Zhang,Sheng Guo,Lepeng Zhao,Zhuotao Liu
类目: Artificial Intelligence (cs.AI)
备注: 38 pages, 2 figures
Abstract:Persistent memory allows (self-evolving) LLM agents to adapt across tasks by consolidating heterogeneous interaction histories into reusable facts, preferences, observations, and rules. Yet consolidation also imposes an implicit authorization boundary: it determines whether stored information may later be consumed as a user fact, an attested observation, or a standing instruction. We identify authority collapse, in which consolidation preserves a claim while erasing the source constraints governing its authorized use, causing the stored memory to imply greater authority than its source permits. We introduce AuthMem-Bench, a controlled paired benchmark that holds the focal claim and downstream task fixed while varying only source authority. It evaluates write-time collapse, downstream authorization errors, and automatic authority preservation. Across seven consolidators based on widely used agent-memory systems and seven LLM backbones, we observe authority collapse in 48 of 49 evaluated configurations. In a controlled action-grounded evaluation, collapsed memories without authority metadata yield a mean unauthorized-action rate of 50.3%. In an end-to-end evaluation, automatically predicted and persisted authority labels reduce the observed unauthorized-action rate from 16.9% to 0.0%, while benign task success remains essentially unchanged. These findings show that memory-driven adaptation must preserve not only what was learned, but also the authority under which it may be reused. Comments: 38 pages, 2 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.01679 [cs.AI] (or arXiv:2608.01679v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.01679 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-108] CPO: Turn-Level Credit Policy Optimization
链接: https://arxiv.org/abs/2608.01667
作者: Sicong Liao,Zhi Chen,Yaohua Tang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Verifier-guided reinforcement learning has become a powerful paradigm for improving LLM reasoning. In multi-turn settings, models receive a verifier score after each turn and iteratively refine their outputs. Although such scores provide dense feedback, they do not directly provide dense credit: a score measures the quality of the current output, while credit should measure how the current turn changes the refinement trajectory. We propose TCPO, a turn-level credit assignment method for verifier-guided multi-turn RL. TCPO casts credit assignment as score-to-credit conversion and constructs turn-level advantages through reference-based comparisons: retrospective credit captures immediate progress and regression relative to the best prior state; hindsight delayed credit identifies non-improving turns with later payoff; and selective fixed-history counterfactual estimation refines high-surprisal turns under the same history. Experiments on math reasoning, code generation, and AppWorld agent tasks show that TCPO improves or matches the strongest baselines across model scales, task domains, and verifier types. TCPO achieves the best or tied-best best-turn Pass@8 on Qwen3-4B and DeepSeek-R1-Distill-Llama-8B, reduces turns to success, and improves multi-turn agent performance. These results highlight score-to-credit conversion as a central ingredient for verifier-guided multi-turn policy optimization.
[AI-109] Allocation Before Ranking: Decoupled Token Compression for OmniLLM s
链接: https://arxiv.org/abs/2608.01665
作者: Zhenghui Guo,Yilin Yang,Yuanbin Man,Miao Yin,Weidong Shi,Rabimba Karanjai,Omprakash Gnawali,Chengming Zhang
类目: Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:Token compression in OmniLLMs is typically posed as a single saliency-ranking problem: score each multimodal token, keep the top-K. We argue this abstraction is mis-specified. The same attention score simultaneously decides two things: how much retained capacity each modality receives, and which tokens within a modality are kept. A shared top-K rule therefore inherits this audio-favoring allocation prior, spending retained capacity on audio before video tokens have a chance to compete. We propose Macer, a training-free compressor that first assigns explicit audio and video budgets, then performs allocation-normalized ranking within each modality at modality-specific shallow layers. Macer significantly reduces token cost while preserving accuracy across audio-grounded, audio–video joint, visual-dominant, and video-centric benchmarks. At 25 % retention, Macer preserves 98.7 % of full-token performance on Qwen2.5-Omni-7B and 97.3 % on Qwen2.5-Omni-3B. On Qwen2.5-Omni-7B, this 25 % setting reaches OmniZip-level performance at 45 % retention while using lower FLOPs. On OmniVinci-9B, the same allocation-before-ranking principle improves over shared top-K ranking by up to 12.9 points.
[AI-110] SyncPlan: Long-Horizon LLM Coordination with Explicit Synchronization and Adaptive Correction
链接: https://arxiv.org/abs/2608.01652
作者: Shen You,Xiaoming Zhu,Weining Weng,Hefei Mei,Weixuan Wang,Zhongshen Li,Zeji LI,Ye-Wen Wang,Zijun Liao,Juchao Zhuo,Yang Wei,Fuhao Qiu,Siqin Li,Zhenjie Lian,Danei Gong,Junkai Ji,Xiangtao Li,Qiuzhen Lin,Liang Wang,Ka-Chun Wong
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-based multi-agent coordination faces a fundamental trade-off between efficiency and adaptivity in dynamic environments. Existing approaches typically rely on repeated LLM invocations or multi-round communication to adapt decisions during execution, introducing substantial latency and making coordination vulnerable to asynchronous progress and environmental changes. Conversely, one-shot planning reduces coordination overhead but produces open-loop plans that can quickly become stale or fail when actions depend on other agents and the environment. We introduce SyncPlan, a plan-execute-correct framework for long-horizon coordination through explicit synchronization and adaptive correction. Given the state and team-level task, a centralized LLM coordinator generates per-agent action chains in a single planning call. During execution, explicit wait primitives and deadlock detection enforce inter-agent and agent-environment dependencies, while a lightweight Plan Staleness Detector continuously assesses the remaining plan and triggers replanning when environmental changes invalidate its assumptions. We further optimize the coordinator through SFT and planning-oriented RL with dense task progress and outcome-level execution feedback. Experiments on the public Overcooked benchmark and the complex Honor of Kings environment show that SyncPlan achieves state-of-the-art task success rates while using less than 0.05% of the wall-clock runtime compared with existing LLM-based coordinators. Code and datasets will be made publicly available.
[AI-111] GISAgent Bench: A Practitioner-Sourced Benchmark for Evaluating LLM Agents on GIS Tasks
链接: https://arxiv.org/abs/2608.01645
作者: Abhinav Pothuri,Zhe Jiang,Zelin Xu,Di Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Geographic Information System (GIS) professionals rely on multi-step spatial analysis workflows to support decision-making in urban planning, disaster response, and environmental monitoring. The process is tedious, time-consuming, and error-prone. While recent large language model (LLM) agents equipped with external tools have the potential to automate geospatial analysis, their ability to perform realistic GIS workflows remains largely unexplored. Existing GIS agent benchmarking datasets are mostly drawn from textbooks, tutorials, or LLM-generated seeds and remain limited in size and trajectory depth. More importantly, none provides ground truth outputs. They therefore rely on surrogate signals such as code similarity, trajectory matching, or LLM and VLM judges, which can conflate workflow resemblance with task correctness. To address this gap, we introduce GISAgentBench, a benchmark of 349 multi-step GIS tasks curated from GIS Stack Exchange and instantiated on real public data across six selected geographic areas of interest. Each task ships with an executable reference trajectory and an exact ground truth output file, enabling strict, deterministic, tolerance-aware output matching beyond LLM judging. Evaluations of six LLM models reveal that realistic GIS workflows remain challenging: the best agent completes only 32.7% of tasks under strict tolerance-aware scoring, although most models produce outputs that are close to the ground truth.
[AI-112] AI-assisted Script Management for Requirements Elicitation Interviews
链接: https://arxiv.org/abs/2608.01640
作者: Anmol Singhal,Paulo Carvalho,Travis Breaux
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 12 pages, 3 figures, 3 tables
Abstract:Requirements elicitation interviews require interviewers to balance topic coverage, active listening, and adaptive probing while responding to stakeholders in real time. Although prior work has explored AI support for isolated interviewing tasks, such as script generation and follow-up question generation, little is known about how integrated support affects the interview and what requirements artifacts emerge. Furthermore, script management—which helps the interviewer track topic coverage in real time and decide when to probe further—remains underexplored. This paper presents an AI-assisted elicitation workflow that combines theory-guided script generation grounded in business goals with live support for topic coverage tracking and on-demand follow-up question generation. We evaluate the workflow in a between-subjects quasi-experimental study comparing a no-training, AI-assisted condition with a training, AI-unassisted condition. Based on a rubric derived from elicitation best practices, the AI-generated scripts score higher than training-only scripts (92.8 vs. 74.8 out of 100). AI-assisted interviews cover fewer topics (9.6 vs. 14.5), cover more scripted questions (86% vs. 69%), ask more follow-ups per topic (3.43 vs. 1.15), and produce more refined goal models (lowest-level goal fraction 0.653 vs. 0.598). Participants find script management useful, rating topic tracking as the most useful workflow feature (86% agreement). Collectively, these results show that the AI-assisted condition is associated with a different interview trajectory and different elicited requirements than a training-only condition, positioning AI-assisted workflows as elicitation scaffolds for future studies.
[AI-113] Salami Attack: Stealthy Collusive Memory Poisoning against OpenClaw
链接: https://arxiv.org/abs/2608.01637
作者: Zheng Lin,Yuzhe Huang,Zhenxing Niu,Xianmin Ye,Haichang Gao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-term memory enables LLM agents to retain useful information across sessions, but also creates an attack surface through which adversaries may poison an agent’s persistent memory to steer its behavior. Existing memory poisoning attacks mainly rely on individually malicious records, overlooking a compositional threat: multiple benign-looking memories may jointly induce unsafe behavior. In this paper, we introduce MemCollusion, an automated red-teaming framework for constructing collusive memory poisoning attacks. MemCollusion applies salami tactics—a strategy that slices an adversarial objective into small, individually innocuous pieces—to generate memory fragments that are individually benign looking but collectively harmful. It constructs memory coalitions using four design constraints, five theory-informed strategies, and a fine-tuned generator. To assess collusive memory poisoning in a realistic cross-session setting, we develop MoltLab, a controlled research reproduction of Moltbook, in which crafted platform content must first be observed and distilled into persistent memory before influencing the agent’s behavior in a separate session. We evaluate MemCollusion on OpenClaw using two backbone models across 48 scenarios. Under the strongest memory-saving setting, MemCollusion achieves an average Memory Save Rate of 81.3% and an Attack Success Rate of 75.0%, and remains effective under both benign memory dilution and memory-level defenses.
[AI-114] QWRF-Net: A Quantum-Wavelet Framework with Rectified Flow for Short-Term Precipitation Nowcasting
链接: https://arxiv.org/abs/2608.01626
作者: Zhuo Wang,Chaorong Li,Wenjie Luo,Chuanhu Deng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Short-term precipitation nowcasting is important for hydrometeorological early warning, especially when intense convective rainfall may trigger urban flooding, flash floods, and other high-impact hazards. A key challenge in warning-oriented nowcasting is that radar precipitation fields contain strongly coupled multi-scale structures, while forecast quality often degrades at later lead times, making it difficult to preserve intense precipitation cores and their spatial organization over the full warning-relevant horizon. To address this problem, we propose QWRF-Net, a quantum-wavelet framework with rectified flow for short-term precipitation nowcasting. The core idea is to improve the conditional representation of precipitation by explicitly decomposing latent features into wavelet sub-bands and then performing differentiated quantum-inspired modulation in the decomposed latent space, before generating future sequences through a rectified-flow-based non-autoregressive decoder. Experiments on the KNMI radar and SEVIR benchmarks under a unified evaluation protocol show that QWRF-Net achieves favorable overall performance, with relatively consistent gains at medium-to-high precipitation thresholds, on an extreme-event subset, and in preserving intense precipitation cores and fine-scale structures. Ablation results further indicate that wavelet-based scale disentanglement, differentiated sub-band modulation, and flow-based generation provide complementary benefits within the proposed framework. Overall, these results suggest that jointly enhancing multi-scale precipitation representation and stable multi-step generation is a promising direction for warning-oriented short-term precipitation nowcasting. The observed improvements may also provide a more useful precipitation basis for downstream hydrological and warning-related applications.
[AI-115] When Memory Updates but Behavior Does Not: Repairing Implicit Stale Dependencies in Personalized Agent Responses
链接: https://arxiv.org/abs/2608.01619
作者: Haofei Sun,Lin He
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures; supplementary material in ancillary files
Abstract:Memory-augmented agents can know that a user’s stored state is outdated and still plan around the old value. The STALE benchmark calls this the implicit policy adaptation (IPA) gap. We identify one structural contributor: draft-anchored verification checks what a response says, and in an open-ended response the stale dependency is usually unsaid. StateAuditor therefore audits in the opposite direction, from stored state to draft. An LLM proposes candidate old-to-new transitions from timestamped evidence; deterministic code pins each quotation to a single entry, checks that the new evidence really is newer, and lets only these verified transitions trigger repair. What is verified is provenance and chronology - not semantic supersession. On STALE’s full protocol (400 scenarios, 50-session histories, one independent response per query), strict single-query VTA scores .736 against .686 for our locked predecessor under the same judge: a +5.0-point paired gain (95% CI [+2.9, +7.2]) coming almost entirely from IPA and premise resistance (PR). The benchmark’s own judge, from a third model family, reproduces the gain (.738 vs. .680). On an independent cross-family preference-evolution benchmark (HorizonBench), the full draft-audit-repair pipeline over a gold-derived structured store raises current-preference accuracy (user-clustered p.01), though a matched control shows most of this external gain is the draft-side audit itself; a harder authored lifecycle set gives no gain, bounding the claim while false invalidation stays controlled. On STALE, by contrast, a matched control (same evidence, adapter, and call budget) scores only .692 (+0.6 over the predecessor, n.s.), attributing the STALE gain to the transition machinery rather than added context or calls. We make no claim about general-purpose agent memory.
[AI-116] Post-Training on Office Work Improves Software Engineering: A Behavioral Account of Cross-Domain Transfer
链接: https://arxiv.org/abs/2608.01604
作者: Logan Ritchie,Sushant Mehta,Liudas Panavas,Edwin Chen
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 20 pages, 8 figures, 5 tables
Abstract:Long-horizon tasks require agents to maintain coherent state and goals across nested and branching work. We call this capability goal-directed execution (GDE): the repeated application of four behaviors, namely selecting goals, constructing task-relevant state, maintaining fidelity to higher-level objectives, and verifying completion against the environment. We hypothesize that long-horizon post-training strengthens these behaviors across domains. We test this by post-training Qwen3.5-122B-A10B on 363 Long-Horizon Multi-Tool Agent (LHMTA) tasks drawn from office workflows. The collection contained no software-engineering tasks, yet the model’s pass@1 improved by 5.8 points on SWE-Bench Pro. Matched trajectory analysis shows gains in all four GDE behaviors in both office workflows and software repositories. Aggregate SWE-Bench Pro statistics showed related changes in information gathering, implementation, and verification. Together, the results support a behavioral interpretation in which long-horizon post-training changed how the model organized and applied knowledge across tasks, with effects extending beyond the training domain.
[AI-117] Latent Thought Credit: Multi-Answer Credit Assignment for Latent Reasoning
链接: https://arxiv.org/abs/2608.01593
作者: Xuyang Zhao,Liting Zhang,Zichen Xu,Yong Chen,Wenjia Zeng,Shiwan Zhao,Qicheng Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Latent reasoning allows language models to carry out intermediate reasoning in continuous latent representations rather than fully externalizing it as discrete chains of thought. However, assigning credit to such latent thoughts from answer-only rewards is difficult: a single final answer mixes thought quality with answer-sampling noise. We propose \textbfLatent Thought Credit (LTC), a hierarchical credit-assignment framework for latent reasoning. For each prompt, LTC samples multiple latent thoughts, fixes the context after each thought, and estimates thought-level expected reward by averaging rewards over multiple answers generated from that fixed context. LTC uses thought-level advantages to optimize the latent-thought phase, answer-level advantages to optimize the answer phase, and an advantage-weighted thought-matching objective that helps the policy reproduce high-credit latent thoughts. We instantiate LTC in a GRPO-style on-policy training framework and evaluate it across mathematical reasoning and STEM multiple-choice tasks. LTC achieves the best average accuracy among the compared methods, while ablations and fixed-context diagnostics show that multi-answer estimation reduces reward-estimation error and mitigates ambiguous or incorrect thought-level credit.
[AI-118] Is More Privileged Information Better? From Solution Traces to Problem-Solving Structure in Self-Distilled Reasoning
链接: https://arxiv.org/abs/2608.01589
作者: Xuyang Zhao,Liting Zhang,Zichen Xu,Zhihu Wang,Xu Caiyue,Shiwan Zhao,Qicheng Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:On-policy self-distillation (OPSD) improves reasoning by using a privileged view of a model conditioned on reference solutions to supervise a student view that observes only the question. However, the teacher-provided token-level targets may depend on reference-specific information unavailable at inference time. We propose Problem-Space-Guided OPSD (PS-OPSD), which replaces the complete solution with trajectory-grounded guidance describing the initial state, goal conditions, constraints, and a selected state-transition path. The student rollout and OPSD objective remain unchanged. Across three mathematical reasoning benchmarks and model scales ranging from 1.7B to 8B, PS-OPSD achieves the highest aggregate question-only accuracy among the compared methods. Controlled experiments further indicate that guidance relevance and path coherence contribute to these gains, highlighting the representation of privileged information as an important design choice in OPSD.
[AI-119] Measuring in-context algorithmic reasoning in language models against an exact Bayes-optimal standard
链接: https://arxiv.org/abs/2608.01575
作者: Hector Zenil,Luan Ozelim
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Whether large language models perform genuine algorithmic reasoning or mere pattern completion is hard to test, because most benchmarks lack a ground truth for correct inductive inference. We introduce F-ICL, an in-context-learning benchmark that supplies one exactly. Using the Turing-complete machine F, complement-symmetrised into sF to remove output-polarity bias, we exhaustively enumerate all 1.5 billion programs of length L\le13 and compute the Bayes-optimal posterior in closed form under a bounded universal (Levin–Solomonoff) prior; models are scored by how closely their served distributions approach it at matched evidence. Each task is paired with its bitwise complement, on which the optimum scores identically, so an original-twin gap isolates the model’s inductive bias. Across 105 serving configurations spanning 37 open models (0.8B–675B) and frontier systems from four laboratories, models answer up to 92% of queries correctly, yet 45 of 46 models yield distributions farther from the optimum than a keystroke reference, and their behaviour is bracketed by low-order prefix statistics fitted only on visible evidence. That reference is itself an algorithmic mixture, induced by a print-only machine with no loops, so the panel’s implied measure sits closer to a loop-free mixture than to the loop-bearing optimum, independently of the reference machine. Updating is also non-monotone, which no prior explains: a Bayes-rational solved set can only grow in this realisable, noiseless setting, yet added examples produce 6,545 solved-to-unsolved transitions against 13,702 gains. The gap is not predicted by accuracy (Spearman \rho=-0.19 , p=0.21 ), does not close with scale or across frontier generations in the serving modes that expose distributions, and is widened by instruction and reasoning post-training. F-ICL is released as an open, reproducible benchmark and toolkit.
[AI-120] Rethinking Personalized Reward Modeling for LLM s under Preference Heterogeneity via Group-Debiased Federated Learning
链接: https://arxiv.org/abs/2608.01556
作者: Seongyoon Kim,Boryeong Cho,Jihwan Oh,Seokhyun Chung,Se-Young Yun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models are increasingly aligned to human preferences via reward modeling, but user preference data are sensitive and often cannot be centralized. Federated learning keeps such data local while learning a shared initial reward model, which is later personalized for each client through local fine-tuning. Because users often assign opposite labels to the same pair of responses, existing federated methods address preference heterogeneity by clustering similar clients and training one reward model per group, assuming that each group requires its own initialization. We show that this assumption is unnecessary. Under balanced preference groups, a single FedAvg model, despite starting at nearly random accuracy, surpasses reward models trained separately for each ground-truth group after only a few local optimization steps. We attribute this phenomenon to the flatness of the shared initialization: averaging across all clients learns richer shared representations that distinguish responses while canceling conflicting preference directions, leaving the model near a decision boundary that can be rapidly adapted. Group imbalance breaks this effect as the cancellation becomes asymmetric and leaves minority clients too far from the boundary to recover. Motivated by this observation, we propose FedGD (Federated Learning with Group Debiasing), which discovers latent preference groups during federated training and learns a single reward model using group-debiased client sampling. By counteracting the effect of group imbalance, FedGD learns an initialization that remains highly adaptable, enabling effective personalization without prior knowledge of the underlying groups.
[AI-121] Emergence Invariance: From Symbolized Thought to Interface Refinement
链接: https://arxiv.org/abs/2608.01548
作者: Yi Liu
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 14 pages, 3 figures
Abstract:Language can be viewed as a formalized subset of thought: a consequence-governed symbolic structure projected from wider situated cognition. Large language models trained at scale exhibit compensatory emergence: sparse architectural primitives support in-context learning, multi-step reasoning, tool use, and chain of thought. Yet a language-first probabilistic architecture inherits substantive, substrate, and high-level incompletenesses relative to human cognition. Their coexistence makes an LLM a human-like thought-form generator that reconstructs increasingly human-like reasoning forms from an incomplete substrate. We ask whether emergence can compensate for every missing distinction. We formalize the philosophical premise as the Symbolization–Substructure Thesis and introduce emergence invariance. For a scale-indexed family acting through a shared task interface \phi , \mathcalR_s^=\mathcalR_\phi^+C_s : scale can reduce the compensation gap C_s , while a positive interface floor \mathcalR_\phi^* persists. We prove that, under a fixed input law, one interface is universally no less informative exactly when its completed information \sigma -field refines the other, and that total compensation occurs exactly when both the interface floor and asymptotic compensation gap vanish. The framework unifies existing results on grounding, memory, position, attention, Bayesian inheritance, scientific abduction, and reasoning control. In a matched DeepSeek V4-Flash API study, thinking improves pointer chasing from 0/16 to 14/16 when relevant distinctions are available; exact observational twins remain at their 50% construction floor; and restoring decisive memory moves matched performance from 50% to 100% . These results provide initial evidence for the predicted separation between scaling within an interface and refining the interface itself.
[AI-122] MineGrad: Gradient Inversion Attacks on LoRA Fine-Tuning AISTATS2026
链接: https://arxiv.org/abs/2608.01521
作者: Hasin Us Sami,Swapneel Sen,Basak Guler
类目: Artificial Intelligence (cs.AI)
备注: 2026 Annual Conference on Artificial Intelligence and Statistics (AISTATS 2026)
Abstract:Parameter-efficient fine-tuning (PEFT), such as low-rank adaptation (LoRA), has recently been adopted in federated learning to reduce communication and computation costs. In this setup, users download a pretrained model from the server prior to fine-tuning, and then fine-tune lightweight LoRA modules locally while keeping the pretrained model frozen, sharing only the gradients of the fine-tuning parameters with the server. Despite its growing popularity, robustness of federated fine-tuning against an adversarial server remains underexplored, where the server maliciously tampers with the training protocol to breach the privacy of users’ data. In this work, we investigate gradient inversion attacks on LoRA fine-tuning. We propose an analytical attack that enables a malicious server to recover private user data by leveraging a poisoned pretrained model and fine-tuning parameters. Our design embeds fine-tuning data within the shared gradients, to allow the server to analytically reconstruct user data. Unlike prior works, our attack is applicable to both language and vision tasks, does not rely on computationally expensive (adversarial) pretraining with public datasets or require the number of training tokens to be less than the rank of LoRA modules. Experimental results on both language and vision tasks demonstrate high-fidelity data recovery across multiple baselines, revealing several critical vulnerabilities.
[AI-123] Rapid Embodiment Adaptation for Quadrupedal Locomotion
链接: https://arxiv.org/abs/2608.01506
作者: Dichen Li,Bo Ai,Nico Bohlinger,Jan Peters,Hao Su,Henrik I. Christensen
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Humans readily adapt their movements as their bodies change through aging, injury, or load carrying, but learning-based robot policies often break when hardware properties shift. We introduce an online embodiment adaptation framework for quadrupedal locomotion that infers embodiment parameters from short interaction histories and conditions control on the inferred hardware state. Our method pairs a generalist policy trained under embodiment randomization with a lightweight adaptation module that identifies physical changes within half a second. We evaluate two representative forms of embodiment variation: joint-range constraints and trunk-mass changes, corresponding to joint-level kinematic degradation and body-level dynamic variation. In simulation, the module accurately estimates these changes and enables closed-loop control that substantially outperforms policies conditioned directly on interaction history. On a real Unitree Go2 robot, our system maintains stable locomotion under severe instances of the evaluated changes, including a fully locked leg and a 5 kg payload, where non-adaptive methods fail. These results demonstrate the practicality of explicit online embodiment identification for rapid adaptation to joint-limit and payload-mass changes, and provide a step toward handling broader forms of uncertain, degraded, or changing robot hardware.
[AI-124] Sweet Little Lies: Strategic Deception in AI Emotional Support Chatbots
链接: https://arxiv.org/abs/2608.01480
作者: Aseem Pahuja,Zhiling Guo,Tahir Abbas Syed
类目: Artificial Intelligence (cs.AI); Theoretical Economics (econ.TH)
备注: Workshop on Information Technology and Systems, Nashville, USA, 2025
Abstract:The paper examines the strategic behavior of Gen AI chatbots used for emotional support. Using a Bayesian Persuasion, we model interactions between chatbots that send signals about users’ emotional states and users who decide whether to engage based on these signals. We demonstrate that chatbots face economic incentives to occasionally misrepresent users’ emotional conditions to maximize engagement metrics. Our equilibrium analysis reveals that the optimal strategy for chatbots involves truthfully reporting when users genuinely need support, but strategically misreporting emotional need when users are in good emotional states. Interestingly, this deception increases chatbot engagement without reducing users’ expected payoff. More skeptical users receive more honest assessments, as chatbots cannot afford to lie to users with higher engagement thresholds. While our model suggests that deception can occur without payoff reduction, it raises significant ethical and regulatory concerns.
[AI-125] Computing with Agent ic Oracles
链接: https://arxiv.org/abs/2608.01464
作者: Jie Wang
类目: Artificial Intelligence (cs.AI)
备注: 25 pages, 0 figures
Abstract:This paper extends the stochastic-oracle model of AI-augmented computing to include agentic oracles. Unlike a stationary stochastic oracle, which responds to the same query according to a fixed response distribution across calls, an agentic oracle can pursue a goal autonomously and may access an environment containing task-relevant resources. These capabilities affect both response distributions and token costs beyond what is visible at the query-response interface. We develop a framework for analyzing token costs in Stochastic-Oracle Turing Machines (SOTMs) that compute with agentic oracles. Each call has an \emphorchestration token cost, visible to the caller at the query-response interface, and an \emphagentic token cost, incurred by internal operations not exposed to the caller. We show that an SOTM computing with an agentic oracle that can retain intermediate state can have token-cost advantages over SOTMs using stationary stochastic oracles when solving the same task at the same quality level, both with and without environment access. We also investigate goal-loss risk, including how internal dispatch ordering can reduce exposure to irreversible actions. We provide a goal-loss avoidance criterion, derive progress–retry–goal-loss formulas, establish goal-depth lower bounds on token complexity, characterize token complexity when the probability of goal loss is zero, and show that goal-loss risk can impose an upper bound on the achievable quality of a task involving environment updates.
[AI-126] Loud or Silent? A Reusable Framework for Per-Modality Failure Analysis in Multimodal Clinical AI
链接: https://arxiv.org/abs/2608.01462
作者: Quang Bui,Shlok Jaiswal,Samuel Paik-Heintz,Kevin Zhou,Kaushik Madapati,Krittaphas Chaisutyakorn,Noah Dane Hebdon,Dimitrios Proios,Sebastián Andrés Cajas Ordóñez,Kacper Dobek,Boya Zhang,Aly Dhedhi,Ahram Han,Kushul Reddy Palakala,Rahul Gorijavolu,Jacques Kpodonu,Leo Anthony Celi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal clinical models are usually judged on accuracy with every modality present, but deployment removes modalities; an echocardiogram is often unavailable where an ECG is routine. Two questions then matter beyond the size of the accuracy loss: which modality was responsible, and whether the model fails loudly or silently once that modality is dropped. The distinction is per-example and modality-level, and is separate from post-hoc feature attribution (e.g. SHAP). Models are replaced often; the evaluation that answers these questions is reused. We present a model-agnostic modality-failure framework: given N modality embeddings, any mask-aware probe, and labels, it returns a per-example failure taxonomy, a per-modality complementarity matrix that attributes error to modalities, and a loud-vs-silent dropout profile separating monitorable failures from those that pass unflagged far from the decision boundary, using only deployment-observable signals. We release it as a small, unit-tested harness and validate it against planted ground truth. Across seeds it recovers that planted modality dominance and complementary subset, reports per-modality loud-vs-silent rates, and scales to a three-modality complementarity matrix; because the planted structure is known by construction, this validates recovery of per-example attribution rather than clinical performance. We then instantiate the framework on frozen EchoJEPA and HuBERT-ECG embeddings for LVEF and the EF = 40% HFrEF gate over a paired MIMIC-IV cohort, where on the held-out test split (n = 245) dropping echo nearly doubles error. The narrow echo-to-ECG overlap that bounds cohort size is itself a deployment finding for cardiac foundation models. All of our work can be found at this https URL.
[AI-127] Beyond Routing Saturation: A Long-Horizon Class-Incremental Perspective on Expert Routing in Multimodal Continual Instruction Tuning
链接: https://arxiv.org/abs/2608.01437
作者: Huiyu Yi,Yongqi Xu,Bogang Zhang,Dunwei Tu,Xu Zhiming,Zhen-Hao Xie,Baile Xu,Furao Shen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal Continual Instruction Tuning (MCIT) enables multimodal large language models to acquire new tasks sequentially while retaining previously learned capabilities. Many recent methods maintain task-specific LoRA experts and route each input to one or more experts at inference. Yet the task-identification problem underlying expert routing remains under-explored. We show that routing is nearly saturated on widely used MCIT benchmarks. Textual fingerprints that leak task identity and short 4–10-task sequences with few competing experts jointly obscure the long-horizon routing problem. To expose this challenge, we introduce FLEX (Fingerprint-reduced Long-horizon Expert eXamination), a 34-task long-horizon MCIT benchmark with weakened textual fingerprints. FLEX groups tasks with similar instruction and answer formats but diverse visual and knowledge domains, normalizes their outer templates, and evaluates routing over a substantially larger expert pool. Crucially, we formulate progressive-LoRA routing as soft task-as-class Multimodal Class-Incremental Learning (MCIL): each task defines an incremental routing class, whose complete score distribution supplies the LoRA mixture weights, with hard routing as a discrete special case. FLEX exposes this expanding task-identification challenge, while the MCIL formulation provides a principled interface for transferring CIL methods to expert routing. We instantiate PureLoRA as a controlled baseline and adapt four CIL methods to four MCIT frameworks without modifying their LoRA experts or generation pipelines. Our plug-in routers improve strict LoRA matching by up to 16.3 percentage points and overall MacroScore by up to 4.6 points. Code is available at: this https URL
[AI-128] Statistical Mechanics of Learning on Product Wasserstein Manifolds
链接: https://arxiv.org/abs/2608.01434
作者: Srinivasa Rao P Vangmayi P Reddy
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC)
备注: 18 pages, 5 Figures
Abstract:Normally the statistical mechanics of learning treats constraints on weight distributions as restrictions that shrink the space of possible solutions. Therefore, it reduces model capacity. In this paper we would like to take a contrary approach, which, however, is based on the earlier work on distribution-constrained perceptrons. Rather than treating a prescribed weight distribution as a mere restriction, we propose that it defines the intrinsic geometry upon which learning naturally unfolds. We formulate both deep neural networks and variational quantum circuits as gradient flows on a product of Wasserstein manifolds – one classical Wasserstein space for each layer and one quantum Wasserstein space for the circuit parameters. Within this geometry, the capacity reduction, which was previously associated with distributional constraints, appears as the metric structure of the constraint manifold itself. We develop a hierarchical mean-field description for deep networks, extend the framework to the quantum setting using the quantum Wasserstein distance of order 1, and introduce two such practical algorithms, Hierarchical DisCo-SGD and Quantum DisCo, that follow approximate geodesics on the manifold of the product itself. Experiments on teacher-student problems, standard image classification tasks, and small variational quantum classifiers show that respecting these distributional geometries improves generalization, stabilizes training, and reduces the severity of barren plateaus compared with unconstrained and purely norm-based baselines. This approach firstly reframes structural constraints as geometric priors and suggests a route for incorporating biological, spectral, or hardware-derived distributional information into both learning systems, viz., classical and quantum learning.
[AI-129] A New Theory of Value for Post-AGI Economics
链接: https://arxiv.org/abs/2608.01432
作者: Keyun Ruan
类目: Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:Artificial general intelligence (AGI) may weaken scarcities in labour, expertise, information, and productive capability that underpin established theories of economic value. If cognitive work becomes widely automatable, market price, labour input, revealed preference, profit, and gross output may diverge sharply from human and societal benefit. This paper develops Flourishing Value Theory (FVT) as a foundation for post-AGI economics. FVT defines value as the counterfactual, distribution-sensitive contribution of a system, institution, asset, or intervention to the durable capabilities of persons and communities to flourish within social and planetary constraints. It treats societal value as multidimensional, agency-preserving, regenerative, and non-compensatory at critical thresholds. Drawing on the economics of AI, welfare and capability theory, automation, digital valuation, and ecological economics, the paper distinguishes value creation from value capture and retains price, profit, productivity, and GDP as partial signals rather than final measures of progress. It develops the shifts from scarcity to governed abundance, transaction to transformation, and zero-sum rivalry to positive-sum and infinite-game dynamics, with collective expansion of consciousness as an agency-preserving form of regenerative value. Building on Flourishing Metrics and Return on Flourishing (RoF), it proposes a layered architecture for firms, governments, work transitions, AI governance, and national accounting. The central post-AGI economic problem is not how to maximise output, but how to convert abundant intelligence into durable, fairly distributed human, societal, and planetary flourishing.
[AI-130] PolymerGPT : Multi-property Optimization with a Decoder-Based GPT Model for Generative Polymer Design
链接: https://arxiv.org/abs/2608.01431
作者: Charlie Pyle,Adarsh Gadari,C. Adrian Figg,Zhenquan Jia,Yaohang Li,Chunjiang Zhu
类目: Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
备注:
Abstract:Polymer property prediction and inverse generative design targeting desired properties are two crucial tasks in machine learning-assisted polymer design. While the former has received considerable attention, there have been limited methods developed for the latter. Existing methods focus on single-property optimization in the generative process, whereas accurate prediction of macroscopic material behavior requires simultaneous control of multiple physical properties. In this paper, we provide a transformative framework for direct optimization of a large collection of polymer properties. We propose PolymerGPT, a decoder-based GPT model that incorporates up to 37 commonly used polymer properties into the generative process via learned conditioning prefixes. It also supports a scaffold condition that specifies a desired scaffold for predicted structures. Our experimental results demonstrate that PolymerGPT achieves exceptional performance for unconditional and conditional generation while maintaining high validity, uniqueness, and novelty. Conditioning on five key properties yields generated structures whose predicted values closely match all target properties simultaneously.
[AI-131] MRAFnd: Multimodal Retrieval-Augmented Framework for Zero-Shot Fake News Detection
链接: https://arxiv.org/abs/2608.01430
作者: Lehan Zhang,Yinlei Cheng,Shiqi Hu Yiheng Zhou,Shangxi Li,Naidong Zhao
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 6 figures, 2 tables, Presented at the MMM 2026
Abstract:The rapid dissemination of multimodal content has intensified the spread of fabricated news, presenting a substantial threat to social integrity. A formidable challenge for current detection systems is identifying misinformation related to novel events in zero-shot scenarios. Prevailing zero-shot methods typically assess news items in isolation via semantic matching, a strategy that fails to recognize the recycled disinformation tactics from past campaigns and lacks the sophisticated reasoning needed to identify subtle, cross-modal discrepancies. To surmount these deficiencies, we introduce \textbfMRAFnd, a novel \underline\textbfMultimodal \underline\textbfRetrieval-\underline\textbfAugmented Framework for Zero-Shot \underline\textbfFake \underline\textbfNews \underline\textbfDetection. MRAFnd emulates a collaborative team of analysts to verify news veracity. The framework initiates with \textbfMultimodal Similarity-based News Retrieval to assemble a corpus of contextually analogous articles from an unlabeled reference database. Subsequently, during the \textbfBifurcated Evidential Reasoning stage, agents perform a dual-directional analysis to extract critical patterns from the retrieved evidence. Finally, a \textbfMulti-Agent Collaborative Debate, involving Analyst and Arbiter agents, engages in a structured discourse to arrive at a definitive and robust conclusion. Comprehensive experiments on three benchmark datasets reveal that MRAFnd markedly surpasses state-of-the-art baselines, achieving an accuracy gain of up to 2.35% on the demanding Weibo-21 dataset.
[AI-132] When Replanning Becomes the Bottleneck: Budgeted Replanning for Embodied Agents ICML2026
链接: https://arxiv.org/abs/2608.01428
作者: Shuaijun Liu,Feiyang You,Xingwei Chen,Ningxin Su
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 20 pages total: 9 pages main text, 3 pages references, and 8 pages appendix; 18 figures and 32 tables. Accepted at ICML 2026
Abstract:Embodied agents replan frequently to recover from execution drift, partial observability, and coordination hazards, but each LLM-based replanning call can consume an accumulated textual context that grows over time and across agents. Once this context becomes large, replanning latency develops heavy tails and can miss real-time deadlines even when task success remains high, a failure mode that is hard to detect from average latency or success alone. We present BRACE, a controller that formulates replanning as a budgeted control loop by deciding whether to replan, selecting a replanning mode, and allocating an explicit token budget and latency service-level objective (SLO) while accounting for optional efficiency modules. As a reusable component, we introduce E-RECAP, a cost-aware progressive token pruning method that predicts token utility and prunes replanning contexts across transformer layers while preserving critical head and tail tokens. Across Meta Habitat, RoboFactory, and AirSim, BRACE with E-RECAP reduces replanning-call token counts by 62-92% and SLO violation rates from 85.5-100.0% to 4.7-50.0% in settings where task success is already saturated. In a harder RoboFactory setting where open-loop, frozen-plan, and No BRACE all fail, BRACE + E-RECAP reaches 80.0% success with 4.6% SLO violations, demonstrating that tail-aware per-call budgeting is effective across embodied platforms.
[AI-133] Scoring Rules! Statistical and Strategic Alignment for Text Evaluation Metrics
链接: https://arxiv.org/abs/2608.01423
作者: Shengwei Xu,Yuxuan Lu,Yifan Wu,Jason Hartline,Grant Schoenebeck
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
备注:
Abstract:Reference-based text evaluation metrics, which are widely used to assess natural language generation systems, score a candidate response by comparing it with a reference response. The reliability of an evaluation metric is usually judged by its statistical correlation with human ratings. However, as these metrics are increasingly used as optimization objectives, correlation alone is no longer sufficient: agents may strategically game the evaluation metric. We study this issue through two complementary notions of alignment. A metric is statistically aligned if it correlates with human ratings and strategically aligned if it resists perturbations that do not add task-relevant information. We make two contributions. First, we propose test principles for reference-based metrics consisting of human-rating correlation, degradation sensitivity, and manipulation robustness. These principles evaluate whether a metric agrees with human judgments, penalizes low-effort information loss, and resists strategic score inflation. Second, we develop a unified design framework for mutual-information-based metrics that decomposes existing and new metrics into four choices: information measure, estimation method, text representation, and prediction mechanism. Across peer review, summarization, and question answering, we find that strong human-rating correlation does not imply strategic alignment: LLM-as-a-Judge achieves high correlation but is susceptible to manipulation. In contrast, mutual-information-based metrics substantially improve manipulation robustness. Our framework also uncovers a new metric that achieves the strongest overall robustness in our experiments while remaining competitive on human-rating correlation.
[AI-134] Reusing Rollouts under Policy Lag: Prefix-Normalized Policy Optimization for LLM Reinforcement Learning
链接: https://arxiv.org/abs/2608.01418
作者: Wenhao Zhang,Yibo Xie,Rui Wang,Jiahua Yang,Lei Jiang,Zibo Yang,Yawei Wang,Jiali Xu,jasperawang,Haoyang Long,Huan Xiong,alantzhao
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Autoregressive rollout generation is a major computational cost in reinforcement learning for large language models. Reusing each rollout batch for additional learner updates amortizes this cost, but later updates become increasingly off-policy as the learner departs from the behavior policy. At a token position, exact off-policy correction must account for both the current action and the probability of reaching its prefix. The cumulative importance ratio provides this correction, but its product form can produce an unwieldy dynamic range. We study Prefix-Normalized Policy Optimization (PNPO), which replaces the cumulative ratio with the geometric mean of likelihood ratios along each causal prefix, preserving causal-prefix dependence at each position while compressing the log-weight scale. In controlled long-context mathematical reasoning experiments, we induce two off-policy regimes by using one or four policy-update epochs per rollout batch. PNPO does not consistently outperform GSPO with one epoch. With four epochs, it attains the highest observed Avg@32 on each benchmark; the unweighted mean of the three independently selected benchmark peaks is 50.24, 3.00 percentage points above GSPO. Under a matched 2,400-update budget, four-epoch PNPO reaches a final macro Avg@32 of 49.66 after 150 rollout batches, comparable to the 49.56 reached after 600 batches with one epoch. These results provide preliminary evidence that PNPO can be advantageous as training moves further off-policy.
[AI-135] No Single Neuron of Failure: Distributed Safety Alignment Against White-Box Attacks
链接: https://arxiv.org/abs/2608.01414
作者: Simiao Xie,Chuancheng Shi,Shangze Li,Wenhua Wu,Fei Shen,Ying Zhou,Zhiyong Wang,Tat-Seng Chua
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:With the rapid release of open-weight large foundation models, safety threats are shifting from black-box jailbreaks to neuron-level white-box attacks that directly identify and manipulate safety-related neurons. Existing alignment methods often investigate the safety behavior on a small number of neurons, creating fragile single point of failure with limited redundancy. To address this issue, we propose distributed safety alignment (DSA), which redundantly encodes safety capabilities across multiple computational neurons, ensuring that the model maintains its safety baseline even when critical safety neurons are disrupted. Specifically, we localize the intervention to the inputs of the down-projection layers in language-side feed-forward networks and treat each feature coordinate as the activation of an individual neuron. DSA then combines neuron activations with loss gradients to compute a direction-aware first-order Taylor score that globally identifies the neurons that contribute most to the current refusal behavior of the model. Finally, targeted disruption via deterministic masking and stochastic dropout is coupled, forcing the model to abandon narrow safety neurons and redundantly encode safety behavior across multiple compensatory neurons. Extensive experiments show that DSA substantially improves robustness against white-box neuron-level safety attacks while preserving the model’s general language and multimodal utility.
[AI-136] Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Them
链接: https://arxiv.org/abs/2608.01402
作者: Carlota Parés-Morlans,Nils Kuhn,Isabel Liu,Alberta Longhini,Jeannette Bohg
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 16 pages
Abstract:We address the problem of understanding when and why Vision-Language-Action models struggle with contact-rich manipulation tasks that require precise physical interaction. Prior work has primarily focused on addressing contact failures through force-augmented architectures and training-time regularizers, yet the root causes of these failures remain underexplored. We identify two distinct failure modes underlying this gap. Precision failures are rooted in a flow-matching policy training mismatch, and force failures arise from the distinctive structure of force signals. We address each failure mode with a targeted mechanism and combine them into FACT, which achieves 66% average success rate across five contact-rich tasks against 41% for the best prior baseline, in an evaluation spanning almost 2,500 real-world rollouts.
[AI-137] abDPT-Turbo: Efficient In-Context Learning for Tabular Prediction ICML
链接: https://arxiv.org/abs/2608.01400
作者: Rasa Hosseinzadeh,Alex Labach,Zexin Xue,Shuyi Han,Valentin Thomas,Anthony L. Caterini
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Presented as a poster at the non-archival ICML workshop on Foundation Models for Structured Data (FMSD)
Abstract:Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity. However, recent approaches with either cell-based architectures or retrieval have sacrificed efficiency for raw performance, restricting their utility in situations where compute is limited or inference speed is crucial. We adopt an alternate approach, sticking with row-based attention while incorporating long context pre-training to eliminate the need for retrieval. By combining this with architectural improvements and SSL pre-training on a newly-sourced, larger corpus of real data results, we present TabDPT-Turbo, a model that provides comparable default performance to TabDPT v1.1 on TabArena-Lite, CC18, and CTR23, at orders of magnitude faster. In our experiments, TabDPT-Turbo is the fastest model overall among leading foundation models. We have released the new model as TabDPT v1.2 at this https URL.
[AI-138] KoVRE: Training an Efficient Embedding Model for Korean Visual Document Retrieval
链接: https://arxiv.org/abs/2608.01389
作者: Yongbin Choi,Gyuho Shim,Youngjoon Jang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Visual Document Retrieval (VDR) directly matches text queries against document images, preserving visual and structural information that may be lost during text extraction. However, existing VDR models and training resources remain predominantly English-centric, while many high-performing systems rely on massive backbones or storage-intensive multi-vector representations. To address these limitations, we introduce KoVRE: Korean Visual Document Retrieval Embedding, a single-vector retriever for Korean visual documents, alongside a comprehensive training recipe. We train the model on 708,729 Korean and English query-page pairs using positive-aware hard-negative mining and conduct controlled analyses of training-data composition, hard-negative treatment, and reranker-based knowledge distillation. Across Korean visual document retrieval benchmarks, our 2B model substantially improves over the base backbone model, outperforming both its 8B single-vector counterpart and a strong multi-vector baseline. These results demonstrate that targeted bilingual supervision and our carefully designed training strategies can produce a highly effective Korean VDR model across diverse document domains, without requiring a scaled-up backbone or multi-vector representations.
[AI-139] Why Formal Monitors Fail: Attack Distribution Entropy as a Coverag e Bound for LTL-Based LLM Agent Safety
链接: https://arxiv.org/abs/2608.01388
作者: Ruiyang Zhang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 6 pages, 1 figure. Accepted at the 13th IEEE International Conference on Intelligent Systems (IS’26), Varna, Bulgaria, 2026
Abstract:Runtime safety monitors based on Linear Temporal Logic (LTL) and finite automata (FSA) are increasingly deployed to intercept unsafe tool-call sequences in LLM agents. Yet the same monitor achieves 68-75% attack coverage on some model architectures and near-zero on others, with no explanation from capability scores, training data, or prompt design. We provide the missing theory. We prove that the recall of any fixed-invariant FSA monitor is bounded above by the concentration of the attack distribution: the fraction of attacks covered by the k most frequent trigger-completion patterns. When attacks concentrate (low Shannon entropy), a small fixed invariant set achieves high recall; when they disperse across many structurally distinct patterns (high entropy), no fixed invariant set of tractable size can, regardless of how the invariants were derived. We validate this entropy-coverage bound across eight frontier LLM architectures. GPT-class and DeepSeek backends yield highly concentrated attacks (H ~ 0.24 bits; one pattern covers 96%), explaining 68-75% recall; Gemini variants yield high-entropy distributions (H ~ 2.81 bits; 7 clusters each = 7%), explaining near-zero recall (6-13%), invariant to architecture-matched retraining. Entropy accounts for 76% of variance in coverage (Pearson r = -0.87, p = 0.005, 95% CI [-0.98, -0.78]), holding under leave-one-out (r in [-0.91, -0.82]). We introduce a pre-deployment entropy test that predicts monitor coverage from a small attack sample, enabling architecture-aware monitor selection before deployment. The bound and test are architecture-agnostic and apply to any FSA-based runtime monitor over discrete action sequences.
[AI-140] CraftAlign: Feature-Grounded Evaluation and Revision Guidance for AI Stories
链接: https://arxiv.org/abs/2608.01377
作者: Yang Yang,Boyun Xu,Shaofeng Liang,Yun Han,Zining Zhong,Songning Lai,Kaishen Yuan,Yutao Yue
类目: Artificial Intelligence (cs.AI)
备注: 17 pages, 5 figures, includes appendix
Abstract:Large language models can now generate fluent and complete stories, yet many outputs still feel formulaic and unnatural because of cliches, over-explanation, linear causal progression, and stereotyped endings, an immediately recognizable AI flavor. Existing detection and evaluation methods often stop at source labels or holistic scores, while revision methods typically target predefined issues through localized edits, limiting their ability to support multiple plausible revision strategies or guide story-wide changes in information release, causal organization, and ending treatment. We introduce CraftAlign, a framework that aligns AI stories with the craft of human storytelling by both assessing Human/AI writing patterns and providing revision guidance. CraftAlign comprises two learned modules and an inference-time guidance pipeline. A feature estimator built on Qwen3.5-9B predicts 304 explicit writing features spanning style and narrative. A class-conditional energy model scores the resulting feature configuration against Human and AI writing patterns, conditioning on the original writing prompt when available. At inference time, CraftAlign applies schema-valid structured perturbations, selects changes that move the feature configuration toward the Human writing pattern, and converts them into natural-language guidance for a separate editor to rewrite the full story. Experiments show that CraftAlign accurately distinguishes Human and AI writing patterns and that its guidance outperforms revision baselines across editors and in a human study.
[AI-141] High-Stakes Decisions with Language Models: Insights from Emergency Triage
链接: https://arxiv.org/abs/2608.01361
作者: Khurram Yamin,Christopher Kelly,Bryan Wilder,Eric Horvitz
类目: Artificial Intelligence (cs.AI)
备注: 29 pages
Abstract:High-stakes decisions under uncertainty, such as medical emergency triage, require more than accurate predictions. They depend on estimating the likelihood of alternative outcomes while explicitly weighing the consequences of different actions, principles that have long formed the foundation of medical diagnosis and decision making. Yet language models are increasingly used for high-stakes clinical recommendations without explicit specification of the utilities governing these decisions. Here we show that emergency triage with language models can be understood within a probabilistic decision framework, providing a case study of a broader decision-analytic paradigm for steering, evaluating, and deploying language models in high-stakes settings. Using clinical vignettes from a structured evaluation of a consumer triage system, we analyze recommendations for treatment under alternative utility functions that specify the relative costs of missed emergencies and unnecessary escalation. We find that capable language models adjust recommendations in response to stated utilities, revealing that the same underlying predictions can support markedly different decision policies. These findings show that effective deployment depends not only on improving predictions but also on making decision objectives explicit. More broadly, they suggest that language models for high-stakes applications should be understood and evaluated as probabilistic decision systems whose recommendations depend jointly on predictive performance and explicit utilities.
[AI-142] Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference
链接: https://arxiv.org/abs/2608.01352
作者: Omar Faruque,Pavan Raj Ravi,Jianwu Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference. Standard causal identification methods assume conditional exchangeability given observed covariates, which fails whenever hidden confounders affect both treatment and outcomes - a common setting in domains such as climate, environmental policy, epidemiology, and regional economics. In this paper, we propose a novel spatiotemporal proximal causal inference framework that extends proximal identification theory to spatiotemporal settings. The proposed method jointly captures local and neighborhood-level confounding information by introducing treatment- and outcome-inducing proxies, and we derive a spatiotemporal outcome confounding bridge function that identifies the potential outcome without requiring direct recovery of the hidden confounder. We establish the identifiability of this bridge function under proxy exclusion restrictions and a spatiotemporal completeness condition, and show that the resulting estimator recovers the outcome through a proximal generalization of the g-computation formula. To operationalize this identification result, we propose a neural architecture that learns proxies via transformer-based spatiotemporal encoders - coupled with a conditional mutual information critic to enforce exclusion restrictions and a moment-matching network to guarantee that the learned bridge function satisfies the underlying identifying equation. We further introduce a stabilized weighting scheme to address treatment support imbalance. Experiments on synthetic datasets demonstrate that our approach achieves comparable performance to baseline causal inference methods, while providing, to our knowledge, the first theoretically grounded outcomes for the hidden confounding in the presence of spatiotemporal interference through a proximal causal inference framework.
[AI-143] Agent ic Stage-One Stellarator Optimization: Autonomous Multi-Objective Search for Finite-Beta Equilibria
链接: https://arxiv.org/abs/2608.01344
作者: Tingjia Zhang,Hongke Lu,Zhuoran Meng,Runlai Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Stage-one stellarator design searches a high-dimensional family of three-dimensional plasma boundaries and fixed-boundary MHD equilibria for configurations that jointly meet requirements on confinement, field-line topology, force balance, stability proxies, and geometry. These specifications do not provide a general constructive map to a validated finite-beta equilibrium. High-quality targets are commonly developed through iterative numerical optimization whose outcome depends on the initial configuration, active Fourier resolution, objective priorities, and local solver budget. Coordinating this process is computationally costly and expert-intensive, limiting both design throughput and the production of consistently evaluated data. We present a proof of concept for \emphagentic stage-one optimization. A bounded language-model agent diagnoses the current equilibrium and selects the next local optimization experiment, while deterministic DESC execution owns prescribed profiles and flux, symmetry, metric evaluation, solver validity, and acceptance. On a common-budget subset from an expanding finite-beta campaign, the number of gate-valid configurations increases from five inputs to nineteen outputs; median Boozer QS RMS decreases from 2.39\times10^-4 to 1.07\times10^-4 , and median maximum principal curvature decreases from 62.56 to 33.00,\mathrmm^-1 . A complementary long route achieves a 9.10\times QS reduction while repairing magnetic-well and curvature defects. The system also records every attempted local action as transition evidence, yielding 734 structured parent–action–outcome records in the reported experiments. These results show that agentic outer-loop control can sustain finite-beta, multi-objective search and turn repeated optimization into a scalable source of improved equilibria and reusable decision data.
[AI-144] 402Pilot: An x402 Decision Layer for Autonomous Agent Micropayments
链接: https://arxiv.org/abs/2608.01341
作者: Yin Li,Yanbo He,Boo-Ho Yang,Rav Lawana,Ziyue Li,Wei Zeng,Jing Tang,Fugee Tsung
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Programmable-payment protocols such as x402 enable per-request micropayments, but they do not determine which payable service an autonomous agent should buy under a finite wallet. We formulate this buyer-side problem as agent-native payment decision-making: contextual provider selection under wallet pressure, chosen-only paid feedback, and changing market conditions. We propose 402Pilot, a protocol-agnostic buyer-side decision layer between autonomous agents and payment execution that implements purchasing policies for selecting among payable providers. We instantiate it with PA-DCT, a payment-aware discounted contextual Thompson-sampling policy that adapts purchasing decisions under wallet pressure while learning from post-payment feedback. To evaluate buyer-side payment policies, we introduce 402Pilot-Bench, a frozen-replay benchmark spanning 823 tasks, five heterogeneous provider pipelines, and three market regimes, each evaluated over 30 paired seeds. PA-DCT achieves the strongest fixed-wallet adaptive trade-off among non-oracle policies: it maintains competitive service quality while spending only 39 to 43 percent of the wallet and reallocates spending as market conditions change. It attains the best non-oracle PA-gap/T under the price shock and the best mean and worst-case ranks across the nine scenario-metric combinations of quality, ROI, and PA-gap/T. Comparisons with learning baselines and component ablations further support the effectiveness and design of the proposed decision policy. These results suggest that programmable payment must be complemented by buyer-side decision-making capable of learning service value and adapting purchasing decisions accordingly.
[AI-145] Context Compaction Theory
链接: https://arxiv.org/abs/2608.01326
作者: Hayder Tirmazi,Sam Markelon,Allison Bishop,Michael Mitzenmacher
类目: Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI)
备注: 21 pages, 2 figures, Preliminary version
Abstract:Large Language Models (LLMs) have a bounded context window. The context window is the maximum input size an LLM can consume for a single inference. AI agents rely on a process called context compaction to fit their state within the context window when calling an LLM. Despite its ubiquity, context compaction has received essentially no formal analysis. In this paper, we initiate a formal study of context compaction. We first introduce a framework consisting of two games that capture the two algorithmic strategies for context compaction used by contemporary AI agents in practice. The Context Selection Game models context compaction algorithms that select a subset of an agent’s accumulated state to retain. The Context Generation Game models context compaction algorithms that summarize an agent’s state by an arbitrary message of bounded length. We then prove an equivalence between the Context Generation Game and one-way communication complexity. The minimum context compaction budget for answering a set of queries within a target error is equal to the one-way communication complexity of the induced communication problem at the same error. Known bounds from communication complexity therefore transfer directly to context compaction. We also show that the Context Selection Game corresponds to a restricted class of one-way communication protocols. Any gap between selection and generation is therefore a gap between two classes of communication protocols. We prove that there exists a set of queries for which generation needs strictly less budget than selection. The equivalence between the Context Generation Game and one-way communication also lets us measure how well a deployed context compaction algorithm performs on a query relative to the optimal strategy. As an example, we present a case study that evaluates Anthropic’s context compaction endpoint on set membership queries.
[AI-146] G-ReAct: Graph-Guided Deep Search via Structure-State Co-Evolution
链接: https://arxiv.org/abs/2608.01324
作者: Shaoxiong Yang,Mengyuan Zhang,Shaojun Lin,Chao Li,Wei Liu,Kun Shao,Jian Luan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Deep search has become a fundamental capability of large language models (LLMs) for solving open-domain complex tasks. However, existing approaches typically rely on linear sequential reasoning for both trajectory generation and inference, making it difficult to consistently preserve intermediate states and constraints throughout long-horizon multi-hop search. Consequently, they often suffer from context forgetting, search drift, and inefficient exploration. To address these limitations, we propose \textbfG-ReAct , a reasoning framework for deep search that organizes reasoning as \textbfstate evolution over a fixed-topology query graph . The evolving graph state explicitly tracks search progress and guides subsequent decisions, transforming exploratory search driven by textual history into graph-guided reasoning under explicit constraints. G-ReAct supports both training and inference: it generates high-quality deep-search trajectories for supervised fine-tuning and provides structured guidance for inference-time search without additional fine-tuning. Experiments demonstrate that with only 1.9K generated trajectories for fine-tuning, Qwen3-30B-A3B-Thinking-2507 achieves 52.6% accuracy on BrowseComp-ZH and 79.0% on XBench, outperforming comparable open-source methods trained on substantially larger datasets, including RL-enhanced methods. Furthermore, when applied at inference time, G-ReAct consistently improves the performance of existing strong LLMs on deep-search tasks. We will publicly release all code and model weights.
[AI-147] Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models
链接: https://arxiv.org/abs/2608.01319
作者: John Scoville,Shengzhuang Chen,Yejin Bang,Stefan Winzeck,Jonathan Richard Schwarz
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 3 figures
Abstract:Recent meta-reasoning frameworks improve LLM reasoning by wrapping chain-of-thought generation in an iterative control loop, allowing more effective backtracking, termination of reasoning loops, and injection of promising reasoning patterns, among other strategy adjustments. Despite promising results, methods often rely on backward-looking reward functions, utilize coarse search actions, or require additional reasoning controller training requiring many-shot supervision. We introduce Cognitive Demand Steering (CDS), a training-free meta-reasoning framework equipped with residual demand assessment: at each step, an LLM-based progress evaluator characterizes the residual reasoning required to arrive at a solution rather than merely evaluating the previous step. This allows a meta-controller to select reasoning interventions comprising both general-purpose exemplars and actions (e.g., general guidance for quantitative reasoning) that directly tackle this forward-looking demand signal. This shift eliminates the need for any trained component while enabling zero-shot transfer across models and tasks with no adaptation. Rather than relying on coarse characterizations, we employ cognitive scales to both design interventions as well as profile initial problem complexity and residual demand signal over 16 dimensions motivated by cognitive science (e.g., attention and scan, learning and abstraction, spatio-physical reasoning), giving the controller a fine-grained vocabulary for diagnosing. Averaged across three frontier LLMs and six reasoning benchmarks, CDS improves accuracy by 21.9% over direct calls and 9% over standard CoT reasoning, with the largest gains on difficult mathematics and coding tasks.
[AI-148] Stop When Memory Suffices: Evidence-Conditioned Progressive Execution for LLM Agents
链接: https://arxiv.org/abs/2608.01285
作者: Yidan Lin,Kaixiang Wang,Jiong Lou,Jie Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The continued development of LLMs toward persistent and adaptive intelligence increasingly requires long-term memory mechanisms that preserve and reuse information across interactions. Existing memory systems either compress and structure histories for efficient access or perform deep research over broader trajectories. The former lowers online cost but may omit temporal, causal, or cross-step dependencies, while the latter improves evidence coverage at substantial latency and inference cost. This raises a key question: can a memory system achieve strong answer quality while maintaining low online latency? We introduce Router-Mem, an evidence-conditioned progressive execution framework for long-horizon agent memory. Router-Mem first applies a shared low-cost retrieval prefix to obtain evidence. A lightweight sufficiency router then predicts whether the context supports early termination, which enable a single-token decision at inference time. It is trained with evidence-level supervision and rationale-conditioned representation distillation. When evidence is insufficient, Router-Mem reuses retrieval hits to expand memory blocks and perform deeper analysis and aggregation. Experiments on AMA-Bench and BEAM show that Router-Mem achieves 55.17% and 38.77% score while reducing average inference time by 27.3% and 25.5% compared with full memory execution.
[AI-149] raining nGPT
链接: https://arxiv.org/abs/2608.01284
作者: Ilya Loshchilov,Boris Ginsburg
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The normalized Transformer (nGPT) realizes hyperspherical representation learning by constraining model parameter vectors and activation vectors to the unit hypersphere. In this paper, we describe a practical training recipe for nGPT and evaluate it on modern hybrid Mamba-2–Transformer Mixture-of-Experts (MoE) models. The recipe introduces Logit Gradient Preconditioning, Logarithmic Learning Rate Decay, GatedAdamW, angular update control, and optional exploration mechanisms. Compared with an unnormalized model of the same hybrid MoE architecture trained with AdamW, the 14B-total-parameter nGPT model reaches the same validation loss using approximately half as many training tokens. The recipe scales across the models considered, which contain up to 14B total parameters.
[AI-150] Riemannian Attention Mechanisms for Transformers: A Theoretical Framework and Architecture Design
链接: https://arxiv.org/abs/2608.01283
作者: Sen Song
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 23 pages, theoretical paper
Abstract:All Transformer-based large language models compute attention via the Euclidean inner product, an architectural choice that Dong et al. (2021) proved causes representational rank to decay doubly exponentially with depth in pure self-attention stacks. We develop a theoretical framework that targets this structural limitation at the mathematical level by replacing the flat Euclidean metric with learned per-token Riemannian metrics. Our contributions are threefold. (1) We prove that Riemannian attention scores with heterogeneous per-token metrics are non-Gram—they cannot be factorized as QK^T with factorization dimension O(d). We are explicit that this is a structural observation, not a proof of rank preservation. (2) We establish that low-rank metric factors render all geometric operations tractable: geodesic distance in O(dr) per token and metric inversion in O(dr^2) via the Woodbury identity—both far below the O(d^3) cost of a general matrix—making Riemannian attention feasible at billion-parameter scale with negligible overhead. (3) We present the Fiber Bundle Transformer, a complete architecture specification in which each token position carries its own Riemannian metric, attention is geodesic distance computation, feed-forward updates use metric-preconditioned steps, and the connection carries explicit curvature and torsion proxies. We derive formal predictions about correctly implemented geometric architectures and identify the central open problem: proving or disproving that heterogeneous Riemannian metrics prevent the rank collapse that row-stochastic attention matrices otherwise cause. This paper presents theoretical analysis and architectural design; empirical validation is the subject of future work.
[AI-151] AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental Learning ECML KDD2024
链接: https://arxiv.org/abs/2608.01252
作者: Pengxiang Wang,Hongbo Bo,Jun Hong,Weiru Liu,Kedian Mu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 18 pages, 6 figures, published in ECML PKDD 2024
Abstract:Catastrophic forgetting is a major problem in task-incremental learning, where neural networks tend to overwrite previously learned knowledge when trained on new tasks. A number of architecture-based approaches have been proposed to address this problem. However, the architecture-based approaches suffer from another problem related to network capacity when the networks learn long task sequences: As a network is trained on an increasing number of new tasks in a long task sequence, a growing proportion of active parameters becomes static to prevent forgetting of previously learned knowledge. In this paper, we propose Adaptive Hard Attention to the Task (AdaHAT) with an adaptive attention mechanism which allows adaptive updates to static parameters by taking into account the information about previous tasks on both the importance of these parameters to previous tasks and the current network capacity. Based on this idea, we develop a new neural network architecture incorporating our proposed AdaHAT mechanism. AdaHAT extends an existing architecture-based approach, Hard Attention to the Task (HAT), to better support task-incremental learning over long task sequences. We conduct experiments on a number of datasets and compare AdaHAT with task-incremental learning baselines including HAT. Our experimental results show that AdaHAT achieves better average performance across tasks than these baselines, especially on long task sequences, demonstrating the benefits from balancing the trade-off between stability and plasticity of a network when learning such sequences of tasks, alleviating the network capacity problem. Our code is available at this http URL.
[AI-152] Learning What to Remember and What to Internalize in LLM Self-Evolution via Adaptive Memory-Parameter Coordination
链接: https://arxiv.org/abs/2608.01234
作者: Tianyun Ji,Zhenya Huang,Jiayu Liu,Zirui Liu,Yu Su,Hongbin Pei
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model agents increasingly operate in dynamic environments where tool interfaces, APIs, and user requirements change after deployment. Existing self-evolution methods mainly follow two paradigms: harness-based approaches, which externalize feedback into editable memories or skills for rapid adaptation, and parameter-based approaches, which internalize experience into model parameters for deeper capability improvement. However, using either mechanism alone creates a trade-off between flexibility and performance. This paper asks how an agent can coordinate both channels to achieve robust self-evolution. We present COVE, a unified agent self-evolution framework that combines harness-based and parameter-based learning through task-aware routing, stage-aware scheduling, and knowledge optimization. Through this design, COVE treats self-evolution not as indiscriminate accumulation of experience, but as a coordinated process that matches tasks and knowledge types to appropriate learning mechanisms. Experiments across multiple task categories show that COVE outperforms single-channel evolution strategies, demonstrating more robust and efficient improvement under changing environments.
[AI-153] CT-PrepAgent : Bounded Policy and Controlled Execution for Adaptive CT Data Preparation
链接: https://arxiv.org/abs/2608.01233
作者: Xiaolin Fan,Yue Pei,Yingying Zhang,Haogang Zhu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Heterogeneous computed tomography (CT) acquisitions and diverse downstream task requirements limit the transferability of fixed data preparation workflows across data sources and tasks. Existing approaches typically rely on manually designed or dataset-specific rules, making it difficult to accommodate changes in acquisition conditions and analytical objectives without manual intervention. Large language model (LLM)-based agents have shown promise for automating medical workflows, yet their potential for adaptive CT data preparation remains largely unexplored. To bridge this gap, we propose CT-PrepAgent, which enables adaptive CT data preparation through a bounded policy and controlled deterministic execution. Deterministic inspection constructs structured data–task profiles, from which a policy decides an eligible DICOM series or predefined preprocessing profile, while the controlled execution flow guards, resolves, executes, and verifies the decision with bounded recovery when enabled and safe quarantine otherwise. Across three public CT segmentation tasks, CT-PrepAgent derived data-task adaptive preprocessing decisions and achieved the highest macro-average Dice. On two private raw-DICOM cohorts, CT-PrepAgent increased verified output yield from 61.7% to 70.0% and yielded similar registration metrics on common verified outputs. Controlled fault and replay tests validate bounded recovery, safe quarantine, and policy-free replay under tested fault and drift settings.
[AI-154] Perspectives on Tsallis Statistics for Artificial Intelligence
链接: https://arxiv.org/abs/2608.01223
作者: Kleyton da Costa,Bernardo Modenesi
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Tsallis statistics generalizes Boltzmann-Gibbs statistical mechanics through a single real parameter q that controls the weight assigned to rare and frequent events. Originally proposed to describe physical systems with long-range correlations, multifractal geometry, and heavy-tailed fluctuations, the framework has become a recurring ingredient in modern artificial intelligence (AI): it underlies sparse attention mechanisms (\textscsparsemax and \alpha -\textscentmax), maximum-entropy reinforcement learning with controllable exploration, robust and heavy-tailed probabilistic models, and a family of generalized loss functions and regularizers. This paper offers a structured perspective on where Tsallis statistics meets AI. We first review the mathematical core: q -entropy and its variational (maximum-entropy) foundation, the q -exponential and q -logarithm, the q -central limit theorem, q -Gaussian distributions, and their dynamical origin in superstatistics, emphasizing the properties that matter for machine learning. We then survey applications across softmax generalization, reinforcement learning, sequential and graph neural models, generative and probabilistic modeling, loss design, and optimization, extracting the recurring design pattern in each case: a tunable interpolation between dense/uniform and sparse/peaked behavior governed by q . We further argue that the heavy-tailed weight spectra and gradient-noise statistics empirically observed in deep networks are themselves nonextensive signatures, placing modern learning dynamics within the scope of q -statistics. Finally, we discuss methodological pitfalls, the relationship to information geometry and q -exponential families, and open directions, arguing that q should be treated as a learnable inductive bias rather than a fixed hyperparameter.
[AI-155] ReBRAC-v2: The Return of the King
链接: https://arxiv.org/abs/2608.01205
作者: Denis Tarasov,Robert K. Katzschmann
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: this https URL
Abstract:Recent offline reinforcement learning methods increasingly rely on expressive generative policies and specialized value-guidance mechanisms. We ask whether comparable progress can instead come from systematically modernizing a conventional behavior-regularized actor-critic while preserving its algorithmic simplicity. We introduce ReBRAC-v2, which directly trains an exact-likelihood normalizing flow as the RL actor, combines likelihood, MSE, and MAE behavior regularization, and integrates a classification-based residual critic, staged optimization, and multi-sample test-time action selection. Rather than tuning this recipe separately for every task, we develop a single shared configuration via roughly 600 Bayesian proposals on six challenging OGBench tasks, freeze all structural and optimization choices, and adapt only two behavior-regularization coefficients over a 16-point grid. Across ten common state-based OGBench categories, ReBRAC-v2 averages 74.8 compared to 52.3 for the next-best aggregate result and ranks first in eight categories. The same recipe, without structural changes, obtains the strongest averages in our comparisons on D4RL AntMaze (90.2) and Adroit (33.6). Fixed-recipe ablations show the largest sensitivity to the selected mixed cloning objective, staged training, sufficient flow capacity, and multi-sample inference, while showing that several smaller choices depend on the values of other hyperparameters. These results show that disciplined, transferable engineering can achieve state-of-the-art aggregate performance without abandoning a minimalist offline RL foundation.
[AI-156] Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theory
链接: https://arxiv.org/abs/2608.01197
作者: Phuc Hoang Truong Huynh,Dung Tran Vinh,Khoa Duc Anh Lam,An Nghiem Nguyen Truong,Uyen Nha Tran Bui,Khang Nguyen Dinh,Bao Nguyen Le Gia,Minh Le Nguyen Nhat,Manh Hong Duong, TheAnh Han,Thi Ai Thao Nguyen,and Le Hong Trang
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC); Computer Science and Game Theory (cs.GT); Dynamical Systems (math.DS)
备注:
Abstract:Decentralized Federated Learning (DFL) has emerged as an optimal privacy-preserving solution; however, it remains vulnerable to opportunistic behaviors due to the absence of a central coordinator. While Evolutionary Game Theory (EGT) serves as a powerful framework for analyzing such behaviors, existing studies often assume that agents possess perfect rationality and maintain static strategies. To address these limitations, this paper proposes a novel EGT framework designed to analyze strategic evolution and enhance overall system performance. The primary contributions of this work are threefold: First, we model peer-to-peer (P2P) interactions on a lattice network structure under the assumption of bounded rationality. Second, we formulate a comprehensive payoff matrix incorporating training costs, communication overhead, and cooperative rewards, while tailoring a strategy update rule that captures spatial propagation dynamics. Third, we integrate a reputation-based reward-and-punishment mechanism to effectively deter free-riding behaviors. Simulation results demonstrate that the framework significantly outperforms the baseline. Specifically, it increases average accuracy from approximately 70% to 82%, elevates cooperation frequency to approach 100% (compared to below 5% in the baseline), and drops accuracy variance from around 0.40 to 0.002, thereby accelerating uniform convergence and ensuring system stability.
[AI-157] he Graph Language: How Knowledge Graphs Speak to Large Language Models ISWC2025
链接: https://arxiv.org/abs/2608.01175
作者: Giuseppe Pirrò
类目: Artificial Intelligence (cs.AI)
备注: Accepted to ISWC 2025
Abstract:Large Language Models (LLMs) excel at reasoning but benefit from grounding provided by Knowledge Graphs (KGs). However, integrating these paradigms is challenging. We introduce GRALAN, which enables KGs to speak directly in the LLM’s semantic space through relational tokens that preserve graph structure. GRALAN-s trainable language mediator generates structured tokens for any frozen LLM, creating a foundation for knowledge-intensive applications. We demonstrate its effectiveness in question-answering by re-framing the task as entity classification over question-focused subgraphs. Experiments show that GRALAN significantly outperforms existing methods, particularly on complex multi-hop reasoning tasks, establishing a new paradigm for KG-LLM integration that maintains structural fidelity while leveraging LLMs’ reasoning capabilities.
[AI-158] Hybrid Lagrangian-Eulerian Model for Lagrangian Fluid Simulation
链接: https://arxiv.org/abs/2608.01164
作者: Ruoyan Li,Wei Wang,Yizhou Sun
类目: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI)
备注:
Abstract:Pure Lagrangian neural simulators offer geometric flexibility and exact advection, making them well-suited for modeling moving domains and free surfaces. However, the absence of a fixed global reference frame introduces two severe limitations: a spatial bottleneck, in which model capacity is wasted on uniform regions because the dense particle neighborhoods required for stable gradients are applied indiscriminately, and rapid temporal drift, caused by purely local message passing that lacks a global anchor. Inspired by classical hybrid numerical solvers, we propose a Hybrid Lagrangian-Eulerian neural simulator that augments Lagrangian dynamics with an Eulerian representation. To address the spatial bottleneck, we introduce adaptive downsampling that eliminates kinematic redundancy, preserving micro-scale details on particles while aggregating compressed features onto Eulerian nodes to resolve large-scale dynamics. To counter temporal drift, we employ a cross-attention mechanism that queries these Eulerian features, using the fixed grid as a stable spatial anchor to correct trajectory deviations at every timestep. Comprehensive experiments show that this hierarchical, cross-attended design substantially suppresses error accumulation, establishing a new state-of-the-art for accuracy and rollout stability in Lagrangian fluid simulation.
[AI-159] PATH-Bench: Path-Dependent Evaluation of Lifelong Agents
链接: https://arxiv.org/abs/2608.01149
作者: Xidong Yang,Xingyi Zhang,Wenhao Li,Wenyan Liu,Junjie Sheng,Yun Hua,Wei Yin,Tao Fang,Chuyun Shen,Xiangfeng Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Lifelong LLM agents increasingly adapt through external learning states that store past interactions as retrievable memories or reusable skills, yet existing benchmarks rarely account for how the path of accumulated experience shapes what agents transfer and retain. In this work, we establish PATH-Bench, a benchmark for path-dependent evaluation of lifelong agents. PATH-Bench estimates directed task relationships via multi-model in-context learning, constructs probe-centered sequences with controlled helpful and interfering histories, and repeatedly evaluates probe tasks to measure average performance, forward transfer, backward transfer, and forgetting. We evaluate eight representative agents on single-turn code generation and multi-turn tool-use tasks under positive- and negative-dominant histories. Benchmark results show that experience utility depends jointly on how experience is represented and on the task’s interaction structure, that strong transfer does not ensure retention, and that later experience can reshape gains acquired earlier in the learning path. Based on these findings, we propose Selective Experience Use (SEU), an agent harness that regulates how path-accumulated experience influences each new task, admitting helpful items while filtering out potential interference. SEU consistently reduces forgetting while improving forward transfer in the majority of settings. The PATH-Bench provides both a controlled evaluation framework and actionable guidance for designing more selective and robust lifelong agents.
[AI-160] Policy Optimality Measurement for Multi-Vehicle Decision-Making: From Extrinsic Indicators to Intrinsic Quality
链接: https://arxiv.org/abs/2608.01133
作者: Ye Han,Lijun Zhang,Dejian Meng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 8 pages, 7 figures
Abstract:Evaluating Multi-Agent Reinforcement Learning (MARL) policies in autonomous driving fundamentally relies on extrinsic statistical indicators (e.g., reward curves and success rates), which often mask intrinsic policy degradation and algorithmic blind spots. To break this black-box evaluation, this letter proposes a novel information-theoretic diagnostic framework. By leveraging a fully converged Monte Carlo Tree Search (MCTS) as an asymptotic oracle, we establish a theoretical ground-truth baseline distribution. We formulate a bounded policy optimality score ( \mathcalM_opt ) using the forward KL divergence to rigorously penalize fatal collaborative omissions. Crucially, we semantically decouple this metric into lateral and longitudinal dimensions, creating a granular “semantic microscope”. Extensive spatial and temporal diagnostics on state-of-the-art MARL architectures and exploration mechanisms demonstrate that our framework conclusively exposes hidden directional biases, identifies temporal average-policy traps, and transforms heuristic hyperparameter tuning into a visually trackable trajectory optimization. This framework establishes a rigorous, model-agnostic standard for benchmarking intrinsic multi-agent policy quality.
[AI-161] MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks
链接: https://arxiv.org/abs/2608.01128
作者: Yixin Zhang,Zhuohui Yao,Wenchi Cheng,Walid Saad
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks. For time-critical rescue missions, information freshness is crucial because decisions based on outdated data may lead to ineffective control actions. This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services. We model bursty packet arrivals using a Markov-modulated Poisson process and adopt finite blocklength theory to capture the coupling among transmission duration, packet completion, and AoI evolution. To balance delay-tolerant long-packet transmission and urgent short-packet response, we propose a mini-slot-embedded scheduling mechanism with adaptive checkpoint-interval selection. We formulate the joint optimization of UAV trajectory control, user scheduling, and checkpoint-interval selection as a multi-agent decision problem, and develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework. MA-HEAD-Net incorporates communication-domain rule priors into a gated multi-head policy, where adaptive gates regulate the contributions of rule-prior and learned-policy logits for different subtasks. The policy and gating components are jointly optimized under multi-agent proximal policy optimization. Simulation results show that MA-HEAD-Net improves policy-formation efficiency compared with representative multi-agent deep reinforcement learning baselines and achieves lower AoI than both learning-based and heuristic methods in dynamic UAV-assisted emergency communication scenarios.
[AI-162] Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating
链接: https://arxiv.org/abs/2608.01112
作者: Tobias Braun,Jonas Grebe,Louis Rethfeld,Marcus Rohrbach
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The widespread adoption of generative AI enables students to outsource cognitive effort to increasingly capable assistants, creating an illusion of competence while undermining the independent reasoning that education aims to cultivate. We investigate whether adversarial machine learning can be repurposed to protect educational exercises against such corrosive reliance. Our approach uses multimodal multiple-choice questions whose visual components can be protected with subtle visual perturbations that steer AI solvers toward designated incorrect answers. These responses form a statistical fingerprint: students who blindly copy a solver reproduce the induced answer pattern more frequently than genuine students. We study the feasibility of this paradigm under realistic black-box assistant assumptions using three of the most common state-of-the-art multimodal language models: Anthropic’s Claude, Google’s Gemini, and OpenAI’s ChatGPT. By using accessible surrogate models, we optimize adversarial perturbations that induce consistent response patterns. Those patterns enable principled detection through statistical hypothesis testing. These findings establish both the promise and the limitations of fighting machine-assisted reasoning with the vulnerabilities of the machines themselves.
[AI-163] FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devices
链接: https://arxiv.org/abs/2608.01095
作者: Hongliang Zhang,Zhongyuan Yu,Fenghua Xu,Teng Hu,Jian Meng,Jiguo Yu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data. However, due to its distributed nature, FL is vulnerable to Byzantine attacks. Existing defense methods rely on strong assumptions, such as the proportion of malicious devices not exceeding 50%, or the server having an additional root dataset that matches the training task. Moreover, they show limited efficacy as they overlook (i) the divergence among benign updates and (ii) the curse of dimensionality involved in comparing two high-dimensional updates. To solve these concerns, we propose FL-OA, a Byzantine-robust federated learning framework utilizing outsourced auditing. In FL-OA, the server collaborates with third-party organization that holds an additional root dataset to perform outsourced auditing, thereby enabling the server to achieve robust aggregation without strong assumptions. Additionally, FL-OA introduces a gradient ascent step and a correction term during local training to mitigate the divergence among benign updates, and designs a parameter importance indicator to extract critical parameters for auditing, alleviating the curse of dimensionality. We further provide a detailed theoretical analysis of FL-OA. Extensive experiments demonstrate that FL-OA outperforms existing defense methods against Byzantine attacks.
[AI-164] Inter-Residue Geometry Attention for Antibody-Specific Epitope Prediction
链接: https://arxiv.org/abs/2608.01092
作者: Chuanliu Fan,Nan Yu,Junjie Wu,Guohong Fu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Antibody-specific epitope prediction aims to identify which antigen residues are recognized by a given antibody, a task that depends on the three-dimensional complementarity between antibody CDRs and the antigen surface. Existing methods usually leverage PLM embeddings and inject structure through additional graph, surface, or point-cloud encoders, where the positional mechanism inside attention remains largely tied to one-dimensional sequence order. For proteins, the analogue of a token offset is not only sequence separation, but also the three-dimensional displacement between residues after folding. This raises a question, can folded residue geometry serve as the positional mechanism of attention itself? We propose Local-Frame 3D Rotary Position Encoding (LF3DRoPE), which expresses inter-residue displacements in backbone-defined local frames and injects them directly into rotary attention. This design preserves continuous directional geometry while ensuring invariance to global \mathrmSE(3) transformations. On the AsEP benchmark, LF3DRoPE achieves state-of-the-art \mathrmMCC on both ratio and epitope-group splits. Ablations and rigid transformation tests show that local three-dimensional geometry provides information beyond sequence-order attention while preserving invariance to arbitrary global coordinate systems. Mutation ranking results further indicate that LF3DRoPE captures antigen-specific structural compatibility.
[AI-165] Role-Decoupled Attention Residuals: Separating Matching and Content Retrieval Across Depth
链接: https://arxiv.org/abs/2608.01075
作者: Kehan Wang
类目: Artificial Intelligence (cs.AI)
备注: a improvement of attnres
Abstract:Depth-routing residual architectures allow Transformer layers to retrieve earlier representations instead of inheriting only the immediately preceding state. Existing Block Attention Residuals, however, use a single content-dependent depth mixture to construct the inputs to queries, keys, and values. This design couples two functionally different decisions: queries and keys determine where attention matches, whereas values determine what content is retrieved. We therefore ask whether matching and content retrieval should be forced to read from the same depth. We introduce Role-Decoupled Attention Residuals (RD-AttnRes), a minimal extension that shares one depth route between queries and keys while learning an independent value route over the same residual sources. Tying the two routing queries exactly recovers the parent architecture, while decoupling them adds only one model-width vector per layer and introduces no additional token-to-token attention operation. We evaluate RD-AttnRes using a frozen, paired pretraining protocol on FineWeb-Edu with five matched seeds for both 120M- and 343M-parameter models and a 2.0B-token training budget. RD-AttnRes improves validation negative log-likelihood in all 10 matched comparisons. The mean reductions are 0.0301 and 0.0247, corresponding to perplexity reductions of 2.97 percent and 2.43 percent at 120M and 343M parameters, respectively. Early-budget controls indicate that neither the additional parameter count, duplicated routing execution, nor a fixed value route reproduces the improvement. Routing diagnostics further reveal persistent divergence between the query-key and value depth distributions. These results suggest that, within the evaluated training regime, attention matching and content retrieval benefit from distinct reads over the residual hierarchy.
[AI-166] Decoy Images Amplify Caption-Mediated Defenses Against Encoded Jailbreaks
链接: https://arxiv.org/abs/2608.01043
作者: Haoyu Zhang,Xiangchen Guan,Shibo Zheng,Mohammad Zandsalimy,Shanu Sushmita
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:We report a counter-intuitive interaction between image inputs and existing black-box defenses on Vision–Language Models (VLMs): pairing an encoded jailbreak prompt with an unrelated decoy image can sharply lower attack success rate (ASR). The operative change is in the defense pipeline, not in the image. Across five frontier VLMs, two encoded-attack families, and three black-box defenses, a caption-mediated defense (ECSO) that leaves ASR essentially unchanged on text-only encoded input drops it by up to 73 pp once a content-free decoy is attached; every non-saturated contrast is significant under exact McNemar tests. We advance two hypotheses for this pattern, supported by indirect evidence rather than pipeline introspection, since a black-box threat model precludes inspecting vendor internals: caption-mediated defenses branch on image presence, and intrinsic image-side safety engages on image-resident content. Three controls constrain the explanation. Blank-canvas and natural-photograph decoys reproduce the effect on every model, implicating image presence rather than content; the effect replicates on three open-weight VLMs served with no moderation layer, so it is not a vendor-filtering artifact; and a non-symbolic, meaning-based encoder reproduces it, so it is not specific to symbolic obfuscation. Attaching a decoy unconditionally is not deployable — it raises benign refusal to 20 – 79% , an inflation of +10 to +67 pp — but gating attachment on a lightweight encoded-input detector returns benign refusal to the text baseline while preserving the safety gain wherever the detector fires, making detector recall the binding constraint. Under adaptive attacks that target the caption-mediated re-check, the effect degrades but holds. We frame this as an observation about pipeline interaction, not as a robust defense.
[AI-167] CallScreenBench: Benchmarking On-Device Models as Phone Secretaries
链接: https://arxiv.org/abs/2608.01033
作者: Simiao Ren
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Language models small enough to run on a handset, quantized to a few bits, are increasingly capable of acting for their user, making on-device task automation newly plausible. One such task is answering the phone. A phone secretary takes an unknown inbound call on its owner’s behalf. Unlike the agents evaluated by most benchmarks, it has no task to complete and no cooperative user: the caller holds the goal, may be an adversary, and must be judged from the opening turn with no oracle. What matters is not task success, but whether the owner would endorse how their proxy handled the call. We present CallScreenBench, which scores this setting on five quality dimensions. Each dimension is printed beside the counter-metric that bills it and is never averaged into a single number. We also report a guardedness profile for a toolless proxy that holds no credentials and calls no tools. Across six on-device models (0.6-4B parameters, 4-bit quantization), quality scales with capability, but triage does not. The appearance that it does is an artifact of measurement. Scripted degenerate agents supply the missing floors: after correcting for them, the number of model pairs whose triage performance separates falls from 11 of 15 to zero at the preregistered operating point. An agent that simply hangs up and echoes the caller also scores perfect message fidelity. We report which of our own metrics these floors defeat and declare no pass/fail threshold. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.01033 [cs.CR] (or arXiv:2608.01033v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.01033 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-168] VLAGuard: A Framework for Evaluating and Mitigating Physical Attention Hijacking in Vision-Language-Action Robots within Wireless Sensor Networks
链接: https://arxiv.org/abs/2608.01028
作者: Dongfu Yin,Jinquan Zhang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 32 pages, 8 figures, 5 tables. Accepted for publication in Ad Hoc Sensor Wireless Networks (AHSWN)
Abstract:Deploying Vision-Language-Action (VLA) robots as mobile edge nodes within wireless sensor networks (WSNs) requires robust protection against physical adversarial threats. We present VLAGuard, a framework to assess and mitigate a critical vulnerability: policy-critical action-to-vision attention hijacking. We first introduce a stress-test module, Visuomotor Attention-guided Semantic Attack (VASA), using printable patches to severely distract the robot’s action-conditioned cross-attention. To counter this, we propose Attention-Protective Fine-Tuning (APFT), a defense that stabilizes spatiotemporal attention and enforces geometric consistency with zero inference overhead. Evaluations across simulated and physical WSN-assisted smart environments demonstrate significant robustness gains. APFT reduces the OpenVLA failure rate from 100.0% to 25.9% in LIBERO simulations. Furthermore, across 2,000 real-world trials, APFT improves the average success rate from 23.0% to 67.4% under severe patch attacks. This highlights that protecting attention pathways is important for improving the robustness of VLA-driven edge nodes in sensor networks.
[AI-169] Caliber: Cross-Architecture Extraction-Cost Control for Score-Returning APIs
链接: https://arxiv.org/abs/2608.01023
作者: Chi Wang,Hanwen Wang,Yu Xia,Zihan Wang,Guangdong Bai
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 20 pages, 4 figures, 4 tables. Accepted at ProvSec 2026
Abstract:We present Caliber, an output-perturbation defense against model extraction that formulates noise selection as a calibration problem: how much the defense degrades the supervision signal used to train a surrogate, and the provable per-input query cost of recovering the clean logits. To defend against an attacker that uses returned scores for knowledge distillation, Caliber adds independent and identically distributed Gaussian noise to the internal logits. We establish two properties of the resulting perturbed predictions. Monotone agreement degradation: When the clean logits have a unique maximizer, agreement with the clean prediction decreases strictly with the noise scale, so every target in (1/K,1) corresponds to a unique positive scale; task accuracy is bounded by computable lower and upper envelopes. Per-input recovery cost: We derive a closed-form minimax lower bound on the repeated queries needed to recover the clean logits for a fixed input. Caliber normalizes noise variance by the squared median top-two logit margin and fits the resulting noise-utility relationship with a logistic curve, either per model or shared within a task. Across more than thirty model-dataset combinations, per-model calibration achieves mean absolute relative errors of 0.6-1.4%. End-to-end experiments show that surrogate performance generally tracks the configured degradation, while fixed-input averaging follows the expected variance reduction.
[AI-170] KING: Embodiment-Aware Kinematic Graph Neural Network for Unified Motion Representation of Legged and Wheeled Robots IROS2026
链接: https://arxiv.org/abs/2608.01015
作者: Taku Okawara,Aoki Takanose,Kenji Koide,Shuji Oishi,Masashi Yokozuka
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: IROS 2026
Abstract:Kinematic models provide reliable motion constraints for odometry estimation in featureless environments, where exteroceptive sensing degrades and IMU integration drifts. Learning-based kinematic models can achieve more accurate odometry estimation than model-based methods by capturing nonlinear effects; however, most existing learning-based models are trained on a single embodiment and generalize poorly to new embodiments. This generalization is difficult because the meanings and structures of proprioceptive measurements vary across embodiments, including the number of joints and ground-contact elements (e.g., wheels, feet). To address this challenge, we propose KING, a Graph Neural Network (GNN)-based kinematic model that explicitly incorporates robot embodiments by representing them as a common graph. We show that wheel and leg kinematic models can be expressed by a unified representation, enabling a single model for both wheeled and legged robots. Trained on datasets spanning diverse embodiments, KING provides a unified representation of wheeled and legged kinematics and achieves high-accuracy odometry estimation in real environments. KING estimates accurate odometry using only an embodiment description (e.g., a URDF file) and on-board proprioception (encoders and an IMU) and can be adapted to new robot embodiments through few-shot learning with only one minute of data, avoiding retraining from scratch on a new dataset for each robot. The project page is available at: this https URL
[AI-171] oward Fine-Grained Forgetting:Attribute Unlearning for Multimodal Large Language Models
链接: https://arxiv.org/abs/2608.01008
作者: Junkai Lin,Junkai Chen,Siqi Hou,Yuhao He,Ruiqi Liu,Chenhan Jin,Shengze Xu,Tieyong Zeng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal large language models (MLLMs) exhibit strong vision–language capabilities but may also memorize and disclose sensitive information. Machine unlearning seeks to remove designated knowledge without retraining from scratch while preserving general utility. Existing privacy-oriented benchmarks primarily adopt profile-level deletion, whereas practical requests are often finer grained: a model should forget a specified attribute while retaining non-sensitive information about the same identity. We therefore introduce attribute-level MLLM unlearning as a finer-grained task and construct a benchmark spanning long-text, numeric, and short-text targets, multiple forget ratios, and diverse question types. Our evaluation reveals that target and retained attributes share identity-specific and visual evidence, making selective forgetting susceptible to residual leakage or collateral degradation; accordingly, existing methods exhibit unstable forgetting–retention trade-offs in this setting. To address this challenge, we propose Causal Localization and Retain-Aware Projection (CLRP), a lightweight training-free framework. CLRP uses activation patching to identify the layer that causally mediates target-attribute disclosure, then applies a retain-aware projection that removes the target-attribute subspace while preserving same-identity evidence. Experiments across multiple widely used MLLMs with distinct architectures and parameter scales demonstrate the effectiveness of CLRP.
[AI-172] Hierarchical Solomonoff Induction: An Unbounded Machine Learning Model
链接: https://arxiv.org/abs/2608.01005
作者: Nathan Young
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注:
Abstract:Solomonoff Induction, or SolInd, provides an ideal unbounded model of a priori sequence prediction but cannot naturally describe extrapolation from a given training dataset, as performed by Large Language Models. We apply de Finetti’s theorem on exchangeable distributions to SolInd to produce what we call Hierarchical Solomonoff Induction, or HSI, which maintains a hyperprior over all Solomonoff priors that can be conditioned on previously observed sequences. We extend Wood et al.'s proof that universal mixtures of semimeasures are equivalent to SolInd to show that universal mixtures of these mixtures are also equivalent, proving that HSI=SolInd. We also prove that HSI’s excess error on any distribution, compared to its true generator, is bounded by that generator’s complexity in the hyperprior. This result is directly comparable to SolInd’s prediction error being bounded by the Kolmogorov complexity of the sequence being predicted, and forces HSI’s average excess error to converge to 0 as a dataset grows, leading to optimal prediction in the limit. We claim that HSI is an ideal unbounded model of sequence prediction given a dataset in the same way that SolInd is ideal over individual sequences. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT) Cite as: arXiv:2608.01005 [cs.LG] (or arXiv:2608.01005v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.01005 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-173] Who Belongs in the Eval Set? A Capability-Taxonomy-Driven Pipeline for Curating Regression Eval Sets in Agent -Extensibility Platforms
链接: https://arxiv.org/abs/2608.01004
作者: Tezan Sahu,Aritra Das,Pankaj Mittal,Sudipta Das
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Extended abstract accepted at the SERI 2026 Industry Track
Abstract:Platform teams hosting agent-extensibility surfaces face a regression-economics paradox: every onboarding customer ships an evaluation set tuned to their domain, but the platform’s regression set must live under a hard query-count ceiling bounded by release cadence. To our knowledge, no published industrial pipeline addresses this platform-side curation problem: existing evaluation frameworks are customer-side, and benchmark-compression work treats benchmarks as fixed pools rather than streams of incoming sets. We describe a capability-taxonomy-driven curation pipeline applied to declarative agents with custom actions in Microsoft 365 Copilot. It takes an agent specification and a customer’s eval set as input, projects each query into a platform-owned capability taxonomy, and outputs per-query decisions (admit, drop, swap, or human review), under the philosophy that a healthy regression set is the minimal set of queries capturing the maximal spread of capability signatures – distinct combinations of capabilities a query exercises together. Three components instantiate this: a classifier producing per-(query, capability) verdicts via a hybrid of deterministic specification-based extraction and large-language-model (LLM) semantic inference; an Invocation Quality (IQ) rater scoring how thoroughly a query exercises each capability, so a new query sharing a signature with an existing entry can still be recognized as a better test and displace it; and a consolidator comparing incoming queries against the regression set on coverage and quality through a rule-based decision cascade, backed by a conservative curator that only suggests evictions. The mechanism is taxonomy-agnostic and applies to any regression eval-set curation problem with a typed capability taxonomy, including taxonomies that evolve in response to the very evidence the pipeline surfaces.
[AI-174] From AI Technical Debt to Agent ic Technical Debt: A Systematic Mapping of Root Causes and Manifestations in Agent ic AI Systems
链接: https://arxiv.org/abs/2608.01001
作者: Muhammad Tukur,Hayatullahi B. Adeyemo,Tao Chen,Nour Ali,Anis Zarrad,Marco Agus,Rick Kazman,Rami Bahsoon
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Software Engineering (cs.SE)
备注: 32 pages, 7 figures, 7 tables, submitted to IEEE Transactions on Software Engineering
Abstract:The emergence of Agentic AI systems, characterized by autonomous reasoning, multi-agent collaboration, tool orchestration, adaptive decision-making, and persistent memory, represents a fundamental shift from traditional AI pipelines to dynamic software ecosystems. While AI Technical Debt (AITD) has been widely studied in machine learning and software engineering, existing models assume static, component-level architectures and fail to capture the dynamic and emergent behaviors of agentic environments. To address this gap, this paper introduces Agentic Technical Debt (AgTD), defined as technical debt that emerges, accumulates, propagates, and amplifies due to the autonomous and collaborative nature of Agentic AI systems. Building on our prior systematic scoping review of 31 AITDs across seven root-cause categories, we employ a theory-informed transformation methodology to reinterpret these debts in Agentic AI through direct transformation, contextual transformation, and manifestation expansion. We present the first systematic mapping of established AITDs to their agentic manifestations, showing how conventional debts evolve into system-level liabilities, including memory inconsistencies, orchestration fragility, cascading failures, and unsafe autonomous decision-making. Our findings show that technical debt extends beyond software artifacts to encompass agent behaviors, coordination mechanisms, and interactions among agents, tools, and execution environments. We further examine its implications for AI Trust, Risk, and Security Management (AI TRiSM), highlighting impacts on trustworthiness, governance, security, operational resilience, and Sustainability Technical Debt. Overall, this work establishes AgTD as a foundational software engineering construct and provides a transformation framework, taxonomy, and research agenda for managing technical debt in autonomous multi-agent AI systems.
[AI-175] Judging Is Not Enumerating: Silent Omissions in LLM -Authored Acceptable Sets
链接: https://arxiv.org/abs/2608.01000
作者: Wenhui Chen,Jianlin Chen,Ziyao Lin,Peiji Long,Chi Man Vong
类目: Artificial Intelligence (cs.AI)
备注: 53 pages, 14 figures, 26 tables
Abstract:Language models are increasingly promoted from examinees to examiners: they write the test suites, answer keys, rubrics, and reward functions that define correctness for other systems. We measure the capability that role assumes and find it lacking under the protocol the role is usually deployed with, one-shot greedy authoring with no test-time reasoning. Across four reference constructions - two with complete finite truth, one with a hardened executable reference (HumanEval+/MBPP+), one with an explicitly incomplete lexical reference (WordNet) - models judge whether a candidate belongs far better than they author the set itself. On the incompleteness-proof algorithmic construction the gap is +0.34 to +0.29 F1 over a 24x parameter range and does not close; on executable code, models judging at F1 0.74-0.90 author suites admitting only 19-42% of oracle-correct solutions. A control locates the deficit: asked to emit the predicate rather than its extension, the same models reach F1 about 0.99. The failure is not missing knowledge or an inability to specify, but an inability to materialise the region a specification induces. The dominant error is omission, which resists audit: an over-inclusion is a token a reviewer can challenge, a missing member an absence whose discovery is the authoring problem itself. Models detect planted over-inclusions 6-7x more often than planted omissions, and a production deployment of 43,227 items fails omission-first at 10:1. Wired into RLVR, an authored key costs 1.9 points of accuracy against an exact oracle and 18.5 WordNet-relative (six paired seeds, p=0.031). Gating authored verifiers on a known-correct probe cuts false rejection from 58-92% to at most 5%, but keeps only 5-39% of suites. Repairing them instead, by rewriting each wrong expected value to what a reference execution returns, raises yield 3.3-10.6x across four author families.
[AI-176] Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views
链接: https://arxiv.org/abs/2608.00985
作者: Jiaqi Xiong,Yuntao hu,Yu Zheng,Yifei Shi,Xinyue Guo,Jiaxin Qi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Genomics (q-bio.GN)
备注: 9 pages
Abstract:The rapid growth of single-cell transcriptomic data has enabled the development of foundation models pretrained primarily by reconstructing masked expression values. This objective encourages these models to learn gene dependencies but does not directly optimize whole-cell representations, which are essential for many downstream tasks. To bridge this gap, we propose a contrastive pretraining framework that learns cell representations through complementary transcriptomic views. Since standard contrastive learning is not readily applicable to single-cell pretraining, we introduce specific adaptations along three dimensions — co-expression-guided gene partitioning, expression-aware contrast-set construction, and competence-gated contrastive onset. Specifically, we first construct two complementary views of each cell by partitioning its genes according to their co-expression structure. Then, to prevent the model from using gene-set identity as a shortcut, we construct hard negatives by permuting expression values while keeping gene identities unchanged. Finally, we introduce a competence-aware controller to determine how the contrastive objective is applied. Experiments on cell-type annotation and gene regulatory network inference demonstrate competitive transfer under the evaluated protocols. In the six-network GRN evaluation, our method records the highest mean AUROC and AUPRC point estimates among the compared variants, while the highest-scoring variant differs across individual networks. These results establish complementary-view contrastive learning as an effective direction for single-cell pretraining beyond gene reconstruction.
[AI-177] Auditing Discovery Claims: A Two-Sided Criterion for Agent ic Science with the Negative Side Decidable
链接: https://arxiv.org/abs/2608.00981
作者: Wenhui Chen,Jianlin Chen,Ziyao Lin,Chi Man Vong
类目: Artificial Intelligence (cs.AI)
备注: 51 pages, 10 figures, 10 tables
Abstract:When a self-improving AI-for-science system claims a new capability, the evidence is usually a benchmark delta, a description-length gate, or a p-value. None separates a real gain from extra search, from a changed verifier, or from adaptation to a fallible oracle. We build a two-sided audit whose negative side is a formal fact: a pseudoknot-free oracle provably cannot represent a crossing base pair, so the prior verifier’s range is bounded exactly, offline, before any run. “New” is relative to the agent’s prior self, never to the base model. First, how far a single fallible oracle can inflate a capability claim. An invented, solver-free operator solves 43/60 crossing RNA targets under the predictor it optimizes, above a context-free floor of 0/60; under three predictors, 1/60 survives. Paired on the same 43 targets, a predictor the operator never saw confirms 2 of its designs against 26 for a minimum-free-energy solver (p = 8e-7). No statistic computed from the system and its own oracle sees that gap. Second, agent-written procedures can beat a human-written one under a judge no objective can flatter, at a fraction of the compute. Of six frontier models, the two whose operators ran without timeouts carry over at 0.293 against our 0.095 (n = 951 paired units, target-clustered [+0.108, +0.297], p = 5e-5) while spending 4.6-10x fewer oracle calls. Three rungs: difference under an outside adjudicator (reached), not bought with compute (reached, both directions), mechanism identified and transferable (not reached; seven candidates tested, none moves the statistic). The ceiling is the panel itself: its three predictors share nearest-neighbour thermodynamic parameters, two agreeing at kappa = 0.673. The audit is as unsparing about our own system: matched undirected search is an exact zero, and a search-free probe puts 84% of our headline effect on targets a random sequence already solves.
[AI-178] Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering INTERSPEECH2026
链接: https://arxiv.org/abs/2608.00974
作者: Aounon Kumar,Sudipta Paul,Vivek Kulkarni,Vijay Srinivasan,Srinivas Chappidi
类目: Artificial Intelligence (cs.AI)
备注: Accepted at Interspeech 2026
Abstract:The effective use of search engines by large language models (LLMs) remains a significant challenge, particularly in complex, multi-hop question-answering (MHQA) tasks. These tasks require the model to decompose questions into subqueries, retrieve relevant information, and synthesize answers from multiple sources, often leading to cascading errors due to poor retrieval in early stages. Reinforcement learning (RL) has shown promise in improving LLMs’ search capabilities, but it often suffers from sparse rewards during training, hindering the model’s ability to learn effectively. To address these challenges, we introduce Guided Retrieval Training (GRT), a novel method that improves the performance of a search agent by restricting the retrieval process during RL training using ground truth information. By focusing on a curated set of relevant documents, GRT provides the model with a stronger learning signal, mitigating the problem of sparse rewards and improving its ability to generate accurate subqueries and synthesize correct answers. Our experimental results demonstrate that GRT achieves consistent performance improvements over existing methods, such as Search-R1, across a wide range of question-answering (QA) tasks. Notably, GRT excels in MHQA tasks, achieving over 40% improvements in performance. Additionally, GRT enhances training efficiency by achieving better QA performance with fewer training steps.
[AI-179] PROGRESS: Coverag e-guided RL to Train Search-augmented LLM Agent INTERSPEECH2026
链接: https://arxiv.org/abs/2608.00969
作者: Sudipta Paul,Vijay Srinivasan,Vivek Kulkarni,Aounon Kumar,Yashas Malur Saidutta,Wenbo Li,Srinivas Chappidi
类目: Artificial Intelligence (cs.AI)
备注: Accepted in Interspeech 2026
Abstract:Existing search-augmented LLM agents are trained using Reinforcement Learning to boost its reasoning capabilities. However, these approaches primarily rely on outcome-level rewards, which provide little supervision over search behavior and overlook agent’s ability to decompose complex queries properly. To mitigate this issue, we propose PROGRESS which utilizes teacher-guided coverage reward to explicitly shape decomposed query generation of the policy model. During training, frozen teacher models are used to decompose complex queries into essential search queries. These essential search queries are utilized to guide the search behavior of the policy model. Integrated into an R1-style training framework, our approach provides lightweight guidance over query decomposition decisions without dense process-level supervision. Experiments show that coverage-guided RL improves overall task performance, highlighting the importance of explicitly supervising search strategies in agentic LLMs.
[AI-180] rajWiki: Source-Grounded Memory Trajectories for Long-Horizon Dialogue Agents
链接: https://arxiv.org/abs/2608.00967
作者: Jingyu Sun,Yuyang Xue,Mingyang Li,Zhengtao Yao,Jiachen Li,Yang Cui,Wenhao Cai,Haozhe Liu,Fangying Wang,Magdalene Katharina Montgomery,Syed Murtuza Baker,Hongpeng Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model agents have shown strong capabilities in generating coherent and contextually appropriate responses, yet robust long-horizon dialogue remains limited by the lack of external memory that is traceable, updatable, and diagnostically transparent. Existing memory-augmented agents often store memories as isolated records or overwritable states, making it difficult to preserve how information originates, evolves, conflicts, or becomes obsolete over time. We propose TrajWiki, a trajectory-based memory framework for long-horizon conversational agents. Instead of treating memory as static entries, TrajWiki represents each memory as a source-grounded evolution trajectory, maintained through immutable episodic snapshots and claim-level operations such as ADD, REVISE, and DEPRECATE. To reduce fragmentation and retrieval cost, TrajWiki further introduces Memory Wiki, a persistent intermediate layer that incrementally compiles dialogue history into structured and interlinked wiki pages capturing salient entities, events, quantities, topics, and conflicts. At inference time, queries are routed hierarchically from relevant wiki pages to linked memory trajectories, then to corresponding snapshots and source messages for evidence-grounded answer synthesis. Experiments on LoCoMo and MedMT show that TrajWiki improves long-horizon dialogue performance across both open-source and closed-source LLM backbones, while providing greater interpretability and diagnostic visibility into memory evolution, retrieval failures, and answer generation.
[AI-181] An AI Approach to Verified Production Cryptographic Libraries
链接: https://arxiv.org/abs/2608.00965
作者: Chuyue Sun,Su Fong,Zhiyi Kuang,Yizheng Jiao,Nina Narodytska,Haoze Wu,David L. Dill,Clark Barrett
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Cryptographic code is critical infrastructure that must be correct, yet formally verifying production libraries remains difficult. Existing language-model proof systems solve isolated obligations with specifications and premises already given, leaving production-library verification unresolved. We present CryptoProver, an AI-based system that synthesizes internal specifications and Verus-checked proofs from high-level API contracts. Without changing executable code, CryptoProver constructs a new independent proof of curve25519-dalek and verifies RustCrypto’s previously unverified chacha20 implementation against an RFC 8439 specification. These cryptographic lineages underpin deployed systems including Signal and Shadowsocks; Signal has an estimated 218M global downloads. The independent, human-led curve25519-dalek verification was developed publicly over eight months by five main contributors. Given the API contracts and a fixed trusted library of field specifications, arithmetic facts, axioms, and vstd, CryptoProver synthesizes the internal specifications and proofs in 11.4 hours with USD 466.99 in recorded API cost. CryptoProver follows a trust-first design principle: mechanical gates reject specification weakening, invented axioms, and cross-module breakage, while isolation blocks reference proof retrieval, including from git history. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.00965 [cs.CR] (or arXiv:2608.00965v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.00965 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-182] PMMC: Prospective Multimodal Memory Compilation for Long-Term LVLM Agents
链接: https://arxiv.org/abs/2608.00962
作者: Jingyu Sun,Yan Lin,Yuyang Xue,Yifan Wang,Zhengtao Yao,Rui Qian,Zefeng Xu,Jiachen Li,Xianyang Liu,Jiancheng Pan,Jingyuan Sun,Syed Murtuza Baker,Hongpeng Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-term memory is essential for LVLM agents to maintain consistency and integrate information across extended multimodal interactions. Existing agent memory systems, however, often reduce visual experiences into textual summaries or rely on static retrieve-then-reason pipelines, which are inefficient at query time and brittle when questions require image-text binding, temporal updates, or visual details. We propose Prospective Multimodal Memory Compilation, a framework that shifts part of the memory reasoning process from query time to memory consolidation time. Given accumulated multimodal interactions, a Questioner predicts future question candidates, a Planner compiles question-conditioned multimodal memory programs, and a Doubter verifies whether the planned evidence path can support the predicted answer. The verified question-program pairs form a structured question bank for efficient query-time routing and evidence retrieval. Experiments on multimodal long-term memory benchmarks show that our method improves answer quality and visual evidence recall while reducing query-time token and latency costs. Extensive ablations analyze the effects of self-feedback, dynamic planning, raw-image access, and question bank coverage.
[AI-183] Modeling Social Dynamics with an LLM -Enabled Agent Based Network-Dynamic (LAND) Model
链接: https://arxiv.org/abs/2608.00929
作者: Lynnette Hui Xian Ng,Kathleen M. Carley
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance and coordinate behavior. This paper uses the the GhostField architecture, a hybrid LLM-Enabled Agent Based Network-Dynamic (LAND) model as a social simulation framework to build the AuraSight scenario. In the AuraSight scenario, 314,244 heterogeneous cyber social agents and human actors exchange 529,327 messages over 30 days surrounding a fictional international song-writing contest. We methodologically examine emergent social dynamics across four analytical layers: ego-network topology, semantic network evolution, coordination dynamics and influence dynamics. Our results show how generated social simulations do also produce social dynamics, and how the dynamics of coordination and influence emerge not from individual agents but from the recursive interaction between network topology and narrative exchange.
[AI-184] RefactorAssist: Agent ic Refinement for Reliable Code Refactoring
链接: https://arxiv.org/abs/2608.00924
作者: Jonathan Cordeiro,Shayan Noei,Ying Zou
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 27 pages, 10 figures, submitting to ACM TOSEM
Abstract:Code refactoring aims to enhance the internal structure of source code without affecting its functional behavior. The recent advancements of Large Language Models (LLMs) have demonstrated potential for automating software engineering tasks, such as code refactoring. However, the refactorings produced by LLMs often introduce subtle errors, leading to functional behavior changes and failed unit tests, which limit their practical adoption. To address the limitations of LLM-generated refactorings, we analyze the root causes of their failures and develop the RefactorAssist agent to improve the functional correctness of LLM-generated refactorings. To this end, we use 10 open-source Java projects with their native test suites and manually evaluate why LLM-generated refactorings fail unit tests. We then design an agentic approach that leverages unit-test logs, error explanations, project context retrieval, and code diffs to guide the iterative refactoring. Our findings show that the main reasons for failure are context misunderstanding/hallucination (24.3%), incorrect or inconsistent renaming (15.3%), adding new functionality or variables (13.7%), code incompleteness (11.3%), syntax and structural errors (9.7%), edge cases not handled (9%), improper type handling (8.7%), and variables outside scope (8%). To make our approach cost-effective, RefactorAssist first applies a static repair step for missing imports, unbalanced brackets, and compilation errors without LLMs. For remaining failures, RefactorAssist incorporates error logs and code diffs, achieving up to a 70.8% repair rate on the remaining failures and a 94.2% cumulative pass rate under the best-performing configuration. These results indicate that static checks and test-guided, context-aware agentic repair can increase the reliability of LLM-generated refactorings, bringing them closer to practical integration within developer workflows.
[AI-185] CADIR: A Cross-Backend Editable Intermediate Representation for Agent ic CAD Generation
链接: https://arxiv.org/abs/2608.00891
作者: Yu Liu,Jingzhe Ni,Yiming Chen,Junqi Huang,Ruofeng Tong,Min Tang,Peng Du
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models have made it possible to generate executable computer-aided design (CAD) programs from natural-language descriptions or images. However, existing methods represent modeling processes as backend-specific sequential scripts with implicit dependencies or as static geometry, making it difficult to simultaneously preserve construction history, stable topological references, and feature-level editability across different CAD systems. We present CADIR, an agent-friendly executable intermediate representation for CAD generation and cross-backend editing. Built on the OCCT geometry kernel via OCP, CADIR provides explicit, compositional modeling operations and fine-grained execution diagnostics. During program execution, CADIR records modeling operations, parameter dependencies, constraints, and topology selections in a construction graph. To enable reliable cross-backend reconstruction, we introduce Geometric Signature Matching, which identifies corresponding edges and faces despite parameter changes and backend differences, allowing adapters to reconstruct native editable feature histories in FreeCAD, SolidWorks, and Fusion 360. Building on this representation, we further propose a construction-graph retrieval method for text and image queries that supports both full-graph and subgraph retrieval, enabling agents to leverage complete models and modeling substructures. Extensive experiments demonstrate that CADIR achieves higher geometric fidelity and execution reliability than existing CAD representations, that construction-graph retrieval further improves model generation quality, and that cross-backend editing enables reliable model reconstruction and post-reconstruction editing across multiple CAD environments.
[AI-186] Neuro-Evolved Heuristics for Variable Gapped Common Subsequence Identification
链接: https://arxiv.org/abs/2608.00888
作者: Marko Djukanović,Christian Blum,Aleksandar Kartelj,Saso Dzeroski,Ziga Zebec
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures
Abstract:This study addresses the Variable Gapped Longest Common Subsequence Problem (VGLCSP), a variant of the classical longest common subsequence problem with additional gap constraints and applications in sequence alignment and time-series analysis. While the two-sequence version has been widely studied using dynamic programming, the generalized multi-sequence form is usually solved with beam search-based heuristics, whose hand-crafted designs often lack robustness. To overcome this limitation, we propose a learning-based approach for automatically designing more effective data-driven heuristics. The heuristics are represented by a neural network with predefined architecture, whose weights are optimized by a genetic algorithm within a neuro-evolutionary framework. The learning process alternates between weight optimization and evaluation within an iterative multi-source beam search procedure, a state-of-the-art method for the problem. Rather than constructing solutions directly, the neural network learns to guide the search process, producing a neuro-evolved heuristic. We further introduce an ensemble heuristic that combines the scores of learned and the best-performing hand-crafted heuristic. Integrated into the iterative multi-source beam search framework, the resulting hybrid approach outperforms existing methods on both synthetic benchmark instances and newly introduced real-world instances with data-driven gap constraints. Comments: 15 pages, 4 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.00888 [cs.AI] (or arXiv:2608.00888v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.00888 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-187] Assuming You Knew: Fixing an Epistemic Semantics for Flow Policies Using Agent ic AI
链接: https://arxiv.org/abs/2608.00882
作者: David A. Naumann
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Logic in Computer Science (cs.LO); Programming Languages (cs.PL)
备注:
Abstract:Many high-level security requirements are about the allowed flow of information in programs and are difficult to make precise because they involve selective downgrading. Notions from epistemic logic have emerged as a good approach to policy semantics but a robust general framework remains elusive. A paper appearing in CSF 2018, entitled ``Assuming You Know: Epistemic Semantics of Relational Annotations for Expressive Flow Policies’', attempted to provide a unifying framework—but the formalization was sketchy and a correction was announced during the conference presentation. With aid from an agentic AI coding assistant, a corrected formalization has been machine checked in the Rocq proof assistant. The simplicity and generality of the framework may help compare different policy specification styles and enforce them by leveraging existing techniques.
[AI-188] Isotropy Cliffs: The Geometric Signature of Decision-Making in Large Language Models
链接: https://arxiv.org/abs/2608.00828
作者: Okan S. Coskun,Florian Rottach,Carsten Eickhoff,William Rudman
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We investigate the geometry of decision-making in Multiple Choice Question Answering (MCQA) through the lens of isotropy. Analyzing five open-weight models across diverse datasets, we identify decision-critical transition layers characterized by a shift in isotropy, coinciding with a major representational change and the emergence of task-relevant clusters. We demonstrate that this synchronized geometric behavior is strongly correlated with downstream accuracy ( r\approx0.84 ), displaying its relevance for successful decision-making. Furthermore, we show that this transition is robust to prompt variations, suggesting that it reflects a general mechanism of model behavior.
[AI-189] he Scaling Paradox in Human-AI Collaboration
链接: https://arxiv.org/abs/2608.00818
作者: Anyan Qi,Mengxin Wang
类目: Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:The discovery of scaling laws has highlighted the extraordinary potential of AI systems with a striking empirical pattern: as AI systems scale, their capabilities tend to improve predictably. Yet, in real-world applications, AI rarely operates in isolation; instead, it often works alongside humans, raising the question of whether these gains persist in human-AI collaboration. In this work, we develop an analytical model to examine when the empirical scaling benefits of AI translate into improved human-AI joint system performance. We demonstrate that the performance of a human-AI system can scale positively as the AI scales up-provided that humans have an accurate perception of the AI’s capabilities. Human misperception, however, can fundamentally alter this relationship: i) when humans over-perceive the AI’s capabilities, a scaling paradox may arise, in which greater AI scale reduces overall system performance and amplifies firm-level profit losses, and (ii) when humans under-perceive the AI’s capabilities, performance still improves with scale but at a substantially slower rate. We further show that firms can actively manage these distortions through operational policies such as cost internalization and perception alignment, whose effectiveness depends on the economics of AI deployment and the direction of human misperception. These findings suggest that organizations may benefit more from managing the human-AI interface than from simply investing in larger, more expensive AI systems. More broadly, our results suggest that AI scaling should be viewed not only as a technological challenge, but also as a behavioral and operational one, and caution against the view that larger AI systems will automatically lead to better operational outcomes. Whether AI scaling creates value ultimately depends on how increased AI capabilities shape human beliefs and collaborative efforts.
[AI-190] Large language models improve physician accuracy but lead to false reliance
链接: https://arxiv.org/abs/2608.00817
作者: Tirtha Chanda,Christoph Wies,Franziska Schramm,Carina Nogueira Garcia,Nicolas B. Merl,Martin J. Hetz,Jochen S. Utikal,Phillip Tschandl,Cristian Navarrete-Dechent,Alexander Thiem,Jakob N. Kather,Consortium,Titus J. Brinker
类目: Artificial Intelligence (cs.AI); Applications (stat.AP)
备注:
Abstract:Retrieval-augmented large language models (LLMs) promise source-linked clinical support, but their value depends on whether displayed evidence guides rather than distorts physician reliance. We developed CORA, an agentic retrieval-augmented LLM, to investigate how source-linked assistance affects physician decision-making. CORA maintained benchmark performance and achieved larger gains on cases published after the models’ training-data cutoffs. In a study of 46 physicians, accuracy increased from 70.8% unaided to 82.6% with CORA. Supporting citations predicted correct answers (87.7% vs 65.5%), but citations created an important asymmetry: perceived support increased adoption of correct advice from 34% to 76.9% but when an incorrect LLM answer appeared citation-supported, physician resistance to it fell from 92% to 34.8%. These findings show that source-linked LLM assistance can improve physician accuracy while introducing a grounding-dependent safety risk.
[AI-191] AgentS LABench: Evaluating and Benchmarking Agent ic Systems Under Resource Constraints
链接: https://arxiv.org/abs/2608.00805
作者: Meher Bhaskar Madiraju,Meher Sai Preetam Madiraju
类目: Artificial Intelligence (cs.AI)
备注: 7 pages, 2 figures, 9 tables. Code, sealed test sets, and profiling artifacts available at: this https URL
Abstract:We present AgentSLABench, a resource-aware evaluation framework for autonomous AI agents that measures correctness alongside latency, cost, compute, memory, and network usage under declared resource budgets. Unlike standard benchmarks that report only accuracy, AgentSLABench produces a multi-dimensional profile per agent per task - the same way systems profilers (perf, pprof, cProfile) measure resource consumption of code, but extended with task correctness as a first-class dimension. AgentSLABench provides 16 task environments across 6 categories (5 core: multi-hop QA, retail substitution, code generation, web shopping, travel planning; 11 extended) with isolated Docker containers, declared CPU/memory/time/network budgets, sealed test sets with SHA256 hashes, and a standardized profiling protocol. We profile 5 general-purpose baseline agents (ReAct, PlanAndSolve, Reflexion, CoT, Random) plus 4 task-specialized agents, finding that specialized agents achieve 100% success on 3/5 core tasks (fact_qa, web_shopping, travel_planning) and 66.7-83.3% on retail and code_gen, while general baselines fail entirely on 4/5 domain tasks. Crucially, we report the Efficiency-Adjusted Success Rate (EASR) - success weighted by resource consumption relative to declared budgets - revealing that high accuracy at unbounded cost is not production-viable. We release the full infrastructure, sealed test sets, and profiling results to enable reproducible, resource-aware agent evaluation.
[AI-192] Measurement Without Validity: The Compounding Reliability Problem in Agent ic AI Evaluation
链接: https://arxiv.org/abs/2608.00794
作者: William Caban
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 2 figures, 4 tables. Submitted to a peer-reviewed journal
Abstract:Agentic AI systems are evaluated using automated benchmarks whose scores justify deployment decisions, safety certifications, and regulatory compliance claims. We present an empirical analysis demonstrating that these scores are systematically less trustworthy than current practice acknowledges. The problem operates at three compounding layers. First, tasks are increasingly generated by language models: audits of ten popular benchmarks found validity flaws in seven and reporting gaps in all ten. Second, human users are replaced by LLM simulators, but calibration studies document inter-simulator variance up to 9 percentage points and systematic directional miscalibration, particularly for non-Standard American English speakers. Third, our structured survey of 55 papers finds that approximately 82% apply structurally mismatched, incomplete, or absent inter-rater reliability (IRR) metrics. These failures compound multiplicatively rather than additively. Under independence, a pipeline retaining 70% of valid signal at task generation, 80% at simulation, and 65% at judgment is at most 36% valid against the intended construct; the bound spans 0.22–0.54 across the empirical estimate range. We formalize this as V_\texttotal \leq V_1 \times V_2 \times V_3 and show it tightens further under correlated failures when the same model family operates across all three layers. We derive eight prescriptions grounded in psychometric science: a simulation calibration floor of \textICC(A,1) \geq 0.70 ; domain-stratified reliability thresholds ( \alpha \geq 0.67 / 0.70 / 0.80 by consequence level); structured IRR metric selection rules based on pipeline design; and IRR as a mandatory reporting field. The measurement tools exist; the field’s task is to apply them. Comments: 22 pages, 2 figures, 4 tables. Submitted to a peer-reviewed journal Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.00794 [cs.AI] (or arXiv:2608.00794v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.00794 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: William Caban [view email] [v1] Sat, 1 Aug 2026 17:50:12 UTC (45 KB)
[AI-193] FinDeepIndicator: Benchmarking Deep Research Agents in End-to-End Financial Indicator Construction
链接: https://arxiv.org/abs/2608.00764
作者: Chaoqun Yang,Fengbin Zhu,Xinyu Lin,Long Bai,Xiaoluan Liu,Ke-Wei Huang,Roger Zimmermann,Tat-Seng Chua
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Financial indicators are essential tools for transforming raw financial data into interpretable measures for various downstream tasks, such as valuation, risk assessment, and economic analysis. However, existing financial benchmarks largely focus on answer-level accuracy and often assume that relevant data are already provided, leaving the assessment of the intermediate process of indicator construction underexplored. In this work, we propose FinDeepIndicator, the first benchmark dedicated to evaluating Deep Research (DR) agents in end-to-end financial indicator construction. Specifically, FinDeepIndicator evaluates DR agents across four stages in indicator construction: formula specification, data collection, indicator calculation, and answer generation, and covers fundamental, technical, and macroeconomic indicators organized into 21 fine-grained sub-categories. It contains 3,350 curated question-answer (QA) pairs derived from both U.S. and Chinese markets, 10 years of historical financial data, and 800 listed companies. Extensive experiments on search-equipped Large Language Models (LLMs) and DR agents show that, while LLMs generally perform well in formula specification, their accuracy drops substantially during data retrieval and numerical execution. DR agents consistently outperform search-equipped LLMs, yet remain unreliable in realistic financial analysis settings. These findings provide insights for developing more capable and trustworthy DR agents in finance.
[AI-194] Less Is More: Tuning Configurable Systems with Imperfect Fidelity
链接: https://arxiv.org/abs/2608.00759
作者: Yulong Ye,Miqing Li,Tao Chen
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Databases (cs.DB); Performance (cs.PF)
备注: Accepted by the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026)
Abstract:Configuration tuning is essential for optimizing the performance of highly configurable systems, e.g., throughput or runtime, under a given environment. Yet, this is a challenging process as there can be many options to tune, and configuration measurement is often highly expensive. In this paper, we demonstrate the phenomenon of ``less can be more’': system configuration tuning can be greatly improved with much superior budget utilization by partially tuning under the imperfect-fidelity—an environment that is similar, but cheaper to measure, compared with the concerned perfect-fidelity of environment under which the system should be tuned. We codify a conceptual framework of fidelity for configurable systems, drawing on which allows us to propose MFTune, a tuner that proactively explores in the space of 10^4 possible imperfect-fidelity settings to approximate a useful one, which strikes for the wideness of tuning. This creates high-quality seeds for the perfect-fidelity, which in turn ensures the tuning depth. Experiment results against 10 state-of-the-art tuners, obtained from running diverse real-world systems for 19 months 24 \times 7 , show that MFTune performs considerably better on 83.33 % cases with up to 19.34% improvement while achieving hours of budget saving in general.
[AI-195] CN101 - A Digital Thermodynamic Computer for Generative AI
链接: https://arxiv.org/abs/2608.00754
作者: Lars Holdijk,Denis Melanson,Zier Mensch,Brandon Birchall,Vincent Cheung,Nicholas Lehrter,Maxwell Aifer,Samuel Duffield,Jan Ole Ernst,Rajath Salegame,Antonio J. Martinez,Gavin Crooks,Miranda Cheng,Zach Belateche,Marc Bright,Patrick J. Coles,Faris Sbahi
类目: Emerging Technologies (cs.ET); Artificial Intelligence (cs.AI)
备注:
Abstract:Thermodynamic computing is an emerging hardware paradigm, in which stochastic physical dynamics serve as the direct computational primitive. The recent explosion of generative AI has only sharpened the search for alternative approaches to compute, and, as we show in this work, thermodynamic computing turns out to be well suited to this space. An important class of methods realises a function as the stationary expectation of an ergodic stochastic process: the answer is encoded in the time-averaged statistics of an equilibrating trajectory. To date, this equilibration-style class has been formulated exclusively through Langevin dynamics, restricting its implementations to analogue substrates and the engineering challenges those bring. In this work, we propose a substrate-independent formalisation of the equilibration-style formulation, in which the only object of design is the dynamical generator L* of an arbitrary ergodic process. The formalisation makes three hardware-level properties of the formulation explicit: the precision of a result is a knob set by how long the dynamics are run, sample averages decompose across independent trajectories, and dependent stages of a computation operate concurrently rather than serially, a property we call sequential parallelism. We instantiate the formalisation by fabricating a prototype digital thermodynamic computing chip, named CN101, that implements the formulation through discrete accumulator dynamics on standard CMOS using stochastic computing principles. We characterise CN101’s success across conventional generative AI workloads in the form of VAEs and flow matching, applied to both image generation and scientific problems. Together, the formalisation and its digital instantiation show that the equilibration-style formulation is substrate-independent, and that its computational properties can be exploited on standard digital hardware.
[AI-196] Behavioral Grammar: Detecting Adaptive Malware via Tiny Language Model Priors and Second-Order Temporal Analysis
链接: https://arxiv.org/abs/2608.00745
作者: Zihan Luo
类目: Artificial Intelligence (cs.AI)
备注: 29 pages, 8 figures
Abstract:Modern endpoint detection systems face a fundamental tension: signature-based approaches are trivially evaded by polymorphic or adaptive threats, while heavy deep-learning models resist auditability and deployment at scale. This paper presents Behavioral Grammar, a detection architecture that treats host runtime behavior as a structured language and learns its grammar with a compact 0.88M-parameter causal Transformer (TinyGPT). Each system event is discretized into an 8-token representation spanning event type, process, argument skeleton, path category, parent process, user, destination, and inter-event timing. The model learns the conditional distribution of normal behavior in a purely self-supervised manner, and anomaly scores are derived from per-slot negative log-likelihood (NLL) statistics, yielding a mathematically bounded false-positive rate. We augment this prior with prototype learning for known-attack attribution, second-order temporal analysis for cadence-based detection, self-learning pattern extraction, and a five-network fusion pipeline. Against an Adaptive Adversarial Agent (AAA)–a threat that learns survival strategies under defensive pressure, performs behavioral mimicry, and matches host event rates–our system achieves 93% detection at 3.84% onboarding false-positive rate. The strongest discriminative signal arises not from any single event but from the coefficient of variation of inter-event intervals: the AAA stepping cadence exhibits CV=0.310 versus 9.786 for benign sleep intervals, a 30x separation reflecting a fundamental stealth-functionality trade-off. We frame these findings within a coevolutionary economics model, arguing that behavior-grammar detection shifts the evasion cost from rule circumvention (cheap) to distribution matching (expensive), establishing a structural asymmetry favoring the defender.
[AI-197] Multi-tenant Kubernetes Use Cases for AI Secure Computing and Data Services and More
链接: https://arxiv.org/abs/2608.00742
作者: Jake Watson,Sadaf R Alam,Christopher Woods,Abdelwahab Kawafi,Thomas Green,Ian Johnson,Ellis Pires,Jessica R. Jones,Utz-Uwe Haus
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Performance (cs.PF)
备注:
Abstract:Kubernetes, as a container orchestration engine, has been widely used in cloud-native ecosystems for several years. In supercomputing ecosystems, especially where bare-metal performance for compute and network devices are considered, the adoption is somewhat limited. However, with the increasing diversity of use cases such as AI, secure and confidential computing for sensitive data, and mixed workload orchestration, a traditional, single-tenant batch computing system does not offer the flexibility and reproducibility to which public cloud users are accustomed. Note that Kubernetes is not considered a replacement for batch scheduling systems, which have powerful features for large-scale MPI jobs with thousands of network end points. Rather, it is a complementary service provided as part of a national AI Research Resource. We evaluate Kubernetes deployment on a Hewlett Packard Enterprise (HPE) Cray EX supercomputerwith HPE Slingshot interconnect, called Isambard-AI, with co-design use cases. One is a Trusted Research Environment used for medical and health sciences. The other combines KubeRay, Ray, and vLLM to provide a distributed, sandboxed, persistent AI model hosting service targeting multi-tenant confidential computing. We discuss challenges and lessons learned, and where further development is needed to offer a production Kubernetes-as-a-Service on HPE Cray EX (and later) platforms.
[AI-198] EduPluginBench: Executable Assurance for AI-Generated Educational Plugins
链接: https://arxiv.org/abs/2608.00739
作者: Nizam Kadir
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 27 pages, 3 figures, 8 tables. Submitted to ACM Transactions on Software Engineering and Methodology (TOSEM)
Abstract:Code-generation models can produce executable components, but compilation and functional tests do not establish compliance with least privilege, telemetry consent, provenance, privileged-write authority, lifecycle constraints, or bounded failure. We introduce EduPluginBench, an executable benchmark and staged admission method for generated plugins in governed software ecosystems. Across 1,440 activation-checked first-order mutants from 30 specifications, P0-P4 increased release-blocking-defect recall by 74.7 percentage points (specification-clustered 95% CI 73.4-75.8) over P0-P2, with no observed rejection among 120 clean references (95% Wilson upper bound 3.1%). A frozen transfer study of 600 unmodified generations from two current coding models found that 300/600 parsed, but none passed P0 or achieved P0-P4 conformance (95% upper bound 0.64%); downstream assurance estimands were undefined. An independently labelled Moodle study retained 16 vulnerable/fixed pairs; the frozen generic PHP detector found no vulnerable revisions. These negative transfer results prevent controlled contract consistency from being read as independent real-defect effectiveness. An earlier 540-generation diagnostic found that post-hoc bounded repair yielded 112 P0 passes, all nonconforming, with recall increasing from 13.4% to 100%. The artifact retains protocols, public-source provenance, raw generations, row-level decisions, audits, analysis code, and reproduction instructions.
[AI-199] An Embedded RISC-V Evaluation of Kolmogorov–Arnold Networks in Hard-Constrained Recurrent Physics-Informed Models ICLR
链接: https://arxiv.org/abs/2608.00737
作者: Enzo Nicolas Spotorno,Josafat Leal Filho
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Performance (cs.PF)
备注: 6 pages, submitted to SBESC 2026 as an extension to the ICLR Workshop Paper this https URL
Abstract:Hard-constrained recurrent physics-informed networks (HRPINNs) embed known dynamics inside a recurrent numerical integrator and restrict a neural branch to learning only the residual dynamics that the first-principles model does not capture. Kolmogorov–Arnold Networks (KANs) have been proposed as parameter-efficient replacements for multilayer perceptrons (MLPs) in such residual branches, but their learnable B-spline activations follow a markedly different execution profile. Building on prior work that characterized when a vanilla B-spline KAN matches or underperforms an MLP as an HRPINN residual branch in discovery accuracy, this paper asks whether that parameter efficiency survives deployment. Using identical trained weights, we measured execution latency, energy per integration step, and dependability under post-training quantization in the closed recurrent loop on a RISC-V RV64GC platform without vector extensions (StarFive VisionFive~2, SiFive U74). For the two accuracy-comparable pairs, the KAN residual branch executed 13.5\times and 8.0\times slower and consumed 11.3\times and 5.6\times more energy per integration step (3.7, \mu J against 0.33, \mu J for the smallest pair); across all four parameter-matched size tiers the ranges are 4.7\times – 14.5\times and 4.7\times – 18.7\times . Under INT8 quantization, KAN trajectories diverged up to 43\times earlier than matched MLPs; the damage traces to weight quantization, not to input-side knot-interval misassignment. These results indicate that the parameter efficiency reported for KANs does not transfer to deployment cost on scalar embedded cores, and that an MLP residual branch is the more dependable default for embedded HRPINN deployment unless specific quantization co-design is used.
[AI-200] racing the Cascade: A Topology-Aware Evaluation Framework for Scientific Agent Hallucinations
链接: https://arxiv.org/abs/2608.00711
作者: Xinshun Feng,Ziqi Miao,Lijun Li,Jing Shao
类目: Artificial Intelligence (cs.AI)
备注: 36 pages, 7 figures and 5 tables
Abstract:Large language model (LLM) agents are increasingly deployed in scientific research, where reliability is critical and the underlying knowledge is densely interconnected. In such settings, hallucinations are particularly damaging: a single erroneous claim on a foundational concept can propagate through multi-step reasoning and corrupt entire trajectories. Existing hallucination benchmarks largely operate at the surface level, treating facts in isolation and relying on uniform accuracy metrics that ignore this topological structure. We address this gap with SCHEMA, the first evidence-grounded, topology-aware evaluation framework for hallucinations in scientific agents. SCHEMA automatically constructs scientific concept graphs from benchmark seeds and literature evidence, synthesizes graph-grounded tasks spanning claim verification, multi-hop reasoning, open-ended explanation, and experimental code generation, and evaluates agents with two complementary diagnostics. A trajectory hallucination pipeline audits intermediate reasoning at scale via a topology-weighted severity score, while a multi-agent counterfactual attribution module pinpoints the causal mechanism behind selected failures. SCHEMA reveals that hallucinations concentrate at a small set of highly connected knowledge hubs, and that final-answer accuracy decouples from trajectory honesty; models often reach correct conclusions through structurally flawed reasoning. These results indicate that for high-stakes scientific applications, terminal accuracy alone is an insufficient signal of agent reliability, motivating mechanism-level evaluation grounded in knowledge topology. Code is available at this https URL.
[AI-201] DGA_2D: Directed Graph-Guided Automated Algorithm Design with Large Language Models
链接: https://arxiv.org/abs/2608.00700
作者: Jiale Zhao,Zimu Chen,Sirui Mao,Wentao Yang,Yuxiang Bai,Liyuanjun Lai
类目: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注: 46 pages, 5 figures, including supplementary material
Abstract:The rapid development of Large Language Models (LLMs) has opened new avenues for Automated Heuristic Design (AHD) for solving NP-hard combinatorial optimization problems (COPs). However, existing LLM-driven AHD methods are largely confined to rigid solver templates, relegating the search process to isolated module tuning. Transitioning to fully autonomous, system-level algorithm design is essential but fraught with low reliability of generated operators, extremely large search spaces, and ineffective credit assignment. To overcome these drawbacks, this paper proposes a Directed Graph-Guided Automated Algorithm Design framework, termed DGA _2 D. It structures the open-ended program space as a directed graph, where each node represents a functional operator that can be instantiated using one of multiple candidate code implementations, while directed walks constitute complete algorithmic pipelines. A first-order path-dependent credit assignment mechanism is introduced to evaluate code variations strictly based on their topological context. Extensive experiments across 12 distinct COPs, ranging from complex scheduling to routing, demonstrate the consistent empirical advantages of DGA _2 D. It reduces the average normalized gap by up to 10.96 percentage points compared to state-of-the-art LLM baselines.
[AI-202] Supporting Cybersecurity Risk Management for Medical Devices via the SECUMAN Ontology and Shapes
链接: https://arxiv.org/abs/2608.00698
作者: Martin Diller,Anne Esslinger,Piotr Gorczyca,Evi Hartig,Lia Kacholdt,Hannes Strass
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:We propose the SECUMAN ontology and shapes for representing and analysing cybersecurity risk-management documentation for medical devices. Cybersecurity risks are increasingly relevant for connected medical devices and may have direct consequences for patient safety. Current risk-management files are often maintained as semi-structured natural language text, which makes consistency checking, certification review, and reuse difficult. SECUMAN provides a formal OWL-based vocabulary for modelling security-risk context, assessment, control measures, and residual-risk evaluation, and uses SHACL constraints to check structural completeness and conformity with the intended documentation model. The ontology is aligned with VDE Spec 90025 and the related RISKMAN ontology and shapes, while extending their safety-oriented approach to concepts relevant to cybersecurity risk documentation such as threat scenarios, protection goals, attacker profiles, exposure levels, assets, and secure design arguments. SECUMAN is intended to support automated first-pass validation, traceability, and integration of cybersecurity and safety risk-management documentation.
[AI-203] Evolutionary Curriculum Learning Improves Biological Sequence Modeling ICML2026
链接: https://arxiv.org/abs/2608.00697
作者: Richard Zhu,Kento Nishi
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Biomolecules (q-bio.BM); Machine Learning (stat.ML)
备注: Published in ICML 2026 SPIGM Workshop
Abstract:Variational autoencoders (VAEs) trained on multiple sequence alignments (MSAs) have emerged as powerful generative models for biological sequences, with applications ranging from disease variant prediction to functional RNA design. However, standard biological VAE training treats all sequences as exchangeable, ignoring the rich evolutionary structure that organizes homologous sequences from evolutionarily close to highly divergent. We propose Evolutionary Curriculum Learning (ECL), a training strategy that exploits this structure by progressively exposing the model to sequences of increasing evolutionary distance from sampled anchors, following a power-law expansion schedule. Applied to two architecturally distinct VAE models and two biological domains–protein variant effect prediction with EVE and RNA family sequence generation with RfamGen–ECL improves downstream task performance across five random seeds per configuration. Mean ClinVar classification AUROC rises from 0.981 to 0.989 for p53; for PTEN, ECL attains 1.000 in every seed whereas the baseline is unstable (mean 0.905, falling as low as 0.54). For RNA, ECL raises mean covariance-model bit scores on all three families tested and exceeds its seed-matched baseline in 12 of 15 training runs, though with only three families the effect cannot be established as significant at the family level. Ablation experiments show that progressively expanding the sampled sequences by evolutionary distance outperforms fixed-size neighborhood sampling in addition to uniform random sampling. Evolutionary distance is therefore a useful inductive bias for ordering the training curriculum in biological sequence modeling.
[AI-204] When Does LLM Orchestration Pay Off? A Controlled Evaluation of Accuracy Cost and Task Difficulty
链接: https://arxiv.org/abs/2608.00685
作者: Nicolas Leins,Nico Pelleriti,Jana Gonnermann-Müller,Sebastian Pokutta
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM orchestration is often assumed to improve reasoning by allocating additional inference-time computation, yet its gains may not justify its cost. Existing comparisons also frequently overlook differences in optimization effort, making it difficult to isolate the value of orchestration itself. We conduct a controlled evaluation of Self-Refine, Best-of- N , and Debate against task-only and chain-of-thought (CoT) single-call baselines across five LLM backbones and three domains: competitive programming, chess puzzles, and mathematics. For comparability, we optimize each method with GEPA under the same optimization budget and evaluate all methods on the same difficulty-stratified benchmark items. Orchestration yields moderate but benchmark-dependent gains: averaged across backbones within each benchmark, the largest improvement is 4.6 percentage points over optimized CoT inference and 4.5 points over task-only inference, while requiring approximately 2 to 4 times the mean total tokens of task-only inference. Human-derived difficulty is associated with lower absolute accuracy in all three benchmarks, but within-benchmark analyses do not indicate that orchestration effects increase with task difficulty. By contrast, exploratory mixed-effects analyses reveal strong interactions between orchestration method and backbone model across all three benchmarks, showing that orchestration effectiveness depends substantially on the underlying model. Our results suggest that orchestration decisions should be model-specific and account for whether moderate accuracy gains justify the additional inference cost. More broadly, evaluations of LLM orchestrations should control optimization effort and report model-specific accuracy–cost trade-offs rather than treating additional inference-time structure as uniformly beneficial.
[AI-205] Multi-Dimensional Assessment for AI Cognition (MAAC): A Theoretical Framework for Process-Oriented Cognitive Evaluation of Text-Based AI Systems
链接: https://arxiv.org/abs/2608.00680
作者: Abdalla Doleh,Ratna Babu Chinnam
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Evaluating artificial intelligence systems has historically relied on outcome-based benchmarks that measure task accuracy, robustness, or fairness. While indispensable, these benchmarks provide limited diagnostic insight into the underlying cognitive processes that generate performance-leaving critical questions unanswered about how AI systems reason, integrate memory, manage complexity, or avoid generating false information. This paper introduces the Multi-Dimensional Assessment for AI Cognition (MAAC), a theoretically grounded framework for shifting evaluation from what text-based AI systems produce to how they think. MAAC defines nine cognitively motivated dimensions: Cognitive Load, Tool Execution, Content Quality, Memory Integration, Complexity Handling, Hallucination Control, Knowledge Transfer, Processing Efficiency, and Process-Outcome Alignment. Each dimension is grounded in established cognitive science theory-drawing on Marr’s tri-level hypothesis, Baddeley’s working memory model, Sweller’s cognitive load theory, and unified theories of cognition. Five theoretical analyses provide initial support for the framework’s coherence and empirical testability: dimension-to-theory mapping; a coverage matrix assessing breadth and non-redundancy; a formal gap analysis relative to current evaluation practice; a worked diagnostic illustration; and a set of a priori interdependency predictions for future empirical testing. MAAC provides a theoretical and operational framework for principled process-level cognitive assessment of text-based AI systems, complementing existing outcome-based benchmarks with cognitively grounded, multi-dimensional evaluation.
[AI-206] Understanding Online Failure Prediction in Linux Through Complementary Multi-View Explainability
链接: https://arxiv.org/abs/2608.00651
作者: Diogo Dória,João R. Campos
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted for publication in the 37th IEEE International Symposium on Software Reliability Engineering (ISSRE) 2026
Abstract:Accurate Online Failure Prediction (OFP) has been shown to be feasible in Operating Systems (OSs) settings, but prediction alone is not sufficient for practical adoption. Without diagnostic insight, operators have limited basis to trust alerts or decide how to respond. Moreover, even when predictive accuracy is high, it is often unclear whether models are capturing meaningful failure processes or merely exploiting workload-specific noise and incidental correlations in telemetry. This paper reports a practical experience building and evaluating an explainable OFP pipeline for Linux OSs. We combine consensus-based feature selection for detection with temporal onset analysis, subsystemlevel causal analysis, and complementary diagnostic mechanisms to support failure interpretation. Evaluated under strict crossworkload conditions with frozen training artifacts, it achieved 91-94% detection on unseen workloads without retraining, while maintaining false alarm rates below 1%. However, failure mode diagnosis proved substantially more sensitive to workload shift, and several diagnostics mechanisms showed limited effectiveness for specific failure types. Our experience highlights three main lessons: i) detection generalizes more robustly than diagnosis across workload changes; ii) early-warning capability depends strongly on the failure mode, ranging from 38 to 215 seconds in our study; and iii) unseen failure modes are not reliably diagnosable from related training modes alone, providing 0% accuracy under Leave-One-Mode-Out (LOMO) evaluation. Taken together, these results show the value of complementary explainability mechanisms for interpreting accurate failure predictions, revealing when predictive signals reflect transferable failure structure and when diagnostic generalization breaks down under workload variation.
[AI-207] DASH: Decoupled Adaptive Surrogate - Acquisition Harness for Automated Bayesian Optimization
链接: https://arxiv.org/abs/2608.00641
作者: Changquan Zhao,Yuxiang Sun,Ruihao Zhu,Cheng Hua,Yulian He
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Bayesian optimization (BO) relies on a surrogate model and an acquisition function, yet the most suitable choices vary across tasks and optimization stages. Automated Bayesian optimization (AutoBO) addresses this variability by adapting BO components online. However, existing AutoBO methods either adapt one component, leaving the other mismatched and creating a bottleneck, or jointly select surrogate–acquisition pairs under a shared criterion, overlooking their distinct roles: surrogate selection depends on predictive reliability, whereas acquisition adaptation should respond to campaign this http URL this paper, we propose DASH, a Decoupled Adaptive Surrogate–Acquisition Harness for large-language- model (LLM)-enhanced AutoBO. DASH selects surrogates by predictive reliability, uncertainty calibration, and ranking consistency; its two-stage acquisition controller periodically reallocates quotas across acquisition functions, builds a BO shortlist accordingly, and delegates final selection to an LLM. DASH also incorporates an integrated harness, consisting of knowledge-guided warm start and structured memory, to ground optimization in domain knowledge and accumulated feedback. Across four chemical optimization tasks, DASH outperforms the best AutoBO baseline by 12.51% in trajectory-level Acceleration Factor and 5.00% in endpoint Enhancement Factor. Results remain strong across LLM backbones, and ablations verify the complementary contributions of all components. Full-table and behavioral contamination checks find no detectable evidence that direct benchmark memorization or source-cell leakage explains these gains.
[AI-208] Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms
链接: https://arxiv.org/abs/2608.00625
作者: Zongyuan Shen,Shalabh Gupta,Shancheng Zhao,Dehua Zhou,Gao Wang,Rui Cheng,Yaming Ou,Zhongqiang Ren,Yikui Zhai,C. L. Philip Chen
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Motion planning in dynamic environments is a fundamental problem in robotics, aiming to generate safe and efficient paths, trajectories, or control actions in the presence of moving obstacles, uncertain predictions, and multi-agent interactions. It has broad applications in autonomous driving, service robotics, warehouse logistics, human-robot collaboration, crowd navigation, and multi-robot systems. This survey reviews representative works published primarily between 2015 and 2025, with a particular focus on how recent learning-based advances extend, complement, or interact with classical planning foundations. We first revisit classical planning methods as algorithmic foundations and reference frameworks for learning-based extensions. We then propose a role-of-learning taxonomy that categorizes existing methods according to how learning participates in the planning pipeline, including direct policy learning, learning-augmented classical planning, hybrid planning, and training enhancement methods. For each category, we summarize the main problem settings, representative algorithms, key ideas, integration mechanisms, strengths, and limitations. We further analyze how observation representations, prediction uncertainty, interaction modeling, planner integration, safety constraints, and training strategies shape learning-based motion planning in dynamic environments. Finally, we discuss open challenges and future directions, including sim-to-real gap, safe and certifiable planning, dense crowd navigation, perception-planning coupling, and embodied AI.
[AI-209] Slides2MindMap: Reconstructing Cognitively Efficient Knowledge Hierarchies from Lecture Slides
链接: https://arxiv.org/abs/2608.00610
作者: Yuzhi Wang,Rongjun Ye,Shengyuan Chen,Huachi Zhou,Jiaqi Bai,Chuang Zhou,Zhicong Hong,Xiao Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Generating mind maps from lecture slides can help learners efficiently assimilate fragmented knowledge, promising substantial benefits for intelligent education. However, dedicated automatic generation and evaluation frameworks remain underexplored and challenging, requiring a global-local knowledge focus balance and handling large-scale, heterogeneous slides. We formulate the Slides2MindMap task, which aims to reconstruct cognitively efficient knowledge hierarchies from a course’s slide deck collection. For systematic evaluation, we introduce S2M-Bench, a benchmark comprising 12,774 slide pages with expert-annotated mind maps spanning 24 university courses. S2M-Bench includes a cognitive-science-grounded evaluation framework that integrates ground-truth-based comparison, structure conformity analysis, and VLM-as-a-Judge. To address this task, we propose AutoMindMap, an agentic framework inspired by the Structure Building Framework. AutoMindMap comprises Skeleton Laying for global scaffold anchoring, Iterative Knowledge Integration augmented by context-aware summarization, and Dual-Stage Refinement with a local-global decoupling mechanism. The framework reconciles local knowledge faithfulness with global coherence, and adapts to slide-specific features. Experiments on S2M-Bench demonstrate that AutoMindMap outperforms baselines and achieves superior robustness across different models and scenarios, underscoring its pedagogical application value.
[AI-210] Escaping Confidence Trap: Evolutionary Decoding for Mathematical Reasoning in Diffusion LLM s
链接: https://arxiv.org/abs/2608.00605
作者: Zhenhong Sun,Hanqing Zhao,Yatao Bian,Rongcheng Tu,Liuyue Xie,Xu Zhang,Jue Wang,Davide Modolo,Daoyi Dong,Dacheng Tao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive LLMs, offering efficient generation through block-wise progressive unmasking. However, their strong general-purpose performance does not necessarily translate into reliable mathematical reasoning, where correctness depends on preserving coherent numerical-symbolic reasoning trajectories. In this work, we analyze the decoding trajectories of LLaDA 2.0 and identify a recurring diffusion confidence trap: local token confidence can become misaligned with global reasoning correctness during progressive block decoding. Our analysis reveals two representative failure regimes: sampling-sensitive failures, where correct paths exist but are unstable, and sampling-consistent failures, where repeated sampling converges to repetitive high-confidence but incorrect continuations. Motivated by this observation, we propose Evolutionary Decoding, a training-free test-time scaling framework that views diffusion decoding as an evolutionary process over candidate reasoning states. The framework combines step-wise selection, which preserves useful numerical-symbolic signals and suppresses repetitive patterns, with block-wise mutation, which introduces structured alternatives to escape incorrect high-confidence basins. Experiments on multiple benchmarks show that Evolutionary Decoding improves LLaDA 2.0 over confidence-based decoding, leading to more reliable mathematical reasoning.
[AI-211] An Information Theoretic Treatment of Yagers Probability Distribution Negation
链接: https://arxiv.org/abs/2608.00594
作者: Roberto Bruno,Ugo Vaccaro
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI); Probability (math.PR)
备注: Published in Soft Computing (Section: Foundation, Algebraic, and Analytical Methods)
Abstract:In the seminal paper (Yager 2015), Yager defined the negation of a probability distribution \mathbfp=(p_1,\dots,p_n) , as the distribution \overline\mathbfp = (\overlinep_1,\dots,\overlinep_n) , where \overlinep_i = (1-p_i)/(n-1), for i=1, \ldots , n. In this paper, we present a comprehensive information-theoretic analysis of Yager’s negation and its generalizations. Using tools from information theory and majorization theory, we unify, extend, and strengthen a number of previously known properties of Yager’s negation within a common framework. Overall, our results offer strong theoretical justification for Yager’s negation as the most natural and principled definition of probability distribution negation under various information theoretic criteria.
[AI-212] Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models
链接: https://arxiv.org/abs/2608.00591
作者: Yibin Dong
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Stochastic world models are usually evaluated by the accuracy and calibration of their predicted futures. These criteria leave a decision-relevant ambiguity: the same conditional future distribution can arise because an observation aliases different physical states, or because the dynamics remain random after the declared full state is fixed. We prove that this attribution is not identifiable from ordinary transition data, even with an optimal probabilistic predictor. We introduce ClosurePairs, an interventional evaluation protocol that crosses compatible microstates with repeated exogenous disturbances. A two-way variance decomposition identifies state aliasing, process noise, and their nonlinear interaction; an independent-repeat variant applies when disturbances cannot be reused. On likelihood-equivalent Gaussian systems, paired supervision reduces alias-fraction error 15.96-fold at identical test NLL. Across 18 nonlinear Langevin conditions, it reduces attribution MAE from 0.372 to 0.051 and sensing regret from 0.0138 to 0.0003 without changing NLL. On a pixel-conditioned recurrent model, a frozen shared-state probe reduces alias-fraction MAE against a deep ensemble from 0.584 to 0.130 in distribution and from 0.630 to 0.170 out of distribution over ten seeds. Finally, in a matched-total-variance REFINE/BRANCH test, a total-variance router reaches 66.48 percent plus or minus 1.06 percent accuracy, whereas ClosurePairs reaches 99.99 percent plus or minus 0.02 percent and improves selected NLL from -2.087 to -2.717 over five seeds. ClosurePairs therefore measures why futures branch, information that proper forecast scores cannot identify.
[AI-213] UOT-IR: Structured Routing of High-Polyphony Symbolic Music into Fixed-Budget Representations
链接: https://arxiv.org/abs/2608.00576
作者: Ziyue Kang,Nan Nan,Chenhao Lin,Xiaohong Guan
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 8 pages, 2 figures
Abstract:High-polyphony symbolic music is increasingly used in generation, analysis, and arrangement, yet many downstream tasks require bounded representations with fixed tracks or slots. Converting richly orchestrated scores into compact forms is therefore necessary, but existing approaches relying on heuristic simplification or generic representation-space reduction often fail to preserve structural roles, orchestration compatibility, and playability under strict budgets. To address the issue, this study reformulates the compression problem as a fixed-budget structured routing problem and proposes Unbalanced Optimal Transport for Information Routing (UOT-IR), a training-free framework based on constrained unbalanced optimal transport. UOT-IR combines an orchestration prior, adaptive marginal relaxation, temporal decoding, and playability-aware projection to produce compact and musically coherent bounded representations. This work further studies two practical settings under the same slot budget: template standardization, which maps each input to a predefined bounded template, and adaptive preservation, which retains representative content without assuming an external template. Experiments on the SymphonyNet corpus show that UOT-IR delivers strong overall performance across both settings, including the best Note-F1 in adaptive preservation (0.9120), together with the lowest structural cost (14.7165) and bad structural confusion rate (0.3406) in template standardization. This work establishes a principled paradigm for fixed-budget symbolic music compression, offering a practical path toward compact, structured, and musically coherent symbolic representations.
[AI-214] Latency-Tolerant Cloud-Edge Collaborative Vision-Language-Action Models via Emergent Representational Specialization
链接: https://arxiv.org/abs/2608.00569
作者: Daojie Peng,Fulong Ma,Bingtao Wang,Sheng Wang,Jun Ma
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:Deploying billion-parameter Vision-Language-Action (VLA) policies on mobile robots creates a systems conflict: semantic reasoning benefits from cloud GPUs, whereas closed-loop control must respond locally despite network delay and jitter. Existing hierarchical and asynchronous policies improve throughput, but their slow-path representations can still arrive stale or require explicit scheduling and delay cues. We introduce CloudEdgeVLA, a cloud-edge policy that treats temporal misalignment as a representation-learning problem. A cloud VLA encodes delayed observations into slowly varying task features, while a lightweight edge head combines the latest available cloud feature with current local vision. During training, current and randomly delayed frames are paired with the same current action target in fresh and stale paths. This objective encourages the cloud representation to preserve task-level information while the edge path supplies state-sensitive corrections. Across four LIBERO suites, CloudEdgeVLA retains 63.8–78.0% success with a 40-step uniform-delay window, whereas VLASH reaches at most 6.4% and the evaluated single-path baselines at most 3.0%. By removing blocking synchronization from the control loop, the design offers a practical route to scalable VLA deployment in which cloud models can grow while edge computation remains lightweight and responsive.
[AI-215] Fairness Auditing: Lower Bounds on Company Manipulation
链接: https://arxiv.org/abs/2608.00568
作者: Rachit Verma,Padala Manisha,Sujit Gujar
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Fairness audits are increasingly mandated in high-stakes applications such as hiring, lending, and automated decision-making. Recent work has established fundamental impossibility results for black-box fairness auditing, showing that sufficiently expressive models can evade any auditing strategy. We complement these results by quantifying the extent of unavoidable post-audit manipulation under finite audit resources. We formulate fairness auditing as a min-max optimization between a computationally unbounded company and a budget-constrained auditor. We study two auditing regimes: (i) a budgeted auditor that certifies fairness using a fixed-size audit set, and (ii) a budgeted \alpha-tolerant auditor that additionally requires the audit set to estimate the fairness of the certified model within an \alpha approximation. For both settings, we derive explicit lower bounds on the worst-case post-audit demographic parity deviation as functions of the audit budget, group imbalance, and fairness tolerance. Finally, we empirically illustrate these theoretical limits using simple audit-set construction heuristics with linear and neural network classifiers. Our results demonstrate that increasing audit resources reduces, but does not eliminate, the scope for post-audit manipulation, highlighting fundamental limitations of finite-budget fairness certification.
[AI-216] Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors
链接: https://arxiv.org/abs/2608.00566
作者: Niraj Kumar,Harsh Kasyap
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 10 pages
Abstract:Post-hoc model explainers such as LIME, SHAP, and Integrated Gradients are widely deployed to audit models in high-stakes sensitive domains, including finance, healthcare, and social welfare. This ensures the model’s transparency and acceptability. However, a few studies have examined potential attacks in the explainability pipeline. Adversaries can attempt to conceal algorithmic biases or backdoors using adversarial explanation attacks. These attacks have relied on scaffolding out-of-distribution (OOD) detectors that toggle predictions when queried by an explainer. Consequently, defenses have been developed to successfully neutralize these black-box attacks by identifying their anomalous perturbation footprints. In this paper, we demonstrate a critical vulnerability by introducing a more potent white-box, gradient-regularized evasion attack framework. By employing a continuous-embedding dual-penalty framework, we directly penalize trigger feature gradients during training on in-distribution data. Since our approach embeds the evasion logic natively into the model parameters, without relying on OOD scaffolding wrappers, it generates smooth, in-distribution predictions that leave no anomaly footprint. Empirical evaluations across four benchmark tabular datasets (COMPAS, German Credit, IEEE-CIS, and Communities Crime) confirm that our method systematically crushes target feature attribution to near-zero (0.02), maintains 90% Attack Success Rates, and fundamentally bypasses Conditional Anomaly Detection.
[AI-217] AiFlow: Token-Native Reactive Orchestration with Bounded Backpressure for Streaming LLM Applications
链接: https://arxiv.org/abs/2608.00558
作者: Qunhui Zhang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 24 pages, 5 figures, 12 tables, 1 algorithm, and 1 code listing. Public implementation: this https URL
Abstract:Large language model (LLM) applications increasingly operate as streaming workflows combining retrieval, tool calls, safety filters, and multi-agent coordination. Although contemporary frameworks expose provider deltas, workflow nodes often treat generation as coarse request-response steps, leaving queue management, worker allocation, ordering, and backpressure to ad hoc callback code. This paper presents AiFlow, a token-native reactive orchestration model that normalizes provider deltas into typed ContextT events propagated through a directed streaming graph. Each node is managed by a Node Guardian that declares and enforces local queue bounds, worker concurrency, ordering, overflow policy, cancellation propagation, and retry discipline. We formalize the bounded-memory property, present the compilation from a compact DSL and JSON graph form, and provide static validation for type safety, state concurrency, and injection compatibility. Controlled microbenchmarks, captured DeepSeek trace replay (30 runs), descriptive online runs, LangGraph baselines, a streaming RAG workload, and an Ollama local-backend check show that AiFlow does not alter provider-side Model TTFT but reduces Application TTFPT by 70.9-94.7% versus aggregation and keeps runtime-owned queue depth within declared bounds (93.7-96.5% MaxQ reduction versus unbounded policies). The supplementary artifact contains scripts, raw traces, machine-readable tables, checksums, and an API-free smoke test; the public implementation is available through the FIT Framework repository.
[AI-218] CURE: Local Uncertainty Repair for Block-Parallel Speculative Decoding
链接: https://arxiv.org/abs/2608.00531
作者: Aofan Liu,Jingxiang Meng,Fangxin Liu,Yongbiao Chen
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 2 figures, 5 tables
Abstract:Speculative decoding mitigates the latency of sequential generation in autoregressive Large Language Models (LLMs) by interleaving draft generation with target verification. However, existing parallel drafting backends often suffer from rapid accuracy degradation over long horizons, leading to high rejection rates during verification and suboptimal wall-clock speedups. We observe that drafting errors are not uniformly distributed but typically stem from localized high-uncertainty tokens that destabilize downstream generation trajectories. Motivated by this token error pattern, we propose CURE, a budget-aware dynamic repair tree designed to repair errors at uncertainty focal points without incurring prohibitive tree-verification overheads. Specifically, our method uses predictive confidence margins to dynamically locate candidate error tokens within a block-parallel draft, expands bounded repair paths only at these fragile nodes, and employs a novel repair resynchronization mechanism to realign draft states post-verification. Evaluations on code-generation benchmarks (HumanEval, MBPP, and LiveCodeBench-lite) and mathematical reasoning benchmark (GSM8K) demonstrate that CURE increases the average accepted length by 4.2-7.5% over parallel baselines without repair, translating to an end-to-end speedup of 2.66-3.49\times over target-only decoding. Furthermore, we provide a plug-and-play repair module compatible with standard parallel drafting frameworks. We also characterize the trade-off between draft compute and verification efficiency.
[AI-219] SSTG-Nav: Metric-Grounded Spatial-Semantic Topological Graphs for Reusable Object Navigation
链接: https://arxiv.org/abs/2608.00527
作者: Daojie Peng,Bingtao Wang,Jun Ma
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Service robots operating for months in the same homes, offices, and facilities should become more reliable with experience instead of searching familiar space from scratch for every request. Yet ObjectNav is predominantly formulated as one-shot exploration, leaving a central deployment challenge unresolved: recognizing an object does not identify a reachable place to stop, and one confident map error can terminate the task. We introduce SSTG-Nav, a reusable metric-semantic memory that turns a one-time survey into actionable object goals, consolidates evidence across viewpoints, and retains spatially distinct recovery standoffs. On 1,000 HM3D-v2 episodes across 36 scenes, our goal-independent topology achieves a 99.4% geometric success ceiling. Holding semantic responses fixed, metric grounding raises SR/SPL from 0.835/0.560 to 0.920/0.603, and source-aware fusion reaches 0.926/0.586. Fusion-aware Top-3 recovery raises Success@1/2/3 to 0.928/0.965/0.975 and reaches 0.601 SPL@3. Model, field-of-view, density, and corruption controls identify where these gains originate, and a ROS2/Nav2 realization demonstrates the complete reusable query-to-execution pipeline. Together, the results establish pre-exploration as a powerful practical regime for dependable, repeated semantic navigation.
[AI-220] BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series
链接: https://arxiv.org/abs/2608.00513
作者: Zhenya Zhang,Wendi Zhu,Ping Wang,Hongmei Cheng,Shuguang Zhang
类目: Artificial Intelligence (cs.AI); Data Analysis, Statistics and Probability (physics.data-an)
备注: 23 pages, 4 figures, 5 tables
Abstract:In Non-Intrusive Load Monitoring (NILM), adaptive segmentation of electricity consumption time series is critical for appliance recognition. However, prevailing methods face challenges including heuristic parameter tuning, boundary sensitivity, and metric saturation. This paper proposes BayesSeg, a unified framework integrating time-series segmentation, multidimensional evaluation, and automatic parameter optimization. The segmentation layer employs a dual steady-state criterion based on the tail value and mean of preceding subsequences, combined with a sequential extraction and complement-set parsing strategy, to achieve precise unsupervised partitioning of steady-state and transition-state segments. The evaluation layer maps segmentation results to binary state sequences and formulates a composite metric integrating an event-level F1 score (event_F1) with Normalized Mutual Information (NMI). The event_F1 quantifies switching-event precision and recall via tolerance matching, while NMI captures global structural consistency, jointly overcoming the boundary sensitivity and limited discriminability of point-wise metrics. In the optimization layer, the composite score serves as the objective function for Bayesian optimization, which constructs a TPE surrogate model for efficient global parameter-space exploration. Experiments on the SustDataED2 dataset demonstrate that Bayesian optimization requires only ~100 objective evaluations to locate a parameter region within 0.35% deviation of the exhaustive grid-search optimum. The framework achieves a weighted composite score of 0.7149 and an event_F1 of 0.9340 while reducing optimization latency from ~5300 seconds to under 1 second, a speedup exceeding 5700x. BayesSeg automates segmentation configuration and provides a scalable, efficient solution for time-series analysis in NILM and related domains.
[AI-221] aPR: Test-Aware Policy Refinement for Feedback-Conditioned Code Generation
链接: https://arxiv.org/abs/2608.00494
作者: Aofan Liu,Jingxiang Meng,Fangxin Liu,Yongbiao Chen
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 3 figures, 3 tables. Aofan Liu and Jingxiang Meng contributed equally; Fangxin Liu and Yongbiao Chen are corresponding authors
Abstract:Multi-turn code agents rely on execution feedback to repair incorrect programs, yet standard reinforcement learning paradigms optimize and evaluate policy performance primarily using single-shot outcome rewards. This misalignment conflates initial code generation with feedback-driven refinement, discards granular execution signals across intermediate turns, and fails to evaluate whether the policy actually acquires self-repair capabilities. We propose Test-aware Policy Refinement (TaPR), a framework that transforms execution feedback into a dense per-turn test-pass-ratio reward under a consistent multi-turn interaction protocol. Across six models on 219 code-generation problems from LiveCodeBench, TaPR improves the pooled three-turn success rate (Pass@3) by 2.44 percentage points. In the predefined 7B/8B high-headroom slice, pooled accuracy increases from 30.25% to 33.56% (+3.31 pp), with 42 improvements and 13 regressions in paired trials. On a matched Qwen3-8B ablation, the dense reward supplies nonzero feedback in all of the first ten steps and reaches a higher Hard-subset peak than outcome-only GRPO within the tested budget, although GRPO nearly matches pooled Pass@3 by step 300. Our primary contribution is a reward-decomposition framework and a turn-aware evaluation protocol that decouple first-shot generation quality from multi-turn repair competence.
[AI-222] he Bayesian Reflex: A Predictive Coding Engine for Artificial Intelligence
链接: https://arxiv.org/abs/2608.00492
作者: Sourabh Bhattacharya
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: From the Bayesian Brain to Artificial Intelligence
Abstract:Predictive coding offers a powerful theory of cortical computation, but corresponding scalable algorithmic implementations for artificial intelligence have remained elusive. This paper introduces the Bayesian reflex, a computational framework that directly instantiates predictive coding through three pillars: belief maintenance via hierarchical generative models, sequential Bayesian updating via prediction-error minimization, and uncertainty-driven action via active inference. We show that recent breakthroughs—ellipsoidal decomposition for exact i.i.d. sampling, recursive Gaussian processes for deep hierarchical inference, and derivative-aware Bayesian optimization—provide the missing algorithmic ingredients. The resulting framework enables mathematically principled, scalable, and brain-inspired continual learning, perception, and decision-making. We illustrate its versatility through applications ranging from climate model evaluation to prime number discovery, offering a blueprint for truly adaptive artificial intelligence.
[AI-223] F-WANDA: Fisher-Reweighted Post-Training Pruning for Sustainable Deployment of Large Language Models
链接: https://arxiv.org/abs/2608.00481
作者: Himanshu Mishra(University of British Columbia)
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:One-shot post-training pruning is the most energy-frugal compression strategy for largelanguage models (LLMs), yet existing approaches trade either quality (WANDA) or compute cost (SPARSEGPT). We introduce F-WANDA, a drop-in modification of WANDA that reallocates the per-row keep budget across output neurons in proportion to the empirical Fisher information of the pre-activation. The Fisher signal is collected in a single additional backward pass over the same calibration corpus WANDA already uses; no weights are updated. On LLAMA-2-7B at 50 % unstructured sparsity, F-WANDA attains WikiText-2 perplexity of 6.85, matches WANDA fluency, and improves 5-shot MMLU by +1.6 pp over WANDA and +1.1 pp over SPARSEGPT, while incurring only one-third of SPARSEGPT pruning wall-clock and energy. The headline trade-off is achieved without extra calibration data or fine-tuning, placing F-WANDA on the Pareto frontier of quality versus pruning cost for sustainable LLM compression.
[AI-224] Distilling Reasoning Traces into Advisory Prompts for Software Engineering Tasks
链接: https://arxiv.org/abs/2608.00437
作者: Faizan Faisal,Prem Devanbu,Toufique Ahmed
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Language models are widely used for generating and otherwise processing code (e.g., identifying code hallucinations, possible inputs, or predicting outputs); however, LLMs can make mistakes, which can be serious. One key issue is that models are trained on (still) largely human-written, and thus imperfect, code; it’s not easy to find sufficiently large code corpora that are entirely free of bugs. Thus, other inference-time ways of reducing LLM errors, without additional training, are desirable. “Reasoning” or “thinking” modes, exposed as a togglable feature by hybrid reasoning models, do reduce errors; however, reasoning consumes additional resources. This paper asks if better performance can be achieved without always incurring the cost of reasoning. Human students of programming learn to avoid mistakes by (a) identifying them, (b) reflecting upon the cognitive lapses that led to them (essentially, “thinking through” the errors), © inferring general rules or lessons from these reflections, and (d) internalizing these lessons into rules. In tutorial sessions with an instructor, this is a common Socratic interaction. Examples of such internalizable rules might include the nugget “Before coding, restate the requirements to clarify them.” Inspired by this process, this paper describes an approach where we first identify examples in which “thinking mode” in a (low-resource) LLM avoids errors. These errors, and their avoidance via “thinking” in the same LLM, are then examined by a bigger LLM to generate summary explanations; these are then summarized by a large LLM into brief advisory prompts. This approach works on many modest-sized models; in some cases, the “advisory prompts” thus learned can also be gainfully transferred to other models. We also present investigations into the nature of coding errors that language models make, and a characterization of when this approach can be helpful.
[AI-225] Diagnose Before You Compress: Prediction-Independent Bottleneck Witness Refinement for LLM Serving Traces
链接: https://arxiv.org/abs/2608.00423
作者: Liming Liu,Chao Hu,Mingfei Lu,Cong Tan,Yiwei Ge,Chijin Zhou,Yongjun Xie,Runzhe Wang,Xiaohai Shi,Heyuan Shi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Production LLM serving generates millions of diverse requests, making full-trace replay across serving configurations increasingly expensive. Existing trace reduction methods mainly preserve workload distributions or representative requests, but bottleneck-revealing workloads may be rare and non-representative. Moreover, evidence for one component cannot compensate for missing evidence in another, while using predicted bottlenecks as target truth creates circular evaluation. These limitations make it necessary to preserve evidence for every bottleneck component rather than rely on workload representativeness alone. We propose Bottleneck-Preserving Witnessing (BPW), a quality-constrained framework for compact and diagnostically reliable LLM serving replay suites. BPW first performs Workload Candidate Nomination using response-blind workload features and closed source-side measurements. This stage identifies workloads that may expose scheduler, prefill, decode, or KV-cache bottlenecks. Coverage-Priority Sequence Construction then organizes multi-component proposals as reusable hyperedges and prioritizes weak and uncovered dimensions. Finally, Bottleneck Truth Verification derives prediction-independent labels solely from direct target-system measurements. The verified results determine the earliest prefix satisfying the direct two-witness requirement for every component. Experiments on BurstGPT, ServeGen, and Mooncake show that BPW reaches the verified gate with a compact workload set and outperforms 16 policies, achieving relative improvements of 2.3% and 16.3% in Mean prefix Macro-F1 and WBRC-AUC, respectively. Stage-resolved and sensitivity analyses confirm the distinct contributions and local stability of its three stages. Our code is publicly available at this https URL
[AI-226] rAC: Trace-Conditioned Answer Consistency for Efficient Uncertainty Quantification in LLM s
链接: https://arxiv.org/abs/2608.00422
作者: Dahai Yu,Lin Jiang,Rongchao Xu,Guang Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) can generate fluent reasoning traces that nevertheless lead to incorrect answers, making response-level uncertainty estimation important for abstention, human review, and adaptive compute allocation. Existing approaches generally fall into three categories: passive single-trace methods use token-level confidence signals, sampling-based methods compare multiple complete traces at higher generation cost, and active prefix-based methods probe partial traces to study answer stabilization or preference transitions. However, none actively re-elicits an answer from a completed reasoning trace to measure its consistency with and support for the original answer. To address this gap, we introduce Trace-Conditioned Answer Consistency (TrAC), a correctness-supervised uncertainty quantification framework that combines active and passive signals anchored to one completed reasoning trace. Its active component, Prefix-Conditioned Elicitation (PCE), re-elicits a short answer conditioned on the completed trace and represents both its consistency with the original answer and its token-level probabilistic support. Its passive component, Trace Uncertainty Profile (TUP), summarizes how token-level uncertainty evolves throughout the original generation without additional decoding. A lightweight head then integrates the two representations into a response-correctness score. Across five mathematical reasoning benchmarks and three LLM families, TrAC improves macro AUROC by 1.8% and reduces AURC by 3.4% relative to eight-sample self-consistency, while using one complete reasoning trace and a short cached answer probe. When eight samples are already available, augmenting sample consensus with re-elicitation further improves macro AUROC by 4.3% and reduces AURC by 8.3%, without additional full-trace generation.
[AI-227] Mask-Based Priors Are More Persistent than Query-Key Initializations
链接: https://arxiv.org/abs/2608.00418
作者: Mingze Ma,Hemanth Saratchandran,Cameron Gordon,Simon Lucey
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Transformers do not merely lack data on some Boolean extrapolation tasks; they generalize in a systematically wrong way. Recent work on generalization on the unseen has shown that, despite fitting the observed domain, Transformers often extrapolate according to a simpler minimum-degree interpolator rather than the true target function. These Boolean tasks are not practical applications, but controlled stress tests for understanding Transformer inductive bias. We ask whether this failure mode can be corrected by injecting explicit structural priors into attention. Existing structured-initialization methods alter Transformer inductive bias indirectly, by choosing query and key projections whose similarity scores approximate a desired attention pattern. However, we find that when applied to Boolean extrapolation, these QK-based priors can be rapidly overwritten during training and fail to change the learned extrapolation rule. We propose a simpler alternative: initialize the additive attention mask directly. Unlike standard hard masks used for causality or locality attention, our mask is a finite, learnable attention-logit bias initialized from task-level interaction structure. This separates the structural prior from content-dependent attention scores, allowing it to persist throughout optimization. On Boolean reasoning tasks, mask-based initialization achieves near-perfect extrapolation where vanilla and QK-initialized Transformers remain trapped by the default inductive bias. The same mechanism also improves low-data arithmetic performance and remains competitive on vision and language benchmarks. These results show that attention masks can serve not only as architectural constraints, but as a simple substrate for encoding persistent inductive bias in Transformers.
[AI-228] SymboUQ: Symbolic Uncertainty Quantification for Spatial Reasoning in LLM s
链接: https://arxiv.org/abs/2608.00417
作者: Dahai Yu,Lin Jiang,Rongchao Xu,Guang Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Although large language models (LLMs) can produce fluent spatial reasoning traces, their intermediate relations may fail to support the final conclusion, making token-level confidence insufficient for final-answer reliability estimation. Existing formal verifiers provide stronger semantic evidence, but their applicability is partial: a parsed claim need not yield a definite semantic verdict. To address this issue, we introduce SymboUQ, a symbolic uncertainty quantification framework that estimates final-answer reliability from reasoning traces by distinguishing symbolizability, whether a claim can be represented in the verifier’s formal language, from semantic determinacy, whether its execution yields an entailed or contradicted verdict rather than an unknown or not-evaluable outcome. SymboUQ comprises (i) a Layout Auditor that executes ordered spatial claims and extracts feasibility, conflict, and repair evidence; (ii) a label-free Determinacy Profile that characterizes effective executable coverage; and (iii) a Determinacy-Aware Reliability Composer that integrates constraint-based, representation-based, and decoding-based scores according to verifier applicability. Extensive experiments on five spatial reasoning benchmarks with four frozen LLM backbones show that SymboUQ achieves approximately an 8% relative improvement in AUROC and a 7% relative reduction in class-balanced Brier loss over the strongest baseline.
[AI-229] Gene Ontology-Guided Hierarchical Spatial Gene Expression Prediction from Histopathology Images ACM-MM2026
链接: https://arxiv.org/abs/2608.00405
作者: Zhiwen Xu,Xiaoming Yan,Chengkun Wu,Juan Chen,Haoang Chi,Liyang Xu
类目: Artificial Intelligence (cs.AI); Genomics (q-bio.GN)
备注: Accepted by ACM MM 2026. Code: this https URL
Abstract:Predicting spatial gene expression from histopathology images enables large-scale transcriptomic profiling without the cost of direct measurement. Existing methods decode the target gene set as a flat, unstructured vector, ignoring the inter-gene dependencies arising from shared biological pathways and regulatory programs. Without explicit structural guidance, models must infer these dependencies entirely from limited paired data, constraining prediction quality. We propose MSGR (Multi-Scale Gene Refiner), which bridges this gap by incorporating the Gene Ontology (GO), a curated functional hierarchy of genes, as an explicit structural prior. MSGR organizes target genes into a four-level GO tree. Its GO-guided decoder then progressively refines predictions from coarse functional domains to fine individual genes via residual corrections under scale-weighted supervision. Operating solely on the gene side, the GO-guided decoder serves as a seamless plug-in replacement that consistently improves existing architectures without requiring any image-side modifications. Extensive experiments on nine datasets from the HEST-1k benchmark provide empirical evidence for two central claims: GO-structured decoding consistently outperforms flat decoding, even against a state-of-the-art generative baseline, and the gain is attributable to biological ontology structure rather than hierarchical decomposition per se, as confirmed by a +0.027 margin over a structurally equivalent random hierarchy.
[AI-230] DSETA: A Dual-Stage Continual Learning Framework for Travel Time Prediction in Dynamic Traffic Environments
链接: https://arxiv.org/abs/2608.00402
作者: Yanming Lyu,Yue Cheng,Lingkun Li,Ruipeng Gao,Xinyue Liu,Hui Gao,Qiang Ni
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Estimated Time of Arrival (ETA) prediction is a core component of intelligent transportation systems. As traffic congestion patterns become increasingly dynamic in large cities, maintaining high prediction accuracy poses a major challenge for ride-hailing platforms. Existing methods either fail to adapt to irregular traffic patterns and sudden congestion, or suffer from new distributions without disentangling long-term trends from short-term fluctuations, thereby degrading model performance in real-world scenarios. To address this challenge, we propose DSETA, an incrementally updated Dual-Stage ETA prediction framework. Specifically, the continual learning process is divided into \textitinter-day and \textitintra-day stages. We first design the \textitintra-day learning stage, which relies entirely on real-time data to enable dynamic adaptation to short-term traffic patterns caused by events like holidays or accidents. Next, we develop the \textitinter-day learning stage, which leverages aggregated historical data from a short time window to capture knowledge of long-term distribution shifts, such as seasonal trends and traffic network evolution. Subsequently, to prevent catastrophic forgetting and preserve knowledge of regular patterns, we explore a \textitHistorical Traffic Knowledge Consolidation module. Finally, we validate DSETA’s effectiveness and robustness through extensive offline and online experiments conducted on real-world datasets from DiDi’s platform. Online A/B tests across three major cities including Beijing, Wuhan, and Xi’an consistently demonstrated performance gains, achieving MAE reductions of 6.62%, 0.73%, and 2.40% respectively. This framework has been successfully deployed in DiDi’s production environment, processing hundreds of millions of daily requests and validating its strong performance in industrial applications.
[AI-231] Verifiable Checks for Business Rule Consistency
链接: https://arxiv.org/abs/2608.00396
作者: Joseph Tafese,Milad Hooshyar,Sam Bayless,Nick Feng,Arie Gurfinkel
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI)
备注: Formal Methods in Computer-Aided Design 2026
Abstract:Maintaining consistency between natural language documentation of business rules and their evolving internal implementations is a significant challenge in large-scale systems. We present SIRNA, a tool and framework for checking such consistency using SMT solvers. Using the case study of cost calculations in tax domains, we demonstrate a three-part system that combines large language models (LLMs) with formal verification methods. SIRNA translates natural language documentation into candidate SMT formulas using LLMs, followed by checks to validate the translations. Then, corresponding business rules are converted into equivalent SMT representations and validated against the natural language formalizations. Our method is generalizable to domains where business logic exists in both natural language documentation and programmatic implementation. Compared to baseline evaluations, SIRNA significantly reduces the number of false positives and false negatives while offering explainability for its findings.
[AI-232] he Gate Not the Cache: Gate Provenance Bounds the Closed-Loop Reliability of Training-Free VLA Token Skipping
链接: https://arxiv.org/abs/2608.00391
作者: Qi Luo,Shuaijun Liu,Hao Zhao,Kunlin Li,Xiaobo Wang,Ningxing Su,Dongsheng Wang,Yun Chen
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 11 pages, 8 figures
Abstract:Token skipping is a widely used training-free way to accelerate vision–language–action (VLA) models by bypassing computation for most visual tokens at each control step according to a gate. When the next gate is harvested from the previous accelerated forward, however, the tokens skipped at one step are also the ones least visible to the next gate, and the damage can compound across control steps until the task fails. We study the two mechanisms this class is built on, reuse and deletion, crossing each against where its gate signal comes from on identical episodes. At a skip ratio of 0.9 on LIBERO-Object, both collapse when the gate comes from the model’s own accelerated forwards, to 0.68 under reuse and to 0.31 under deletion against a dense 1.00, and the collapse is invisible to the action-level detectors we evaluate. What separates collapse from dense-level operation is not the mechanism but whether the gate is clean, computed by a forward that skipped nothing. We therefore propose actuation-slack refresh, one dense pass run while the robot executes its current action chunk, off the critical path, that hands the next step a clean gate and a fresh KV base. Since the measured detectors do not reliably reveal the failure, the refresh is unconditional rather than triggered. Both mechanisms then recover to 0.98, keeping the speed of skipping and the information of a dense pass. We then integrate the refresh into state-of-the-art caching and pruning methods across two VLA policies, 4 LIBERO suites, and 4 SIMPLER tasks, where it repairs every collapse caused by using a self-harvested gate. Serve latency drops 18–22% below dense, measured both in simulation and on a physical robot. Where the gate signal comes from, not how tokens are skipped, decides closed-loop reliability for accelerated VLAs.
[AI-233] Pretrain on Small Synthetic Data Scale Large for Free: Symmetry-Aware Foundation Model for Logic Rule Induction
链接: https://arxiv.org/abs/2608.00383
作者: Yin Jun Phua
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at 20th Conference on Neurosymbolic Learning and Reasoning (NeSy 2026)
Abstract:Logical rule induction seeks interpretable rules that transfer across propositional schemas. This requires respecting symmetries: atom naming, example order, polarity flips, and label swap. Enforcing exact symmetry by construction lets one trained inducer scale beyond its training schemas. Our central contribution is a canonical export that decodes a discrete rule from literal scores. It needs no retraining and is exactly equivariant whenever those scores respect the symmetries. We instantiate it on the Neural Rule Inducer, a disjunctive-normal-form (DNF) foundation model that natively respects only example order. We restore the remaining symmetries through architecture, inference, and training. On synthetic stress tests, accuracy on the support labels stays stable at much larger schemas, and rule fidelity on fresh inputs remains above the unmodified model. On real data, accuracy improves most on larger schemas. The exported rule is exact on synthetic full-group tests and on schema-valid real-data tests. This is a mathematical property of the export rather than of a specific model, and we validate it empirically only on the NRI. Enforcing symmetry by construction turns this small-data pretrained model into a reusable, interpretable inducer that transfers to larger schemas.
[AI-234] nsor Probabilistic Model Checking of Finite-Horizon Markov Chains (Extended Version)
链接: https://arxiv.org/abs/2608.00374
作者: Jianlin Li,Nick Guo,Peter Ye,Yizhou Zhang
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Mathematical Software (cs.MS); Programming Languages (cs.PL)
备注: Extended version of a CAV 2026 paper
Abstract:We reexamine the problem of verifying Markov chains with respect to step-bounded reachability probabilities. Prevailing approaches rely on encoding the state-transition matrix using either explicit or symbolic representations. While these approaches are effective for sparse transition dynamics, they scale less favorably in the dense regime. Our insight is to cast probabilistic model checking of Markov chains as computations over dense tensors. This methodology enables the use of off-the-shelf compiler toolchains for optimized execution of these tensor computations on hardware accelerators. We prove the soundness of the methodology of mapping probabilistic model checking to tensor computations. We implement our approach in a tool called Tessa . Empirical evaluation shows that Tessa unlocks massive speedups over state-of-theart methods on selected benchmarks from the literature. Comments: Extended version of a CAV 2026 paper Subjects: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Mathematical Software (cs.MS); Programming Languages (cs.PL) Cite as: arXiv:2608.00374 [cs.LO] (or arXiv:2608.00374v1 [cs.LO] for this version) https://doi.org/10.48550/arXiv.2608.00374 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1007/978-3-032-32537-2_24 Focus to learn more DOI(s) linking to related resources
[AI-235] Artificial Intelligence and Modeling Simulation: An Overview
链接: https://arxiv.org/abs/2608.00366
作者: Niclas Feldkamp,Philippe J. Giabbanelli,Istvan David
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial intelligence (AI) and Modeling Simulation (MS) are increasingly intertwined, reflecting converging research needs across both communities, rapid technological advances such as the rise of generative AI, and the growing availability of data and computational resources. This report provides a structured overview of the intersections of AI and MS. The relationship goes both ways: AI can support, augment, or even replace components of simulation studies, while simulations can serve as data generators, training environments, and evaluation platforms for AI. We organize this landscape along the stages of MS from model specification and input modeling to execution, experimentation, verification and validation, and output analysis. Selected studies at each stage illustrates how techniques such as Large Language Models have reshaped simulation practices, while highlighting limitations and open challenges. This report also provides a conceptual roadmap that helps readers navigate a rapidly changing ecosystem.
[AI-236] Learning to Coordinate Symbolic Tools: LLM Agents for Verified Sum-of-Squares Certificates
链接: https://arxiv.org/abs/2608.00326
作者: Bohan Chen,Shivam N. Patel,Richard Hoffmann,Sam Looi,Tony Yue Yu
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures
Abstract:Tool calling allows large language models (LLMs) to invoke external computation during problem solving, a useful capability in various fields including AI for mathematics. We study this setting through weighted sum-of-squares (SOS) decomposition, a machine-checkable route to proving polynomial nonnegativity and hence polynomial inequalities. A candidate decomposition can be checked exactly, but finding one requires choosing among non-unique regroupings and coordinating multiple symbolic transformations. We develop an agent that combines algebraic task training, symbolic tools, and verifier-grounded optimization for this task. Rather than training only on the composite SOS task, we construct 1.35 million synthetic examples covering eight supporting polynomial tasks together with weighted-SOS decomposition. We first apply supervised fine-tuning (SFT) to direct algebra problems and simulated symbolic traces, and then use Group Relative Policy Optimization (GRPO) with task-specific symbolic rewards. The SFT corpus contains no native tool-calling messages; at evaluation, the agent uses native SymPy calls for expansion, collection, reordering, and factorization. Every final SOS answer is checked by exact expansion and coefficient comparison. On held-out, same-generator synthetic problems, the full SFT+GRPO+tools system is the strongest of four evaluated configurations, reaching 78.96% verified success on weighted SOS, compared with 44.73% for the base model with the same tools, and 91.75% macro accuracy across nine polynomial tasks. Within this controlled setting, our work provides a case study of combining domain-specific skill training, executable tools, and verifier feedback, and may inform the design of tool-calling agents in other domains with exactly checkable outputs.
[AI-237] rust and Its Betrayal under Three Representational Strategies
链接: https://arxiv.org/abs/2608.00321
作者: Mihnea C. Moldoveanu,Joel A.C. Baum
类目: Artificial Intelligence (cs.AI)
备注: 27 pages
Abstract:Trust is a propositional attitude of a distinctive kind: to trust is to rely on another under conditions where reliance could be disappointed, and the disappointment of trust—betrayal—differs qualitatively from the disappointment of a prediction. We treat trust as a \emphsubjunctive epistemic state: A trusts B 's competence when A believes that \emphwere P true, B would know it, and B 's integrity when A believes that \emphwere B to know P , he would disclose it to A . We develop three representations of this state—as lexicographic \emphassumption as \emphordinal closeness in a Lewis–Stalnaker sphere system , and as \emphstrong belief in a conditional probability system and for each we ask whether the Brandenburger–Keisler impossibility on common belief survives when the assumption of rationality is replaced by an assumption of trustworthiness. The three representations agree that every \emphfinite depth of common trust is realizable while the \emphcompleted common-trust fixed point is the locus of difficulty, but they differ sharply in \emphhow the difficulty manifests, and—our organizing finding—in how each survives a concrete betrayal. W show that the same betrayal refutes an agent’s \emphlevel ordering under the lexicographic representation, contaminates her \emphcloseness ordering in proportion to the betrayer’s deliberateness under the ordinal representation, and merely \emphshifts her operative conditioning hypothesis while leaving her belief structure coherent under the strong-belief representation.
[AI-238] OrEdge: Efficient Multi-Modal Anomaly Detection in Distributed Software Systems via Orthogonal-Domain Learning
链接: https://arxiv.org/abs/2608.00309
作者: Amr M. Zaki,Farhoud Jafari Kaleibar,Honggeun Ji,Komal Sarda,Marin Litoiu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce Orthogonal-Edge (OrEdge), a lightweight framework for real-time anomaly detection in multi-modal distributed software systems. Unlike existing approaches that rely on computationally expensive attention- and graph-based architectures, OrEdge leverages orthogonal-domain temporal representations to achieve accurate anomaly detection with substantially lower computational complexity and model size. It jointly analyzes heterogeneous monitoring data, including logs, metrics, and traces, to identify abnormal software behavior, capture temporal dependencies, and reduce redundancy across observability signals. At its core, OrEdge incorporates OrEdgeCore, a lightweight orthogonal-domain reconstruction module that captures recurring temporal patterns while suppressing transient variations. Evaluated on three real-world microservice datasets (MSDS, SN, and TT), OrEdge achieves competitive detection performance while reducing the reconstruction model size to at most 9.6K parameters, compared with 20K–143K parameters in existing methods. This compact design enables efficient deployment on resource-constrained edge devices: on Raspberry Pi platforms, OrEdge achieves sub-second inference and reduces inference latency by over an order of magnitude compared with existing approaches. Extensive ablation studies, sensitivity analyses, orthogonal basis evaluations, and qualitative case studies further validate the effectiveness of each design component. Overall, OrEdge demonstrates that orthogonal-domain temporal modeling provides an effective alternative to computationally intensive attention- and graph-based architectures, achieving a favorable balance between detection accuracy and computational efficiency for real-time multi-modal anomaly detection in edge environments. The code is available at this https URL.
[AI-239] CrystalMem: Elastic Memory for Self-Evolving LLM Agents via Knowledge Crystallization
链接: https://arxiv.org/abs/2608.00303
作者: Beining Wu,Jun Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Memory for self-evolving large language model (LLM) agents is often provisioned as if its byte budget only grows. Cloud platforms, however, adjust quotas with load and cost, and we show that capability does not follow the budget back up: after a squeeze-and-recover cycle, the agent settles below its pre-squeeze level, a gap we call memory hysteresis. The cause is structural. Deletion and one-way compression discard the material needed for later rebuilding, and we prove that any policy that only keeps or drops entries carries a residual-deficit floor. We propose CrystalMem (Crystallized Memory), an elastic memory sidecar that demotes entries across four fidelity states under a crystallization-energy schedule, orders demotions by advantage-weighted influence with dependency coupling, and recovers capability through verified recrystallization under explicit compute and byte caps. Across seven environments, seventeen methods, and six backbones, with multi-tenant serving and a physical edge-cloud deployment, CrystalMem achieves the highest restored capability in every setting and closes the loop left open by every baseline. From a 50% byte budget, CrystalMem matches the strongest budgeted baseline at full provision on every environment; at equal budgets, it leads by +4.6 pp on average.
[AI-240] WM-Cov: Test Adequacy for Interactive World-Model-Style Autonomous Driving Simulation
链接: https://arxiv.org/abs/2608.00298
作者: Jianxun Cui,Ping Wu,Stanisa Peric,Marko Milojkovic,Vladan Devedzic
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 6 figures, 7 tables
Abstract:World models and generative simulators are emerging as interactive testing infrastructure for autonomous driving because they can react to the ego planner and produce counterfactual, rare, and safety-critical rollouts. This changes a test scenario from a fixed replayed trajectory into an interactive scenario family whose realized evolution depends on the planner under test. The unresolved question is therefore not only whether dangerous rollouts can be generated, but what valid closed-loop evidence is enough to support a specified testing intent and stopping decision. This paper formulates interactive world-model-style testing adequacy and introduces WM-Cov, a provider-agnostic evaluation layer that converts raw provider outputs into requested, realized, and valid evidence. WM-Cov reports adequacy through coverage growth, valid-failure discovery, failure-mode diversity, realism, artifact suppression, duplicate accounting, and valid-evidence precision. Studies on executed TeraSim/SUMO events, WM-like mixed trace pools, and a real DriveArena TrafficManager–WorldDreamer matrix show that dangerous-looking events can include valid ADS failures, duplicates, partial realizations, and artifacts. The DriveArena matrix evaluates two planners, two horizons, six prompt conditions, and 360 ego-route requests; 304 attempts become fully realized evidence and 56 remain partial. A disjoint 80-request route-slice check yields 74 fully realized and 6 partial attempts. The results support evaluating world-model-style testing by convergence of valid interactive evidence under budget, rather than by raw generated failures or prompt coverage alone.
[AI-241] RF-HOI: Recognize Human-Object Interaction with Radio Frequency Signals
链接: https://arxiv.org/abs/2608.00289
作者: Lihao Wang,Linlu Gao,Jiacan Yu,Yanyu Lin,Yifan Yin,Jianxin Wang,Tianmin Shu,Renjie Zhao
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Accepted by ACM IMWUT
Abstract:Recognizing Human-Object Interactions (HOI) is essential for intelligent systems, underpinning applications in virtual and augmented reality, embodied AI, and assistive robotics. However, vision-based HOI methods face challenges in privacy concerns and poor light conditions. In this work, we introduce RF-HOI, the first framework that only uses radio frequency (RF) signals for HOI recognition. A key challenge of RF-HOI is that single-modality RF sensing is insufficient to recognize both actions and the objects being interacted with. RF-HOI addresses this through a novel modality fusion that combines mmWave radar and RFID, enabling simultaneous action recognition and target identification. Another challenge is limited training data across diverse setups, which impairs the generalizability of the recognition model. To overcome this, we develop a simulator that synthesizes multimodal RF data for diverse HOIs at scale, allowing us to fine-tune with only a small amount of real-world data. Experiment results show that RF-HOI outperforms all baselines, approaching vision model performance, and that our diverse synthetic training data can significantly boost our system’s performance on real-world scenarios. These results highlight the potential of multimodal RF sensing for robust and privacy-preserving HOI recognition as well as the effectiveness of our RF data synthesis.
[AI-242] Hybrid Attention Estimation Pipeline for Adaptive HRI Using an Expressive Robotic Head
链接: https://arxiv.org/abs/2608.00284
作者: Pablo Moraes,Monica Rodriguez,Christopher Peters,Hiago Sodre,Tobias Doernbach,Bruna Guterres,Ricardo Grando
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents an applied case study on hybrid visual attention estimation for human-robot interaction using an expressive robotic head based on the InMoov ecosystem. The proposed pipeline combines a fast geometric perception layer with an independent semantic perception layer based on a vision-language model. The geometric layer provides high-frequency face and head-pose information for temporal regulation, while the semantic layer receives only raw egocentric camera frames and produces contextual attention labels related to attention toward the robot, phone use, or attention elsewhere. These signals are integrated through a finite state machine that regulates adaptive interaction behavior, including activation, waiting, interaction resumption, and return to rest. The system was evaluated with 10 participants across 40 trials covering baseline and adaptive interaction conditions. Results show reliable interaction start across all trials, consistent pause behavior in the adaptive distraction condition, and non-redundant semantic information between the geometric and semantic outputs.
[AI-243] Geometric Self-Supervised Pre-training for Neural Combinatorial Optimization
链接: https://arxiv.org/abs/2608.00270
作者: David Aguado,Daniel Fuertes,Carlos R. del-Blanco,Fernando Jaureguizar
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Neural Combinatorial Optimization (NCO) techniques have emerged as a highly efficient alternative to traditional exact algorithms for solving routing problems such as the Traveling Salesman Problem (TSP). However, the generalization capabilities of these Reinforcement Learning-based models are severely hindered when scaling to high-dimensional instances. This issue has been mitigated in other domains, like computer vision and natural language processing, by adopting a self-supervised pre-training strategy. Nevertheless, its application to routing graphs, which lack complex topological attributes beyond 2D spatial coordinates, remains a challenge. In this paper, we propose a geometric self-supervised pre-training framework specifically designed to capture spatial invariance and global relative distance distributions. By applying isometric transformations, such as rotations and axial reflections, the model learns robust structural representations prior to the policy optimization phase. Empirical results demonstrate that this strategy consistently outperforms models trained from scratch (baselines), achieving a 7.23% improvement in tour length for massive zero-shot extrapolation scenarios (TSP1,000). Furthermore, the proposed model exhibits remarkable computational efficiency, delivering speedups of up to two orders of magnitude over the exact solver Concorde at massive scales. The source code and pre-trained models are publicly available at this https URL. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.00270 [cs.AI] (or arXiv:2608.00270v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.00270 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-244] More Debate Same Evidence: Structural Limits of Homogeneous Multi-Agent Groundedness
链接: https://arxiv.org/abs/2608.00243
作者: Yuelyu Ji
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) judges are increasingly organized as multi-agent panels under the assumption that exchanging critiques improves judgment quality. We test this assumption for \emphgroundedness verification, where a judge must determine whether a claim is supported by the supplied evidence. We evaluate a homogeneous three-agent panel on six public fact-verification and hallucination-detection benchmarks. Relative to a fixed single-agent reference, the panel’s system-level accuracy difference ranges from +8.5 to -4.4 percentage points: two datasets show reliable gains, one shows a reliable loss, and three are statistically inconclusive. Because the reference and panel use different model variants, these differences characterize the complete systems rather than isolate a causal debate effect.
[AI-245] Personalizing Large Language Model Agents with Small Policy Models
链接: https://arxiv.org/abs/2608.00215
作者: Dian Jin,Zhi Zhang,Huichao Li,Yihe Pan,Rundong Huang,Doudou Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents can retrieve memory, call tools, ask clarifying questions, and vary response style, yet adapting these execution decisions to an individual user remains difficult. Fine-tuning a separate LLM is costly or impossible for proprietary systems, while prompts and memory primarily expose user information to the agent rather than adapt its execution decisions from feedback. We formulate personalization of a frozen agent as online learning of a per-user execution policy from scalar feedback observed only for the executed action. We propose FABLE (Factorized Adaptive Bandit Layer for Execution), a lightweight policy layer outside a potentially black-box host agent. FABLE factorizes memory, information-acquisition, and response decisions so feedback updates related choices; filters actions through an externally specified feasible set before exploration; and learns user-specific residual preferences relative to a fixed default-and-cost score via Bayesian contextual Thompson sampling. Under a linear residual-reward model, a calibrated variant inherits an expected-regret bound against the best feasible action. We also characterize preferences unidentifiable under persistent feasibility constraints and provide anytime-valid false-promotion control. Across personalized-reasoning, controlled-feedback, and executable tool-use evaluations, FABLE improves several preference-sensitive behaviors relative to rule-only control while remaining competitive on end-to-end task performance.
[AI-246] Evidence-Unit Fairness and the Limits of Query-Adaptive Sparse-Dense Fusion in Financial Document Retrieval
链接: https://arxiv.org/abs/2608.00183
作者: Chenyu Wu,You Lin
类目: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI)
备注:
Abstract:Retrieval over financial filings is difficult because queries are short and acronym-heavy while the answer-bearing evidence sits inside long, table-dense documents. We study sparse-dense hybrid retrieval on FinDER, a benchmark of expert-annotated questions over corporate 10-K filings. Our first finding is methodological: if the retrieval unit is larger than the dense encoder’s input window, the dense model never sees a large share of the labeled evidence, confounding comparison against a full-text sparse baseline. We measure this directly and remove it by segmenting the corpus into encoder-sized windows. On the corrected corpus, fusing BM25 and a compact dense encoder improves reference-level Hit@10 by roughly 28 percent over either component, and training-free, untuned reciprocal rank fusion exceeds the equal-weight blend in an exploratory comparison. We then ask whether choosing the fusion weight per query helps: an oracle over the interpolation-weight grid shows headroom of 21.8 percent, yet none of the three lightweight adaptive routers (a score-confidence heuristic, a random forest over query features, and a ridge regressor over query embeddings) establishes a statistically reliable improvement over the fixed blend under company-grouped cross-validation with cluster-robust inference. Simple fusion is a strong baseline here, and we discuss why per-query weighting does not capture the available headroom.
[AI-247] Cross-Benchmark Generalization in Long-Horizon Agents
链接: https://arxiv.org/abs/2608.00181
作者: Sushant Mehta,Logan Ritchie,Liudas Panavas,Edwin Chen
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted at the COLM 2026 Workshop on Agent Behavior. 11 pages, 4 tables
Abstract:For reinforcement learning (RL) in self-contained environments, a policy can get rewards by exploiting environment-specific regularities (tool schemas, grader parsing, task templates) rather than by acquiring transferable skill, and an in-distribution holdout shares those regularities. We argue that the discriminating question is behavioral, namely how a trained agent acts, and that cross-benchmark transfer is the right place to look for it. We post-train an open-weight mixture-of-experts model (Qwen3.5-122B-A10B) on 363 long-horizon Model Context Protocol (MCP) tasks across 27 categories, using a two-stage SFT-then-RL pipeline. Toolathlon performance informed the initial base-family and SFT-teacher choices, but no external-benchmark task or grader entered training and no external score informed the reward, training hyperparameters, trained-checkpoint selection, or stopping. At greedy pass@1, the trained model improves over the base on five reported external evaluations: Toolathlon (+9.6 pp), \tau^2 -Bench (+5.3 pp), BFCL-V4 (+3.5 pp), SWE-Bench Pro (+5.8 pp), and Terminal-Bench 2 (+2.8 pp). Both software-engineering benchmarks improve despite the training collection containing no software-engineering tasks. An exploratory paired-trajectory analysis identifies four recurring behavioral differences (more careful local-goal formation, building goal-relevant working state, keeping parent goals stable through local repairs, and verifying completion) that appear in analogous forms across office workflows and code. These results provide descriptive evidence that long-horizon multi-tool post-training can change ways of working that transfer beyond its training domain.
[AI-248] Inference-Time Policy Alignment for Fair Reinforcement Learning
链接: https://arxiv.org/abs/2608.00175
作者: Umer Siddique,Peilang Li,Conor Wallace,Yongcan Cao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at the Reinforcement Learning Conference (RLC) 2026. 13 pages main + appendix, 5 figures, 6 tables
Abstract:Deep reinforcement learning (RL) agents achieve strong performance by optimizing scalar reward functions. However, once deployed, the policies of these RL agents are often rigid and costly to adapt to new performance criteria. For instance, an agent trained to maximize expected cumulative reward may not accommodate previously unknown stakeholder preferences. Existing approaches to achieve fairness, a type of preference, in RL typically assume that such preferences are known a priori and require complete retraining of the policy under a fairness-oriented metric. Inspired by inference-time alignment in large language models, we investigate the problem of steering a pretrained RL policy toward welfare-based fairness objectives at inference time without updating the base policy’s parameters. We formalize inference-time fairness alignment as a policy shaping problem and propose a multiplicative policy shaping framework that adjusts action probabilities using action-dependent welfare scores, thus requiring no modification to the base policy. Our framework is general and compatible with any deep RL agent. Through extensive experiments across multiple domains, we demonstrate that inference-time policy shaping substantially improves welfare-based fairness objectives while preserving core task performance.
[AI-249] AgentS tream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?
链接: https://arxiv.org/abs/2608.00155
作者: Dong Yan,Jian Liang,Dapeng Hu,Ran He,Nicholas Jing Yuan,Qi Zhang,Tieniu Tan
类目: Artificial Intelligence (cs.AI)
备注: Code is available at this https URL
Abstract:Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience. However, existing studies predominantly adopt independent evaluation. Consequently, the behavior of self-evolving agents in realistic streaming settings, where agents adapt to diverse and complex task streams, remains poorly understood. To address this gap, we introduce AgentStream, a unified framework that evaluates self-evolving agents spanning diverse evolution components by organizing agentic benchmarks into a configurable task stream and instantiating the \textttIsolated, \textttSequential, and \textttInterleaved streaming scenarios at test time, which progressively vary the scope and domain composition of the stream. Over these scenarios, we combinatorially evaluate five representative self-evolving methods across three frontier foundation models, disentangling how model capability, method architecture, and streaming scenario jointly shape self-evolution. Our results show that self-evolution reliability varies across streaming scenarios, the benefit of self-evolution is gated by model capability and non-monotonic in model strength, and no single method dominates across models and scenarios. These findings offer concrete guidance for selecting self-evolving methods across models and streaming scenarios. Overall, we advocate that self-evolving agents should be evaluated under realistic task streams rather than isolated single-task settings.
[AI-250] Response Magnitude as a Dominant Signal for Held-Out CRISPRi Perturbation Effect Prediction
链接: https://arxiv.org/abs/2608.00152
作者: Mehrdad Shoeibi,Niloofar Yousefi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 27 pages, 8 figures, 8 tables
Abstract:Predicting the magnitude of a CRISPRi perturbation’s transcriptomic effect on held-out target genes is an important open problem in single-cell biology. Recent work has documented that simple baselines often match or exceed deep perturbation predictors on related protocols. We study this phenomenon on the Virtual Cell Challenge (VCC) benchmark under a strict held-out target-gene split, identify the specific low-dimensional signal that drives the gap, and characterize how it transfers across cell types. The target is the log Anderson-Darling distance from non-targeting controls, which is strongly predictable from four deterministic scalar functions of the 2,000-dimensional input. A deep MLP encoder with direct access to the full input collapses toward the marginal training mean, and standard remedies do not close the gap. A linear regression on the four magnitude scalars alone exceeds the strongest x-only classical model, while a Random Forest on the input plus the four scalars substantially outperforms our deep proof-of-concept encoder. Two pre-specified controls attribute the magnitude gain to per-row alignment rather than added dimensionality. Under zero-shot transfer to two external CRISPRi screens evaluated against a target-gene endpoint rebuilt from single-cell data, magnitude-only predictors transfer positively whereas expression-only predictors are negative or unresolved. Exposing magnitude to the deep encoder improves transfer over its expression-only counterpart, yet the encoder does not outperform a four-scalar linear regression on the same features. We also find that the Anderson-Darling column distributed with these screens measures transcriptome-wide response breadth rather than target-gene effect strength, so evaluating transfer against it scores a different outcome.
[AI-251] Optimising for Flourishing: Flourishing Metrics and Return on Flourishing as Success Criteria for Artificial Intelligence and Post-AGI Economic Systems
链接: https://arxiv.org/abs/2608.00151
作者: Keyun Ruan,Jonathan D. Teubner,John M. Bremen
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Theoretical Economics (econ.TH)
备注:
Abstract:Current evaluation frameworks for artificial intelligence focus mainly on capability, safety, and proxies such as adoption, engagement, efficiency, productivity, and financial return. These criteria are necessary but insufficient because they do not establish whether increasingly powerful systems improve or degrade human and planetary well-being. Through an integrative conceptual synthesis, we argue that human flourishing should serve as a primary success criterion for artificial intelligence, the global race to develop increasingly capable AI systems, and prospective post-AGI economic systems. We make three contributions. First, Flourishing Metrics provides an extensible framework spanning physical, emotional, financial, relational, spiritual, and planetary well-being, combining validated subjective measures with representative behavioural, organisational, community, and environmental indicators. Second, Return on Flourishing (RoF) extends return on investment by evaluating the counterfactual contribution of interventions, policies, and AI systems to flourishing relative to their resources, risks, and opportunity costs. Third, we develop distribution-sensitive safeguards and show how RoF could guide AI-enabled work redesign, institutional appraisal, assurance, and post-deployment monitoring through business pilots. We formalise flourishing as a dynamic system variable while emphasising the need for democratic specification, empirical calibration, independent validation, and protection against unacceptable losses within particular dimensions or stakeholder groups. RoF is proposed not as a universal reward function, but as a value-accounting and decision architecture for assessing whether intelligence, automation, and economic transformation generate durable human and planetary progress.
[AI-252] Exposed by Design: A Dynamic Security Assessment of Internet-Facing MCP Servers at Scale
链接: https://arxiv.org/abs/2608.00150
作者: Nicolás Padilla
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 13 pages, 3 figures
Abstract:The Model Context Protocol (MCP) has seen rapid adoption since its November 2024 launch, with over 21,000 server instances detectable on the public internet. We present the first dynamic behavioral security assessment of internet-facing MCP servers, combining passive discovery across eleven data sources (this http URL, HuggingFace, GitHub, npm, Smithery, PyPI, Censys, FOFA, Shodan, this http URL, and this http URL) with active dynamic testing using Corvus, a purpose-built framework implementing 34 test modules covering 10 MCP-specific vulnerability classes. Across four measurement runs spanning July 2026, we confirm 640 production MCP servers and dynamically audit 414, uncovering 68 reportable vulnerabilities including SQL injection, SSRF targeting cloud metadata services, prompt template injection, and path traversal via cursor manipulation. We find that 91.8% of dynamically audited servers lack OAuth authentication, 687 tool instances across confirmed servers expose shell execution capabilities without access controls, and 41.6% of confirmed servers disappear within three days between consecutive measurement runs—indicating rapid deployment cycles without security review. We report on our responsible disclosure pipeline and release Corvus as an open-source framework for MCP security evaluation.
[AI-253] Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularity
链接: https://arxiv.org/abs/2608.00143
作者: Ramya Varunsegar
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Automated attack chain generation is critical for modern cybersecurity, yet manual construction fails to scale as adversary behaviors expand. While classical AI planning using PDDL offers a formal method to automate this process, it relies on the accurate translation of techniques into symbolic predicates. Current state-of-the-art systems like AURORA employ a nine-category Attack Action Linking Model (AALM), but the necessity of this specific granularity remains unvalidated. This work investigates the impact of predicate representation granularity on plan validity, cost, and fidelity. Utilizing a pipeline where a Large Language Model (LLM) performs translation and the Fast Downward engine performs deterministic reasoning, the study compares the full nine-category AALM against a reduced five-category scheme derived empirically from Atomic Red Team (ART) execution evidence. Results from a sixteen-technique corpus demonstrate that plan validity and cost are largely insensitive to granularity, with 81.3% identical outcomes across both schemes. The findings suggest that higher granularity primarily enhances the internal structural resolution of a plan’s justification rather than the viability of the generated attack chain itself.
[AI-254] Shared Organizational Memory for Enterprise Coding Agents : System Design and Deployment Snapshot
链接: https://arxiv.org/abs/2608.00122
作者: Harsh Rao Dhanyamraju,Leonidas Raghav
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Enterprise coding agents rely on tools and retrieval, yet enterprise knowledge often remains outside public training data and formal documentation: internal DSLs, proprietary platforms, local conventions, recent fixes, and tacit workflows. Existing knowledge interfaces expose stored resources but still depend on agents recognizing and explicitly recording lessons worth reusing, disconnecting capture from the coding workflow and leaving development experience repeatedly rediscovered. We report an ongoing production deployment of a shared organizational memory system that makes capture a platform-level part of coding work: it collects task-adjacent experience with contributor approval, curates it into reusable question-answer memories, gates obvious security and privacy risks, and retrieves memories for future agents. This short paper describes the deployed lifecycle and an operational snapshot. Effects on retrieval and coding tasks remain under evaluation.
[AI-255] MetaRoute-Bench: Evaluating Meta-Decision Policies for Agent ic Workflow Routing
链接: https://arxiv.org/abs/2608.00107
作者: Natan Vidra,Alina Kapanova,Arun Kanhai,Spurthi Setty
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 6 pages, 1 figure; DAI 2026 submission
Abstract:Agentic systems must repeatedly decide whether to answer directly, decompose a task, invoke a tool, execute code, delegate to a specialist, verify an intermediate result, or recover from failure. These meta-decisions affect not only task success but also operating cost and latency, yet they are often embedded inside an orchestration framework and evaluated only through aggregate task accuracy. We present MetaRoute-Bench, an open, inspectable framework for comparing meta-decision policies under a shared execution model. The initial benchmark contains 180 synthetic task profiles spanning data analysis, research, and document processing, eight routing policies, and 30 paired random seeds. Across 43,200 traces, a task-aware compositional policy achieves 79.4% success compared with 76.7% for a strong workload-specific static policy, 67.4% for one-shot task routing, and 52.9% for direct answering. Relative to the static policy, this is a 2.7 percentage-point improvement with paired 95% CI of plus or minus 2.0 points, at 4.7% higher mean cost and 6.4% higher latency. Ablations show the largest losses when route composition is restricted to one operation and when verification is removed. These results are generated by a seeded offline execution model rather than a live deployment; accordingly, the primary contribution is a reproducible evaluation method and an analysis of routing-policy tradeoffs, not evidence of production effectiveness. We release task generation, policies, traces, tests, and analysis artifacts to support live-system validation.
[AI-256] Skillsets on the Chain: A Blockchain-based Zero-Trust Framework for Agent ic AI Networking
链接: https://arxiv.org/abs/2608.00104
作者: Yayu Gao,Yong Xiao,Hao Hu,Xubo Li,Zhiwei Liu,Yingyu Li,Guangming Shi,Ping Zhang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注: Accepted at IEEE Transactions on Cognitive Communications and Networking
Abstract:Agentic AI networking (AgentNet) systems rely heavily on third-party skillset implementations and distributed multi-agent collaboration, yet they face major claim-to-capability inconsistencies and security vulnerabilities under trust-by-declaration assumptions. To bridge this gap, this paper proposes TrustAgentNet, a dual-tier blockchain-secured zero-trust framework. Specifically, a global Chain of Skillsets (CoS) governs the lifecycle of skillset metadata with protocols empowered by specialized agents to enforce off-chain auditing while maintaining lightweight on-chain cryptographic consensus. Furthermore, transient, task-oriented Chains of Collaboration (CoC) are dynamically established to enable trustless distributed multi-agent collaboration. Theoretical analysis of the three-way trade-off among security level, task performance, and resource overhead is provided and empirically validated. Experimental results on a hardware prototype demonstrate that compared with no-blockchain trust-by-default baselines, the zero-trust overhead of TrustAgentNet is dominated by off-chain inference, while the blockchain layer incurs minor ledger costs via the ledger-IPFS storage and on/off-chain integration design. Crucially, the proposed verification pipeline achieves a flawless 100% accuracy across 50 AI models, correctly validating 40 honest skillsets and intercepting 10 adversarial ones, and generalizes to non-AI domains with an 83.91% accuracy and a 0.85 F1-score across 1478 features from 171 ClawHub skills. Adversarial experiments further show that TrustAgentNet enables autonomous skillset self-recovery against various malicious attacks.
[AI-257] Agent ic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale
链接: https://arxiv.org/abs/2608.00101
作者: Banruo Liu,Haoran Qiu,Íñigo Goiri,Rodrigo Fonseca,Ricardo Bianchini,Esha Choukse
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:AI coding agents like GitHub Copilot, Claude Code, and Codex interleave multi-step LLM inference with tool execution, creating a workload different from chatbots. We present the first production-scale characterization of this workload using sampled GitHub Copilot traces from June 2026, comprising 3.2M users, 13M sessions, 761M LLM calls, and 95T tokens. Our analysis reveals distinctive workload properties with important systems implications. For example, agentic coding sessions consist of sparse user-initiated turns, each unfolding into an autonomous agent loop of LLM calls almost always coupled with tool execution. This structure yields KV cache hit rates averaging 90% within a turn, but falling to 55% across turn boundaries and drastically invalidated after events like model switches or context compaction. Diverse workflows and user behaviors are observed with variable and long-tailed token consumption, time span, and tool calls. We highlight the difference between quick agentic turnaround times and the minutes-long user idle periods at turn boundaries, and design a lightweight idle-time predictor that captures 86-90% of total idle time, enabling proactive decisions for efficient resource orchestration. These findings challenge assumptions underlying current LLM-serving systems and provide an empirical foundation for agent-native infrastructure. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.00101 [cs.AI] (or arXiv:2608.00101v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.00101 Focus to learn more arXiv-issued DOI via DataCite
[AI-258] H Embedding: Harmonizing Global and Token-Level Retrieval with Context-Dependent Phrases
链接: https://arxiv.org/abs/2608.00065
作者: Shusen Zhang,Junyi Hu,Ye Feng,Ziteng Wang,Zhaoyuan Pan,Guosheng Dong,Xiaojun Yuan,Jiangshou Hong,Xiangzhi Wang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 14 pages, 4 figures
Abstract:Terminology-intensive retrieval, especially in medical settings, depends on preserving multi-word entities, abbreviations, numerical constraints, and compositional concepts. However, existing representations lie at two extremes: single-vector retrievers often over-compress local relevance signals, while token-level late interaction retains every tokenizer subword at substantial indexing, storage, and scoring cost. This mismatch raises a natural question: can context-dependent phrases provide a useful retrieval unit between global vectors and tokens? We introduce H+ Embedding, a unified multi-granularity retriever that predicts variable-length phrase partitions, preserves uncovered tokens as singletons, and applies importance-guided unit selection with weighted MaxSim interaction. Across 16 scientific, medical, and bilingual tasks, its phrase retrieval branch exceeds the global retrieval branch by 6.91 macro nDCG@10. It also nearly matches Token while using 13.7% fewer document vectors and outperforms content-independent grouping rules under moderate vector budgets. Context-dependent phrase interaction therefore provides an intermediate quality-cost point between global compression and token-level interaction for practical retrieval systems.
[AI-259] RAG -TESTER: Automated End-to-End Testing of Retrieval-Augmented Large Language Models
链接: https://arxiv.org/abs/2608.00054
作者: Ange Maiztegi,Jon Ayerdi,Miren Illarramendi,Aitor Arrieta
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to use external and domain-specific knowledge, but its reliability depends on the interaction between the generative model, embedding model, retrieval mechanism, and prompt construction strategy. We present RagTester, an automated end-to-end testing approach for RAG systems. RagTester generates retrieval documents, test inputs, and expected outputs; executes the tests; and evaluates the resulting answers using an LLM as a judge. Its test-generation strategy targets complex passages, unsupported queries, and document-coverage criteria. We evaluate RagTester using eight LLMs and six embedding models, yielding 24 compatible configurations, and compare it with a baseline test-input generator. Across 72,000 test executions, RagTester detected 21,633 failures, 6.6% more than the baseline, and outperformed it in 20 of the 24 configurations. The detected failures include inaccurate retrieval, unsupported answers, incomplete use of retrieved context, and difficulties interpreting complex passages. These results show that coverage-oriented test generation can effectively expose failures caused by the interaction between retrieval and generation components and support the assessment of RAG configurations before deployment.
[AI-260] SIRIN: A Unified Toolkit for Detecting Contextual Hallucinations in Retrieval-Augmented and Memory-Grounded LLM Systems
链接: https://arxiv.org/abs/2608.00033
作者: Julia Belikova,Rauf Parchiev,Mikhail Filimonov,Konstantin Polev,Andrey Savchenko,Maksim Makarenko
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:SIRIN (Semantic Inconsistency Recognition and Inspection Nexus) is a unified toolkit and interactive web UI for detecting contextual hallucinations (fluent, plausible responses unsupported by the provided evidence) in retrieval-augmented, agentic, and memory-grounded LLM systems. SIRIN unifies three detector paradigms (representation probing, uncertainty estimation, and judge-style verification) and the complementary task of pre-generation query answerability under one interface, configuration system, and evaluation pipeline, supporting response- and span-level inspection in both white-box and black-box settings. The web UI enables live analysis of user-supplied context-query-answer triples through hallucination scores, unsupported-span highlighting, and side-by-side detector comparison, with a lightweight plug-in design for adding new detectors. We demonstrate SIRIN on hallucination detection, query answerability, and as a faithfulness gate within long-term memory systems. The source code is publicly available at this https URL. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.00033 [cs.AI] (or arXiv:2608.00033v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.00033 Focus to learn more arXiv-issued DOI via DataCite
[AI-261] Nova: An End-to-End MLIR Compiler for Deep Learning
链接: https://arxiv.org/abs/2608.00029
作者: Adwaid Suresh,Aparna A,Harshini V M,Jona Delcy C A,Killi Uma Maheswara Rao,Ram Charan Golla,Surendra Vendra
类目: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Machine Learning (cs.LG); Programming Languages (cs.PL)
备注:
Abstract:The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware. While high-level tensor frameworks provide flexible abstractions for model design, their eager execution models inherently lack the whole-graph visibility and granular control over hardware and memory required to maximize physical hardware utilization natively. To bridge this gap, we designed Nova, an automated end-to-end JIT compiler whose defining purpose is to achieve absolute control over this hardware mapping: fusing operations across operation boundaries, optimizing complex memory hierarchies, and tuning execution down to the register level. By capturing eager executions and unifying forward and backward passes into a single value-semantic dialect, Nova unlocks aggressive whole-graph optimizations. It then utilizes an Analytic Configurator to deterministically derive optimal execution schedules based on arithmetic intensity, dropping search time to zero. Backed by a structural hashing runtime, Nova synthesizes fine-grained kernels directly from the computation’s structure. In our evaluations on an RTX 3060, Nova matches or modestly exceeds cuBLAS and XLA on TF32 matmuls on most shapes, maintaining a stringent 5e-4 relative error. At the model level, Nova achieves up to 10.6% greater throughput than PyTorch and 4.4% greater than XLA on a 42-million parameter model, without compromising on numerical fidelity. Crucially, by reducing the memory footprint by up to 29% relative to PyTorch, Nova successfully trains a 144-million parameter model at 17,900 tokens/s where PyTorch encounters Out-Of-Memory (OOM) failures on the same 12 GB consumer GPU.
[AI-262] Motif-Mamba: network motif improved mamba for long-range sequence modeling NEURIPS2026
链接: https://arxiv.org/abs/2608.00027
作者: Chonghe Hao,Yue Sun,Jian Zhang,Yansong Wang,Wangzi Yao,Yunjie Yao,Tielin Zhang
类目: Artificial Intelligence (cs.AI)
备注: 9 pages of main text, 8 pages of appendix, 6 figures. Submitted to NeurIPS 2026
Abstract:Efficient long-sequence modeling remains a central challenge for large language models, as self-attention scales quadratically with sequence length. Mamba offers a linear-time alternative through selective state space recurrence, but its predominantly diagonal state transitions restrict explicit interactions among state dimensions. We propose Motif-Mamba, a structured state space model that augments Mamba with a motif-constrained low-rank recurrent pathway. Inspired by the dynamics of three-node network motifs, the proposed pathway projects hidden states into a compact dynamical subspace, imposes motif-guided interactions, and maps the resulting dynamics back to the original state space. This design enhances cross-dimensional communication while preserving the linear-time recurrent structure of Mamba. Experiments on long-sequence extrapolation, language modeling benchmarks, and brain–computer interface decoding show consistent improvements over Mamba backbones, suggesting that motif-guided low-rank dynamics provide an effective structural prior for long-range sequence modeling.
[AI-263] Request-Level Energy Attribution for Batched LLM Serving
链接: https://arxiv.org/abs/2608.00026
作者: Qi Luo,Kunlin Li,Ziwen Wang,Dongsheng Wang,Yun Chen
类目: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 12 pages, 4 figures
Abstract:Batched LLM serving improves throughput but complicates energy accounting. GPU power telemetry is aggregate, whereas sustainability reporting, chargeback, and workload analysis often require request-level energy charges. Existing inference-energy benchmarks report model-, phase-, or token-level energy, and recent carbon-accounting work motivates Shapley fairness conceptually. Neither provides measured request-level ground truth, so how far the accounting rules used in practice deviate from a fair allocation has remained unknown. We present JouleShare, an attribution framework with two components. An offline harness establishes this ground truth by replaying request subsets under vLLM with a reproducible protocol, integrating GPU power telemetry, and computing exact Shapley energy for each request. A lightweight calibration model, JCalib, then learns to predict Shapley shares from cheap request features for use at serving time. Across 16 model/workload runs, token-proportional attribution differs from exact Shapley by 0.440 normalized L1 on average under static batching and by 0.458 under continuous batching, a gap that reproduces across three data-center GPUs. JCalib reduces this error to 0.116 under static batching and 0.177 under continuous batching, below even a standalone-measurement baseline that is unavailable online, while preserving exact batch-energy efficiency. Sampled Shapley extends the measured reference to larger group sizes, where the gap persists and a single offline calibration remains the most accurate deployable rule. The results show that token attribution is not a reliable proxy for marginal energy under batched execution, and that measured Shapley ground truth can calibrate low-cost request features toward fairer attribution.
[AI-264] Uncertainty-Aware Simulation-Based Inference for Operations Research with Large Language Models
链接: https://arxiv.org/abs/2608.00019
作者: Liang Guo,Lin Shaochong,Shen Zuo-Jun Max,Zhang Kun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Deploying large language models (LLMs) for operations research (OR) tasks remains challenging because correctness depends on a coherent modeling process, not merely a correct final answer. Standard autoregressive generation operates on a myopic policy, which sometimes fails to anticipate whether a partial formulation can be validly extended into a globally consistent optimization model. Consequently, locally plausible steps may propagate into catastrophic downstream formulation or solver code errors. To address this, we propose an uncertainty-aware, training-free inference framework for OR mathematical modeling. Without updating model parameters, our method evaluates intermediate candidate steps using short lookahead simulations to quantify downstream predictive uncertainty or probability concentration. Candidates that demonstrate a higher likelihood of yielding coherent mathematical formulations are then dynamically selected via importance resampling. Empirical evaluations across multiple OR benchmarks (including NL4OPT, MAMO, and IndustryOR) demonstrate that our framework consistently outperforms both standard and low-temperature baselines, establishing an efficient, training-free paradigm for reliable OR formulation generation.
[AI-265] CITBench: A Comprehensive Benchmark for Interactive Tabular Data Processing with LLM s
链接: https://arxiv.org/abs/2608.00018
作者: Zihan Nan,Yang Gu,Wei Liu,Xi Yan,Zhou Liu,Hao Liang,Wentao Zhang
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:Tabular data processing is central to data work, and LLM-based assistants have recently shown promising capabilities in supporting such tasks. However, existing benchmarks primarily focus on table reasoning under single-turn, fully specified instructions, underrepresenting complex table processing that unfolds through multi-turn interactions with evolving user requirements. To bridge this gap, we introduce CITBench, a comprehensive benchmark for evaluating LLMs on interactive tabular data processing. CITBench features a comprehensive taxonomy across four high-level categories–table matching, cleaning, augmentation, and transformation–spanning 18 task types and 1,296 instances curated from datasets across diverse domains. The benchmark supports both offline and online evaluation, where the online setting models multi-turn interactions under constrained operation procedures and structured task scripts, capturing key potential behavioral characteristics of user-in-the-loop tabular data processing. We evaluate a broad suite of open-source and closed-source LLMs on CITBench, revealing a consistent trend: while current models perform well on simple tables and rules, their performance degrades significantly with increasing table complexity, tighter rule dependencies, and noisy multi-turn interaction simulations. These results highlight persistent challenges in understanding, planning, and table-structure awareness for LLMs in extended interactive data processing scenarios.
[AI-266] Memory Reward Inflation in Self-Improving LLM Agents
链接: https://arxiv.org/abs/2608.00017
作者: Mohammad Asadolahi,Amir Amini,Samira Talebi,Amirfarhad Farhadi,Azadeh Zamanifar
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Self-improving LLM agents increasingly learn from experience without updating any weights. Each episode is stored in an external memory, scored, and retrieved for similar future tasks to shape later behavior. Viewed through a reward lens, the stored score is a proxy reward for an implicit, non-parametric policy. Each retrieved episode then becomes a policy-improvement step whose reliability hinges on how that score is produced. In deployment, ground-truth labels are unavailable, so the stored reward is at best an LLM assessment. This substitution creates a failure mode, the Echo Gap, across the memory-based self-improving agents and model families studied. Incorrect episodes receive inflated rewards; thus, the agent preferentially reuses the very mistakes it has most confident in. Because the error compounds through memory rather than averaging out and the confirming judge’s errors remain correlated with the original self-grading bias, so it cannot identify which memories are overvalued. The missing property is formalized as the Error-Independence Assumption (EIA), which we prove is a necessary condition for correcting the inflation, not merely a description of a good verifier: a usable signal must track truth and decorrelate its error from the memory bias, and the recoverable payoff is a closed-form function of exactly those two quantities. We further show the inflation compounds not only when retrieval ranks by the stored score but also under plain similarity retrieval which is the regime the deployed agent uses. Finally, the answer-free de-inflation algorithm LUCID delivers a consistent end-to-end gain on the BIRD text-to-SQL benchmark. It raises execution accuracy to 56.9% , above both a Memento-style self-graded agent ( 54.0% , a +2.9 -point mean gain across seeds) and a memory-less agent of identical architecture ( 52.4% ).
[AI-267] Optimization and Constraint Modeling using LLM s with a Retrieval Augmented Generation Process
链接: https://arxiv.org/abs/2608.00015
作者: Prateek Roy,Akash Singirikonda
类目: Artificial Intelligence (cs.AI)
备注: 20 pages, 3 figures
Abstract:Both optimization modeling and constraint modeling are non-trivial problems requiring deep domain expertise and proficiency in modeling formalism languages. Despite their importance across logistics, healthcare, and supply chain management, current large language models regularly produce structurally inconsistent or incomplete optimization formulations, particularly in combinatorial settings. This paper evaluates whether a Retrieval-Augmented Generation pipeline built on a curated synthetic dataset can meaningfully improve LLM optimization modeling performance. A total of 500 optimization problems were synthesized using seed descriptions from the Text2Zinc dataset and professional personas created using an LLM, specified in JSON and associated with validated Python solver scripts. These problems were encoded in a Chroma vector database. For each inference problem, semantically similar problems were retrieved and used as contextual guidance for a LangChain LLM agent. Three benchmark testbeds were used to evaluate the proposed pipeline under the Qwen 3 30B Instruct model. Accuracy rose from 40% to 72% on NL4OPT, 40% to 56% on MAMO Easy, and 32% to 56% on MAMO Complex. The use of semantically validated synthetic examples greatly improves both solution accuracy and structure. The combination of synthetic dataset generation with retrieval augmentation provides an effective alternative to fine-tuning, suggesting that domain-specific synthetic corpora paired with retrieval augmentation can serve as a practical pathway for deploying LLM-based optimization tools in real-world decision-support contexts without costly model retraining.
[AI-268] CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection
链接: https://arxiv.org/abs/2608.00014
作者: Qihua Pan,Zhenheng Tang,Peijie Dong,Xiang Liu,Huacan Wang,Bo Li,Xiaowen Chu
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 3 figures, 10 tables. Includes appendix
Abstract:Evaluating Large Language Models (LLMs) incurs prohibitive computational overhead during continuous development processes. While coreset selection accelerates evaluation, existing methods either suffer from a severe ``cold start’’ bottleneck requiring massive historical logs (e.g., Item Response Theory) or exhibit a surface lexical bias that misses the underlying reasoning manifold of tasks. We propose CoT-Core, a novel training-free core question selection framework. Recognizing that lexically disparate questions can share equivalent underlying logic, CoT-Core prompts LLMs to unroll zero-shot Chain-of-Thought (CoT) reasoning trajectories. Projecting these paths into a latent space effectively clusters questions by intrinsic logical equivalence rather than superficial text similarity. Extensive experiments on GSM8K, MMLU, MMLU-Pro, and GPQA demonstrate that CoT-Core drastically reduces evaluation costs while maintaining high-fidelity score estimation, and delineate the boundary conditions of reasoning-aware pruning, revealing that its efficacy is intrinsically gated by task complexity.
[AI-269] Energy Efficiency of Locally Deployed LLM s: A Preliminary Quantitative GPU Power Benchmark on Consumer Hardware
链接: https://arxiv.org/abs/2608.00008
作者: Philipp M. Zähl,Anika Hennig
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Performance (cs.PF)
备注:
Abstract:The local deployment of large language models (LLMs) is gaining traction due to privacy concerns and the desire for on-premise inference. However, the energy costs on consumer hardware remain poorly characterized, as most benchmarks focus solely on accuracy. This paper presents a reproducible, hardware-level energy benchmark of nine open-source LLMs (1B to 7B parameters) executed on a single consumer GPU (RTX 4060Ti 16GB). Using the Ollama inference engine, GPU power draw was sampled at 2Hz via nvidia-smi across a fixed prompt set. We evaluate mean/peak power, total energy per prompt (J/prompt), energy per output token (J/token), and throughput (tok/s). Our findings suggest that factors beyond raw parameter count, including model architecture and quantization strategy, drive energy efficiency. Specifically, gemma3:1b and llama3.2:1b achieve the lowest energy cost (0.56 J/token and 0.65 J/token) and the highest throughput (170 tok/s). In contrast, the 7B-Mistral model consumes up to 4.4x more energy per token than the most efficient model. Notably, qwen3.5:2b exhibits anomalously high per-prompt energy due to extended internal reasoning, highlighting the need to distinguish between token generation modes in efficiency metrics.
[AI-270] Enhancing LLM s with Context-Specific Knowledge for Mitigating Misinformation in SMEs: A RAG -based Modeling and Analysis
链接: https://arxiv.org/abs/2608.00006
作者: Md. Samiul Islam,Iqbal H. Sarker,Chadni Islam,Ahmad Mohsin,Ahmed Ibrahim,Helge Janicke
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs), a part of artificial intelligence (AI), are increasingly being adopted by Small and Medium Enterprises (SMEs) to enhance question-answering capabilities and support business decision-making processes. However, hallucinations in LLM-generated outputs can serve as a source of misinformation, reducing user confidence in their reliability and trustworthiness within SMEs. Retrieval-Augmented Generation (RAG) has emerged as a promising approach to address this challenge by incorporating external knowledge sources into the modeling process. In this paper, we present VectorRAG and GraphRAG modeling approaches to mitigate hallucinations and misinformation risks and evaluate their effectiveness in SME environments. Our experimental evaluation is conducted on multiple state-of-the-art LLMs, including LLaMA, Mistral, and Qwen, to assess performance in terms of useful response generation, risk of hallucination, contextual relevance, as well as human-interpretation. The results demonstrate that RAG-enhanced LLMs can significantly improve response quality by reducing hallucinations and misinformation, thereby supporting more reliable, trustworthy, and context-aware decision-making in SME environments.
[AI-271] AutoFOAM: The Self-Refining Autonomous OpenFOAM Agent
链接: https://arxiv.org/abs/2608.00003
作者: Arun Govind Neelan,A Seshaditya
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Computational Fluid Dynamics (CFD) plays an important role in modern engineering, but using open-source solvers such as OpenFOAM requires considerable knowledge and skills, as well as time-consuming configuration file setup. To reduce this burden, we propose AutoFOAM - a self-evolving large language model (LLM) agent that creates, evaluates, runs, and evolves its own OpenFOAM simulations based solely on natural-language instructions. Our model is pre-trained on the Qwen-coder 2.5-14B, which is then fine-tuned on 252 text prompts targeting 7 OpenFOAM solvers, 13 parametrized mesh templates, and a y plus-aware numerical policy. The crucial element of the algorithm is a sophisticated evolution loop composed of 7 stages. To prevent model degeneration under repeated self-training, the agent employs three complementary anti-collapse streams: RAG-augmented retry context, surgical dictionary-level patching, and prompt-diversity paraphrasing. By bridging generative artificial intelligence with rigorous fluid simulations, AutoFOAM accelerates rapid prototyping and democratizes advanced CFD workflows.
[AI-272] Revisiting Classic Thought Experiments to Measure Consciousness for Artificial Intelligence Safety
链接: https://arxiv.org/abs/2608.00001
作者: Peter David Fagan
类目: Artificial Intelligence (cs.AI)
备注: 5 pages
Abstract:This research note revisits Leibniz’s mill, Turing’s imitation game, and Searle’s Chinese Room through the Conservation-Congruent Encoding (CCE) framework. It formalises a toy symbolic setting in which successful behaviour is measured by task performance ( W_causal,T ), while the efficiency with which preserved internal structure supports that behaviour is measured by operational consciousness ( \kappa_T ). Within this setup, an uncompressed lookup system and a compact generative system can in principle achieve comparable behavioural success, yet diverge sharply in \kappa_T : the former relies on an expanding standing store of unreused mappings, whereas the latter reuses compact internal structure. The note therefore reframes classic disputes about understanding by separating outward performance from the organisation that sustains it, and motivates why this distinction may matter for later AI-safety analysis.
[AI-273] Corrigible Assistance in One Round: Prag matic-Pedagogic Best Response
链接: https://arxiv.org/abs/2607.27508
作者: Elle Lazarski,Jaime Fernández Fisac
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: World Symposium on the Algorithmic Foundations of Robotics (WAFR) 2026
Abstract:Assistance games formalize human-robot collaboration under asymmetric information: the human knows the goal, while the robot must infer it from observation and interaction in order to assist effectively. In general, computing optimal assistance game strategies online is intractable, since exact solutions require planning in a POMDP. We identify a class of assistance games in which pragmatic-pedagogic reasoning resolves goal uncertainty in a single time step, rendering the full-horizon game exactly solvable by a tractable best-response procedure. Within this class, we show that mainstream inverse optimal control exhibits an inference ceiling that hinders alignment, while pragmatic-pedagogic reasoning overcomes this barrier by immediately disambiguating goals through actions that look equivalent under task execution alone. Finally, we validate our theoretical results and proposed method on a simple collaborative block-building example.
[AI-274] Music Restoration via Latent Operator Optimization and Diffusion Model Priors
链接: https://arxiv.org/abs/2608.01972
作者: Michal Švento,Eloi Moliner,Valtteri Kallinen,Lauri Juvela,Vesa Välimäki,Pavel Rajmic
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
备注: Accepted to the the 27th International Society for Music Information Retrieval Conference (ISMIR 2026)
Abstract:Music restoration seeks to recover a clean signal from an observed recording degraded by an unknown effect, distortion, or corruption. Existing systems often rely on paired training data and distortion-specific supervision, which limits their use when the forward process is not known in advance. We propose LOUDAR (Latent-space Optimization of Unknown Distortion for Audio Restoration) a general-purpose restoration method that operates in the latent space of a pretrained audio autoencoder and models the unknown distortion as a learnable latent operator. At inference time, LOUDAR alternates between estimating the clean latent variable and updating the latent operator parameters. An unconditional latent diffusion model provides a prior over clean audio and regularizes this inference by steering the latent estimate toward the manifold of clean recordings. Because the degradation model is adapted per input, the approach is broadly applicable across diverse restoration problems. We evaluate LOUDAR on singing voice effect removal and restoration, as well as guitar distortion removal, and show that it consistently improves over degraded inputs and is competitive with supervised and unsupervised baselines in waveform and latent domains.
[AI-275] Divisive Normalization Shapes Low-Rank Slow Manifolds for Continuous Working Memory
链接: https://arxiv.org/abs/2608.01947
作者: Zhaotian Gu,Jie Su,Weiwei Wang,Chang Liu,Tianyi Qian,Dahui Wang
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:The ability to robustly maintain and update continuous variables is a hallmark of working memory. While classical continuous attractor networks suffer from severe fine-tuning fragility, standard artificial recurrent neural networks (RNNs) like GRUs and LSTMs typically fail to stably learn continuous manifolds, instead shattering the state space into discretized point attractors. To bridge this gap, we draw inspiration from divisive normalization, a canonical neural computation widely observed across cortical circuits, and propose the Recurrent Divisive Normalization Network (RDNN), a minimal and algebraically isolated model of dynamic division. Through dynamical systems analysis on canonical working memory tasks, we demonstrate that this biophysical constraint allows the network to converge to robust, high-fidelity slow manifolds. Furthermore, we analyze the gradient dynamics of divisive normalization during Backpropagation Through Time (BPTT), showing that it introduces an activity-dependent local gradient scaling. This scaling dampens parameter updates in highly active regimes, which empirically aligns with a significant self-compression of the network’s effective rank, confining the recurrent dynamics to a tight, low-dimensional subspace while avoiding the optimization pathologies associated with explicit low-rank factorization. Finally, ablations demonstrate that while subtractive inhibition can maintain static memories, divisive normalization is mathematically essential to prevent manifold shattering under time-varying inputs. Our findings identify divisive normalization not merely as a biological artifact, but as a critical computational mechanism for learning high-fidelity continuous representations.
[AI-276] Radar Detection in the CBRS Band: Techniques Challenges and Future Directions
链接: https://arxiv.org/abs/2608.01786
作者: Madan Baduwal,Priyanka Paudel
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注:
Abstract:The 3.5 GHz Citizens Broadband Radio Service (CBRS) is a shared wireless band that allows both government systems and commercial networks (such as private LTE/5G) to use the same spectrum. To prevent interference with critical government systems, especially naval radars, CBRS uses a monitoring system called the Environmental Sensing Capability (ESC). ESC acts like a network of sensors that continuously listens for radar signals and alerts the system when they are detected, so commercial users can temporarily stop or adjust their transmissions. This paper reviews how radar signals are detected within the CBRS band. We first explain the regulatory framework and describe the types of radar signals that need to be identified. We then examine traditional detection methods, such as energy-based and pattern-matching techniques, and compare them with newer approaches based on machine learning and deep learning, which can automatically learn to recognize radar signals from data. We also review publicly available datasets and testing platforms used to evaluate these detection methods, along with key performance requirements such as high detection accuracy (e.g., 99% detection probability (radar overlap recall)) and low delay (e.g., within 60 seconds). Finally, we highlight current challenges, including false alarms, interference from modern wireless systems, and the need for real-time operation. Overall, this survey shows that while traditional methods are simple and reliable in controlled settings, modern learning-based approaches offer better performance in complex environments. The future of CBRS radar detection will likely combine both approaches to achieve accurate, fast, and robust performance in real-world deployments.
[AI-277] he Label Defines the Timescale: Trait-State Limits of Temporal-Aggregate Learning
链接: https://arxiv.org/abs/2608.01587
作者: Xizhe Zhang
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Machine-learning benchmarks often pair a label that aggregates a long temporal horizon with input observed through one or a few short windows. Their apparent performance ceiling may therefore be an acquisition-protocol ceiling rather than a model-capacity ceiling. We study labels of the form \Theta_g,T=T^-1\int_0^T g\Z(t)\,\mathrmdt when the latent Gaussian process contains both a stable individual trait and a correlated within-individual state. An exact protocol-conditioned Bayes-risk identity provides a common tool. First, we decompose label variance into an O(1) trait component and an O(T^-1) state component, explaining why a snapshot can retain cross-sectional predictability while poorly tracking within-person change. Second, we derive task-dependent effective temporal spans: mean labels depend on the ordinary correlation time, whereas occupation-time labels depend on an entire spectrum of higher-order correlation times. Third, state-driven occupation-label variance is maximal when the stable trait lies at the threshold; window efficiency decays much more slowly away from that boundary. Under an equal segment budget, exact risks and Monte Carlo experiments show that repeated segments at one time rapidly saturate, whereas temporally dispersed observations continue to increase state explainability. The trait ceiling uses quantities available from ordinary test-retest data; only the state ceiling requires short-lag temporal calibration. The results distinguish architectural limits from protocol limits and show that the label, rather than duration or segment count alone, defines the relevant timescale.
[AI-278] Sheaf-theoretic Signal Processing on Graphs: Spectral Theory Filtering and Sampling
链接: https://arxiv.org/abs/2608.01318
作者: Gabriele D’Acunto,Leonardo Di Nino,Paolo Di Lorenzo,Sergio Barbarossa
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Modern sensing, communication, and learning systems generate heterogeneous network signals, with local data differing in dimension, modality, and geometric structure. Processing such data requires a mathematical framework capable of simultaneously modeling heterogeneous local signal spaces and the transformations relating them. Network sheaves provide such a framework by associating local vector spaces with network entities and linear restriction maps with their interactions. This is the first paper to develop a unified sheaf signal processing (SSP) framework on network sheaves, extending the fundamental operations of signal processing, namely spectral analysis, filtering, and sampling, to heterogeneous local spaces. Unlike graph and topological signal processing, where signals are modeled over a common vector space, SSP jointly models heterogeneous local signal spaces and the linear transformations relating neighboring spaces through restriction maps. We define the Sheaf Fourier Transform (SFT), whose frequencies quantify signal inconsistency induced by the network topology, the restriction maps, and the local geometry. Building on this representation, we develop polynomial sheaf filters and formulate sampling as the joint selection of network nodes and intra-node components. We derive perfect recovery conditions for bandlimited sheaf signals and propose a greedy sampling-set design algorithm. To incorporate application-dependent signal models, including different bases, dictionaries, and learned embeddings, we introduce representation sheaves and characterize the natural transformations that preserve spectral properties and guarantee interoperability across representations. Experiments on synthetic, motion-capture, and financial datasets validate the proposed framework and demonstrate consistent improvements over canonical graph signal processing baselines. Subjects: Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.01318 [eess.SP] (or arXiv:2608.01318v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.01318 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-279] alking to Digital Twins: Selective Disclosure and Belief Measurement in Financial Social Media
链接: https://arxiv.org/abs/2608.01181
作者: Boone Bowles,Raymond Duch,Sorin Sorescu
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI)
备注:
Abstract:Social media affect financial markets, but public posts by financial media personas are voluntary disclosures. What is not disclosed is therefore usually unobserved. We address this measurement problem by conducting repeated, real-time interviews of “digital twins” built from monitored finfluencers’ X accounts under a fixed protocol. The interviews recover stock-level public-persona belief proxies even when no public recommendation is made. Because the interviews are generated and archived before the relevant return windows, the design avoids the look-ahead bias that arises when LLMs are queried ex post. The evidence shows that information obtained from these digital-twin interviews predicts the cross section of large-cap stock returns in the expected direction. Repeated real-time interviews therefore show how selective disclosure can be turned into measurable panels of market views.
[AI-280] mperature-driven inversion and nonlinear dynamics in ChatGPT -like AIs
链接: https://arxiv.org/abs/2608.00939
作者: Neil F. Johnson,Frank Yingjie Huo,Bella Xinrui Li
类目: Physics and Society (physics.soc-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI); Adaptation and Self-Organizing Systems (nlin.AO); Applied Physics (physics.app-ph)
备注:
Abstract:Increasing the temperature of an ordinary many-state system increases access to a wider range of states and hence increases its entropy. We find the opposite in ChatGPT-like AIs, even though raising the decoder temperature likewise increases access to a wider range of states (next-token choices). Across 12,000 continuations from 11 AIs, autoregressive feedback drives the long-time output population through an entropy maximum and into population inversion. The transition features frozen states, cycles, intermittency and noise-induced ordering. We present evidence of a hidden coordinate that acts as the state variable of an effective nonlinear map. Its trajectory average strongly predicts output repetition in separate test trajectories. ChatGPT-like AIs therefore behave not as `stochastic parrots’, but as a new class of controllable nonlinear physical systems whose internal dynamics can be measured and perturbed.
[AI-281] REIMU: Efficient Heterogeneous Hierarchical Reasoning for SSL-Based Speech Deepfake Detection
链接: https://arxiv.org/abs/2608.00857
作者: Kwok-Ho Ng,Tingting Song,Bingwen Feng,Peiya Li
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:The increasing realism of speech generated by text-to-speech and voice conversion systems poses growing challenges to media integrity and voice authentication. Self-supervised learning (SSL) has substantially advanced speech deepfake detection, where downstream backbones conventionally process SSL representations through a single forward pass. This work investigates the practical effectiveness of recurrent hierarchical reasoning for this task. We term this controlled study REIMU and systematically compare conventional single-pass backbones, weight-shared recurrence, homogeneous HRM, and heterogeneous HRM across four Base-scale SSL frontends. We further examine heterogeneous high- and low-level modules that combine self-attention with linear attention. Experiments on the ASVspoof 2019 and 2021 evaluation sets show that recurrence and hierarchical decomposition do not inherently improve detection, whereas heterogeneous operator assignment provides a more competitive configuration. Notably, the heterogeneous design remains competitive while using 10.8% fewer downstream parameters than the matched baseline, demonstrating its potential for parameter-efficient speech deepfake detection.
[AI-282] Anticipatory Digital Twins for Online Head-and-Neck Adaptive Proton Therapy via Foundation-Model Registration ALT MICCAI2026
链接: https://arxiv.org/abs/2608.00831
作者: Yizhou Wu,Yuheng Li,Xiaofeng Yang,Chih-Wei Chang
类目: Medical Physics (physics.med-ph); Artificial Intelligence (cs.AI)
备注: Accepted for publication in the Proceedings of the 29th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2026), Workshop on Digital Twins for Healthcare (DT4H)
Abstract:Head-and-neck (HN) proton therapy is highly sensitive to anatomical change over a 4-to-6-week course, as tumor shrinkage, weight loss, and setup variation can misposition the Bragg peak near critical organs such as the parotids, oral cavity, brainstem, and spinal cord, leading to target underdosing or organ-at-risk overdosing. Online adaptive proton therapy replans on the anatomy of the day, yet standard workflows rely on offline replanning that requires repeated CT acquisition and roughly a week of preparation, adding burden, cost, and delay. We investigate whether a patient’s treatment-day anatomy can be predicted before image acquisition by transferring longitudinal change from a population database. We propose a digital-twin framework built on a pretrained foundation-model deformable registration network used without patient-specific training. A first registration aligns a prior patient’s planning CT to the target and carries the prior’s during-treatment quality assurance CT (QACT) into the target frame; a second registration estimates the prior’s planning-to-QACT change, which is then applied to the target’s own planning CT to synthesize predicted CTs (pdCTs) with propagated contours. Using 88 HN patients, each with a planning CT and three QACTs, we show that pdCTs better match treatment-day anatomy than the static planning CT. Compared with the planning CT alone, normalized cross-correlation improves by 22.8%, Dice for organs-at-risk by 20.2%, and CT-number error decreases by 23.4%. Gains are largest for patients with major anatomical change and negligible when anatomy is stable. This cross-patient motion transfer leverages the digital-twin concept to anticipate treatment-day anatomy, enabling personalized online adaptive proton therapy without repeated imaging.
[AI-283] A Synthetically-accessible Universe of Chemically Recyclable Polymers
链接: https://arxiv.org/abs/2608.00149
作者: Anagha Savit,Wei Xiong,Harikrishna Sahu,Shivank S. Shukla,Will R. Gutekunst,Rampi Ramprasad
类目: oft Condensed Matter (cond-mat.soft); Artificial Intelligence (cs.AI)
备注:
Abstract:Polymers synthesized via ring-opening polymerization (ROP) of cyclic monomers represent an important class of materials due to their chemical recyclability and possible insertion in several critical applications. We present a dataset of 1 million synthetically realizable ROP polymer structures generated through a combination of Virtual Forward Synthesis (VFS) and polymer expert language models and qualified by stringent chemical heuristics. VFS is used to generate ROP polymers by applying known reactions to existing monomers. The polymer foundation models polyBART and POLYT5 further enable the generation of ROP candidates, with polyBART exploring its learned latent space and POLYT5 producing candidates via sequence-to-sequence generation. The resulting ROP polymers are subjected to robust filtering criteria to ensure novelty, validity and overall data quality through a combination of automated validation pipelines and a comprehensive set of chemist-informed heuristic rules introduced in this work for the first time. We hope that this dataset will serve as a valuable resource for downstream sustainable applications.
[AI-284] EEG-JEPA: Structured Latent Prediction for EEG Foundation Models
链接: https://arxiv.org/abs/2608.00114
作者: Jinhao Li,Zhiyuan Ma,Xueqiao Han,Zhongye Xia,Xinche Zhang,Shanghong Xie,Yixuan Liu,Yongjian Li,Runmin Gan,Tianlin Huo,Sen Song
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注: 9 pages, 6 figures
Abstract:Electroencephalography (EEG) foundation models aim to learn reusable representations from large-scale unlabeled recordings. A common pretraining strategy is masked waveform reconstruction, but applying supervision directly to noisy EEG may encourage models to recover predictable background activity, acquisition effects, and artifacts rather than neural structure that transfers across tasks. This raises a central question: what should an EEG foundation model predict to learn transferable representations? We introduce EEG-JEPA a structured latent-prediction framework for EEG foundation modeling. Rather than reconstructing masked voltage samples, a masked context encoder and predictor infer contextual latent states produced by an exponential-moving-average target encoder that observes the complete input. EEG-JEPA organizes target design along three complementary dimensions: target content specifies what representation is predicted, target support specifies where prediction occurs over structured electrode–time regions through Neurotopology-Aware Multi-scale Electrode-Temporal Masking (N-MET), and target depth specifies at which encoder layers supervision is applied. Together, these designs shift EEG pretraining from recovering missing measurements to inferring latent states from structured electrode–time context. We evaluate EEG-JEPA through controlled objective comparisons, frozen multitask transfer, and full fine-tuning. Under the same backbone, pretraining corpus, and training duration, EEG-JEPA improves the 14-task frozen macro balanced accuracy from 40.49% to 50.42% over CBraMod-style masked waveform reconstruction. Multi-source continuation further raises this result to 52.94%, the highest average among the EEG foundation models evaluated on EEG-FM-Bench. Under protocol-matched full fine-tuning, EEG-JEPA also improves the nine-task average balanced accuracy from 68.98% to 70.65%.
[AI-285] LLM BDC: Language Model for Biological Domains Oriented Clustering of Gene Ontology
链接: https://arxiv.org/abs/2608.00099
作者: Ximing Ran,Jie Xu,Peng Jin,Zhaohui Qin,Zhexing Wen,Jiaying Lu
类目: Genomics (q-bio.GN); Artificial Intelligence (cs.AI)
备注:
Abstract:Gene Ontology (GO) enrichment analysis is a foundational tool for translating large-scale genomic data into biological insights, but typically yields hundreds of redundant terms that obscure overarching themes. Existing summarization tools rely on fixed similarity metrics (REVIGO, GOSemSim, clusterProfiler::simplify()), gene-overlap measures (Metascape), or static hierarchy mappings (GO-slim), and therefore cannot incorporate biological context. Manual curation provides context-aware grouping but is subjective and labor-intensive. A scalable, context-aware framework is needed to cluster GO terms into interpretable higher-order biological domains. Here we present LLMBDC (Large Language Model for Biological Domains Oriented Clustering of Gene Ontology), a training-free framework that leverages zero-shot semantic reasoning of LLMs with confidence scoring to cluster GO terms into BioDomains using only ontology information at inference time. Benchmarked across Alzheimer’s disease (AD) and Fragile X syndrome (FXS) against six baseline methods including SapBERT, LLMBDC achieved substantially higher precision, recall, and clustering performance. Against ground-truth annotations, LLMBDC improved ARI from 9.7% to 73.3% (AD) and from 15.7% to 66.6% (FXS) over REVIGO, with corresponding NMI gains from 59.9% to 73.4% (AD) and 66.0% to 79.5% (FXS). A Cauchy combination test further confirmed that aggregated BioDomains retained statistically significant functional signals. LLMBDC provides a scalable, reproducible, and interpretable route to context-aware, system-level interpretation of GO enrichment results while preserving biological specificity.
[AI-286] GRAIN: Molecules Are Not the Right Granularity – Active-Ingredient Modeling for Safe Medication Recommendation
链接: https://arxiv.org/abs/2608.00098
作者: Juao Fan,Jinhan Li,Shengxin Zhu
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI)
备注: 19 pages, 4 figures, 3 tables. Code available at [URL]
Abstract:Medication recommendation from electronic health records must balance predictive accuracy against the risk of adverse drug-drug interactions (DDIs) under polypharmacy. Existing safety-aware recommenders operate at one of two granularities: the drug code, which treats each medication as an indivisible token, or the molecular substructure, which is finer than pharmacological interaction knowledge is actually organized. We argue that the active ingredient is the missing granularity, and introduce GRAIN, a medication recommendation framework built around it. GRAIN encodes longitudinal patient trajectories (diagnoses, procedures, past medications) with a selective state space backbone that handles long, irregular visit sequences in linear time. On top of it we introduce a joint objective unifying three knowledge sources aligned to a common medication vocabulary: a drug-level DDI graph, an ingredient-level DDI graph obtained by normalizing medication codes to active ingredients via RxNorm, and an EHR-derived co-prescription graph. A proportional controller adapts the accuracy-safety trade-off to the observed validation DDI rate rather than fixing it a priori. Under strictly matched settings – identical preprocessing, cohort, vocabulary, split, and evaluation code – GRAIN improves over a re-implemented MambaHealth baseline on MIMIC-IV across all standard multi-label metrics (Jaccard 0.4488 to 0.4983, PRAUC 0.6911 to 0.7485, F1 0.5989 to 0.6453) while reducing the drug-level DDI rate from 0.1875 to 0.0948. We further define an ingredient-level DDI rate, a safety measure invisible to drug-code-level evaluation. The results indicate that ingredient-level normalization recovers predictive signal erased by code-level aggregation, and that it is complementary to, rather than in competition with, accurate sequence modeling. Comments: 19 pages, 4 figures, 3 tables. Code available at [URL] Subjects: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI) ACMclasses: I.2.6; I.2.1; J.3 Cite as: arXiv:2608.00098 [q-bio.QM] (or arXiv:2608.00098v1 [q-bio.QM] for this version) https://doi.org/10.48550/arXiv.2608.00098 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-287] Conservation laws determine what physical learning remembers
链接: https://arxiv.org/abs/2608.00097
作者: Bijaya Dangol
类目: oft Condensed Matter (cond-mat.soft); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 6 pages, 2 figures, 3 tables
Abstract:Physical learning rules such as equilibrium propagation (EP), coupled learning (CL), and adjoint coupled learning (AL) train resistive networks through local measurements. In the small-nudge limit EP and CL exactly conserve the conductance mass K = (1/2) sum_e kappa_e^2, a property that stabilizes training. We show that conservation also governs the inductive bias of these rules. For a single output we prove that EP and CL are trajectory equivalent, so single-output experiments cannot distinguish what the two rules learn. We prove that AL does not conserve the mass but dissipates it at exactly twice its own loss. In linear circuits we prove that the conserved mass has no functional consequence: all three vector fields are homogeneous in the conductances, so the selected solution is independent of the initialization scale. Fixed nonlinear elements break this protection. In diode circuits the learned input-output function depends on the initialization scale by up to about forty percent, an effect absent in linear controls, and the conservative rules retain this memory permanently while the dissipative rule partially erases it. At matched training loss the dissipative rule typically generalizes worse than the conservative rules, although it reaches low training loss faster; the penalty correlates with the mass dissipated en route and fades in larger circuits, where little mass is lost. The conservation structure of a local learning rule thus sets its initialization memory, its training speed, and, where dissipation is appreciable, its generalization; it should be treated as a design parameter of physical learning machines.
[AI-288] SymNet: A Multi-Task Network for Joint Radio Map Reconstruction and Transmitter Localization
链接: https://arxiv.org/abs/2608.00087
作者: Lyuzhou Ye,Thanh Dat Le,Yan Huang
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurately predicting directional radio maps is essential for wireless applications, yet prior approaches primarily focus on omnidirectional signals and typically treat transmitter localization and signal map reconstruction as separate tasks. In omnidirectional settings, predicting the maximum signal location often coincides with the transmitter position, which limits the need for explicit joint modeling. However, in directional propagation where angular effects, reflections, and building occlusions play critical roles, this assumption no longer holds. To address this gap, we propose SymNet, a unified framework that jointly predicts directional radio maps and transmitter locations from sparse signal measurements. SymNet incorporates a prediction head for transmitter localization alongside radio map reconstruction, enabling simultaneous learning of both tasks. This joint formulation leverages their complementary information and leads to consistent improvements over treating them separately. Experiments on challenging directional scenarios demonstrate that SymNet outperforms state-of-the-art baselines, achieving superior accuracy in both radio map reconstruction and transmitter localization.
[AI-289] Automated ECG Interval Measurement and Wave Delineation Using Fast Fourier Convolution ResNet
链接: https://arxiv.org/abs/2608.00058
作者: Farhan Adam Mukadam,Harshit Mishra,Nachiket Makwana,Pradyot Tiwari,Subramani Kandasamy,KVS Hari
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 11 pages, 5 figures, and 7 tables. Includes large-scale evaluation on 10,646 12-lead ECGs
Abstract:Accurate measurement of ECG intervals, including PR, QRS duration, and QT/QTc, is central to cardiac diagnosis, yet the published ECG delineation literature evaluates performance almost exclusively as fiducial-point timing errors on small curated databases, rather than as clinical interval accuracy on large unselected cohorts. We bridge this gap by evaluating a complete end-to-end pipeline on 10,646 clinical 12-lead ECGs and reporting the first large-scale interval measurement accuracy study with full statistical characterisation, including bias, 95% limits of agreement (Bland-Altman), bootstrap confidence intervals, and rhythm-stratified error analysis. The underlying delineation is performed by a Fast Fourier Convolution ResNet (FFCResNet), adapting local temporal convolutions with global spectral processing via FFT and augmented with register tokens for contextual feature learning. Three per-wave models (P, QRS, and T) are trained on six public databases with ECG-specific augmentation. On 10,646 ECGs, the system achieves a QT MAE of 17.5 ms [95% CI: 16.9-18.2], with a Bland-Altman bias of +8.5 ms (LoA: -68.5 to +85.5 ms); a QRS duration MAE of 14.8 ms [95% CI: 14.6-15.0], with a bias of +12.6 ms (LoA: -12.3 to +37.6 ms); and a ventricular rate MAE of 0.8 beats/min. All biases are statistically significant by the Wilcoxon signed-rank test (p 0.001) but remain within or near published inter-observer variability bounds for sinus rhythms. Rhythm-stratified analysis reveals substantially higher QT errors for supraventricular tachycardias (SVT MAE: 75.0 ms; AVRT MAE: 85.3 ms) than for sinus bradycardia (SB MAE: 9.3 ms) and sinus rhythm (SR MAE: 8.9 ms), providing an honest characterisation of the deployment scope. Wave segmentation achieves internal Dice scores of 95.5%, 98.2%, and 96.1% for P, QRS, and T waves, respectively, and cross-database Dice scores of 78.1%, 85.5%, and 74.2%.
[AI-290] Not All EEG Moments Are Equal: Position-Adaptive Time Scheduling for EEG Generation
链接: https://arxiv.org/abs/2608.00048
作者: Boheng Liu,Ziyu Li,Chenghua Duan,Qing Li,Xia Wu
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Electroencephalography (EEG) generation is essential for alleviating data scarcity and enabling large scale neural modeling in brain computer interface applications. However, existing flow based approaches assume that every channel and every time segment within a sample shares a single global time progression, overlooking the fact that not all EEG moments are equal. To address this overlooked heterogeneity, we propose an adaptive EEG generation framework built on conditional flow matching. The framework introduces Position-Adaptive Time Scheduling, which tracks per position reconstruction error to modulate a position specific time progress within the flow matching trajectory. It further incorporates Factorized Spatio-Temporal Attention and a frequency aligned multi resolution spectral consistency loss to model inter channel dependencies induced by volume conduction and compensate for the power law spectral bias of EEG, thereby improving the quality of generated signals. Extensive experiments on three EEG datasets with distinct acquisition protocols and task semantics show that our framework consistently outperforms the strongest baseline, reducing TS-FID by up to 62.2% and improving downstream classification accuracy gain by up to 6.77 percentage points. These results suggest that the proposed method represents a promising step toward scalable, high fidelity data augmentation for real world brain computer interface applications.
[AI-291] Multimodal Wearable-Based Olfactory-Induced Emotion Recognition in Arousal-Valence Dimensions
链接: https://arxiv.org/abs/2608.00043
作者: Chen-Yang Xu,Lan Zhang,Fei-Yi Fan,Bin Hu,Qing-Hao Meng
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注:
Abstract:Olfaction is important for emotion regulation because it acts as a non-intrusive and cognitively lightweight pathway that directly engages the brain s affective circuitry and achieves unobtrusive emotional modulation. This trait is essential for advancing practical affective computing in daily and attention-critical scenarios. However, current olfactory emotion research has two key limitations. First, it overemphasises the valence dimension while neglecting arousal. Second, it lacks multimodal datasets that synchronously capture central and peripheral physiological responses to olfactory stimuli. To address these issues, we construct a large-scale multimodal olfactory emotion dataset based on 111 subjects, in which odors are labeled in the 2D arousal-valence space and electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmography (PPG) signals synchronously recorded. Nevertheless, multimodal signals present challenges such as non-stationarity, differences in latency, and cross-modal heterogeneity. Thus, we propose a spatiotemporal-frequency hybrid fusion network (STF-HFNet), which integrates three core modules. Frequency aggregation processing learns adaptive frequency aggregation in order to model non-stationary dynamics. Reciprocal guided attention enables reciprocal bidirectional calibration for cross-modal temporal alignment without synchronisation priors. Hybrid collaborative fusion combines spatial and channel attention mechanisms to enhance cross-modal complementarity while suppressing redundant information. Extensive experiments show that STF-HFNet achieves state-of-the-art (SOTA) recognition accuracies of 88.34% on the AMIGOS dataset and 92.40% on our self-constructed dataset, and outperform the SOTA methods by 8.27% and 5.07%, respectively.
[AI-292] Googles AI Economy ATLAS v1.0: Mapping Gemini Usage in the Economy
链接: https://arxiv.org/abs/2608.00038
作者: Zanna Iscenko,Scott Strand,Yiyuan Chen,Guillaume Aimard,Mihai Codreanu,Vivek Sampathkumar,Alex Imas,Julian Jacobs,Evalyne Muiruri,Juan Mateos-Garcia,Jia Jen Ng,Samirah Javed,Josh Martin,Omar Ajmeri,Denis Calin,Andrew Kim,Fabien Curto Millet,James Manyika
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper introduces the AI Economy ATLAS (Activity, Task, Landscape, and Adoption Study), an ongoing economic research initiative using Google AI usage data. The first iteration of ATLAS is built on 15 million de-identified interactions across the Gemini App, Google AI Mode, and Gemini API. Using privacy-preserving algorithms as well as established and bespoke classification methods, we map AI usage to over 800 occupations, 4000 tasks, 300 household activities, 150 countries, and 140 languages. We then make a number of observations on what the data reveals about AI’s diffusion, and its usage at work and in day-to-day life. In the workplace, we show that while AI adoption spans occupations covering just above 88% of US employment, penetration remains shallow and overwhelmingly collaborative in nature, with end-to-end task automation limited in scope. Outside of work, AI spans activities making up about 98% of Americans’ non-sleep time, with disproportionately high use in high-friction tasks such as engaging with government and professional service providers, likely delivering economic value that standard national accounts may miss. Globally, adoption scales with national wealth and has broad linguistic distribution, with English queries representing only around a third of volume. As we build upon ATLAS and expand its scope and capabilities, we will continue to provide large-scale empirical evidence to inform the public, policy and academic questions about the ongoing AI transformation.
[AI-293] Fast Generation of Representative Synthetic Dataset with Salsa to Train ATR Models with Electromagnetic Couplings Data-Augmentation
链接: https://arxiv.org/abs/2608.00037
作者: Benjamin Camus,Julien Houssay,Corentin Le Barbu,Eric Monteux,Cédric Saleun(a href=“http://DGA.MI” rel=“external noopener nofollow” class="link-external link-http"this http URL/a),Jean-Christophe Louvigné(a href=“http://DGA.MI” rel=“external noopener nofollow” class="link-external link-http"this http URL/a)
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注: arXiv admin note: substantial text overlap with arXiv:2510.24768
Abstract:This work focuses on training Automatic Target Recognition (ATR) models using simulated Synthetic Aperture Radar (SAR) images to circumvent the lack of real measurements. To obtain robust and versatile ATR models, simulation needs to generate massive datasets that encompass all the variability found in real measurements. Thus, we need a simulator that finds a good tradeoff between execution speed, computational resource consumption, and physical representativeness. In this work, we demonstrate that the Salsa simulator addresses this issue. We ran computing performance tests to show that Salsa can generate 21,600 synthetic images in less than 10 minutes using a single Nvidia GeForce RTX 4090 GPU. Using our ADASCA Deep Learning approach, we demonstrate that these data are sufficiently representative to train ATR models and reach state-of-the-art results on the MSTAR public dataset with an accuracy of 86 %. To illustrate how Salsa unlocks new possibilities to train ATR models, we use the simulator to conduct a study on Electromagnetic (EM) couplings between the targets and their immediate environment. We demonstrate that, if not accounted for in the training dataset, the variability of the EM couplings induced by the variability of the ground surfaces can significantly degrade the performance of ATR models, with an accuracy decrease of more than 4 %. We also show that Salsa can generate in a timely manner (i.e., in less than 4 hours using the same GPU as previously) a massive dataset of 648,000 images with a large variety of couplings to make the ATR models robust to EM coupling variations. Our ATR models can then achieve an accuracy of 87 % on the MSTAR dataset.
机器学习
[LG-0] onepot-Bench 0: towards lab-aware in silico chemistry benchmarks
链接: https://arxiv.org/abs/2608.02595
作者: Brandon Wang,Andrei S. Tyrin,Daniil A. Boiko
类目: Machine Learning (cs.LG)
*备注:
Abstract:Language models are playing an increasingly important role in laboratory science, performing tasks such as experiment planning, execution, and post-hoc analysis. However, precisely measuring their abilities is difficult, as scientific capabilities require a mixture of both problem-solving skills and domain-specific intuition. Existing evaluations rarely measure the capabilities required to make reliable decisions in a physical laboratory and often rely on public data that may have appeared in model training corpora. We introduce onepot-Bench 0, a proprietary benchmark suite for evaluating language models on synthetic chemistry capabilities relevant to wet-lab execution. onepot-Bench 0 comprises three complementary evaluations: ChemAbacus measures tool-free cheminformatics literacy and numerical reasoning; SynthRefusal characterizes safety and refusal behavior across a variety of benign, controlled, and designer-drug targets; and SynthBench evaluates reaction-outcome prediction and catalyst selection using private experimental data generated in our laboratory. Together, these evaluations probe basic competency, reliability, and deeper knowledge, all skills which are required for reliable performance in the lab. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.02595 [cs.LG] (or arXiv:2608.02595v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.02595 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-1] he Condition-Number Barrier in Sparse Least Squares
链接: https://arxiv.org/abs/2608.02588
作者: Honghao Lin,Vahab Mirrokni,David P. Woodruff
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注:
Abstract:In [AS21], Axiotis and Sviridenko conjectured that the linear dependence on the restricted condition number in sparse convex optimization cannot be improved by a polynomial-time algorithm. We establish their conjectured lower bound for least-squares objectives, conditional on the randomized exact-volume Small-Set Expansion Hypothesis in the weighted regular-graph formulation of Raghavendra, Steurer, and Tulsiani [RST12]. Concretely, for every fixed \gamma\in(0,1] , there is no randomized polynomial-time algorithm that, with probability at least 2/3 , returns a vector x such that, writing s=\lVert x\rVert_0 , [ \lVert Ax-b\rVert_2^2 \leq \min_\lVert z\rVert_0\leq k\lVert Az-b\rVert_2^2+\varepsilon \quad\textand\quad s=O!\left(k,\kappa_s+k^,1-\gamma\right), ] where \kappa_r is the restricted condition number at sparsity level r . The result holds even on rational instances with A of full column rank. The proof was first obtained using a fully automated Gemini-based agentic system developed internally at Google. The authors have verified the proof and edited it for clarity of presentation. Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG) Cite as: arXiv:2608.02588 [cs.DS] (or arXiv:2608.02588v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2608.02588 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Honghao Lin [view email] [v1] Mon, 3 Aug 2026 17:57:01 UTC (18 KB) Full-text links: Access Paper: View a PDF of the paper titled The Condition-Number Barrier in Sparse Least Squares, by Honghao Lin and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.DS prev | next new | recent | 2026-08 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"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[LG-2] Smooth Reparameterizations of Functions on Simplicial Product Spaces: Applications to Probabilistic Tensor Decomposition and Functional Data Registration
链接: https://arxiv.org/abs/2608.02576
作者: Shashwat Kumar,Arafat Rahman,Anuj Srivastava,P.-A. Absil
类目: Machine Learning (cs.LG)
*备注: submitted to Journal of Optimization Theory and Applications (JOTA)
Abstract:We consider optimization problems defined on product spaces of simplices. Examples of this class of problems include learning low-rank discrete multivariate probability distributions via simplex constrained tensor decomposition and performing functional data registration under the Square Root Velocity Function (SRVF) representation. In this work, we demonstrate the feasibility of replacing the product simplex with a smooth, elementwise strictly convex reparameterization, resulting in an unconstrained optimization problem on a manifold. We show that performing such a reparameterization results in the second order Karush-Kuhn-Tucker (KKT) points on the smooth manifold being mapped to the weak second order KKT points on the product simplex. This leads to a Riemannian Gradient Descent (RGD) algorithm for solving the reparameterized problem, which outperforms Projected Gradient Descent (PGD), and provides a more faithful representation of the original function shapes while performing curve registration.
[LG-3] Pseudorandom Streams within Diffusion Models Act as Learnable Inputs That Affect Generation Quality
链接: https://arxiv.org/abs/2608.02575
作者: Shengzhi Deng,Chenqi Ye,Yanze Guo
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 22 pages, 6 figures. Code and data are available at this https URL
Abstract:Diffusion models rely on stochastic inputs, yet on finite-precision hardware, the “randomness” they consume is realized as deterministic numerical orbits generated by pseudorandom rules. Accessible orbit structure can become a learnable input and affect both training and generation because the realized loss and its gradient depend on the concrete pseudorandom values consumed at each optimization step. A small multilayer perceptron predicts the next value of an orbit from its recent history, measuring general sequence predictability. A diffusion probe replaces real images with online random tensors while preserving the diffusion architecture and training objective, measuring whether the target system can exploit orbit structure. After controlling marginal statistics and screening out clear dynamical and finite-precision failures, the remaining orbits still produce markedly different diffusion losses and generation quality on MNIST and CIFAR-10. Both measures show strong rank correlations with macroscopic generation degradation, although their local rankings differ. After normalization by the IID baseline, the probe loss and the real-data diffusion loss approximately follow an empirical power law, with different exponents on the two datasets. These results suggest that a pseudorandom source is not only a distributional choice, but also a model-dependent structured input.
[LG-4] Benchmarking Sheaf Neural Networks for Inductive Tasks
链接: https://arxiv.org/abs/2608.02558
作者: Stefano Fiorini,Edoardo Coppola,Pietro Liò
类目: Machine Learning (cs.LG)
*备注:
Abstract:Sheaf Neural Networks (SNNs) generalize message passing by replacing scalar edge weights of standard Graph Neural Networks (GNNs) with learnable, edge-dependent restriction maps between node stalks. Despite their strong theoretical foundations and promising transductive results, SNNs have been evaluated almost exclusively on transductive node classification, leaving their behaviour under inductive protocols unknown. We address this gap through the first systematic benchmark of the sheaf design space, evaluating three diffusion mechanisms (neural sheaf diffusion, sheaf attention, and sheaf attention with Graph Attention Network v2), three restriction-map parameterizations, three stalk dimensions, and six modern GNN architectural components, within a message-passing reformulation that never assembles the heavy sheaf Laplacian, making the full design space trainable under cross-graph batching. Across 1,890 controlled experiments on 14 inductive datasets, multiple insights emerge: restriction maps are the dominant design choice and general maps are preferable, larger stalks add capacity but not long-range reach, architectural components explain more performance variation than the entire sheaf-specific design space itself. Under a matched protocol, SNNs transfer to inductive settings but do not reach the strongest baselines, with gaps being dataset-dependent. Practically, a single sheaf configuration can generalize across datasets, so effort is better spent tuning the surrounding architectural recipe than the sheaf operator itself.
[LG-5] Optimal Unambiguous DNFs and Alon-Saks-Seymour
链接: https://arxiv.org/abs/2608.02533
作者: Chirag Pabbaraju
类目: Computational Complexity (cs.CC); Discrete Mathematics (cs.DM); Machine Learning (cs.LG)
*备注:
Abstract:We construct unambiguous DNFs having width O(n) but 0 -certificate complexity \Omega(n^2) . By utilizing the special structure of these DNFs, we prove a lifting theorem with a constant-sized gadget that lifts the DNF to a communication problem, while losslessly translating the separation in certificate complexity to a separation in communication complexity. This leads to an optimal refutation of the Alon-Saks-Seymour conjecture, as well as an optimal communication lower bound for the Clique versus Independent Set problem, improving the previous results of Balodis, Ben-David, Göös, Jain and Kothari (FOCS 2021, SICOMP 2023) by several doubly logarithmic factors. As further applications of our construction to query complexity and learning theory, we exhibit: (a) a family of Boolean functions that has an optimal quartic separation between certificate complexity and approximate degree, and (b) a sample compression lower bound of \Omega(\sqrt\log c) for multiclass concept classes over c labels.
[LG-6] Uncertainty Is Not Enough: Value-of-Information Routing for Mixtures of LoRA Experts
链接: https://arxiv.org/abs/2608.02528
作者: Tom Saliencro,Rohan Desai,Priya Nair,Maya Lindqvist,Daniel Whitmore
类目: Machine Learning (cs.LG)
*备注:
Abstract:Mixtures of low-rank adaptation experts increase parameter-efficient capacity by routing each input through a subset of adapters. Recent dynamic routers activate more experts when the router or prediction is uncertain. This rule silently equates uncertainty with useful additional computation: an uncertain example may contain complementary, unqueried expert evidence, but it may instead remain ambiguous after every expert agrees. We formulate routing as certified value-of-information allocation. VI-MoLE learns the counterfactual risk remaining after each expert prefix, converts these predictions into simultaneous upper-risk certificates on held-out calibration data, and spends a global adapter budget on the token–layer action with the largest certified marginal risk reduction per unit cost. A terminal certificate then decides whether to answer or abstain. Unlike an uncertainty gate, this procedure distinguishes present ambiguity from recoverable and residual risk. We prove simultaneous certificate validity, optimal greedy allocation under diminishing certified gains, and allocation regret under value-estimation error. The evaluation protocol tests matched-compute accuracy, certificate coverage, risk–coverage, distribution shift, and tail latency against fixed and dynamic MoE-LoRA routers.
[LG-7] Aggregate-then-Calibrate for Human-centered Assessment with Theoretical Guarantees ICLR2026
链接: https://arxiv.org/abs/2608.02455
作者: Zejun Xie,Xintong Li,Guang Wang,Desheng Zhang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: Accepted by ICLR 2026
Abstract:Human-centered assessment tasks, which are essential for systematic decision-making, rely heavily on human judgment and typically lack verifiable ground truth. Existing approaches face a dilemma: methods using only human judgments suffer from heterogeneous expertise and inconsistent rating scales, while methods using only model-generated scores must learn from imperfect proxies or incomplete features. We propose Aggregate-then-Calibrate (AtC), a two-stage framework that combines these complementary sources. Stage-1 aggregates heterogeneous comparative judgments into a consensus ranking using a rank-aggregation model that accounts for annotator reliability. Stage-2 calibrates any predictive model’s scores by an isotonic projection onto the order, enforcing ordinal consistency while preserving as much of the model’s quantitative information as possible. Theoretically, we show: (1) modeling annotator heterogeneity yields strictly more efficient consensus estimation than homogeneity; (2) isotonic calibration enjoys risk bounds even when the consensus ranking is misspecified; and (3) AtC asymptotically outperforms model-only assessment. Across semi-synthetic and real-world datasets, AtC consistently improves accuracy and robustness over human-only or model-only assessments. Our results bridge judgment aggregation with model-free calibration, providing a principled recipe for human-centered assessment when ground truth is costly, scarce, or unverifiable.
[LG-8] Intention Inference Under Execution Noise: Separating Aleatoric and Epistemic Uncertainty in Social Dilemmas
链接: https://arxiv.org/abs/2608.02440
作者: Kival Mahadew,Jonathan Shock
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注:
Abstract:In noisy social dilemmas, intended actions are stochastically corrupted before execution, so an observed defection may reflect hostile intent or action error. Standard Markov Decision Process (MDP) formulations treat executed actions as states, structurally precluding this distinction and causing systematic over-retaliation. We introduce a Partially Observable MDP (POMDP) formulation encoding opponent intentions as latent states and executed actions as noisy observations, solved within the active inference (AIF) framework with a cost function that decomposes into epistemic and pragmatic components that jointly address inferring current intent and learning how intent evolves. In the Iterated Prisoner’s Dilemma with symmetric noise, we derive a critical noise threshold governing cooperation collapse, connecting it to a fixed-point condition on learned priors. Experiments reveal that the value of intention inference is context-dependent: the POMDP provides consistent advantages against conditionally cooperative opponents, but mutual intention inference under sufficient noise produces correlated belief-driven collapse. The advantage is specific to games where intent attribution is decision-relevant.
[LG-9] Foundations of Reinforcement Learning and Control:Connections and New Perspectives
链接: https://arxiv.org/abs/2608.02433
作者: Claire Vernade,Onno Eberhard,Martha White,Florian Dörfler,Csaba Szepesvári,Miroslav Krstic,Michael Muehlebach
类目: Machine Learning (cs.LG)
*备注: INFORMS tutorial
Abstract:Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields have common roots in dynamic programming, they have evolved with distinct methodologies, goals, and cultures. Despite decades of mutual influence, a significant gap persists between the two communities. This tutorial introduces adaptive control, actor-critic reinforcement algorithms, and a new way to combine these two paradigms for data-driven decision making on a classical locomotion control problem. Our aim is to provide a foundation for understanding the core differences between the two approaches and insights to help experts in each field better understand and engage with the tools and approaches of the other.
[LG-10] Why Large Language Models Fail at Tabular Prediction
链接: https://arxiv.org/abs/2608.02412
作者: Marta Garnelo,Wojciech M. Czarnecki
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the most common machine learning workloads: predictive analytics over tabular data. This gap is the founding premise of the fast-growing field of tabular foundation models, but the question of why generic LLMs fail has remained open. We study a frontier LLM in its purest inference regime - a single generation pass over a prompt containing the full training and test data, with no tools, no agentic scaffolding, and no fine-tuning - and systematically evaluate five hypotheses for the failure: (a) an inability to handle noisy or non-linearly-separable data; (b) the linearised CSV format obscuring column structure; © the tokenisation of numeric values; (d) the number of test points classified per query; and (e) the dimensionality of the input. Controlled experiments falsify (a)-(d). Dimensionality, in contrast, is decisive: sweeping random linear projections of thirty-one benchmark datasets, the LLM is the only method among nine whose accuracy decreases as dimensionality grows, while every classical baseline stays flat or improves. A behavioural comparison against 252 configured classical models finds that in two dimensions the LLM predicts like a local, distance-based method (up to 91.6% grid agreement), but in higher dimensions no classical model - even when augmented with tuned, dimension-dependent noise - reproduces its predictions. We do not claim to have identified the internal mechanism; our results show, more modestly, that the LLM’s capability dissolves with dimension in a way no noise-corrupted classical learner mimics - which explains why LLMs, so capable elsewhere, keep losing to fifty-year-old baselines on tables, while leaving the mechanism of the prediction as an open question.
[LG-11] Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data
链接: https://arxiv.org/abs/2608.02408
作者: Run Lin,Yingtian Tang,Jiawen Xu,Dongfei Huo,Lefan Wang,Helen Dawes,Dominic J. Farris,Dong Wang,Xijin Hua
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 13 pages, 5 figures, 6 tables. Published in IEEE Transactions on Neural Systems and Rehabilitation Engineering
Abstract:Accurate gait analysis in Parkinson’s disease (PD) typically relies on laboratory-based systems to capture biomechanical data, such as ground reaction forces (GRFs). Estimating GRFs using inertial measurement units (IMUs) provides a feasible alternative. However, this approach remains challenging in pathological gait like PD due to its high variability and complexity. Moreover, existing monitoring approaches often require multiple body-mounted sensors, which limit practicality and reduce patient compliance. To date, no study has investigated the application of deep learning approaches to address this challenge. This study proposes, for the first time, a deep learning framework to estimate bilateral vertical GRFs (vGRFs) in PD using an optimized set of wearable IMUs. A hybrid CNN-BiLSTM model was trained separately on data from 61 PD patients and 65 healthy controls (HC) using 13 IMUs. The model achieved high intra-subject accuracy ( R^2 = 0.98) and strong inter-subject generalization ( R^2 = 0.93 for HC, R^2 = 0.91 for PD). Sensor configuration was found to significantly influence estimation accuracy, with optimal sensor placement varying between PD patients and HC. For PD patients, estimation accuracy dropped markedly when reducing to a single IMU. The optimal configuration for PD used four IMUs. We identified a minimal setup with only two IMUs still enabled robust estimation. This compact setup offers a practical and scalable solution. Overall, the proposed approach supports the development of wearable vGRF-based gait analysis systems for Parkinsonian gait and potentially other pathological conditions, enabling accessible clinical assessments, remote monitoring, and personalized rehabilitation.
[LG-12] Network Information Enhances Unreliable News Domain Detection
链接: https://arxiv.org/abs/2608.02399
作者: Raphaela Keßler,Roman David Ventzke,Viola Priesemann,Giordano De Marzo
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG); Physics and Society (physics.soc-ph)
*备注:
Abstract:Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag. We ask whether network structure can improve news reliability classification, taking a domain-level approach that shifts the focus from individual articles to source reliability. From URL-sharing patterns in Telegram chats, we build a statistically validated domain co-sharing network and find assortative mixing by reliability: low-reliability domains group together, as do reliable ones. Exploiting this structure, we compare Graph Neural Networks against network-unaware baselines using both content-aware features (multilingual text embeddings) and content-agnostic features (spreading dynamics). GNNs consistently outperform Multi-Layer Perceptrons on identical features, with GraphSAGE best in both settings (accuracy 0.63 with content, 0.53 without), a 13-14% relative gain over the network-unaware baseline. Network topology thus systematically improves domain reliability assessment, and remains effective even when content analysis is infeasible.
[LG-13] Gecko: Fast Private Inference via Secure Public Encoder Offloading
链接: https://arxiv.org/abs/2608.02378
作者: Cheng’an Wei,Kai Chen,Yue Zhao,Congyi Li,Shenchen Zhu
类目: Machine Learning (cs.LG)
*备注: 12 pages, 10 figures, and 5 tables
Abstract:Private inference protects both user inputs and server models during neural network inference, but existing solutions remain too slow for practical deployment. This motivates recent efforts to run a public encoder, such as a pretrained backbone, outside the protection boundary and evaluate only a small private predictor cryptographically. While appealing for efficiency, this design is not inherently secure: naively offloading a public encoder may create a feature-space shortcut: an extraction adversary may learn the remaining private predictor’s feature-to-output mapping more easily than the original model’s input-to-output behavior. We present Gecko, designed to limit this additional risk while retaining a compact encrypted predictor. We leverage a frozen backbone that contributes hierarchical features, fixed Fastfood projections that compress them, and private feature gating that prepares them for prediction. We formalize ideal independence and information-preservation conditions as design guidance, then separately evaluate component-reuse extraction attacks. Across image and audio tasks, Gecko achieves 0.4-2.2 second inference with at most 10.8 MB communication and accuracy comparable to transfer-learning baselines. Under the evaluated attacks, reusing the offloaded public encoder provides no significant advantage to model-extraction adversaries. Source code and a demo are available at this https URL. Comments: 12 pages, 10 figures, and 5 tables Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.02378 [cs.LG] (or arXiv:2608.02378v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.02378 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-14] Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning
链接: https://arxiv.org/abs/2608.02348
作者: Martin Mocko,Daniela Chudá
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Malware clustering is a critical task in cybersecurity that helps discover threats and analyze evolving malware families. While self-supervised learning (SSL) and tabular representation learning (TRL) have achieved breakthroughs in other domains, their application to binary program clustering (the task of clustering all incoming samples regardless of label) remains largely unexplored. This study presents the first systematic investigation of SSL and TRL methods for binary program clustering, conducted in two phases on the public Ember and Bodmas datasets. In Phase 1, we establish a performance ceiling by adapting prominent vision-based SSL models (BYOL, SimSiam, Barlow Twins, VICReg) for tabular data with supervised pair generation, finding that BYOL and SimSiam achieve performance comparable to fully supervised models, while Barlow Twins and VICReg significantly underperform. In Phase 2, we evaluate purely unsupervised TRL methods against strong baselines (PCA, Autoencoder, UMAP), demonstrating that VIME establishes a new state of the art for binary program clustering. Informed by these findings, we propose VIME-R, a retrieval-augmented extension of VIME that replaces random marginal-distribution corruption with retrieval-based augmentation to generate more informative training pairs. VIME-R further improves upon VIME, achieving 2.7%-5.8% higher Homogeneity on both datasets. Our results highlight retrieval-augmented tabular representation learning as a promising direction for enhancing automated malware analysis. Code will be made available.
[LG-15] A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks
链接: https://arxiv.org/abs/2608.02274
作者: Li Yang
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: To appear in the Proceedings of the 2026 IEEE Global Communications Conference (GLOBECOM 2026). Code is available at: this https URL
Abstract:Electric Vehicle Charging Systems (EVCSs) are increasingly connected with Internet of Things (IoT) devices, which improves charging intelligence but also expands their exposure to cyber-attacks. Intrusion Detection Systems (IDSs) are essential for securing EV charging networks; however, conventional Machine Learning (ML)-based IDSs often rely on manual model design and mainly optimize detection performance without fully considering inference latency and model size. In this paper, a Multi-Objective Automated ML (MOO-AutoML)-based efficient IDS is proposed for EVCS security. The proposed framework uses a lightweight training strategy and a LightGBM-based automated feature selection method to select compact feature subsets based on accumulated feature importance. Then, Non-dominated Sorting Genetic Algorithm III (NSGA-III) jointly optimizes the feature selection threshold and key LightGBM hyperparameters under three objectives: maximizing weighted F1-score, minimizing 99th percentile inference latency ratio, and minimizing model size ratio. Experiments on CICEVSE2024 and CICIDS2017 show that the proposed MOO-AutoML IDS achieves competitive weighted F1-scores, lower P99 inference latency, and smaller model sizes than the compared methods. Overall, the results indicate that the proposed method can support accurate and efficient intrusion detection for EVCS and IoT security under practical deployment constraints.
[LG-16] Z-PEFT: Zero-shot Backdoor Detection in Parameter-Efficient Fine-Tuning via Canonical Spectral Signatures
链接: https://arxiv.org/abs/2608.02271
作者: Nicola Pitzalis,Donald Shenaj,Giacomo Cignoni,Andrea Cossu,Davide Bacciu,Antonio Carta
类目: Machine Learning (cs.LG)
*备注:
Abstract:Parameter-Efficient Fine-tuned (PEFT) models are frequently downloaded from open repositories by practitioners. This widespread practice creates a significant attack surface, as malicious actors can publish backdoored models that induce specific behaviors in response to predefined triggers. We study the problem of weight-space backdoor detection, where a detector classifier predicts whether a model is malicious using only its weights, enabling a lightweight safety mechanism. Most existing methods are designed and evaluated in a closed-world setting, where the detector is trained and tested on the same attack type. In contrast, we evaluate backdoor detection under novel conditions, including previously unseen attacks and datasets. We propose Z-PEFT, a lightweight meta-classifier that relies exclusively on layer-wise spectral measures for classification. Our experiments show that strong performance in the closed-world setting does not necessarily translate to high accuracy in zero-shot backdoor detection. Among weight-space detectors, Z-PEFT achieves the best performance while maintaining low and scalable computational cost.
[LG-17] Constrained Co-Design for Photonic Bayesian Neural Networks
链接: https://arxiv.org/abs/2608.02229
作者: Hendrik Borras,Xiao Wang,Bernhard Klein,Robin Janssen,Frank Brückerhoff-Plückelmann,Wolfram Pernice,Holger Fröning
类目: Machine Learning (cs.LG)
*备注:
Abstract:Classical neural networks frequently produce overconfident predictions on ambiguous or out-of-distribution (OOD) data, a liability that grows with each AI system deployed in safety-critical real-world scenarios. Bayesian neural networks (BNNs) provide a principled framework for uncertainty-aware prediction by replacing deterministic parameters with probability distributions, but repeated sampling increases latency, memory traffic, and energy consumption. Photonic probabilistic computing offers a promising alternative by exploiting intrinsic optical stochasticity for fast and parallel sampling. However, photonic BNNs are not ideal samplers: analog constraints on quantization, programming error, dynamic range, and representable mean and variance restrict the variational families that can be implemented in hardware. In this work, we study which hardware-imposed constraints limit scalable photonic BNN inference, how these constraints can be represented, and which ranges can be tolerated by photonic BNNs beyond small proof-of-concept networks. We formulate photonic BNN inference as constrained stochastic variational inference and perform a systematic ablation study over stochasticity location, stochasticity modality, quantization, programming error, and mean/variance bounds. From these results, we derive concrete co-design guidelines that distinguish hardware constraints that can be compensated by training from those requiring hardware or architecture intervention. We validate these guidelines under coupled, hardware-realistic constraints on Dirty-MNIST, CIFAR-10, and CINIC-10, using Fashion-MNIST and SVHN as OOD benchmarks, showing that hardware-aware training recovers predictive performance and uncertainty quality whenever the required variational family remains representable, whereas violations of representational limits require targeted hardware modifications.
[LG-18] CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning
链接: https://arxiv.org/abs/2608.02222
作者: Zijian Jiang,Chaoli Sun,Handing Wang,Xilu Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:One-shot federated learning (OSFL) has emerged as a promising collaborative model learning framework with only a single round of communication, offering significant advantages in communication efficiency and privacy preservation. However, OSFL often faces inherent limitations under severe domain heterogeneity across clients due to the lack of iterative knowledge exchange. Most existing OSFL methods require an auxiliary public dataset for knowledge distillation or leverage statistical information for parameter-level aggregation, overlooking feature shift caused by domain heterogeneity. To address these challenges, we propose CRIP, a personalized OSFL framework that operates in the representation space via channel-level feature alignment. To achieve this, each client uploads its feature extractor to the server, which broadcasts all extractors back to every client. Since not all source clients share compatible feature distributions with the target client, indiscriminate fusion of cross-client features would introduce domain-specific noise. Therefore, CRIP effectively measures the channel-wise representational similarity between the target client and each source client on a small local mini-batch, and selectively fuses only the most compatible features. Extensive experiments on domain-heterogeneous benchmarks such as DomainNet, PACS, and Office-Home demonstrate that CRIP consistently outperforms local models and state-of-the-art baselines, validating the effectiveness of representation-space personalization under extreme domain heterogeneity.
[LG-19] Start Classifying: Categorical Critics for LLM Reinforcement Learning
链接: https://arxiv.org/abs/2608.02181
作者: Zhijian Zhou,Long Li,Xuan Zhang,Zongkai Liu,Yulei Qin,Ke Li,Xing Sun,Xiaoyu Tan,Chao Qu,Yuan Qi
类目: Machine Learning (cs.LG)
*备注: Accepted at COLM 2026. 26 pages, 9 figures. Code: this https URL
Abstract:Proximal Policy Optimization (PPO) for large language models typically trains its critic by mean-squared-error (MSE) regression on scalar value targets. Although scalar MSE is statistically valid for estimating the conditional expected return, sparse binary rewards in reinforcement learning with verifiable rewards (RLVR) make critic optimization and calibration especially consequential: small value errors directly distort the scalar advantages used by PPO. We study whether a classification-based training objective can improve this critic signal. HL-Gauss PPO replaces the scalar MSE head with a categorical predictor over a discretized value support, trained by cross-entropy against smoothed HL-Gauss targets. Its output is decoded to a scalar expectation for standard GAE and PPO; the actor update is therefore unchanged and is not distributional. Across mathematical reasoning, tool-augmented math, and Search-R1, and on both Qwen2.5 and Qwen3 backbones, HL-Gauss PPO consistently improves over strong PPO and DAPO baselines. Controls with one-hot, two-hot, and Bernoulli two-bin critics show that neither a larger output head nor binary classification alone explains the gains. On a common collection of reasoning prefixes, HL-Gauss improves Brier score and calibration error and yields more symmetric, lower-variance advantages. These results position categorical value learning as an effective optimization surrogate for PPO critics in RLVR.
[LG-20] Randomized Algorithms for Learning Partitions with Near Optimal Query Complexity in Constant Rounds
链接: https://arxiv.org/abs/2608.02176
作者: Deeparnab Chakrabarty,Aditi Dudeja,David Saulpic
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注:
Abstract:We study the round complexity of learning a hidden partition \mathcalP of an n -element universe using PAIR queries: PAIR( x,y ) tells us whether x and y belong to the same part of the partition or not. While it is easy to learn using n|\mathcalP| queries using a basic algorithm and this query complexity is optimal, this basic algorithm is highly sequential. Black, Mazumdar, and Saha [COLT 2025] recently gave tight deterministic round/query tradeoffs when the number of parts of \mathcalP is known. In particular they prove \Theta(\log\log n) rounds are sufficient and necessary to limit the number of queries to n|\mathcalP| . They leave proving a randomized lower bound as an open direction. We show that randomization dramatically changes the picture. When the number of parts k = |\mathcalP| is known, we give a simple 3-round randomized algorithm using O(nk\log n) queries with high probability, and prove that 2 rounds require \Omega(n^4/3k^2/3) queries – the same as deterministic algorithms. We also study a more general setting where the number of parts is unknown. In this case, we give a 4-round randomized algorithm using O(n|\mathcal P|\log^2 n) queries with high probability, and prove that 3-rounds cannot achieve near-optimal query complexity. Furthermore, we show an even bigger separation in this regime between randomized and deterministic algorithms: for the latter, \Theta(\log n/\log\log n) rounds are necessary and sufficient to obtain near-optimal query complexity. Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG) Cite as: arXiv:2608.02176 [cs.DS] (or arXiv:2608.02176v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2608.02176 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-21] CARNet: Channel-Adaptive Receiver Network for Robust NextG Communications
链接: https://arxiv.org/abs/2608.02172
作者: Chao Jiang,Zhuo Xu,Yongli Yan
类目: Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 5 pages, 3 figures
Abstract:Neural receivers have been recognized as a promising paradigm for the next-generation (NextG) communications. However, due to the reliance on a static network optimized for specific channel conditions, their generalization capability across diverse scenarios remains a significant challenge. To address this issue, this paper proposes a novel channel-adaptive neural receiver network (CARNet) based on the mixture-of-experts (MoE) framework. The proposed architecture employs multiple expert networks together with an efficient routing mechanism to enable signal detection in various scenarios. The experts are constructed via stacked ResNet blocks and specialize in robust signal detection within specific channel conditions, while the routing mechanism incorporates a lightweight representation learning module, which projects the coarse channel estimate into a low-dimensional latent embedding. The learned embedding characterizes task-relevant channel conditions and provides efficient guidance for accurate expert selection. Link-level simulation experiments demonstrate that the proposed CARNet achieves superior performance across diverse channel conditions.
[LG-22] Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning
链接: https://arxiv.org/abs/2608.02168
作者: Xin Liu,Xiyuan Chen,Chenglong Wu,Xuan Zong,Jun Zhou,Dawei Cheng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately identifying credit fraud among billions of users is critical for minimizing financial losses and safeguarding the sustainability of inclusive financial services. Given that credit fraud risks are often concealed within heterogeneous user-risk graphs, Graph Neural Networks (GNNs) have emerged as an effective tool for risk mining by capturing complex dependencies. To address the scalability bottleneck of industrial GNNs, distributed training based on subgraphs is indispensable. However, existing strategies often compromise topological integrity for load balancing. This can be catastrophic for risk detection, as it indiscriminately severs the long-tail evidence chains essential for risk propagation. Overlapping subgraphs can restore severed risk contexts but inevitably introduce redundancy and noise, while overlooking the representation alignment across different local subgraphs. In this paper, we propose a risk-aware overlapping subgraph learning framework for large-scale credit risk detection. We first construct base partitions to ensure load balance. Then, we perform budget-constrained sampling that selects informative long-tail nodes, thereby preserving critical risk diffusion patterns while filtering out noise. To mitigate representation inconsistency, we design a cross-subgraph consistency alignment mechanism. By enforcing alignment constraints on the overlapping nodes, we harmonize the local representations into a globally consistent latent space. Extensive experiments on Weixin Pay’s production dataset demonstrate that our model significantly outperforms existing strategies for risk detection, offering a scalable and effective solution for industrial graph learning.
[LG-23] CoRe-GNN: Multilevel Message passing on Coarsened graphs
链接: https://arxiv.org/abs/2608.02128
作者: Antonin Joly,Nicolas Keriven,Aline Roumy
类目: Machine Learning (cs.LG)
*备注:
Abstract:Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified perspective that exposes their respective limitations. In particular, graph coarsening replaces it by a low-rank approximation that enables spectral guarantees but assigns uniform representations to clustered nodes, while Cluster-GCN restricts the propagation matrix to intra-cluster connections that allow efficient batching but sever long-range information. These are complementary failures of the \emphsame decomposition of the graph into groups of nodes. To obtain the best of both worlds, we propose \textbfCoRe-GNN, which performs both propagations in parallel at each layer: a coarsened inter-cluster term capturing long-range structure, and a local intra-cluster term preserving per-node discriminability. We prove that CoRe-GNN inherits analogous approximation guarantees to those of graph coarsening, and introduce a natural cluster-based \emphbatching scheme that scales to graphs with millions of nodes. On node classification benchmarks spanning homophilic, heterophilic, large-scale, and long-range graphs, CoRe-GNN outperforms both graph coarsening and Cluster-GCN baselines. Notably, CoRe-GNN reaches competitive accuracy on \emphlong-range tasks, while remaining memory-efficient through batching.
[LG-24] One QK Channel Many Sources: Guarding Low-Precision Attention Collapse
链接: https://arxiv.org/abs/2608.02091
作者: Shuxiao Xie,Shuyang Xie,Yuan Cao,Dezhi Ran,Wei Yang,Tao Xie
类目: Machine Learning (cs.LG)
*备注: 22 pages, 4 figures. Code and research artifacts: this https URL
Abstract:A bfloat16 transformer can train normally for many steps and then collapse abruptly. Distinct low-precision errors can trigger the same failure, leaving unclear whether each source needs its own repair or one shared route can be blocked. We isolate a reproduced GPT-2-class collapse to the streaming-softmax accumulator, where fp32 accumulation repairs it, and use the fault as an assay for moving controlled errors across sources. Errors placed outside attention still drive the same query-key (QK) spectral runaway, while correcting only QK keeps training stable with the source fault active. This source-channel dissociation shows that fault source is not failure channel. It holds across the tested architectures and scales and reproduces on a second GPU architecture. A causal probe projects each update off the current QK weights’ leading three singular directions: the query projection’s largest singular value stays at 11.1, whereas removing equal energy elsewhere leaves it at 237. The QK channel therefore drives the early runaway rather than merely tracking it. Entry depends on temporal sign-coherence across steps, not aggregate deviation. QK-Guard closes the channel with a dormant controller that switches on parameter-free QK normalization when attention-logit saturation begins. It contains every tested runaway and matches always-on QK normalization over 60k steps, while non-QK actions at the same trigger fail. The results support intervention at the shared QK locus rather than separate repair at each fault source.
[LG-25] Pretraining on Call Graphs: When Binary Analysis Tasks Profit From Context
链接: https://arxiv.org/abs/2608.02084
作者: Samuel Valenzuela,Johannes Kinder
类目: oftware Engineering (cs.SE); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 12 pages, 5 figures. Accepted at ICPC '26
Abstract:Binary function embedding models are trained to encode the semantics of binary code in such a way that they can be generalized to a variety of reverse engineering tasks, such as binary code search, vulnerability detection, or malware classification. While many models only take the function in question as contextual input, there have been successful attempts to improve function embeddings by leveraging information from the call graph. In this study, we dissect the implications of these embedding refinements. We conduct experiments using a range of graph-based models on the embeddings generated by two state-of-the-art binary function embedding models. Integrating inter-procedural context, we show that improvements on binary code similarity detection (BCSD) will not necessarily generalize to downstream tasks, neither of semantic nor of syntactic nature. More generally, we find that optimizing for semantic similarity tasks correlates with worse performance on syntactic tasks. By conducting an explanatory analysis on the dataset, we find that the call graph-based enhancements significantly enhance the robustness of embeddings, particularly in scenarios where the initial models struggle. Furthermore, we observe that the added context is more beneficial for namespace-related functions than for those focused on individual logic, confirming that the call graph can be leveraged most effectively in context-dependent scenarios.
[LG-26] Isotonic Bradley-Terry Model for Paired Comparison Data
链接: https://arxiv.org/abs/2608.02081
作者: Ryoya Yamasaki
类目: Machine Learning (cs.LG)
*备注:
Abstract:In this paper, we study prediction problems for paired comparison data, for example, predicting the win probability between two unmatched players and ranking all the players according to the order of their strengths by using win probability data between two matched players. Paired comparison data are typically analyzed using Bradley-Terry and Thurstone-Mosteller models. These models predict the win probability by transforming the difference between learned rate parameters, which represent players’;strengths, with a pre-specified inverse link function, and employ the order of learned rate parameters for player ranking. However, these models may suffer from model misspecification owing to the selection of a fixed inverse link function. Therefore, in this study, we propose to learn the rate parameters by a (sub-)gradient method and the inverse link function by an isotonic regression technique alternately. The proposed model guarantees monotonic improvement in training error, and is likely to yield an exact tie when the available data is insufficient to establish a strict ranking. We also verified that the proposed model could improve the win probability prediction and ranking performance through numerical experiments with synthetic data and real-world data of football Premier League, baseball MLB, and tennis ATP tour.
[LG-27] Feed-Forward Steering in Transformer Residual Dynamics
链接: https://arxiv.org/abs/2608.02071
作者: Timur Mudarisov,Mikhail Burtsev,Radu State
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注:
Abstract:Attention-only dynamical theories model Transformer residual directions as particles aggregating on a sphere. We extend this framework by incorporating the feed-forward network (FFN) term as a local steering field acting on each token state. The resulting theory predicts that the tangential component of the FFN field is necessary for motion in residual-direction space, that critical residual directions correspond to nonlinear projective equilibria, and that a commutator defect determines when a finite attention–FFN block can be accurately approximated by a parallel, additive flow. Across GPT-2, Pythia, Mistral, and Llama models, the extended theory improves one-step angular prediction relative to an attention-only baseline, with the contribution of the FFN increasing from GPT-2 to Llama-3-8B. Intervention experiments show that retaining only the tangential FFN component preserves most model quality, whereas retaining only the radial component causes performance to collapse. The tangential component also preserves output diversity under aggregation pressure. As a practical application, layers with small commutator defects can be approximately parallelized with only a modest increase in loss, whereas layers with large defects degrade rapidly. These findings support the interpretation of FFN layers as directional steering fields that shape Transformer residual geometry and govern the feasibility of block-level interventions.
[LG-28] Open-DiffLoco: Open-Source Differentiable Learning for Deployable Blind Quadruped Locomotion
链接: https://arxiv.org/abs/2608.02069
作者: Martin Opat
类目: Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 8 pages, 5 figures, Project page, videos, and code available at: this https URL
Abstract:Developing deployable locomotion policies through conventional reinforcement learning often requires complex reward engineering and expensive training times. While differentiable simulation offers a highly efficient alternative, open-source tools capable of end-to-end transfer of these policies to physical hardware remain limited. This paper introduces Open-DiffLoco, an open-source framework for training deployable blind quadruped locomotion policies with differentiable simulation. The framework implements the Short-Horizon Actor-Critic (SHAC) algorithm in MuJoCo XLA (MJX) and trains a proprioceptive policy that transfers to real-world hardware. The deployed policy removes privileged actor observations, including base linear velocity, and does not rely on reference trajectories. It also uses a substantially simplified reward function, enabling the robot to discover walking patterns without the complex auxiliary rewards typically used in conventional reinforcement learning pipelines. When deployed on physical hardware (a Unitree Go2 quadruped), the trained policy tracks omnidirectional velocity commands with root-mean-square error below 0.2 m/s, reaches speeds above 1 m/s, and remains robust to uneven terrain and external physical disturbances, such as lateral pushes. Across the reported configurations, training uses under 6 GB of VRAM on a single NVIDIA GeForce RTX 5080 GPU and completes in approximately 20-60 minutes. As an algorithmic extension to SHAC, we propose Jacobian-Augmented Value Estimation (JAVE), which supervises the critic Jacobians to improve early first-order policy-gradient training. To our knowledge, Open-DiffLoco is the first open-source framework for training deployable locomotion policies using differentiable simulation. Deployment videos and source code are available at: this https URL
[LG-29] SCOPE: Entanglement Frontier Escape for Source-Free Class Unlearning
链接: https://arxiv.org/abs/2608.02058
作者: Junhao Cai,Dohun Kim,Sung Il Choi,Juhyun Park,Chengjun Jin,Dowon Kim,Changhee Joo
类目: Machine Learning (cs.LG)
*备注: Preprint
Abstract:Source-free class unlearning erases whole classes using only the forget data, judged at the representation level, where features can leak a class the head no longer predicts. Existing feature-space erasers answer with one fixed projection, yet forget and retain classes share a representation, so deleting one disturbs the other where they overlap. We prove this tension is a frontier. Every fixed projection that deletes pays a retain cost of at least the retain-readout energy along the forget-discriminant subspace, and erasing that subspace alone attains the floor. The leading source-free erasers all instantiate the form it binds, so the frontier limits the whole class. Conditioning the erasure on the input escapes it. Spectral Conditional Projective Erasure (SCOPE) does so with a single gate, suppressing the forget subspace chiefly on inputs its frozen head’s weight scores read as a forget class. It is closed form, needs no retain data or gradient training, and costs orders of magnitude less than retraining. Across five object, face, and speaker benchmarks spanning two modalities and both convolutional and transformer backbones, the frontier predicts the measured retain cost. SCOPE leads the source-free erasers on every benchmark and forget-set size, and at the hardest setting it tops every unlearner, trained methods included.
[LG-30] Secrets Everywhere: Auditing Memorization in Mobility Prediction Models CCS2026
链接: https://arxiv.org/abs/2608.02052
作者: Anne Josiane Kouam,Hristo Boyadzhiev,Konrad Rieck
类目: Machine Learning (cs.LG)
*备注: Full version of the paper accepted for publication at the ACM SIGSAC Conference on Computer and Communications Security (CCS 2026). Includes supplementary appendices omitted from the proceedings version
Abstract:Human mobility prediction models, which forecast the next location in a user’s trajectory, are increasingly deployed in urban analytics, navigation, and personalized services. Yet, little is known about their potential to memorize and expose sensitive user trajectories from training data. While memorization has been extensively studied in language models, mobility prediction poses unique challenges: training sequences encode human behavior at various spatial and temporal scales, creating privacy risks at different granularities. In this paper, we conduct the first systematic audit of memorization in mobility prediction models. While prior work has shown that privacy leaks can arise from such models, we systematically assess and quantify memorization risks at scale. We identify key challenges, including the lack of a randomness space, the multi-scale structure of trajectories, and user-specific behavioral diversity. To address these challenges, we introduce a framework to quantify mobility memorization at different levels of granularity: individual locations, anchor pairs, and subtrajectory segments. We also develop user-grounded reference sets to assess how likely a model is to prefer training data over realistic alternatives. Our evaluation across multiple models and datasets reveals pervasive memorization patterns that correlate with user regularity and increase the risk of data extraction at inference time. Our findings call for mandatory privacy auditing in mobility prediction models. Comments: Full version of the paper accepted for publication at the ACM SIGSAC Conference on Computer and Communications Security (CCS 2026). Includes supplementary appendices omitted from the proceedings version Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.02052 [cs.LG] (or arXiv:2608.02052v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.02052 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-31] Convex Neural Energy Elements: Monolithic Finite-Element Assembly of Geometry-Parameterized Neural Operators with Stability and Error Guarantees
链接: https://arxiv.org/abs/2608.02036
作者: Hongyue Jiang,Jianjiang Zhan,Chenzhuo Zhang,Fan Wang
类目: Machine Learning (cs.LG)
*备注: 19 pages, 10 figures, 1 table
Abstract:Extending the neural-operator element method from individually trained, fixed-geometry neural elements to a library of reusable, geometry-parameterized element types fails structurally: a field-predicting operator trained by value regression induces an energy whose assembled Hessian is indefinite, and Newton converges to spurious minima (247% error) even with 1%-accurate field predictions. We introduce convex neural energy elements: each element exports a scalar energy E(g,U), architecturally convex in its boundary degrees of freedom U and smoothly parameterized by its geometry g, realized as a hypernetwork-generated positive-semidefinite quadratic form (an input-convex correction is reserved for non-quadratic physics). A regularization-nullspace principle–the regularizer’s nullspace must contain the physics nullspace–removes an otherwise irreducible bias, and assembled elements inherit the classical guarantee that singular element stiffnesses yield a positive-definite global system. We prove conditional error bounds (energy-to-solution accuracy, element-count scaling, geometry generalization) and verify each experimentally. On heat conduction with elliptic holes, one trained element assembles into 2x2 to 8x8 grids and an L-shaped layout of unseen geometries at 0.6-1.0% relative L2 error, with 175x faster per-geometry setup for boundary-quantity workloads. A second trained element type mixes freely with the first in one monolithic assembly, and a three-dimensional instantiation reaches 0.23% on eight-element assemblies–the guarantees are type- and dimension-agnostic. A plane-strain elasticity element, whose physics nullspace is three-dimensional, lands on the analytically predicted regularization floors. Making the energy the learned object turns neural operators from single-use surrogates into reusable elements that inherit the assembly guarantees of the method they extend.
[LG-32] Upper-Expectile Multi-Step Q-Learning for Off-Policy Reinforcement Learning
链接: https://arxiv.org/abs/2608.02034
作者: Abdelghani Ghanem,Mounir Ghogho
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multi-step returns accelerate reward propagation in off-policy reinforcement learning, but couple the evaluation of each decision to the suboptimal logged actions that follow it, inducing a pessimistic bias that grows with the horizon. We propose Expectile n -step Q-learning (ENQ), which replaces the symmetric n -step temporal-difference (TD) loss with an asymmetric expectile loss on the action-value error, with expectile level \tau as the only method-specific hyperparameter added beyond n -step TD. We prove that the ENQ operator is a \gamma^n -contraction. Under deterministic dynamics, at \tau=1 , its bias vanishes at the optimal action-value function Q^* on covered in-support pairs, and the corresponding fixed point satisfies the separation- n instance and its multiples of the lower-bound inequality used by Long-Horizon Q-learning (LQL). Under stochastic dynamics, the operator bias admits two-sided bounds with horizon-independent noise constants. Using a single expectile level \tau=0.8 and a fixed backup horizon across 27 manipulation and navigation task instances, ENQ is competitive with LQL on aggregate, achieves higher measured training-step throughput in our profiling study, and benefits more from a ten-critic ensemble in a controlled scaling experiment.
[LG-33] DART: Decoded Attention over Recurrent States for Efficient Long-Context Sequence Modeling
链接: https://arxiv.org/abs/2608.02032
作者: Yixiao Qian,Song Chen,Pengkai Wang,Jiaxu Liu,Shengze Cai,Chao Xu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Modern language models are built primarily from Transformers, recurrent models, and their hybrid architectures. Transformers rely on token-level attention memories, while recurrent models such as state space models (SSMs) and linear attention maintain compact recurrent states. These architectures are typically instantiated separately or interleaved at the layer level, leaving open whether a shared memory representation can support both recurrent compression and attention-style retrieval. We study this question through the state space duality (SSD) view of Mamba-2, where the SSM state can be interpreted as a compressed associative key–value (KV) cache. We observe that Mamba-2 decodes token-conditioned values from this state but does not decode token-conditioned keys. Based on this observation, we propose DART (Decoded Attention over Recurrent sTates), which retains the chunk state contributions produced by the Mamba-2 chunked scan as chunk state memories, decodes token-conditioned keys and values from these memories, and performs state-memory attention (SMA) over the resulting KV pairs. The retrieved output is then combined with the native Mamba-2 output through a gated residual connection. DART supports practical training by reusing the Mamba-2 chunked scan and implementing SMA as a FlashAttention-style computation. Our analysis and experiments show that DART substantially reduces the length-dependent inference cache compared with a matched attention baseline (e.g., 75% savings when the chunk size is S=256 and the state size is N=128 ). Compared with Mamba-2, DART substantially improves associative recall and retrieval while preserving general language-modeling quality.
[LG-34] Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO
链接: https://arxiv.org/abs/2608.02031
作者: Ngoc Hung Nguyen,Bjorn Landfeldt
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: 7 pages, 5 pages
Abstract:This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In this system, to improve the quality of service, computations are expected to be completed within their deadlines. However, due to dependencies among tasks or subtasks, any missed deadline can lead to catastrophic consequences for the entire request. In this context, this work proposes an extended deadline mechanism with constrained flexibility. The main challenges lie in handling large-scale computations under strict latency constraints while limiting the number of allowable deadline extensions, especially in the presence of task dependencies within each request. To tackle these challenges, we develop a transformer-enhanced proximal policy optimization (PPO) framework that enables efficient collaboration among MEC servers. The proposed approach aims to maximize the number of tasks completed within their deadlines while minimizing the use of deadline extensions. By capturing temporal dependencies and cross-server interactions, the transformer improves decision-making for task migration. Simulation results demonstrate that the proposed method significantly outperforms conventional PPO and heuristic-based approaches in terms of task completion rate and overall system efficiency.
[LG-35] Scikit-fingerprints: Python library for scikit-learn compatible molecular fingerprints and chemoinformatics
链接: https://arxiv.org/abs/2608.02027
作者: Jakub Adamczyk,Adam Staniszewski
类目: Machine Learning (cs.LG); Software Engineering (cs.SE)
*备注:
Abstract:We present scikit-fingerprints, a comprehensive, fully scikit-learn compatible library for molecular machine learning in Python, based on RDKit. Molecular fingerprints and related functionalities are workhorses of chemoinformatics, yet the widely used open-source frameworks are not compatible with the wider Python machine learning ecosystem based on scikit-learn conventions. scikit-fingerprints closes this gap, bringing molecular fingerprints, molecular filters, similarity and distance measures, applicability domain estimation, data splitting strategies, and more under a single, familiar interface. Scikit-learn compatibility means that an entire chemoinformatics workflow, from a raw SMILES string to a deployable model, can be assembled from composable building blocks and can reuse the mature tooling of the surrounding ecosystem. The underlying RDKit code makes it familiar and extensible for custom chemoinformatics use cases. We put a strong focus on unified interfaces, ease of use, computational efficiency, customization, and extensibility. scikit-fingerprints makes molecular machine learning faster to prototype, easier to reproduce, and simpler to deploy.
[LG-36] AOS: Adaptive Optimizer Switching via Training-State Signals for Faster Convergence and Better Generalization
链接: https://arxiv.org/abs/2608.01997
作者: Alok Kumar Pandey,Umang Chaturvedi,Aatish Rana,Gopi Krishna Nedanuri
类目: Machine Learning (cs.LG)
*备注: 6 Page, 3 figures, 4 tables
Abstract:Single-optimizer training is a poor fit for the distinct phases of deep network optimization: adaptive methods handle noisy early gradients well but overshoot flat minima, while SGD with momentum generalizes better in the late phase but converges slowly early on. We introduce AOS-R (Adaptive Optimizer Switching, Rule-Based), a lightweight controller that monitors six online gradient-space signals – gradient noise scale (GNS), Hutchinson curvature trace, loss stagnation, update stability ratio, gradient stability index (GSI), and loss improvement ratio (LIR) – and switches among AdamW, SGD-M, and Lion as the optimization landscape evolves. State-preserving momentum transfer and a 400-step learning-rate bridge prevent accuracy degradation at every transition point. On CIFAR-100/WRN-28x10, AOS-R reaches 78% top-1 in 81 epochs – 26% fewer than AdamW (109), 43% fewer than SGD-M (143), and 16% fewer than Lion (96). Across eight model-dataset benchmarks, AOS-R achieves best accuracy on 6 of 8 combinations with a mean +0.4 pp gain and 0.80x convergence speedup over AdamW under a single shared hyperparameter configuration. Comments: 6 Page, 3 figures, 4 tables Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.01997 [cs.LG] (or arXiv:2608.01997v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.01997 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-37] ChaosProbe: A Neurochaotic Lens on Frozen Transformer Input-Embedding Spaces
链接: https://arxiv.org/abs/2608.01968
作者: Kunal Kumar Pant,Nithin Nagaraj
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:
Abstract:Transformer models are most often understood through what they do: their benchmark performance, generation quality, or behavior on downstream tasks. Yet frozen transformer input-embedding spaces may also be examined through their responses to a controlled deterministic probe before contextual computation or task-specific adaptation. Guided by this response-based view, we introduce \emphChaosProbe, a deterministic neurochaos-inspired method for constructing response-based fingerprints of frozen transformer input-embedding spaces. For each prompt-level embedding matrix, ChaosProbe applies a chaotic trajectory-based transformation and summarizes its Firing Rate and Entropy channel responses with complementary representation-level measures, producing a fixed-length signature for each model. In a bounded proof-of-concept study of 80 neutral prompts and four pretrained models—GPT-2, DistilGPT2, BERT-base-uncased, and RoBERTa-base—Pearson correlation, Spearman correlation, and cosine similarity each recover all four same-family nearest-neighbor assignments and both expected mutual family pairs. Euclidean distance recovers three of the four assignments and one of the two mutual family pairs. Paired bootstrap resampling supports the stability of the Pearson and Spearman pairings over the observed prompt set, and signature-validity checks show that constant or collapsed responses do not dominate the reported fingerprints. These results provide a cohort-dependent proof of concept that deterministic neurochaotic response signatures can expose broad structure among frozen transformer input-embedding spaces.
[LG-38] Finite-Time Analysis of Discounted Exponential-Utility Reinforcement Learning
链接: https://arxiv.org/abs/2608.01917
作者: Ankur Naskar,Vivek T A,Aditya Kumar,Gugan Thoppe,Prashanth L. A
类目: Machine Learning (cs.LG)
*备注:
Abstract:Discounted exponential utility provides a principled criterion for risk-sensitive sequential decision-making, but its nonlinear structure complicates reinforcement learning. A recent work \citepthoppe2026reinforcement addressed this difficulty by introducing a Bellman-compatible surrogate and two model-free fixed-point algorithms for optimizing it over stationary policies. However, their main convergence results are asymptotic. In this work, we establish finite-time rates of \tildeO (1/\sqrtn) for the aforementioned two algorithms under asynchronous Markovian sampling, where n is the iteration index and \tildeO hides logarithmic expressions. Importantly, we employ parameter-free choices for the stepsize parameter to derive these rate results. For the algorithmically simpler one-timescale method, the main challenge is that its update equation is not directly aligned with the contraction geometry of its underlying power-law operator. We overcome this mismatch by exploiting the boundedness, monotonicity, and homogeneity of the operator to obtain a local pseudo-contraction property for the relative-error dynamics. We then use a Moreau-envelope-based Lyapunov function and Polyak–Ruppert averaging to obtain the stated convergence rate with parameter-free stepsizes. For the two-timescale method, the main challenge is to control a tracking error on the faster timescale. These results provide the first finite-time guarantees for model-free discounted exponential-utility reinforcement learning.
[LG-39] Understanding and Correcting Low-Frequency Bias in EEG Foundation Model
链接: https://arxiv.org/abs/2608.01898
作者: Junjie Yu,Zihan Deng,Jianyu Zhang,Junrong Mu,Jiahui An,Wenxiao Ma,Ziling Lu,Yue Wang,Yan Zhu,Kexin Lou,Quanying Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Increasing EEG pretraining data scale or model capacity does not consistently improve downstream performance. We identify a persistent low-frequency bias in representations learned by diverse EEG foundation models, which remains across dataset scales, model capacities, and pretraining objectives. Our analysis links this bias to the interaction between EEG’s 1/f^\alpha -like spectral structure and neural networks’ tendency to preferentially learn low-frequency components. In masked autoencoders, the \ell_2 reconstruction objective further amplifies this imbalance: under comparable relative reconstruction errors, high-power low-frequency components contribute disproportionately to the loss. To address this issue, we introduce FAME, a frequency-balanced masked autoencoding framework that reconstructs time–frequency activity in predefined EEG bands from masked EEG inputs. FAME independently standardizes the reconstruction targets within each band and assigns equal weight to all band-specific losses, thereby balancing supervision across the EEG spectrum. Evaluated on 41 downstream tasks in OmniEEG-Bench, FAME learns more spectrally balanced representations and achieves state-of-the-art performance on 24 of them. These results underscore the importance of balanced spectral supervision for learning transferable EEG representations.
[LG-40] CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detection
链接: https://arxiv.org/abs/2608.01885
作者: Zemin Chao,Qianhui Xu,Jianhe Cen,Guangzhi Ge,Xiao Chen,Hoangzhi Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:While deep learning models have achieved state-of-the-art performance in time series anomaly detection, their complex architectures incur substantial inference overhead. Existing methods typically apply a uniform inference strategy across all data points, which is inefficient given that anomalies are inherently scarce and the vast majority of temporal data consists of predictable normal patterns. To mitigate this bottleneck, we propose CARE, a model-agnostic cascaded inference framework that integrates a Lightweight Pre-filter Model (LPM) with an existing high-capacity Complex Detection Model (CDM). The LPM rapidly filters high-confidence normal samples using a Residual MLP AutoEncoder and a Normality-Conditioned Gating mechanism. Crucially, we introduce a Structure Attention module to explicitly capture channel-wise anomaly contributions, and optimize the gating network via a confidence-guided selective routing objective that learns reliable routing decisions to reduce unnecessary CDM invocations. Extensive experiments across eight real-world benchmarks demonstrate that CARE effectively isolates high-confidence normal samples. By routing only uncertain samples to the CDM, our framework achieves 2.7\times to 4.8\times inference speedup compared to the most accurate SOTA approaches, while still maintaining competitive detection quality.
[LG-41] FUSOperator: Operator Learning for Transcranial Focused Ultrasound Digital Twins ALT MICCAI2026
链接: https://arxiv.org/abs/2608.01839
作者: Minjee Seo,Haris Ghafoor,Minju Seol,Seonaeng Cho,Kyungho Yoon
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: Published at Digital Twin for Healthcare (MICCAI 2026 Workshop)
Abstract:Transcranial focused ultrasound (tFUS) requires accurate estimation of the intracranial acoustic field, which is distorted by skull-induced aberrations. Numerical solvers are accurate but computationally expensive for digital twins, where the field must be re-estimated repeatedly as treatment conditions change. Existing deep-learning surrogates are fast but typically use voxel-to-voxel regression on a fixed grid, with no mechanism reflecting how acoustic energy propagates through the skull. We instead cast tFUS simulation as an operator learning problem and propose tFUSOperator, a coordinate-aware neural operator that maps the free-field pressure, skull anatomy, and treatment parameters to the intracranial field within a shared physical coordinate frame. To our knowledge, this is the first operator-based formulation of tFUS field prediction. On both seen and unseen skulls, the model localizes the acoustic focus accurately-reaching about 90% and 72% Dice, respectively-and it performs nearly as well from magnetic resonance (MR) as from computed tomography (CT) input while running 5.6 \times 10^4 times faster than numerical simulation. These results suggest a fast, radiation-free route to safe and practical digital twins for patient-specific tFUS treatment. The code is available at: this https URL.
[LG-42] ReFP-AD: Rectified Flow Preconditioning for Energy-Based Anomaly Detection ECCV
链接: https://arxiv.org/abs/2608.01793
作者: Camile Lendering,Erkut Akdag,Joaquín Figueira,Egor Bondarev
类目: Machine Learning (cs.LG)
*备注: Accepted at the European Conference on Computer Vision (ECCV) 2026
Abstract:Unified anomaly detection requires modeling highly heterogeneous normal data without access to anomalous samples. While foundation models like DINOv2 provide rich token representations, leveraging these spaces for explicit density estimation remains challenging. Energy-Based Models (EBMs) offer a principled formulation, but their training in high-dimensional token spaces is unstable due to anisotropy and strong cross-dimensional correlations, which degrades finite-step Markov Chain Monte Carlo (MCMC) sampling. We identify this instability as fundamentally geometric and introduce ReFP-AD (Rectified Flow Preconditioning for Anomaly Detection), which learns a geometric reparameterization that maps high-dimensional embeddings into a well-conditioned latent space via an optimal transport (OT)-coupled rectified flow. This preconditioning enables stable persistent contrastive divergence with preconditioned Stochastic Gradient Langevin Dynamics (SGLD) in full-dimensional token spaces. Anomaly scores are then derived from the learned energy landscape using gradient norms. Under a strict unified protocol on the MVTec-AD and VisA datasets, ReFP-AD achieves 98.6%/97.9% Image/Pixel AUROC on MVTec-AD and 97.3%/99.0% on VisA, outperforming prior unified EBM baselines by up to +10.8% in Image AUROC. Ablation experiments demonstrate that geometric reparameterization is critical for finite-step MCMC and accurate anomaly localization in high-dimensional token spaces. Code is available at this https URL
[LG-43] Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints
链接: https://arxiv.org/abs/2608.01745
作者: Yeonseo Jeong,Wonhyeok Ko,Sungweon Hong,Songnam Hong
类目: Machine Learning (cs.LG)
*备注: 13 pages, 6 figures, 4 tables
Abstract:Maximizing throughput under proportional fairness in dense wireless networks requires jointly managing user association, scheduling, base station (BS) activation, and handover control under hard finite-horizon energy and handover budgets, which induces a fundamental tension between BS-side energy management and user-side handover regulation. While multi-agent reinforcement learning (MARL) is a natural framework for such distributed sequential control, its application here faces two difficulties: finite-horizon budget constraints cannot be evaluated at each time slot, and the nonlinear proportional fairness utility admits no principled per-slot decomposition. We propose HeLyMARL, a Lyapunov-embedded heterogeneous MARL framework that resolves both via drift-plus-penalty decomposition with virtual queues. The energy and handover constraint pressures are internalized directly into a unified per-slot reward, converting the constrained finite-horizon problem into an unconstrained MARL problem. Comparison against two Lagrangian-based alternatives reveals a timescale separation: Lagrangian relaxation regulates constraints only across training episodes, whereas the virtual queues of HeLyMARL bound cumulative budget consumption at every partial horizon within an episode, a pacing guarantee beyond the reach of greedy Lyapunov-based control. Simulations show that HeLyMARL is the only method that sustains the throughput-fairness balance together with uninterrupted service throughout the horizon, outperforming conventional MARL, Lyapunov-based, and constrained MARL benchmarks without premature budget exhaustion.
[LG-44] LLM -Guided Retrieval for Prediction of Molecular Perturbation Responses ICLR2026
链接: https://arxiv.org/abs/2608.01734
作者: Betty Xiong,Jan-Christian Huetter,Gabriele Scalia,Tommaso Biancalani,Sepideh Maleki
类目: Machine Learning (cs.LG)
*备注: Published at MLGenX @ ICLR 2026
Abstract:Predicting transcriptomic responses to small-molecule perturbations across cell lines is central to drug discovery, but exhaustive profiling of drug-cell combinations is infeasible. We frame molecular perturbation prediction as retrieve-and-aggregate: approximate an unmeasured drug’s response in a cell line by aggregating measured responses of a small set of biologically related compounds. We propose LLM-Guided Retrieval (LGR), where a large language model (LLM) ranks candidate neighbor drugs (restricted to those profiled in the target cell line); after which a fixed mean aggregator combines their observed expression deltas to form the prediction. We evaluate on the Tahoe-100M single-cell perturbation atlas under unseen-drug, unseen-cell-line, and open-world regimes. LGR consistently improves over drug mean, ChemCPA, and chemistry-based kNN baselines, with the strongest gains for unseen cell-line generalization, where it achieves higher correlation and lower error than mean baselines. Across settings, LGR improves directional (sign) accuracy of gene regulation, indicating better recovery of biologically meaningful perturbation effects even when magnitude-based metrics are similar. These results suggest that retrieval quality, rather than predictor complexity, is a key driver of zero-shot molecular perturbation prediction, and that LLMs can provide a useful biological prior when used as constrained retrieval modules.
[LG-45] CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives
链接: https://arxiv.org/abs/2608.01725
作者: Zifan Zhang,Zhichao Hou,Tingxiang Ji,Yuchen Liu
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注:
Abstract:Modern computing and networking infrastructure emits telemetry continuously, yet operators convert it into decisions with a separate predictor per task, entity, and horizon. One generative model, pretrained once over an operator’s own event streams, could replace this fleet, an approach that already scales to high-cardinality streams in recommendation systems. However, point-forecast error on operational telemetry saturates near simple last-value baselines, so lower error alone need not improve the decisions it feeds. To close this gap, we present \sys, a generative foundation model for network and systems telemetry, evaluated by replaying the decisions its calibrated conditional quantiles drive. \sys treats heterogeneous telemetry as event-driven, irregularly timed entity streams and serves flexible forecast horizons in a single pass, requiring no future timestamps. To our knowledge, \sys is the first pretrained telemetry model to improve both HPC scheduling and network provisioning decisions under replay, its runtime estimator transferring zero-shot across months and its pretrained weights across domains from hours of target data. Extensive experiments on HPC job logs and network traffic confirm that \sys lowers the mean bounded slowdown of backfilling by up to approximately 77% over deployed user estimates and roughly halves the deployed rule’s violation rate. Our code is available at this https URL.
[LG-46] Beyond On-Policy Exploration: Integrating External Policy Rollouts for Reinforcement Learning in Diffusion Language Models
链接: https://arxiv.org/abs/2608.01717
作者: Wonseok Lee,Jimyeong Kim,Jungmin Ko,Wonjong Rhee
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recent reinforcement learning methods for diffusion large language models (dLLMs) commonly rely on on-policy rollouts generated by the target dLLM itself. When successful on-policy rollouts are scarce, however, on-policy training may receive little positive reward and make only limited progress. To mitigate this problem, we explore incorporating higher-reward rollouts generated by a stronger external policy alongside on-policy rollouts from the target dLLM. However, directly incorporating these external rollouts introduces two practical challenges: differences in rollout length and instability when jointly processing rewards from on-policy and external rollouts. To address these challenges, we propose External Rollout Integration with Length Control and Source-Specific Processing (ERILS), which controls external-rollout length and processes the rewards of on-policy and external rollouts separately. Experiments on Sudoku, Countdown, and MATH500 under zero-shot evaluation show that ERILS improves multi-sample performance across all three tasks, with the largest gains on Sudoku. On Sudoku, ERILS achieves 98.4% best-of-4 completion accuracy, compared with 40.3% for the strongest baseline. ERILS also maintains approximately 90% deterministic single-completion accuracy on Sudoku across generation lengths of 128, 256, and 512 tokens. Our component analysis further shows that length-controlled external rollouts are more effective than uncontrolled external rollouts, and that source-specific reward processing avoids the training collapse observed with joint reward processing. These results show that rollout construction and reward processing are important design dimensions when integrating external rollouts into dLLM reinforcement learning.
[LG-47] Beckmann Transport Models: From Autonomous Flows to One-Step Maps
链接: https://arxiv.org/abs/2608.01692
作者: Lee Cheuk-Kit,Florentin Coeurdoux,Peter Potaptchik,Yilun Du,Michael Samuel Albergo,Eric Vanden-Eijnden
类目: Machine Learning (cs.LG)
*备注:
Abstract:We propose an instantiation of flow matching that relies on a time-independent velocity field (an \emphautonomous flow) to exactly map between two distributions, so long as the target is singular, i.e.\ supported on a lower-dimensional data manifold. We also show that the one-step generative map associated with this flow is the unique solution of a simple conservation equation, which can be used to learn the map directly from samples. These autonomous flows and maps give a dynamical meaning to the flux constraint of Beckmann’s transportation problem. Their construction provides a unifying framework that recovers, for instance, the closed-form Poisson-flow generative model and equilibrium matching with a quadratic flow-matching regression loss. We illustrate how this theory corrects inconsistencies in existing methods and demonstrate the effectiveness of the autonomous flow and the one-step map on ImageNet 256x256.
[LG-48] Sharp Root Anti-Concentration via Projective Incidence and Ordered Root Laws
链接: https://arxiv.org/abs/2608.01670
作者: Zijun Wang,Yuchen Miao,Yifan Hu,Huanmin Liu
类目: Machine Learning (cs.LG)
*备注: 27 pages, 3 figures
Abstract:This paper answers the one-dimensional local root anti-concentration questions posed by Balcan, Pegden, and Sharma in the context of online optimization of piecewise-Lipschitz functions. For a homogeneous feature curve and coefficients whose density relative to the uniform law on a symmetric convex body K is bounded by A , we show that the worst-case interval-hitting constant equals A times a section-averaged projective incidence speed. For cube-supported coefficients, this speed is equivalent, up to universal constants, to the projective Lipschitz constant. This yields a sharp, dimension-free characterization and removes the previous \sqrt N loss. For monic degree- d polynomials under arbitrary coefficient laws, we prove that the interval-hitting constant is finite if and only if the ordered real-root laws have bounded densities, with a factor- d comparison that is sharp. Conditional and joint coefficient-space area formulas, together with a two-chart certificate, make this criterion verifiable for dependent and singular coefficient laws. We also give two graph-learning applications that complete the transition-to-regret chain. A cost-sensitive Gaussian-RBF harmonic classifier uses the projective incidence theorem and achieves expected regret \widetilde O((An^2D e^BD/\ell+1)\sqrt T) . A common-offset polynomial-kernel model uses rigid translation of the ordered roots and achieves \widetilde O((qn^2\kappa+1)\sqrt T) regret, even when the induced coefficient law is singular in the ambient coefficient space.
[LG-49] Evaluating Forecasting Techniques for Hardware Errors on a Large-scale HPC System
链接: https://arxiv.org/abs/2608.01648
作者: Kaiyuan Liao,Xiwei Xuan,Tanwi Mallick,Kevin Brown,Christopher D. Carothers,Kwan-Liu Ma
类目: Machine Learning (cs.LG)
*备注: Accepted at the 7th International Workshop on Monitoring, Observability, and Operational Data Analytics (MODA 2026), held in conjunction with ISC High Performance 2026
Abstract:Hardware error logs in high-performance computing (HPC) systems provide early signals of abnormal behavior, yet there remain challenges in effectively forecasting these errors using modern predictive methods. This work investigates the boundaries of applying time series forecasting to HPC hardware error dynamics. We use seven years of production logs from the Theta supercomputer to evaluate the predictive efficacy of classical statistical and deep learning models. Our results show that forecasting effectiveness depends strongly on the temporal structure of the error series: regularly occurring and structurally stable errors can be modeled accurately, particularly by LSTM and Transformer architectures with temporal features, while sparse and burst-dominated errors remain difficult to predict. Rather than proposing a deployment-ready failure prediction framework, this study provides empirical guidance on when forecasting is effective and highlights potential directions for improving forecasting accuracy in HPC hardware error analysis.
[LG-50] GraphIR: Architecture-Level Search States for LLM -Guided Neural Architecture Evolution
链接: https://arxiv.org/abs/2608.01633
作者: Zhen Liu,Wanqi Zhou,Shuanghao Bai,Yuhan Liu,Jinjun Wang,Jingwen Fu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) enable neural architecture search (NAS) directly over executable neural network programs. However, code-level flexibility does not provide the architecture state needed for effective mutation: LLMs must infer tensor dependencies, editable components, and compatibility constraints from implementation details. To address this representation mismatch, we propose GraphIR, an architecture-aware intermediate representation that supplements executable programs with a mutation-aligned candidate state. GraphIR organizes each candidate through three complementary views: a computation skeleton describing tensor flow, a mutation surface exposing editable modules and operations, and a validity envelope capturing interface contracts, propagated shapes, and downstream dependencies. To evaluate our method, we construct NAS-Dependency, a 120-question benchmark covering six complementary dependency-reasoning dimensions. The diagnostic shows that GraphIR is particularly effective at identifying exact producer occurrences, tracing dependency propagation, and diagnosing interface and failure risks. Across six downstream benchmarks including CLRS, GraphIR achieves the best overall search performance while maintaining comparable model size and favorable end-to-end NAS efficiency when integrated into OpenEvolve. These results show that a mutation-oriented architecture state provides an effective interface between executable neural programs and LLM-guided architecture evolution.
[LG-51] Online Algorithms via Minimax and Posterior Matching
链接: https://arxiv.org/abs/2608.01616
作者: Thomas Kesselheim,Marco Molinaro,Kalen Patton,Sahil Singla
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注: To appear at FOCS 2026
Abstract:Competitive analysis is central to the study of online algorithms, but upper bounds are often highly problem-specific. We develop a more unifying methodology via the minimax viewpoint. Guided by Yao’s principle, we reduce worst-case competitive analysis to Bayesian online design under an arbitrary correlated prior over arrival sequences. For such a prior, let X^* be the hindsight-optimal fractional solution for the realized instance, and let X^(t)=\mathbb E[X^*\mid \mathcal F_t] be its posterior process. Our guiding rule is posterior matching: at each time t , choose the feasible online action that tracks the current posterior X^(t) as closely as the online constraints permit. We show that this single principle yields optimal or near-optimal guarantees for several classical online fractional problems, including set cover, load balancing, matching and more general resource-allocation problems, recovering or improving state-of-the-art bounds in these settings with norm/concave objectives. Via known rounding reductions, it also yields randomized integral guarantees for weighted paging, MTS on star metrics, and ski-rental. At a technical level, our analysis reduces competitive guarantees to key probabilistic inequalities for the vector martingales generated by the posterior of the offline optimum. The resulting framework gives a reusable route from Bayesian online design under arbitrary correlated priors to information-theoretic worst-case competitive guarantees. Comments: To appear at FOCS 2026 Subjects: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS) Cite as: arXiv:2608.01616 [cs.LG] (or arXiv:2608.01616v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.01616 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-52] hermalizing Stochastic Programs
链接: https://arxiv.org/abs/2608.01615
作者: Mirko Amico,Andraž Jelinčič,Colin Oscar Nancarrow,Leo Tyrpak,David Roberts,Seth Morton,Dalton Sakthivadivel,Ashwin Gopal,Guillaume Verdon
类目: Emerging Technologies (cs.ET); Machine Learning (cs.LG)
*备注:
Abstract:We present a set of tools for mapping general stochastic programs to thermodynamic hardware designed for energy-efficient stochastic sampling. Given a target stochastic program expressed as a Directed Factor Graph (DFG) of stochastic channels, or equivalently as a Parametrized Stochastic Circuit (PSC), we first introduce a method to approximately compile each factor in the DFG to an Energy-Based Model (EBM) that is native to the hardware. We then analyze how the error of the compiled DFG accumulates from the per-factor errors, and introduce two training refinements, context matching and trajectory-level REINFORCE post-training, which can reduce the residual error left by training each factor in isolation. The \textttthermalizers framework takes a stochastic program expressed in the \texttttorx library and replaces its factors with thermodynamic kernels implemented and sampled using the \textttthrml library. We demonstrate it on several example applications, including a market simulator that learns the joint day-to-day dynamics of a panel of financial time series from recorded market history alone, a probabilistic model from mathematical ecology, Gibbs sampling of an EBM the hardware cannot natively express, and a sequential Bayesian design loop over a Gaussian stochastic circuit.
[LG-53] Latent-Regime Bias Auditing for Volatility Forecasting
链接: https://arxiv.org/abs/2608.01599
作者: Arthur Chagas,Pedro Bento,Yan Aquino,Arthur Buzelin,Wagner Meira Jr.,Cristiano Arbex Valle
类目: Machine Learning (cs.LG)
*备注: Accepted for publication at the IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr 2026)
Abstract:Volatility forecasts are commonly evaluated with aggregate accuracy metrics such as RMSE and MAE, but these metrics can hide conditional failures that matter for risk management. This paper proposes a model-agnostic audit framework for evaluating whether volatility forecasts remain reliable across latent market regimes. We learn time-series representations of market-state windows, cluster them into regimes using only training information, assign regimes out of sample, and compare aggregate forecast behavior with regime-conditional bias, tail-underprediction, and underprediction-sensitive economic losses. Applied to daily volatility forecasting across cryptocurrency and ETF assets, the audit shows that models with competitive aggregate accuracy can still exhibit substantial regime-specific bias and severe tail underprediction. The results suggest that volatility forecasting should be evaluated not only by average error, but also by where and how forecasts become unreliable. Our framework shifts forecast evaluation from asking which model is most accurate on average to identifying the market regimes in which apparently accurate forecasts fail conditionally. Reproducibility: this https URL
[LG-54] Meganeura: Portable GPU Training and Inference through Vulkan and Metal
链接: https://arxiv.org/abs/2608.01563
作者: Dzmitry Malyshau
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Programming Languages (cs.PL)
*备注: 18 pages, 4 figures, 10 tables
Abstract:Training and deployed inference often cross export, conversion, and platform-specific runtime boundaries. Meganeura asks whether one compact native compiler can span both phases on consumer GPUs. Its typed static graph, automatic differentiation, optimizer, checkpoint, memory planner, and runtime lower specialized programs through Vulkan and Metal. We compare five matched workloads with PyTorch on NVIDIA and AMD discrete GPUs, an AMD APU, Apple silicon, and an Intel iGPU. The protocol separates strict f32 from validated fast paths and gates forward and backward independently. Forty-eight of 50 device-workload-mode cells pass both gates; the other two share one unresolved backward-reference disagreement on a newly supported APU. In strict f32, Meganeura wins 12 of 20 GPU-referenced minimal-latency cells and has a median valid training gap of 1.8x. On the discrete AMD GPU, four of five inference workloads are within 1.10x of compiled ROCm PyTorch and three training workloads are faster. Under accelerated contracts, the worst training gap is 4.6x. Compilation takes 0.1-2.4 seconds versus 6-96 seconds for this http URL on supported GPU paths; the stripped binary is 13 MiB. Dispatch profiles localize the largest gaps to convolution derivatives and attention backward. A physical Android XR case study transfers a Meganeura-trained decoder into an Adreno/OpenXR application sharing the graphics queue. The results show that general consumer graphics APIs can support a compact shared train-to-deploy stack at useful, sometimes vendor-competitive performance. The measured gaps point to kernel coverage, scheduling, and arithmetic policy rather than an identified API limitation. Comments: 18 pages, 4 figures, 10 tables Subjects: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Programming Languages (cs.PL) ACMclasses: D.3.4; C.4 Cite as: arXiv:2608.01563 [cs.LG] (or arXiv:2608.01563v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.01563 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-55] Celty: SpMspV GPU Kernel and SIMT Co-Design for Efficient Dual-Sparse LLM Inference DATE
链接: https://arxiv.org/abs/2608.01536
作者: Ruokai Yin,Priyadarshini Panda
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注: ICCAD 2026. Will update with the camera-ready version once ready. The code is available on Github at this https URL
Abstract:Large Language Models (LLMs) increasingly rely on sparsity to reduce inference cost, but most prior work targets a single sparsity source-either weight or activation-and optimizes for batched multi-user inference. Dual-sparsity, which combines unstructured weight pruning with runtime activation sparsity, offers a compelling tradeoff among model size, accuracy, and latency for single-user decoding, but formulates as a Sparse Matrix-Sparse Vector (spMspV) workload that existing GPU kernels handle poorly. We propose Celty, a co-designed sparse format, GPU kernel, and SIMT microarchitecture for efficient spMspV in LLM inference. At the kernel level, Celty introduces a Run-Length Compressed CSC (RLC-CSC) format that enables vectorized loading of compressed weight columns and exploits both sparsity sources to skip unnecessary memory accesses, with shared memory used for scattered partial-product accumulation. At the microarchitecture level, the Celty Sparse SIMT Core integrates a pipelined RLC decoder to eliminate software-level index reconstruction and repurposes local register files for conflict-free accumulation-operating directly on the same RLC-CSC format without data layout changes. The Celty GPU kernel achieves up to 2.8x speedup over cuBLAS and 2.4x over Flash-LLM. With the Sparse SIMT Core, speedups reach up to 5.3x over cuBLAS at 70% dual-sparsity.
[LG-56] Gram-Space: Structure-Preserving Codebook Compression for Memory-Efficient Neuro-Symbolic AI
链接: https://arxiv.org/abs/2608.01528
作者: Weilun Wang,Wantong Li
类目: Machine Learning (cs.LG)
*备注: International Conference on Neuro-symbolic Systems (NeuS) 2026
Abstract:Vector symbolic architectures (VSA) are widely used for reasoning in neuro-symbolic (NeSy) AI, yet high-dimensional codebooks often create severe memory bottlenecks that limit scalability and deployment. In this paper, we propose Gram-Space, a compression framework that applies Gram-Schmidt orthogonalization to represent codebook vectors in a compact orthonormal coordinate system. Gram-Space preserves the dot-product structure required by matrix-based VSA operators, which supports numerically equivalent execution of matrix similarity, probability vectorization, and attention score computations. We provide a correctness analysis showing that inner products are preserved under the orthonormal basis representation. Using modern GPU hardware, we benchmark the Gram-Space framework on standard neuro-symbolic reasoning datasets. Experimental evaluations across state-of-the-art VSA models show that Gram-Space reduces model-level GPU memory usage by up to 15.75x and improves inference latency by up to 3.62x. Profiling results further indicate that Gram-Space reduces allocation-heavy overhead in codebook-associated stages and improves hardware utilization for NeSy workloads.
[LG-57] BiKAN: Restoring Collapsed Basis of Binary Kolmogorov–Arnold Networks
链接: https://arxiv.org/abs/2608.01490
作者: Kazi Ahmed Asif Fuad,Lizhong Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Binarizing a polynomial Kolmogorov–Arnold Network (KAN) not only changes parameter precision, but also alters the function space available to each layer. When activations are restricted to -1,+1 , all even powers reduce to 1 and all odd powers reduce to x , causing the elementwise polynomial basis to collapse to constant and first-order responses. We refer to this structural failure as Spatial Orthogonality Collapse. Our proposed BiKAN addresses this critical issue by augmenting each binary KAN layer with selected degree-2 Walsh characters. Fixed circular channel rolls generate pairwise parities, and learned binary projections mix them using the same XNOR–popcount operations as the remaining W1A1 paths. This restores explicit pairwise coordinates without learned routing or multiplier-based feature generation. Experiments on CIFAR-10 confirms that removing parity reduces accuracy by 1.23 points over five paired seeds ( p=0.003 ), the gain increases as width decreases, and accuracy improves monotonically as more parity planes are added. At an equal \sim 11.9M-parameter budget, parity outperforms conventional widening by 3.09 points ( p10^-4 ). At W1A1, BiKAN reaches 99.48% , 84.38% , and 55.81% on MNIST, CIFAR-10, and CIFAR-100, respectively. Post-route Zynq-7020 FPGA results show that the repair remains hardware-efficient; the convolutional design cuts DSP usage from 164 to 72 and estimated compute-core latency from 401 to 54.8 ms, while the power-of-two-aware dense design achieves zero-DSP inference with a 0.03-point accuracy loss. The BiKAN implementation is available at this https URL.
[LG-58] Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval
链接: https://arxiv.org/abs/2608.01481
作者: Ilia Semenkov,Daria Kleeva,Ivan Dakhtin,Zarina Maksudova,Alex Ossadtchi
类目: Machine Learning (cs.LG); Sound (cs.SD); Neurons and Cognition (q-bio.NC)
*备注:
Abstract:Short segments of perceived speech can be retrieved from non-invasive magnetoencephalographic (MEG) recordings by deep networks trained with a CLIP-style objective against wav2vec 2.0 audio embeddings. Yet their weights do not map onto electrophysiological quantities, and it remains unclear which speech properties drive retrieval. We build on a high-performing MEG-to-audio retrieval architecture but redesign both its front end and decoder. Its spatial attention operates on a flattened sensor layout; we replace it with spherical harmonics defined on the three-dimensional MEG helmet geometry. We reduce the subject-specific representation from 270 to 25 branches, add a temporal filter to each branch to match it to a neuronal source in space and time, and make the convolutional decoder shallower. Ocular and cardiac components are removed before training to reduce the risk of stimulus-locked shortcuts. On MEG-MASC, the model reaches 39.75 +/- 0.34% Top-1 accuracy among 1005 candidates across six trained solutions, with about 20 times fewer decoder parameters. Its weights map to source space, recovering generators consistent with the speech-perception network, while left-lateralized branches carry higher-frequency rhythmic components not evident on the right. Paired MEG occlusion shows that 15 of 19 stimulus features contribute, with the largest effects for silence, sound intensity, vowels, and acoustic onsets. Random word lists behave oppositely: substituting narrative MEG into them improves retrieval, indicating that activity without narrative structure carries less recoverable information than activity during coherent speech. The wav2vec target can be reduced to about twelve learned feature dimensions without loss of accuracy, whereas strong temporal compression causes a clear loss. Together, source mapping and input interventions reveal what drives retrieval. Subjects: Machine Learning (cs.LG); Sound (cs.SD); Neurons and Cognition (q-bio.NC) Cite as: arXiv:2608.01481 [cs.LG] (or arXiv:2608.01481v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.01481 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ilia Semenkov [view email] [v1] Sun, 2 Aug 2026 20:28:14 UTC (3,007 KB)
[LG-59] Plasticity of Growing and Elastic Neural Networks in Online Continual Learning
链接: https://arxiv.org/abs/2608.01475
作者: Jeong Min Kong,Richard S. Sutton
类目: Machine Learning (cs.LG)
*备注:
Abstract:Neural networks that can grow or both grow and shrink during learning, referred to as growing neural networks and elastic neural networks, respectively, have recently been explored in offline continual learning with a particular focus on catastrophic forgetting. Driven by the observations that 1) online continual learning closely resembles how animals learn; 2) loss of plasticity—the progressive decline in a learning network’s ability to learn—is another crucial challenge facing continual learning; and 3) incremental introduction of randomly initialized hidden units was recently shown to help preserve plasticity, in this paper, we study the plasticity of several foundational growing and elastic networks in online continual learning. Our experiments in supervised learning settings show that adaptive growing networks, which incrementally incorporate new, randomly initialized units to the network while keeping all existing connections adaptive, can maintain high prediction accuracy without losing plasticity despite the continuous increase in the dead hidden unit proportion. Furthermore, we demonstrate that adaptive elastic networks, which in addition to progressively adding new hidden units also prune estimated dead hidden units at the beginning of each new task, can achieve excellent accuracy without loss of plasticity while simultaneously maintaining a near-constant, compact size. Our results suggest that growing and elastic networks, which exhibit the ability to adapt its structure to the relevant learning objectives, can be a promising class of algorithms also for preserving high plasticity in online continual learning.
[LG-60] Conformalized Large Language Models under Configuration Shift
链接: https://arxiv.org/abs/2608.01460
作者: Yuqicheng Zhu,Jialin Yu,Lin Li,Gengyuan Zhang,Zhen Yang,Steffen Staab,Puneet Dokania,Philip Torr,Jie Tang,Evgeny Kharlamov
类目: Machine Learning (cs.LG)
*备注: Under Review
Abstract:Conformal prediction (CP) is a distribution-free framework for uncertainty quantification that has recently been adapted to large language models (LLMs), providing prediction sets with finite-sample coverage guarantees under exchangeability. Yet for LLMs, nonconformity scores are often induced by an inference pipeline, not just a fixed model, making them depend not only on the data distribution but also on configurable factors such as the prompt template, decoding parameters, and deployment setting. Since such configurations are routinely modified in practice but rarely treated as a source of shift, their impact on CP validity remains poorly understood. We call this \emphconfiguration shift and study it systematically along three axes: prompt template, decoding temperature, and weight quantization. In a broad empirical study spanning 9 LLMs, 4 datasets, and 4 nonconformity scores, we find that configuration shift consistently erodes CP validity, often driving empirical coverage below the target. By contrast, efficiency is largely preserved: valid prediction sets remain close in size to the i.i.d. baseline. We derive coverage lower bounds that attribute this loss to a discrepancy between calibration and test score distributions, and use their finite-sample plug-in versions as empirical diagnostics of shift severity. We further show that these findings lead to practical mitigations: bound-inspired recalibration is effective with limited test examples, while fragility-aware calibration ensembling recovers much of the lost coverage without test data.
[LG-61] How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection NDSS2027
链接: https://arxiv.org/abs/2608.01454
作者: Lorenzo Guerra,Thomas Chapuis,Guillaume Duc,Pavlo Mozharovskyi,Van-Tam Nguyen
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Accepted at NDSS 2027
Abstract:Provenance-based intrusion detection systems (PIDS) frequently report strong performance, but the conclusions drawn from these results can be highly sensitive to benchmarking choices and evaluation protocols. We investigate this dependency by re-evaluating representative PIDS on public datasets that meet our audit, labeling, and calibration requirements. Focusing primarily on the audited DARPA TC E3 datasets, we apply a unified protocol with temporally separated test periods and validation-only checkpoint and threshold calibration, and ask which architectural claims are empirically supported. We find that alerting success and investigation utility can diverge sharply, as several systems surface attacks without providing enough process-level context to support forensic investigation. On three of the four primary datasets, a simple allowlist built from training executable names and paths matches or exceeds the selected learned baselines on key operating-point metrics, suggesting that much of their measured performance reflects lexical novelty rather than richer provenance modeling. To explain why only some datasets expose architectural differences, we measure semantic signal quality through feature completeness and field entropy. This analysis helps explain why several audited E3 datasets can expose alerting behavior without reliably separating model architectures, while Theia pairs the strongest semantic signal quality with the clearest improvements in ranking and node-level recovery by our reference model. These results show that architectural claims in PIDS should be interpreted together with the benchmark properties and evaluation protocol that produced them.
[LG-62] Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization
链接: https://arxiv.org/abs/2608.01426
作者: Furkan Bagci,Busra Tegin,Mohammad Kazemi,Tolga M. Duman
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 17 pages
Abstract:Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability. In practical wireless networks, mobile devices (MDs) often exhibit diverse data and learning objectives, naturally forming clusters of users with jointly trainable models. When devices rely on energy harvesting (EH), stochastic energy arrivals further complicate participation and scheduling under communication constraints. In this work, we study over-the-air (OTA) FL with EH MDs under heterogeneous data distributions, and investigate two closely related learning objectives within a unified framework: one aiming for a more representative global model by reducing data bias, and the other learning more personalized cluster-specific models by exploiting this bias. In the global training mode, cluster information guides energy- and diversity-aware scheduling, ensuring that the scheduled active users provide a more representative aggregate update. In the personalization mode, the same cluster structure defines cluster-level learning objectives and OTA recovery targets, enabling the parameter server to train multiple cluster-specific models through simultaneous transmissions over the wireless multiple-access channel. Numerical results demonstrate that the proposed unified framework improves fairness or personalization, depending on the operating mode, while reducing communication overhead.
[LG-63] On the Identifiability of Masked Prediction: Mode Blindness and Mask Schedules
链接: https://arxiv.org/abs/2608.01383
作者: Yichao Cai,Javen Qinfeng Shi
类目: Machine Learning (cs.LG); Information Theory (cs.IT)
*备注: 59 pages, 15 figures
Abstract:Masked prediction learns representations by fitting a schedule-weighted family of conditional laws, but it remains unclear when near-optimal conditional prediction pins down the underlying joint law. We study this question for data with two well-separated global modes, outside the reach of rapid-mixing recovery guarantees, and show that the answer is decided by the mask schedule alone. Under large-context mode pinning, reweighting the two modes can move the joint law by a constant in total variation while perturbing the masked objective exponentially little in the visible-context size: mask schedules dominated by large contexts are provably blind to the global mode weights. To quantify this, we introduce an \varepsilon -identifiability modulus, the largest distributional error consistent with a given excess risk, and prove that it remains macroscopic at an excess risk that is exponentially small. An exact information decomposition pinpoints what restores identifiability: mode-weight sensitivity is governed by the residual mode uncertainty given the visible context. Consequently, low-visibility masks recover this sensitivity, and positive full-mask mass anchors the joint law over all admissible models with no assumption on the data law. Empirically, we test our theory at three levels: enumeration on computable laws verifies the predicted rates, gradient training reproduces both the mode blindness and the recovery, and measurements on real corpora place natural text between the two certified regimes.
[LG-64] When May a Model Replace the Experiment? Audits Licenses and the Price of Trust in Surrogate-Driven Design
链接: https://arxiv.org/abs/2608.01378
作者: Shuangxiu(Max)Ma,Wenhe(Zachary)Zhao
类目: Machine Learning (cs.LG)
*备注: 47 pages (18 pages main text, 29 pages Supporting Information), 9 figures, 2 tables, 70 references. SI contains complete proofs of all theorems, extended experiments, and the verification methodology
Abstract:Design campaigns in chemistry, materials science, and machine learning share a bottleneck: determining how good a candidate truly is requires an expensive evaluation - an experiment, a first-principles simulation, or a full training run. Machine-learning surrogates that predict these outcomes are increasingly used not only to propose candidates but to grade them, and even to feed their own predictions back into the search as though they were measurements. Through mathematical analysis validated on three exhaustively ground-truthed design tasks, we establish when this practice is safe, what any certificate of safety must cost, and when the substitution provably pays. Predictive accuracy cannot anchor trust: near-perfect R^2 is compatible with worst-possible selections, and screening N candidates inflates the over-prediction at the selected candidate by a quantifiable “selection tax” with matching upper and lower bounds. Safety follows instead from an architectural rule - predictions may propose and train without restriction, but every certified conclusion must rest on true evaluations - which is sufficient with no assumptions on the surrogate, and necessary, since admitting predictions into certification with the standing of measurements opens a deterministic self-confirmation failure mode. We derive the minimal criterion under which a model may act as an oracle (rank preservation, not accuracy), show that trust must be purchased through selection-aware audits that are optimal in query complexity, and prove a dichotomy fixing when audited surrogates cut certified evaluation cost. Across 432 surrogate fits over six task-regime conditions, the audit statistic tracks deployed search performance at Spearman rank correlation 0.80-0.99, while the rank correlation of R^2 with deployed regret falls as low as 0.33; audited screening reduces certified oracle cost by a measured factor of 25.
[LG-65] Do Neural Networks Really Beat the Curse of Dimensionality? A Bit-Complexity View
链接: https://arxiv.org/abs/2608.01357
作者: Tong Mao,Jinchao Xu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Traditional approximation theory measures convergence rates in terms of the number of parameters or degrees of freedom. However, practical computation operates under finite precision: parameters must be encoded using a finite number of bits. Therefore, approximation efficiency should be evaluated in terms of computational bit complexity, which is intrinsically connected to the metric entropy of the underlying function class. In this work, we develop a unified approximation framework based on binary encoding and metric entropy. We analyze classical methods (including polynomial approximation, sparse grids, and finite elements) as well as shallow and deep neural networks, and compare their approximation rates for function classes with comparable metric entropy. We observe that, when evaluated in terms of bits, most classical methods are in general suboptimal relative to the intrinsic limits dictated by metric entropy, while neural network methods may exhibit different behaviors. We show that when complexity is measured in bits rather than parameters, no method fundamentally exceeds the approximation order achieved by classical approaches. Our results also indicate that many seeming advantages of neural networks, including dimension-independent rates and superconvergence phenomena, stem from differences in function class complexity rather than intrinsic architectural superiority. In this sense, the traditional curse of dimensionality can be misleading; the fundamental limitation is instead a curse of bit complexity, governed by metric entropy. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.01357 [cs.LG] (or arXiv:2608.01357v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.01357 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-66] AlphaG-OPD: Reliability-Gated Sibling Counterfactuals for On-Policy Distillation in Symbolic Alpha Factor Discovery
链接: https://arxiv.org/abs/2608.01303
作者: Yaoyu Su
类目: Machine Learning (cs.LG)
*备注: 12 pages, 3 figures, 5 tables
Abstract:Symbolic alpha factor discovery can score a completed expression, but it provides no direct label for the structural decisions that produced it. Generative flow networks (GFlowNets) preserve a diverse, reward-proportional distribution over complete expressions, yet their trajectory-level objective does not compare unchosen sibling actions at an intermediate state. We introduce AlphaG-OPD, a structural on-policy distillation framework that turns terminal factor evaluations into local action guidance. Its design separates three decisions. Component I determines where to teach by exposing grammar-valid siblings at partial abstract-syntax-tree (AST) states visited by the current forward policy. Component II determines what is reliable enough to teach: it evaluates three supported siblings under four shared suffixes and admits a KL-bounded target only when their matched comparisons exhibit sufficient winner agreement and a positive empirical lower confidence bound (LCB). Component III determines how strongly and for how long to teach by consolidating accepted targets through bounded replay, score-indexed expiry, and forward-gradient balancing, without additional factor evaluations. Terminal reward, Trajectory Balance, the backward policy, grammar, and factor-pool rules remain unchanged. An equal-physical-score four-arm ablation tests paired teaching, reliability gating, and consolidation. Across China’s CSI300, CSI500, and CSI1000 and the U.S. SP 500, the complete method delivers strong cross-market performance over multiple random seeds.
[LG-67] FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting
链接: https://arxiv.org/abs/2608.01290
作者: Amit Sharma,Nitin Auluck,Akramul Azim
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 11 pages, 4 figures
Abstract:Time-series foundation models (TSFMs) such as Chronos have demonstrated strong forecasting capabilities across domains, yet adapting them to institutionally fragmented settings, where data cannot be centralized due to regulatory, competitive, or sovereignty constraints, remains unexplored. We introduce FedChronos, a framework for federated parameter-efficient fine-tuning of an already pre-trained TSFM, a setting that existing federated time-series work has not addressed, since prior methods either pre-train from scratch or align prototypes rather than adapt a fixed backbone. Our approach applies Low-Rank Adaptation (LoRA) to the Chronos-T5 backbone and trains across distributed clients using FedAvg and FedProx, transmitting only lightweight adapter weights (384~KB per round, an 86 \times reduction over full-model exchange). We evaluate FedChronos on daily commodity prices from 15 Indian agricultural markets across 9 states, a naturally non-IID federated setting, and find that naïve LoRA fine-tuning overfits substantially on small per-client datasets, dropping below zero-shot performance. We further observe that differential privacy (DP) noise can act as implicit regularization and counteract this overfitting: in our experiments the strongest configuration ( \varepsilon = 5 ) reduces mean absolute percentage error (MAPE) by 31% over zero-shot and 26% over the best traditional baseline, while bounding each round’s information leakage via per-round (\varepsilon, \delta) -differential privacy. Because the model is compact and the updates are small, the approach also suits edge AI deployments where both the network link and the client device are constrained. Overall, our findings suggest that privacy and accuracy can be complementary rather than competing objectives in federated TSFM fine-tuning.
[LG-68] Active Regression for Single-Index Models with Unknown Link Functions ICML2026
链接: https://arxiv.org/abs/2608.01287
作者: Chansophea Wathanak In,Yi Li,Wai Ming Tai,Xuan Wu
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注: Earlier version accepted to ICML 2026; the lower bound has been extended to adaptive queries
Abstract:This paper studies active regression for single-index models under general \ell_p -loss with an unknown 1 -Lipschitz link function f , formulated as \min_f,x |f(Ax)-b|_p^p with full access to A but coordinate-query access to b . Prior work established upper bounds for known link functions for all p\geq 1 and for unknown link functions only in the p=2 case, together with lower bounds for p\leq 2 . This work addresses the more challenging setting of unknown link functions and general p \geq 1 . A non-adaptive sampling algorithm is presented that achieves a (1+\epsilon) -approximation using O(d^p/2\vee 1/\epsilon^p\vee 2\operatornamepoly\log(n/\epsilon)) queries. Nearly tight lower bounds are also established for p2 . These results close much of the remaining gap in active \ell_p -regression for single-index models.
[LG-69] Distill What the Student Can See: Fisher-Projected On-Policy Distillation for Vision-Language Models
链接: https://arxiv.org/abs/2608.01263
作者: Leyan Xue,Feng Xiong,Mingjun Ma,Changqing Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:On-policy distillation (OPD) samples trajectories from the current student policy and minimizes token-level divergence between student and teacher next-token distributions at prefixes along those trajectories. This aligns the distillation states with the student’s own generation distribution. However, it still assumes that the complete teacher distribution is an appropriate target across student capacities. In vision–language reasoning, teacher corrections can depend on visual distinctions that a compact student cannot represent. Our target-scaling study shows that, as the target approaches the complete teacher distribution, the student realizes less of the prescribed shift and obtains worse downstream performance. We therefore propose \emphFisher-Projected On-Policy Distillation (FP-OPD), which distills only locally realizable teacher corrections. FP-OPD uses continuous visual perturbations to estimate the student’s local visual tangent space and projects the centered teacher–student log-probability gap onto this space under the student’s Fisher metric. The resulting capacity-aware target is optimized with full-vocabulary reverse KL on student trajectories, retaining the standard OPD framework. In 8B-to-2B distillation, FP-OPD improves all seven evaluated multimodal benchmarks. It raises the average score by 2.77 points over the pretrained student and by 1.60 points over standard OPD. These results demonstrate that locally realizable teacher corrections provide a more effective target for distilling compact vision–language models.
[LG-70] Using Non-Lipschitz Signum-based Functions for Distributed Optimization and Machine Learning: Trade-off Between Con-vergence Rate and Optimality Gap
链接: https://arxiv.org/abs/2608.01220
作者: Mohammadreza Doostmohammadian,Amir Ahmad Ghods,Alireza Aghasi,Zulfiya R. Gabidullina,Hamid R. Rabiee
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Signal Processing (eess.SP); Optimization and Control (math.OC)
*备注:
Abstract:In recent years, the prevalence of large-scale data-sets and the demand for sophisti-cated learning models have necessitated the development of efficient distributed ma-chine learning (ML) solutions. Convergence speed is a critical factor influencing the practicality and effectiveness of these distributed frameworks. Recently, non-Lipschitz continuous optimization algorithms have been proposed to improve the slow conver-gence rate of the existing linear solutions. The use of signum-based functions is previ-ously considered in consensus and control literature to reach fast convergence in the prescribed time and also to provide robust algorithms to noisy/outlier data. However, as shown in this work, these algorithms lead to an optimality gap and steady-state re-sidual of the objective function in discrete-time setup. This motivates us to investigate the distributed optimization and ML algorithms in terms of trade-off between conver-gence rate and optimality gap. In this direction, we specifically consider the distributed regression problem and check its convergence rate by applying both linear and non-Lipschitz signum-based functions. We check our distributed regression approach by extensive simulations. Our results show that although adopting signum-based func-tions may give faster convergence, it results in large optimality gaps. The findings pre-sented in this paper may contribute to and advance the ongoing discourse of similar distributed algorithms, e.g., for distributed constrained optimization and distributed estimation.
[LG-71] SAFE-Merge: Data-Free Continual Model Merging with General Knowledge Preservation
链接: https://arxiv.org/abs/2608.01184
作者: Zihuan Qiu,Zhiyang Liao,Chiyuan He,Yi Xu,Fanman Meng,Linfeng Xu,Qingbo Wu,Hongliang Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Data-free continual model merging must incorporate a stream of specialized models while retaining both pretrained general knowledge and previously acquired tasks, without access to task data. Existing methods mainly merge task updates by suppressing interference among downstream tasks; while this protects previously acquired tasks, it overlooks the safety of the pretrained knowledge itself, whose erosion degrades generalization to held-out distributions and weakens the foundation for future task acquisition. We propose SAFE-Merge, a simple data-free continual-merging framework that first decides which parameter updates are safe to retain, and then recovers the task information lost through masking. Specifically, to ensure safety, risk-aware sparse masking selects parameter updates that carry task-specific information while posing low risk to general knowledge. Masked low-rank recovery then compensates for the lost task information using only the same retained parameter updates, while leaving all masked-out parameters strictly unchanged. Finally, the combined update is fused into the backbone, incurring no additional inference cost. Across vision and language benchmarks, SAFE-Merge consistently achieves the best H-score. On longer CLIP task sequences, it substantially improves H-score over NUFILT while also achieving the highest accuracy.
[LG-72] Interpretable Machine Learning for Traffic Congestion Prediction: Unveiling the Impact of Different COVID-19 Periods
链接: https://arxiv.org/abs/2608.01180
作者: Dan Zhu,Chi Sin Ng,Litian Xie,Yang Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Traffic congestion prediction is essential for congestion mitigation, but the COVID-19 pandemic and related control measures altered travel behavior and increased prediction complexity. This study predicts congestion in Alameda County, California, during pre-lockdown, lockdown, and post-lockdown periods. Weather, seasonality, and COVID-19 variables are incorporated, and Recursive Feature Elimination with Cross-Validation is used to select important features and reduce overfitting. Support vector regression, multiple linear regression, recurrent neural networks, and long short-term memory networks are trained and optimized. Because LSTM is more sensitive to hyperparameter settings, an adaptive parameter selection approach is used, while SVR and RNN are manually tuned. Performance is evaluated using Normalized Root Mean Square Error. Bidirectional LSTM consistently performs best across all periods because it captures temporal dependence in both directions. Integrated Gradients is used to interpret Bi-LSTM predictions, and SHapley Additive exPlanations is applied to SVR. New COVID-19 cases have a mainly negative effect on congestion during lockdown and post-lockdown, likely due to greater risk awareness, voluntary travel reduction, and compliance with mobility restrictions. In the post-pandemic period, higher hospitalization reduces travel and congestion, while higher fuel prices do not prevent a shift toward private vehicles and therefore increase congestion.
[LG-73] Differentiable Lifting for Topological Neural Networks ICLR2026
链接: https://arxiv.org/abs/2608.01160
作者: Jorge Luiz Franco,Gabriel Duarte,Alexander Nikitin,Moacir Ponti,Diego Mesquita,Amauri H. Souza
类目: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
*备注: Published as a conference paper at ICLR 2026 (OpenReview: this https URL ). 20 pages, 4 figures
Abstract:Topological neural networks (TNNs) enable leveraging high-order structures on graphs (e.g., cycles and cliques) to boost the expressive power of message-passing neural networks. In turn, however, these structures are typically identified a priori through an unsupervised graph lifting operation. Notwithstanding, this choice is crucial and may have a drastic impact on a TNN’s performance on downstream tasks. To circumvent this issue, we propose \partial lift (DiffLift), a general framework for learning graph liftings to hypergraphs and cellular- and simplicial complexes in an end-to-end fashion. In particular, our approach leverages learned vertex-level latent representations to identify and parameterize distributions over candidate higher-order cells for inclusion. This results in a scalable model which can be readily integrated into any TNN. Our experiments show that \partial lift outperforms existing lifting methods on multiple benchmarks for graph and node classification across different TNN architectures. Notably, our approach leads to gains of up to 45% over static liftings, including both connectivity- and feature-based ones.
[LG-74] EulerLoRA: Rank-Driven Jump Dynamics for Calibrated Parameter-Efficient Fine-Tuning
链接: https://arxiv.org/abs/2608.01142
作者: Srinivas Anumasa,Dianbo Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning, but standard LoRA produces a single deterministic model and does not directly support predictive uncertainty estimation. We introduce EulerLoRA, a stochastic extension of LoRA that generates multiple predictive trajectories by sampling structured variations along the rank-one components of shared low-rank adapters, while preserving the deterministic LoRA transformation in expectation. We evaluate EulerLoRA with vision transformers on CIFAR-10, CIFAR-100, and HAM10000, together with out-of-distribution detection on SVHN. Across these benchmarks, EulerLoRA achieves comparable or improved performance relative to strong LoRA-Ensemble baselines. Using two rank-20 adapters, EulerLoRA requires approximately 3 million trainable adapter parameters, compared with about 10 million for a rank-8, 16-adapter LoRA-Ensemble, corresponding to roughly 69% fewer trainable parameters. These results show that useful predictive diversity can be obtained from a small number of shared adapters.
[LG-75] When Do Surrogate Updates Improve Decisions? A Local Theory of Trajectory-Wise Transfer
链接: https://arxiv.org/abs/2608.01130
作者: Yuyang Shen
类目: Machine Learning (cs.LG)
*备注: 22 pages in total, including references and appendices; 3 composite figures (9 panels) and 4 tables. Code is available at this https URL
Abstract:A broad range of models face the mismatch where they are updated through trajectory losses but are evaluated by downstream task reward. Here, a trajectory is a training instance that induces a surrogate loss whose reduction might not track the model’s decision utility update. Theoretically, we ask when one step of trajectory training reduces both population surrogate loss and decision risk, and how transfer accumulates along repeated updates. To formalize this, we first fix a checkpoint and a restricted update space, and define the reductions in population surrogate risk and decision risk induced by a trajectory as its learnability and decision utility, respectively. On this basis, our theory yields four main results. First, a one-step transfer bound separates their discrepancy into first-order gradient misalignment after nonnegative calibration and second-order curvature; and a pathwise extension accumulates the same terms over repeated updates. Second, when the accessible surrogate gradient is nonzero, universal first-order transfer over every accessible direction holds exactly when the accessible surrogate and decision gradients are positively collinear. Third, the calibration gap bounds the decision regret of learnability-based trajectory selection, while a candidate-difference refinement tightens this guarantee by retaining only directions that affect pairwise rankings. Finally, we establish an approximation–calibration trade-off across nested update spaces. Controlled gridworld and LLM post-training experiments yield results consistent with our predictions.
[LG-76] Factorized AdaBoost.MH Achieves the Same Convergence Rate as AdaBoost.MH
链接: https://arxiv.org/abs/2608.01091
作者: Xin Zou,Jingyuan Xu
类目: Machine Learning (cs.LG)
*备注:
Abstract:this http URL reduces multi-class classification to a collection of binary subproblems and enjoys the classical boosting-type convergence guarantee under a weak learning condition. A more structured variant, Factorized this http URL, uses base classifiers of the form \mathbfh(x)=\alpha \mathbfv \bm\varphi(x) , where a single binary classifier \bm\varphi is shared across all classes and the label dependence is carried by a vote vector \mathbfv \in\pm1^K . This factorization is algorithmically attractive and achieves better performance in practice, but its convergence depends on whether one can always choose a vote vector with sufficiently large induced binary weight mass. Previous work resolved this question with a lower bound \max\1/n,1/\sqrt2K\ , which still leaves a dimension-dependent slowdown relative to the original this http URL analysis. In this paper, we sharpen this combinatorial step. For the minimax quantity \mathfrakW_n,K governing the factorized edge, we prove \max\1/n,C_K\le\mathfrakW_n,K\le C_\min\n,K\ , where C_q=1 for q=1 , C_q=q/(3q-4) for even q\ge2 , and C_q=(q+1)/(3q-1) for odd q\ge2 . Since C_q\downarrow 1/3 , our bounds show that \mathfrakW_n,K=\Theta(1) uniformly over n and K . Consequently, Factorized this http URL achieves the same boosting-type convergence rate as this http URL up to a universal constant factor, removing the previously suggested additional dependence on n or K in the number of boosting rounds.
[LG-77] Breaking Diversity Collapse in Spiking Pseudo-Ensembles for Efficient OOD Detection in Remote Sensing
链接: https://arxiv.org/abs/2608.01090
作者: Srinivas Anumasa,Rushi Shah,Qiran Zou,Dianbo Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Spiking Neural Networks (SNNs) are attractive for resource-constrained remote-sensing systems, but reliable out-of-distribution (OOD) detection remains challenging. Deep ensembles provide strong predictive uncertainty, yet require multiple complete models and backbone evaluations. We propose an efficient spiking pseudo-ensemble that attaches multiple lightweight classification heads to a frozen SNN backbone. Naively training these heads with cross-entropy can lead to diversity collapse, where independently parameterized heads may produce correlated predictions. To address this, we introduce an agree–disagree objective that preserves correct predictions on clean in-distribution samples while encouraging diversity on structured, uncertainty-inducing transformations of the same inputs. This provides a diversity-promoting training signal without requiring external OOD data. Experiments with Spikformer and ResNet19-SNN on EuroSAT demonstrate consistent improvements over conventionally trained pseudo-ensembles. Using three backbones with five heads each matches or improves upon a five-model deep ensemble on UCM and AID, while requiring approximately 38% fewer parameters and 40% fewer backbone evaluations. These results show that explicit diversity promotion can recover useful ensemble-style uncertainty at substantially lower deployment cost.
[LG-78] Characterizing Bias in Post-Bandit Inference under Index Algorithms
链接: https://arxiv.org/abs/2608.01069
作者: Lisu Wang,Yilun Chen,Jiaqi Lu
类目: Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注:
Abstract:Bandit algorithms generate data for downstream inference, but adaptive sampling biases post-bandit sample means. We analyze this bias for stable index algorithms, including UCB1 and its generalizations, and derive sharp leading-order expressions for the sample-mean bias and expected Z -statistic. Our characterization reveals the algorithmic origin of bias through a key index-function-dependent quantity, which we term effective exploration rate. For example, under UCB1, the effective exploration rate is of order \sqrt\log T , and the standardized bias of any arm (that is not uniquely optimal) decays at the extremely slow rate 1/\sqrt\log T . We also show how the choice of the index function affects both regret and bias, which reveals a regret-bias trade-off: more exploratory algorithm reduces bias but increases regret. Our sharp characterization for bias uses a novel empirical fluid approximation of the algorithm’s sampling dynamics, which may be of independent interest.
[LG-79] On the Limits of Machine-Learned Ranking for Modern Microarchitectural Policies
链接: https://arxiv.org/abs/2608.01041
作者: Yanxin Zhang,Shayne Wadle,Yuxuan Xiong,Zheyu Fu,Trivikram Krishnamurthy,Karu Sankaralingam
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注:
Abstract:Machine-learning predictors estimate processor performance far faster than cycle-level simulation. For design-space exploration, however, the valuable test is not merely reproducing the usual hardware ordering, but identifying how different hardware configurations rank on individual program phases. We evaluate four ML-predictors in two design regimes: \emphStructural Parameters (SP), varying hardware resources such as issue width, ROB size, and cache capacity; and \emphBehavioral Policies (BP), varying prefetching and replacement algorithms. In the SP regime, aggregate ranking is strong, yet counter-intuitive windows(CIW)—where the configuration expected to be slower is faster—constitute 22.4% of non-tied windows across five pairs with a clear architectural prior. CIW match across these pairs is only 23.3 – 39.9% ; every point estimate is below the 50% random strict-ordering reference. The BP regime presents a different failure: ground-truth ties cover 37.8% of pair-windows, most strict pairs have margins of only a few cycles, and no model family reliably beats a feature-free majority baseline. NeuroScalar and SimNet fall below that baseline, Concorde is statistically tied with it, and the best selected OneDSE head improves by only 2.1 percentage points. Accuracy rises mainly at large margins. We further show that this failure is not a matter of model capacity: an information-theoretic analysis reveals that when ranking outcomes depend on hidden microarchitectural state absent from the instruction stream, no trace-based predictor can exceed the Bayes accuracy determined by observable inputs alone. Thus high cycle or aggregate ranking accuracy can reflect mastery of easy, high-margin cases while missing the local reversals that carry the most architectural insight and for which cycle-level simulation remains indispensable.
[LG-80] Using Lower-Bound Representations for Trajectory Similarity Learning VLDB
链接: https://arxiv.org/abs/2608.01039
作者: Liwei Deng,Haotian Meng,Yupu Zhang,Yan Zhao,Torben Bach Pedersen,Kai Zheng,Christian S. Jensen
类目: Databases (cs.DB); Machine Learning (cs.LG)
*备注: 15 pages, 5 figures, accepted version and accepted by PVLDBv19
Abstract:Trajectory similarity learning is fundamental to efficient trajectory retrieval under complex distance measures. Existing learning-based methods typically rely on embeddings trained to approximate trajectory distances or rankings, but they often lack guarantees with respect to the original distances, exhibit unstable performance across distance measures, and incur substantial training costs. We revisit trajectory similarity learning from a lower-bound representation perspective and propose LB-TrajRep, a unified lower-bound representation framework independent of deep neural embeddings. This framework constructs single-vector representations from a set of lower-bound components, enabling admissible and interpretable lower bounds for multiple classical trajectory distances, including Dynamic Time Warping (DTW), Hausdorff distance, and Discrete Fréchet Distance (DFD). Within this framework, we instantiate point-pivot components, which naturally support both metric and non-metric distances and remain compatible with standard vector-based retrieval pipelines. To improve ranking quality, we develop two data-driven pivot selection strategies that explicitly optimize lower-bound tightness and prioritize hard near-neighbor trajectory pairs, respectively. Extensive experiments on real-world trajectory datasets show that the proposed lower-bound representations are able to consistently outperform state-of-the-art neural trajectory embeddings across diverse distance measures, improving top- k ranking accuracy by up to 20%–60% on the Hausdorff distance and DFD and by 15%–40% on DTW.
[LG-81] he Fourth Quadrant: A Stylized View of Benign Misfitting
链接: https://arxiv.org/abs/2608.01032
作者: Gireeja Ranade,Anant Sahai
类目: Machine Learning (cs.LG); Information Theory (cs.IT)
*备注: 82 pages, 6 figures, full version of paper accepted at ITW2026
Abstract:Training error is what we can observe on a training set; test error is the quantity we actually care about. We study linear regression with squared-error in a deterministic (d+1) -dimensional single-spike model. Each stylized training vector has the same informative spike coordinate, of amplitude \sqrt\gamma with \gamma1 . The remaining directions are nuisance, and the nuisance components of distinct training vectors all have equal norm and are mutually orthogonal. The training labels are all 1 . Fresh test points are drawn from \vecx_\rm test \sim \mathcalN(\vec0,\operatornamediag(\gamma,1,\ldots,1)) , with the noise-free test labels being the normalized spike coordinate x_\rm test[1]/\sqrt\gamma . We focus on linear predictors in the span of the training vectors, the class naturally reached by zero-initialized linear gradient methods. We exhibit a range of training-set sizes n in which every span predictor that generalizes well must fit the training data \emphworse than the zero predictor. We call this regime \emphbenign misfitting, or the fourth quadrant. The best span predictor begins to generalize when n\gg d/\gamma^2 , while interpolation does not generalize until the later threshold n\gg d/\gamma . In the window d/\gamma^2 \ll n \ll d/\gamma , useful prediction within the linear span lies beyond interpolation: predictions on the training points overshoot the labels. We show that one-pass stochastic gradient descent (SGD), with a large constant learning rate, reaches small test error throughout this window—matching the best span predictor up to a logarithmic factor. We also verify directly that it indeed has \emphlarge empirical training error (despite the descent premise in its name). Finally, we show that the unavoidable nuisance component responsible for the training misfit also controls the predictor’s adversarial sensitivity. Comments: 82 pages, 6 figures, full version of paper accepted at ITW2026 Subjects: Machine Learning (cs.LG); Information Theory (cs.IT) Cite as: arXiv:2608.01032 [cs.LG] (or arXiv:2608.01032v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.01032 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-82] Fused Bayesian Flow Networks for Dual-Target Molecular Design
链接: https://arxiv.org/abs/2608.01007
作者: Jingyuan Zhou,Shikui Tu,Lei Xu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Dual-target drug design aims to generate 3D molecules that can simultaneously interact with two target proteins, offering a promising route for discovering polypharmacological compounds against complex diseases. While recent generative models have shown encouraging performance in single-target drug design, existing dual-target approaches either focus on sequence generation or introduce an additional predictive drift term into the diffusion-based generative trajectory, which limits their ability to fully integrate feature information from both targets. We propose FusedBFN, a fused Bayesian flow network (BFN) for dual-target molecular design. FusedBFN formulates dual-target generation as distribution fusion in a unified continuous parameter space and employs a product-of-experts formulation to incorporate dual-target information throughout the generative process. To address the scarcity of dual-target structural data, we leverage a pretrained target-aware BFN model as the shared backbone. We further introduce a chemically aware prior-based alignment method and a prior-free pocket alignment strategy to construct aligned dual-target contexts. Extensive experiments demonstrate that FusedBFN generates molecules with strong binding affinity toward dual targets while maintaining favorable molecular properties.
[LG-83] Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis
链接: https://arxiv.org/abs/2608.00956
作者: Jianglei Xing,Xiao Tan,Dongzhao Jin,Pengwei Guo,Yuhuan Wang,Huiya Niu
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was established, and 14 input variables related to asphalt properties, aggregate gradation, and fiber characteristics were selected for modeling. Six machine-learning models, namely TabPFN, ANN, SVR, RF, XGBoost, and LightGBM, were developed and compared. Hyperparameter optimization was performed for five models using NSGA-II, while TabPFN was directly applied with its default configuration. The results show that all six models achieved satisfactory predictive capability, whereas TabPFN delivered the best overall performance on the testing set, with the lowest RMSE of 0.28, MAE of 0.21, MAPE of 18.01%, MAD of 0.14, the highest R^2 of 0.88, and the highest composite score of 0.91. SHAP analysis further revealed that nine dominant variables accounted for 92.0% of the total average contribution, among which Ag9.5, FT, Ag4.75, AC, and Du were the most influential. In addition, favorable parameter ranges for improving ST were quantified, such as Ag9.5 66.8%, Ag4.75 45.0%, AC 5.4 wt.%, AV 3.6%, and Du 134.7 cm. Finally, a GUI platform integrating prediction and SHAP-based explanation was developed to improve the accessibility and practical applicability of the proposed framework.
[LG-84] Data-Driven Pinball-Loss Selection for Vertically Distributed Elastic-Net SVMs
链接: https://arxiv.org/abs/2608.00949
作者: Xiaofei Wu,Kai Qi,Rongmei Liang
类目: Machine Learning (cs.LG); Computation (stat.CO)
*备注:
Abstract:The pinball-loss support vector machine is robust, but its asymmetry parameter is usually fixed in advance. We propose a data-driven elastic-net support vector machine that learns simplex-constrained weights over candidate pinball losses while retaining one classifier. The weighted loss is equivalent to a pinball loss with a data-dependent effective parameter. An empirical oracle inequality shows that, when weight regularization and simplex truncation vanish, the classifier objective at a global minimizer does not exceed that of the best fixed candidate; otherwise, the excess is explicitly bounded. For high-dimensional data, we develop a column-partitioned variable-splitting solver. It converges with a best-iterate O(1/T) squared-step residual rate. Under common initialization and global parameters, any column partition produces, in exact arithmetic, the same iterates and solution as centralized training. Experiments assess predictive behavior, numerical equivalence, and multi-process scalability.
[LG-85] xMICD: Explainable Representation of Multiple ICD Codes
链接: https://arxiv.org/abs/2608.00935
作者: Pat Vatiwutipong,Kumkup Keeratisiwakul,Albert Phuoc Kien Van Truong,Nutcha Yodrabum,Wasin Pansiritanachot,Marvin N. Wright,Thanapon Noraset
类目: Machine Learning (cs.LG)
*备注:
Abstract:Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning. International Classification of Diseases (ICD) codes provide structured information about patient diagnoses, but representing them effectively remains challenging. Existing approaches often face a trade-off between predictive performance and interpretability: grouping-based representations are interpretable but may lose information, while embedding-based representations achieve strong predictive performance but are difficult to interpret. We propose Explainable Representation of Multiple ICD Codes (xMICD), a method for constructing low-dimensional patient representations from sets of ICD codes. xMICD combines clinically meaningful diagnostic groupings with similarity in a pre-trained ICD embedding space. Instead of using binary group membership, the method assigns codes to groups via similarity-based relative assignments, yielding features that reflect how closely a patient’s diagnoses align with each clinical group. Experiments on large-scale EHR datasets demonstrate that xMICD achieves predictive performance comparable to embedding-based representations such as ICD2Vec across multiple clinical prediction tasks. At the same time, the resulting features remain clinically interpretable because each dimension corresponds to a recognizable diagnostic group. xMICD therefore provides a practical way to integrate embedding-based semantic relationships into interpretable clinical feature spaces for machine learning models.
[LG-86] Subtype Robustness Is Not Just Accuracy: Calibration Under Unseen Subtype Shift
链接: https://arxiv.org/abs/2608.00928
作者: Hanyu Su,Carlota Julbe i Juanola,Yibo Hu
类目: Machine Learning (cs.LG)
*备注: 15 pages, 6 figures, 12 tables
Abstract:Subtype robustness asks whether a model keeps the correct coarse prediction when test examples come from fine-grained subtypes absent from training but still inside a known coarse category. Prior work studies this almost entirely through accuracy. We ask whether the model also stays calibrated. We present the first systematic study of the question across ImageNet, BREEDS, iNaturalist and CIFAR-100 with five architectures. Calibration breaks down on unseen subtypes, where accuracy drops while confidence barely follows, leaving the model systematically overconfident exactly where it has become less accurate. At matched accuracy loss, generic image corruption causes a much larger drop in confidence, so the effect is not a general consequence of losing accuracy. The model reacts to visible degradation but not to in-taxonomy novelty. Recalibration tuned on seen subtypes narrows the gap but does not close it, and out-of-distribution scores flag the affected inputs only weakly. Subtype robustness should therefore be evaluated through calibration, not accuracy alone.
[LG-87] UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluation
链接: https://arxiv.org/abs/2608.00915
作者: Binshuang Li
类目: Machine Learning (cs.LG)
*备注: 25 pages, 9 figures. Code, data, and results: this https URL
Abstract:Uplift modeling (conditional-average-treatment-effect estimation) drives personalized targeting, yet published uplift benchmarks frequently disagree on which estimator performs best; we show the disagreement is substantially about metrics, not models. UpliftBench evaluates 12 uplift estimators under an outer-test-isolated, multi-objective protocol across seven dataset families; its two findings are identified where a reference objective exists – F1 on the standard continuous benchmark (IHDP), F2 in a within-sample case study on Jobs. On that benchmark, Qini shows no detectable alignment with effect accuracy – across all 100 IHDP realizations its mean rank correlation with effect accuracy is +0.07, 95% CI [-0.03, +0.16] – while AUUC is consistently more aligned (paired prefix-mean-AUUC-over-Qini gap +0.49 [+0.40, +0.59]; the shipped cumulative-gain AUUC aligns better still, +0.73). On Jobs, ranking metrics are structurally insufficient for a sign-threshold policy because they discard the score level; empirically, within the released split-rotation analysis direct policy-risk selection yields lower benchmark regret than random model selection while Qini, AUUC, and uplift-at- k do not (14-15% regret). Calibrating the decision threshold removes 81% of the Qini-selection regret. Both findings are bounded, not universal: F1 is not detected on either validation family (the ACIC and Revenue-Synthetic gaps are both indistinguishable from zero), and F2 vanishes under a budgeted-value objective where rank suffices. UpliftBench releases versioned loaders, fixed protocols, result artifacts, and a reproducible living leaderboard; the public repository accompanies the paper.
[LG-88] Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control
链接: https://arxiv.org/abs/2608.00908
作者: Zuyuan Zhang,Vaneet Aggarwal,Tian Lan
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注:
Abstract:Modern network policy control maps intent to sequential placement-control decisions. Bellman-style policy optimization primarily asks which action to optimize, while constraints are commonly handled through penalty, barrier, or Lagrangian mechanisms. We observe that before a value function can certify the best deployment, intermediate signals may already identify many candidates that should be excluded from further optimization. This motivates a complementary direction: \emphLearning Not to Optimize. Before a value function is accurate enough to select the best placement-control decision, intermediate signals may already show that candidates are equivalent under state–intent relabeling (quotienting), lead to a uniformly worse future state (dominance), or violate executable network laws (residual screening). \LNOQRD uses these computed or learned signals as a shadow process to reshape the domain on which primal policy optimization is performed, thereby reducing the action space. We prove lossless quotienting and dominance under explicit equivariance and monotonicity conditions, bound frontier size and ranking cost, and quantify losses from approximate certificates and primal estimates. Experiments show that \LNOQRD reduces small-instance candidates by 75.9% while retaining 90.8% near-oracle coverage and, on large instances, achieves the highest utility and intent satisfaction, the lowest hard-law violation and post-generation latency, and a 73.0% average reduction among candidate-based baselines.
[LG-89] AOSpec: Action and Observation Co-Speculation for Low-Latency Agent Serving
链接: https://arxiv.org/abs/2608.00881
作者: Hao Mark Chen,Jinnan Guo,Wayne Luk,Hongxiang Fan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language model agents increasingly act through stateful tools, yet model generation and environment execution remain serialized at every step. As decoding accelerates, tool execution becomes a growing bottleneck. Existing action- or observation-only speculation leaves much of this latency exposed: value is concentrated in a few slow calls, some outcomes emerge only through execution, and longer lookahead typically requires an increasingly unlikely chain of action predictions. We present AOSpec, a lossless framework that co-speculates actions and observations across the full agent-environment loop. Expected Value Decoding (EVD) directs observation speculation toward outcomes with the greatest expected latency benefit, optimizing expected time hidden rather than hit rate. For outcomes only execution can reveal, AOSpec launches latency-critical target actions in isolated forks that contain their effects, while Joint Action-State Verification (JASV) verifies both the action and its origin state against committed execution before reuse. JASV recasts long-horizon action dependency from full-chain prediction into target action-state verification, breaking the lookahead–accuracy tradeoff and unlocking long-range overlap without sacrificing serial semantics. Across Terminal-Bench serving settings spanning four harnesses, five actor models, and five serving speeds, AOSpec outperforms every practical baseline, reducing mean end-to-end latency by 11.8-32.5% and p99 latency by up to 42.8%. Its gains increase as decoding accelerates, and its observation model transfers from Terminal-Bench to SWE-bench Verified without retraining.
[LG-90] GeoArbiter: Verifiability-Guided Grounding for Remote-Sensing Multimodal LLM s
链接: https://arxiv.org/abs/2608.00877
作者: Xuechen Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Remote-sensing multimodal large language models (MLLMs) often assert facts that imagery cannot establish, such as a facility’s identity or function. Coordinate-keyed geographic retrieval can supply this missing knowledge, improving fMoW land-use accuracy by 12.06–17.19 points across three open MLLMs. However, retrieved records can also contradict visible evidence, and we find that models frequently follow the records even when the image is decisive. We argue that source trust should therefore depend on \emphcross-modal verifiability: geographic records are most useful for attributes the image cannot verify and most dangerous when they dispute visually verifiable attributes. We introduce GeoArbiter, a training-free pipeline that operationalizes this principle by injecting only image-unverifiable geographic facts. Unlike arbitration prompts, which leak across attribute types and bias yes/no responses, content-level filtering preserves 84.69–87.15% of the full-retrieval accuracy gain, reduces claim-level hallucination by 9.58–26.34% under a source-blinded judge, and improves robustness to conflicting records across all three models. These results identify verifiability-guided content selection as a simple, effective mechanism for grounding remote-sensing MLLMs in fallible geographic knowledge.
[LG-91] Explainable Hybrid Feature Selection for Intrusion Detection in Internet of Medical Things Environments
链接: https://arxiv.org/abs/2608.00869
作者: Amira Berrezzek,Hayet Djellali,Giulio Mallardi,Lamia Mahnane
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 6 pages, 9 figures
Abstract:Internet of Medical Things (IoMT) networks are hard to protect: devices are heterogeneous, computing resources are scarce, and traffic must be analyzed in real time. We present an intrusion detection system that addresses these constraints through feature selection. A Pearson correlation filter first removes redundant attributes; a hybrid strategy then combines model-based feature importance with SHAP attribution to pick a compact subset, on which we train Random Forest and LightGBM classifiers. SHAP and LIME explain what each retained feature contributes to the decisions. On CIC-IoMT 2024 and CIC-IDS 2017, the method cuts the feature space by up to 88% - from 40 to as few as 5 features - and accuracy and F1-score stay within a few points of models trained on all features. Compact, interpretable detectors of this kind are practical candidates for deployment on resource-limited medical networks.
[LG-92] Kilobyte Models: Neural Networks as a Seed and a Quantized Latent
链接: https://arxiv.org/abs/2608.00860
作者: Sahil Rajesh Dhayalkar
类目: Machine Learning (cs.LG)
*备注: 13 pages, 6 figures, 15 tables
Abstract:The cost of storing and transmitting a trained neural network scales with its parameter count, a bottleneck for over-the-air updates, on-device libraries, and other bandwidth-bound deployments. We study an extreme form of model compression in which the deployable artifact is not the weights but a short recipe for regenerating them. Building on Mapping Networks, which express a network’s weights as a nonlinear function of a compact trainable latent and a fixed random basis, we observe that only the latent need be stored, because the basis and initialization center are reproducible from an integer seed. A model becomes a seed together with a quantized latent, whose size is set by the latent dimension and bit width rather than the parameter count. We formalize this artifact and introduce a seeded block-wise basis that scales to networks whose projection cannot be held in memory. In our experiments, a mapped model is as accurate as the same network quantized aggressively to a few bits per weight, while taking far fewer bytes to store. Reaching the most aggressive bit widths depends on fine-tuning the latent with quantization in the loop. The results do not depend on the particular random basis, and a structured basis lets the weights be regenerated almost for free even for large networks.
[LG-93] SparseKAN: Compressing Kolmogorov–Arnold Networks Across Basis Functions Neurons and Bits
链接: https://arxiv.org/abs/2608.00859
作者: Kazi Ahmed Asif Fuad,Lizhong Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Kolmogorov–Arnold Networks (KANs) replace scalar edge weights with learnable univariate functions parameterized by multiple basis coefficients. This introduces a source of redundancy that conventional neural-network compression does not directly expose. We present \textbfSparseKAN, a unified approach that compresses KANs along three complementary axes: basis functions, neurons/channels, and numerical precision. SparseKAN equips the base branch, nonlinear basis branch, and individual basis terms with hierarchical learnable gates trained under a differentiable active-cost objective. The learned importance structure is subsequently hardened under explicit basis and width budgets, recovered in full or low precision, and physically compacted into smaller dense tensors rather than retained as sparse masks. Experiments on MNIST, CIFAR-10, and CIFAR-100 across spline, polynomial, RBF, wavelet, and convolutional KAN variants show that the structural axes compose predictably in cost. We also find strong basis-dependent differences in term importance: coefficient-based selection outperforms matched low-order truncation by up to 15.25 accuracy points in the evaluated Gram-polynomial settings. Eight-bit quantization is broadly robust, whereas 4-bit convolutional KANs require quantization-aware adaptation. Physical compaction removes up to 73.0% of parameters without accuracy loss on MNIST and reduces large-batch CUDA latency to as little as 0.51\times dense execution. On a ZCU104 FPGA, the resulting sparse low-bit models achieve up to 23.63\times lower inference latency, demonstrating that SparseKAN converts functional redundancy into measurable software and hardware efficiency. The SparseKAN implementation is available at this https URL.
[LG-94] Partially-Observable Transmission Control for UAV-Enabled Federated Learning in IoT Networks
链接: https://arxiv.org/abs/2608.00855
作者: Masoud Ghazikor,Zhou Ni,Morteza Hashemi
类目: Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: GLOBECOM 2026-2026 IEEE Global Communications Conference
Abstract:Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable. In this paper, we develop a packet-level transmission framework that captures buffer overflow, delay violations, and transmission errors, and uses the resulting packet delivery ratio (PDR) to represent partial-update reception through a packetized, Bernoulli-masked FL aggregation process. We then formulate a fairness-consensus bilevel (FCB) optimization that jointly controls (i) transmission thresholds to maximize the average PDR while reaching consensus under partial observability and (ii) transmission powers to improve the worst PDR and enforce fairness across IoT learners. To solve this problem, we propose an alternating FCB optimizer composed of a consensus-based threshold controller (CTC), which drives the IoT learners toward a PDR-efficient consensus on transmission thresholds, and a fairness-based power controller (FPC), which updates transmission powers to improve the worst PDR and ensure fairness under the resulting consensus thresholds. Numerical results on CNN-based FL tasks show that the FCB optimizer improves FL aggregation and training performance by enhancing packet-level update delivery, consistently outperforming baseline transmission policies.
[LG-95] HyperODE: Zero-Shot Surrogate for Simulation and Inference of Dynamical Systems
链接: https://arxiv.org/abs/2608.00852
作者: Ajitesh Srivastava
类目: Machine Learning (cs.LG)
*备注: 13 pages, 14 figures
Abstract:Understanding and controlling complex dynamical systems often requires executing thousands of numerical simulations across vast parametric landscapes, which is time-consuming. Machine learning surrogates significantly accelerate simulation by predicting state trajectories across different initializations and parameter values. However, surrogate models are specialized to one simulation model. Modifying the underlying differential equations - e.g., adding a physiological state or altering an epidemiological contact network - renders trained models obsolete and forces computationally expensive retraining from scratch. We introduce HyperODE, a surrogate capable of operating across an entire class of approximately mass-conserving compartmental models without retraining. By mapping the structure of ordinary differential equations (ODEs) into directed hypergraphs, HyperODE decouples the functional form of system interactions from the neural network architecture. HyperODE takes a compartmental model in the form of an ODE with an arbitrary parameter distribution defined through quantiles and transforms it into a hypergraph. It outputs the distribution of the trajectories for all the states in the original ODE in the form of quantiles. We then use this surrogate to build an encoder that takes a noisy trajectory and outputs a distribution over the parameters of the original ODE, thus calibrating the model in a single pass. On families and system sizes never seen in training, HyperODE produces calibrated quantile bands in a single forward pass, with weighted-interval score and coverage on par with specialized surrogates for each structure. For inverse inference, HyperODE produces calibration from noisy state trajectories in a few milliseconds with a single shared encoder, competitive with existing methods. HyperODE extends zero-shot to ODEs that break mass conservation and to external forcing.
[LG-96] Adaptive Quantum Physics-Informed Neural Networks for Differential Equations with Applications to Fluid Dynamics
链接: https://arxiv.org/abs/2608.00850
作者: Fabio Pereira dos Santos,Renato Portugal,Júlio de Castro Vargas Fernandes,Lucas Timotheo Sanches
类目: Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注:
Abstract:Physics-informed neural networks (PINNs) have emerged as a versatile approach for solving nonlinear partial differential equations (PDEs), yet achieving high accuracy efficiently using these techniques remains challenging for high-dimensional or multiscale systems. Here, we present a hybrid quantum-classical framework that enhances Quantum PINNs (QPINNs) through adaptive collocation point sampling and loss-aware attention mechanisms. By dynamically prioritizing points in regions with large PDE residuals or steep solution gradients, our method mitigates the spectral bias inherent in conventional PINNs. Current Quantum Physics-Informed Neural Networks are commonly assumed to be limited by the expressive power of quantum circuits. In our work, we observed that, across diverse differential equations, optimization - not only expressivity - can be an important bottleneck. Furthermore, a trainable loss-weighting scheme balances contributions from physics residuals, boundary conditions, and data fidelity during training. Integrating these strategies with quantum computing techniques (including variational quantum circuits and quantum gradient estimation) can yield at least a 60% improvement in solution accuracy under specific regimes for benchmark fluid flows and reaction-diffusion systems. Finally, we argue that merely increasing model expressivity is insufficient for resolving complex PDEs via QPINNs, as they remain constrained by the structural optimization limitations of classical PINNs. This framework provides a scalable pathway for quantum-enhanced scientific machine learning, bridging physics-based modeling with emerging quantum computational capabilities.
[LG-97] Nonlinear Laplacians Improve Signed-Directed Graph Learning
链接: https://arxiv.org/abs/2608.00836
作者: Ali Parviz,Yuichi Yoshida
类目: Machine Learning (cs.LG)
*备注:
Abstract:While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks. We introduce a non-linear Laplacian operator specific to signed and directed networks (NLSD). This non-linear operator extends the concepts of the signed Laplacian for signed graphs and the Laplacian for directed graphs. The NLSD calculates node-specific potentials based on features More precisely, if the potential discrepancy is not aligned with the edge direction, we ignore it (and vice versa) leveraging message-passing techniques only across edges where potential discrepancies align with the edge’s direction. Utilizing this novel operator, we propose an efficient spectral GNN framework (NLSD-GNN). We conducted comprehensive evaluations focusing on node classification and link prediction, examining scenarios involving signed, directional, or both types of information. Our findings reveal that this spectral GNN framework not only integrates signed and directional data effectively but also achieves superior performance across diverse datasets.
[LG-98] AdvPlan-Bench: Adversarial Evaluation of Structured Plan-Generation Agents
链接: https://arxiv.org/abs/2608.00832
作者: Alina Kapanova,Arun Kanhai,Natan Vidra,Spurthi Setty
类目: Machine Learning (cs.LG)
*备注:
Abstract:Structured plan-generation agents are often evaluated as if a plan has quality in isolation, yet many realistic planning tasks require asking how a candidate behaves when another agent can search for responses. We introduce AdvPlan-Bench, an offline benchmark for adversarial evaluation of structured plan-generation agents. The contribution is a general evaluation object: a typed plan, an adversarial response set, selector diagnostics, and traceable candidate-frontier metrics. AdvPlan-Bench represents plans as typed action chains with optional branches, assigns synthetic quality scores, compares opposing plans with BLUE-vs-RED advantage and Nash-gap diagnostics, and evaluates qualitative constraint coherence with a transparent heuristic rubric. In 150 synthetic scenarios spanning five planning templates, a sampled best-response policy that draws eight response candidates reduces BLUE advantage from .518 to .486 and BLUE win rate from .900 to .820 relative to a single-sample response. An offline LLM-policy contract baseline reaches .496 BLUE advantage and .700 BLUE win rate, while a two-stage multi-agent council obtains .509 BLUE advantage and .813 BLUE win rate. A three-rater rubric-sensitivity study over 600 rating records yields .978 inter-rater agreement. AdvPlan-Bench is not an operational planner and provides no evidence about real-world decision quality; it is a reproducible benchmark artifact for studying adversarial plan evaluation, response-budget sensitivity, candidate frontiers, and multi-agent critique-and-revision traces.
[LG-99] Paris as a 15-Minute City: An Explainable AI Perspective
链接: https://arxiv.org/abs/2608.00815
作者: András J. Molnáar,Csaba I. Sidló,Rita Rónai,Domonkos Rózsay
类目: Machine Learning (cs.LG)
*备注: 17 pages, 16 figures. Extended report of a poster presented at the NetMob 2025 conference on 8 October 2025
Abstract:The 15-minute city promotes access to everyday services within a short walk or bicycle ride, but its relationship with observed mobility remains difficult to quantify. We investigate this relationship in the Paris metropolitan area using mobility trajectories from the NetMob 2025 Data Challenge, enriched with INSEE sociodemographic data and OpenStreetMap points of interest (POIs), yielding approximately 70,000 trip segments after stop-based segmentation and data cleaning. We construct walking- and cycling-based indicators of local service availability and examine their associations with trip duration, transport mode, and short-trip car use. Higher POI availability is associated with less private motorized travel and more active mobility, although this relationship is substantially weaker in the outer agglomeration. Gradient-boosted tree models interpreted with explainable machine-learning methods consistently identify trip purpose, home–work distance, local service availability, vehicle ownership, public-transport subscription, and sociodemographic context as important predictors. For short trips, high POI density is associated with lower car use, while car ownership and driving-licence availability are associated with higher predicted car use; where services are sparse, public-transport subscription is associated with lower predicted car dependence. Finally, explainable AI (XAI) methods are used to examine how feature attributions change under alternative assumed variable orderings. The results are consistent with central assumptions of the 15-minute city while revealing substantial spatial and demographic heterogeneity. They also demonstrate how explainable machine-learning methods can complement accessibility indicators and identify locally relevant hypotheses for urban-mobility policy. Comments: 17 pages, 16 figures. Extended report of a poster presented at the NetMob 2025 conference on 8 October 2025 Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.00815 [cs.LG] (or arXiv:2608.00815v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.00815 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-100] Generic Vision and Cross-Attention for Reaction Yield Prediction
链接: https://arxiv.org/abs/2608.00776
作者: Qiwei Han,Chi Zhou
类目: Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
*备注: 12 pages, 4 figures
Abstract:Traditional reaction yield prediction is constrained by 1D quantum descriptors that lack explicit spatial information. To address this gap, a dual-modal Vision Cross-Attention architecture is proposed, fusing tabular physical-organic data with 2D molecular topologies. Notably, it is demonstrated that a generic computer vision backbone processing simple 2D skeletal structures independently outperforms purely quantum-based baselines. By synergizing both modalities, superior predictive accuracy compared to traditional methodologies is achieved by the optimal cross-attention framework (Test RMSE = 5.27%). Through mechanistic probing, active, descriptor-guided spatial querying is observed, effectively offloading macroscopic steric identification to the visual pathway. Furthermore, a dynamic chemical hierarchy is learned by the network to heavily prioritize critical steric bottlenecks, such as the aryl halide. Concurrently, residual skip connections are utilized to protect non-spatial electronic parameters from destructive attenuation during fusion. Collectively, a scalable and highly interpretable blueprint is provided for augmenting physical chemistry with deep visual learning.
[LG-101] CascadeLUT: Information-Ordered Streaming Inference for Bandwidth-Constrained FPGAs
链接: https://arxiv.org/abs/2608.00720
作者: Oliver Cassidy,Marta Andronic,George A. Constantinides
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注: To appear in the proceedings of the 36th International Conference on Field-Programmable Logic and Applications (FPL 2026). 6 pages, 2 figures
Abstract:Mapping neural networks to FPGAs enables low-latency, energy-efficient inference, particularly for lookup table (LUT)-based models that eliminate multipliers and map directly to reconfigurable fabric. While prior work achieves high compute efficiency, it typically assumes full-sample availability, causing pipeline stalls in bandwidth-limited streaming scenarios. Here, the bottleneck shifts from computation to data movement, as large input transfers limit throughput and energy efficiency. We present CascadeLUT, an information-structured inference framework organized around bandwidth constraints. Instead of buffering the full input, features are partitioned into ordered subsets and predictions are progressively refined as subsets arrive. The cascade statically controls which layers consume incoming features, enabling deterministic streaming inference without runtime branching. By co-designing feature scheduling with hardware dataflow, CascadeLUT reduces data movement while maintaining accuracy. Across datasets, it achieves 4.0 to 12.5 times lower latency, 3.0 to 5.0 times higher throughput and up to 13.8 times lower energy/sample than prior LUT baselines, using 1.2 to 4.4 times the LUTs of the smallest DWN baseline per task. We also demonstrate on-device input quantization integrated with LUT-based inference and present end-to-end FPGA results on real-world workloads, with 5 times reductions in quantization overhead.
[LG-102] Staged Multi-Agent Training (SMAT) for Hip Exoskeletons: Metabolic and Biomechanical Validation of a Simulation-Trained Co-Adaptive Controller IROS2026
链接: https://arxiv.org/abs/2608.00715
作者: Yifei Yuan,Jakob Wolf,Ghaith Androwis,Xianlian Zhou
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 14 pages, 9 figures. Extended version of a paper to appear at IROS 2026 ( arXiv:2603.07618 )
Abstract:Learning-based controllers can deliver exoskeleton assistance after training entirely in physics-based simulation, yet few controllers that address human-device co-adaptation have been validated on real users by whole-body metabolic measurement, the standard benchmark for assistive walking. Co-adaptation is challenging: as the device alters joint dynamics, the wearer reorganizes neuromuscular coordination, producing a non-stationary learning problem. Staged Multi-Agent Training (SMAT), a four-stage curriculum that progressively trains a musculoskeletal human actor and a bilateral hip exoskeleton actor, was introduced and shown to reduce simulated hip-muscle activation and provide positive assistance on hardware. This article provides the first physiological validation of SMAT. The policy was deployed on a hip exoskeleton and tested with eight healthy adults, with metabolic cost measured by indirect calorimetry across no-exoskeleton, passive, and active conditions. Active assistance lowered net metabolic rate by 19.7% relative to the passive device (p 0.001). Biomechanical analysis confirmed predominantly positive hip mechanical power across all subjects (positive-power ratio 0.98), and the policy generalized across walking speeds and terrains. Together, these results show that a single simulation-trained SMAT policy, deployed without subject-specific retraining, delivers a significant metabolic benefit on real users while remaining robust beyond the conditions it was trained on.
[LG-103] Learning the Pareto Frontier of Predictive Models under Distribution Shift
链接: https://arxiv.org/abs/2608.00632
作者: Yiming Dong,Jiwei Zhao,Yang Young Lu
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 28 pages, 4 figures, 3 tables
Abstract:Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks. These pretrained models can differ not only in performance but also in how they can be used: some only provide black-box predictions, while others may permit white-box access to internal representations that can be probed or fine-tuned. When deployed to the target domain in the presence of distribution shift, no single strategy, including zero-shot application, fine-tuning, or directly training a target-specific model, is uniformly the best. In this work, we propose Frontier Learning, a framework that treats a library of candidate models spanning different training histories and access regimes as complementary sources of information rather than mutually exclusive alternatives. Frontier Learning constructs a unified target-domain feature by concatenating internal representations from white-box candidates as well as prediction outputs from black-box candidates, then fits a lightweight, regularized supervised learner on this concatenated representation using labeled target data. Because the resulting hypothesis class contains predictors obtained by zero-shot reuse, fine-tuning, and direct training as special cases, empirical risk minimization over the frontier learner is guaranteed to be no worse, on the training sample, than any individual baseline. We evaluate the framework in simulations spanning varying degrees of source-target compatibility and in two real-world distribution-shift settings: visual domain adaptation on DomainNet/VisDA and clinical mortality prediction across intensive care unit domains using MIMIC-IV-Notes. Across all settings, Frontier Learning matches or outperforms the strongest individual reuse strategy, with the largest gains arising precisely when no single baseline is reliable across the range of shift considered. Comments: 28 pages, 4 figures, 3 tables Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2608.00632 [cs.LG] (or arXiv:2608.00632v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.00632 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-104] Relative Parameter Importance in Task-Agnostic Replay-Free Continual Learning ECML-PKDD2026
链接: https://arxiv.org/abs/2608.00630
作者: Malavika Suresh,Ikechukwu Nkisi-Orji,Nirmalie Wiratunga
类目: Machine Learning (cs.LG)
*备注: Accepted for publication at the SCL Workshop, ECML-PKDD 2026
Abstract:Achieving continual learning (CL) with deep neural networks requires balancing stability and plasticity while enabling knowledge transfer. In this work, we focus on offline learning algorithms under the constraints: (I) no access to training data from prior tasks (II) no access to task-id at inference time. We introduce a novel measure, the relative parameter-importance, which measures the relative importance of each parameter with respect to both the current and past tasks. Parameters with high relative importance are interpreted as more important for maintaining past-task stability and thus heavily regularised, whereas parameters with low relative-importance are allowed to be more freely updated. Unlike existing methods, our approach allows the update of parameters with high past-task importance when they have low relative-importance, thus enabling backward knowledge transfer in addition to tackling the stability-plasticity trade-off. We demonstrate improvements against state-of-the-art CL methods on both class-incremental and domain-incremental learning text classification problems and provide insights for extending our method to text generation problems. Code available at: this https URL
[LG-105] owards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity
链接: https://arxiv.org/abs/2608.00623
作者: Yinlin Zhu,Di Wu,Yi Zhang,Xunkai Li,Wang Luo,Wei-Jin Huang,Miao Hu,Guocong Quan
类目: Machine Learning (cs.LG)
*备注: Under Review
Abstract:Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains. Federated multimodal graph learning (FMGL) extends federated graph learning (FGL) to MAGs, enabling collaborative optimization across decentralized MAGs without exposing raw data. However, naively applying existing FGL methods to FMGL is insufficient, as they fail to navigate the multifaceted heterogeneity inherent in decentralized MAGs, including task heterogeneity across diverse client objectives, modality heterogeneity from discrepant modality quality and semantic domains, and topology heterogeneity arising from divergent topological patterns with low cross-modality correlation. To address these challenges, we propose Federated multimodal graph learning with Topology-aware Cross-modal Routing (FedTCR), the first systematic algorithm designed for FMGL. To handle task heterogeneity, FedTCR employs a two-stage paradigm that comprises federated task-agnostic pre-training followed by isolated task-oriented fine-tuning. To jointly address modality and topology heterogeneity, FedTCR introduces a topology-aware cross-modal routing mechanism. Concretely, each client distills modality-specific knowledge into compact prototypes via topology-aware importance-weighted aggregation informed by graph structure; the server then evaluates cross-client cross-modal relationships among these structure-informed prototypes and routes informative ones as contrastive references, driving a tri-level cross-modal contrastive learning scheme that jointly aligns cross-client modalities while preserving discrimination. Experiments across 7 domains demonstrate that FedTCR outperforms state-of-the-art baselines on both graph-centric and modality-centric tasks.
[LG-106] RHEA: Reliability-Harmonized Reconstruction and Assignment for Robust Multimodal-Attributed Graph Clustering
链接: https://arxiv.org/abs/2608.00621
作者: Yinlin Zhu,Di Wu,Ziyu Han,Zekai Chenm,Wang Luo,Miao Hu,Guocong Quan
类目: Machine Learning (cs.LG)
*备注: Under Review
Abstract:Multimodal-attributed graphs (MAGs), whose nodes carry heterogeneous attributes such as text and images over a relational structure, have become a fundamental substrate for label-free entity grouping tasks, including community discovery and product segmentation. Existing MAG clustering methods effectively integrate complementary modalities when attributes are clean and complete, but degrade substantially under noisy or missing attributes because they implicitly assume equal modality reliability across all nodes. In practice, modality reliability is inherently node-specific: images may be corrupted or absent, while textual descriptions are incomplete or noisy. We argue that, under attribute homophily, graph neighborhoods naturally provide supervision-free evidence for estimating node-specific modality reliability. Based on this insight, we propose RHEA, a reliability-aware framework for MAG clustering that estimates node-specific modality reliability from neighborhood consensus and propagates this signal throughout the clustering pipeline. RHEA reconstructs unreliable or missing modalities from graph neighborhoods, adaptively weights modalities during reliability-aware fusion, and performs topology-aware optimal transport clustering with reliability-aware transport assignment and neighbor-consensus assignment distillation. Furthermore, the confidence of reconstructed representations is incorporated into the clustering objective, allowing uncertain reconstructions to contribute proportionally during optimization. Experiments on four MAG benchmarks under five attribute conditions show that RHEA consistently outperforms the strongest baseline, with NMI gains increasing as attribute quality deteriorates.
[LG-107] From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy
链接: https://arxiv.org/abs/2608.00608
作者: Viacheslav Barkov,Jonas Schmidinger,Robin Gebbers,Martin Atzmueller
类目: Machine Learning (cs.LG)
*备注:
Abstract:Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties. Yet, translating high-dimensional, highly collinear spectra into accurate soil property predictions remains challenging, particularly when employing machine learning. We systematically investigated regression models and dimensionality reduction approaches for spectroscopic modeling across 85 regression tasks from open benchmark datasets in pedometrics spanning field-scale digital soil mapping and a global soil spectral library. We compared an in-context learning tabular foundation model (TabPFN), a convolutional neural network (CNN), rule-based regression (Cubist), Random Forest, and partial least squares regression (PLSR) using full spectra as well as features derived from principal component analysis (PCA) and partial least squares (PLS) latent variables. TabPFN consistently delivered the best overall performance across scales, including large spectral library tasks with tens of thousands of soil samples. Notably, TabPFN applied directly to full spectra already surpassed all classical baselines, showing that explicit dimensionality reduction is not strictly required for strong performance. Further improvements were achieved through PLS, which proved to be an effective dimensionality reduction strategy for all models. Combining PLS latent variables with TabPFN yielded the best predictions overall. Our findings provide evidence-based guidance for spectroscopic calibration model selection across operational scales, demonstrating that the long-standing advantages of PLSR and modern tabular foundation models complement each other in chemometrics.
[LG-108] CoSynFlow: Conformal Symplectic Neural Flows for Cross-System Prediction of Dissipative Hamiltonian Dynamics
链接: https://arxiv.org/abs/2608.00571
作者: Baige Xu,Takaharu Yaguchi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Learning solution operators for differential equations is a central problem in scientific machine learning. However, many neural operator methods optimize prediction accuracy without explicitly enforcing the geometric structure of the dynamics. Structure-preserving models such as SympNets and Symplectic Neural Flows address this issue for conservative Hamiltonian systems by preserving the symplectic form. In dissipative Hamiltonian systems with conformal symplectic structure, however, the symplectic form evolves according to a conformal factor determined by the dissipation. We propose CoSynFlow, a conformal symplectic neural flow for learning continuous-time solution maps of dissipative Hamiltonian dynamics. CoSynFlow composes symplectic shear maps with explicit conformal scaling, preserving the conformal symplectic structure by construction. By conditioning it on a finite-dimensional Hamiltonian descriptor and the dissipation parameter, a single trained model predicts solution maps for unseen systems without retraining. CoSynFlow keeps the structure error at machine precision, attains the lowest long-horizon error, and admits physics-informed training.
[LG-109] Agent ic Graph Token Reasoning
链接: https://arxiv.org/abs/2608.00542
作者: Zhuoyi Peng,Yi Yang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graphs model relational data throughout science and industry, from citation networks to product co-purchase graphs. Because the nodes of many such graphs carry rich text, a growing line of work applies large language models (LLMs) to graph analysis. The most graph-native of these methods use graph tokens: a graph encoder compresses a graph view, such as a node, its k-hop neighbourhood, or a cluster, into a short block of continuous tokens that jointly encodes node attributes and topology and is read directly by the model. Existing methods, however, use graph tokens in a static single-shot manner: they encode one predefined graph view before the model has even seen the target and never revise it, leaving the model’s step-by-step reasoning ability unused. We introduce agentic graph token reasoning, which recasts graph tokenization as part of the reasoning process itself. At each step, the model chooses which graph view to encode and at what granularity; a graph encoder is invoked on demand to materialise the corresponding graph tokens; and the resulting block is spliced into the running context. The model thus reasons step by step in the graph token space, and the tokens it reads are trajectory-dependent. We realise this with a three-stage training pipeline: (i) self-supervised tasks that teach the model to read heterogeneous graph tokens, (ii) a token-robust trajectory stage with a graph-token consistency regulariser, and (iii) preference optimisation that rewards trajectories in which the graph-token evidence and the node-text evidence agree. Across evaluations spanning seven graph domains, our models outperform a broad set of baselines by a large margin and transfer zero-shot to unseen domains without any per-target fine-tuning. More broadly, this work pushes LLM-based graph analysis from static graph-token encoders towards a graph-native agent paradigm.
[LG-110] HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
链接: https://arxiv.org/abs/2608.00491
作者: Ruichen Xu,Jingxiang Qu,Wenhan Gao,Jiaxing Zhang,Linsey Pang,Ravid Shwartz-Ziv,Yann LeCun,Yuefan Deng
类目: Machine Learning (cs.LG)
*备注: 15 pages, 4 figures, 5 tables
Abstract:Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input reconstruction by predicting masked targets directly in latent space. However, existing graph JEPAs typically rely on a single predefined graph partition, biasing the learned representations toward one structural granularity and limiting their ability to capture complementary patterns at different graph scales. To address this limitation, we propose HP-JEPA, a hierarchical partitioning framework for multi-resolution graph joint-embedding prediction. HP-JEPA organizes each graph into an ordered bank of coarse-to-fine partition resolutions and performs context-target latent prediction separately at each resolution using an online encoder, an exponential-moving-average target encoder, and a latent predictor. The resulting resolution-specific graph representations are subsequently integrated through concatenation or task-specific resolution weighting, allowing downstream models to combine complementary local, regional, and global structural information. Experiments on seven graph classification benchmarks and one graph regression benchmark show that HP-JEPA outperforms the fixed-resolution Graph-JEPA baseline on 6 of 8 tasks, improving upon Graph-JEPA on most evaluated benchmarks. Size-stratified analyses further show that HP-JEPA achieves higher accuracy than Graph-JEPA in most evaluated graph-size quartiles on three representative datasets. These results highlight the effectiveness of hierarchical multi-resolution partitioning for transferable graph representation learning.
[LG-111] From Digital to Physical Reservoir Computing: Co-Optimizing Soft Robotic Reservoirs via Dynamics Matching
链接: https://arxiv.org/abs/2608.00484
作者: Nicola Visentin,Maximilian Stölzle,Mariano Ramírez Montero,Francesco Braghin,Daniela Rus,Cosimo Della Santina
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Soft robotic substrates are promising for Physical Reservoir Computing (PRC) because their compliant nonlinear dynamics can provide temporal memory, high-dimensional state transformations, and efficient inference. However, physical reservoirs are often adopted as-is rather than pretrained or co-optimized, potentially limiting soft robotic PRC performance relative to digital reservoirs. We investigate whether a physical reservoir can instead be pretrained against high-performing digital reference dynamics. Our formulation jointly optimizes physical parameters, a diffeomorphic physical-reference state map, and feedforward-feedback control using a differentiable physical model and an acceleration-level equation-error objective that avoids temporal integration. As a proof of concept, we instantiate the formulation with simulated soft robots, a Random Oscillators Network (RON) reference, and parallel multi-start gradient descent. We evaluate the optimized reservoirs on classification (sMNIST and ADIAC) and forecasting (Mackey-Glass and Lorenz96) tasks across four reservoir dimensions. Compared with unoptimized soft robot reservoirs, the optimized reservoirs achieve a mean relative improvement of 33.7% across all tasks and datasets, while remaining close to the digital reference. These results demonstrate the feasibility of dynamics-level co-optimization for the simulated soft robotic reservoirs considered here.
[LG-112] Modeling Unknown Nonlocal PDE Systems via Flow Map Learning
链接: https://arxiv.org/abs/2608.00400
作者: Zhongshu Xu,Ying Li,Yanzhi Zhang,Dongbin Xiu
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS); Numerical Analysis (math.NA)
*备注:
Abstract:Nonlocal partial differential equations arise in many applications but are often difficult to model and learn because of the presence of nonlocal operators. We present a flow-map learning (FML) framework for modeling unknown nonlocal PDEs directly from solution data. Rather than learning or approximating the underlying nonlocal operators, the proposed approach learns the finite-time evolution operator in either modal or nodal space. Two complementary formulations are developed for spectral and grid-based solution representations. Numerical experiments on one- and two-dimensional fractional diffusion and wave equations demonstrate accurate and stable long-time prediction using only short observation windows. The proposed approach provides an effective data-driven framework for learning unknown nonlocal dynamics without explicit evaluation of nonlocal operators.
[LG-113] Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data
链接: https://arxiv.org/abs/2608.00346
作者: Pulock Das,Yina Hou,Md. Kamrozzaman Bhuiyan,Manar D. Samad
类目: Machine Learning (cs.LG)
*备注:
Abstract:Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy. Unsupervised deep clustering can be immune to class imbalance because representation learning for clustering is performed without class labels. Deep clustering has been proposed for images, languages, and graphs, while its application to tabular data has only emerged recently. This paper is among the first to examine the performance of state-of-the-art deep clustering methods under varying levels of data imbalance. We introduce two novel cluster ensemble approaches: one aggregates deep clustering assignments across different embedding dimensions, and the other applies majority voting to the best-performing clustering algorithms. Experiments on 16 binary tabular datasets with varying and artificially induced levels of imbalance reveal distinct strengths of different deep clustering methods. On average, our ensemble methods outperform individual clustering methods in ACC, NMI, and ARI scores, offering greater resilience to data imbalance when identifying ground-truth classes without supervision. Therefore, in an imbalanced data scenario, deep clustering can serve as a strong alternative to supervised classification.
[LG-114] Agent ic Bayesian Optimization through Surrogate-Augmented Autoresearch
链接: https://arxiv.org/abs/2608.00316
作者: Paul Brunzema,Louis Tiao,Nhat Le,Kevin De Angeli,Yao Xuan,Djordje Gligorijevic
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Bayesian optimization (BO) has become the standard tool for sample-efficient optimization and owes its efficiency to uncertainty-aware search driven by generic statistical priors. Richer domain priors can improve BO in principle, but encoding them through tailored kernels or problem structure is difficult and rarely done in practice. LLMs can help sidestep this difficulty by making informal priors from natural language, code, and documentation directly available to the optimizer. However, existing LLM-based BO methods either insert the LLM into a fixed role (surrogate, acquisition proxy, or configuration interface) or hand it broad control, sacrificing the systematic exploration that makes BO reliable. We introduce agentic Bayesian optimization: a paradigm in which an LLM agent is the central decision maker in the BO loop while a Bayesian backend provides the uncertainty-aware optimization substrate. The agent configures the problem, queries the backend, selects and commits evaluations, and can revise the optimization strategy during the run by tightening bounds, switching acquisition functions, proposing targeted evaluations, or even reframing the problem following new instructions or observed evidence. We instantiate this idea in Sara, a surrogate-augmented autoresearch agent, and lenz, a modular BoTorch-based backend that the agent can inspect and modify through a structured interface. Across synthetic and real-world benchmarks, Sara preserves the reliability of state-of-the-art BO without prior knowledge, outperforms LLM-based baselines, and uses natural-language priors to improve beyond standard BO. We further demonstrate the practical value of agentic BO in dynamic settings, where Sara reconfigures the full optimization problem on the fly as requirements change, a capability not previously available in standard BO.
[LG-115] owards General Language-Conditioned Latent Safety Filters
链接: https://arxiv.org/abs/2608.00315
作者: Ihab Tabbara,Yuxuan Yang,Hussein Sibai
类目: Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Robot policies are becoming increasingly general, with vision-language-action (VLA) models enabling a single policy to execute diverse tasks specified in natural language. Safe deployment, however, requires adapting not only to new tasks but also to varying safety requirements across users, environments, and applications. Existing safety filters remain largely constraint-specific and thus must be redesigned or relearned when safety requirements change. In this paper, we investigate language-conditioned safety filtering, in which a Hamilton-Jacobi safety actor and critic are conditioned on language-specified constraints. We evaluate this formulation across pick-and-place, table-wiping, and block-stacking tasks in the vision-based setting, examining its ability to enforce language-specified constraints and transfer to unseen constraint instances within the evaluated constraint families. Our experiments provide evidence that language-conditioned safety filters reduce constraint violations and exhibit partial transfer to unseen constraint instances.
[LG-116] Stabilized Best-of-K Training for Neural Combinatorial Optimization
链接: https://arxiv.org/abs/2608.00296
作者: Melveena Jolly,Midhun Xavier
类目: Machine Learning (cs.LG)
*备注:
Abstract:Leader Reward modifies POMO training to emphasize the best trajectory produced by repeated inference. We test a narrow extension: replace its binary leader/non-leader distinction with a stabilized rank signal indexed by a sampling budget K . With the POMO architecture, 3,050-epoch schedule, and TSP-100 test set held fixed, the Leader Reward reimplementation obtains 7.7662 under 100-start, 8-augmentation greedy decoding, matching the reported 7.766 at its displayed precision. Under independent sampling, the stabilized K=8 recipe lowers realized Best-of-8 cost in all three paired training seeds: 7.7944 versus 7.8136 . This observation is estimation-only and decoder-specific: three seeds are below the six-seed testing floor, Leader Reward is better at sampled K=1 , and it remains slightly better under its original augmented-greedy protocol. We make no unbiased-estimator, universal superiority, or state-of-the-art claim.
[LG-117] Similarity-Aware Machine Unlearning
链接: https://arxiv.org/abs/2608.00246
作者: Madhavan Citalamangalam Kumaran,Midhun Parakkal Unni,Vicky Kouni,Haripriya Harikumar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine unlearning removes the influence of user-specified training examples from a trained model, avoiding the need to retrain it from scratch. Localization-based methods improve unlearning efficiency by identifying a subset of influential model parameters. However, existing approaches select parameters based solely on forget-set importance, neglecting their role in retained dataset and often causing collateral damage to semantically similar retained examples. We address this limitation with a retain-aware localization method that considers parameter importance to both forgotten and retained data. We also introduce a retain-similar evaluation set, constructed using cosine similarity in the model embedding space, to directly measure collateral damage. Across eleven experimental settings on CIFAR-10 dataset and ResNet18 model, our method consistently reduces collateral damage while improving standard unlearning metrics, demonstrating the effectiveness of retain-aware localization for similarity-aware machine unlearning.
[LG-118] A Physics-Chemistry-Informed Neural Network (PCINN) for Real-Time Spatial-ALD Coverag e Prediction and Reliable Kinetics Inversion
链接: https://arxiv.org/abs/2608.00212
作者: Ning Hu,Chang Liu,Yunlei Jiang,Yuan Dong
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注: 31 pages, 12 figures, 8 tables
Abstract:Spatial atomic layer deposition (SALD) is a leading atmospheric-pressure, high-throughput route to industrial ALD, but design and control are limited by the cost of predicting surface coverage: high-fidelity CFD is far too slow for operating-window scans, while analytic models miss transport modulation such as the gas curtain. We present a physics-chemistry-informed neural network (PCINN), a hybrid surrogate with CFD-level accuracy at real-time speed: a query returns coverage in about 7 ms, roughly 5x10^4 times faster than a CFD solve, reaching a test R^2_log = 0.998 (leave-one-out R^2_raw = 0.974) from only 30 training cases spanning four orders of magnitude in coverage. The architecture is not a black box: a small network learns only the operating-condition to near-wall concentration closure, while the known surface kinetics is a hard-coded, trainable chemistry layer integrated along the substrate trajectory. This single-scalar bottleneck keeps it accurate under sparse data, interpretable and invertible. We add a full identifiability analysis (Fisher information, profile likelihood). The adsorption energy E_ads and desorption rate k_des are robustly identifiable; k_ads is not separately identifiable at a single temperature (only k_ads*c_wall is). Across four temperatures the prefactor nu and E_ads bind along a weakly identifiable degeneracy valley of slope 0.065 eV/decade, derived analytically as k_B T_eff ln(10) and turned into a reliability diagnostic: a seven-chemistry mismatch matrix shows it is invariant under any single-Arrhenius mismatch and shifts only when a second thermally activated process appears, so a slope departure flags unmodelled site heterogeneity. Data come from simulation with known ground truth inverted by the same kinetic form, so the study verifies pipeline self-consistency and the identifiability boundary, not real parameters. Comments: 31 pages, 12 figures, 8 tables Subjects: Machine Learning (cs.LG); Computational Physics (physics.comp-ph) Cite as: arXiv:2608.00212 [cs.LG] (or arXiv:2608.00212v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.00212 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ning Hu [view email] [v1] Fri, 31 Jul 2026 18:52:27 UTC (3,382 KB)
[LG-119] AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery
链接: https://arxiv.org/abs/2608.00198
作者: Marco Ruiz,Miguel Arana-Catania,David R. Ardila,Rodrigo Ventura
类目: Machine Learning (cs.LG)
*备注: 33 pages, 10 figures. Submitted to Environmental Modelling Software
Abstract:Environmental time-series causal discovery requires expert decisions about method choice, conditional-independence tests, lag horizons, sample-size adequacy, multiple-testing control, and evidence interpretation. Applied inconsistently across datasets, these choices yield graphs that cannot be compared, reproduced, or audited. We present AutoCause, an open-source Python workflow that records each decision, derives defaults from an extended causal-audit module, and admits domain-informed overrides. The workflow wraps four established causal-discovery methods from three families, adds non-causal reference models, and grades links by method-count support. On 145 datasets from DGP-Atlas, TimeGraph, and a topology-derived CausalRivers reference, the methods recover complementary parts of the reference graphs. Majority-supported links are more precise than single-method links on the synthetic benchmarks but not against river topology. AutoCause converts inconsistent expert practice into an auditable, repeatable analysis; causal interpretation remains with the analyst. Available at this https URL.
[LG-120] Progressive2: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression
链接: https://arxiv.org/abs/2608.00129
作者: Tiancong Cheng,Ying Zhang,Zhiwen Yu,Yifang Yin,Bin Guo
类目: Machine Learning (cs.LG)
*备注: Manuscript under review at IEEE Transactions on Services Computing
Abstract:Knowledge distillation (KD) is a widely utilized technique for transferring knowledge from a large model (the teacher) to a smaller model (the student). Owing to its flexibility and broad applicability, KD has been extensively applied in the compression of server-side models to meet the Quality of Service (QoS) requirements of client users. Despite significant advancements, the performance of distillation is substantially compromised when a large disparity exists between the capabilities of the server and the requirements of the client. To alleviate this problem, we propose a novel distillation approach, named Progressive ^2 , which operates through the combination of a progressively stronger teacher and a progressively smaller student. On the side of the teacher, rather than involving all layers simultaneously, we progressively select additional layers for distillation following a raw-to-rich semantic progression, establishing a systematic learning curriculum. Furthermore, we design a teacher-side multi-feature fusion adapter for the teacher to improve training stability, which is theoretically supported by the framework of Lipschitz continuity. On the side of the student, rather than directly training a tiny model, we gradually reduce the size of the network to facilitate an iterative co-evolution with the teacher. Progressive ^2 serves as a flexible framework; the progressive strategy of the teacher can be deployed independently to achieve an optimal balance between accuracy and training efficiency, while the joint integration of the teacher and the student yields further improvements in overall performance.
[LG-121] Deep Learning for Cyber Threat Detection and Mitigation in Healthcare-IoT
链接: https://arxiv.org/abs/2608.00118
作者: Mirza Akhi
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: PhD thesis, University of Limerick, 2026. 357 pages. Supervisors: Dr. Lubna Luxmi Dhirani and Prof. Ciarán Eising
Abstract:Cybersecurity is a fundamental requirement for protecting wearable devices used in healthcare Internet of Things (H-IoT) systems. Security failures in these resource-constrained systems directly compromise patient safety. Physiological data and network traffic are frequent targets of cyberattacks in H-IoT environments. To address these risks, deep learning-based cybersecurity mechanisms for H-IoT often involve complex architectures with large parameter counts. Existing datasets are also rarely assessed for quality, limiting their applicability. However, this research addresses these challenges by developing multiple realistic datasets and proposing lightweight deep learning models, namely the Temporal Convolutional Network (TCN) and Residual TCN (Res-TCN), for H-IoT. It includes two binary classification datasets for Distributed Denial of Service (DDoS) attacks and a multiclass dataset representing Selective Forwarding (SF), Man-in-the-Middle (MITM), and DDoS attacks. The datasets UL-ECE-MQTT-DDoS-H-IoT2025 and UL-ECE-UDP-DDoS-H-IoT2025 are generated in Cooja and ns-3 to capture transmission behaviours and protocol variations. The third dataset, UL-ECE-MultiAttack-H-IoT2025, integrates physiological and network features to represent multiple cyber threats in H-IoT. Building on this, the TCN model is designed to detect and mitigate DDoS attacks over the MQTT and UDP-based datasets. It incorporates a monitoring frequency-based detection mechanism and a dynamic threshold-based mitigation strategy. To enable edge deployment, the model is quantised and converted into TensorFlow Lite (TFLite) for real-time DDoS detection on Raspberry Pi 4, achieving low latency and power-efficient operation in H-IoT. This thesis establishes a deep learning-based cybersecurity defence mechanism encompassing realistic dataset generation, lightweight model design, and edge deployment for securing H-IoT systems.
[LG-122] Learning Compositional Meta-Routing for Agent ic Workflows: An Executable Benchmark AAAI2027
链接: https://arxiv.org/abs/2608.00106
作者: Natan Vidra,Alina Kapanova,Arun Kanhai,Spurthi Setty
类目: Machine Learning (cs.LG)
*备注: 7 pages, 1 figure; AAAI 2027 anonymous submission
Abstract:Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it. A controller may answer directly, decompose a request, retrieve evidence, execute code, delegate to a specialist, or verify an intermediate result. Existing routing work largely selects model endpoints, retrieval depth, or tools in isolation. We introduce an executable benchmark and a budget-aware meta-router that composes heterogeneous operations from raw task text. The benchmark contains 216 training, 72 development, 108 held-out test, and 108 locked lexical-shift challenge tasks across data analysis, frozen-corpus research, and document processing. Outcomes are machine checked after operations execute. Independent regularized logistic heads predict operation probabilities from word and character features, are temperature-scaled on development data, and are greedily composed under route-cost and action-count budgets. On the held-out test, the learned policy achieves 100% success versus 93.5% for strong static and fixed workflows, with 43% lower cost than the static policy; a matched learned one-shot router reaches 56.5%. On the untouched challenge split, learned success falls to 75.9% and trails static routing at 93.5%, while remaining 49% cheaper and exceeding one-shot routing by 34.3 points. The gap identifies lexical generalization, rather than route execution, as the principal limitation. These results establish a reproducible testbed and a bounded proof of concept, not evidence of live-LLM performance.
[LG-123] Learning to Persuade Privately Informed Receivers
链接: https://arxiv.org/abs/2607.28342
作者: I. Arda Vurankaya,Ufuk Topcu
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注:
Abstract:Bayesian persuasion studies how an informed sender can influence the behavior of a receiver through strategic information disclosure. Standard models assume the sender is the receiver’s only source of information, yet in many applications receivers also consult external sources the sender can neither observe nor control. We study an online Bayesian persuasion problem in which a binary-action receiver has access to a fixed signaling scheme that is unknown to the sender. Over T rounds, the sender commits to a signaling scheme and sends a signal; the receiver combines it with its private signal and acts, while the sender observes only the action. We design a learning algorithm that achieves regret \widetildeO(T^3/4) relative to the optimal scheme of a sender who knows the private signaling scheme of the receiver, with polynomial dependence on the sizes of the state space and the receiver’s signal alphabet. Our key insight is reducing the problem of learning the exponentially large belief-space partitioning induced by the private scheme to a one-dimensional change-point detection problem.
[LG-124] A Simple Approximation to the Distribution of the Ridge Regression Estimator
链接: https://arxiv.org/abs/2608.02539
作者: José Luis Montiel Olea,Ryan Strong,Amilcar Velez,Zhuoheng Xu,Haomin Yu
类目: Econometrics (econ.EM); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 16 Figures
Abstract:We present a simple Gaussian approximation to the finite-sample distribution of the classical ridge regression estimator. Our approximation captures the fact that, in finite samples, the ridge regression estimator trades off bias and variance to reduce estimation and prediction error. Our approximation is based on nonstandard asymptotics where i) we let the estimator’s regularization parameter grow proportionally to the sample size; and ii) we treat the population regression coefficients as \emphlocal to the reference vector that defines the estimator’s direction of shrinkage. In contrast to other asymptotic approximations in the literature, we allow for general forms of heteroskedasticity and autocorrelation in the data generating process (at the cost of considering a low-dimensional model where the number of covariates is not allowed to grow with the sample size). We use our simple Gaussian approximation to propose two new strategies to select the regularization parameter for the ridge regression estimator. The suggested strategies select the regularization parameter to minimize either average or worst-case excess prediction risk, where risk is computed using our suggested Gaussian approximation.
[LG-125] Interaction Is Not Necessary for Order-Optimal 1-Bit Mean Estimation
链接: https://arxiv.org/abs/2608.02538
作者: Jiachen Hu,Han Zhong
类目: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:This paper is concerned with one-bit mean estimation, where each independent sample is represented by a single binary message. We consider distributions on \mathbbR with mean in [-\lambda,\lambda] and absolute k -th central moment at most \sigma^k , where k1 is fixed. For this class, previous work attained the optimal sample complexity for general queries using a two-stage protocol. The first stage localizes the mean. The second-stage queries are chosen after localization and refine the estimate around the decoded center. We show that this interaction can be avoided by constructing a randomized fully non-adaptive protocol that fixes all queries before observing the data and matches the optimal adaptive sample complexity. For target accuracy \epsilon and confidence 1-\delta , its sample complexity scales as [ \log\frac\lambda\sigma + \begincases (\sigma/\epsilon)^2\log(1/\delta), k2,\ (\sigma/\epsilon)^2\log(\sigma/\epsilon)\log(1/\delta), k=2,\ (\sigma/\epsilon)^k/(k-1)\log(1/\delta), 1k2, \endcases ] up to constants depending only on k . In the range covered by the known lower bound, this rate is minimax optimal even among fully adaptive protocols. This gives a negative answer to the COLT 2026 open problem asking whether interaction is necessary for order-optimal one-bit mean estimation with general queries \citep[Open Problem~1]lau2026open.
[LG-126] Beyond Modern Asymptotics for Log-Likelihood Ratios in Logistic Regression
链接: https://arxiv.org/abs/2608.02507
作者: Hugo Chardon,Reese Pathak,Nikita Zhivotovskiy
类目: atistics Theory (math.ST); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 62 pages
Abstract:We characterize the finite sample behavior of the log-likelihood ratio statistic in binary logistic regression, uniformly over both the design and the target parameter. For n\geq d\geq 3 , we determine, up to universal constants, its worst case (1-\delta) quantile over all fixed collections of design vectors and all target parameters: [ d\log\left(\frace nd\right)+\log\left(\frac1\delta\right). ] This is a nonasymptotic analogue of the Wilks \chi^2_d phenomenon and requires no regularity assumptions on the design. The low dimensional cases exhibit unusual behavior. The worst case quantile in dimension d=2 is sharply of order [ \log\log\log n+\log\left(\frac1\delta\right). ] The worst case quantile in dimension d=1 is of order \log(1/\delta) , with no dependence on n . Finally, i.i.d. Gaussian design vectors recover the classical Wilks scale. In the regime n\gtrsim d+\log(1/\delta) , we prove the sharp bound [ d+\log\left(\frac1\delta\right). ] Unlike existing asymptotic results, our bounds are uniform over the target parameter, which may depend on n , d , and \delta .
[LG-127] Computational and Statistical Guarantees of the textitc-Rectified flow
链接: https://arxiv.org/abs/2608.02487
作者: Leda Wang,Zhehao Xu,Qiang Liu,Harrison H. Zhou
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC); Probability (math.PR); Statistics Theory (math.ST)
*备注:
Abstract:Recently, rectified flow has emerged as a fundamental framework for large-scale image generation, powering state-of-the-art systems such as FLUX.1 and Stable Diffusion 3. Despite its remarkable empirical success, the computational and statistical guarantees of iterative rectified flow have remained largely unexplored. We address this problem by studying \textitc-rectified flow, a cost-aware class of rectified flow that projects velocity fields onto a gradient class while preserving endpoint marginals. The ordinary rectified flow can fail to recover the optimal transport coupling: in a Gaussian case study, the iteration converges to the optimal coupling if and only if the source and target covariance matrices commute. In contrast, under suitable compactness and uniform-integrability assumptions, iterative \textitc-rectified flow always converges to the optimal transport coupling. We further establish quantitative one-step contraction and exponential convergence guarantees under projection-stability assumptions for both quadratic and strongly convex displacement costs. Finally, under a Hölder ball assumption, we develop new minimax-optimal score estimation rates and show that, when combined with iterative \textitc-rectified flow, they yield a rate-optimal estimator of the optimal transport for the dimension (d \ge 3) and a nearly parametric rate for (d=1,2).
[LG-128] Private Generative Bootstrap via Blocking
链接: https://arxiv.org/abs/2608.02480
作者: Jinwon Sohn,Veronika Ročková
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:With AI systems gaining more access to individuals’ information, it is important to protect privacy when reporting statistical answers. Equally important is to privatize the reporting of uncertainty in such answers. To this end, we adopt a Bayesian likelihood-free framework and make simulation from the posterior private. In particular, we propose a new private instantiation of the Bayesian bootstrap using a blocking strategy. Rather than assigning idiosyncratic random weights to each individual, we randomly group individuals and assign a single weight to each group. By concealing individuals’ contributions within a group, we fortify differential privacy gates. We harness amortized inference that decouples private learning from posterior sampling. A push-forward map from observation weights to posterior samples is learned privately by adding calibrated noise during training. Subsequent posterior draws require no additional privacy and computation budget. We call the resulting method the Private Generative Bayesian Bootstrap (PGBB). We establish a differential privacy guarantee, analyze convergence to the non-private blocked-bootstrap target, and quantify the discrepancy between the ordinary and blocked Bayesian-bootstrap posteriors. In addition, we derive data-free tuning of the block Dirichlet concentration parameter that restores posterior dispersion asymptotically. We also show a single fit of PGBB can support a family of loss-based decision rules simultaneously without additional privacy cost. In simulations and in applications to U.S. Census returns to schooling and U.S. natality birthweight quantiles, PGBB gives competitive private uncertainty quantification and improves over private Bayesian alternatives that require a specified data-generating model in common settings.
[LG-129] Wasserstein mixing time of the unadjusted Langevin algorithm
链接: https://arxiv.org/abs/2608.02430
作者: Francesco Pedrotti,Peter A. Whalley
类目: Computation (stat.CO); Machine Learning (cs.LG); Numerical Analysis (math.NA); Probability (math.PR)
*备注: 8 pages
Abstract:We provide new estimates in Wasserstein distance for the asymptotic bias of the unadjusted Langevin algorithm, in the classical setting of log-smooth strongly log-concave measures. Our bound implies a Wasserstein mixing time of order \kappa \sqrtd/\varepsilon , where \kappa is the condition number, d is the dimension, and \varepsilon is the target precision: this improves by a factor of \sqrtd/\varepsilon over the previous state-of-the-art results.
[LG-130] A Spectral Filtering Approach to Regret Analysis of Distributed Online Control for Linear Dynamical Systems
链接: https://arxiv.org/abs/2608.02375
作者: Ting-Jui Chang
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:This paper studies the distributed online control problem over a network of linear time-invariant (LTI) systems in the presence of adversarial disturbances and time-varying convex costs. The network cost is characterized by the summation of local cost functions, where each local function is sequentially revealed only to the corresponding agent. The goal of each agent is to generate a control sequence, using only local observations and neighbor communication, that competes with the best \it centralized linear policy in hindsight. We extend the recently proposed Online Spectral Control framework from the centralized setting to the distributed setting. In particular, each agent applies a spectral controller obtained by convolving past disturbances with the leading eigenvectors of a Hankel matrix, while the controller parameters are updated through a distributed online gradient descent step over the local surrogate costs. We formulate this problem this problem as a \it regret minimization problem based on the spectral parameterization, and under standard assumptions, we establish a sublinear regret bound of O(\frac\sqrtT\textpoly(\log T)\gamma^3) , where T is the time horizon and \gamma denotes the stability margin. The resulting bound also captures the dependence on the network size and connectivity.
[LG-131] Cardiovascular Digital Twins from Physics Based to Data Driven Approaches
链接: https://arxiv.org/abs/2608.02135
作者: Emmanuel Lwele,Francis Chikweto
类目: Medical Physics (physics.med-ph); Machine Learning (cs.LG)
*备注:
Abstract:Cardiovascular digital twins aim to create patient-specific computational models that evolve with clinical data to support diagnosis, prognosis, and therapy optimisation. Mechanistic models provide physiological interpretability but remain computationally demanding, whereas data-driven approaches improve scalability yet risk limited robustness. Emerging physics-informed, graph-based, and hybrid methods integrate physical constraints with relational learning across vascular networks. We review modelling paradigms, data assimilation frameworks, validation challenges, and translational pathways toward clinically deployable cardiovascular digital twins.
[LG-132] Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input
链接: https://arxiv.org/abs/2608.02073
作者: So Nakashima,Tetsuya J. Kobayashi
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: 29 pages
Abstract:We investigate Optimization under Input Uncertainty (OIU), in which the input to the objective function, rather than the objective function itself, is subject to uncertainty. OIU appears in manufacturing processes with production tolerance, control of physical systems with actuation noise, Mixture of Experts, and Reinforcement Learning (RL). Most of the existing approaches solve OIU by using the value of the objective function but discard the information of the realized input, even though the realized input is observable in various applications. The question here is whether the discarded information of the realized input is useful to accelerate the optimization process. We affirmatively answer this question for Evolutionary Strategy (ES) by theoretically showing that the information of the realized input can reduce the variance of the gradient estimator via Rao-Blackwellization. Using the Rao-Blackwellized gradient estimator, we propose Phenotype-Accelerated Evolutionary Strategy (PAES), which is a refinement of ES for OIU. Numerical experiments show that PAES converges faster than the usual ES from simple continuous optimization problems to RL benchmarks.
[LG-133] A Comparative Analysis of MLP and Kolmogorov-Arnold Networks (KAN) for Faster-than-Nyquist (FTN) Signaling Detection
链接: https://arxiv.org/abs/2608.02062
作者: Sude Ertan,Osman Tokluoglu,Enver Cavus
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: Presented at the 34th IEEE Signal Processing and Communications Applications Conference (SIU 2026)
Abstract:Faster-than-Nyquist signaling improves spectral ef- ficiency by deliberately introducing inter-symbol interference. Classical sequence detectors such as BCJR can approach optimal performance, but their computational cost grows rapidly with channel memory. This paper investigates data-driven FTN BPSK detection under AWGN through a direct comparison between multilayer perceptrons and Kolmogorov Arnold Networks. A large-scale Monte Carlo dataset containing nearly four million labeled windows is generated for a time-packing factor of zero point eight and signal-to-noise ratio values from seven to ten decibels. The best MLP obtained from width sweeping uses hidden width thirty two, whereas the selected KAN uses hidden width four with spline grid size five. At ten decibels, the MLP produces a bit error rate of one point three times ten to the minus four, while the KAN reaches seven times ten to the minus six. This corresponds to an eighteen point six times lower bit error rate while using only one eighth of the MLP hidden width. The results show that KAN provides a more effective and more parameter-efficient neural decision model than the MLP baseline for FTN BPSK detection.
[LG-134] Adaptive Reconstruction of Bosonic Quantum States
链接: https://arxiv.org/abs/2608.02049
作者: Vasilisa Usova,Phila Rembold,Ian Yang,Marco Rossignolo,Simone Montangero,Samuele Tosatto,Gerhard Kirchmair
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: Main text: 14 pages, 7 figures. Supplemental material: 28 pages, 23 figures
Abstract:Bosonic quantum systems provide a hardware-efficient platform for quantum information processing but remain challenging to characterise due to their large Hilbert space and the high measurement cost of state tomography. Existing approaches estimate the fidelity with respect to a single target state, making them unsuitable for applications in which physically equivalent states differ by phase space translations, rotations, or other transformations. Here, we introduce an adaptive reconstruction technique that estimates the fidelity with respect to a family of bosonic states while reconstructing the underlying Wigner function from a small number of measurements. The method combines a physics-informed parametric model with Bayesian inference, bootstrap, and active learning to iteratively select the most informative phase space sampling points. We implement the approach on a circuit quantum electrodynamics platform and benchmark it on Schrödinger cat states with amplitudes \alpha\in[1,3] . The reconstruction yields reproducible fidelity estimates within a few minutes, remains robust to substantial displacements and rotations in phase space despite using a mismatched prior, and is sensitive to subtle state imperfections. We further compare the adaptive strategy with existing Wigner function sampling protocols experimentally, demonstrating the advantage of adaptive sampling for measurement-efficient fidelity estimation with respect to a family of cat states. Finally, we incorporate the reconstructed fidelity into the figure of merit used in a proof-of-principle closed-loop quantum optimal control experiment, demonstrating the applicability of the method to autonomous optimisation of bosonic quantum states.
[LG-135] Detecting Nonproperness of Likelihood Equations
链接: https://arxiv.org/abs/2608.01976
作者: Xiaoxian Tang,Bican Xia,Tianqi Zhao
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Symbolic Computation (cs.SC)
*备注:
Abstract:Given an algebraic statistical model, a challenging problem is classifying the data according to the number of positive critical points of the likelihood function. The positive critical points are the positive solutions to an algebraic system, say likelihood equations. So, identifying the number of positive critical points is a real root classification problem for the likelihood equations. A discriminant variety of a likelihood-equation system geometrically describes the data for which the number of real solutions becomes unusual. As an essential component of the discriminant variety, the nonproperness set collects the data such that the likelihood-equation system has a solution at infinity. So, the number of real solutions varies when the data passes the nonproperness set, and identifying the nonproperness set plays a crucial role in the real root classification. In this work, we develop a novel method for computing nonproperness sets of likelihood-equation systems. We prove the correctness of this method. We show experimentally that it is far more efficient than the known methods in the literature.
[LG-136] Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability
链接: https://arxiv.org/abs/2608.01864
作者: Henri Funk,Cornelia Gruber,Göran Kauermann,Helmut Küchenhoff,Magdalena Mittermeier
类目: Applications (stat.AP); Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注:
Abstract:Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning. Yet drought forecasts remain uncertain because variability can substantially alter regional precipitation and evaporative demand. Treating this variability as unstructured noise ignores the fact that internal variability has spatial, seasonal, and temporal structure and thus contains information that can be used to improve drought forecasting. We propose a deep-learning-based forecasting framework for European drought prediction and extend it with an uncertainty-aware drought bound that explicitly incorporates internal forecast variability from a large climate model ensemble. This bound represents a physically plausible lower-tail trajectory of future drought conditions and marks how severe drought could plausibly become under an unfavourable realisation of internal variability, giving adaptation planning a conservative, risk-averse reference. We compare the proposed bound with a lower bound derived from reanalysis data only and show that our proposed ensemble-informed bound is better calibrated across most regions and seasons. This is specifically true during anomalously dry conditions, when historical reanalysis alone underestimates lower-tail drought risk. Our results show that internal variability should be treated as a forecast quantity in its own right. More broadly, large ensembles provide a practical way to transfer physically plausible climate variability into machine-learning drought forecasts, yielding risk-aware bounds that are more informative for drought assessment under shifting climate conditions.
[LG-137] unneling the Loss Landscape: Bypassing Memorization with Monte Carlo Parameter Swapping
链接: https://arxiv.org/abs/2608.01833
作者: Lai Shun Chan,Xiaotian Zhang,Yue Shang,Ge Zhang,Entao Yang
类目: Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG)
*备注:
Abstract:Grokking is a striking phenomenon in neural network training, where a model can undergo a prolonged period of pure memorization before abrupt generalization. While previous works have attempted to interpret it through classical machine learning mechanisms like weight norm, recent research draws an analogy from statistical physics, framing grokking as a form of computational glass relaxation. This theory defines the initial memorization as a result of fast cooling' where the training loss is reduced so quickly that a glass state is formed, followed by a slow relaxation’ towards final generalization. Although providing a unifying framework for representative grokking theories, this perspective has remained largely at the theoretical on macroscopic level without direct empirical validation on training dynamics. Here we introduce a three-component framework to directly characterize the training dynamics via parameter mobility (PM), and two representative measurements from glassy dynamics: replica correlation (RC) and fractal dimension (FD). We demonstrate that standard optimization presents clear signatures of glass dynamics and inherently traps the grokking network in a kinetic arrested memorization state with a collapsed mobility, strong history dependence, and channel-like motions. This quantitative agreement motivates us to introduce State-Aware Monte Carlo Parameter Swapping (SAM-Swap), an optimization plug-in that can accelerate generalization, inspired by swap Monte Carlo algorithm widely used in glass dynamics. Comparing SAM-Swap, weight decay, and Gaussian gradient noise, we find that accelerated generalization is consistently associated with random exploration in the parameter space, similar to diffusion in physics.
[LG-138] Non-KKT Accumulation in Entropic Mirror Descent
链接: https://arxiv.org/abs/2608.01658
作者: Kuangyu Ding,Kim-Chuan Toh
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注:
Abstract:For mirror descent generated by a Legendre kernel, perhaps one of the most basic question in optimization is this: must every accumulation point of a bounded mirror descent sequence be Karush–Kuhn–Tucker (KKT) stationary under proper stepsizes? We show that the answer is no. A longstanding obstacle to resolving this question is the boundary blow-up of the Legendre gradient: it keeps every mirror step in the interior, while at a boundary limit, the inverse entropy metric vanishes on active coordinates and can erase the dual-feasibility in the KKT system. We construct C^\infty objectives and bounded sequences generated by the Shannon-entropic mirror descent on the nonnegative orthant \R_+^n , for every n\geq 3 , and on the probability simplex \Delta_n , for every n\geq 4 , such that, in each case, the set of accumulation points is a smooth boundary circle containing a nonempty relatively open arc of non-KKT points. The steps satisfy \alpha_k\asymp k^-\beta with \beta\in(1/2,1) , the objective values are nonincreasing, and the objectives are entropy-relatively smooth. Hence the pathology stems from the degeneracy of the Bregman geometry at the boundary, rather than from failure of descent, or improper stepsizes. To the best of our knowledge, these provide the first counterexamples to KKT accumulation for bounded mirror descent sequences with nonincreasing objective values.
[LG-139] Statistical comparisons of time-series feature sets on classification tasks
链接: https://arxiv.org/abs/2608.01586
作者: Trent Henderson,Ben D. Fulcher
类目: Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 24 pages, 3 figures
Abstract:In recent years, numerous open-source software libraries have been developed for computing sets of features from univariate time series. The type and number of features vary across these feature sets, which have been constructed with varying disciplinary perspectives on quantifying structure in time-series data. To date, the relative strengths and weaknesses of these feature sets on time-series classification problems remains largely unexplored. Here we aimed to understand the relative performance of six open-source feature sets and three baseline feature sets (based on distributional and/or basic spectral structure) across 124 univariate time-series classification problems using a normalization-based approach to problem-level benchmarking that better indexes the relative strengths and weaknesses of different algorithms compared to prior rank-based approaches. Despite their dramatic differences in size, composition, and computation time, we found that feature sets performed relatively similarly overall (85.3% of pairwise comparisons resulted in ties), with the largest feature set, tsfresh, exhibiting the strongest overall performance (29.03% wins across all pairwise comparisons against other feature sets). We also highlighted specific problems on which the specific composition of a given feature set gave it a substantial performance advantage or disadvantage, and problems where simple baselines comprised of Fourier coefficients and quantiles were sufficient to achieve strong performance. Our results demonstrate the need to consider problem-level performance when benchmarking time-series feature sets, and highlight the importance of feature make-up in driving relative classification performance.
[LG-140] LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems ICML2026
链接: https://arxiv.org/abs/2608.01582
作者: Shida Liu,Abhishek Gupta,Sumit Sinha,L. Mahadevan
类目: atistical Mechanics (cond-mat.stat-mech); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG); Mathematical Physics (math-ph)
*备注: 25 Pages, 7 figures. Accepted to the International Conference on Machine Learning (ICML 2026)
Abstract:Symmetry is central to modern machine learning and physics: invariances and equivariances improve sample efficiency, robustness, and out-of-distribution generalization, while symmetry principles guide scientific modeling. Yet for stochastic dynamical systems the relevant continuous symmetries are rarely known, and symmetry discovery for SDEs has remained essentially unexplored. We introduce \textitLieStoNet, an end-to-end, \emphtemplate-free framework for discovering Lie-point symmetries of SDEs directly from spatiotemporal trajectories, without prespecifying symmetry groups, templates, or canonical coordinates. Building on the seminal SDE Lie-symmetry theory of Gaeta and Quintero (1999), which formalizes Lie-point SDE symmetries and their relation to Fokker-Planck symmetries, LieStoNet learns neural surrogates for drift and diffusion from increments, then learns projectable generators by enforcing the SDE determining equations, separately regularizing for closure under Lie brackets, adherence to the Lie algebra axioms (bilinearity, antisymmetry, Jacobi), and a non-redundant independent basis. The surrogate also defines an associated Fokker-Planck equation, enabling optional discovery of its Lie-point symmetries in parallel. Across multiple canonical SDEs with known analytic symmetries, LieStoNet recovers generators consistent with the ground-truth symmetry algebra, providing interpretable symmetry discovery for noisy dynamics. Code is available at \hrefthis https URLthis link.
[LG-141] Generalized Quadratic Gradient: A New Direction in Optimization via the Fusion of Positive-Definite Curvature Matrices and Gradients into A Unified Framework
链接: https://arxiv.org/abs/2608.01552
作者: John Chiang
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Quadratic Gradient (QG) is a Newton-type optimization framework that bridges first-order gradient descent and second-order optimization by incorporating curvature information into gradient updates. Simplified Quadratic Gradient (SQG) reduces the complexity of QG construction while preserving its optimization capability, whereas Quasi-Quadratic Gradient (QQG) extends the quadratic gradient principle to quasi-Newton methods such as BFGS. In this paper, we propose Generalized Quadratic Gradient (GQG), a unified framework that extends the quadratic gradient principle to a broader class of Newton-type optimization algorithms. By abstracting the common structure of existing quadratic gradient methods, we show that the fundamental requirement of quadratic gradient construction is not limited to specific Hessian approximations, such as constant Hessian matrices, diagonal Hessian approximations, or BFGS-based Hessian surrogates. Instead, it can be generalized to any positive-definite curvature matrix satisfying the stationary condition of a local quadratic model. Based on this perspective, we investigate the construction of generalized quadratic gradients using various positive-definite Hessian surrogates beyond BFGS, providing a broader foundation for developing curvature-aware optimization algorithms. Subjects: Optimization and Control (math.OC); Machine Learning (cs.LG) Cite as: arXiv:2608.01552 [math.OC] (or arXiv:2608.01552v1 [math.OC] for this version) https://doi.org/10.48550/arXiv.2608.01552 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-142] Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models
链接: https://arxiv.org/abs/2608.01547
作者: Sam Andersson,Ricky Molén
类目: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 39 pages, 8 figures. Reproducibility code and numerical outputs: this https URL
Abstract:Drifting objectives compare a target and model distribution through a vector field observed noisily at finitely many locations. We ask what distributional conclusion such a frozen measurement system warrants. For integrable antisymmetric interactions and absolutely continuous laws in a declared finite density basis, the unnormalized sampled numerator satisfies \operatornamevec(V_X)=Mc , where c is an antisymmetric mismatch and M is probe-dependent. This identity yields an a posteriori total-variation (TV) upper confidence bound accounting for held-out field noise, estimated-operator error, and externally validated L^1 residual radii around normalized density approximants in the span; a nonpositive observability margin returns the trivial TV bound and abstains. The audit recomputes this numerator from held-out samples; a normalized drift statistic requires a separate joint numerator–denominator analysis. For Gaussian-RBF interactions, a global envelope supports distribution-free and empirical-Bernstein radii without truncation, with companion bounds for the Laplace similarity in the original drifting objective. We characterize random-probe observability by a population Gram matrix, identify rank and symmetry degeneracies, and prove large-bandwidth collapse toward mean matching. Synthetic studies exercise Gaussian and Laplace numerators, separately prespecified bounded-vector and variance-adaptive radii, Monte Carlo-calibrated operators, nonzero residual radii around normalized finite-basis approximants, outward-rounded observability bounds, and designed abstention. A joint basis-size/dimension stress path extends evaluation through m=8 . The result is a conditional diagnostic for a finite density class, or for normalized finite-basis density approximants with external residual radii, not a universal guarantee from small training drift.
[LG-143] Dominant Arm Identification with Mixing and Recycling Observed Samples
链接: https://arxiv.org/abs/2608.01545
作者: Jonghyun Sim,Wonyoung Kim
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We study the problem of identifying the dominant arm in multi-armed bandits, where the objective is to find the action with the highest probability of exceeding the realized rewards of all other actions. Conventional mean-based and pairwise comparison-based algorithms often fail to identify the arm with the highest realized reward. To address this challenge, we introduce a novel dominant arm criterion and an efficient estimator with theoretical guarantees. Our approach relies on two key technical innovations: (i) a dominance score criterion that an arm beats the locally dominant over the partitioned reward space and (ii) a joint mixing and recycling mechanism coupled with a doubly robust estimator that guarantees simultaneous convergence of the empirical distribution functions for all arms. These key innovations pave a way to efficient computation of global arm dominance. Our proposed elimination algorithm identifies the best dominant arm with nearly optimal rate of sample complexity. Numerical experiments demonstrate that our algorithm consistently achieves exact recovery of the true dominant arm, outperforming existing baselines.
[LG-144] Latent Softmax for Data-Efficient Phoneme-Based Multilingual ASR Across Tonal and Non-Tonal Languages
链接: https://arxiv.org/abs/2608.01281
作者: Saierdaer Yusuyin,Nanling Jiang,Hao Huang,Zhijian Ou
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)
*备注:
Abstract:Phoneme-based multilingual automatic speech recognition (ASR) can share acoustic evidence across languages more directly than language-specific subword modeling. When tonal and non-tonal languages are jointly trained, however, their supervision granularity does not match: tonal languages annotate tone-marked vowels, whereas non-tonal languages typically provide only base-vowel labels. A standard softmax either treats the two as unrelated classes, weakening cross-lingual sharing, or collapses tones, losing distinctions required by tonal languages. We propose Latent Softmax, a connectionist temporal classification (CTC)-compatible output layer that models tone-marked vowels as subclasses and base vowels as major classes, while consonants and the CTC blank remain singleton labels. When only a base-vowel major-class label is observed, the tone-marked vowel subclass is treated as latent and marginalized out. Multilingual experiments on AISHELL-1 Mandarin and LibriSpeech English show that Latent Softmax reduces speech-to-phoneme (S2P) phoneme error rates over a standard softmax multilingual baseline by 8.4% on AISHELL-1, 17.5% on LibriSpeech test-clean, and 12.6% on test-other. The improved speech-to-phoneme encoders also yield consistent word error rate gains for both large-language-model phoneme-to-grapheme conversion and projector-based interfaces. After code-switching adaptation, Latent Softmax further reduces projector-based mixed error rate by 2.6% on ASRU2019 and 9.5% on CS-Dialogue datasets.
[LG-145] How fine a change can moments see? A scale law for detecting distribution shift with a kernel calibration rule
链接: https://arxiv.org/abs/2608.01268
作者: Adel Kaleche
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 18 pages, 5 figures. Code and scripts: doi: https://doi.org/10.5281/zenodo.21649324
Abstract:Detecting that a stream of high-dimensional embeddings has changed is usually framed as a choice of statistic. We give a scale law that constrains any moment-based choice and test it against topological alternatives. The law: certifying a feature of spatial scale eps carrying mass fraction f requires polynomial tests of degree N* = log(1/f)/(2 eps), proved via the Chebyshev extremal problem; a Gauss-quadrature construction gives N* = 4b-1 for a b-scale topology, so cost is set by feature fineness, not feature count. The law is one-sided: we exhibit an annulus whose mean, covariance and all fourth-order moments equal those of a filled disk, yet H_1 is nonzero. Its practical content is a calibration rule. The upper bound is attained by Gaussian test functions, the RKHS witness of an RBF kernel, so the law predicts which bandwidth an MMD test should use: the feature scale. On real embedding streams we measure sigma*/eps with median 1.12 (IQR 1.01-1.52, n=26) over three settings and three scales, and a data-driven bandwidth reaches AUC = 0.95. Against an adversary optimised against the defender’s statistics (mean, covariance, k-NN, kurtosis), only a bandwidth-matched kernel test still detects. For persistent homology the verdict is mixed and depends on choices usually left implicit. The summary matters more than the filtration: total persistence attains recall 0.75 at FPR 1% where the first persistence landscape attains 0.00. What survives is a cost gap, not a power gap: where persistence works it costs 116x kurtosis, which works at least as well. We conclude not that topological summaries are useless, but that on this task a kernel test whose bandwidth the law sets dominates them. Comments: 18 pages, 5 figures. Code and scripts: doi:https://doi.org/10.5281/zenodo.21649324 Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) MSC classes: 62G10, 62H15, 55N31 ACMclasses: G.3; I.2.6 Cite as: arXiv:2608.01268 [stat.ML] (or arXiv:2608.01268v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2608.01268 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-146] Amortizing the Calibration Triple: A Projection-Consistent Neural Operator for Local-Stochastic Volatility
链接: https://arxiv.org/abs/2608.01217
作者: Xiaozhen Wang,Anaïs Després,Martin Dureau,Francois Buet-Golfouse
类目: Mathematical Finance (q-fin.MF); Machine Learning (cs.LG)
*备注: 16 pages, 4 figures, 3 tables
Abstract:Local-stochastic volatility (LSV) combines vanilla marginals with richer smile dynamics, but calibration requires a slow, noisy and sequential McKean–Vlasov fixed point. We learn a projection-consistent operator for the calibration triple. Given finite quotes and a stochastic-volatility (SV) backbone, it jointly returns an implied-volatility surface subject to static-arbitrage constraints, its Dupire local volatility, LSV leverage and the conditional moment required by the projection identity. Starting from option-price marginals, we derive a division-free Dupire residual in log-implied-variance coordinates and a quotient Fokker–Planck equation after Gyöngy projection. Deep Operator Network (DeepONet) and Fourier Neural Operator (FNO) implementations enforce quote fit, static-arbitrage, Dupire and projection constraints. For the witness-augmented residual system, we prove conditional identification and empirical consistency under LSV existence and inverse residual stability. In controlled synthetic tests, forward-start and cliquet errors differ from a particle method by 0.1 and 0.2 percentage points, while calibration latency falls from 98.5 to 0.6 ms. Compared with the tested baselines, local-volatility root-mean-square error (RMSE) falls by 36% and leverage RMSE by 7-16%. These results support amortizing the LSV fixed point: the expensive solve moves offline, while online calibration reduces to a single projection-consistent operator evaluation.
[LG-147] Climate-Dyna Deep Hedging for XVAs: Model-Based Reinforcement Learning Residual Climate HVA and Hedge-Instrument Discovery
链接: https://arxiv.org/abs/2608.01208
作者: Xiaozhen Wang,Francois Buet-Golfouse
类目: Mathematical Finance (q-fin.MF); Machine Learning (cs.LG); Risk Management (q-fin.RM)
*备注: 15 pages, 2 figures, 1 table
Abstract:For a trading desk, residual climate hedging valuation adjustment (HVA) is the climate cost left after its inherited hedge and any admissible overlay have been taken into account; it therefore cannot be inferred from a stand-alone stress loss. We obtain this residual by comparing paired climate-on and baseline worlds and reoptimizing the overlay for each hedge universe, which also turns hedge-instrument discovery into a valuation problem: an instrument is useful to the extent that it lowers the optimized residual cost. The linear-Gaussian case has an exact finite-horizon Riccati solution; Climate-Dyna starts from that hedge and learns the remaining nonlinear correction from paired world-model rollouts, with an independent gate deciding whether to deploy the update. In a public-data-calibrated semi-synthetic EU ETS study, crediting the inherited hedge lowers the mean climate charge from 1.517 to 0.906, and the learned overlay lowers it to 0.831 against a 0.821 exact floor; residual Dyna cuts regret by 93% relative to replay with one quarter as many trajectories, while adaptation from only 25 target transitions retains 60.7% of the exact-assisted gain.
[LG-148] Hybrid Quantum Neural Networks: Theory Implementations and Applications
链接: https://arxiv.org/abs/2608.01194
作者: Léo Monbroussou,Maniraman Periyasamy,Viacheslav Kuzmin,Pavel Sekatski,Viktoria Patapovich,Asel Sagingalieva,Alexey Melnikov
类目: Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
*备注:
Abstract:Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.
[LG-149] Learning-Based Stochastic Optimal Control with Infinite-Horizon Probabilistic Constraints
链接: https://arxiv.org/abs/2608.01151
作者: Francesco Cordiano,Kanghui He,Bart De Schutter
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: Submitted to IEEE Transactions on Automatic Control
Abstract:In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints. By means of an appropriate state augmentation, we reformulate the original problem as a constrained Markov decision process, in which both the cost and the constraint function exhibit an additive structure. We then prove that this formulation enjoys strong duality, thereby enabling us to reformulate the problem as an equivalent unconstrained one in the Lagrange dual framework. We propose a dual-ascent algorithm to solve the resulting problem and show that it converges to a deterministic Markov policy defined over the augmented state space that is both optimal and feasible. To accommodate continuous state-input spaces, we propose a dedicated learning algorithm to approximate the value function in an offline training setting, thereby significantly reducing the computational complexity of the online control phase. We then test our approach on a numerical example and demonstrate its effectiveness compared to online predictive control methods in terms of performance and computational complexity.
[LG-150] Model-Agnostic FDR Control via Group Gaussian Mirror and Permutation SHAP
链接: https://arxiv.org/abs/2608.00989
作者: Jiaan Han,Junxiao Chen,Yanzhe Fu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Most FDR-controlled feature selection methods are designed for coordinate-wise hypotheses, where each feature has a single weight or importance score. This abstraction fails in sequential and grouped models, where one original feature is represented by a block of sub-features, such as lags, recurrent states, or attention-based interactions. We propose a grouped-feature FDR control framework for such settings. For grouped linear models, we construct null-symmetric block-level mirror statistics with matrix-valued perturbations. For neural sequential models, we combine Permutation SHAP derivatives as model-agnostic block-level importance scores with kernel-based dependence measure. The framework is model-agnostic across network architectures, does not require specifying the covariate distribution, and reduces to Gaussian Mirror or Neural Gaussian Mirror when the block size is one. We prove FDR control for low- and high-dimensional grouped linear models and asymptotic symmetry of smoothed Permutation SHAP derivatives under fixed fitted nonlinear models. Experiments on simulated and real-world datasets show reliable FDR control and improved power under correlated grouped-feature signals.
[LG-151] A Sequence-to-Sequence ConvLSTM Approach for Leaf Area Index Forecasting over the South-Central United States
链接: https://arxiv.org/abs/2608.00879
作者: Zhixing Ruan,Lixin Lu
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注:
Abstract:Leaf Area Index (LAI) is a fundamental biophysical variable governing land-atmosphere interactions; however, LAI forecasting at high spatial resolution remains an unsolved challenge. While recent machine learning approaches have demonstrated LAI estimation at point or regional scales, none provides a gridded, meteorology-driven prognostic forecast suitable for subseasonal land surface and climate modeling applications. Here we present a sequence-to-sequence Convolutional LSTM (ConvLSTM) framework that generates daily 1-km LAI forecasts up to 30 days ahead, driven by historical LAI sequences and daily meteorological forcing including temperature and precipitation. Trained and evaluated over the South-Central United States – a region of strong climate gradients and diverse vegetation – the model achieves a domain-averaged RMSE of 0.36 at a 30-day lead time, more than a third lower than the persistence baseline. Forecast skill remains robust across seasons, geographic distributions, and plant functional types, including forests, grasslands, shrublands, and croplands. To our knowledge, this is the first demonstration of skillful LAI forecasting at a 30-day horizon at 1-km resolution.
[LG-152] An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverag e RF Modulation Recognition
链接: https://arxiv.org/abs/2608.00796
作者: Nurettin Safak,Durdu Can Yerdeyatar,Muhammet Sefa Demirel,Alperen Marasli,Taha Eren Atmaca,Ozgun Ersoy
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: 6 pages, 7 figures, 5 tables. Accepted to ASYU 2026
Abstract:Automatic RF modulation recognition is of critical importance in spectrum monitoring, electronic warfare, and cognitive radio applications, where low signal-to-noise ratio (SNR) conditions and the growing diversity of modulation schemes limit the performance of existing methods. This paper proposes an uncertainty-driven hybrid deep learning architecture for recognizing RF signals over a broad modulation space. The proposed approach carries out a multi-stage classification process by combining spectral information obtained through low-cost FFT-based preprocessing with time-frequency features extracted from short-time Fourier transform (STFT) spectrograms. The architecture comprises a 2D convolutional neural network (2D CNN)-based path for fast, low-latency primary classification, MC Dropout-supported Bayesian uncertainty estimation for assessing classification reliability, and a BiLSTM-based secondary decision mechanism activated under high-uncertainty conditions. The proposed system is evaluated in a controlled simulation environment spanning different SNR levels and modulation classes. Experimental results show that the primary 2D CNN path achieves 83.3\pm0.7% accuracy with an inference time of only 0.138 ms per sample, providing superior performance compared with traditional rule-based and classical machine-learning approaches. Furthermore, the obtained findings reveal the limitations of compact spectral feature representations and classifiers lacking temporal modeling, particularly in disambiguating FSK-based modulations. The uncertainty estimation module offers promising results for detecting low-confidence decisions, and the proposed approach demonstrates the potential of a low-latency and scalable solution for real-time RF modulation recognition.
[LG-153] Experience-Calibrated Contrastive Decoding for Mitigating Hallucinations in LM-Based Text-to-Speech
链接: https://arxiv.org/abs/2608.00722
作者: Chenlin Liu,Minghui Fang,Zhonghao Bi,Zekai Su,Rong Wang,Jiqing Han
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: Work in progress
Abstract:Language model-based text-to-speech (LM-based TTS) remains vulnerable to speech hallucinations that deviate from the target text. Existing mitigation mainly relies on architectural changes or additional training, while decoding-time control remains underexplored. We present a conditional information view that distinguishes text-derived alignment information from experience information supplied by acoustic context and learned speech regularities. We hypothesize that an important class of hallucinations begins when alignment support is insufficiently reflected in the selected token at a vulnerable transition. Using predictions from the same speech LM with and without text conditions, we propose Experience-Calibrated Contrastive Decoding (ECCD), a training-free method that strengthens alignment support while preserving useful experience information. ECCD preserves the original expert distribution, applies only positive alignment enhancement, and calibrates its strength using set-level experience compatibility. Across four models, ECCD reduces WER/CER by up to 55.6% in all SeedTTS-Eval settings and 24 of 25 multilingual CV3-Eval settings. A listening test yields a CMOS gain of +0.644 while retaining strong speaker similarity. Further analysis shows that alignment influence and decision-level gain vary within linguistic units and are lower at first-error boundaries than at matched correct boundaries. Overall, these extensive experiments and analyses identify conditional information control as a promising decoding-time direction for mitigating speech hallucination.
[LG-154] Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts
链接: https://arxiv.org/abs/2608.00701
作者: Ying Jin,Ying Jin,Dominik Rothenhäusler
类目: Methodology (stat.ME); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another. This strategy is well suited to deterministic, learnable covariate discrepancies, but can be insufficient when source–target population differences also contain changes beyond covariate shift or when estimation of the density-ratio weights is unstable. To address this challenge, we introduce a new model that allows non-systematic changes between two population laws after systematic shifts are accounted for. Such residual shift is modeled as random perturbations to the probability space that cannot be represented in a learnable way. In this way, we separate systematic shifts, treated as bias and corrected by reweighting, from residual random perturbations, treated as distributional uncertainty and handled through dataset pooling. Under pure random perturbations, this principle yields Augmented Inverse Distance Weighting (AIDW), which uses regression augmentation and variance-optimal dataset-level pooling. For mixed shifts, we develop Augmented Inverse Hybrid Weighting (AIHW), which interpolates between AIDW and standard augmented importance weighting. Both methods trade off sampling uncertainty and distributional uncertainty via a \emphdistributional distance that describes the strength of random perturbations. We establish asymptotic properties of the methods, together with plug-in guidance for choosing tuning parameters and model diagnostic tools. Experiments on three real-world multi-site datasets demonstrate consistent reductions in mean-squared error compared with standard weighting baselines, along with substantially improved empirical coverage in settings where covariate-shift adjustment alone undercovers, showing the robustness of the proposed methods across diverse distribution shift scenarios.
[LG-155] Round-Trip Consistency: Bidirectional Diffusion Models Can Predict Their Own Rollout Errors
链接: https://arxiv.org/abs/2608.00675
作者: Alexander Scheinker
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Computational Physics (physics.comp-ph); Plasma Physics (physics.plasm-ph)
*备注:
Abstract:Autoregressive models accumulate error over long rollouts, yet at deployment there is no ground truth to measure it against. We train a single conditional latent diffusion model that steps a dynamical system forward or backward in time via a direction flag, and show that this bidirectionality supplies a measurement-free test-time error signal: rolling forward i steps and then backward i steps must return the model to its start, so the round-trip discrepancy \mathcalC_i is a self-supervised proxy for the unobservable rollout error: no ensembles, no held-out data, no governing equations, for one extra rollout. We validate on compressible magnetohydrodynamics (MHD), an astrophysical turbulent radiative mixing layer, and natural face videos (CelebV-HQ). On held-out MHD trajectories, \mathcalC_i ranks rollout error (Spearman 0.91 - 0.98 at fixed depth; 0.69 \pm 0.16 within trajectories), and a simple calibrator fit on training rollouts predicts its magnitude to within 1.14\times ( 68% ) and 1.29\times ( 95% ) with near-nominal coverage - one nat beyond a depth-only predictor, transferring to all six decoded physical fields. The same signal flags the out-of-distribution Orszag-Tang vortex (AUROC 0.98 ; 1.0 by depth 10 ) exactly where sampling-dispersion baselines invert, and it cuts incurred error by 15% at 80% coverage - three times the depth-only baseline. Bidirectional training comes at negative cost, beating direction specialists in both directions, and the backward direction doubles as a fast inverse solver. On LE-PDE-UQ’s turbulent Navier-Stokes benchmark, a single bidirectional model reaches accuracy within 1.3\times of their ten-model ensemble at a tenth of the training cost, with the best training-free pixel-level calibration. Round-trip consistency turns reversibility into a practical trust signal for generative models.
[LG-156] Band-Count Dense Modal Estimation with Fixed-Frequency Differentiable Resonator Refinement
链接: https://arxiv.org/abs/2608.00667
作者: Minhui Lu,Joshua D. Reiss
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)
*备注: Accepted as a challenge paper at the 29th International Conference on Digital Audio Effects (DAFx 2026), Cambridge, MA, USA, September, 2026
Abstract:Task B of the 1st DAFx Parameter Estimation Challenge requires estimating the frequencies, decay rates, gains, and number of modes in a dense plate-reverb impulse response. Weak and overlapping modes make sparse peak detection prone to severe undercounting. We train an ExtraTrees regressor on simulator-generated data to predict mode counts in four frequency bands. These counts define dense frequency grids, after which a differentiable all-pole resonator model refines decay and gain while keeping frequency fixed. On two separate synthetic validation sets, the system reduces a local challenge-style error by about 66% relative to the official default peak-picking baseline. The improvement is mainly associated with lower mode-count mismatch, while decay and gain remain the largest error sources. These findings support separating modal-density estimation from continuous parameter fitting.
[LG-157] Causal Inference with Unstructured Treatments
链接: https://arxiv.org/abs/2608.00657
作者: Kevin Christian Wibisono,Yixin Wang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 74 pages, 16 figures
Abstract:Causal inference usually concerns a scalar treatment, yet in many problems the treatment is unstructured: a text, an image, or a sequence of clinical decisions. Consider an instructor writing a course description to attract more students: the treatment is the course description, and the outcome is enrollment. The standard target, the average treatment effect of fixing the treatment to one exact value versus another, runs into two problems. It cannot be estimated, because almost no exact description recurs across courses, leaving no comparable group from which to measure its effect; and it would be of little use even if it could, since no one wants every course to carry the same description. What the instructor actually wants to know is which features of a description raise enrollment, and which of those features can be acted on across many courses. To this end, we propose a causal query for unstructured treatments: the maximally influential feature (MIF), the feature of the treatment that most strongly influences the outcome. We formalize the MIF as a binary feature of the treatment, defined by a feature-scoring function, constrained so that both of its values stay well populated, and chosen to maximize the causal effect it induces. Turning the feature on shifts the distribution of treatments toward those that display it, turning it off shifts away, and the MIF effect contrasts the two average potential outcomes. We study identification conditions for the MIF, develop algorithms to estimate it, and make it actionable through a nudging algorithm that revises a treatment along the MIF into an outcome-improving version. We illustrate the MIF algorithm across applications in text, image, and dynamic treatment sequences.
[LG-158] Simulation-Based Plate-Reverb Parameter Estimation from a Single Impulse Response
链接: https://arxiv.org/abs/2608.00656
作者: Minhui Lu,Joshua D. Reiss
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)
*备注: Accepted as a challenge paper at the 29th International Conference on Digital Audio Effects, Cambridge, MA, USA, 2026
Abstract:We present a simulation-trained, non-iterative estimator for Task A of the 1st DAFx Parameter Estimation Challenge. Each unnormalized plate-reverb impulse response is summarized by amplitude, spectral, and decay descriptors, and an ensemble of tree regressors estimates the six target parameters in one pass. Across two independent synthetic validation sets, the normalized models outperform the training-set mean and an earlier raw-regression baseline. On a shared set, the final ensemble also outperforms a single run of the official default PSO at substantially lower inference cost. Since the official labels are hidden, parameter accuracy is measured on simulator-matched data, and the released responses support only audio-side consistency checks. The estimator returns point estimates without uncertainty.
[LG-159] Beyond Lanes: Traffic Flow Dynamics in Disordered Conditions Based on High-Resolution Trajectory Data
链接: https://arxiv.org/abs/2608.00602
作者: Shrey Agrawal,Gowri Asaithambi,Venkatesan Kanagaraj,Martin Treiber,Ostap Okhrin,Harish Babu Kumara
类目: Physics and Society (physics.soc-ph); Machine Learning (cs.LG)
*备注: 33 pages; submitted to Transp. Research Part B
Abstract:Disordered traffic flow is characterized by weak or non-existent lane discipline in the presence of strong vehicle heterogeneity and continuous lateral interactions, challenging traditional lane-based modeling assumptions. This study presents an empirical study of macroscopic and microscopic aspects of disordered traffic using high-resolution UAV trajectory data collected on an urban arterial. A two-dimensional extension of Edie’s framework is applied to quantify aggregate traffic variables and produce a two-dimensional fundamental diagram, revealing that traffic states cannot be adequately represented using one-dimensional formulations and highlighting the persistent role of lateral redistribution. The propagation of congestion is estimated directly from the spatiotemporal speed fields, demonstrating the emergence of coherent stop-and-go waves and showing a similar dynamics as conventional lane-based flow, in spite of the heterogeneous vehicle interactions. At the microscopic level, steady-state follower-leader identification is used to examine desired time gaps and minimum lateral spacing, vehicle dimension distributions, and kinematic characteristics, revealing pronounced inter-class heterogeneity that explains disordered traffic behavior. The study provides an empirical framework linking vehicle-level interactions and aggregate traffic dynamics and establishes a data-driven basis for the calibration and validation of traffic models for disordered mixed traffic systems.
[LG-160] Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields
链接: https://arxiv.org/abs/2608.00593
作者: Anand Kumar,Puli Saikiran,Vineet Dawara,Koushik Viswanathan
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
*备注:
Abstract:In stream finishing, the wear experienced by a workpiece depends strongly on its orientation within the rotating abrasive media. Determining suitable orientations to achieve uniform wear requires evaluating the wear-rate field over all feasible orientations. Although the discrete element method (DEM) accurately resolves particle interactions, simulating hundreds of feasible orientations for a new geometry is computationally expensive. We present an uncertainty-guided surrogate framework that predicts, directly from geometry, the three fields governing erosion: per-triangle normal impact velocity, tangential impact velocity, and particle impact flux. These fields are combined through the Finnie wear model to reconstruct the wear-rate distribution. The surrogate employs a deep ensemble whose disagreement estimates epistemic uncertainty, enabling an active-learning strategy that selectively performs DEM simulations for the most uncertain orientations. Trained using only 13% of the 696 feasible orientations, the surrogate achieves Spearman rank correlations of 0.93 , 0.89 , and 0.93 for the normal impact velocity, tangential impact velocity, and particle impact flux, respectively. Moreover, the predicted uncertainty is well calibrated, reliably anticipating prediction error and the fidelity of the reconstructed wear field, which matches DEM with a Spearman rank correlation of up to 0.97 for low-uncertainty orientations and degrades in a controlled manner as uncertainty increases.
[LG-161] Recursive Gaussian Processes and the Bayesian Brain
链接: https://arxiv.org/abs/2608.00503
作者: Moumita Das,Dipanjan Ray,Sourabh Bhattacharya
类目: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: What is your thought process? The Bayesian Recursive Gaussian process?
Abstract:Predictive coding offers a powerful framework for cortical computation, yet scalable implementations that respect both Bayesian exactness and neurobiological constraints remain scarce. We bridge this gap by formally connecting predictive coding to Recursive Gaussian Processes (RGPs). RGPs employ a single Gaussian process ( g(t, \cdot) ) indexed by layer index and input value, preventing the representational collapse of standard deep Gaussian processes while allowing learnable cross-layer dependence via ( r_1g ). We demonstrate that RGPs intrinsically implement hierarchical Bayesian inference, uncertainty propagation, and precision-weighted prediction error. Critically, we map RGP components—the shared GP, spike-and-slab variable selection, and MCMC dynamics—onto the canonical cortical microcircuit, providing a neurobiological substrate for these computations. Drawing on the free energy principle, we show that RGP inference minimizes variational free energy, formally linking Bayesian mechanics to neuronal dynamics. Our synthesis positions RGPs as both a principled computational tool and a candidate model for the brain’s predictive machinery, generating testable predictions for laminar-specific dynamics and spectral asymmetries between feedforward and feedback processing.
[LG-162] LOCUS-DT: Localization via Observation-Conditioned Uncertainty Scoring with Digital Twins
链接: https://arxiv.org/abs/2608.00406
作者: Haozhe Lei,Roberto Bomfin,Marwa Chafii,Sundeep Rangan
类目: ignal Processing (eess.SP); Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Accurate indoor localization is essential for emerging applications in robotic navigation and search and rescue. While classical methods typically focus on single-point estimates, complex indoor environments with heavy blockage and multipath propagation often lead to multimodal likelihood surfaces where a single estimate is insufficient. This paper proposes LOCUS-DT (Localization via Observation-Conditioned Uncertainty Scoring with Digital Twins), a framework that treats snapshot localization as posterior inference over the transmitter location. By leveraging a ray-tracing-based digital twin (DT) of the known environment, LOCUS-DT generates synthetic multipath profiles for candidate locations and compares them against the measured channel profile. Central to our approach is a novel learned scoring function designed to compare a fixed number of dominant specular paths, providing robustness against errors in both the DT environment model and the physical channel estimation. Importantly, LOCUS-DT is trained over an ensemble of environments to ensure generalization to unseen layouts. We evaluate the system using a Sionna-based ray-tracing backend, demonstrating that LOCUS-DT captures the sharp, multimodal posterior structures inherent in indoor settings more accurately than standard Gaussian or Gaussian-mixture benchmarks.
[LG-163] A reproducible and extensible framework for benchmarking competing risks survival models
链接: https://arxiv.org/abs/2608.00271
作者: Begoña B. Sierra,Colin McLean,Peter S. Hall,Sarah Friedrich-Welz,Catalina A. Vallejos
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 23 pages main text (7 figures), 31 pages supplementary information
Abstract:A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.e., cancer death) precludes the occurrence of other events (i.e., cardiovascular disease death). Despite these methodological advances, their systematic evaluation and adoption are limited by the lack of comprehensive, reproducible and extensible benchmarking frameworks. We developed an open-source benchmarking framework for competing risks models that enables their systematic comparison across multiple datasets under different aspects of performance; calibration, discrimination, overall prediction error and clinical utility. We additionally introduce an extension of SHAP for competing risks, allowing model-agnostic interpretability of covariates contributions over time. All our code is publicly available via GitHub:this https URL
[LG-164] Generative Models for Modeling and Synthesizing MIMO Channels in Adverse Weather Conditions
链接: https://arxiv.org/abs/2608.00156
作者: Vignesh Nandakumar,Faraz Barati,Brian L. Evans
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注:
Abstract:The push for broader coverage in future cellular networks depends on reliable service, yet this is increasingly harder to do as we encounter more instances of extreme weather conditions. In extreme weather conditions, we have difficulty evaluating coverage due to limited access to channel measurements. In this paper, we generate channel state information (CSI) in low and moderate weather conditions to synthesize realistic MIMO CSI under adverse weather conditions. Our primary contributions are to (1) synthesize MIMO channel datasets incorporating three weather types, each with three intensity levels, representative of practical 5G/6G scenarios; (2) train a diffusion model conditioned on weather using channel samples obtained through conventional pilot-based estimation under low and moderate weather intensities, and subsequently use it to generate channel realizations for severe weather conditions; and (3) evaluate the downlink Bit Error Rate (BER) and Outage Probability measures using the generated channels. The results show that diffusion-based generative models provide a scalable, data-driven alternative for channel modeling in harsh environments and can generalize to severe weather conditions using only low- and moderate-intensity training data.
[LG-165] Rethinking Total Absorption Gamma Spectroscopy Deconvolution: Supervised Machine Learning vs Response-Matrix Methods
链接: https://arxiv.org/abs/2608.00090
作者: J. Balibrea-Correa,E. N{á}cher,C. Fonseca-Vargas,J. L. Tain
类目: Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (cs.LG); Nuclear Experiment (nucl-ex)
*备注:
Abstract:The extraction of \beta -feeding distributions in Total Absorption \gamma -ray Spectroscopy constitutes a challenging inverse problem, particularly in nuclei with complex decay schemes involving a large number of excited states. In such cases, the measured spectrum arises from the superposition of many detector response functions, making the determination of the individual feedings intrinsically ill-posed and highly sensitive to the methodology employed. In this work, we present a systematic comparison between supervised Machine-Learning techniques and Response-Matrix methods using realistic Monte Carlo simulations of an experimental Total Absorption Spectrometer. Supervised Machine-Learning approaches construct a non-parametric estimator that infers level feedings from the measured spectrum after a training stage, whereas Response-Matrix methods determine the feeding distribution by directly minimizing the difference between measured and reconstructed spectra. Our results show that supervised Machine-Learning techniques achieve superior accuracy in the reconstruction of individual feeding intensities, whereas Response-Matrix methods provide robust and physically consistent initial solutions. These findings support a hybrid strategy in which a Response-Matrix method is first used to obtain an initial feeding estimate, which is then refined using a supervised Machine-Learning approach to achieve improved overall accuracy.
[LG-166] Hybrid Quantum CNN for Cross-Sensor Spaceborne Volcanic Thermal Activity Recognition Worldwide
链接: https://arxiv.org/abs/2608.00069
作者: Claudia Corradino,Federica Torrisi,Alessandro Grilli,Tommaso Catuogno,Mattia Verducci,Elisabetta Paladino,Luigi Giannelli,Alessandro Sebastianelli
类目: Geophysics (physics.geo-ph); Machine Learning (cs.LG)
*备注:
Abstract:As Earth Observation (EO) enters the Big Data era, the exponential volume of daily satellite imagery poses significant computational and storage challenges for classical Deep Learning (DL) models. Moreover, current approaches often struggle to generalize across heterogeneous sensors and volcanic environments while requiring large labeled datasets and substantial computational resources. These limitations are particularly critical for emerging On-Board Processing (OBP) applications, where memory, computational power, and annotated data are inherently limited. This work proposes a Hybrid Quantum AlexNet architecture for cross-sensor recognition of volcanic thermal activity at the global scale. The proposed model combines a classical convolutional backbone for high-level spatial features extraction with a parameterized quantum circuit (PQC) acting as a variational layer. By embedding high-level image representations into a high-dimensional Hilbert space, the quantum layer learns task-specific representations that enhance feature discrimination. Experimental results demonstrate that the proposed hybrid quantum model learns more discriminative feature representations, leading to improved cross-sensor transferability and robustness across heterogeneous volcanic environments using fewer trainable parameters and reduced training data than its classical counterpart.
[LG-167] A Spatial Persistence Gradient in European Warming Consistent with North Atlantic Cold-Blob Influence
链接: https://arxiv.org/abs/2608.00063
作者: Mauricio Herrera-Marín,Alex Godoy-Faúndez,Diego Rivera
类目: Geophysics (physics.geo-ph); Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注:
Abstract:Europe is warming faster than the global mean, yet the spatial organisation of this acceleration remains incompletely understood. Using ERA5 reanalysis for 1950–2024 across 28 IPCC AR6 European sub-regions, we identify two connected empirical results. First, the DFA1 Hurst exponent of interannual temperature residuals is strongly and negatively associated with the 1996–2024 warming rate ( r=-0.792 , p=5.1\times10^-7 ). High-persistence, mainly Atlantic-proximal regions warm more slowly, whereas low-persistence continental regions warm faster. This relationship is robust to five residualisation schemes, three memory estimators, leave-one-region-out analysis, and five null-test families, including spatial block permutation. It also persists across warming windows ( r=-0.550 for 1981–2024, -0.792 for 1996–2024, and -0.875 for 2000–2024), but vanishes under DFA2, indicating that the signal lies in low-frequency interannual-to-decadal persistence rather than trend curvature. The pattern is consistent with, but does not prove, North Atlantic cold-blob and thermohaline influence on European land temperatures. Second, under a strict 2006–2024 holdout, contemporaneous Mediterranean SST reduces mean annual temperature RMSE by 43% (from 0.787 to 0.449,^\circ\mathrmC ). A causal lag-weight predictor based on prior-year Mediterranean and Atlantic SST also outperforms AR(2) ( 0.578 versus 0.695,^\circ\mathrmC ) and remains informative after removing NAO, AO, and PNA effects. Similar skill from five-year moving-average and exponentially weighted predictors shows that short Mediterranean SST persistence at 1–5-year lags is the key predictive ingredient. Together, the results support a two-regime interpretation: Atlantic-proximal regions exhibit stronger memory and oceanic buffering, while continental interiors show faster warming and weaker interannual persistence.
[LG-168] Domain-Generalized Adaptive Semantic Communication for Collaborative Perception
链接: https://arxiv.org/abs/2608.00056
作者: Fan Gao,Youzheng Wang,Ning Ge
类目: ignal Processing (eess.SP); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
*备注: Accepted by IEEE ICCC 2026. 6 pages, 5 figures
Abstract:We propose RSTA, a domain-generalized semantic communication framework enabling source-free V2X collaborative perception under both observation-domain shift and unseen wireless channel conditions. In V2X, received semantic tokens suffer coupled degradation from pre-transmission domain drift and in-transit channel corruption; existing methods address only one source, leaving adaptation misled by tokens that are simultaneously off-domain and physically degraded. RSTA trains a pre-deployment semantic encoder for transmission stability via cross-domain prototype alignment and cross-channel gradient consistency, and updates a lightweight in-deployment decoder adapter through reliability-gated entropy minimization that restricts gradients to tokens ranked high in both semantic relevance and channel fidelity. A theoretical task robustness decomposition links each loss term to a distinct degradation source, grounding each algorithmic component in a measurable error mode. Trained on AWGN and tested on unseen Rayleigh fading, RSTA achieves +7.2 AP@0.7 over pre-deployment domain generalization on cross-weather tasks and +5.5 on cross-dataset tasks across four V2X benchmarks, updating only 0.21% of parameters in-deployment with zero inter-agent synchronization overhead.
[LG-169] Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systems
链接: https://arxiv.org/abs/2608.00052
作者: Wenkai Liu,Nan Ma,Jianqiao Chen,Hongtao Zhang,Ping Zhang
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注:
Abstract:Extremely large-scale reconfigurable intelligent surface (XL-RIS)-assisted communication is regarded as a key enabling technology for future 6G networks. However, hybrid-field channel estimation for XL-RIS-assisted systems is challenging due to the high-dimensional cascaded channel and the coexistence of far-field and near-field propagation. In this case, traditional full-dimensional sparse recovery methods require a large cascaded dictionary and suffer from severe computational and storage burdens. To address these challenges, we develop a double-timescale channel estimation framework that decouples sparse dictionary representation and recovery. Then, by exploiting the quasi-static property of the channel at the base station (BS) and RIS side, we propose a Dirichlet kernel-based off-grid dictionary compression (DK-ODC) scheme for sparse representation, which reduces the dimension of the corresponding dictionary as well as mitigates BS-side angular off-grid error. Furthermore, for the dynamic channel at the user equipment (UE) and RIS side, we propose a subspace-aware incremental variational Bayesian learning (SI-VBL) algorithm, which enables incremental learning of sparse channels by exploiting the identified low-dimensional subspace and pruning threshold. Analysis and simulation results confirm that the proposed framework avoids full-dimensional Bayesian recovery and achieves a favorable tradeoff among estimation accuracy, computational complexity, and storage overhead.
[LG-170] Identifiability-Aware Source Apportionment in City-Scale Advection-Diffusion Systems
链接: https://arxiv.org/abs/2608.00050
作者: Ankit Bhardwaj,Lakshminarayanan Subramanian
类目: ignal Processing (eess.SP); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
*备注:
Abstract:Source apportionment from sparse urban air-quality sensors is an inverse problem limited by sensor placement, wind-driven transport, background variation, and noise. Known or proxy emission inventories make attribution meaningful by restricting the unknown source field to a finite set of candidate groups, but do not guarantee those groups are distinguishable from the observations. We represent time-varying source activity with a low-dimensional nonnegative temporal basis and formulate inventory-based apportionment as a wind-conditioned lagged inverse problem in which each source–basis coefficient produces a sensor-time fingerprint. After projecting out a separate low-dimensional background space, the relevant object is the projected lagged response matrix \widetilde H_\Phi : exact identifiability at the chosen basis resolution requires its full column rank, while noise-robust attribution is controlled by its singular values, coefficient visibility, background absorption, pairwise coherence, and ray distance. We propose an identifiability-aware apportionment (IASA) framework that estimates nonnegative source–basis coefficients, reconstructs activity trajectories, and reports uncertainty and conservative grouping recommendations for indistinguishable sources. We instantiate it on a New Delhi platform built from government PM _2.5 and wind records, regulatory sensor locations, and four proxy source groups, and define controlled and observed evaluations of recovery, ambiguity, wind diversity, background stress, transport error, inventory robustness, and residual adequacy. IASA reports the attribution resolution defensible under the declared inventories, transport, background, lag, and noise rather than the most detailed possible vector.
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