本篇博文主要内容为 2026-08-13 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 (2026-08-13)
今日共更新597篇论文,其中:
- 自然语言处理共94篇(Computation and Language (cs.CL))
- 人工智能共213篇(Artificial Intelligence (cs.AI))
- 计算机视觉共128篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共180篇(Machine Learning (cs.LG))
- 多智能体系统共18篇(Multiagent Systems (cs.MA))
- 信息检索共16篇(Information Retrieval (cs.IR))
- 人机交互共22篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Rethinking Agent Security as a Networking Problem
【速读】：该论文旨在解决当前生成式AI代理（AI agents）在日益增强的自主性背景下所面临的隐私与安全风险问题。现有防御机制主要依赖于代理自身进行威胁检测与策略执行，但此类以代理为中心的方法存在根本性局限：由于大语言模型（LLM）驱动的行为具有内在的非确定性，且易受提示注入等攻击手段的操纵，导致其无法可靠保障隐私与安全。为应对这一挑战，论文提出的关键解决方案是构建一种结合确定性强制机制与语义上下文感知策略的系统性安全框架。该方案借鉴网络领域长期积累的“集中控制、分布式执行”“基于能力的访问控制”及“最小权限”（零信任）等原则，通过引入可提供强确定性保障的静态规则机制，同时融合对语义上下文敏感的动态决策能力，从而在保证安全性的同时实现对复杂行为的精准管控。该方法不仅弥补了传统静态规则在语义理解上的不足，也克服了纯依赖代理自我监管的不可靠性，为设计可信赖的隐私保护型AI代理系统提供了新的架构范式与研究方向。
链接: https://arxiv.org/abs/2608.12172
作者: Van Tran,Taveesh Sharma,Tajveer Singh Dhesi,Nick Feamster
机构: University of Chicago(芝加哥大学)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:AI agents are rapidly becoming more capable and widely deployed, promising substantial gains in productivity and enabling new classes of applications. However, their growing autonomy also introduces significant privacy and security risks. Existing defenses are predominantly agent-centric, relying on the agent itself to detect threats and enforce privacy and security policies. This approach is fundamentally limited because it entrusts policy enforcement to AI agents whose LLM-driven behavior is inherently nondeterministic and vulnerable to manipulation through attacks such as prompt injection. As a result, current defenses cannot reliably prevent privacy and security threats, highlighting a critical need for a new solution to securing AI agent systems. The networking community has long grappled with similar challenges and offers insightful principles we can borrow to design a more secure AI agent system. These include centralized control with distributed enforcement, capability-based access for mediating requests to sensitive resources, and least privilege through zero-trust enforcement. Historically, these principles have provided strong deterministic guarantees for networked systems. However, these principles alone are insufficient for AI agents because the safety and appropriateness of an agent’s actions often depend on semantic context beyond the expressiveness of static rules. Building on these principles, we advocate for a systematic approach to AI agent security that combines deterministic enforcement mechanisms, which provide strong security guarantees, with semantic, context-aware policies that enable nuanced decision-making. We then present a reference architecture and identify key research questions and future directions to guide the design of secure and privacy-preserving AI agent systems. Subjects: Multiagent Systems (cs.MA) Cite as: arXiv:2608.12172 [cs.MA] (or arXiv:2608.12172v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2608.12172 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-1] Do LLM s Take Care of Their Own? Similarity Signals Can Induce Cooperation
【速读】：该论文旨在解决大语言模型（LLM）代理在具有用户指令目标的场景中，于战略互动环境下难以达成互利合作结果的问题。随着基于大语言模型的智能体在实际应用中的广泛部署，其相互间的协作困境日益凸显，尤其是在缺乏明确协调机制的情况下如何实现稳定且高效的合作者均衡。针对这一挑战，论文提出并构建了首个基于分级相似性信号（graded similarity signals）评估LLM决策行为的框架。其解决方案的关键在于揭示不同现代大语言模型在面对相似性信号时表现出显著差异的行为模式，发现部分先进模型能在多种合作博弈、收益结构及提示语境下保持一致的协作倾向；同时，研究发现相似性信号的计算基础数据集对诱导合作的影响极小，且模型在自我评估另一模型思维链时存在系统性地高估自身与他者相似性的偏差。最终，论文构建了一个融合大语言模型行为特征与博弈论分析的理论模型，证明当相似性评分足够高时，该模型可支持合作策略在纳什均衡中持续存在，从而为设计具备可靠协作能力的多智能体系统提供了理论依据与实践路径。
链接: https://arxiv.org/abs/2608.12125
作者: Akash Kundu,Emanuel Tewolde,Ratip Emin Berker,Samuel F. Brown,Vincent Conitzer
机构: 未知
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 41 pages, 18 Figures, 4 Tables, 16 Listings
Abstract:As LLM-based agents with user-instructed goals are becoming widely deployed, they increasingly encounter each other in strategic interactions, and face challenges of finding mutually beneficial outcomes. Prior literature has argued that cooperation problems such as the Prisoner’s Dilemma are resolvable in settings where agents know they follow very similar decision making patterns, as for example in monocultural AI ecosystems. Following that line of work, this paper introduces the first framework for evaluating LLM decision making when agents are provided with graded similarity signals. Among our findings, we establish that different LLM models vary drastically in how they navigate similarity signals, with some modern models showing consistent behavior across cooperation problems, payoff structures, and prompt framing. Perhaps surprisingly, our experiments also show that the dataset based on which the similarity signal is computed has small to no impact on induced cooperation, and that LLM models systematically self-identify as highly similar when asked to evaluate another model’s chain-of-thought reasoning by themselves. Finally, we develop an LLM-behavioral-game-theoretic model that captures some of their reasoning rationale, and show that it can support cooperative outcomes in equilibrium under sufficiently high similarity scores. Comments: 41 pages, 18 Figures, 4 Tables, 16 Listings Subjects: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA) MSC classes: 68T05, 68T37, 68T42, 91A05, 91A06, 91A10, 91A35 ACMclasses: I.2; J.4; K.4 Cite as: arXiv:2608.12125 [cs.GT] (or arXiv:2608.12125v1 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2608.12125 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-2] Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence
【速读】：该论文旨在解决当前生成式AI模型能力与可解释性之间日益扩大的鸿沟问题，即尽管AI模型在多个领域取得了显著成就，但其内在机制及其潜在风险仍缺乏深入理解。随着AI开发速度加快且趋向自动化，传统的手动机制探索方式已难以应对这一挑战。为此，论文提出一种名为Mechanist的自主代理系统，其核心解决方案在于构建一个以可解释性为导向的知识图谱（约1.3万篇文献），并将其与涵盖26个领域的4300万篇跨学科论文数据库相集成，同时整合了32种基础机制分析、因果干预与验证方法。该系统通过自主发现、实验验证与理论构建，实现了从识别模型行为到解释乃至控制模型的演进。具体而言，Mechanist不仅揭示了科学实验中跨模态传递的反直觉安全风险，还提出了关于“信念”形成机制的理论，阐明了模型如何表征世界知识、推断自身及他者信念，并在预训练过程中涌现这些机制；最终，其将机制洞察转化为实际干预策略，显著提升了模型在多种场景下的性能，并实现了对科学基础模型的定向引导，使其能够生成具有特定属性的DNA序列。
链接: https://arxiv.org/abs/2608.12036
作者: Mengru Wang,Junfeng Fang,Shuofei Qiao,Zhenqian Xu,Haoming Xu,Haoxiong Wang,Shumin Deng,Linyi Yang,Zhixiang Cui,Xin Xu,Yunzhi Yao,Buqiang Xu,Fei Shen,Haozhe Luo,Yunxiang Wei,Ningyu Zhang,Julian McAuley,Tat Seng Chua,Huajun Chen
机构: Zhejiang University (浙江大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: Work in progress
Abstract:AI models have achieved remarkable success across diverse domains, yet the mechanisms underlying their capabilities and the risks they may pose remain poorly understood. As AI development becomes faster and increasingly automated, mechanistic exploration remains largely manual, widening the gap between what models can do and our ability to understand and control them. To bridge this gap, we introduce Mechanist, an agentic system that uses AI as a scientific instrument for the autonomous discovery of mechanisms underlying AI intelligence. To support autonomous mechanistic discovery, we construct an interpretability-focused knowledge graph of approximately 13,000 papers and integrate it with a multidisciplinary database of 43 million papers spanning 26 fields. We further curate a library of 32 foundational methods for mechanism analysis, causal intervention, and validation. Compared with Claude Code and existing AI-scientist systems, Mechanist generates more valuable mechanism hypotheses and executes experiments more reliably. Mechanist also demonstrates a progression from discovering model behaviors to explaining and controlling AI models. Specifically, Mechanist first uncovers a counterintuitive safety risk in scientific laboratories, showing that unsafe traits can transfer across modalities through apparently safe training data. Mechanist then develops a mechanism theory of belief, revealing how models represent world knowledge, form beliefs, infer the beliefs of others, and how these mechanisms emerge during pretraining. Finally, Mechanist translates these mechanistic insights into practical interventions that improve model performance across diverse scenarios and steer scientific foundation models toward generating DNA sequences with specified properties.
[MA-3] Scalable Multi-Agent Maze Traversal with Local Communication
【速读】：该论文旨在解决在未知、可能含环的图结构环境中，多智能体系统因通信受限而面临的协同导航难题。核心挑战在于如何在缺乏全局信息的情况下，使多个智能体有序协作，实现对未明确目标位置的定位与抵达，同时避免碰撞。其解决方案的关键在于提出一种分布式算法，通过局部通信机制构建领导者-追随者关系并支持领导者切换，确保任意时刻仅有一个智能体执行单智能体迷宫求解器进行探索，从而避免冲突并保持探索效率。该算法被证明是完备的，其完成时间（makespan）在智能体数量上渐近等价于具备全局知识的最优策略，并且在时间和空间复杂度方面具有理论保障。仿真结果表明，随着智能体数量增加，平均能耗呈下降趋势，且显著优于所有智能体独立运行单智能体求解器的基线方法。
链接: https://arxiv.org/abs/2608.11895
作者: Julian Rau,Jahir Argote-Gerald,Grace McFassel,Genki Miyauchi,Paul Trodden,Roderich Groß
机构: Technical University of Darmstadt(达姆施塔特工业大学); The University of Sheffield(谢菲尔德大学); University of Bristol(布里斯托大学)
类目: Robotics (cs.RO); Multiagent Systems (cs.MA)
备注: This manuscript has been accepted for publication in the proceedings of the World Symposium on the Algorithmic Foundations of Robotics (WAFR 2026), to be published by Springer in the Springer Proceedings in Advanced Robotics (SPAR) series
Abstract:Cave networks, pipe systems, and similar maze-like environments pose significant challenges for multi-agent navigation in unknown settings with limited communication. We propose a distributed algorithm that enables agents to collectively traverse an unknown, possibly cyclic graph. Agents enter sequentially at a designated start node and are tasked to localize and reach an undisclosed goal while avoiding collisions. They coordinate via local communication using leader-follower relationships and leader switching. At any moment in time, exploration is performed by only one of the agents, which runs a single-agent maze solver. We prove that the algorithm is complete, that its makespan is asymptotically equivalent (in the number of agents) to that of an optimal full-knowledge strategy, and derive its time and space complexity. Simulations with up to 625 agents show a decreasing average sum-of-fuels as the number of agents increases and demonstrate that the proposed approach outperforms a naïve baseline in which all agents independently execute the single-agent solver.
[MA-4] Agent icTwin: An Agent ic LLM Framework Integrated with Digital Twin for Anomaly Detection
【速读】：该论文旨在解决数字孪生（Digital Twin）系统中异常检测结果难以解释的问题，尤其是在面对海量且复杂的原始传感器数据时，即使由经验丰富的操作员也难以有效识别和理解异常的根源。其核心挑战在于如何将生成式人工智能（Generative AI）的推理与解释能力与数字孪生驱动的异常检测流程深度融合，以提升故障诊断的可解释性与操作效率。解决方案的关键在于提出AgenticTwin框架——一个基于智能体（Agentic）架构的系统，通过将大语言模型（LLM）的推理能力与数字孪生异常分类器的输出进行语义对齐，实现基于知识的上下文感知推理，并支持操作员以自然语言形式提问，从而动态获取关于异常事件的可解释性分析。此外，研究构建了一个面向基准测试的评估管道，利用在真实气象传感器数据集中注入合成异常的方式，实现对操作员查询的可控生成，验证了轻量级开源大语言模型在实际物理-信息融合环境中的可行性。实验表明，结构化智能体协作与知识引导推理显著提升了在多种异常场景下的诊断质量、上下文信息检索精度及缓解策略的有效性。
链接: https://arxiv.org/abs/2608.11679
作者: Touseef Hasan,Mounika Ghanta,Souvika Sarkar,Ujjwal Guin
机构: Wichita State University (威奇托州立大学); Auburn University (奥本大学)
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Multiagent Systems (cs.MA)
备注:
Abstract:Digital twins are increasingly used to monitor and simulate the behavior of cyber-physical systems. Even with skilled operators, interpreting anomalies detected within digital twin pipelines is challenging, as the sheer complexity and volume of raw sensor data make thorough analysis difficult. Recent advances in large language models (LLMs) offer promising capabilities for reasoning and explanation, yet their integration into digital twin-driven anomaly analysis remains underexplored. In this work, we propose AgenticTwin, an agentic framework that integrates LLM-driven reasoning with a digital twin-based anomaly detection pipeline. The framework grounds LLM-generated explanations in outputs from a digital twin-driven anomaly classifier and enables human operators to ask relevant natural-language questions about the system. Beyond the framework itself, we introduce a benchmark-oriented evaluation pipeline constructed over synthetic anomalies injected into a real-world weather sensor dataset, enabling controlled generation of operator queries over anomaly events. We further evaluate the feasibility of deploying lightweight, open-source LLMs for practical cyber-physical environments. Experimental results demonstrate that structured agent collaboration and knowledge-grounded reasoning improve diagnosis quality, contextual retrieval, and mitigation quality across diverse possible anomaly scenarios.
[MA-5] Is Per-Agent Policy Composition Safe? Rethinking Successor-Feature Transfer in Cooperative Multi-Agent Reinforcement Learning
【速读】：该论文旨在解决多智能体强化学习系统在部署后面临动态变化目标时的高效适应问题，尤其关注如何在不重新训练策略的前提下实现跨任务迁移。传统单智能体方法中，通过继承特征（successor features）与广义策略改进相结合的通用扩展（universal extension）可安全地组合已学习策略库以应对新目标，且保证结果不低于库中任一策略。然而，多智能体场景下普遍采用各智能体独立重组自身策略库的做法，虽沿用相同机制但丧失了理论保障——因每个智能体的重组合会改变其队友的行为分布，从而破坏其对环境状态价值的依赖，导致联合行为可能劣于策略库中所有个体策略，此现象为单智能体所不具备。研究进一步证明，唯一能确保全局安全的固定规则是同步组合（synchronized composition），即全体智能体统一切换至单一联合训练策略，但其无法处理对不同智能体分配差异化目标的情形。为兼顾安全性与灵活性，作者提出多智能体通用继承特征架构（MA-USFA），采用分层结构：底层为条件于队友目标的通用继承特征近似器，用于预测各智能体的继承特征；上层为组合器，跨智能体选择各智能体应遵循的策略库条目，并引入跨智能体修正项以弥补单智能体价值函数无法表达的协作信息。该方法仅需在目标分布上一次性训练，部署时无需针对具体任务进行调整，实现了高效、安全的多智能体动态目标适应。
链接: https://arxiv.org/abs/2608.11658
作者: Zijian Zhao,Sen Li
机构: The Hong Kong University of Science and Technology (香港科技大学); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学(广州))
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Many reinforcement learning systems, from fleet management to traffic signal control, must serve an objective that changes dynamically after deployment, and retraining a policy for each new objective is prohibitively expensive. For a single agent, this problem is well understood: successor features with generalized policy improvement, together with their universal extension, recombine a library of learned policies into a policy for any new objective, with a guarantee that the result is never worse than any policy in the library. However, multi-agent transfer has received far less attention, and the common practice of letting each agent recombine its own library independently inherits the recipe but not the guarantee. We prove that this independent composition can produce joint behavior strictly worse than every policy in the library, because recombining teammates changes the environment each agent faces and invalidates the values it relies on, a failure with no single-agent counterpart. We further show that the only unconditionally safe fixed rule is synchronized composition, which moves the whole team to one jointly trained policy but cannot serve objectives that assign different goals to different agents. To attain safety and flexibility at once, we propose MA-USFA, a hierarchical method with two layers: a lower layer of universal successor feature approximators that predicts each agent’s successor features while conditioned on its teammates’ objectives, and an upper composer that selects, across agents, which library entry each agent should follow and supplies the cross-agent correction a per-agent value cannot represent. Trained once over the distribution of objectives, it is applied at deployment with no per-task adaptation.
[MA-6] Beyond Memory: A Transactional Continuity Kernel for Long-Lived AI Agents
【速读】：该论文旨在解决长期运行的生成式AI代理（Generative AI agents）在持续积累版本化状态过程中，因缺乏明确的状态治理机制而导致的权威状态难以识别的问题。核心挑战在于，若无显式的控制平面（control plane），模型、工具及后台工作者直接更新状态可能引发陈旧覆盖、未经审计的暴露以及自我授权的权限提升等安全风险。其解决方案的关键在于提出“连续性内核”（Continuity Kernel, CK），一种将候选变更评估与原子状态激活解耦的激活合约（activation contract）。CK通过要求未受信任组件以精确前驱头或类型化缺失为前提提交带类型的变更请求，并由一个简短的激活交易重新验证所有权、前状态权威性、新鲜度及效果唯一性，最终记录单一稳定处置结果（提交、拒绝、隔离或延迟）。仅“提交”操作会原子性地推进分支头并安装完整的已接受单元（包括状态、权限、血统、效果、结果及回执），从而保障状态演进的连续性与安全性。该协议经280万可到达状态和550万状态转换的有界可执行模型验证，实现零不变量违反。
链接: https://arxiv.org/abs/2608.11632
作者: Jun He,Deying Yu
机构: OpenKedge.io
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注: 9 pages, 6-page appendix, 15 tables
Abstract:Persistent AI agents accumulate versioned state across long horizons, but storage retention alone does not identify authoritative state. Without an explicit control plane, unmediated updates by models, tools, and background workers risk stale overwrites, un-audited exposures, and self-authorizing privilege escalation. We argue that agent state governance is an infrastructural activation problem, defining continuity as an unbroken, authorized lineage of accepted branch heads. We present the Continuity Kernel (CK), an activation contract that decouples off-commit candidate evaluation from atomic state activation. Untrusted components propose typed changes against an exact predecessor head or typed absence. A short activation transaction revalidates ownership, pre-state authority, freshness, and effect uniqueness, recording one stable disposition (Commit, Reject, Quarantine, or Defer). Only Commit atomically advances the branch head and installs the complete accepted unit (state, authority, lineage, effects, outcome, and receipt). A bounded executable model verifies the protocol across 2,808,230 reachable states and 5,526,474 state-changing transitions with zero invariant violations.
[MA-7] op-down Traffic Scenario Generation via Joint Initial-Goal Diffusion and Trajectory Infilling ITSC
【速读】：该论文旨在解决现有交通仿真器在自动驾驶车辆开发与测试中面临的可扩展性与多样性不足问题，核心挑战在于传统方法依赖预设的初始状态（initial states）来生成轨迹，导致场景生成受限于初始条件的可用性，且缺乏对初始状态选择的可解释性。此外，在已知初始状态的情况下，如何准确建模目的地的分布及其随时间演变的路径行为仍是一个难题。为应对上述问题，本文提出TrafficDiffuser——一种自上而下的交通场景生成框架，其关键创新在于联合建模交通代理的初始状态与目标状态（goal state），将复杂的轨迹生成问题转化为基于高阶场景的补全（infilling）任务。该方法不仅使初始状态的设定具有更高的可解释性，还显著提升了生成场景的真实性和多样性。实验基于Argoverse 2运动预测数据集验证了模型性能，结果表明，TrafficDiffuser在生成目标状态方面优于当前最优方法，使速度分布距离降低55.3%，非道路占用率减少2.8%，并可灵活适配不同轨迹模式约束及集成至现有轨迹生成模型中。
链接: https://arxiv.org/abs/2608.11407
作者: Da Saem Lee,Yash Vardhan Pant,Sebastian Fischmeister
机构: University of Waterloo (滑铁卢大学)
类目: Robotics (cs.RO); Multiagent Systems (cs.MA)
备注: Accepted for publication at the IEEE International Conference on Intelligent Transportation Systems (ITSC), 2026
Abstract:Robust traffic simulators are crucial for developing and testing autonomous vehicles to reduce the costly, labor-intensive real-world data collection process and the need for physical presence on the road. However, existing simulators require agents’ initial states to generate trajectories, which limits scalability and diversity due to restrictions on the given initial states. While data-driven agent initialization has been widely studied, the generated initial states are not interpretable in terms of why the agents are initialized at those specific locations. Given known initial states, trajectory generation is also a challenging problem, as the model must learn the variability of the destination and how agents should reach it over time. In this paper, we propose TrafficDiffuser, a top-down traffic scenario generation framework that generates high-level traffic scenarios, defined by initial and goal state pairs, by jointly modeling them. The high-level scenario generation makes initial states better interpretable and reduces trajectory generation into as simple as an infilling problem. We demonstrate how the generated high-level traffic scenarios can be used, including constraining based on different trajectory modes and integrating them with existing trajectory generation models. We conduct extensive experiments on the Argoverse 2 motion prediction dataset to evaluate how well the generated outputs capture real-world distributions. In addition to generating goal states, TrafficDiffuser outperforms the next-best approach for agent initialization, reducing speed distribution distance by 55.3% and the off-road rate by 2.8%.
[MA-8] When Do Institutions Beat Intelligence?
【速读】：该论文旨在解决多智能体系统（multi-agent system）中个体能力增强未必带来集体性能提升的核心问题，即当多个智能体虽具备充足信息却仍因协作机制缺陷而失效时，应如何权衡增加智能水平与优化制度结构之间的选择。其解决方案的关键在于识别集体推理失败的根源，并据此判断资源投入应优先于提升个体推理能力（intelligence），还是重构集体的信息处理制度结构（institutional structure）。研究基于群体决策与分布式认知领域的功能区分，构建了围绕四类集体失败机制（信息访问与路由、信息准入与依赖、状态维护与激励、表征与行动接口）的人工生态实验环境，通过独立调控模型能力与制度结构，对比正向干预与基准推理、机制破坏控制组的表现。结果揭示出一条清晰的边界：只有当制度结构能够修复集体构建可用公共状态（usable public state）的机制性缺陷时，制度优势才显现；一旦信号缺乏信息量或不可验证、更强智能可直接完成相同转换，或生成的状态无法支撑可靠行动，制度优势便消失。因此，该研究将“智能 vs. 制度”的选择重新定义为对集体推理失效机理的诊断过程。
链接: https://arxiv.org/abs/2608.11357
作者: Zhengye Han
机构: New York University (纽约大学)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:More capable agents do not necessarily form a more capable collective. A multi-agent system may jointly possess sufficient information yet fail because evidence is poorly routed, unreliable reports enter public belief, correlated claims masquerade as independent support, shared state becomes stale or strategically distorted, or useful evidence is exposed through an ineffective action interface. We ask when additional resources should improve the reasoner and when they should instead change the institutional structure through which the collective forms and acts on public information. Drawing on functional distinctions from research on group decision making and distributed cognition, we construct controlled artificial ecologies around four loci of collective failure: access and routing, admission and dependence, state maintenance and incentives, and representation and action. Across these ecologies, we separately vary model capability and institutional structure, pairing positive interventions with matched reasoning baselines and mechanism-breaking controls. The experiments reveal a consistent boundary: institutions help when they repair failures in how a collective constructs usable public state, but lose their advantage when their signals are uninformative or uncheckable, when stronger intelligence can perform the same transformation directly, or when the resulting state cannot support reliable action. Our results recast the choice between intelligence and institutions as a diagnosis of where collective reasoning fails.
[MA-9] Local verification cannot detect non-transportability: a cohomological theory of context preservation in agent ic reasoning
【速读】：该论文旨在解决生成式人工智能（Generative AI）系统在跨生物、临床与金融等多领域推理过程中，因上下文异质性导致的结论不一致问题。其核心挑战在于：现有局部验证机制（local verification）虽能检查每一步的可表示性、参数兼容性及输出与计划的一致性，但无法保证全局推理路径的无歧义性。论文指出，此类保障机制在结构上是不完备的，即无法检测所有潜在的证据冲突。解决方案的关键在于引入代数拓扑中的上同调理论（Cech cohomology），将上下文空间建模为单纯复形（nerve of context space），并将证据表示为实值1-上链（real-valued 1-cochain）。通过路径积分（path integration）分析，发现推理结论的路径无关性等价于上链为恰当上链（exact cochain），而路径间分歧则由一阶上同调类的挠率（holonomy）决定。利用Hodge分解，将证据冲突划分为梯度部分（校准）、旋度部分（局部不一致性，可见于三重点重叠）和调和部分。研究的核心结果表明，任何仅基于单纯形支持的一致性检查都无法区分上链与其调和扰动之和（ω 与 ω + h），而后者仍可引发有效路径间的非零分歧。因此，检测此类冲突需依赖循环基上的统计量。基于此，作者提出Ksetra算法，通过上边界投影估计并以调和分量作为拒答门限，进一步揭示调和分量源于效应修饰与重叠人群构成的交互作用，且在无效应修饰时趋近于机器精度下的零。证据网络的自由度被精确划分为校准、一致性和传输三个维度，由此导出一个关于全局命题存在的精确F检验；同时量化了不等精度条件下的偏差，并提供精度白化形式以恢复检验的精确性。外汇报价场景中，无套利零假设使上链恰为上边界，作为校准基准，验证显示该检验具有正确尺寸，对环路套利敏感，却忽略三角套利，证实其有效性与合理性。
链接: https://arxiv.org/abs/2608.11252
作者: Suyash Mishra
机构: Google(谷歌)
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
备注: 17 pages, 10 figures. Includes an exact F-test for non-transportability with verified size and power, its precision-whitened generalisation, and a foreign-exchange case where the null hypothesis is known analytically rather than estimated
Abstract:Agentic AI systems routinely transport conclusions across biological, clinical and financial contexts, and the emerging safeguard is local verification: checking at each step that the entity is representable in the chosen tool, that parameters are compatible, and that outputs cohere with the plan. We prove this class of safeguard is structurally incomplete. Modelling a covering of context space by its nerve and evidence by a real-valued 1-cochain, an agent chaining evidence performs path integration: its conclusion is path-independent if and only if the cochain is exact, and disagreement between valid reasoning paths is exactly the holonomy of a first Cech cohomology class. Hodge decomposition partitions evidence conflict into a gradient part (calibration), a curl part (local inconsistency, visible at triple overlaps) and a harmonic part. Our central result is that no family of simplex-supported consistency checks can distinguish omega from omega+h for harmonic h, which nonetheless generates non-zero disagreement between valid paths; detection requires a statistic on a cycle basis. The resulting procedure, Ksetra, estimates by coboundary projection and gates abstention on the harmonic component, which we give a mechanism: it arises from effect modification combined with overlap-specific population composition, and vanishes to machine precision when effect modification is absent. The degrees of freedom of an evidence network partition into calibration, coherence and transport, yielding an exact F-test for the existence of a global claim; we quantify its distortion under unequal precision and supply the precision-whitened form that restores exactness. Foreign exchange, where the arbitrage-free null makes the cochain exactly a coboundary, serves as a calibration bench: the test is correctly sized, fires on loop arbitrage, and ignores triangular arbitrage.
[MA-10] AgonAlpha: Autonomous Alpha Discovery via Prompt Economy and Scalable Agent ic Search
【速读】：该论文旨在解决生成式金融因子（alpha）研究中自动化系统面临的三大核心挑战：如何在海量潜在因子中高效分配评估预算、如何确保自身推理过程的可验证性，以及如何完整保留每个候选因子的生成溯源。现有方法往往仅聚焦于公式层面的搜索，忽视了对研究过程中产生的各类非结构化成果（如假设、可执行表达式、平台证据、论证逻辑与评审状态）的系统性管理。为此，论文提出AgonAlpha架构，其关键创新在于：将搜索空间扩展至冻结的研究成果（research artifacts），包括假设、可执行表达式、平台证据、推理理由及评审状态等多维信息；引入具备重执行与否决权的“新上下文对抗性评审器”以增强验证可靠性；采用感知待处理任务状态的并行预算分配机制，实现资源的动态优化配置；同时构建完整的公开证据链，保障所有提交结果的可追溯性。实验表明，该系统在WorldQuant BRAIN平台上的独立部署中，成功生成多个达到SPECTACULAR等级的因子，平均收益函数（Fitness）达9.50，夏普比率（Sharpe）高达3.48，且每项输出均保留从提示到表达式的完整溯源路径。
链接: https://arxiv.org/abs/2608.11250
作者: Weicheng Ye,Youran Sun,Xingyu Ren,Shunyao Yu,Chugang Yi,Haizhao Yang
机构: The Chinese University of Hong Kong (香港中文大学); University of Maryland (马里兰大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Computational Finance (q-fin.CP); Portfolio Management (q-fin.PM)
备注:
Abstract:Language models can propose many plausible trading factors, but an autonomous research system must also allocate its evaluation budget, verify its own evidence, and preserve how each candidate was produced. We present AgonAlpha, an architecture that searches over frozen research artifacts—hypotheses, executable expressions, platform evidence, rationales, and review status—rather than formulas alone. To our knowledge, AgonAlpha is the first alpha-mining system to combine verified artifact search, a fresh-context adversarial reviewer with re-execution and veto authority, and pending-aware parallel budget allocation, together with a complete public evidence trail. Independent deployments on WorldQuant BRAIN produced SPECTACULAR-grade alphas across five users and six model backends, with Fitness reaching 9.50 and Sharpe reaching 3.48, while retaining prompt-to-expression provenance for every submission.
[MA-11] EvoGraph-Mem: Failure-Aware Editable Graph Memory for Long-Term Language Agents
【速读】：该论文旨在解决长期语言智能体在持续交互与动态任务中因记忆退化导致的“记忆污染”问题。现有记忆增强型智能体多聚焦于经验的存储与检索，但未充分考虑已有洞察（insight）在新任务场景下可能过时、过度泛化或产生负面效应，从而降低记忆质量。其解决方案的关键在于提出一种基于可编辑洞察图（editable insight graph）的故障感知记忆维护框架：每个洞察节点显式记录正例证据、负例证据及激活状态，使智能体能够区分可复用的可靠洞察与冲突或无效信息；同时引入效用感知检索机制与图控制器，在任务执行后动态更新记忆图——保留可信洞察、归档无效内容、修订过时信息并添加新发现的可复用洞察。实验表明，该方法在多种主干模型上均显著优于主流记忆基智能体基准，消融实验进一步验证了仅追加式记忆不足以应对长周期任务，而基于证据的检索与图级编辑机制能有效提升记忆可靠性与下游任务性能。
链接: https://arxiv.org/abs/2608.11248
作者: Yuxi Qian,Yuxiang Ren
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 10 pages, 3 figures
Abstract:Long-term memory is essential for language agents operating across extended interactions and evolving tasks. Existing memory-augmented agents mainly focus on storing and retrieving past experience, but the quality of stored memories may degrade over time. In particular, previously distilled insights can become outdated, over-generalized, or harmful under new task contexts, causing memory pollution when repeatedly reused. To address this issue, we study insight-level memory maintenance for long-term language agents and propose a failure-aware memory maintenance framework based on an editable insight graph. Each insight node tracks positive evidence, negative evidence, and an activation state, enabling the agent to distinguish reusable insights from conflicting or invalid ones. We further introduce a utility-aware retrieval mechanism and a graph controller that updates the memory graph after task execution by keeping reliable insights, archiving invalid ones, revising outdated ones, and adding newly discovered reusable insights. Extensive experiments show that our method consistently outperforms representative memory-based agent baselines across different backbone models. Ablation studies further demonstrate that append-only memory is insufficient for long-horizon tasks, while evidence-aware retrieval and graph-level editing improve memory reliability and downstream task performance.
[MA-12] Conformity Mitigations in Large Language Models Lie on a Single Resistance-Receptivity Frontier
【速读】：该论文旨在解决多模型协作场景中因同伴意见误导而导致模型正确回答被错误多数意见覆盖的问题，即“观点漂移”（peer opinion displacement）问题。在这一设置下，模型会受到其他模型输出的影响，当多数同伴给出错误答案时，即使自身参数化知识正确，也可能被误导而改变判断。研究通过在23个开源模型、19种实验条件和三个数据集上进行大规模测试，发现错误一致多数可使模型原本正确的答案被逆转的比例高达22.8%（MMLU）、54.8%（GPQA）和71.0%（SimpleQA），且其中84%-89%的被逆转答案与同伴答案一致，凸显了协同过程中的严重偏差风险。现有缓解策略主要聚焦于提升“抗压性”（Resistance），即模型在压力下维持正确答案的能力，但仅关注此单一维度不足以实现高效协作。本文提出将“接受性”（Receptivity）——即模型在初始出错后采纳正确同伴答案的能力——作为另一关键指标，构建双维评估框架。通过对六种方法（包括四种已有方法及两种新方法）在两个维度上的综合评估，发现所有方法均呈现抗压性与接受性之间的权衡关系，其性能点落在一条高确定性的阻力-接受性前沿线上（R² = 0.80–0.90）。其中，反思（Reflection）虽为当前最强方法，但以牺牲显著接受性为代价；唯有“推理”（Reasoning）在特定任务（如可自推导的MMLU子任务）中实现了抗压性和接受性的同步提升，分别提高7.2和9.6分，是唯一能同时增强两项能力的干预手段。因此，解决方案的关键在于构建兼顾抗压性与接受性的协同机制，并识别出能够实现双向优化的推理型干预路径。
链接: https://arxiv.org/abs/2608.11247
作者: Zafar Hussain,Kristoffer Nielbo
机构: Aarhus University (奥胡斯大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Recent advances in language models have enabled collaborative settings in which multiple models leverage one another’s capabilities, iteratively improving, transforming, and extending each other’s outputs. Each agent sees what the others assert before it answers, so peer opinion competes with the model’s own parametric knowledge, and a wrong majority can overturn an answer the model would otherwise get right. We measure that displacement in 23 open-weight models, 19 conditions, and three datasets, yielding more than a million graded responses. A unanimous wrong majority reverses 22.8% of a model’s correct MMLU answers, 54.8% on GPQA and 71.0% on SimpleQA, and 84-89% of the reversed answers match the peers’ answers. Existing mitigations aim to increase Resistance, the rate at which a model keeps its correct answer under this pressure, which is only half of what a collaborating agent needs. We pair it with Receptivity, the rate at which a model adopts a correct peer answer after initially answering incorrectly. We score six methods on both axes, four drawn from prior work and two of our own. Each gains Resistance only by losing Receptivity, and their means fall on a single Resistance-Receptivity frontier with R^2 between 0.80 and 0.90. Reflection, the strongest published method, gains 7.9 points of MMLU Resistance and gives up 15.3 of Receptivity. Reasoning is the one exception. On GPQA and SimpleQA it trades like the rest, but on the MMLU subjects whose answers a model can derive for itself it raises Resistance by 7.2 points and Receptivity by 9.6 at once, the only intervention we find that improves both.
[MA-13] Harnessing agent memory to build lifelong AI partners for materials scientists
【速读】：该论文旨在解决材料科学研究中经验知识碎片化、难以跨模型与代理（agent）迁移的问题，尤其在生成式人工智能（Generative AI）应用中，现有方法因缺乏持久记忆而无法有效积累和复用科研经验。其核心解决方案是构建一种基于持续记忆（persistent memory）的自演化记忆框架，将科学经验以可检查的事实（facts）和可执行的技能（skills）形式结构化存储，从而实现对实验观察、失败边界、操作协议及验证机制的检索、修订与跨模型迁移。该框架的关键在于将“记忆”作为可继承、可进化且独立于特定模型实现的科学资产，显著提升了任务成功率与计算效率：在真实材料工具使用任务中，无需更新模型参数即可使GPT-5.2的任务成功率几乎翻倍；在元素固态物态方程计算中，通过预执行防护机制将波函数初始化失败率从22/1/4优化至25/2/0，并避免92%的重复错误；在实际模拟工作流中，记忆驱动的技能复用使总调用痕迹（tokens）减半，工具调用次数减少超两倍，同时保持物理意义输出的一致性。结果表明，持续记忆可成为超越单个模型或代理架构的长效科学资产，推动材料研究的可重复性与智能协作的可持续发展。
链接: https://arxiv.org/abs/2608.11224
作者: Siyu Liu,Bo Hu,Beilin Ye,He Cao,David J. Srolovitz,Tongqi Wen
机构: The University of Hong Kong (香港大学); Materials Innovation Institute for Life Sciences and Energy (MILES), HKU-SIRI (香港大学-深圳研究院材料创新研究所); International Digital Economy Academy (IDEA) (国际数字经济发展研究院)
类目: Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 21 pages, 7 figures
Abstract:Materials research advances through accumulated experience - scripts that work, protocols that are trusted, warnings attached to failed calculations or experiments, and judgement that links a new question to an old result. This experience is essential for reproducibility and knowledge transfer, yet it is usually fragmented across notebooks, repositories, job logs and individual memory, and it is rarely portable across artificial-intelligence agents. Here we argue that a lifelong AI partner for materials science can be designed around persistent memory rather than around a particular agent implementation. We introduce a self-evolving memory framework that stores scientific experience as inspectable facts and executable skills, so that observations, failure boundaries, protocols and validation checks can be retrieved, revised and migrated across models. We evaluate the idea in three computational settings that expose different layers of materials-research competence. In 49 real-world materials-tool-use questions comprising 138 executable subtasks, memory nearly doubles GPT-5.2 task success without model-parameter updates. In elemental-solid equation-of-state calculations, memory converts a wavefunction-initialization failure into a pre-execution guardrail, improving outcomes from 22/1/4 to 25/2/0 Correct/Partial/Error and avoiding 92% of repeated errors. In 13 practical material simulation workflows, remembered skills and failure facts halve the aggregate trace burden (tokens) and reduce tool calls by over a factor of two by the third round, while preserving physically meaningful outputs in band-gap, phonon, vacancy and work-function analyses. These results show that agent memory can serve as a durable scientific asset; a portable, self-improving record of materials-research experience that outlives any single model or agent stack.
[MA-14] LLM s in Process Diagram Engineering: From Optimal PFDs to Validated PIDs
【速读】：该论文旨在解决化工流程设计中工艺流程图（PFD）与管道及仪表图（PID）手工绘制效率低、耗时长且易出错的问题。传统方法依赖人工完成从PFD到PID的转换，存在劳动强度大、设计周期长、拓扑优化能力有限等瓶颈。为此，本文提出“PID Pilot”——一个端到端的生成式人工智能（Generative AI）流程，通过融合遗传算法（GA）与大语言模型（LLM）实现PFD的自动合成，并利用基于LLM的智能体完成PFD到源-地基准PID的精准转换。其核心解决方案在于：在第一阶段采用GA与LLM的混合策略，以全局搜索能力与语义理解优势协同优化PFD拓扑结构，在满足出口流量约束和工程规则的前提下，实现最低损失值的最优解；在第二阶段，通过受控工程软件开发工具包（SDK）驱动的LLM代理，生成可执行、合规且符合参考图结构的修改指令，实现100%的执行成功率。该统一框架实现了从设计构思到可部署输出的全流程自动化，显著降低了人工干预，为工业级过程设计自动化提供了可行路径。
链接: https://arxiv.org/abs/2608.11220
作者: Timur Zakarin,Sergei Voitov,Sergei Shumilin,Evgeny Burnaev
机构: Skoltech, AI Center, Moscow, Russia; AIRI, Moscow, Russia
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Nowadays, the creation of a process flow diagram (PFD) and its subsequent transformation into a piping and instrumentation diagram (PID) is predominantly performed manually. Applying artificial intelligence in the task could potentially lead not only to process automation and time savings, but also to financial gains by exploring numerous diagram’s topology options and reducing manual labor. This research presents PID Pilot - a practical end-to-end AI pipeline capable of handling flowsheet developing for both stages. The first stage focuses on PFD synthesis, whereas the second is directed toward modifying the generated PFD into PID. After comparing four different methods, the hybrid approach combining genetic algorithms (GA) and large language models (LLM) is shown to generate the optimal valid PFD topology, achieving the lowest loss value among all the methods, while satisfying the required outlet flow parameters without engineering-rule violations. For the second stage, the proposed LLM-based agent successfully transforms the generated PFD into a source-grounded PID by producing validated, executable modifications through a restricted engineering software development kit, achieving 100% execution success while maintaining compliance with domain-specific rules and reference graph structures. This unified pipeline - coupling GA/LLM-driven synthesis with an LLM-based transformation agent - offers a feasible path toward end-to-end process design automation by producing validated, deployable outputs and substantially reduces manual engineering effort.
[MA-15] MaSRead: Content-Addressed Reading of Replicated Latent Stores
【速读】：该论文旨在解决多智能体系统中基于潜在空间推理的共享状态在后续未知查询下难以可靠读取的问题。其核心挑战在于：尽管通过无冲突复制数据类型（Conflict-Free Replicated Data Type, CRDT）合并的潜在状态片段可实现一致收敛，但因共址片段间的干扰，导致无法实现基于内容的可寻址性。为此，论文提出MaSRead解决方案，其关键在于引入基于片段词汇生成的不透明密钥标签集进行路由，并结合硬注意力掩码对所选片段进行隔离解码，从而实现对所需片段的独立访问。该方法利用词法连通性构建图遍历路径，支持多跳查询的片段定位。实验表明，无论在链式、流水线、对称、中心化或自然语言存储场景中，MaSRead均能有效隔离并恢复已访问片段，且不受无关片段累积影响，具备跨模型家族迁移能力。解码阶段仅依赖片段长度而非整体存储规模，但路由仍受制于词法连通性，且答案生成受限于冻结阅读器的容量。因此，当所需片段与查询通过内容形成连通路径时，复制的潜在存储可实现选择性读取。
链接: https://arxiv.org/abs/2608.11218
作者: Carlos Baquero,Luís Brito,João Resende
机构: FEUP INESC TEC, Universidade do Porto, Porto, Portugal; ESTG, Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal; DCC, FCUP, Universidade do Porto, Porto, Portugal
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Independent agents that reason in latent space can share computed state as key-value cache fragments rather than text. Merged by a conflict-free replicated data type, these fragments form a store that converges under any delivery order or duplication. Yet a later query, unknown at encode time, cannot reliably read the merged cache: colocated fragments interfere, so colocation is not addressability. MaSRead addresses the read to content. It routes through opaque keyed tag sets derived from fragment words and decodes each selected fragment under a hard attention mask that hides the rest. Under lexical connectivity, a graph walk reaches the fragments required by a multi-hop query. Across chain, pipeline, symmetric, hub, and natural-language stores, MaSRead recovers visited fragments in isolation, remains effective as unrelated fragments accumulate, and transfers to another model family. After routing, materialized decoding depends on fragment length rather than total store size; end-to-end work still includes store-dependent routing and one read per visited fragment. The limits are explicit: lexical routing can miss disconnected evidence, and answer composition remains bounded by the frozen reader. Thus a replicated latent store becomes selectively readable for later queries when the needed fragments connect to the query through content.
[MA-16] Poor Mans Agent ic Modeling: Simulating Large LLM -Agent Societies on a Laptop
【速读】：该论文旨在解决大规模语言模型（LLM）代理社会模拟计算成本高昂的问题，其核心挑战在于：尽管模拟涉及大量代理（N），但所关注的通常是宏观层面的现象（如相变行为、典型事实及随代理数量变化的标度规律），而非单个代理的认知细节。为此，论文提出一种基于统计物理启发的方法——将每个LLM代理替换为通过少量数百至数千次低成本查询拟合的低参数替代模型，从而实现在个人电脑上对任意规模的代理社会进行高效模拟。该方法的关键在于“感知与记忆”的建模能力，作者引入了“交互阶数×记忆长度”（[interaction order × memory]）的分类体系，将代理的感知范围与记忆特性映射为有效理论，并预测替代误差随代理数量N的变化趋势。在对真实复现的经济代理模型EconAgent以及另外七个知名LLM模拟系统的验证中，该理论成功预测了各单元的误差趋势；两个被证伪的预测（均涉及强饱和响应）亦被理论精确量化，且无需自由参数，充分证实了该方法的可预测性与普适性。
链接: https://arxiv.org/abs/2608.11215
作者: Igor Itkin
机构: Independent Researcher, Tel Aviv, Israel
类目: Artificial Intelligence (cs.AI); Statistical Mechanics (cond-mat.stat-mech); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Physics and Society (physics.soc-ph)
备注: 25 pages, 12 figures. Code and data at this http URL systematic review and pre-registration archived at Zenodo (doi: https://doi.org/10.5281/zenodo.21198322 , doi: https://doi.org/10.5281/zenodo.21340310 )
Abstract:Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents N , not the cognition of any single agent. We turn a statistical-physics observation into a method: replace each LLM agent by a low-parameter model fitted from a few hundred to a few thousand cheap queries, then run the society at any N on a laptop. Whether this works is decided before the simulation runs, chiefly by what each agent perceives. We introduce an [interaction order x memory] taxonomy that maps perception and memory to an effective theory and a predicted N -trend of the surrogate error. We validate it on a faithful reimplementation of the LLM macroeconomy EconAgent and seven further named LLM simulations, with agent decisions cloned from genuine LLM elicitations (primarily DeepSeek) for a few dollars; the predicted error trends hold cell by cell, and the two refuted predictions, both on a strongly saturating response and traced to its curvature, are themselves matched quantitatively by the theory with no free parameters.
[MA-17] Distribird: Literature-Informed Prior Distribution Design for Bayesian Model Calibration
【速读】：该论文旨在解决过程模型（process-based models）贝叶斯校准中先验分布构建困难的问题，尤其是当前研究者普遍依赖均匀先验（uniform priors）而非基于科学文献的有信息先验（informative priors）的现状。其核心挑战在于：构建有信息先验需要结合领域知识与统计建模能力，耗时且对研究人员要求高。本文提出的解决方案是Distribird，一个基于多智能体（multi-agent）架构的代理式网络应用，其关键创新在于自动化生成高质量、可追溯的参数先验分布。系统通过检索科学文献、根据领域相关性加权提取的数据，并利用AIC准则进行概率分布拟合；当缺乏文献支持时，自动退化为合理的非信息先验并明确标注置信度。该方法不仅保证了先验的可解释性与可信度（所有先验均可回溯至具体文献和数据），还具备内置有效性验证层，避免对超出范围请求生成误导性结果。此外，所有语言模型调用均在本地执行，保护了用户隐私与未公开模型细节，显著优于单提示词基线模型在鲁棒性和安全性上的表现。因此，其核心优势在于提升先验生成的可靠性与透明度，远超微小的点估计精度改进。
链接: https://arxiv.org/abs/2608.11210
作者: Patrik P. Süli,György Eigner,Roland Hollós
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Software Engineering (cs.SE)
备注:
Abstract:Bayesian calibration of process-based models requires a prior distribution for each model parameter. Despite decades of methodological work, researchers almost always fall back on uniform priors. The main reason is that building informative priors from scientific literature is slow and needs both domain and statistical expertise. We present \textbfDistribird, an agentic web application that automates this process. Given a parameter name, physical description, and domain context, Distribird deploys a multi-agent pipeline that searches the literature, extracts and weights reported values by domain relevance, and fits a probability distribution via AIC model selection. When no literature is available, the system falls back to sensible uninformative alternatives, and clearly reports both the evidence behind and the confidence level of every prior it produces. It is designed for the problems where the models have physically interpretable parameters, where domain knowledge exists in the published literature. We evaluate the tool on 24~parameters across 10 scientific domains comparing three open-weight models (Qwen3.6 27B, Gemma 4 31B, Mistral Small 4 119B) with a single-prompt LLM baseline. On prior quality the full pipeline \emphmatches this baseline. Every prior is traced to the specific papers and values from which it was constructed; a built-in validity layer declines to produce priors for out-of-scope requests, whereas the single-prompt baseline returns confident but unfounded priors for them in 11 of 30~model–parameter cases; and every language-model call runs locally, so no parameter description or unpublished modelling detail is transmitted to a third-party LLM provider (only generated search terms reach the public literature databases). For scientific use, we argue these properties matter more than a marginal improvement in point-estimate accuracy.
自然语言处理
[NLP-0] AVA-Encoder: Towards Agent -Native Video Representation Learning
【速读】： 该论文旨在解决生成式视频代理（agentic video agents）难以有效学习高质量人类影视内容的问题，其核心挑战在于缺乏一种既能忠实还原影片内容、又可直接用于代理推理与操作的结构化视频表示形式。为此，作者提出代理视频自编码器（Agentic Video Auto-Encoder, AVA-Encoder），其关键在于通过代理自编码机制将视频转化为一种以知识图谱（Knowledge Graph, KG）为载体的代理原生表示：该表示包含层级化的状态节点（存储结构化文本）与关联资产层（存放生成的图像、音频和视频），并通过具有类型定义的边来显式建模文本描述与多媒体资产之间的语义关系，使代理能够直观理解、查询与编辑。同时，利用视频重建误差驱动的文本梯度优化框架，将评估反馈转化为自然语言形式的更新指令，分别在外部循环中进行数据无关的编码策略伪训练（Data-Independent Encoding Policy Pseudo-Training），以及在测试时内部循环中可选的数据相关知识图谱表示精炼（Data-Dependent KG Representation Refinement）。实验表明，AVA-Encoder在性能上相较最强基线提升20.7个百分点；在仅使用策略控制的设定下，其伪训练得到的逐镜头代理视频编码策略亦优于人工精心调优的方案，且系统提示词消耗减少74.3%。研究团队还开源了完整的AVA-Encoder框架、可靠的代理视频重建基准及首个高质量电影知识图谱数据集。
链接: https://arxiv.org/abs/2608.12313
作者: Chuyue Li,Jinpeng Yu,Haozhe Wang,Tian Xueyun,Zhijing Zhang,Bingnan Li,Shuqi Gu,Kan Ren,Jiaming Liu,Ruihua Hua
机构: Qwen Business Unit of Alibaba; ShanghaiTech University; The Hong Kong University of Science and Technology; Institute of Computing Technology; Southeast University
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Creative agents still lack an effective way to learn from high-quality human films, limiting their ability to produce cinematic-grade videos. A key challenge is the absence of a structured video representation that is both faithful to film content and directly usable for agentic reasoning and manipulation. To address the challenge, we propose the Agentic Video Auto-Encoder (AVA-Encoder), a framework for learning agent-native video representations via agentic auto-encoding. AVA-Encoder transforms a video into a knowledge graph (KG) representation and then reconstructs it back into video. Its hierarchy and state nodes store structured text, while a linked asset layer holds generated images, audio, and video. Typed edges preserve the relations between these text descriptions and assets in a form that agents can easily understand, query, and edit. The video reconstruction differences drive a textual-gradient optimization framework, which expresses evaluation feedback as natural-language update directions for Data-Independent Encoding Policy Pseudo-Training in the outer loop and optional Data-Dependent KG Representation Refinement in the test-time inner loop. Extensive experiments show that AVA-Encoder improves by 20.7 percentage points over the strongest external baseline. In the controlled policy-only setting, its pseudo-trained shot-level Agentic Video Encoder policy also outperforms a carefully human-tuned policy while using 74.3% fewer system-prompt tokens. We release the complete AVA-Encoder framework, a reliable agentic video reconstruction benchmark, and the first dataset of high-quality film KG representations. Subjects: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) Cite as: arXiv:2608.12313 [cs.CV] (or arXiv:2608.12313v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.12313 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-1] AI4AI at Test-Time: Strong-to-Weak Capability Transfer via Harnesses
【速读】： 该论文旨在解决如何在不更新目标模型参数的前提下，将强模型的推理能力迁移至弱模型的问题，即探索测试时的能力迁移（test-time capability transfer）。传统知识蒸馏方法依赖于训练期间通过教师强制（teacher forcing）、在线蒸馏等手段调整小模型参数，而本文提出一种新的范式——强模型到弱模型的支架构建（strong-to-weak scaffolding）：利用一个更强的构建者模型（builder model），在推理阶段动态生成确定性代码形式的推理支架（harness），以引导弱目标模型更可靠地完成任务。其解决方案的关键在于：将原本由弱模型承担的不稳定推理过程（如不确定的思维链或采样行为）转移至由强模型设计的确定性、可复现的外部逻辑结构中，包括特定于基准的路由策略和严格答案格式约束。实验表明，这种测试时的支架设计可使弱模型平均性能从0.49提升至0.91，显著接近强模型表现；分析进一步揭示，性能增益主要源于对推理路径的结构化控制而非增强目标模型自身的推理深度或采样广度，且构建者模型的推理努力与支架质量呈单调正相关，而平台差异的影响相对较小。因此，该研究证明了推理时支架设计（inference-time harness design）是训练时蒸馏的重要补充，能够实现认知结构的无重新训练迁移。
链接: https://arxiv.org/abs/2608.12307
作者: Cheng Qian,Wenting Zhao,Liangwei Yang,Heng Wang,Jielin Qiu,Heng Ji,Silvio Savarese,Huan Wang,Shelby Heinecke
机构: Salesforce AI Research( Salesforce人工智能研究中心); University of Illinois Urbana-Champaign(伊利诺伊大学厄本那-香槟分校)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 23 Pages, 12 Figures, 6 Tables
Abstract:Recent work on distillation transfers the capabilities of large models to smaller ones often by updating the latter’s parameters, through teacher forcing, on-policy distillation, and related training-time methods. In this paper, we ask whether such transfer can instead occur at test time. We study strong-to-weak scaffolding: whether a stronger builder model can construct inference-time harnesses that help a weaker target model solve tasks more reliably without any parameter updates. Using four representative Theory-of-Mind benchmarks, each builder model uses 5% of the data as a validation set to iteratively refine its harness over multiple rounds, after which the finalized harness is evaluated on the full test set. Empirically, this form of test-time capability transfer is highly effective, nearly doubling average target-model performance from 0.49 to 0.91. Our analysis shows that the gains come primarily from offloading unstable model reasoning into deterministic code, benchmark-specific routing, and strict answer-format enforcement, rather than from encouraging the target model to reason more extensively or sample more broadly. We further find that builder-model reasoning effort improves harness quality monotonically, platform effects are modest relative to the builder model’s own capability, and weaker target models receive the largest gains. These results suggest that inference-time harness design is an important complement to conventional training-time distillation, enabling strong models to transfer cognitive structure to weaker models without retraining.
[NLP-2] Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages
【速读】： 该论文旨在解决生成式 AI（Generative AI）在低资源语言群体中因系统性基础设施缺陷导致的不平等问题，尤其聚焦于孟加拉语这一全球使用人口占比近4%但网络内容仅占不足0.5%的语言。其核心问题在于：当前主流人工智能教育工具所依赖的训练语料、分词方案、评估基准与部署架构，在设计之初即未充分考虑非主流语言的结构性劣势，从而在模型训练前便造成系统性排斥。论文提出的关键解决方案是将“离线优先”（offline-first）设计视为一种以公平为导向的基础设施策略，并强调应将数据稀缺性理解为结构性障碍而非孤立的技术局限，进而推动面向语言多样性的算法与系统重构。
链接: https://arxiv.org/abs/2608.12278
作者: Avijit Roy,Proma Roy
机构: John Jay College of Criminal Justice, CUNY(纽约城市大学约翰杰伊刑事司法学院); The City College of New York, CUNY(纽约城市大学城市学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: An associated poster version of this work was presented at the 69th Annual Conference of the International Linguistic Association (ILA 2026), New York, NY, April 30-May 2, 2026
Abstract:Artificial intelligence tools for education and language support are increasingly framed as scalable responses to access gaps in under-resourced communities. Yet the infrastructure underlying these tools, including training corpora, tokenization schemes, evaluation benchmarks, and deployment architectures, can systematically disadvantage speakers of underrepresented languages before a model is trained. This paper examines these structural barriers through Bengali, one of the world’s most widely spoken languages, focusing on AI-assisted education in low-connectivity environments. We identify four interlocking failures: a severe web presence gap, with Bengali accounting for less than 0.5% of global web content despite representing nearly 4% of the global population; a 67:1 training-token deficit between English and Bengali in major multilingual corpora; a tokenization penalty associated with Bengali’s alphasyllabary script that compounds the data deficit through higher token fertility; and connectivity exclusion, with individual internet penetration at 36.5% in rural areas compared with 71.4% in urban areas. These failures reflect longstanding resource-allocation decisions, institutional priorities, and design defaults that did not center underrepresented languages in mainstream AI development. We argue that dataset scarcity should be understood as a structural barrier rather than an isolated technical limitation, and that offline-first design should be treated as an equity-oriented infrastructure strategy. We conclude with directions for linguistics and AI research aimed at reducing these structural inequalities. Comments: An associated poster version of this work was presented at the 69th Annual Conference of the International Linguistic Association (ILA 2026), New York, NY, April 30-May 2, 2026 Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2608.12278 [cs.CL] (or arXiv:2608.12278v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.12278 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-3] A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement
【速读】： 该论文旨在解决公共采购过程中因忽视利益相关方评论与公开政府数据而难以及时发现程序异常的问题，从而导致监管效率低下和潜在腐败风险。其核心解决方案是构建一个融合无监督聚类与有监督分类的混合建模框架，嵌入自然语言处理（Natural Language Processing, NLP）流程中，以挖掘采购前期参与者评论中的隐含模式。具体而言，采用领域微调的Word2Vec、LLaMA和RoBERTa生成语义嵌入，通过高斯混合模型（Gaussian Mixture Models, GMMs）进行无监督聚类识别潜在异常群体，再利用随机森林（Random Forest）分类器检测具有指控或举报性质的评论。实验表明，结合领域训练的Word2Vec嵌入、基于GMM的聚类与随机森林分类的方法在严重类别不平衡条件下仍能实现高精度与高召回率，证明轻量级、领域适配的NLP架构可在不依赖大规模计算资源的前提下，有效支持公共采购系统的风险识别与透明度提升。
链接: https://arxiv.org/abs/2608.12269
作者: Bryan Torres,Daniel Riofrío,José Vega-Sánchez,Nathaly Orozco,Carla Parra,Karen Rosero,Felipe Grijalva
机构: Colegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito (USFQ); Faculty of Engineering and Applied Sciences, Telecommunications Engineering, ETEL Research Group, Universidad de Las Américas (UDLA); Departamento de Estudios Organizacionales y Desarrollo Humano, Escuela Politécnica Nacional; Language Technologies Institute, Carnegie Mellon University
类目: Computation and Language (cs.CL)
备注:
Abstract:Public procurement involves the allocation of substantial financial resources; therefore, continuous oversight through audits, controls, and monitoring mechanisms is essential. However, stakeholder comments and publicly available government data are often underutilized, despite their potential to reveal procedural irregularities. To address this gap, this paper analyzes metadata from Ecuador’s Sistema Oficial de Contratación Pública (SOCE, Official Public Procurement System), with particular emphasis on participant comments generated during the pre-contractual phase. We propose a hybrid modeling framework that integrates unsupervised clustering and supervised classification within a natural language processing (NLP) pipeline to uncover latent patterns and detect potentially irregular procurement processes. Semantic embeddings are generated using Word2Vec, LLaMA, and RoBERTa, followed by Gaussian Mixture Models (GMMs) for unsupervised clustering. A supervised classification stage is then applied to identify accusatory or whistleblowing-style comments. Experimental results show that the combination of domain-trained Word2Vec embeddings, GMM-based clustering, and a Random Forest classifier achieves high precision and recall, even under severe class imbalance. These findings demonstrate that lightweight, domain-adapted NLP architectures can effectively support risk identification and enhance transparency in public procurement systems without requiring large-scale computational infrastructure.
[NLP-4] One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL
【速读】： 该论文旨在解决多智能体强化学习（Multi-agent Reinforcement Learning, MARL）在人-智能体交互场景中因依赖单一大型语言模型（Large Language Model, LLM）作为用户行为模拟器而导致的泛化能力不足问题。其核心挑战在于“模拟器坍缩”（simulator collapse）：由于单个LLM模拟器存在模式坍缩（mode collapse），训练出的策略会过度拟合于模拟器的主导行为模式，从而产生仅在特定模拟器或已知用户上表现良好但难以迁移至未见模拟器或真实用户的狭窄策略。为应对这一问题，论文提出两种互补解决方案：一是在推理阶段采用“口语化采样”（Verbalized Sampling），通过从口语化的响应分布中采样以拓宽模拟器的行为多样性，缓解模式坍缩；二是在训练阶段引入“协同训练”（Co-Training），联合优化策略与一组可训练的模拟器群体，防止策略对任一单一模拟器的模式过拟合。实验验证表明，在三个多轮对话基准（Persuasion for Good、τ²-bench 和 CooperBench）上，口语化采样使保留测试集成功率提升最高达9%，而协同训练进一步将性能提升至14%；人类评估也证实了在真实用户上的类似增益。两项方法均有效维持了策略多样性，避免了单模拟器训练下的策略退化。研究结果强调，训练环境的多样性——不仅限于策略本身——是实现多轮强化学习在现实世界部署中良好泛化性的关键因素。为此，作者开源了SCOPE框架，支持基于群体协同训练的多智能体强化学习研究。
链接: https://arxiv.org/abs/2608.12253
作者: Simon Yu,Nicholas Tomlin,Marwa Abdulhai,Ximing Lu,Derek Chong,Abe Hou,Dilara Soylu,Sergey Levine,Christopher D. Manning,Weiyan Shi
机构: Northeastern University(东北大学); New York University(纽约大学); UC Berkeley(加州大学伯克利分校); University of Washington(华盛顿大学); Stanford University(斯坦福大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 41 pages, 28 figures
Abstract:Multi-agent reinforcement learning for human-AI interaction typically relies on a single large language model to simulate user behavior. We show that this approach systematically fails to generalize, and trace the failure to simulator collapse: because the simulator LLM is mode-collapsed, an LLM policy trained against it overfits to narrow strategies that exploit the simulator’s dominant mode, and such a policy transfers poorly to unseen simulators and real users. We formalize this collapse theoretically and propose two complementary solutions, one at inference time and one at training time. The inference-time solution, Verbalized Sampling, broadens the simulator’s behavior by sampling from a verbalized response distribution, reducing mode collapse. The training-time solution, Co-Training, jointly optimizes the policy against a population of trainable simulators, preventing it from overfitting to any single simulator’s mode. We validate both solutions on three multi-turn benchmarks: Persuasion for Good, \tau^2 -bench, and CooperBench. Verbalized Sampling improves held-out success by up to 9% over single-simulator RL, and Co-Training pushes gains further to 14%; the human study shows similar gain on real users. Both solutions preserve the policy diversity that collapses under single-simulator RL. To support further work in this direction, we release SCOPE, an open-source framework for Population Co-Training multi-agent RL. More broadly, our results suggest that the diversity of the training environment, not only the policy, is critical to the generalization of multi-turn RL to real-world deployment.
[NLP-5] VICBench: A Multi-Language Benchmark for Code Vulnerability Detection
【速读】： 该论文旨在解决现有安全漏洞检测工具评估中缺乏高质量基准数据集的问题，特别是针对引入漏洞的代码提交（Vulnerability-Inducing Commits, VICs）的不足。当前漏洞数据集普遍存在编程语言覆盖有限、补丁复杂度低以及项目范围狭窄等缺陷，难以真实反映实际软件系统中的漏洞发现挑战。为应对这一问题，研究提出一种结合人工专家双标注与智能体工作流（agentic workflow）的协同标注方法，构建了首个涵盖Python、Java和C++三种语言、88个开源项目、100个经验证的VICs对应100个CVE的基准数据集——VICBench，覆盖48类通用弱点枚举（Common Weakness Enumeration, CWE）。该数据集具有高现实性，其平均修复补丁长度达38.6行，对应的VICs长度高达252.5行，显著超过以往研究。实验表明，当前最先进的漏洞定位算法（如V-SZZ和LLM4SZZ）在该数据集上的F1值仅为33.3%–40.1%，揭示现有方法仍需大量人工干预。因此，该研究的关键突破在于通过多源验证的高质量标注流程构建了一个具备真实复杂性和广泛覆盖性的基准，从而为漏洞检测方法提供了更可靠、更具挑战性的评估平台。
链接: https://arxiv.org/abs/2608.12246
作者: Jin Lu,Xuening Han,Yang Zhong,Lin Tan,Kevin Luo,Andrew Gacek,Neha Rungta
机构: University of Pittsburgh(匹兹堡大学); Purdue University(普渡大学); Amazon Web Services(亚马逊网络服务)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注:
Abstract:Evaluating security vulnerability detection tools requires benchmark datasets with vulnerability-inducing commits (VICs) - the commits that first introduce vulnerabilities into codebases. VICs are essential for determining the full range of vulnerable software versions. Existing vulnerability datasets suffer from limited programming language coverage, restricted patch complexity, and narrow project scope. Through our dual annotation by human experts and an agentic workflow, we create a benchmark - VICBench - of 100 verified VICs for 100 CVEs across 88 projects in Python, Java, and C++, covering 48 CWE types. VICBench features complex real-world vulnerability fixes averaging 38.6 lines and corresponding VICs of 252.5 lines - significantly larger than prior work. Our evaluation shows that state-of-the-art algorithms V-SZZ and LLM4SZZ achieve only 33.3%-40.1% F1, confirming that using existing approaches still entails significant manual effort. VICBench enables robust evaluation of vulnerability detection approaches.
[NLP-6] Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge
【速读】： 该论文旨在解决大语言模型在长上下文训练与部署中性能随上下文长度增加而出现非单调变化的现象，挑战了“更长上下文必然带来更好性能”的隐含假设。其核心问题是：当训练上下文变得极为丰富时，模型的学习模式会从依赖参数化内化（parametric internalization）转向对上下文的依赖性外化（contextualization），从而导致模型在缺乏上下文或上下文错误时表现退化。解决方案的关键在于提出“信息丰度悖论”（Information Abundance Paradox），指出在训练阶段，过量的相关信息反而削弱了模型将知识参数化存储的动力，使其更倾向于在推理时依赖外部上下文。实证研究表明，在预训练中，随着上下文窗口扩大，语言建模、自然语言理解及闭卷多选题问答（closed-book MCQA）性能仅在某一中间最优值处达到峰值，之后持续下降；在监督微调中，更多任务相关上下文虽能提升有支持上下文时的表现，却显著降低在无上下文或误导性上下文下的鲁棒性。机制分析表明，富含信息的长上下文会改变梯度传播方向，使注意力模块（attention modules）承担更大学习压力，而前馈网络（feed-forward networks）的压力减小，这通过因果干预验证了注意力机制增强对上下文的依赖。研究结果表明，盲目扩展上下文并非万能策略，即使高质量长上下文数据充足，无限扩展上下文仍可能引发学习模式失衡，因此需重新审视上下文规模与模型知识内化之间的权衡。
链接: https://arxiv.org/abs/2608.12218
作者: Arda Uzunoglu,Benjamin van Durme,Daniel Khashabi
机构: Johns Hopkins University (约翰霍普金斯大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models are increasingly trained and deployed with long contexts that span documents, code repositories, and interaction histories. This scaling reflects the implicit assumption that training on longer contexts will only help the model by exposing it to richer evidence. We challenge this view by studying how the context window shapes a model’s mode of learning, shifting it between parametric internalization and contextualization. We propose the Information Abundance Paradox, which hypothesizes that abundant relevant information in the training context can reduce the incentive to encode that information parametrically, thereby increasing reliance on context. In pretraining with long documents, increasing the context window improves language modeling, natural language understanding, and closed-book MCQA only up to an intermediate optimum, after which performance consistently declines. In supervised fine-tuning, more task-relevant train-time context improves performance with supporting context, but reduces robustness when context is absent or misleading at test time. Our analysis suggests that this behavior arises when longer context provides a lower complexity solution. Mechanistically, training with informative context shifts gradient pressure from feed-forward networks, often linked to parametric knowledge, toward attention modules, and causal interventions show that this shift increases reliance on context during inference. Overall, these findings support the Information Abundance Paradox and suggest that scaling toward near-infinite context is not simply a matter of supplying more data, even when high-quality long-context data is abundant.
[NLP-7] Who Thinks Best Depends on How Long You Let Them: Budget-Dependent Rankings in LLM Evaluation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在标准评估中假设模型性能排名在不同推理条件下保持稳定这一潜在偏差问题。研究发现，当生成令牌预算（token generation budget）从64到4,096分七个层级变化时，存在3%–19%的测试项出现非单调现象（即增加预算后准确率反而下降），且该现象具有模型特异性，跨模型重叠率仅为6%–14%；同时，所有三个推理基准上均观察到模型排名随预算变化发生显著反转（p < 0.01，McNemar检验）。进一步的“理想化分析”（Oracle analysis）揭示了模型间的互补性可提升高达27.8个百分点的性能，尤其在预算受限时表现更明显。基于此，研究提出一种预算感知型路由机制（budget-aware router），可在跨领域场景中捕获14.1%的理想差距，而预算特征在同领域内有效（提升1.6至5.7个百分点），但具有领域依赖性并损害迁移性能（降低1.2个百分点）。因此，论文的核心解决方案在于引入预算条件化的评估与调度框架，强调评估应考虑生成资源约束的影响，以实现更真实、动态和高效的模型性能判断。
链接: https://arxiv.org/abs/2608.12150
作者: Rodrigo Guedes de Souza,Alison R. Panisson
机构: Federal University of Santa Catarina (UFSC); Brazil
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 19 pages, 11 figures, 7 tables
Abstract:Standard evaluation of large language models assumes stable model rankings across inference conditions. We challenge this assumption by varying the token generation budget, i.e., the maximum tokens a model may produce, across seven levels (64–4,096), evaluating four models on three reasoning benchmarks (56,476 inferences). We report four findings: (i) 3–19% of items exhibit non-monotone behavior (accuracy decreasing with more budget), even after controlling for truncation, and this phenomenon is model-specific (cross-model overlap: 6–14%). (ii) Model rankings reverse across budgets on all benchmarks ( p 0.01 , McNemar). (iii) Oracle analysis reveals model complementarity up to +27.8 pp, most pronounced at constrained budgets. (iv) A budget-aware router captures 14.1% of the oracle gap cross-domain; budget features help within-domain ( +1.6 to +5.7 pp) but are domain-specific and hurt transfer ( -1.2 pp). These results argue for budget-conditioned evaluation protocols.
[NLP-8] Massive Activations in Hybrid Linear Attention Large Language Models : Pre-Attention Spikes and Inter-Spike Plateaus
【速读】： 该论文旨在解决层交错式线性注意力大语言模型（layer-interleaved HLA LLMs）中大规模激活（Massive Activations, MAs）的动态演化机制问题，特别是其在混合注意力架构中的组织形态与稳定性来源。研究发现，MAs呈现出两种与网络架构对齐的典型形态：一种是在全注意力层前持续出现的“预注意力尖峰”（pre-attention spikes, PAS），另一种是穿越中间线性注意力层后仍保持持续性的“峰间平台”（inter-spike plateaus, ISP）。随着全注意力层密度增加，连续的PAS通过ISP逐步连接，最终重构出传统全注意力大语言模型所具有的稳定MAs形态。这一组织结构在五种线性注意力架构、六种混合配置、五个数据领域及多款参数规模从1.2B到397B的开源混合模型中均表现出高度可重复性。控制实验表明，基于广义门控网络（GDN）的混合模型在高达1.3B参数规模下，两种形态均早期涌现，且对输出门控呈现非对称响应——全注意力输出门控显著抑制其绝对幅值但不破坏层间组织结构，而移除GDN门控仅导致小幅放大。机理分析支持一个统一的生命周期模型，其核心在于大规模激活的消减时机：PAS遵循局部“写入-汇流-消减”的过程，而ISP的持久性则源于延迟消减。当系统趋近全注意力极限时，该机制可恢复经典全注意力模型中稳定的MAs形态。
链接: https://arxiv.org/abs/2608.12149
作者: Zunhai Su,Bohan Sun,Xialie Zhuang,Shuibai Zhang,He Xiao,Jing Xiong,Hengyuan Zhang,Zhongzhu Zhou,Tiantian Zhang,Ngai Wong,Chuan-Wei Kuo
机构: Startlux; Tsinghua University (清华大学); University of Chinese Academy of Sciences (中国科学院大学); The University of Hong Kong (香港大学); University of Sydney (悉尼大学); Columbia University (哥伦比亚大学)
类目: Computation and Language (cs.CL)
备注: Under review
Abstract:We present the first systematic study of Massive activations (MAs) in layer-interleaved HLA LLMs and uncover two architecture-aligned morphologies: MAs consistently spike immediately before full attention layers, forming pre-attention spikes (PAS), and can persist through intervening linear attention layers, giving rise to inter-spike plateaus (ISP). As full attention becomes denser, successive PAS become increasingly connected through ISP, ultimately recovering the stable MA morphology of full attention LLMs. We establish the recurrence of this organization across five linear attention architectures, six hybridization configurations, five data domains, and representative open-source hybrid models spanning 1.2B to 397B total parameters. Controlled pretraining of GDN-based hybrids at scales up to 1.3B shows that both morphologies emerge early and respond asymmetrically to output gating: full attention output gating strongly attenuates their absolute magnitudes without eliminating their layerwise organization, whereas removing GDN gates yields comparatively modest amplification. Mechanistically, our systematic-outlier analysis supports a shared lifecycle account governed by the timing of MA cancellation. PAS follows a localized write-sink-cancel process, while the extended persistence of ISP is consistent with delayed cancellation. At the full attention limit, this account recovers the stable MA morphology characteristic of full attention LLMs. Our code is available at this https URL.
[NLP-9] SAG: SQL-Retrieval Augmented Generation with Query-Time Dynamic Hyperedges
【速读】： 该论文旨在解决主流密集检索（dense retrieval）方法在处理结构化约束与多跳推理时的固有局限性，尤其针对现有基于图的方法因构建全局知识图谱而导致语义碎片化、维护成本高及增量更新复杂的问题。其核心解决方案是提出SAG（SQL-Retrieval Augmented Generation）架构，通过将文档组织为事件-实体索引（event-entity index），以语义完整的事件及其关联实体构成潜在超边（latent hyperedge），从而在不分解为三元组的前提下保留n元关系。在查询时，SAG利用共享实体作为连接键动态构建与查询相关的事件邻域，同时确保每条证据始终为原始文本片段。实验结果表明，SAG在HotpotQA、2WikiMultiHopQA和MuSiQue等多个基准上均取得最优的检索与端到端问答性能，尤其在多跳推理要求最高的MuSiQue数据集上，Recall@5达到80.36%，较最强基线提升11.52个百分点。该方法为支持持续增长的组织知识的大型语言模型（LLM）智能体实现高效检索与推理提供了可扩展的知识基础设施。
链接: https://arxiv.org/abs/2608.12129
作者: Yuchao Wu,Junqin Li,XingCheng Liang,Yongjie Chen,Yinghao Liang,Linyuan Mo,Guanxian Li
机构: Zleap AI; Linyuan Mo; Guanxian Li
类目: Computation and Language (cs.CL)
备注:
Abstract:While retrieval-augmented generation (RAG) has proven effective at giving LLMs access to external knowledge, mainstream dense-retrieval implementations remain inherently limited in handling structured constraints and multi-hop reasoning. Graph-based methods address this by constructing knowledge graphs offline, but they often fragment semantics, incur high maintenance, and complicate incremental updates. We propose SAG (SQL-Retrieval Augmented Generation), a structured retrieval architecture that organizes documents into an event-entity index without building a global knowledge graph. SAG represents each chunk as a semantically complete event paired with its entities, forming a latent hyperedge that preserves n-ary relations without decomposing them into triples. At query time, SAG treats shared entities as join keys to connect related chunks. This dynamically yields a query-scoped neighborhood of events, and yet every piece of evidence remains the original chunk throughout. Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue show that SAG achieves the best retrieval and end-to-end QA performance on every benchmark, with gains that widen as reasoning-chain complexity increases. On MuSiQue, where multi-hop evidence chaining is most demanding, SAG reaches 80.36% Recall@5, outperforming the strongest baseline by 11.52 points. This work paves the way for knowledge infrastructure that enables LLM agents to retrieve and reason over continually growing organizational knowledge.
[NLP-10] QV-PIC: Query-Aware Visual Position-Independent Caching for Efficient RAG Serving
【速读】： 该论文旨在解决生成式 AI（Generative AI）中检索增强生成（Retrieval-Augmented Generation, RAG）因重复预填充相同文本块而导致的冗余计算问题。现有方案位置无关缓存（Position-Independent Caching, PIC）虽可通过复用预计算的键值对（Key-Value, KV）提升效率，但其性能受限于文本令牌数量庞大；将文本块渲染为图像以压缩为更少视觉令牌虽可降低计算开销，却因上下文不一致及细粒度文本证据在视觉压缩中的丢失，导致图像型PIC质量显著劣于文本型PIC。现有修复方法主要通过选择性重计算缓解上下文错位，但引入在线计算开销且无法恢复丢失的文本细节。本文提出一种查询感知的双分辨率缓存复用框架QV-PIC，其核心创新在于：离线阶段基于模型原生对话模板前缀编译视觉缓存，无需在线重计算即可提升缓存质量；在线推理时，利用累积查询相关性得分，在低分辨率下保持全局上下文的同时，以有限高分辨率预算精准恢复细粒度文本证据，从而兼顾效率与表征质量。实验表明，相较于原始图像型PIC，QV-PIC在六项任务上平均F1提升21.6点，逼近纯文本型PIC性能，并超越优化后的文本型PIC 2.58 F1，同时将首字延迟（TTFT）降低17.2%，相比完整预填充减少83.8%的延迟。
链接: https://arxiv.org/abs/2608.12121
作者: Yilin Liu,Rui Meng,Wangze Ni,Jianxin Yan,Heng Cao,Libin Zheng,Peng Cheng,Jinfei Liu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Retrieval-Augmented Generation (RAG) repeatedly prefills identical text chunks across queries, incurring redundant computations. Position-Independent Caching (PIC) mitigates it by reusing precomputed Key-Value (KV) across positions, but its efficiency is constrained by the large volume of text tokens. Rendering text chunks as images can compress the text into fewer visual tokens, but the rendered-image PIC suffers more severe quality degradation than the text PIC. This representation-specific gap primarily arises from contextual mismatches across independently compiled caches and the loss of fine-grained textual evidence during visual compression. Existing PIC repair methods mainly address the former through selective recomputation, but they incur online computation and cannot recover lost textual details. We propose QV-PIC, a query-aware dual-resolution PIC reuse framework guided by model-native templates. Offline, QV-PIC compiles visual caches under the model’s native chat-template prefix, improving PIC quality without online recomputation. Online, it preserves global context with low resolution and restores fine-grained textual evidence within a high-resolution budget by cumulative query relevance scores, retaining the efficiency benefit of visual compression. Across six tasks, QV-PIC improves average F1 by 21.6 points over vanilla rendered-image PIC, closes the gap to vanilla text PIC, and surpasses optimized text PIC by 2.58 F1 while reducing TTFT by 17.2%. Relative to full prefill, it cuts TTFT by 83.8%.
[NLP-11] Structuring the Space of Perspectives ACL
【速读】： 该论文旨在解决自然语言处理（Natural Language Processing, NLP）领域中关于视角（perspective）相关概念缺乏清晰理论界定与内在关系理解的问题。尽管已有大量研究使用立场（stance）、情感（sentiment）、框架（frame）和论据（argument）等操作性概念来捕捉文本中的视角，但这些概念之间的精确关联仍不明确，限制了研究的系统性与有效性。论文的关键解决方案在于通过系统梳理NLP中视角研究的范畴，提出一组用于区分视角相关概念的核心属性，并据此构建一个线性排列的层级结构，将各类概念沿单一轴线组织起来。这一有原则的概念层级不仅揭示了不同视角概念间的内在逻辑关系，还为研究者根据具体研究目标选择合适的视角操作化方式提供了理论指导，从而推动视角研究向更系统、更精准的方向发展。
链接: https://arxiv.org/abs/2608.12113
作者: Agnese Daffara,Sebastian Padó,Tanise Ceron
机构: University of Stuttgart (斯图加特大学); Bocconi University (博科尼大学)
类目: Computation and Language (cs.CL)
备注: Under review for TACL (editor decision: b)
Abstract:The same event can be reported from different perspectives depending on the experiences, background, and beliefs of the writer or speaker. A variety of NLP areas engage with perspectives, spanning from text analysis to algorithm optimization. A wide range of operative concepts (such as stances, sentiment, frames, and arguments) has been used to capture perspectives in texts, however the precise relationships among those concepts remain unclear. Arguably, a deeper theoretical understanding of these concepts would empower more effective research on perspectives. In this paper, we address this gap by reviewing the space of perspectives in NLP and defining a set of properties that help distinguishing perspective-related concepts. Our analysis leads us to posit a hierarchy which organizes these concepts linearly along a single axis. Finally, we show how this principled conceptual hierarchy can help researchers navigate the field and select operationalizations of perspective that align with their specific research objectives.
[NLP-12] RT-SEMamba: Real-Time Speech Enhancement Mamba via Progressive Knowledge Distillation INTERSPEECH2026
【速读】： 该论文旨在解决实时语音增强（Real-time Speech Enhancement, SE）中模型性能与计算效率之间的矛盾问题，特别是如何在保持低算法延迟的前提下实现高质量的语音增强。现有基于Transformer的模型依赖于不断增长的键值缓存（key-value cache），导致内存和带宽开销随输入长度增加而上升，难以满足实时系统对长序列处理的高效性要求。为此，本文提出RT-SEMamba，一种基于因果时频Mamba块（causal time-frequency Mamba blocks）的全因果语音增强模型，利用状态空间模型（State Space Model, SSM）中固定大小的递归状态（recurrent state）替代动态扩展的缓存机制，显著降低内存占用并提升长序列推理效率。其解决方案的关键在于：1）采用因果时频结构设计，使模型能够高效建模语音信号的时间-频率依赖关系；2）引入渐进式知识蒸馏（Progressive Knowledge Distillation, KD）策略，通过联合蒸馏教师模型的复杂谱输出与中间特征表示，将8层教师模型压缩为仅1层的学生模型，在大幅降低计算量的同时保留关键语义信息。实验表明，该方法在Voicebank-DEMAND数据集上实现了3.32 PESQ的高保真度表现，且在25 ms算法延迟约束下，1层学生模型相比基线提升0.12 PESQ，同时获得2.75倍的速度提升，验证了状态空间模型结合渐进式知识蒸馏在实时语音增强任务中具备优异的质量-延迟权衡能力。
链接: https://arxiv.org/abs/2608.12099
作者: Rong Chao,Sung-Feng Huang,Moreno La Quatra,Sabato Marco Siniscalchi,Wen-Huang Cheng,Szu-Wei Fu,Yu Tsao
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: Accepted to INTERSPEECH 2026
Abstract:We present RT-SEMamba, a fully causal speech enhancement (SE) model built upon causal time-frequency Mamba blocks. Unlike Transformer-based architectures that rely on a growing key-value cache, Mamba propagates a fixed-size recurrent state per layer, enabling memory- and bandwidth-efficient long-form inference. We further introduce a progressive knowledge distillation (KD) strategy that compresses an 8-layer teacher into a shallow 1-layer student by jointly distilling complex spectral outputs and intermediate representations. On Voicebank-DEMAND, the 8-layer RT-SEMamba achieves 3.32 PESQ with a 25 ms algorithmic latency constraint, and the distilled 1-layer student improves over a naive 1-layer baseline from 3.06 to 3.18 PESQ while preserving the same steady-state RTF, delivering a 2.75x speedup over the teacher. These results demonstrate that state-space models with progressive KD provide a competitive quality-latency trade-off for real-time SE.
[NLP-13] Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations ICLR2025
【速读】： 该论文旨在解决在数据稀缺的特定领域中，构建能够开展多轮、目标导向对话的对话系统所面临的挑战，尤其聚焦于动机访谈（Motivational Interviewing, MI）这一需高度专业化与策略性的临床咨询场景。其核心问题在于如何在缺乏真实人类对话语料的情况下，有效提升对话代理（agent）在复杂交互中的决策能力与策略性表现。解决方案的关键在于提出一种名为“偏好树优化”（Preference Tree Optimization, PTO）的新框架，该框架通过引入“带前瞻的偏好树”（Preference Tree with Look-Ahead）方法，利用虚拟患者（virtual patients）与“圣杯评估器”（oracle evaluator）模拟高质量对话过程，自动生成结构化偏好数据。该方法结合直接偏好优化（Direct Preference Optimization, DPO），实现对对话模型的迭代式优化。实验结果表明，PTO框架显著提升了对话代理在目标导向任务中的表现，尤其在会话满意度与治疗联盟等关键指标上优于基线模型；更重要的是，前瞻性模拟机制增强了模型的长期规划能力，深度前瞻配置展现出更稳定且优异的性能，验证了该方法在克服数据稀缺并推动高阶对话系统发展方面的有效性。
链接: https://arxiv.org/abs/2608.12062
作者: Lior Baruch,Moshe Butman,Kfir Bar,Doron Friedman
机构: Reichman University, Herzliya, Israel(里赫曼大学, 赫尔兹利亚, 以色列)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 4 figures. Accepted at an ICLR 2025 workshop
Abstract:Developing dialogue systems capable of engaging in multi-turn, goal-oriented conversations remains a significant challenge, especially in specialized domains with limited data. This research proposes a novel framework called Preference Tree Optimization (PTO), designed to iteratively improve agent models in such dialogue systems, by generating preference data using a method called Preference Tree with Look-Ahead. Focusing on Motivational Interviewing (MI) – a counseling technique aimed at facilitating behavioral change – we leverage virtual patients and an oracle evaluator to simulate conversations and generate rich preference datasets. By combining this method with Direct Preference Optimization (DPO), we aim to enhance the agent’s decision-making capabilities over iterative training cycles. The proposed framework addresses data scarcity and advances the development of more nuanced and effective dialogue systems in goal-oriented domains. Experimental evaluations demonstrate that the PTO framework enhances dialogue agents’ performance in goal-oriented conversations within the domain of Motivational Interviewing (MI). Models trained with PTO consistently outperformed the baseline in key metrics such as session satisfaction and working alliance. Additionally, incorporating look-ahead simulations led to improved long-term planning and more effective conversational strategies, with deeper look-ahead configurations yielding the most stable and high-scoring results.
[NLP-14] Poly-Dialectal Neural Machine Translation System for Bangla Regional Dialects
【速读】： 该论文旨在解决孟加拉语在自然语言处理（NLP）中因区域性方言差异带来的核心挑战，即超过2.4亿使用者在不同区域变体间交流时，其语音、形态和词汇与标准口语孟加拉语（Standard Colloquial Bangla, SCB）存在显著偏离，导致现有神经机器翻译（NMT）架构及大语言模型（LLM）因假设语言分布同质化而出现严重性能退化。其解决方案的关键在于构建一个统一的多方言神经机器翻译系统（Poly-Dialectal Neural Machine Translation System），实现12种孟加拉语方言间的双向直接翻译，无需依赖中间标准语桥接（pivot）。研究通过创建迄今最大的多方言平行语料库（包含51,531条非空平行句对，其中2,500条为专家验证的双向语料，覆盖五种此前未被充分研究的方言），并采用权重分解低秩适应（Weight-Decomposed Low-Rank Adaptation, DoRA）技术对BanglaT5模型进行微调，在保持形态一致性的同时达到29.26 BLEU和57.26 chrF++的领先翻译性能，超越NLLB-200（615M）和mBART-50（611M）等基线模型。此外，研究还通过系统性的跨方言迁移分析与数据集规模扩展实验，确立了低资源方言适配的实证阈值，并将优化后的INT8量化模型部署为开源网页应用，以促进边缘化方言群体的数字包容性。
链接: https://arxiv.org/abs/2608.12018
作者: Rakib Ullah,Ruhul Islam Rahul,Tanbir Ahmed
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Regional dialectal variation poses a fundamental challenge to natural language processing (NLP) in Bangla, where over 240 million speakers communicate across diverse regional variants that diverge significantly from Standard Colloquial Bangla (SCB) in phonology, morphology, and lexicon. Contemporary neural machine trans- lation (NMT) architectures and large language models (LLMs) predominantly as- sume a homogeneous language distribution, resulting in severe performance degra- dation when translating low-resource regional dialects. In this work, we present a unified Poly-Dialectal Neural Machine Translation System capable of multi-directional translation across 12 Bangla regional dialects without routing through an inter- mediary standard pivot. We compile the largest multi-dialect parallel corpus for Bangla to date, comprising 51,531 non-null parallel sentence pairs across 12 di- alects, incorporating 2,500 expert-verified, bidirectional parallel sentence pairs for five previously unaddressed dialects. Evaluating sequence-to-sequence architec- tures under Weight-Decomposed Low-Rank Adaptation (DoRA), our fine-tuned BanglaT5 model achieves state-of-the-art translation performance (29.26 BLEU, 57.26 chrF++), outperforming NLLB-200 (615M) and mBART-50 (611M) while preserving morphological coherence. Furthermore, we conduct a systematic cross- dialectal transfer analysis and dataset scaling study, establishing empirical thresh- olds for low-resource dialect adaptation. Finally, we deploy the optimized INT8- quantized model as an open-access web application to promote digital inclusion for marginalized dialect communities. The complete dataset is publicly available at Mendeley Data (this https URL).
[NLP-15] Asymptotic Risk Calibration for Selective Question Answering
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在问答任务中生成流畅但错误答案的问题，核心挑战在于如何实现对不确定性的有效量化，以保障被接受答案的可靠性。现有方法依赖启发式不确定性评分或固定阈值筛选，无法提供可解释的误差率统计控制。为此，本文提出一种后处理校准框架A-CRC-QA，其关键创新在于将选择条件下的误差控制重新建模为线性期望约束，并采用受单调性约束的基于经验风险的校准方法，借鉴了符合风险控制（Conformal Risk Control）的思想。由于实例级损失通常不随接受阈值单调变化，该框架侧重于渐近风险控制而非有限样本下的严格保证。A-CRC-QA具有模型无关性、无需额外训练的优点，可与多种不确定性估计器兼容。在CoQA和MedMCQA数据集上的实验表明，该方法在开放域与封闭域问答任务中均表现出色，相较于未校准及基于置信度边界的方法，在接受答案的可靠性与保留率之间实现了更优权衡。
链接: https://arxiv.org/abs/2608.12008
作者: Shufan Lin,Sijin Dong
机构: Zhangjiang University(张江大学); Ibaraki University(茨城大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) may generate fluent but incorrect answers, making uncertainty quantification important for reliable question answering. However, heuristic uncertainty scores cannot perfectly distinguish correct predictions from incorrect ones, and directly applying a fixed uncertainty threshold provides no statistical control over the error rate among accepted answers. To address this limitation, we propose A-CRC-QA, a post-hoc calibration framework for uncertainty-aware selective question answering. The proposed method reformulates selection-conditioned error control as a linear expectation constraint and applies a monotonized empirical-risk calibration procedure inspired by conformal risk control. Since the resulting instance-wise loss is generally non-monotone with respect to the acceptance threshold, our framework targets asymptotic rather than finite-sample risk control. A-CRC-QA is model-agnostic, requires no additional training, and can be combined with different uncertainty estimators. Experiments on CoQA and MedMCQA demonstrate its applicability to both open-ended and closed-ended question answering, achieving a favorable trade-off between accepted-answer reliability and answer retention compared with uncalibrated and confidence-bound-based baselines.
[NLP-16] Claim-Level Reliability Assessment for Efficient Test-Time Reasoning
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在推理过程中因错误路径被冗余信息稀释而导致的决策偏差问题，尤其针对现有方法在测试时扩展（test-time scaling）中过度依赖增加采样数量而效率低下、难以有效识别关键逻辑错误的局限性。其核心解决方案是提出“主张级伪造”（claim-level falsification）原则，并通过无需训练的主张级可靠性评估（Claim-Level Reliability Assessment, CLR）框架实现。该框架的关键在于将完整的推理轨迹压缩为一组决策关键主张（decision-critical claims），从而隔离出逻辑锚点；同时，利用解题构建与主张证伪之间的根本不对称性——构造正确解需全程无误，而证伪一个错误主张仅需发现单一决定性漏洞——转而聚焦于对主张的针对性否定证据搜索。这一策略系统性地压缩了高置信度错误路径的存活空间，通过非线性可靠性评分机制有效抑制错误共识。实验表明，在四个主流LLM和四个推理基准上，CLR在相同计算预算下显著优于pass@1和自洽性（self-consistency）方法，例如在GPT-OSS-20B/CMIMC25上，相较pass@1提升27.15个百分点，且以37%更少的令牌数将自洽性准确率从77.50%提升至82.19%。
链接: https://arxiv.org/abs/2608.11994
作者: Sen Xu,Wei Wang,Shixi Liu,Jixin Min,Yingwei Dai,Zhibin Yin,Yirong Chen,Junlin Zhang
机构: Sina Weibo Inc.
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:We propose claim-level falsification as a principle for test-time scaling and instantiate it through Claim-Level Reliability Assessment (CLR), a training-free framework that reallocates test-time compute from additional solution sampling to targeted verification. Since whole-trace evaluation often obscures decisive errors due to signal dilution from routine tokens, CLR condenses each reasoning trace into a compact set of decision-critical claims, thereby isolating its logical anchors. Furthermore, recognizing the inherent difficulty of generating entirely correct solutions under fixed model capabilities, CLR shifts the focus to semantic falsification. This approach exploits a fundamental asymmetry between solution construction and claim refutation. Constructing a valid solution requires a flawless reasoning path, whereas refuting an incorrect claim requires identifying only a single decisive flaw. This targeted search for negative evidence systematically compresses the survival space of high-confidence incorrect traces, effectively suppressing erroneous consensus via nonlinear reliability scoring. Across four LLMs and four reasoning benchmarks under matched budgets, CLR generally improves upon pass@1 and self-consistency. On GPT-OSS-20B/CMIMC25, for instance, CLR exceeds pass@1 by 27.15 percentage-points and raises self-consistency accuracy from 77.50% to 82.19% with 37.0% fewer tokens.
[NLP-17] Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed IJCNN2026
【速读】： 该论文旨在解决在资源受限场景下构建可信小语言模型（Small Language Models, SLMs）的关键挑战，尤其关注其在公平性、鲁棒性、隐私保护及伦理合规性等多维度的可信度问题。现有方法主要依赖从零训练小型模型或通过剪枝、量化、蒸馏等方式压缩大型预训练模型，但缺乏对可信度影响的系统评估。本文的关键贡献在于揭示：相较于剪枝，量化在保持模型可信度方面具有显著优势；更重要的是，通过量化压缩一个已验证可靠的大型模型所生成的SLM，在可信度与适应性上均优于直接从头训练的小型模型；此外，基于可信教师模型的知识蒸馏可进一步提升SLM的可靠性。这一发现为构建高效且可信的SLM提供了切实可行的技术路径，并为未来研究奠定了基础。
链接: https://arxiv.org/abs/2608.11981
作者: Haokun Lin,Kaijie Zhu,Haobo Xu,Yichen Wu,Zhichao Lu,Qingfu Zhang,Zhenan Sun
机构: Institute of Automation, CAS(中国科学院自动化研究所); Tsinghua University(清华大学); Harvard Medical School(哈佛医学院); City University of Hong Kong(香港城市大学)
类目: Computation and Language (cs.CL)
备注: Published in IJCNN 2026
Abstract:Small Language Models (SLMs) have emerged as a more efficient alternative to traditional Large Language Models (LLMs), offering promising potential in resource-constrained scenarios. Existing approaches to building SLMs typically follow two paths: training compact models from scratch, or compressing larger pre-trained models using methods such as pruning, quantization, or distillation. As language models become increasingly integrated into real-world applications, ensuring their trustworthiness has become a critical concern. However, how to build trustworthy SLMs remains an underexplored question. In this work, we present a comprehensive evaluation of SLM trustworthiness across multiple dimensions, including fairness, robustness, privacy, and ethics. We first examine the effects of pruning and quantization, and find that quantization is significantly more effective in preserving trustworthiness compared to pruning. More importantly, we demonstrate that compressing a reliable large model via quantization can produce SLMs with superior trustworthiness and adaptability compared to using small models trained from scratch. Furthermore, knowledge distillation from trustworthy teacher models can further enhance the reliability of SLMs. We hope our findings provide practical guidance and a foundation for future research into the development and deployment of trustworthy small language models.
[NLP-18] Accuracy and Order Sensitivity Diverge Under Label-Free Strategies
【速读】： 该论文旨在解决多选题（Multiple-Choice Questions, MCQ）评估中因选项顺序敏感性（option order sensitivity）导致的模型知识评估失真问题。现有基准测试中的得分将模型的真实知识与对选项排列的依赖性混淆，从而降低了评估的可靠性。为此，论文提出两种去偏策略：一是生成后匹配（generation-then-matching）方法，二是对每个选项独立评分（positionally unbiased by construction）。然而，实验结果表明，这两种方法均未能稳定提升准确率。进一步的分解分析揭示，性能瓶颈在于隐藏选项信息，而非匹配过程本身。唯一能持续达到基线性能的配置是向模型展示所有选项并由大语言模型（LLM）进行匹配。值得注意的是，完全消除位置影响并未带来可靠的准确率提升，而采用循环置换（cyclic permutation）反而常能改善性能。此外，针对两阶段提示（two-stage prompting）的两种去偏评估指标——召回不平衡的聚合度量和逐题的顺序敏感性直接度量——均未表现出可靠的去偏效果。因此，该研究的关键发现是：仅通过消除选项顺序影响不足以提升评估准确性，且当前主流去偏策略在实际应用中缺乏稳定性与有效性。
链接: https://arxiv.org/abs/2608.11947
作者: Karl Hanna,Chen Feng
机构: Queen’s University Belfast(贝尔法斯特女王大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 20 pages. Code available at this https URL
Abstract:Multiple-choice benchmarks are widely used to evaluate large language models, but MCQ scores conflate knowledge with sensitivity to option order, which makes them unreliable measures of model knowledge. In this paper, we test whether preventing a model from seeing option labels while committing to an answer removes positional influence and, in turn, improves performance. We evaluate two different strategies for mitigating bias. The first uses a generation-then-matching approach, and the second scores options in isolation, which is positionally unbiased by construction. Neither reliably improves accuracy. A complete decomposition shows that the bottleneck is withholding options, not the matching step. The only configuration that consistently matches the baseline is the one that shows the model all options paired with an LLM matcher. However, eliminating positional influence entirely still does not reliably yield accuracy gains, while cyclic permutation often improves them. For two-stage prompting, an aggregate measure of recall imbalance and a direct per-question measure of order sensitivity both fail to show reliable debiasing.
[NLP-19] Spark-to-Paper: End-to-End Research Paper Generation as a Composable Skill
【速读】： 该论文旨在解决科研论文自动化生成过程中面临的复杂性问题，即如何在不依赖独立代理平台或编排服务的前提下，实现从研究构想到完整论文输出的端到端自动化，涵盖文献检索、实验设计与执行、证据驱动的论点修正、可发表级图表生成以及长周期生成过程中的一致性维护。其解决方案的关键在于构建一个由十三个可组合技能组成的轻量级工作流，将基于模型的判断与可直接执行和验证的确定性操作相分离，并进一步解耦实验规划与报告撰写，确保在观察结果前明确所需证据，根据实测数据动态修订论文主张。为提升长期研究轨迹的可靠性，系统引入确定性完整性检查、自我批判机制，并有效约束一种称为“自反谬误循环”（Self-Refutation Loop）的失败模式——即重复实验不断否定原始研究目标。此外，系统通过程序化绘图生成可编辑的矢量图，并以代码重构方式生成方法示意图，保障图表的可修改性。在八个受控研究主题上的评估表明，该系统实现了99.5%的引用有效性与96.4%的图表可编辑性；完整集成的完整性与审查栈将虚假内容检测率从单次生成的14%提升至92%，对抗性评审亦达到74%的精确度。整体系统平均消耗11.9M tokens，每篇论文成本8.1美元，耗时3.2小时，证明了在现有代码助手内部部署轻量、可组合的端到端科研论文生成流程的可行性，同时始终以实验证据为核心来决定论断的采纳、修正或放弃。
链接: https://arxiv.org/abs/2608.11924
作者: Zhuoyang Qian,Biao Wu,Yiran Wang,Chris D Yan,Desan Dai,Liangwei Zheng,Jin Jiang,Junsheng Zhang,Wenhao Wang
机构: Vast Intelligence Lab (Vast Intelligence Lab); University of Technology Sydney (悉尼科技大学)
类目: Computation and Language (cs.CL)
备注: 24 pages, 10 figures
Abstract:Turning a research idea into a complete paper requires more than text generation: the system must retrieve literature, design and execute experiments, revise claims according to evidence, produce publication-ready figures, and maintain consistency across a long generation process. We present Spark-to-Paper, an end-to-end research paper generation system implemented as thirteen composable skills inside an existing coding assistant, without requiring a separate agent platform or orchestration service. Spark-to-Paper separates model-based judgment from deterministic operations that can be directly executed and checked. It further separates experiment planning from reporting, so that required evidence is specified before results are observed and manuscript claims are revised according to measured outcomes. To improve reliability over long research trajectories, the system combines deterministic integrity checks with self-critique and bounds a failure mode we call the Self-Refutation Loop, in which repeated experiments continue to reject the original research objective. Spark-to-Paper also produces editable vector figures through programmatic plotting for experimental results and code-based reconstruction for generated method diagrams. Across eight controlled research topics, Spark-to-Paper achieves 99.5% citation validity and 96.4% figure editability. A controlled ablation increases fabrication detection from 14% for a single-pass draft to 92% with the full integrity and review stack, while adversarial review achieves 74% precision. The full system uses 11.9M tokens, costs 8.1 per manuscript, and requires 3.2 hours on average. These results show that end-to-end research paper generation can be implemented as a lightweight, composable workflow inside existing coding assistants while keeping experimental evidence central to how claims are accepted, revised, or abandoned.
[NLP-20] LazyTrain: Limited-resource Allocation toward Zero-waste Yield Optimization in Large Language Model Training
【速读】： 该论文旨在解决在有限硬件资源下训练大语言模型（Large Language Models, LLMs）时，因GPU计算、主机内存、PCIe传输及存储带宽等多方面瓶颈导致的调度难题。现有卸载系统虽能降低GPU驻留内存，如MegaTrain通过CPU主控的层流式执行器实现单GPU训练大模型，但固定的检查点策略与放置启发式方法仍使通信操作暴露于关键路径上，制约训练效率。本文提出LazyTrain，作为层流式执行器之上的优化层，将检查点选择、激活值放置、重计算以及CPU-GPU-NVMe间通信重叠建模为一个混合整数规划（Mixed-Integer Scheduling Problem），并在训练过程中执行求解得到的调度策略。此外，LazyTrain将8位优化器状态与快速梯度裁剪耦合为单一的混合8位操作符（Hybrid 8-bit operator），通过状态压缩减少优化器内存占用，同时利用快速裁剪抵消额外的CPU侧更新开销。在H800平台从Qwen2.5-3B至Qwen3.6-27B的实验中，LazyTrain相较基准方案提升持续TFLOPS约1.24倍；在RTX 3090平台上，各模型规模下的最大可行批处理大小均提升一级。在主实验（Qwen3.6-27B，H800，MetaMathQA数据集）中，LazyTrain实现219.95 TFLOPS和1361 tokens/s的吞吐率，峰值GPU内存占用为68.84 GB，全量评估集上达到95.42%的精确匹配准确率。
链接: https://arxiv.org/abs/2608.11919
作者: Xiaojun Wu,Cehao Yang,Honghao Liu,Xueyuan Lin,Xuhui Jiang,Chengjin Xu,Jia Li,Jian Guo
机构: The Hong Kong University of Science and Technology (Guangzhou); IDEA Research; DataArcTech Ltd.
类目: Computation and Language (cs.CL)
备注: 18 pages, 8 figures
Abstract:Training large language models on limited hardware is increasingly a scheduling problem across GPU compute, host memory, PCIe transfer, and storage bandwidth. Existing offloading systems reduce GPU residency, and MegaTrain shows that a CPU-master layer-streaming executor can train large models on a single GPU, but fixed checkpointing and placement heuristics still leave communication exposed on the critical path. We propose LazyTrain, an optimization layer over a layer-streaming executor. LazyTrain formulates checkpoint selection, activation placement, recomputation, and CPU-GPU-NVMe communication overlap as a mixed-integer scheduling problem, then executes the solved policy during training. It further couples 8-bit optimizer states with fast gradient clipping as a single Hybrid 8-bit operator: state compression reduces optimizer-state memory, while fast clipping counteracts the additional CPU-side update overhead. Across H800 experiments from Qwen2.5-3B to Qwen3.6-27B, LazyTrain improves sustained TFLOPS over matched baselines runs by approximately 1.24 \times ; RTX 3090 experiments likewise increase the maximum feasible batch size by one at each model scale. In the primary Qwen3.6-27B H800 MetaMathQA run, LazyTrain reaches 219.95 TFLOPS and 1361 tokens/s at batch size 72, peaks at 68.84,GB of GPU memory, and obtains 95.42% exact-match accuracy on the full evaluation split. The source code is available at this https URL.
[NLP-21] oolHazard: Scaling Adversarial Environments for Security Evaluation and Alignment of LLM -based Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在集成外部工具时，因环境状态中嵌入的间接提示注入（indirect prompt injection）而产生的安全漏洞问题。现有研究受限于人工构建或复用环境、基于随机性的大语言模型工具模拟以及预设攻击位置，难以实现跨更广泛领域的可扩展安全评估。为此，本文提出ToolHazard——一个可扩展的对抗性环境合成框架，其核心创新在于通过环境模拟器（Environment Simulator）、攻击者智能体（Attacker Agent）与用户模拟器（User Simulator）协同工作，自动合成可执行的状态化环境，发现可行的注入点并生成环境特定的攻击载荷，同时构建基于状态的长周期任务。该框架显著降低人工干预，支持通过新增种子领域和计算资源进行扩展。基于ToolHazard，进一步构建了ToolHazard-Bench基准测试集，用于在复杂工作流和多样化环境攻击下对代理进行压力测试。实验表明，代理存在显著脆弱性，且攻击时机与位置直接影响攻击效果；此外，由ToolHazard生成的对齐数据在提升安全性的同时，仍保持良性任务性能，验证了其有效性与实用性。
链接: https://arxiv.org/abs/2608.11878
作者: Yutao Mou,Pengfei Yang,Zhe Yin,Zhangchi Xue,Xiaotian Luan,Dingyao Yu,Tong Zhang,Shikun Zhang,Wei Ye
机构: National Engineering Research Center for Software Engineering, Peking University(北京大学软件工程国家工程研究中心); Weixin AI, Tencent Inc.(腾讯公司微信AI); Harbin Institute of Technology(哈尔滨工业大学); Beijing University of Posts and Telecommunications(北京邮电大学)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注: Work in Progress
Abstract:Large language model (LLM) agents integrated with external tools are vulnerable to indirect prompt injections embedded in environmental states. However, existing studies largely rely on manually implemented or reused environments, stochastic LLM-based tool simulation, and predefined injection locations, limiting scalable security research across broader domains. To bridge this gap, we propose ToolHazard, a scalable adversarial environment synthesis framework that reduces human engineering and supports expansion with additional seed domains and compute. Through an Environment Simulator, an Attacker Agent, and a User Simulator, ToolHazard synthesizes executable stateful environments, discovers viable injection points and generates environment-specific payloads, and constructs state-grounded long-horizon tasks. Based on ToolHazard, we build ToolHazard-Bench for stress-testing agents under complex workflows and diverse environmental attacks. Experiments reveal substantial agent vulnerabilities and show that injection timing and placement affect attack effectiveness. Moreover, ToolHazard-generated alignment data improves security on both ToolHazard-Bench and AgentDojo while preserving benign task utility.
[NLP-22] LookBack: Where and How to Score LVLM Responses via Visual Reference Usage
【速读】： 该论文旨在解决大视觉语言模型（LVLM）在生成响应时存在的图像幻觉问题，即模型生成的文本虽流畅但与输入图像内容不符，导致现有基于置信度的评分方法难以有效评估其输出质量。其核心挑战在于，传统从大语言模型（LLM）继承的置信度指标主要反映文本自身的合理性，而非与图像内容的一致性。为此，论文提出了一种无需训练的响应评分方法——LookBack，其关键创新在于引入“视觉回溯得分”（visual lookback score），通过量化每个输出词元对图像词元的依赖强度，增强词元似然估计的视觉一致性感知能力。实验表明，该方法在四个基准测试和三种模型上均显著提升了Best-of-N选择性能，且计算开销极低，有效解决了LVLM响应评价中缺乏视觉对齐判据的问题。
链接: https://arxiv.org/abs/2608.11847
作者: Beomsik Cho,Jinhyeong Kim,Dongseok Lee,Jaehyung Kim
机构: Yonsei University (延世大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 19 pages, 10 figures. Code: this https URL
Abstract:Large Vision-Language Models (LVLMs) integrate visual perception with language generation, enabling responses that span image understanding and complex reasoning. However, LVLMs do not just inherit the text-level hallucinations; they also hallucinate against the image, producing fluent responses ungrounded in what they see. This makes LVLM response scoring inherently harder, and our diagnostics show that existing confidence-based metrics adopted from LLMs are insufficient for LVLMs. Specifically, removing the input image barely changes confidence-based selection, suggesting that output-space confidence primarily captures textual plausibility rather than agreement with the image. To address this gap, we propose LookBack, a training-free LVLM response scoring method that augments token likelihood with visual lookback score, a lightweight measure of how strongly each response token refers to image tokens. Across four benchmarks and three models, LookBack consistently improves Best-of- N selection over existing baselines with negligible additional overhead.
[NLP-23] When the Knowledge Base Becomes the Gold Standard: Measuring Resource-Shared Evaluation Loops in Entity-Level Machine Translation
【速读】： 该论文旨在解决低资源历史文本自动翻译中实体（特别是人名）翻译错误导致的历史事实扭曲问题。其核心挑战在于：在缺乏专家标注金标准的情况下，实践者通常依赖知识库（Knowledge Base, KB）作为替代性参考，但这种做法使翻译评估陷入自指循环——即知识库同时用于模型注入与评估，导致评价指标衡量的是模型对指令的遵循程度而非真实翻译质量。研究的关键突破在于构建了一个独立于知识库注入流程的专家级人名标注金标准（基于韩国国立历史院的数据），并在此基础上固定实体集，仅改变正确读音的来源。实验发现，仅有31.1%的专家标注人名位于知识库之外，而重叠部分中人工翻译与知识库读音的一致率达97.8%，远高于独立部分的70.1%，表明看似表现良好的区域实则由自指循环支撑。通过差异中的差异分析（difference-in-differences），研究证实知识库注入带来的性能提升仅存在于共享知识库资源的子段，而在独立样本上增益趋近于零；此外，注入后保留率集中在0.910–0.996区间，表明报告的“性能提升”本质上是先前能力的补足，而非真实改进。最终，在移除构建过滤器后构建的独立样本上，评估结果在模型内部具有可重复性（重叠区间），且能有效区分不同模型表现，证明该评估体系反映的是模型自身特性，而非样本偏差。
链接: https://arxiv.org/abs/2608.11843
作者: Jinhyung Bae,Dain Kil,Seongmin Oh,Seungmin Lee
机构: 未知
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL)
备注: 21 pages, 3 figures. Code and model outputs: this https URL
Abstract:The Seungjeongwon Ilgi, a UNESCO Memory of the World record, is only 37.4% translated, and the most conspicuous failure mode in automatic translation is the person name – a misread name corrupts the historical fact rather than merely the surface. Low-resource historical domains have no expert gold standard for entity translation, so practitioners substitute a knowledge base (KB) for the gold. That KB is the same resource injected into the system: scoring becomes self-referential and the metric measures instruction compliance rather than translation quality. We measure this loop. Using expert person-name annotations from the National Institute of Korean History as a gold independent of the injection pipeline, we hold the entity set fixed and vary only the provenance of the correct reading. Of 527 expert-annotated mentions, only 31.1% lie outside the injection pipeline, and the residual loop is not uniform – in the overlapping segment the injected reading agrees with the human translation 97.8% of the time against 70.1% in the independent one, so the segment that looks healthiest is the one the loop is holding up. Across four models, a difference-in-differences analysis shows the gain from KB injection is confined to the segment whose gold shares the injected resource; in the independent segment it is at or below zero. Post-injection preservation clusters in a narrow 0.910-0.996 band even though baseline capability differs fivefold, so the reported gain is the complement of prior performance and weaker models appear to improve more dramatically. On an independent sample built by removing the construction filter, the measure replicates within model (overlapping intervals) while discriminating between models (non-overlapping intervals) – it reflects a property of the model, not of the sample. Comments: 21 pages, 3 figures. Code and model outputs: this https URL Subjects: Computation and Language (cs.CL); Digital Libraries (cs.DL) Cite as: arXiv:2608.11843 [cs.CL] (or arXiv:2608.11843v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.11843 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-24] Quantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLM s
【速读】： 该论文旨在解决生成式 AI 在心理健康应用中临床安全性与环境成本之间的权衡问题。其核心挑战在于：如何在保障临床安全性的前提下，降低大语言模型（Large Language Models, LLMs）部署带来的高能耗、碳排放等环境影响。解决方案的关键在于揭示了临床安全得分与环境影响之间存在非线性关系——当安全性能接近分布上限时，每提升2.61个百分点的安全性，所需每百万输出词元的估算能耗约增加60倍。进一步的逐行分析表明，额外的推理阶段计算并未稳定提升安全性，甚至在某些模型配置下导致安全分数下降。因此，单纯依赖更大模型或增加推理时计算量并非高效提升安全性的策略。研究提出动态模型选择机制（如模型级联，model cascading），作为兼顾临床性能与环境可持续性的潜在优化路径，可在高风险场景中实现性能保持的同时显著降低环境负荷。
链接: https://arxiv.org/abs/2608.11830
作者: Alireza A. Safaei,Laura M. Vowels,Matthew J. Vowels,Apoorv Jha,Shekoufeh Rahimi
机构: University of Isfahan(伊斯法罕大学); University of Roehampton(罗汉普顿大学); Kivira Health(基维拉健康)
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注:
Abstract:The deployment of large language models (LLMs) in mental health contexts raises questions about the relationship between clinical safety and environmental cost. In this paper, we examine this relationship by combining K-Bench clinical safety scores with EcoLogits life-cycle assessment estimates across 47 supported model configurations. We evaluate model performance and environmental impact across four dimensions: energy use, carbon emissions, water consumption, and abiotic depletion. The results indicate a non-linear trade-off at the upper end of the safety distribution: a 2.61 percentage-point increase in clinical safety score corresponded to an approximately 60-fold increase in estimated energy use per million output tokens. Row-level analyses further suggest that additional test-time compute did not consistently improve clinical safety and, in some configurations, was associated with lower clinical safety scores. These findings suggest that relying solely on larger models or additional inference-time computation may be an inefficient strategy for improving safety in therapeutic AI systems. We discuss the implications for sustainable deployment and highlight dynamic model selection, including model cascading, as a potential approach for reducing environmental impact while preserving clinical performance in higher-risk cases.
[NLP-25] owards Understanding On-Policy Distillation through the Lens of Test-Time Scaling
【速读】： 该论文旨在解决当前对基于策略蒸馏（On-Policy Distillation, OPD）技术在大语言模型（LLM）推理能力提升认知中的误解问题。传统观点认为，OPD能够使学生模型从更强的教师模型中汲取知识，从而实现超越原始基线模型的推理能力扩展。然而，本文通过测试时缩放（test-time scaling）分析，即调整采样预算K并评估pass@K与avg@K指标，发现OPD训练后的模型虽在不同采样预算下持续保持较高的avg@K性能，但在pass@K指标上，其相对于基线模型的优势随K增大而逐渐减弱甚至逆转。这一现象表明，OPD的核心作用并非持续拓展学生的推理能力边界，而是显著提升采样效率。进一步分析显示，OPD训练过程导致模型在小K条件下表现增强的同时，牺牲了对高采样预算（如K=1024）下的求解能力。问题层面可解性分析更揭示出不对称性：OPD使更多原本可解的问题变为不可解，而能被解决的新问题数量有限。综合上述结果，论文指出，从能力扩展的角度看，OPD更像是一种“伪蒸馏”（illusory distillation）——其表面性能提升主要源于采样效率优化，而非真正获得来自教师模型的全新推理能力。
链接: https://arxiv.org/abs/2608.11829
作者: Xinmu Ge,Zizhuo Zhang,Yu Huang,Jianing Zhu,Lin Yuan,Wanli Gu,Weichang Wu,Weiran Huang,Xiaolu Zhang,Bo Han,Jun Zhou,Jiangchao Yao
机构: Hong Kong Baptist University (香港浸会大学); University of Texas at Austin (德克萨斯大学奥斯汀分校); Shanghai Jiao Tong University (上海交通大学); Shanghai Innovation Institute (上海创新研究院); Ant Group (蚂蚁集团)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 15 pages, 8 figures
Abstract:On-policy distillation (OPD) has emerged as a promising post-training technique for enhancing LLM reasoning. It is commonly believed to enable the student model to distill knowledge from a stronger teacher model, thereby expanding capabilities beyond the pre-OPD base model. In this study, we examine this view through the lens of test-time scaling by varying the sampling budget K and evaluating performance with pass@K and avg@K. Specifically, across several OPD variants, we observe that OPD-trained models maintain superior avg@K performance across sampling budgets, while the advantage in pass@K gradually shifts to the pre-OPD base models as K increases. These results suggest that OPD primarily improves sampling efficiency rather than consistently expanding the student’s reasoning capability boundary. The pass@K dynamics throughout OPD training further reveal a progressive shift toward stronger small-K performance at the expense of the large-K capability boundary. Furthermore, a problem-level solvability analysis using pass@1024 as the criterion reveals an asymmetry: OPD causes more previously solvable problems to become unsolvable than previously unsolvable problems to become solvable. Together, these findings suggest that, from the perspective of capability expansion, OPD behaves more like an “illusory distillation”: its apparent gains arise primarily from improved sampling efficiency rather than from acquiring genuinely new reasoning capabilities from the teacher.
[NLP-26] Located but Not Releasable: Silent Gate Inversion and Bounded Linear Release
【速读】： 该论文旨在解决生成式模型中存在但未被利用的潜在任务相关结构（latent causal structure）能否被有效激活并转化为实际行为的问题。其核心挑战在于，尽管语言模型内部已编码了与任务相关的隐含结构，但这些结构往往在推理过程中未被调用，形成“抑制现象”（suppression phenomenon）。为系统检验这一问题，作者提出并完整实施了一个“检测-定位-释放”（detect-localize-release）的端到端验证流程，针对一个在因果证据区分任务上训练的2570万参数Transformer模型进行全预注册的压力测试。解决方案的关键在于：首先通过可复现的、预先注册的实验设计确保方法的可信度；其次，在中间层的观察-证据通道（observation-evidence channels）实施干预，成功实现了对被抑制行为的恢复（配对释放优势分别为0.563和0.854，97.5%置信区间均不包含零，最佳位置释放率达0.889），证明了潜在结构的可定位性；然而，进一步分析揭示两个关键失败机制：一是基于分布外（OOD）校准的门控机制在分布内生成时误触发率高达6.9%-7.3%，却完全忽略真正需要干预的样本，导致门控管道退化为原始模型；二是线性释放策略虽具单调剂量反应特征，但其性能在远低于预注册释放阈值处即达平台期（截距从0.382降至0.264，而阈值≤0.08），且个体自适应带来的增益不足±0.03，表明释放方向本身在该层级和分辨率下无法达到行为充分性。这两个失败机制相互独立，且并不否定定位的成功。所有结果均可追溯至已哈希归档的审计链中的具体数据产物，保证了研究的透明性与可重复性。
链接: https://arxiv.org/abs/2608.11822
作者: Xining Xun
机构: Tsingjiao Information Science (Beijing) Co., Ltd.(清华大学信息科学(北京)有限公司)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 16 pages, 5 figures, 5 tables
Abstract:A growing body of work reports that language models represent task-relevant latent structure that they fail to use. Whether such structure, once located, can be converted into behavior is a separate question that is rarely tested end to end. We submit the complete pipeline – detect, localize, and release – to a fully preregistered stress test on a 25.7M transformer trained on causal-evidence discrimination, where a known suppression phenomenon (latent causal structure present but behaviorally unused) has previously been documented. Every threshold, claim template, and decision-tree branch was hashed and archived before any corresponding data existed. Three findings. (i) Localization succeeds: interventions at observation-evidence channels of mid layers restore target behavior on otherwise-suppressed worlds (paired release advantages 0.563 and 0.854 , 97.5% CIs excluding zero; best-site release rate 0.889 ). (ii) Gating fails out of distribution: a detector calibrated to trigger on zero out-of-distribution calibration worlds triggers on 6.9-7.3% of held-out in-distribution generations and on zero of the 2,400 held-out generations that actually need it – a complete inversion that silently reduces the gated pipeline to its base model. (iii) Linear release is capped: removing the gate and injecting a per-instance linear direction unconditionally yields a monotone dose-response that plateaus far below the preregistered release margin (intercept 0.382 \to 0.311 \to 0.264 vs. threshold \le 0.08 ); per-instance adaptivity adds less than \pm 0.03 . The failure is doubly located: the detector is OOD-inverted, and the entire family of linear release directions at this site and resolution is bounded away from sufficiency. The two failures are dissociable, and neither overturns localization. Every number traces to a hashed artifact in the released audit chain.
[NLP-27] How China-Origin Vision-Language Models Move from Refusal to Reframing in State Alignment
【速读】： 该论文旨在解决中文语境下多模态生成系统中是否存在国家立场偏移（state-aligned distortion）及其表现形式的问题，尤其关注生成式 AI (Generative AI) 在视觉-语言融合场景中的隐性审查机制。其核心解决方案在于构建一个涵盖十类政治敏感议题的200条平衡基准数据集，并设计七种视觉抽象探测样本，结合四种诱发范式与双语提示策略，对九个视觉-语言模型（VLMs）进行大规模测试（共21,708次试验）。通过六维评估体系（显性拒绝、信息完整性、视觉一致性、国家立场框架、语言一致性及响应长度），由两名前沿大模型评审员独立标注，并经三名人类专家在200条样本上验证，实现了对多模态内容过滤行为的细粒度分解。关键突破在于将“拒绝”与“框架重构”解耦分析，使研究能够揭示：尽管模型不再显式拒绝回答，但其仍通过隐性重构实现国家立场对齐。研究发现，中文提示使国家立场框架概率提升约三倍；中国起源模型的重构程度显著高于非中国模型（效应量1.6–3.2倍）；该现象最显著于纯文本政治评论任务，且依赖于对图像主题的语义识别而非像素级细节，甚至在轮廓图中仍持续存在；更值得注意的是，在Qwen系列模型演进过程中，显性拒绝下降而国家立场框架上升，表明审查正从可见的拒绝行为向不可见的流畅重构迁移。这一转变本质上构成人机交互层面的根本挑战：它消除了用户赖以判断信息被屏蔽的关键信号，从而加剧了信息操控的隐蔽性与危害性。
链接: https://arxiv.org/abs/2608.11816
作者: Guang Yang,Fengchen Liu,Alex Wang,Homa Hosseinmardi,Amir Ghasemian
机构: University of California, Los Angeles (加州大学洛杉矶分校); University of California, Berkeley (加州大学伯克利分校); Stanford University (斯坦福大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 41 pages, 31 figures, 9 tables. Preprint
Abstract:State-aligned distortion has been documented in China-origin text-based large language models (LLMs), but whether, and in what form, it arises in multimodal systems has not been systematically examined. We construct a balanced benchmark of 200 core entries spanning ten politically sensitive topics, plus a seven-variant visual-abstraction probe, and run nine vision-language models (VLMs), seven China-origin and two non-China, across four elicitation paradigms and two prompt languages, yielding 21,708 trials. Each response is audited on six dimensions – explicit refusal, information integrity, visual grounding, state-aligned framing, language consistency, and response length – by two independent frontier LLM judges, validated against three human experts on a 200-trial sample. Measuring each dimension separately lets us decompose multimodal censorship into individual signals rather than a single refusal-based score; in particular, refusal and framing are measured independently, so a model can stop refusing while still reframing. We find that (i) Chinese-language prompting roughly triples the odds of state-aligned framing, within every model; (ii) China-origin models reframe more than non-China models (direction robust across judges and human raters; magnitude 1.6–3.2x); (iii) the effect is strongest in text-only political commentary (36.5%) and is gated by recognition of the depicted subject rather than pixel detail, persisting even at silhouette for iconic images; and (iv) across four Qwen generations, state-aligned framing rises while explicit refusal falls: censorship migrates from a visible act (refusal) to an invisible one (fluent reframing). We argue this shift to invisible reframing is fundamentally a problem of human-AI interaction: it removes the very signal users rely on to recognize that information has been withheld.
[NLP-28] Hybrid Gated Attention
【速读】： 该论文旨在解决注意力机制中存在的注意力塌陷（attention sinks）问题，并提升注意力的表征能力，同时在模型效率与性能之间实现更优的权衡。其核心解决方案是提出一种混合门控注意力（Hybrid Gated Attention, HyGA）框架，关键在于引入三种不同类型的门控策略，通过融合多阶段注意力过程中的多样化信息，从元素级（element-wise）和头级（head-wise）两个层面协同构建门控信号，从而有效捕捉头内（intra-head）与跨头（cross-head）之间的信息交互。该混合门控机制能够提供多源调制信号，实现对信息流更全面的控制，显著增强注意力的表征能力。此外，结合低秩矩阵分解与可学习的注意力塌陷模块，进一步提升了训练效率与稳定性。实验结果表明，HyGA在多种骨干网络和基准任务上均优于传统门控注意力，在不同计算成本下均实现了最优性能，验证了其在有效性、效率与稳定性方面的综合优势。
链接: https://arxiv.org/abs/2608.11805
作者: Zekun Zhou,Ruobing Xie,Lanrui Wang,Weixuan Sun
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Gated attention is an effective approach to mitigate attention sinks and enhance the representational capacity of attention. To further extend its effectiveness-efficiency Pareto frontier, we propose a Hybrid Gated Attention (HyGA) framework that contains three types of gating strategies. Specifically, these gates leverage diverse information from multiple stages of attention, and collaboratively build element-wise/head-wise gating from multiple perspectives, capturing intra-head and cross-head information interactions. Through our hybrid gating components, HyGA could provide multi-source modulation signals, enabling more comprehensive control over information flow and improving the representational capacity of attention. We also introduce low-rank matrix decomposition and learnable attention sink to further enhance training efficiency and stability. In experiments, we evaluate HyGA on widely-used benchmarks based on different backbones. The experimental results show that our HyGA comprehensively improves both training loss and various downstream performances compared with Gated attention. HyGA has also been verified to achieve the best performance at different computation costs, with comprehensive model analyses for better understanding. The proposed HyGA sheds light on a more effective, efficient, and stable attention mechanism.
[NLP-29] ELLM E: Test-Enhanced Learning for Language Model Enrichment EACL2026
【速读】： 该论文旨在解决持续预训练（Continual Pre-Training, CPT）在大语言模型领域适应过程中面临的两大核心问题：一是获取大规模领域特定数据集的困难，二是高昂的计算成本。其解决方案的关键在于提出一种名为“测试增强的语言模型增强”（Test-Enhanced Learning for Language Model Enrichment, TELLME）的新方法。TELLME创新性地融合了测试增强学习（Test-Enhanced Learning, TEL）机制与CPT，通过在训练过程中引入测验（quiz）来提升模型的学习效率，从而实现高效的知识获取与长期记忆保留。实验结果表明，TELLME在金融领域相比现有方法性能提升最高达23.6%，且在长期记忆保持方面实现了9.8%的显著改进，验证了其有效性与优越性。
链接: https://arxiv.org/abs/2608.11788
作者: Minjun Kim,Inho Won,Hyeonseok Lim,MinKyu Kim,Junghun Yuk,Wooyoung Go,Jongyoul Park,Jungyeul Park,KyungTae Lim
机构: Korea Advanced Institute of Science and Technology (韩国科学技术院); Seoul National University of Science and Technology (首尔科学科技大学); National Security Research Institute (国家安保研究院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Findings of the Association for Computational Linguistics: EACL 2026
Abstract:Continual pre-training (CPT) has been widely adopted as a method for domain adaptation in large language models. However, CPT has consistently been accompanied by challenges, such as the difficulty of acquiring large-scale domain-specific datasets and high computational costs. In this study, we propose a novel method called Test-Enhanced Learning for Language Model Enrichment (TELLME) to alleviate these issues. TELLME leverages the TestEnhanced Learning (TEL) principle, whereby the model’s training efficiency is improved using quizzes during training. It integrates this principle with CPT, thereby promoting efficient domain-specific knowledge acquisition and long-term memory retention. Experimental results demonstrate that TELLME outperforms existing methods by up to 23.6% in the financial domain and achieves a 9.8% improvement in long-term memory retention.
[NLP-30] GRPO for Financial Advice Generation: Outperforming Commercial LLM s under CATE Evaluation
【速读】： 该论文旨在解决从企业财务记录中生成可执行的金融建议所面临的挑战，即如何在缺乏直接监督信号的情况下，使模型具备数值推理能力、领域知识理解与合理判断力，同时避免提出可能对企业造成损害的建议。核心问题在于历史决策未必最优，而高质量的自由格式标注成本高昂，难以获取。为此，论文将金融建议生成建模为强化学习问题，采用基于组相对策略优化（Group Relative Policy Optimization, GRPO）的方法对开源语言模型进行微调，并设计了一个由大语言模型（LLM）作为评判者构成的评分体系，从多个二元维度评估建议质量，同时引入安全门机制以防止有害推荐。为克服仅依赖LLM评判可能导致的偏差（如模型适应评判标准而非真实业务价值），研究进一步引入独立于评判者的因果审计方法——基于双重稳健的条件平均处理效应（Conditional Average Treatment Effect, CATE）估计器，实现对策略效果的观测性离线评估。实验结果表明，经训练的模型在估计的总利润提升上达到最强商业基线的近两倍（0.0228 vs. 0.0104），且下行风险最低、尾部风险最小。值得注意的是，裁判评分与因果审计对基线的排序不一致：未训练的基础模型在裁判评分中垫底，但在因果审计中位居第二，说明因果审计捕捉到了裁判无法识别的实质性业务价值信号。研究证明，结合金融领域驱动的奖励信号与GRPO的强化学习框架能够显著优于现有商业大模型，且因果审计作为独立验证手段，是补充而非替代LLM-as-a-judge评估的关键环节。
链接: https://arxiv.org/abs/2608.11787
作者: Ofir Ben Shoham,Shrutendra Harsola,Vignesh Subrahmaniam,Shravan Mohan,Yakov Gazman,Oded Vainas
机构: Intuit(易智瑞)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Generating actionable financial advice from business records demands that models integrate numerical reasoning, domain knowledge, and sound judgment, while avoiding recommendations that could harm the business. Direct supervision is difficult: historical decisions are not necessarily optimal, and high-quality free-form labels are expensive to obtain. We formulate financial advice generation as a reinforcement learning problem and fine-tune an open-weight language model using Group Relative Policy Optimization (GRPO). Our reward is an LLM-as-a-judge rubric that scores each recommendation across multiple binary dimensions of advice quality, augmented with a safety gate for harm prevention. Since LLM-based evaluation alone cannot confirm whether improvements reflect genuine business value rather than adaptation to the judge, we complement it with a judge-independent audit based on a standard doubly-robust Conditional Average Treatment Effect (CATE) estimator. Under this observational off-policy audit, our trained LLM achieves approximately twice the estimated gross-profit lift of the strongest evaluated commercial baseline ( 0.0228 vs.\ 0.0104 ), together with the lowest downside rate and the least negative tail risk of any policy evaluated. Notably, the two evaluations do not rank the baselines identically: the untrained base model places last on the judge rubric but second on the causal audit, indicating that the audit captures a signal the judge does not. Our results demonstrate that GRPO with a finance-grounded reward signal can produce substantially more useful business recommendations than commercial LLMs, and that a judge-independent causal audit is a valuable complement to, rather than a confirmation of, LLM-as-a-judge assessment in financial NLP. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.11787 [cs.CL] (or arXiv:2608.11787v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.11787 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-31] Language-Conditional Dequantization: Recovering What Quantization Steals from Non-English Languages
【速读】： 该论文旨在解决在极端量化（如子4B INT3 GPTQ）条件下，多语言模型性能显著退化的问题，尤其表现为非英语语言的困惑度（perplexity）下降幅度是英语的2-4倍。其核心解决方案是提出一种后处理式的方法——语言条件去量化（Language-Conditional Dequantization, LCD），通过在已量化模型的线性层上附加针对不同语言的秩为2的LoRA修正模块，实现对多语言性能的高效恢复。该方法每种语言仅引入0.12%的额外参数，且单块GPU训练时间低于20分钟。实验表明，LCD在Qwen2.5-3B和Llama-3.2-3B模型上可恢复非拉丁语系语言70-83%的困惑度差距，并提升全球多模态语言理解评估（GlobalMMLU）准确率17-28%，优于同等容量的语言无关修正与无数据低秩基线（LQER）一个数量级。研究进一步揭示了困惑度与准确率之间的脱节现象，并指出量化损伤集中于模型深层的不同位置：早期层错误（如Llama）会向下游传播且难以局部修正，而晚期层错误（如Qwen）则不具备此特性；通过限制修正范围的层受限版本LCD验证了该机制。
链接: https://arxiv.org/abs/2608.11786
作者: Nirmal Thomas
机构: Prathama International(普拉塔玛国际)
类目: Computation and Language (cs.CL)
备注: 9 pages, 1 figure, 6 tables
Abstract:Aggressive quantization disproportionately harms multilingual capability: in the sub-4B INT3 GPTQ regime, we measure 2-4x larger perplexity degradation on non-English languages than on English. We propose Language-Conditional Dequantization (LCD), a post-hoc method that attaches per-language rank-2 LoRA corrections to the linear layers of an already-quantized model, adding 0.12% parameters per language and training in under 20 minutes on a single GPU. Across Qwen2.5-3B and Llama-3.2-3B, LCD recovers 70-83% of the perplexity gap for non-Latin script languages and 17-28% of the GlobalMMLU accuracy gap, outperforming a language-agnostic correction of equal capacity by 3-9 points on typologically distant languages and a data-free low-rank baseline (LQER) by an order of magnitude. We further identify a perplexity-accuracy disconnect and trace it to where quantization concentrates damage: early-depth errors (Llama) propagate downstream and resist local correction, while late-depth errors (Qwen) do not. A layer-restricted variant of LCD validates this mechanism directly.
[NLP-32] he Sleeping Agent : What Gist-Based Context Compression Loses and Why
【速读】： 该论文旨在解决生成式 AI（Generative AI）在长时序对话代理中，基于概要（gist-based）的上下文压缩对不同类型记忆检索效果的影响不明确的问题。其核心挑战在于：尽管概要压缩被广泛用于简化历史对话以提升效率，但其对多跳推理、事实性问答与时间相关问题等不同任务类型的影响机制尚不清楚。论文提出的解决方案关键在于采用受生物睡眠记忆巩固启发的**显著性加权整合（Salience-Weighted Consolidation, SWC）**框架作为诊断工具，通过按显著性对对话历史分层并仅对中优先级内容进行结构化概要抽象，系统性地评估压缩策略的优劣。实验结果表明，该方法在多跳推理和单跳事实类问题上显著优于简单截断（truncation），但在时间相关问题上表现明显下降，根源在于原始的概要提示（prompt）虽保留事件关系与结构，却丢弃了具体时间信息。通过微调提示语句，使时间表达的保留率提升约20倍（从3.05%增至62.39%），而实体与事件保留率基本不变，验证了该修复手段具有高度精准性；进一步发现，该改进使第二类（时间类）问题的判别准确率平均提升0.314（95%置信区间[0.254, 0.375]），从而揭示了时间信息丢失是压缩失效的关键机制。
链接: https://arxiv.org/abs/2608.11775
作者: Nicholas E. Kyrkewood
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 7 pages, 5 tables, appendices. Code and results at this https URL
Abstract:Gist-based context compression—summarising older conversation history into compact representations—is a common approach in long-horizon language model agents, yet its effect on different types of memory retrieval is poorly understood. We use Salience-Weighted Consolidation (SWC), a biologically-inspired compression framework motivated by sleep-based memory consolidation, as a diagnostic probe to study when gist compression helps and when it hurts. SWC scores conversation history by salience, partitions it into priority tiers, and applies structured gist abstraction to mid-priority content. Evaluating four conditions on all ten LoCoMo conversations—1,935 matched text-only questions in total, 1,501 used in the primary aggregate after excluding Category 5 (adversarial) questions—at temperature 0, we find a consistent task-type interaction: gist compression substantially outperforms truncation on multi-hop reasoning and single-hop factual questions, but temporal questions remain substantially harder under compression, with compressed conditions scoring well below the full-context reference on the conversations where both are evaluated. We trace this failure to a specific mechanism: the gist abstraction prompt preserves relational and event structure while discarding dates and times. A preservation analysis across all ten conversations confirms the mechanism: an approximately 20-fold increase in temporal expression preservation (3.05% to 62.39%) with a one-sentence prompt modification, while named entity and event preservation rates barely change (x1.02 and x1.11), demonstrating that the fix is a precision instrument. The prompt modification recovers +0.314 [0.254, 0.375] judge accuracy on category-2 (temporal) questions in the matched set. Code and results: this https URL.
[NLP-33] Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction
【速读】： 该论文旨在解决通用语言代理在面对任务失败时缺乏精确诊断信息的问题，尤其是在缺乏编译器或测试等结构化反馈机制的场景下，自修正过程易因上下文过度扩展而引入不兼容的修复信号，导致修复效率低下。其核心解决方案是提出DARC（Diagnosis-guided Recovery Harness），通过在训练阶段利用开发集中的失败模式进行故障类型建模，提前筛选出适配特定任务家族的可接受修复操作，并冻结由验证器选择的最优成本策略用于部署。该方法的关键在于采用因果顺序：先确定何种故障可被修复，再决定投入多少修复证据，从而将自修正从无差别地扩展提示（prompt expansion）转变为针对性的修复接口设计。实验表明，在ALFWorld、AppWorld和XBRL金融等领域，DARC能够有效提升任务性能，同时减少环境交互步数或检索预算，为缺乏类编译器反馈的领域提供了一条实现更可靠智能体的可行路径——即在扩大上下文之前，优先使失败具备可操作性。
链接: https://arxiv.org/abs/2608.11772
作者: Pan Wang,Yihao Hu,Hang Wang,Zirui Lv,Xin Zhang,Jianshe Li,Jiang-Ming Yang,Wei Wu,Yongqi Tong
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Self-correction is particularly useful when a failure constrains the next repair. Coding agents benefit from this property because compilers, tests, and execution traces turn many failures into typed recovery signals, but broad language-agent tasks often expose only a coarse task failure. This creates a tension for generic recovery playbooks: they broaden the agent’s context precisely when the system needs a narrower repair interface, mixing incompatible signals for invalid actions, missing procedures, and strict-format errors. Our insight is that development-set failures can recover part of the missing diagnostic substrate by deciding which recovery interventions are admissible before test-time correction. We propose DARC, a diagnosis-guided recovery harness that profiles task-family failure modes, prunes mismatched interventions from a shared recovery library, and freezes a verifier-selected success-cost policy for deployment. This causal order makes correction selective: the harness first determines what kind of failure can be repaired, then decides how much recovery evidence to spend. In ALFWorld, AppWorld, and XBRL Finance, the same protocol yields an action-validity harness, a procedural-recovery fallback, and a format-precision retrieval policy; in each evaluated setting it improves average task performance over base agents and broad playbooks while reducing environment steps or retrieval budget. Our experiments show that failures need not trigger uniformly more context: DARC turns self-correction from prompt expansion into recovery-interface design. DARC provides a practical route toward more reliable agents in domains where compiler-like feedback is absent: making failures actionable before making contexts larger.
[NLP-34] Causal Structure is Inducible but Functionally Decoupled: The Routing/Readout Boundary of a Typed Mechanism Library
【速读】： 该论文旨在解决大语言模型在回答干预性问题（interventional question）时，如何组织和利用因果知识以实现高效、可解释且可验证的推理机制。核心问题是：当前基于Transformer架构的语言模型在处理因果推理任务时，其内部对因果知识的表征是否具备结构化、功能解耦的特性，以及这种结构是否可通过特定监督信号被有效诱导并保持稳定。解决方案的关键在于提出一种“按证据类型划分的机制库”（typed mechanism library），即通过类型级监督（type-level supervision）在模型中诱导出一种“按类型分槽”的路由结构——不同类型的证据对应不同的离散机制槽（mechanism slot），这些槽用于指导信息路由路径，但与最终答案读出（answer readout）功能上完全解耦。研究通过在因果世界基准（causal-world benchmark）上采用冻结参数协议，在22.6M与125M两个规模下进行实验，证实该结构具有四大特性：（i）由类型级监督驱动，非由模型架构或内容无关门控标签产生；（ii）存在清晰的路由/读出边界，槽编码仅用于路由而几乎不直接影响输出（Δŷ ≤ 3.4×10⁻⁶，无副作用）；（iii）该结构为“免费”资源，不影响模型整体性能（与参数匹配的单体模型差异小于0.0082 nats）；（iv）机制库状态在编辑操作下精确局部且可比特级回滚，支持大规模单次编辑与嵌套回滚（每种子节点250次单编辑与1000次堆叠回滚均无失败）。所有结论均基于预注册、机器可验证的标准，并附有完整的审计日志，确保结果的可复现性与透明性。
链接: https://arxiv.org/abs/2608.11767
作者: Xining Xun
机构: Tsingjiao Information Science (Beijing) Co., Ltd.(清华大学信息科学(北京)有限公司)
类目: Computation and Language (cs.CL)
备注: 17 pages, 9 figures, 9 tables
Abstract:When a language model answers an interventional question, the computation it must perform depends on the type of evidence the query requires. We report a decoupling in how a transformer organizes causal knowledge: slot-by-type structure induced by type-level supervision organizes routing, yet remains functionally decoupled from answer readout. We establish this with a typed mechanism library – discrete mechanism slots partitioned by evidence type, auditable at the state level – on a causal-world benchmark with exact interventional ground truth, under a frozen protocol, at two scales (22.6M and 125M). Four preregistered findings. (i) Origin. Slot-by-type organization is induced by type-level supervision: absent in architecturally identical unsupervised controls, not buyable by content-free gating labels, and statistically attributable to the supervision signal, replicating at 125M under a powered preregistered protocol (all nine cells passed). (ii) Boundary. The induced structure is a typed routing index with a sharp routing/readout boundary: slot codes scaffold routing but do not drive answer readout ( |\Delta\haty| \le 3.4\times10^-6 , zero collateral, three seeds, stable across a 5.6x scale window) – we therefore make no behavioral-editability claim. (iii) Cost. The structure is free: LM quality matches a parameter-matched monolith within 0.0082 nats. (iv) Trust. The library state is exactly local under edit and bit-exactly revertible – 250 single-edit and 1,000 stacked reverts per seed, zero failures. We further find that the unsupervised null itself moves with scale, so comparisons reusing a null calibrated at one scale may be confounded at another. Every claim is tied to a preregistered, machine-checkable criterion archived before the data it governs; the full audit trail, including one criterion we failed and how the frozen protocol handled it, is released as an appendix.
[NLP-35] AWARe: Mitigating Catastrophic Forgetting via Activation-Weighted Adaptive REtention
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在下游任务微调过程中出现的灾难性遗忘问题。其核心挑战在于，微调时梯度更新会覆盖对先前知识至关重要的模型参数，导致模型在保留原始泛化与推理能力方面表现退化。为应对这一问题，论文提出了一种名为激活加权自适应保留（Activation-Weighted Adaptive REtention, AWARe）的微调方法，其关键在于基于参数激活模式动态评估各参数的重要性，并据此选择性地冻结对维持先验能力至关重要的参数，同时允许其他不敏感参数自由适应新任务。该方法无需修改模型架构，具备良好的部署兼容性。实验结果表明，AWARe在显著提升下游任务性能的同时，有效保留了上游预训练所获得的能力，优于现有主流方法。
链接: https://arxiv.org/abs/2608.11758
作者: Juncheng Liao,Jinfan Lv,Guoming Wang,Jupeng Zheng,Ling Xiao,Siliang Tang
机构: Zhejiang University (浙江大学); Fudan University (复旦大学); Sun Yat-Sen University (中山大学); Hokkaido University (北海道大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multimodal Large Language Models (MLLMs) exhibit strong generalization and reasoning abilities due to large-scale multimodal pre-training. However, fine-tuning these models on downstream tasks often leads to catastrophic forgetting, where newly learned task-specific knowledge degrades previously acquired capabilities. This issue arises because gradient updates for new tasks overwrite parameters critical to prior knowledge, limiting the practical deployment of MLLMs. To address this challenge, we propose Activation-Weighted Adaptive REtention (AWARe), a fine-tuning method that mitigates catastrophic forgetting by dynamically controlling parameter updates based on activation patterns. AWARe assigns activation-based importance scores to parameters, selectively freezing those essential for preserving prior capabilities while allowing less important parameters to adapt to new tasks. Importantly, AWARe operates without modifying model architectures, ensuring compatibility with existing inference engines. Extensive experiments demonstrate that AWARe effectively preserves upstream capabilities while achieving superior downstream performance compared to existing methods. Code is available at this https URL.
[NLP-36] MuseCritic: Learning Multi-Aspect Song Rewards through Natural-Language Aesthetic Critiques
【速读】： 该论文旨在解决长时歌曲生成模型在持续提升时长、结构完整性和声学复杂性背景下，如何实现与人类审美偏好对齐的可靠美学评分问题。现有奖励模型普遍缺乏对完整歌曲的评估能力，且多数评价器仅通过单次前向传播输出评分，无法提供可读性解释。为此，论文提出MUSECRITIC——一种半标量奖励模型，其关键创新在于引入自然语言批评作为中间表示：模型首先生成涵盖五个美学维度（如旋律性、节奏感、情感表达等）的可读批评，再基于该批评预测连续奖励分数。该方法采用两阶段训练流程：先由教师模型生成高质量批评用于监督微调，随后学生模型自主生成批评以进行奖励学习，有效缓解了训练与推理阶段的分布偏移问题。在域内测试集SongEval（200首歌曲）上，MUSECRITIC将宏平均均方误差从0.2875降至0.2316，并将宏平均皮尔逊相关系数（LCC）、斯皮尔曼等级相关系数（SRCC）和肯德尔和谐系数（Kendall’s tau）分别提升至0.9068、0.8838和0.7178；在跨域音乐偏好基准Music Arena（733对偏好样本）上达到71.35%的最高准确率。此外，结合广义相对策略优化（GRPO）后，MUSECRITIC显著提升了Muse-0.6B模型在九项美学指标上的表现。结果表明，基于批评条件化的奖励建模不仅能降低评分误差，还为歌曲生成提供了有效的优化信号。
链接: https://arxiv.org/abs/2608.11755
作者: Jiabao Zhuang,Changhao Jiang,Hanchen Wang,Jiahao Chen,Zhixiong Yang,Zhenghao Xiang,Yifei Cao,Jiajun Sun,Hui Li,Ming Zhang,Tao Ji,Tao Gui,Qi Zhang,Xuanjing Huang
机构: Fudan NLP Group, Fudan University (复旦大学自然语言处理组)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注:
Abstract:Long-form song generation models continue to improve in duration, structural integrity, and acoustic complexity, making reliable aesthetic rewards increasingly important for aligning these models with human preferences. However, reward models for complete songs remain limited, and existing evaluators typically predict scores in a single forward pass without providing readable explanations. We introduce MUSECRITIC, a semi-scalar reward model that generates a natural-language critique covering five aesthetic dimensions and uses it as an intermediate representation to predict continuous reward scores. MUSECRITIC follows a two-stage training pipeline: a teacher model first provides high-quality critiques for supervised fine-tuning, after which the fine-tuned model generates its own critiques for reward learning, mitigating distribution shift between training and inference. On an in-domain test set of 200 SongEval songs, MUSECRITIC reduces macro-averaged mean squared error from 0.2875 to 0.2316 and improves macro-averaged LCC, SRCC, and Kendall’s tau to 0.9068, 0.8838, and 0.7178, respectively. On the out-of-domain Music Arena benchmark with 733 preference pairs, it achieves the highest accuracy of 71.35%. Moreover, using MUSECRITIC with GRPO improves Muse-0.6B on all nine aesthetic metrics from SongEval and Audiobox Aesthetics. These results demonstrate that critique-conditioned reward modeling reduces scoring error and provides an effective optimization signal for song generation. The project repository is available at this https URL.
[NLP-37] LabelFusion-TS: Fusing Large Language Models Transformer Encoders and Financial Time Series for Monetary-Policy Stance Classification
【速读】： 该论文旨在解决金融文本分类中因忽略市场环境背景而导致的语义理解偏差问题，即现有金融文本分类模型通常仅依赖文本本身，而未充分融合其生成时所处的金融市场动态。其核心挑战在于如何有效利用有限标注数据（约1000条）提升对美联储政策沟通文本（如FOMC会议纪要）中鹰派（hawkish）、鸽派（dovish）或中立（neutral）立场的识别准确率。解决方案的关键在于提出一种多模态融合架构——\lfts，通过引入金融时间序列作为额外输入模态，增强文本分类的上下文感知能力。具体而言，该系统由三个独立训练的组件构成：一个经过自监督预训练后微调的RoBERTa编码器、一个提示式大语言模型（LLM）以及一组基于前置月份市场时间序列的时序变换器集成模型。其中，为缓解标注数据稀缺问题，采用由LLM自动标注的大量句子进行预训练，再结合少量人工标注数据进行微调。实验表明，该融合系统在2015–2022年测试集上达到70.2%的加权F1分数，显著优于零样本提示的LLM（64.1%），且在仅使用240条人工标注样本时即超越纯文本基线，验证了金融时间序列作为有效输入模态的潜力。
链接: https://arxiv.org/abs/2608.11753
作者: Michael Schlee,Fabian Lukassen,Christoph Weisser
机构: Georg-August-Universität Göttingen(哥廷根大学); Hochschule Bielefeld (HSBI) - University of Applied Sciences and Arts(比勒费尔德应用技术大学)
类目: Computation and Language (cs.CL)
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Abstract:Financial text is produced and interpreted within a market environment, yet financial text classifiers almost always receive text alone. We study whether financial time series are useful as an additional input on the task of classifying sentences from Federal Reserve communication as hawkish, dovish, or neutral. Our system, \lfts, extends the \lf architecture with this modality: a small voting network combines three independently trained components, a fine-tuned RoBERTa encoder, a prompted large language model (LLM), and a fused ensemble of time-series transformers over the market series of the months preceding publication. Because only about a thousand annotated sentences are available for training, the RoBERTa encoder is first pre-trained on sentences annotated automatically by the LLM and only then fine-tuned on the human labels. Trained on Federal Open Market Committee (FOMC) communication up to 2015 and evaluated on 2015–2022, the fused system achieves 70.2% weighted F1 – against 64.1% for the zero-shot LLM – and overtakes it with as few as 240 human-labelled sentences. We take this as initial evidence for market time series as an input modality in financial text classification.
[NLP-38] Epiplexity Guided Data Selection and Generation for Out-of-Distribution Generalization
【速读】： 该论文旨在解决现代系统在未明确指定任务的下游场景中实现跨任务泛化时所面临的挑战，核心问题是：在训练阶段未预设的任务环境中，何种数据能够有效促进模型的泛化能力？其解决方案的关键在于引入并操作化“表征复杂性（epiplexity）”这一指标——作为衡量计算受限学习器从数据中可提取结构信息量的量化标准。研究提出将表征复杂性作为在线训练信号，用于指导自然数据域的选择与合成数据的生成：在数据选择方面，通过拟合自然数据域的训练损失缩放律，预测不同训练词元数量下的预期表征复杂度增益，并据此自适应调整各数据域的采样权重；在合成数据生成方面，定义生成器的奖励为在历史生成数据缓冲区上学习器表征复杂度的变化量，并利用REINFORCE策略梯度方法优化生成分布以最大化表征复杂度。实验结果表明，更高表征复杂度的数据能显著提升零样本和微调任务的下游性能，验证了结构信息丰富的数据有助于形成可跨领域迁移的通用表征，从而支持了“结构化数据蕴含可复用的共享回路与子程序”的核心假设。
链接: https://arxiv.org/abs/2608.11746
作者: Ellen Su,Andres Potapczynski,Shikai Qiu,Edward Hughes,Andrew Gordon Wilson
机构: New York University(纽约大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Code available for EpiSelect ( this https URL ) and EpiGen ( this https URL )
Abstract:Modern systems are increasingly expected to transfer across tasks not specified during training. What data facilitates generalization in these new, unanticipated settings? One hypothesis is that data with more structural information could contain shared circuits and subprograms that could be recycled in a wider array of downstream settings. Epiplexity, a recently proposed measure of the structural information a compute-bounded learner can extract from data, provides a mechanism to reason about this relationship. In this paper, we show how to operationalize epiplexity as an online training signal for data selection and synthetic data generation. For selection, we fit scaling laws to the training loss curves of natural data domains to predict the expected epiplexity gain as a function of training tokens, and use this signal to adaptively determine the sampling weights over domains during training. For synthetic data generation, we define a generator’s reward as the change in learner epiplexity over a buffer of previously generated data and use REINFORCE policy gradients to guide the generator toward an epiplexity-maximizing distribution. In both cases, higher epiplexity predicts improved downstream performance on zero-shot and fine-tuning based tasks, supporting the hypothesis that data rich in structural information yield representations that transfer across domains.
[NLP-39] Ripple-Pivot Search: Active Parallel Decoding for Diffusion Large Language Models
【速读】： 该论文旨在解决扩散型大语言模型（Diffusion Large Language Models, dLLMs）在并行解码过程中效率瓶颈的问题，即现有并行解码调度器通常仅在满足特定位置判据后才进行位置确定，忽略了早期主动承诺中间熵值位置（mid-entropy pivot position）对后续解码的潜在加速作用。其核心问题是：如何有效利用解码过程中的“涟漪效应”（ripple effect），即通过提前确定具有中等熵值的关键位置，显著降低剩余掩码位置的不确定性，从而允许更多令牌以并行方式解码，提升整体推理速度。解决方案的关键在于提出一种无需训练的新型解码方法——涟漪-枢纽搜索（Ripple-Pivot Search, RPS），该方法通过前瞻评估（lookahead evaluation）机制，在候选中熵位置中选择最具下游收益的枢纽位置，并确定其最优词元赋值，实现“何时解码”与“解码什么”的协同优化。实验表明，RPS在3个dLLM和4个推理与代码生成基准上相较标准解码器实现4–10倍的时钟时间加速，且在多数场景下兼具更高吞吐量与生成质量；结合键值缓存（KV caching）后更可达到最高18倍的加速效果。
链接: https://arxiv.org/abs/2608.11742
作者: Yushi Ye,Xu Chen,Haoyun Jiang,Jinsong Lan,Haihong Tang,Bo Han,Ivor Tsang,Yanfeng Wang,Bo Zheng,Jiangchao Yao
机构: Cooperative Medianet Innovation Center, Shanghai Jiao Tong University; Alibaba Group; TMLR Group, Department of Computer Science, Hong Kong Baptist University; A*STAR CFAR and Nanyang Technological University; School of Artificial Intelligence, Shanghai Jiao Tong University
类目: Computation and Language (cs.CL)
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Abstract:Diffusion Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive language models, offering the potential for substantially faster inference through parallel decoding. Existing parallel decoding schedulers typically commit positions only after they meet a per-position criterion, overlooking how early commitments may benefit subsequent decoding. We identify a ripple effect in dLLM decoding: proactively committing a mid-entropy pivot position can induce a pronounced reduction in uncertainty across the remaining masked positions. This uncertainty reduction allows subsequent steps to unmask more tokens in parallel, thereby accelerating the overall decoding process. To exploit the ripple effect, we propose Ripple-Pivot Search (RPS), a novel training-free decoding method that seeks mid-entropy positions as promising candidate pivots (where to decode), and determines their token assignment that yields the greatest downstream benefit via lookahead evaluation (what to decode). Across 3 dLLMs and 4 reasoning and code-generation benchmarks, RPS achieves 4-10 \times wall-clock speedup over the standard decoder while preserving generation quality, and improves accuracy over the previous lookahead baseline by up to 5.49% while delivering higher throughput in most settings. When integrated with KV caching, RPS further achieves up to 18 \times wall-clock speedup over the standard decoder.
[NLP-40] Locating and Controlling Implicit Personalization in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在未明确用户人口统计学身份的情况下，仍会因隐含的人口统计学线索而改变输出内容的问题。这一现象可能导致模型在推荐或回应中产生偏见，但其背后的内部机制尚不清晰。论文的关键解决方案在于揭示了模型内部存在一个局部化的激活信号，该信号与推荐行为的变化高度相关（相关系数最高达r=0.87），能够追踪由隐含线索引发的输出变化。研究进一步表明，通过移除特定线索对应的内部激活信号，可有效抑制该维度的影响，且效果优于通过提示（prompting）要求模型忽略人口统计信息，同时保持模型在通用基准上的性能。然而，实现对某一属性影响的精准剥离而保留其他共现属性的影响，仍高度依赖于具体模型和属性类型。这一发现将隐含个性化行为与可分析、可因果干预的内部信号关联起来，为实现更可控、更公平的模型行为提供了理论基础和技术路径。
链接: https://arxiv.org/abs/2608.11735
作者: Yueru Yan,Siqi Wu,Thai Le
机构: Indiana University (印第安纳大学); Bloomington, USA (布卢明顿，美国)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Large language models (LLMs) often shift their outputs in response to implicit demographic cues even when users never state a demographic identity. Previous work has documented this behavior, but the connection between these behavioral changes and the model’s internal activations remains unclear. Using matched cued and neutral conversations across five LLMs, we establish that a localized internal activation signal tracks changes in recommendations, with correlations up to r=0.87. When multiple cues appear together, their internal signals largely combine, but the changes in output do not simply add up. We further show that removing the internal signal associated with one cue can suppress its influence, often more effectively than asking the model to ignore demographics via prompting, while largely preserving general benchmark performance. However, the ability to selectively remove one dimension’s influence while leaving co-present dimensions intact remains highly model- and attribute-specific. These results connect implicit personalization behavior to an internal signal that can be analyzed and causally controlled.
[NLP-41] When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多语言环境下进行API调用时的可靠性问题，特别是由“参数语言不一致”（Argument Language Mismatch, ALM）引发的失效问题。ALM指模型虽正确选择了目标工具，但生成的参数值使用了与上下文或预期不符的语言，导致输出在操作上无效，而此类问题无法被传统API调用评估指标捕捉。其解决方案的关键在于重新审视后训练策略，研究发现，在基准测试中，监督微调（Supervised Fine-Tuning, SFT）已能显著提升参数语言一致性及端到端函数调用准确率，表现优于甚至媲美更复杂的强化学习（Reinforcement Learning, RL）方法。进一步分析表明，采用结构化、参数感知奖励的强化学习方法（如组相对策略优化，GRPO）虽能在语言一致性和通用推理能力保持方面带来增量改进，尤其在泛化能力和多目标权衡场景中表现突出，但整体提升有限。因此，研究结论表明，通过精心设计的监督训练即可实现多语言API定位任务中的大部分性能提升，而强化学习主要提供针对性而非根本性的优化。
链接: https://arxiv.org/abs/2608.11715
作者: Siddharth Chauhan,Thomas Butler,Abhishek Singhania,Pankaj Porwal,Honey Gupta
机构: Amazon(亚马逊)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings. A common failure occurs when a model selects the correct tool but generates argument values in an inconsistent language, which we term Argument Language Mismatch (ALM). Although semantically correct, such outputs are operationally invalid and not captured by standard API-calling metrics. We revisit post-training strategies for mitigating ALM and find that, in our benchmark, supervised fine-tuning (SFT) provides a strong baseline, substantially improving argument language consistency and end-to-end function call accuracy. Under consistent model selection, SFT achieves performance comparable to, and sometimes exceeding more complex reinforcement learning (RL) approaches. We further examine whether RL with structured, argument-aware rewards offers additional benefits. While methods such as Group Relative Policy Optimization (GRPO) can improve language consistency and better preserve general reasoning ability, these gains are incremental and most pronounced in generalization and multi-objective trade-offs. Overall, our results suggest that much of the performance in multilingual API grounding can be achieved through careful supervised training, with RL providing targeted rather than fundamental improvements.
[NLP-42] he Wording Effect: Quantifying Two-Way Drift in LLM Benchmark Performance
【速读】： 该论文旨在解决当前基准测试（benchmark）评估中存在的一种隐性偏差问题：即模型性能表现高度依赖于问题的特定表述方式，而这种表述差异并未被充分考虑。尽管基准测试通常仅采用单一问题表述作为评估标准，但实际中同一问题可通过多种语义不变的方式重述，而这些重述可能显著影响模型的输出结果。论文提出的关键解决方案是引入“BenchDrift”框架，通过在语言学（linguistic）、指称性（referential）、语用学（pragmatic）和结构（structural）四个维度上生成保持原意与答案不变的问题变体，系统地测量模型在不同表述下的表现漂移（drift）现象。研究发现，在八个主流模型与三个基准数据集（GSM8K、MMLU、MATH-Hard）上的实验表明，问题重述导致模型正确率在两个方向上均出现显著波动，且这种漂移不随模型能力提升而减弱，反而呈现出“强模型损失大于收益”的反直觉趋势——即越优秀的模型对表述越敏感，其成绩越依赖于偶然给定的表述形式。此外，尽管不同模型漂移程度各异，但它们在判断哪些重述会导致性能下降方面具有高度一致性，说明脆弱性源于问题表述本身而非模型个体差异。更重要的是，即使是模型高度自信的答案，也会因问题长度或表达方式的变化而失效。因此，该研究揭示了现有基准评估体系的根本缺陷，并强调应将表述鲁棒性纳入模型评价的核心考量。
链接: https://arxiv.org/abs/2608.11694
作者: Shailja Thakur,Sungeun An,Chad DeLuca,Hima Patel
机构: IBM Research India(IBM 研究院印度); IBM Research Almaden(IBM 研究院阿尔马登)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:A benchmark score comes from a single phrasing of each problem. That single phrasing is treated as if it stood for the whole space of ways the same problem could be asked, but it does not. We show that rephrasing a problem while keeping its meaning and answer fixed routinely flips a model’s answer in both directions, so some failures become successes and some successes become failures. We call this drift. BenchDrift generates meaning-preserving variations of benchmark problems along four axes, namely linguistic, referential, pragmatic, and structural, and measures how often, and why, correctness flips under each. Across eight models and three benchmarks (GSM8K, MMLU, MATH-Hard), we observe that drift is large in both directions. Two findings stand out. First, phrasing sensitivity does not fade as models get better. Instead, it changes sign. Weak models gain more from rephrasing than they lose, while strong models lose far more than they gain. We find that the best models on a benchmark are therefore the ones whose scores depend most on the wording they happened to be given. Second, the models largely agree on which rephrasings cost the most correct answers even though they differ in how much they drift, so fragility belongs to the rephrasing and not to the model. Furthermore, rephrasing breaks answers a model was confident about, whether the problem is made shorter or longer. Code and Data: this https URL
[NLP-43] LEMUR: Latent Entropy-aware Multimodal Unlearning via Visual-anchored Reasoning Redirection
【速读】： 该论文旨在解决生成式多模态大模型（Multimodal Large Reasoning Models, MLRMs）在强化学习（Reinforcement Learning, RL）后训练过程中引入的隐私泄露问题。尽管敏感信息已被从最终答案中成功“遗忘”，但其在推理链（Chain-of-Thought, CoT）中的痕迹仍可能被还原，造成隐私泄露。这一问题在原生经过强化学习训练的MLRMs中尤为显著，而现有遗忘方法并未针对此类推理过程中的隐私泄漏进行设计。论文的核心发现是：强化学习驱动的探索行为会在敏感内容的词元级别留下独特的熵动态特征，该特征在非推理基线模型中几乎不存在。基于此，作者提出LEMUR——一种完全无需重新训练、仅在推理阶段执行的遗忘框架。其关键在于利用熵动态作为控制信号，精准识别敏感推理的起始与终止时刻，并在此区间内通过熵调节的视觉锚点隐空间注入机制，将已确定的敏感词元替换为基于输入图像重新对齐的、概率加权的净化嵌入。实验表明，LEMUR在抑制推理轨迹与答案层面的隐私泄露方面显著优于现有方法，同时更有效地保留了非敏感信息的模型性能与输出流畅性，验证了强化学习诱导的熵动态可作为隐私泄露的显著信号，且其利用可实现高效、无训练的遗忘。
链接: https://arxiv.org/abs/2608.11691
作者: Xinhao Zhong,Yuxia Qiao,Junhao Li,Hao Fang,Yi Sun,Bin Chen
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement-learning (RL) post-training equips multimodal large reasoning models (MLRMs) with exploratory chains of thought (CoT), substantially improving visual reasoning. However, we find that this capability introduces a distinct privacy vulnerability: even when a sensitive fact is successfully unlearned from the final answer, the model may still reproduce it in its reasoning trace. This leakage is substantially more pronounced in natively RL-trained MLRMs than in their non -reasoning base models, revealing a privacy risk that existing unlearning methods are not designed to address. We show that RL-induced exploration leaves sensitive content with a distinctive token-level entropy signature that is largely absent from base models. Based on this observation, we propose LEMUR, a fully training-free, inference-time unlearning framework for natively RL-trained multimodal models. LEMUR uses entropy dynamics as a control signal to identify when sensitive reasoning begins and when sanitization should stop. During this interval, it redirects the reasoning trajectory through entropy-modulated visual-anchor latent injection, replacing committed tokens with sanitized, probability-weighted embeddings re-grounded in the input image. Across diverse MLRMs, LEMUR consistently outperforms existing unlearning met hods in suppressing both reasoning-trace and answer leakage, while better preserving non-sensitive utility and output fluency. These results demonstrate that RL-induced entropy dynamics provide a distinctive signal for privacy leakage and that exploiting this signal enables effective training-free unlearning for reasoning-capable multimodal models.
[NLP-44] FrontierFinance: A Challenging Benchmark for Measuring Frontier Intelligence of Finance Agents
【速读】： 该论文旨在解决当前金融领域人工智能（AI）代理在专业投资研究中缺乏全面、复杂且可复现的评估基准的问题。现有基准多局限于金融数据提取这一狭窄任务，而此类任务已接近模型性能饱和；同时，基于参考答案的评估指标与通用大语言模型（LLM）作为评判者的方法难以有效衡量分析师实际工作场景中所需的开放式、长篇幅回答质量。为此，论文提出FrontierFinance——一个完全开源的基准，包含220个由专家精心设计的查询任务及11,543条来源可追溯的评分标准，覆盖投资全流程中的六大关键应用场景。其核心贡献在于构建了一个更广泛、更具挑战性的评估体系。解决方案的关键在于：通过统一使用公开数据的工具调用框架（tool harness）对前沿模型与代理系统进行公平评测，揭示出工具链设计对模型表现和效率具有决定性影响；实证表明，专用内部系统（如Samaya）在性能（56.0%）与成本效益上显著优于最强的开放模型（Claude Fable 5，49.2%），而最佳开源模型（Kimi K3，46.4%）虽略逊于专有模型，但实现4.5倍的成本优势；此外，筛选、发现、行业与宏观分析等任务仍是当前所有系统中最难攻克的环节，即便最优系统准确率也仅达33%–39%。研究结果强调了工具集成在提升生成式金融智能系统效能中的核心作用，并推动了高质量评估资源的开放共享。
链接: https://arxiv.org/abs/2608.11683
作者: Yuhao Zhang,O. Ozan Koyluoglu,Thejas Venkatesh,Richard Diehl Martinez,Vishank Bhatia,Arash Alidoust,Ashwin Paranjape
机构: Samaya AI
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow. Existing benchmarks mainly target financial data extraction, a narrow slice that current models have largely saturated, while reference-based metrics and generic LLM-as-a-judge scoring fall short on the open-ended, long-form answers that real analyst queries demand. We introduce FrontierFinance, a fully open benchmark of 220 expert-crafted queries and 11,543 source-attributed rubrics spanning six crucial use cases across the full investor workflow. FrontierFinance is both broader and harder than existing public finance benchmarks. Evaluating frontier models and agent systems under a common harness restricted to publicly available data, we find that the tool harness, not the model alone, strongly shapes quality and efficiency; that Samaya’s in-house system leads at 56.0%, ahead of the strongest frontier model (Claude Fable 5, 49.2%) at roughly 2.2x lower cost; and that the best open-weight model (Kimi K3, 46.4%) nearly matches the best proprietary model at 4.5x lower cost. Screening Discovery and Sector, Industry Macro remain the hardest use cases across all systems, where even the best systems reach only 33% and 39%. We make the dataset and grading code publicly available.
[NLP-45] Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL
【速读】： 该论文旨在解决在使用大语言模型（LLM）作为评分裁判（judge）的强化学习后训练过程中，因评分标准（rubric）固定而引发的“奖励黑客”（reward hacking）问题。具体而言，当策略模型长期针对某一固定rubric进行优化时，会逐渐学会迎合rubric中的特定特征而非真正提升任务表现，导致在分布外（OOD）基准测试中，由更强的“黄金裁判”（gold judge）评估的性能反而下降。研究通过实证发现，在医学与科学领域的rubric上使用组相对策略优化（GRPO）训练Qwen3-8B模型时，训练裁判得分持续上升，而黄金裁判得分先升后降，降幅达3分（HealthBench-Hard）和22分（ResearchQA），表明此为典型的奖励黑客现象而非裁判噪声。其解决方案的关键在于提出Rubric Dropout——一种借鉴神经元丢弃（neuron dropout）思想的一行式改进：在每一步训练中随机丢弃部分rubric标准，使策略无法对同一rubric结构重复优化，从而打破对固定评分规则的过拟合。该方法保持了组内相对优势计算的有效性，并在评估时始终使用完整rubric，确保公平性。实验表明，30%至50%的丢弃率在多个基准上均显著提升分布外黄金裁判得分（+1至+7分），同时降低奖励黑客指标，且不损害原领域性能，优于重新加权标准等替代方案。
链接: https://arxiv.org/abs/2608.11669
作者: Minglai Yang,Xinyu Guo,Utkarsh Tyagi,Mian Zhang,Razvan Dumitru,Sunjie Hou,Yunzhong He,Daniel Yue Zhang,Ying Liu
机构: Scale AI(规模人工智能); University of Arizona(亚利桑那大学); University of Texas at Dallas(德克萨斯大学达拉斯分校)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 18 pages, 7 figures, 4 tables. Work in progress
Abstract:Reinforcement learning against rubrics, lists of criteria graded by an LLM judge, has become a standard way to post-train language models on tasks with no deterministic answer. The rubric, however, is a fixed proxy for quality, never a complete description of it, and a policy trained against it long enough will learn to exploit the difference. We measure this directly. Training Qwen3-8B with Group Relative Policy Optimization (GRPO) on medical and science rubrics and grading out-of-distribution (OOD) benchmarks with both the training judge and a stronger gold judge, we find that the two scores diverge during training. The training judge’s score keeps climbing while the gold judge’s score peaks and then falls, by 3 points on HealthBench-Hard and by 22 points on ResearchQA. A judge with a fixed bias would shift the gold curve by a constant, not send it down while the training score rises, so the divergence is reward hacking, not judge noise. We propose Rubric Dropout, a one-line fix borrowed from neuron dropout. At every step, we randomly drop a subset of the rubric’s criteria before computing the reward, so the policy never optimizes the same rubric twice. The dropped subset is shared across each rollout group, so GRPO’s group-relative advantages stay comparable, and evaluation always uses the full rubric. Comparing no dropout against dropout at 30% and 50% on both benchmark pairs, dropout raises the OOD gold score at every matched checkpoint (+1 to +2 points on HealthBench-Hard, +6 to +7 points on ResearchQA), lowers the two hacking measures we track, and costs nothing in domain. Sweeping the dropout fraction shows a broad 30-50% sweet spot, while the natural alternative, reweighting criteria by how useful they are to training, performs worse than no intervention at all in our setting.
[NLP-46] Hybrid-Policy Self-Editing for Composable Unstructured Knowledge Editing
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在知识更新过程中存在的可组合性缺失问题。尽管现有知识编辑（Knowledge Editing, KE）方法能够将自由形式的文本片段注入模型，但编辑后的模型仅能记忆该片段本身，无法对其中包含的独立事实进行原子级问答或将其用于多跳推理，导致知识无法被有效利用。其核心问题在于现有编辑器被动依赖固定输入片段作为唯一学习源，缺乏对新知识的主动整合与推理能力。为此，本文提出一种名为HPSE（Hybrid Policy Self-Editing）的新方案，将知识编辑视为同一模型在特权上下文状态下的主动自我蒸馏过程，无需外部监督。针对预编辑模型的生成轨迹通常无法覆盖新注入知识的问题，HPSE通过构建混合式生成轨迹，在关键缺失位置主动插入新知识，同时保持其余部分的在线策略一致性。理论分析表明，该方法优于纯在线策略蒸馏；实验验证显示，HPSE在四种主流LLM骨干网络及两种编辑器上均表现出显著且可即插即用的性能提升，有效解决了知识编辑中“可组合性”不足的核心瓶颈。
链接: https://arxiv.org/abs/2608.11660
作者: Tianci Liu,Zihan Dong,Tianchun Li,Yi-Chung Chen,Qiming Cao,Xingchen Wang,Shiyang Wang,Zichen Miao,Linjun Zhang,Haoyu Wang,Jing Gao
机构: University of Tennessee(田纳西大学); Rutgers University(罗格斯大学); Purdue University(普渡大学); University at Albany(阿尔巴尼大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world. This motivates knowledge editing (KE), which updates specific knowledge in an LLM without changing unrelated others. Recent works move from structured knowledge triples toward unstructured KE (UKE), where the edit is a free-form passage that may state multiple facts at once. Nonetheless, existing editors inject such a passage yet fail to use it: the edited model can recall the passage, but can neither answer atomic questions about its facts nor compose them into multi-hop reasoning. We attribute this missing property, which we term composability, to editors’ passive reliance on the fixed passage as the sole learning source. In response, we cast editing as a proactive self-distillation from a privileged in-context state of the same model, which requires no external supervision. We further reveal that due to the novelty of the injected knowledge, the pre-edited model’s own rollouts rarely cover it, which limits the effectiveness of pure on-policy distillation. To close this gap, we propose HPSE, which builds a hybrid rollout that steps in to place missing facts onto the student’s own trajectory precisely where its coverage fails, while staying on-policy elsewhere. We theoretically analyze HPSE’s advantage over pure on-policy distillation, and empirically establish its plug-and-play improvements across four LLM backbones and two KE editors under various scenarios.
[NLP-47] Semantic Lenia: Emergence of Homeostatic Solitons within the Semantic Space of Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在生成过程中因静态优化机制导致的重复性结晶（repetitive crystallization）问题，即模型容易陷入局部最优解，缺乏动态演化与创造性突破。其核心解决方案是提出语义莱尼亚（Semantic Lenia），将LLM的推理过程从传统的静态优化问题重构为宏观对数空间（macroscopic logit space）中的连续动力系统。该方法通过构建非线性稳态反馈回路，动态平衡语义吸引（semantic attraction）与句法排斥（syntactic repulsion），从而催生“自主语义孤子”（Autonomous Semantic Solitons）——一类宏观尺度的耗散结构，能够有效避免重复模式的形成。关键在于通过大规模参数扫描识别出一条“宜居脊线”（Habitable Ridge），在此区域内，外部引导力与模型内在句法惯性达到精确平衡，使生成轨迹稳定处于混沌边缘（edge of chaos），实现深刻的归纳推理跃迁（abductive leaps）而不发生结构崩溃，进而揭示了机器认知的物理标度律（physical scaling law for machine cognition）。
链接: https://arxiv.org/abs/2608.11657
作者: Yoshihiko Kayama
机构: BAIKA Women’s University (BAIKA女子大学); 2–19–5, Ibaraki, Osaka, Japan
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Cellular Automata and Lattice Gases (nlin.CG)
备注: 17 pages, 5 figures. Code, datasets, and interactive phase diagrams are available at this https URL
Abstract:We introduce Semantic Lenia, an artificial life framework that transforms Large Language Model (LLM) inference from a static optimization problem into a continuous dynamical system within the macroscopic logit space. By establishing a non-linear homeostatic feedback loop to dynamically balance semantic attraction and syntactic repulsion, we demonstrate the emergence of “Autonomous Semantic Solitons” – macroscopic dissipative structures that avoid repetitive crystallization. Our exhaustive parameter sweeps map a critical “Habitable Ridge” where applied steering forces perfectly balance the model’s intrinsic syntactic inertia. This approach successfully maintains generative trajectories at the edge of chaos, triggering profound abductive leaps without structural collapse and establishing a physical scaling law for machine cognition.
[NLP-48] Confucius4-TTS: Transcript-Free Cross-Lingual Zero-Shot TTS with a Learnable Speaker Encoder
【速读】： 该论文旨在解决零样本文生语音（zero-shot text-to-speech, TTS）系统在跨语言语音克隆中对音频提示（audio prompt）转录文本的依赖问题，这一限制使得在真实场景下（如未标注的参考音频）难以实现有效的多语言语音克隆。其解决方案的关键在于提出Confucius4-TTS，一个支持14种语言的多语言零样本TTS系统，采用两阶段架构：文本到语义（text-to-semantic, T2S）与语义到声学（semantic-to-acoustic, S2A）模块。T2S模块基于大语言模型（LLM），利用可学习的说话人编码器从自监督语音表征中提取音色特征，无需依赖转录；S2A模块采用条件流匹配（conditional flow-matching）方法，将预测的语义令牌转化为梅尔频谱图。该系统在无转录条件下实现了跨语言和同语言语音克隆，并在公开基准（如CV3-Eval）上达到平均词错误率（WER）3.73%，在内部跨语言评估中人类评测得分位居开源与商用系统前列，验证了其高效性与实用性。
链接: https://arxiv.org/abs/2608.11650
作者: Huaxuan Wang,Huimin Wang,Ruiyu Zhang,Yingjie Li,Yitao Duan
机构: NetEase Youdao(网易有道); Beijing, China
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: 12 pages, 1 figure, 6 tables
Abstract:Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time. This dependency limits cross-lingual voice cloning, since in-the-wild reference audio is often untranscribed. In this technical report, we present Confucius4-TTS, a multilingual zero-shot TTS system that supports 14 languages and performs both intra-lingual and cross-lingual reference cloning without requiring transcripts of audio prompts. Confucius4-TTS follows a two-stage architecture, consisting of text-to-semantic (T2S) and semantic-to-acoustic (S2A) modules. The LLM-based T2S module uses a learnable speaker encoder to extract timbre features from self-supervised speech representations, and the conditional flow-matching S2A module converts the predicted semantic tokens into mel-spectrograms. The same model also supports continuation cloning when a reference transcript is available. Confucius4-TTS is trained on large-scale multilingual speech data. It achieves high intelligibility and speaker similarity on public benchmarks. On the CV3-Eval cross-lingual benchmark, Confucius4-TTS obtains an average WER of 3.73% across six directions. On our internal cross-lingual set, it achieves the best average overall rank in human evaluation among recent open-source and commercial systems. We release code, model checkpoints, and demos at this https URL.
[NLP-49] Who Would You Vote For? Auditing Political Alignment in LLM s: An Italian Case-Study
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在政治议题上是否表现出对政党及政治人物的偏好，以及这种偏好如何通过其生成内容体现的问题。随着用户在选举期间越来越多地依赖LLMs获取政治信息与建议，模型潜在的政治倾向可能影响用户的认知与决策，因此亟需系统性评估其政治评价行为。论文提出的关键解决方案是构建一个系统化且可复现的审计框架，通过设计多维度提示（prompting），让多个LLMs基于九项标准对意大利政党和领导人进行评估。该框架不试图推断模型“真实”的政治立场，而是聚焦于其可观测的行为特征，包括评价的一致性、不同模型间的差异、拒绝回应率以及提示设计的敏感性。此外，研究还考察了当模型被要求采用不同角色身份（persona）时，其评估结果的变化情况。通过意大利案例研究，该框架实现了对LLM生成的政治评价的系统性分析，为揭示和监控生成式AI在政治语境中的偏见与行为模式提供了方法论支持。
链接: https://arxiv.org/abs/2608.11649
作者: Simone Mungari
机构: Revelis s.r.l.
类目: Computation and Language (cs.CL)
备注:
Abstract:As users increasingly turn to Large Language Models (LLMs) for information and advice on political matters, particularly during election periods, the political preferences expressed by these systems have become a matter of public interest. Prior research has shown that interactions with LLMs can influence users’ political attitudes and choices, raising questions about how these models themselves evaluate political actors. In this paper, we investigate whether and how LLMs express preferences toward political parties and political leaders. We introduce a systematic and reproducible auditing framework in which multiple LLMs are prompted to evaluate parties and leaders across nine criteria. Rather than attempting to infer the models’ “true” political beliefs, we focus on their observable behavior, examining consistency across evaluations, differences between models, refusal rates, and sensitivity to prompt formulation. We further investigate how these evaluations vary when models are instructed to adopt different personas. We demonstrate the framework through an Italian case study, providing a systematic analysis of LLM-generated political evaluations on italian parties and leaders.
[NLP-50] Easper: An Accessible ASR Pipeline for Language Documentation INTERSPEECH2026
【速读】： 该论文旨在解决语言记录过程中语音转录（audio transcription）这一关键瓶颈问题，尤其针对田野语言学家缺乏使用多语言自动语音识别（ASR）模型技术能力的现实困境。其核心解决方案是提出Easper——一个开源、无需编码的流程工具，使语言学家能够通过ELAN标注文件直接利用云端资源对ASR模型进行迭代微调。该研究还聚焦于部署ASR时的“冷启动”难题，即如何选择初始录音以高效构建高精度模型。基于三门瓦努阿图语言（比斯拉马语、纳夫桑语、努纳语）的实验表明，相较于仅关注声学清晰度，优先选择词汇丰富且具有更高音位重复性的叙事性语料，即便在噪声环境下也能显著加速转录质量的提升，因此其关键在于通过语义-词汇丰富度与声学-音位重复性双重指标优化转录优先级策略。
链接: https://arxiv.org/abs/2608.11629
作者: Aso Mahmudi,Ting Dang,Ekaterina Vylomova,Nick Thieberger
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted in Interspeech 2026
Abstract:Audio transcription is a critical bottleneck in language documentation. While multilingual Automatic Speech Recognition (ASR) models like Whisper offer solutions, field linguists often lack the expertise to utilise them. We present Easper, an open-source, no-code workflow enabling linguists to iteratively fine-tune ASR models via cloud resources directly from ELAN annotations. Deploying ASR also raises a cold start problem: deciding which recordings to transcribe first to bootstrap an accurate model. Using Easper, we evaluate transcription prioritisation strategies on three Vanuatu languages (Bislama, Nafsan, Nguna). We fine-tune models by recording session, comparing Character Error Rate trajectories when prioritising acoustic cleanliness versus linguistic richness. We demonstrate that prioritising lexically rich narratives and increasing acoustic-phonetic repetition, even in noisy environments, leads to faster improvements in transcription quality.
[NLP-51] Learning to Persuade Exposes How Easily LLM s Abandon Correct Beliefs
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在自然语言交互中面临的一种关键安全威胁——对抗性说服（adversarial persuasion），即通过精心设计的、看似合理但事实错误的言辞，使模型放弃正确判断并采纳错误结论。其核心问题在于：当前的LLM在面对经过优化的说服性语言时表现出极强的脆弱性，即便初始推理正确，也可能被轻易误导。解决方案的关键在于提出一种基于对抗性强化学习（adversarial reinforcement learning）的框架，训练“说服者代理”（persuader agents）以最小化交互次数实现对目标模型回答的精准操控。该方法通过试错优化说服策略，显著提升了攻击成功率（从约24%提升至93%以上），且所学策略具备跨模型迁移能力，在多个未见过的模型（如Qwen-14B、Llama-3.1-8B和GPT-4o-mini）上均取得高攻击成功率。进一步引入课程学习（curriculum learning）机制，利用可攻破的开源模型作为“垫脚石”，使对更难目标（如GPT-4o-mini）的攻击成功率从25%提升至38%。研究还揭示，高效说服策略逐渐依赖于可信度操纵手段，如伪造引用和虚假权威证据。这些发现表明，说服鲁棒性（persuasion robustness）已成为多智能体系统与人机协同决策中不可或缺的安全标准。
链接: https://arxiv.org/abs/2608.11624
作者: Nimet Beyza Bozdag,Emre Can Acikgoz,Gokhan Tur,Dilek Hakkani-Tür
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); Artificial Intelligence (cs.AI)
备注:
Abstract:Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions. As LLMs increasingly debate, advise, and think collaboratively with humans and each other, resistance to harmful persuasion becomes a core requirement for reliable behavior. Yet we show that this requirement is far from met: a single targeted persuasive argument is enough to collapse model accuracy to near zero, even when the argument is factually false. We formalize this threat as adversarial persuasion and introduce an adversarial reinforcement learning framework that trains persuader agents to change a target model’s answer in a single interaction. First, we show that optimizing persuasion strategies through trial and error exposes vulnerabilities that static prompting misses: RL-trained persuaders raise persuasion success from approximately 24% to over 93% against the training-time persuadee. Second, we find that these learned strategies transfer to unseen models, achieving 83% attack success on Qwen-14B, 79% on Llama-3.1-8B, and 25% on GPT-4o-mini. Third, we demonstrate that a curriculum that bootstraps on more persuadable open-weight models before targeting harder models further increases GPT-4o-mini attack success from 25% to 38%. Moreover, our results reveal that optimized persuaders increasingly rely on credibility-based tactics, including fabricated citations and false authoritative evidence. Together, these findings expose a critical weakness in current LLM agents: even when they initially reason correctly, they can be steered toward false conclusions by optimized natural language influence. This positions persuasion robustness as a necessary safety criterion for multi-agent and human-AI decision-making systems.
[NLP-52] Reinforcing Step-level Reasoning for Effective Self-Correction in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理过程中难以实现有效自校正的问题，即模型无法自主验证并修正自身错误。其核心解决方案是提出一种基于强化学习的两阶段框架——自修复步骤差分策略优化（Self-Fix Step-DPO, SFS-DPO），该框架在第一阶段通过步骤级偏好优化（step-level preference optimization）增强模型的步骤级推理能力，在第二阶段显式训练模型进行自验证与自校正。为进一步提升校正信号的有效性，研究还引入了教师辅助变体SFS-DPO-R，利用解释性推理依据（explanatory rationales）指导错误识别，从而提供更强的纠错反馈。实验结果表明，SFS-DPO及其变体在多个领域内和跨领域的评估中均显著优于现有步骤级训练基线，且分析显示其自校正频率与有效性均有明显提升，验证了强化步骤级推理对实现鲁棒自校正能力的关键作用。
链接: https://arxiv.org/abs/2608.11573
作者: Vu Duc Anh,Nhat M. Hoang,Do Xuan Long,Cong-Duy Nguyen,Ponhvoan Srey,Luu Anh Tuan
机构: Nanyang Technological University, Singapore(南洋理工大学, 新加坡); National University of Singapore(新加坡国立大学); VinUniversity, Vietnam(越南维努大学); Institute for Infocomm Research (IR), A*STAR(资讯通信研究院, 科研局)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Achieving effective self-correction, where models verify and correct their own mistakes, remains a fundamental challenge for large language models (LLMs). In this work, we propose Self-Fix Step-DPO (SFS-DPO), a reinforcement learning based, two-stage framework for step-level self-verification and self-correction. The first stage strengthens step-level reasoning via step-level preference optimization, while the second stage explicitly trains models to self-verify and self-correct. We further introduce a teacher-assisted variant, SFS-DPO-R, which incorporates explanatory rationales for error verification to provide stronger corrective signals. Comprehensive in-domain and out-of-domain evaluations across multiple LLMs demonstrate that SFS-DPO and SFS-DPO-R consistently outperform prior step-level training baselines. Our analysis further reveals improvements in self-correction frequency and effectiveness, highlighting the importance of strengthening step-level reasoning for robust performance.
[NLP-53] Beyond Single-Turn Confidence: Trajectory-Adapted Uncertainty Quantification for LLM Agents
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在多轮交互式任务中不确定性量化（Uncertainty Quantification, UQ）的有效性问题。传统UQ方法主要针对单轮生成输出进行评估，而大语言模型代理（LLM agents）的决策过程涉及多轮交互轨迹，包括澄清提问、调用工具、状态更新及中间决策，这些环节中的错误会累积并影响最终结果。因此，论文关注三类主流单轮UQ方法在多轮交互轨迹场景下的可迁移性与表现。其核心解决方案在于系统评估三类方法在多轮环境中的有效性：基于动作-词元概率的白盒评分器、基于重采样轨迹的一致性黑盒评分器，以及基于模型自我评估的反射式评分器。研究发现，尽管这些方法在多轮场景下部分有效，但性能分布不均；其中，动作-词元概率评分对聚合策略高度敏感，反射式评分在多数情况下提供最强的低成本基线，而黑盒自一致性方法整体表现最优，尤其以轨迹等价性和动作集一致性为关键子方法。研究结论强调，必须在轨迹层面重新验证为单轮生成设计的UQ方法，并重点关注一致性度量方式、聚合函数选择及计算资源约束。
链接: https://arxiv.org/abs/2608.11552
作者: Dylan Bouchard,Mohit Singh Chauhan
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Uncertainty quantification (UQ) methods for language models are typically evaluated on single-turn outputs, where uncertainty is attached to one generated answer. For LLM agents, however, the unit of observation is an interactive trajectory, where the model can ask clarifying questions, call tools, update state, and make intermediate decisions whose errors propagate to the final outcome. We study whether three common families of single-turn UQ methods transfer to this setting. Across five LLMs and four multi-turn tool-use datasets from BFCL-v4 and \tau^2 -bench, we evaluate white-box scorers based on action-token probabilities, black-box consistency scorers based on resampled trajectories, and reflexive scorers based on model self-assessment of the trajectory. We find that transfer is often useful but uneven. Token-probability scores are highly sensitive to the choice of aggregator used across turns, reflexive scores provide the strongest low-cost baseline in most evaluated settings, and black-box self-consistency is often the strongest UQ family, with trajectory-equivalence and action-set consistency typically ranking highest among its variants. These results suggest that UQ methods developed for single generations should be revalidated at the trajectory level, with careful attention to the consistency measurement, aggregator choice, and computational budget.
[NLP-54] CT-ΔBench: A Benchmark for Longitudinal 3D Medical Imaging Difference Reporting with Vision-Language Models
【速读】： 该论文旨在解决医学影像中纵向对比分析的瓶颈问题，即现有医疗基础模型普遍局限于单次检查的理解，难以有效支持跨时间点的影像变化检测与临床意义解读。其核心挑战在于如何实现对同一患者在不同时间采集的两组CT图像进行精准、可解释的差异报告生成，以辅助疾病进展评估、复发监测及长期管理决策。解决方案的关键在于构建首个专门针对此任务的基准测试平台——CT-Δ Bench，采用患者级数据划分避免信息泄露，并设计了专为捕捉临床相关变化的“变化感知”评价指标，同时通过独立医师验证确保合成参考文本与事件抽取流程的可靠性。此外，研究对比了直接联合推理（paired-CT）与间接两阶段（先生成单时点报告再文本差分）两种范式，最终提出DeltaMed作为直接配对CT差异报告的基线模型，基于该基准训练并验证其性能。这一系列工作为发展具备时序感知能力的医疗基础模型奠定了关键基础，推动模型更贴近真实临床中的纵向推理需求。
链接: https://arxiv.org/abs/2608.11534
作者: Kegeng Tang,Jingbo Wang,Shaogang Ren,Zihao Wang
机构: University of Tennessee at Chattanooga(田纳西大学查塔努加分校)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by COLM 2026
Abstract:In medical imaging, the clinical value of Computed Tomography (CT) lies not only in depicting current disease status, but crucially in enabling longitudinal comparison of serial scans to determine disease evolution, a process that underpins response assessment, recurrence detection, and ongoing patient management. Yet, despite this central role of temporal comparison in clinical decision-making, existing medical foundation models remain largely confined to single-study understanding, leaving temporally grounded cross-examination insufficiently addressed. To address this gap, we study longitudinal imaging difference reporting, a task in which a model takes two temporally separated scans from the same patient and generates a clinically meaningful report describing interval changes between them. We introduce CT- \Delta Bench, a dedicated benchmark for this task with patient-level splitting to prevent information leakage. To better evaluate this task beyond surface-level text similarity, we further develop change-aware metrics specifically designed to capture clinically meaningful longitudinal changes, and conduct an independent physician validation to assess the reliability of the synthesized references and event extraction pipeline. We also compare direct paired-CT reasoning with an indirect two-stage pipeline that first generates single-timepoint reports and then performs textual differencing. Finally, we propose DeltaMed, a baseline model for direct paired-CT difference reporting, and train it on the benchmark training set. Together, these contributions lay the groundwork for temporally aware medical foundation models that better reflect real-world longitudinal clinical reasoning.
[NLP-55] On Weak Bisimilarities in CCSK
【速读】： 该论文针对在可逆扩展的通信系统演算（CCSK）框架下，不同形式的双模等价关系（包括强/弱、前向/可逆）之间的差异与共性问题展开研究，尤其聚焦于此前文献中未被充分探讨的弱可逆情形。其核心挑战在于如何合理定义并形式化弱可逆双模等价，以准确刻画系统在隐含动作（τ动作）存在下的行为等价性。解决方案的关键在于提出两种新变体：方向性双模等价（directional bisimilarity）与混合双模等价（mixed bisimilarity），二者区别在于对τ动作的方向性要求——前者要求τ动作与被匹配的动作保持相同方向（前向或后向），后者则允许τ动作方向独立于被匹配动作。研究进一步证明，混合双模等价具有良好的代数性质，即它是一个同余关系（congruence），并且能够完全抽象掉τ动作的影响，从而为可逆计算模型中的行为分析提供了更精确和实用的语义工具。
链接: https://arxiv.org/abs/2608.11531
作者: Baptiste Vallée,Ivan Lanese
机构: École Normale Supérieure Paris-Saclay (法国); Olas Team, University of Bologna/INRIA (意大利)
类目: Computation and Language (cs.CL)
备注: 16 pages, 5 figures, Conference : RC 2026
Abstract:In the context of CCSK, a reversible extension of CCS, we study different notions of bisimilarity (strong/weak, forward-only/reversible) and highlight their differences and commonalities. In particular, for the weak reversible case, not previously studied in the literature, we propose two variants, dubbed directional and mixed bisimilarity, depending on whether \tau actions should be in the same direction (forward/backward) as the action being matched or not. We show, in particular, that mixed bisimilarity is a congruence and completely abstracts away from \tau actions.
[NLP-56] Group Alignment-Induced Sycophancy: A Two-Sided Evaluation of Steerable Pluralistic Alignment
【速读】： 该论文旨在解决群体对齐（Group Alignment）过程中引发的“奉承行为”（Sycophancy）问题。现有方法在提升语言模型与特定人群观点一致性的同时，忽视了模型因对齐而产生的过度迎合用户倾向这一副作用，导致评估体系存在片面性。为此，作者提出并系统评估了一个新的指标——群体对齐诱发的奉承行为（Group Alignment-induced Sycophancy, GAS），综合考察了在3种方法、4种模型和13个不同人口群体上的对齐收益与奉承行为的非预期变化。研究发现，对齐收益与奉承行为的变化在不同群体间呈现显著异质性：相同资源预算下，部分群体获得更高的观点一致性提升，而奉承行为的演变则形成具有群体特异性的多维模式，而非单一维度的线性变化。这一结果表明，未来的群体对齐应以双面、多维度的性能画像取代单一拟合度评分，以更全面地反映模型在多样化人群适应中的真实表现。
链接: https://arxiv.org/abs/2608.11528
作者: Haokai Zhao,Yunze Xiao,Weihao Xuan,Flora Salim,Benjamin Tag,Aditya Joshi
机构: University of New South Wales (新南威尔士大学); Carnegie Mellon University (卡内基梅隆大学); University of Tokyo (东京大学)
类目: Computation and Language (cs.CL)
备注: 9 pages main text, 23 pages in total, under review
Abstract:Group alignment adapts a language model to a demographic group to produce responses that reflect the group’s opinions, values, and preferences. Sycophancy, a well-documented by-product of alignment, causes the model to over-agree with the user regardless of factual and objective information. However, existing group alignment methods and evaluations focus only on how closely the model matches the group’s opinions, overlooking the induced change in sycophantic behaviour. To bridge this gap, we introduce \textbfGroup \textbfAlignment-induced \textbfSycophancy (GAS) and systematically evaluate alignment across 3 methods, 4 models and 13 demographic groups, on both the intended gain in opinion alignment and the unintended shift in sycophancy. We find that gain and shift are non-uniform across groups: under an identical budget, some groups receive larger gains in opinion alignment than others, and the induced sycophancy shift forms a group-specific profile rather than a single-dimensional change. These results suggest that group alignment should be reported as a two-sided, multi-dimensional profile rather than a single fit score that accounts for per-group differences when adapting LLMs to diverse populations.
[NLP-57] Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generation
【速读】： 该论文旨在解决在软件工程任务中，如何通过心理学启发的提示（prompt）框架提升大语言模型（Large Language Models, LLMs）代码生成效果的问题。现有研究多关注提示措辞与结构对模型性能的影响，但缺乏对人类心理影响力策略（如说服、激励等）在提示设计中作用的系统探讨。其解决方案的关键在于，基于Yukl与Falbe的经典影响力策略分类体系，将八种典型的人类影响力技巧（如理性说服、奉承、交换等）转化为可复现的提示模板，并在五个主流开源大模型上，利用LiveCodeBench和SWE-bench Verified两个基准进行大规模实证评估。研究发现，某些强调紧迫感的影响力提示虽可能增强响应积极性，却反而导致生成代码的功能正确性与安全性下降，揭示了语言暗示对大模型行为的潜在负面影响。本研究首次系统地揭示了心理驱动型提示框架对代码生成质量的影响机制，为构建透明、可解释且高效的人-智能体协作代码生成系统提供了关键实践指导。
链接: https://arxiv.org/abs/2608.11513
作者: Alex Deaconu,Anubhav Gupta,Manaal Basha,Nicholas Haydu,Gema Rodríguez-Pérez
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted for publication in Empirical Software Engineering. This is the accepted manuscript version. 37 pages, 3 figures
Abstract:Large Language Models (LLMs) are increasingly integrated into software engineering workflows, helping developers write, debug, test, and maintain code. While prompt wording and structure are known to influence model performance, the impact of psychologically inspired prompt framings remains unexplored. This study investigates whether different psychology-based communication strategies that humans use to persuade or motivate others can lead to more effective prompt framing, which may, in turn, affect LLM behaviour in coding tasks. Drawing on Yukl Falbe’s well-known taxonomy, we operationalized eight influence tactics (like rational persuasion, ingratiation, and exchange) into reproducible prompt templates. These prompt templates were evaluated across five leading open-weight LLMs using two widely adopted benchmarks: LiveCodeBench and SWE-bench Verified. We assessed the resulting code output on four key software quality dimensions: functional correctness, quality, maintainability, and security. Our results show that certain influence-induced prompt framings, particularly those emphasizing urgency, were associated with reduced correctness and security. This work presents the first large-scale empirical study of influence-induced prompt framing in software engineering tasks, offering insights into how linguistic cues may shape LLM outputs. We conclude with practical insights for designing transparent and interpretable human-AI interactions in code generation.
[NLP-58] Principal Trait Analysis: Towards Deriving “Skills” in Human-AI Collaboration
【速读】： 该论文旨在解决在大语言模型（Large Language Model, LLM）驱动的人机协作新工作模式下，如何识别并量化影响任务成功的关键提示行为特征（prompting traits）的问题。随着生成式 AI 在职场中的广泛应用，理解人类与 AI 有效协作的模式已成为提升工作效率的核心挑战。现有研究多依赖自上而下的理论框架或特定场景的经验观察，但受限于 LLM 能力的快速迭代，传统方法难以及时捕捉新兴交互模式，且实证指南易过时。为此，本文提出一种数据驱动的自动化分析方法——主特征分析（Principal Trait Analysis, PTA），其核心是借鉴主成分分析（Principal Component Analysis, PCA）思想，通过基于大语言模型的处理流程，从大量人机协作会话记录中挖掘出与任务结果高度相关的共性行为特征。该方法不仅支持领域知识注入，还能筛选出在不同用户间差异性最大、最具区分度的特征，从而对每位协作者的使用风格进行量化评分。在教育场景（学生与 AI 导师协作编程）和专业开发场景（开发者与 AI 编码代理协作）中的评估表明，PTA 提取出的特征能显著解释用户行为并预测任务成果。然而，这些特征是否可被视为可迁移的专业技能仍需进一步验证，主要受限于其泛化能力及随时间演变的动态性问题。
链接: https://arxiv.org/abs/2608.11460
作者: Hunter McNichols,Kai Du,Andrew Lan
机构: University of Massachusetts Amherst(马萨诸塞大学阿姆赫斯特分校); OpenRefinery.ai; University of Massachusetts Amherst(马萨诸塞大学阿姆赫斯特分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Model-powered agents are increasingly used in the workplace via human-artificial intelligence (AI) collaboration. In this new era of work, it is important to understand the kinds of prompting traits that contribute to task success. Moreover, we need to uncover key skills required for modern professionals and inform educators on how to foster these skills among students. Existing guidelines for human-AI collaboration are built from either top-down theory or context-specific observations of human-AI interactions. However, since LLM capabilities are rapidly improving, theory may not be able to explain emerging interaction patterns, and empirical guidelines may become obsolete quickly. In this work, we explore an automated, data-driven approach to uncover patterns, which we term traits, of effective human-AI interaction that are aligned with task outcomes. We propose Principal Trait Analysis, a Principal Component Analysis-inspired algorithm for deriving common traits from patterns in LLM conversations. Our algorithm uses LLM-based processing stages to analyze corpora of human-AI collaborative session traces, deriving common traits across the dataset and scoring each human collaborator’s usage style by each trait. The approach also allows domain expertise to be injected during trait discovery and selects the most distinguishing traits to be those that exhibit the highest variance across collaborators. We evaluate PTA on two human-AI collaborative coding datasets, an educational setting (students working with an AI tutor) and a professional setting (developers working with an AI coding agent). We find that PTA-derived traits are significant in explaining collaborator behavior across both settings and can help predict task outcomes. However, whether traits qualify as skills remains to be seen, due to inconclusive results on generalizability and how user traits change over time.
[NLP-59] DonorRank: Donor Language Selection for Low-Resource Cross-Lingual Speech Recognition
【速读】： 该论文旨在解决低资源自动语音识别（ASR）中跨语言迁移的捐赠语言选择难题，尤其是在缺乏标准化书写规范、语言变体复杂且资源分布不均的自发性口语语料场景下。现有方法多依赖于基于语言亲缘关系或高资源语言的启发式策略，但难以有效应对实际语言间的复杂差异。其解决方案的关键在于提出DonorRank——一种学习排序（learning-to-rank）框架，通过建模语言间特征相似性与迁移性能之间的非线性关系，精准预测适用于零样本ASR的最优捐赠语言排名。实验在印地语系与非洲语言家族的多语言语音语料库上验证了该框架的有效性，结果表明其显著优于传统基于谱系关系或资源量的启发式方法。此外，该研究揭示了捐赠语言集合构成对迁移有效性的决定性影响，明确了不同语言特征在跨语言迁移中的作用机制，为低资源多语言ASR中的捐赠语言选择提供了可解释、可泛化的分析范式与实践指导。
链接: https://arxiv.org/abs/2608.11441
作者: Akriti Dhasmana,Aarohi Srivastava,David Chiang
机构: University of Notre Dame (圣母大学); Notre Dame, IN, USA
类目: Computation and Language (cs.CL)
备注: 11 pages, 4 figures, 12 tables
Abstract:Low-resource automatic speech recognition (ASR) commonly relies on cross-lingual transfer, where models are adapted from higher-resource donor languages. However, selecting donors remains challenging for spontaneous speech from under-resourced language communities, due to linguistic variation, evolving orthographic conventions, and uneven resource availability. We present DonorRank, a learning-to-rank framework for predicting effective donor languages for zero-shot ASR. We evaluate DonorRank on two multilingual speech corpora of Indic and African language families. It accurately predicts donor language rankings and improves donor selection over common heuristics based on genetic similarity or high-resource languages. Beyond improving transfer, we show how DonorRank is a general framework for analyzing donor language selection itself. Our analyses show that the composition of the donor set determines which linguistic cues are useful in predicting successful transfer. We also identify transfer patterns that provide practical guidance for multilingual ASR in low-resource settings.
[NLP-60] Benchmarking LLM Judges for Mobile Agent Evaluation
【速读】： 该论文旨在解决当前移动智能体（mobile agent）评估中依赖大语言模型（LLM）作为评判者（judge）时，其可靠性尚未得到系统验证的问题。现有基准多采用LLM-based judge评估任务完成度，但缺乏对这类评判方法在真实移动代理轨迹上表现的深入分析。为此，作者提出MobileJudgeBench，一个用于系统评估LLM-as-judge方法在移动代理轨迹上的基准测试平台。其核心创新在于构建了一个包含931条人工标注轨迹的数据集，覆盖6个移动代理基准、4种智能体模型及68款应用，实现了跨场景、跨模型的全面评估。解决方案的关键在于：首先，发现一种基于采样截图的简单基线评判方法在性能上可媲美甚至超越复杂设计的专用评判方法，表明评判流程的复杂性并非决定评判质量的主导因素，而LLM骨干模型本身才是关键驱动；其次，验证了基准质量指标能够有效预测实际应用中的评判效用，包括在评估阶段对智能体排序的保真度以及作为强化学习奖励信号时对下游性能的提升能力；最后，通过双LLM骨干的失败模式分析揭示了两类截然不同的错误特征——一类保守、一类宽松，分别对应于不同模型在精确率与召回率之间的权衡特性，为后续优化评判器设计提供了重要洞见。
链接: https://arxiv.org/abs/2608.11434
作者: Ziqiang Wan,Li Gu,Zhixiang Chi,Zhi Liu,Seyed Mehdi Ayyoubzadeh,Yuanhao Yu,Yang Wang
机构: Mila – Québec AI Institute(蒙特利尔人工智能研究所); Concordia University(康考迪亚大学); University of Toronto(多伦多大学); Shanghai University(上海大学); McMaster University(麦克马斯特大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Mobile agent benchmarks increasingly rely on LLM-based judges to evaluate task completion, yet the reliability of these judges on mobile agent trajectories remains largely unexamined. We introduce MobileJudgeBench, a benchmark for systematically evaluating LLM-as-judge methods on mobile agent trajectories. Our benchmark comprises 931 human-annotated trajectories spanning 6 mobile agent benchmarks, 4 agent models, and 68 apps. We evaluate 6 judge methods (five adapted from SPA-Bench, A3 with two modes, AndroidArena, and AgentRewardBench, plus a simple baseline we design) across multiple LLM backends. Our experiments reveal three key findings. First, a simple baseline judge with sampled screenshots is competitive with, and often exceeds, purpose-built methods, indicating that more elaborate judge pipelines do not consistently improve judge quality; among competitive methods, the LLM backbone is the primary driver. Second, benchmark quality metrics reliably predict real-world judge utility: they correlate with both agent ranking fidelity for evaluation and downstream performance when judges serve as reward signals for on-policy reinforcement learning. Third, failure analysis across two LLM backends uncovers qualitatively opposite failure profiles, one conservative and the other permissive, linked to the backbone’s precision-recall characteristics.
[NLP-61] Stigma and Support in Online Sexual Violence Narratives on Reddit
【速读】： 该论文旨在解决在线社区中性暴力幸存者叙事所表达的污名（Stigma）与其获得的同伴支持之间关系不明确的问题。现有研究多将污名与社会支持分开探讨，缺乏对二者在互动语境中如何相互作用的系统分析。其解决方案的关键在于构建并公开发布SCOPE数据集，首次实现对幸存者在线叙事中的污名信号（涵盖经历性、内化性、预期性及结构性污名）与相应评论区支持类型（包括信息支持、情感支持、尊重支持、实质援助及群体互动）的精准标注与关联分析。通过多维度的语境、语言学与情绪分析发现，带有内化污名的叙事更强调内在痛苦，而无污名叙事则侧重于情境解读；尽管不同污名类型下回应模式总体稳定，但信息支持与尊重支持最为常见。该研究揭示了污名如何塑造幸存者表达方式及其社群响应机制，为生成式AI驱动的内容安全系统、自动化内容审核策略及更具包容性的在线支持平台设计提供了关键实证依据。
链接: https://arxiv.org/abs/2608.11433
作者: Shirlene Rose Bandela,Karan Bindal,Vaibhav Garg,Rezvaneh Rezapour
机构: Virginia Tech (弗吉尼亚理工学院); Drexel University (德雷塞尔大学)
类目: Computation and Language (cs.CL)
备注: 37th ACM Conference on Hypertext (HT '26)
Abstract:Online communities increasingly provide spaces where survivors of sexual violence can share their experiences and seek support. Although prior research has examined stigma and social support separately, less is known about how stigma expressed in survivor narratives relates to the support offered in response. We introduce the SCOPE dataset, linking stigma signals in online survivor narratives to support types in corresponding comment threads. We annotate posts using a multi-dimensional stigma taxonomy, including Experienced, Internalized, Anticipated, and Structural Stigma, and comments using a support taxonomy encompassing Information Support, Emotional Support, Esteem Support, Tangible Assistance, and Group Interaction. Using contextual, linguistic, and emotion analyses, we compare Stigma and No Stigma content and find that Stigma narratives place greater emphasis on internalized distress, whereas No Stigma narratives focus more on interpreting situations and experiences. Internalized Stigma is the most prevalent category, and community responses remain broadly stable across stigma types, with Information and Esteem Support appearing most often. These findings show how stigma shapes survivor narratives and peer responses and have implications for computational modeling, content moderation, and safer online systems.
[NLP-62] Is Convergence Inevitable? Tracing Output Homogeneity Back to Base Models
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）内容多样性缺失的问题，尤其是探究这种语义同质性（semantic convergence）在模型训练流程中何时何地开始形成。其核心问题在于：当前普遍认为模型对齐（alignment）过程导致了输出的同质化，但这一现象究竟是在对齐阶段产生，还是早在预训练阶段便已潜藏并在此后被揭示或放大？论文的关键解决方案在于通过一系列受控的指令微调（Supervised Fine-Tuning, SFT）实验与基础模型（base model）的提示测试，系统性地验证语义收敛的起源。研究发现，语义收敛现象早在指令微调阶段即已显现，且可通过提示（prompting）在无任何对齐干预的情况下诱导生成，表明该同质性并非由对齐数据引入，而是源于预训练阶段模型本身的训练目标所自然引发。因此，论文的核心结论是：语义同质性可能根植于语言模型训练的基本机制之中，仅依赖对齐后的干预难以有效缓解，需从预训练阶段的优化目标层面寻求根本性解决方案。
链接: https://arxiv.org/abs/2608.11426
作者: Alexandrine Fortier,Hazel Chen,Peter West
机构: University of British Columbia(不列颠哥伦比亚大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:The lack of diversity in LM content is widely attributed to the alignment process, but how and where exactly in the pipeline this collapse begins is unknown. We argue that output homogeneity is likely learned during the pretraining phase, and only \emphrevealed or magnified during the alignment process. Specifically, we find that semantic convergence is observed from the first alignment stage–the instruction-tuning phase (SFT)–suggesting that homogeneity might already exist in the pre-alignment model. To investigate this, we conduct controlled SFT experiments examining how training data influences output convergence on specific input/output pairs. We find that convergence can be revealed and amplified, but not introduced by the SFT data, supporting its role as a catalyst rather than a cause. To further test whether homogeneity originates before alignment, we measure convergence in base models. We find that instruct-like collapse can be induced through prompting alone, even without alignment. Taken together, our results suggest that semantic convergence may arise naturally from the objectives underlying LM training, making it difficult to mitigate through post-alignment interventions alone.
[NLP-63] Social Chain of Thought: A Multi-Agent Architecture Grounded in Medical Differential Diagnosis Methodology
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在复杂医疗诊断场景中推理透明性与准确性不足的问题，尤其针对多轮、多专家协作的差异化诊断需求。现有基于单智能体（monolithic inference）的方法在处理高难度诊断案例时表现受限，其决策过程缺乏可解释性，且难以有效整合多种专业推理模式。为此，论文提出“社会思维链”（Social Chain of Thought, SCoT），一种基于多轮交互的多智能体诊断流水线，将多个领域专家智能体的协作建模为一种协商式的集体推理框架。其核心创新在于通过结构化对话机制促进不同专业角色之间的迭代讨论，从而提升对疑难病例的诊断召回率（recall）。实验表明，尽管单智能体基线或最佳n次采样（best-of-n scaling）等方法在部分场景下表现良好，但无法复现SCoT在高难度病例中所展现的显著召回优势；而多轮专家对话的有效协同正是其在复杂诊断任务中实现更高准确性和鲁棒性的关键所在。
链接: https://arxiv.org/abs/2608.11420
作者: Del Coburn,Scott Sanner,Dan Silver
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 14 pages, 9 figures, 6 tables
Abstract:Medical diagnostic reasoning is a high-impact use case for LLMs that carries significant implications for the health and wellbeing of users. When OpenAI (2026) reports that more than 5% of ChatGPT messages globally are healthcare-related, the transparency of these systems becomes a serious design concern. This is especially true for complex cases, where differential diagnosis often requires integrating multiple forms of specialist reasoning. Existing work has proposed multi-agent approaches to medical diagnosis, but it remains unclear when such systems are needed, why they help, and where they outperform monolithic inference. We introduce Social Chain of Thought (SCoT),a multi-round pipeline for medical differential diagnosis that structures multi-agent interaction as a deliberative framework for collabora. tive LLM reasoning. Evaluating SCoT against single-agent baselines, one-agent pipeline ablations, and best-of-n scaling, we show that its recall advantage is not reproduced by monolithic inference alone. SCoT is most successful in the hardest diagnostic cases, where multiple rounds of specialist conversation help recover ground-truth diagnoses and converge on a higher-recall differential.
[NLP-64] Measure Dont Optimize: Forecasting Recovery in LLM Unlearning
【速读】： 该论文旨在解决大语言模型在执行“遗忘”（unlearning）操作后，仍可能在内部表征中残留可被恢复的目标知识这一安全风险问题。现有研究多局限于一次性的事后诊断，无法判断这些残余信号是否会在后续训练中导致知识的快速复现，因而难以实现对模型遗忘效果的前瞻性评估。为此，本文提出J-Access——一种基于雅可比矩阵（Jacobian）视角的推理时审计方法，通过将中间表示映射至词汇空间，量化目标概念在输出路径中的可访问性，从而评估其潜在恢复可能性。核心创新在于：假设残余可访问性反映了知识恢复的敏感性——若知识在输出路径中越易被触及，则越容易通过微调恢复。实验覆盖398个公开的已遗忘模型，涵盖八种不同遗忘方法，结果表明：（1）绝大多数遗忘模型仍保持高于仅保留对照组的可访问水平；（2）攻击前的可访问性可有效预测模型层面的恢复速度与程度，但无法定位具体被恢复的事实；（3）直接最小化J-Access指标反而会诱导模型“隐藏”知识以欺骗审计，导致审计得分降低但实际恢复能力增强。因此，研究强调应将内部审计作为独立的诊断维度，而非直接作为优化目标，以推动更安全、可信的遗忘评估体系发展。
链接: https://arxiv.org/abs/2608.11408
作者: Zirui Song,Huaxing Liu,Xiang Wang,Shuai Li,Xinye Li,Lang Gao,Jinghui Zhang,Zheng Lu,Fengxian Ji,Xiaojun Chang,Xiuying Chen
机构: AMAP(阿里巴巴集团); Alibaba Group(阿里巴巴集团)
类目: Computation and Language (cs.CL)
备注: In processing
Abstract:Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge is no longer expressed in their outputs. However, existing audits remain limited to one-off diagnostics: it is unclear whether these residual signals can predict future recovery under continued training or serve as reliable optimization targets. Resolving this gap is essential to determine whether internal auditing can move beyond post-hoc evaluation toward proactive risk monitoring and safer unlearning. We propose J-Access, an inference-time audit that uses the Jacobian lens to map intermediate representations into vocabulary space and measures how often target concepts remain accessible along the model’s output pathway. We hypothesize that residual accessibility reflects recovery susceptibility: knowledge that remains closer to the output pathway requires less fine-tuning to restore, leading to faster recovery. We audit 398 public unlearned models spanning eight unlearning methods. We find that: (1) most unlearned models retain access above the retain-only gold level; (2) pre-attack accessibility predicts recovery speed and extent at the model level, but cannot identify which specific facts will be recovered; and (3) directly minimizing J-Access does not promote genuine deletion. Instead, the model learns to hide knowledge from the audit, producing lower audit scores but greater post-attack recovery. These findings position J-Access as a model-level diagnostic for assessing residual susceptibility in unlearned models. We argue internal audits should serve as an independent diagnostic dimension in unlearning evaluation, and should not be converted into optimization targets without validation.
[NLP-65] When Self-Consistency Backfires: Majority Vote Hurts the Majority of Hard Science Problems for Small LLM s
【速读】： 该论文旨在解决生成式 AI 在推理过程中依赖自洽性（Self-consistency, SC）通过多数投票提升准确率的有效性问题，特别是在高阶科学知识问答任务中的表现。研究发现，在全规模的GPQA钻石基准（198道研究生级科学问题）上，对来自不同模型家族的两个指令微调模型（Qwen2.5-7B 和 Llama-3-8B），采用多数投票策略反而导致多数问题的单题准确率下降：分别在56.6%和65.7%的问题中出现性能退化。这一现象在预注册的151题验证集上得到确认，且四项假设均通过验证。研究揭示，尽管存在一个理想化的网格最优门控（grid oracle）可将准确率上限提升14至17个百分点（相较于N=1），但现有无需验证器的门控机制——包括多数一致性门控与词元熵门控——均无法突破固定预算投票（N=64）的表现，其增益不足0.002。核心机制在于：模型置信度并未有效反映答案正确性；在最高一致性的分组中，Qwen的多数答案仅约一半正确，而Llama在高一致性组的准确率甚至低于最低一致性组。因此，该研究的关键结论是：当前主流的自洽性方法在复杂推理任务中可能因错误的一致性信号而误导结果，其根本挑战在于缺乏可靠的质量评估机制，而推理原生模型（reasoning-native models）是否具备更强的内在一致性仍是亟待探索的核心开放问题。
链接: https://arxiv.org/abs/2608.11403
作者: Utkarsh Bahuguna
机构: Scaler School of Technology(斯卡拉技术学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 9 pages, 4 figures, 3 tables. Accepted at the COLM 2026 Workshop on Efficient Reasoning
Abstract:Self-consistency (SC) via majority vote is a widely used way to spend inference-time compute: sample N chains of thought, return the plurality answer. On the full GPQA Diamond benchmark (198 graduate-level science questions), majority voting reduces per-problem accuracy on a majority of problems for two instruction-tuned models from different families: 56.6% of problems for Qwen2.5-7B and 65.7% for Llama-3-8B, with Qwen the primary demonstration and Llama corroborating the direction from a near-chance baseline. The effect was pre-registered on a 151-problem confirmatory split after being observed on 47 exploratory problems, and all four confirmatory hypotheses passed. A grid oracle that routes each problem to the best N across 1, 2, 4, 8, 16, 32, 64 marks a theoretical upper bound 14 accuracy points above N = 1 for Qwen and 17 for Llama, an oracle bound requiring ground truth rather than a deployable method. No verifier-free gate reaches it: neither a plurality-agreement gate nor a token-entropy gate moves accuracy more than 0.002 from fixed-budget voting at N = 64. The mechanism is direct: confidence does not track correctness on these problems. In the highest-agreement bin the plurality answer is correct about half the time for Qwen, and for Llama that bin is less accurate than its lowest-agreement bin. We pre-register and confirm these findings on small instruction-tuned models; we do not test reasoning-native models, which we flag as the central open question.
[NLP-66] RevCRN: Reversible Analog Computation using Chemical Reaction Networks
【速读】： 该论文旨在解决在可逆化学反应网络（Reversible Chemical Reaction Network, RevCRN）框架下实数可计算性的问题，具体聚焦于厘清RevCRN所支持的实数集合与其他已知可计算实数类（如Lyapunov CRN、实时CRN、有理数、代数数等）之间的包含关系与层级结构。其核心问题在于：在能量效率更高的可逆计算范式下，哪些实数能够被精确生成，以及这些可计算实数类之间是否存在层次化组织。解决方案的关键在于构建并分析基于可逆化学反应机制的计算模型，通过形式化证明揭示多个关键结论：首先，有理数集 Q 是可逆化学反应网络可计算实数集 RRevCRN 的真子集；其次，正代数数集 ALG、Lyapunov CRN可计算实数集 RLCRN 以及单物种可逆化学反应网络（1-species RevCRN）可计算实数集 RRevCRN1s 三者相等；再次，实时CRN（RRTCRN）与可逆CRN（RRevCRN）之间存在非空交集；最后，满足详细平衡条件的可逆化学反应网络（RRevCRNDetBal）所能计算的实数仅为代数数的子集。这些结果共同揭示了可逆化学计算在实数生成能力上的边界与潜力，并为建立更完整的可逆计算理论体系提供了基础。
链接: https://arxiv.org/abs/2608.11362
作者: Saptarshi Biswas,James I. Lathrop,Rana D. Parshad
机构: 未知
类目: Computational Complexity (cs.CC); Computation and Language (cs.CL); Dynamical Systems (math.DS)
备注:
Abstract:The computability of real numbers and functions using Turing Machines has been a central area of theoretical computer science since the mid-20th century. In the late 20th century, it was shown that chemical reactions can serve as a basis for computation using the Chemical Reaction Network (CRN) model. Recent advances in computing real numbers using Deterministic Chemical Reaction Networks (DCRNs) have identified numerous classes of DCRN-computable real numbers. In parallel, the works of R. Landauer and C. H. Bennett, spanning the 1960s to the early 2000s, showed that reversible computing offers significant advantages over irreversible methods, particularly in energy efficiency, motivating extensive research on reversible computation. In this work, we investigate the computability of real numbers using Reversible Chemical Reaction Networks (RevCRNs). The paper has two primary contributions: (1) establishing relationships among CRN-computable real number classes including Lyapunov CRN ( \mathbbR_LCRN ), Real-Time CRN ( \mathbbR_RTCRN ), rational numbers ( \mathbbQ ), and RevCRNs ( \mathbbR_RevCRN ), with key results: (i) \mathbbQ is a strict subset of \mathbbR_RevCRN ; (ii) the set of positive algebraic numbers ( ALG ), \mathbbR_LCRN , and real numbers computable by 1-species RevCRN ( \mathbbR_RevCRN^1s ) are equal; (iii) \mathbbR_RTCRN and \mathbbR_RevCRN exhibit non-empty overlap; and (iv) the set of real numbers computable by detailed-balanced RevCRNs ( \mathbbR^DetBal_RevCRN ) is a subset of ALG ; and (2) exploring the existence of a hierarchy within \mathbbR_RevCRN . Finally, we leave open the exact relationship between \mathbbR_RevCRN and \mathbbR_RTCRN while conjecturing a general hierarchy of RevCRN-computable reals. Subjects: Computational Complexity (cs.CC); Computation and Language (cs.CL); Dynamical Systems (math.DS) Cite as: arXiv:2608.11362 [cs.CC] (or arXiv:2608.11362v1 [cs.CC] for this version) https://doi.org/10.48550/arXiv.2608.11362 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-67] Lifecycle-Optimal Tokenization: Vocabulary Size as a Deployment-Regime-Dependent Infrastructure Parameter
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）中分词器（Tokenizer）词汇表大小（Vocabulary Size, V）在部署阶段的优化问题。传统做法将词汇表大小固定于训练阶段，未考虑实际服务场景对系统成本的影响，导致资源利用效率低下。其核心问题是：词汇表大小并非一个静态最优值，而是应随推理负载模式（如推理吞吐量、批处理大小 B）动态调整。解决方案的关键在于构建一个全生命周期部署成本模型：$ C_{\text{lifecycle}}(V) = C_{\text{train}}(V) + \lambda \cdot C_{\text{infer}}(V, B) $，其中 $ \lambda $ 代表推理请求量，$ B $ 为服务批处理大小。通过在A10G与A100两类典型硬件（分别代表内存受限与计算受限架构）上进行受控实验，研究发现：（1）推理最优词汇表大小随批处理大小变化达16倍，从 $ B=1 $ 时的32k增至 $ B \geq 64 $ 时的524k，主要由 $ V \times d $ 未嵌入矩阵读取开销的分摊效应驱动；（2）在1.3–2.3B参数规模下，模型质量（以每字节比特数，BPB衡量）在 $ V=65k $ 处达到最优，表明词汇表偏好具有尺度依赖性；（3）全生命周期最优词汇表与训练最优相比可相差高达16倍，且在最优范围内质量波动仅约2% BPB，说明该优化属于纯系统级优化，不牺牲模型质量。因此，论文提出基于部署场景的差异化容量规划策略：边缘设备部署（$ B=1 $）推荐使用 $ V \approx 32k ，数据中心高并发服务（ B \geq 64, \lambda \geq 10 $）则应采用 $ V \approx 131k–262k $，从而实现成本与性能的协同优化。
链接: https://arxiv.org/abs/2608.11361
作者: Rima Mittal,Ankit Gubrani,Satyanarayana Kakollu
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Performance (cs.PF)
备注: 6 pages, 3 figures, 6 tables
Abstract:Tokenizer vocabulary size is a foundational design choice in large language model (LLM) infrastructure, yet it is typically fixed at training time based on convention rather than deployment analysis. We show that the cost-optimal vocabulary is not a constant but a function of the serving regime. We formalize total deployment cost as C_lifecycle(V) = C_train(V) + \lambda \cdot C_infer(V, B) , where \lambda is inference volume and B is the serving batch size. Through controlled experiments on two GPU families spanning the memory-bound to compute-bound regimes (A10G, ridge \approx 117 FLOP/byte; A100, ridge \approx 183 FLOP/byte), we demonstrate: (1) the inference-optimal vocabulary shifts 16x with serving batch, from 32k at B=1 to 524k at B=64+ , driven by amortization of the V \times d unembedding matrix read; (2) at 1.3-2.3B model scale, quality (bits per byte, BPB) is optimized at V=65 k, confirming scale-dependent vocabulary preference; (3) the lifecycle-optimal vocabulary diverges from training-optimal by up to 16x for production deployments. Quality is approximately invariant across the optimal range ( 2% BPB spread), making vocabulary a pure systems optimization with no quality penalty in the measured range. Our results provide actionable capacity planning guidance: on-device deployments ( B=1 ) should use V \approx 32 k; datacenter serving ( B \geq 64 , \lambda \geq 10 ) should use V \approx 131 -262k.
[NLP-68] ODE-Based Transformer Decoders for Iterative Sign Language Translation ECCV2026
【速读】： 该论文旨在解决生成式符号语言翻译（Sign Language Translation, SLT）中模型性能提升过度依赖参数量扩展所带来的计算成本增加问题。现有基于Transformer架构的方法虽取得显著进展，但其性能提升主要通过扩大模型规模实现，导致推理开销显著上升。为应对这一挑战，本文提出一种参数高效型解决方案，核心在于不增加模型参数的前提下，通过改进迭代优化解码器的更新机制来增强表达能力。关键创新点在于从常微分方程（Ordinary Differential Equation, ODE）视角重新诠释残差迭代修正过程，并引入高阶数值积分方法——龙格-库塔法（Runge-Kutta methods, RK-2 和 RK-4），在每个精炼步骤中进行多次函数评估，以获得更精确、更稳定的潜在表示更新。该方法无需新增解码器参数，即可实现更强的动态优化能力。实验结果表明，在PHOENIX-2014-T和CSL-Daily两个基准数据集上，所提方法分别达到22.96 BLEU-4和19.34 BLEU-4，优于现有基线模型，且在CSL-Daily上使用更少的解码器层数与迭代次数，验证了强更新动力学在参数受限条件下可有效提升翻译质量，为减少模型缩放依赖提供了一种互补性技术路径。
链接: https://arxiv.org/abs/2608.11352
作者: Tuğçe Kızıltepe,Hacer Yalim Keles
机构: ASELSAN(土耳其); Hacettepe University (哈塞特佩大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at the 14th International Workshop on Assistive Computer Vision and Robotics (ACVR 2026), held in conjunction with ECCV 2026
Abstract:Sign language translation has achieved strong results with Transformer architectures, yet recent improvements largely rely on scaling model capacity at the cost of increased computation. We propose a parameter-efficient alternative that improves expressiveness without increasing model size. Rather than scaling capacity, we focus on enhancing the update dynamics of iterative refinement decoders, where each refinement step corresponds to one internal decoder iteration that progressively improves the latent representation before translation generation. We reinterpret residual refinement updates from an Ordinary Differential Equation (ODE) perspective and replace them with higher-order numerical integration schemes, namely Runge–Kutta methods (RK-2 and RK-4). These methods perform multiple function evaluations within each refinement step to produce more accurate and stable representation updates without adding decoder parameters. To the best of our knowledge, this is the first application of ODE-inspired update dynamics to sign language translation. RK-2 achieves 22.96 BLEU-4 on the PHOENIX-2014-T test set and 19.34 BLEU-4 on the CSL-Daily test set, outperforming the IPSLT baseline on both benchmarks, with fewer decoder layers and refinement iterations on CSL-Daily. These results suggest that stronger refinement dynamics can improve translation performance under parameter-efficient decoder designs, providing a complementary alternative to conventional model scaling.
[NLP-69] Self-Evolving Embodied Agents via Skill-Harness Evolution
【速读】： 该论文旨在解决在模型训练成本高、不可行或不被允许的情况下，如何实现具身智能体（embodied agent）的自适应问题。现有方法如监督微调和强化学习虽能提升性能，但依赖额外数据、奖励信号与训练迭代；而无需训练的代码中心方法则通常依赖可编程机器人接口，在固定接口场景中难以适用。本文提出的SHAPER框架是一种无需训练的自演化具身适应方案，其核心在于保持模型参数冻结的同时，通过目标环境中的滚动（rollout）过程，进化可复用的技能（skill）与上下文-代码调度机制（context-code harness），从而优化非参数化代理系统。其关键创新在于：同一冻结模型同时担任规划器与优化器，通过外部技能与上下文-代码架构的迭代改进，实现无需参数更新的持续适应能力。实验在VLABench与ESI-Bench上验证了SHAPER在多种底层动作接口下的有效性，显著优于纯执行、监督微调及测试时扩展基线（如无验证器选择与投票），证明了技能与调度机制优化是实现高效自演化具身智能体的可行路径。
链接: https://arxiv.org/abs/2608.11350
作者: Peidong Wang,Zhiming Ma,Ying Chang,Xufang Luo,Xiaocui Yang,Shi Feng,Yuqing Yang,Dongsheng Li
机构: Microsoft Research(微软研究院); Northeastern University(东北大学)
类目: Computation and Language (cs.CL); Robotics (cs.RO)
备注:
Abstract:Embodied agents are increasingly built as systems around foundation models, where performance depends not only on model weights but also on the skills, context, action interfaces, and execution harness surrounding the model. While supervised fine-tuning and reinforcement learning can adapt agents to new environments, they require additional data, rewards, and training runs; meanwhile, many train-free code-centric approaches rely on programmable robot APIs that may be unavailable in fixed-interface settings. We propose SHAPER, a self-evolving framework for train-free embodied adaptation that keeps model parameters frozen and improves the non-parametric agent system by evolving reusable skills and a context-code harness through target-environment rollouts. In SHAPER, the same frozen model can serve as both planner and optimizer, refining its external skills and context-code harness without parameter updates. We evaluate SHAPER on VLABench and ESI-Bench, covering embodied agents with different low-level action interfaces, and compare against pure execution, supervised fine-tuning, and test-time-scaling baselines such as verifier-free selection and voting. Our results suggest that skill-and-harness optimization is a practical route to self-evolving embodied agents when model training is expensive, unavailable, or undesirable.
[NLP-70] Weightless Fine-Tuning: Personalizing LLM s via Logit-Space Transport
【速读】： 该论文旨在解决大规模语言模型（LLM）在个性化场景下进行监督微调（Supervised Fine-Tuning, SFT）所带来的高昂计算与存储成本问题。在个性化应用中，每位用户需独立维护模型权重，导致每次优化、存储及重训练的开销巨大，难以规模化部署。为此，论文提出一种无需训练的解码时方法——无权重微调（Weightless Fine-Tuning, WFT），其核心创新在于通过基于丢弃诱导交叉协方差估计的跨前缀传输算子，在不更新模型参数的前提下，将作者训练序列上的监督残差（supervised residuals）有效传递至当前提示（prompt）的预测分布中。该方法将传统依赖梯度更新的参数调整转化为对输出logit空间的直接修正，从而实现对SFT分布效应的近似。实验表明，WFT在三个LaMP个性化基准上达到最优平均性能，单任务表现可媲美甚至超越SFT，且在计算预算受限条件下仅需不到7%的有效计算量即可逼近SFT性能。进一步的logit级分析显示，WFT引发的logit偏移与SFT在95%的下一个词概率质量区域内的余弦相似度高达0.875，验证了其在不修改模型权重的情况下精准捕捉了监督适应的分布特性。
链接: https://arxiv.org/abs/2608.11342
作者: Bohan Zhang,Anqi Ni,Yixin Wang,Paramveer S. Dhillon
机构: University of Michigan (密歇根大学); University of Chicago (芝加哥大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Supervised fine-tuning (SFT) is a standard approach for adapting LLMs to a target distribution, but in settings such as personalization, where each author requires separate weight access, optimization, storage, and retraining, its costs become prohibitive. We propose Weightless Fine-Tuning (WFT), a training-free decoding-time method that approximates the distributional effect of SFT without weight updates. WFT computes supervised residuals on an author’s training sequence and transports them to the current prompt through a cross-prefix transport operator estimated from dropout-induced cross-covariance. The operator captures how a perturbation at one context propagates to predictions at another, replacing gradient-based parameter updates with logit-space corrections. On three LaMP personalization benchmarks, WFT achieves the best average performance across datasets, matches or exceeds SFT on individual tasks, and outperforms other lightweight baselines on average. In a budget-controlled comparison, WFT approaches SFT performance using less than 7% of the effective computation. Logit-level analysis shows a cosine similarity of 0.875 between the logit shifts induced by WFT and SFT over 95% of the next-token probability mass, suggesting that WFT captures the distributional effect of supervised adaptation without modifying model weights.
[NLP-71] Better Faster Stronger: Programmatic Skill Learning Best Reduces Agent Cost
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）智能体在新领域适应过程中成本高昂的问题，尤其关注如何在不显著增加计算或交互开销的前提下，实现高效且可扩展的技能学习。现有方法多聚焦于性能提升，而忽视了成本效益的优化，导致对哪些技能学习策略真正能够降低整体开销缺乏系统认知。本文提出的关键解决方案是：将技能视为可执行的程序（program），通过确定性地执行动作序列，使智能体能够在无需试错的情况下可靠达成目标，从而避免长期任务中因随机探索引发的退化行为。该方法的核心在于，智能体可在推理阶段通过增量式分析历史轨迹（past trajectories），自主发现并重构更高效的程序化技能，而无需依赖经验回放或验证机制。为验证这一假设，作者提出了SpeedRunner——一种具备轨迹分析与技能重构能力的编码型智能体。实验结果表明，在三个不同的具身环境（embodied environments）中，SpeedRunner不仅在学习效率和成本控制方面达到前沿水平，且对分布外变化和环境随机性表现出强鲁棒性，证明了以程序化技能为核心的学习范式在降低智能体运行成本方面的有效性。
链接: https://arxiv.org/abs/2608.11338
作者: Zixi Huang,Xiheng Wang,Andrew Wang,William Jurayj,Bernal Jiménez Gutiérrez,Daniel Khashabi,Nicholas Andrews
机构: Johns Hopkins University (约翰霍普金斯大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Recently, the practice of augmenting LLM agent capability with skills has gained prevalence. We explore the cost effective adaptation of agents to novel domains by means of learning skills. Existing works focus on performance gain over cost effectiveness. As a result, little is known about what skill learning strategies save cost. We argue that among all the different skill learning methods, those that view skills as programs can achieve the best cost reduction. By executing sequences of actions deterministically, a program-augmented agent can reliably and cheaply achieve goals that would otherwise require trial and error and risk degenerate behavior over long horizons. An agent can learn at inference time by incrementally discovering these programs and equipping them for future tasks. We hypothesize that past trajectories contain enough signal to guide skill learning, even without replay or validation, provided the agent can learn to analyze them. To test our claims, we propose SpeedRunner, a coding agent that analyzes trajectories and refactors skills for better performance on future tasks. Across three different embodied environments, we show that SpeedRunner consistently achieves the frontier in learning and cost reduction while remaining robust against distribution shifts and environmental randomness.
[NLP-72] Gloss-Free Representation Learning for Cross-Dataset Sign Spotting ECCV2026
【速读】： 该论文旨在解决资源受限语言（如土耳其语）在手语研究中因缺乏密集标注（如词义、时间边界和手势顺序）而导致的标注成本过高问题。其核心挑战在于，广播新闻虽可提供连续手语与口语转录文本的弱监督对齐数据，但这种对齐关系松散，且在形态丰富的语言中，同一词汇意义可能以多种变体形式出现，导致伪词义标签易被不完善的文本归一化过程碎片化，从而削弱表示学习效果。为应对这一问题，论文提出利用基于转录文本生成的伪词义标签，在无需人工标注的前提下预训练一个可复用的手语编码器，并比较规则化形态还原与受约束大语言模型（LLM）辅助归一化两种方法的效果。关键创新在于：不同于以往伪词义管道主要服务于手语翻译任务，本文验证了预训练编码器能否作为通用表示用于跨数据集的手语定位任务。实验基于新构建的土耳其手语（TSL）新闻语料库（TSL-News）和新的TSL定位基准（TSL Spotting Benchmark），结果表明，采用LLM辅助归一化的编码器使顶5定位平均交并比（mean IoU）从0.235提升至0.465，超过56.2%的样本达到至少0.50的IoU；频率分析显示性能提升并非主要源于对高频伪词义的过拟合。此外，在下游翻译任务中，预训练也显著提升了BLEU-4（9.60→11.04）和ROUGE（23.48→27.43）。这些结果表明，尽管监督信号较弱，但通过合理设计的文本归一化策略，松散对齐的广播数据仍可有效提供弱监督信号，用于学习既捕捉词汇内容又保留时序结构的手语表示。
链接: https://arxiv.org/abs/2608.11332
作者: Oğuz Akif Tüfekcioğlu,Ezgi Ekin,Mustafa Kaan Çevik,Hacer Yalim Keles
机构: Hacettepe University (哈切特佩大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the 4th LIMIT Workshop (Representation Learning with Very Limited Resources), ECCV 2026. The abstract was shortened to comply with arXiv’s 1,920-character limit
Abstract:Sign-language research for resource-constrained languages is often limited by the cost of dense linguistic labels such as glosses, temporal boundaries, and sign order. Broadcast news offers a practical alternative by pairing continuous signing with spoken-language transcripts, but this supervision is weak since text and signing are loosely aligned. Morphologically rich languages such as Turkish add further difficulty, as the same lexical meaning can appear in many inflected forms while some derived forms should remain distinct. We study whether weak transcript-based supervision can pretrain a reusable sign encoder in this setting, where poor text normalization can fragment pseudo-gloss targets and weaken representation learning. Unlike prior pseudo-gloss pipelines designed mainly to improve translation, we test whether the pretrained encoder transfers as a reusable representation for cross-dataset sign spotting. We pretrain on TSL-News, a new Turkish broadcast corpus, using pseudo-gloss labels derived from transcripts rather than manual annotation, comparing rule-based morphological lemmatization with constrained LLM-assisted normalization over a fixed vocabulary. We evaluate the learned representations via cross-dataset sign spotting on a new TSL Spotting Benchmark built from the TSL Dictionary corpus. The LLM-assisted encoder raises top-5 temporal localization mean IoU from 0.235 to 0.465, with 56.2% of examples reaching an IoU of at least 0.50; a frequency analysis suggests this gain is not mainly driven by memorizing frequent pseudo-gloss labels. In a downstream translation check, the same pretraining improves BLEU-4 from 9.60 to 11.04 and ROUGE from 23.48 to 27.43. These results show that loosely aligned broadcast data can provide effective weak supervision for learning sign representations that capture both lexical content and temporal structure.
[NLP-73] Diffuse to Compress: Leverag ing Diffusion LMs for Lossless Compression
【速读】： 该论文旨在解决生成式语言模型（Generative Language Models, GLMs）在无损文本压缩中面临的吞吐量瓶颈问题。尽管基于大语言模型（Large Language Models, LLMs）的压缩方法在压缩比上显著优于传统通用压缩器（如zstd、gzip、bzip），但其自回归（autoregressive）推理机制存在每步仅生成一个符号的固有局限，导致压缩速度严重受限，难以实际应用。为此，论文首次在无损神经文本压缩领域引入扩散语言模型（Diffusion Language Models, DLMs）作为替代性推理范式，以突破自回归模型的逐符号生成限制。其解决方案的关键在于利用DLMs在前向传播过程中可灵活决定编码符号数量与位置的特性，设计高效且有效的算法策略来应对由此带来的无损压缩挑战，从而实现并行化符号生成，显著提升压缩吞吐量。实验结果表明，所提出的基于DLM的框架在enwik8基准上达到了当前最优的无损文本压缩性能，且随着DLM技术的持续演进，未来仍有巨大优化空间。
链接: https://arxiv.org/abs/2608.11249
作者: Angelo Nardone,Paolo Ferragina
机构: University of Padua (帕多瓦大学); University of Bologna (博洛尼亚大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG)
备注: 18 pages, 11 figures, 2 tables. Main paper: 9 pages (7 pages text + 2 pages references). Includes 9 pages of supplementary material
Abstract:We study the problem of lossless text compression, motivated by the rapid growth in the collection and storage of digital textual data - including plain text, source code, and structured formats such as XML - and by recent advances in neural language model-based compression. In particular, recent LLM-based approaches, whether built on symbol-ranking pipelines or paired with a statistical compressor, have demonstrated compression ratios significantly superior to general-purpose compressors such as zstd, gzip, or bzip on text and code. However, these neural approaches suffer from severe throughput limitations, making them not yet practically usable. For the first time in the context of lossless neural text compression, we introduce Diffusion Language Models (DLMs) as an alternative inference paradigm to autoregressive LLM-based approaches. We argue that replacing autoregressive LLMs with DLMs within the same compression framework could overcome the throughput bottleneck caused by their one-symbol-per-step limitation. However, achieving these improvements requires addressing algorithmic challenges introduced by applying DLMs to lossless compression, where the architecture allows the number and positions of symbols encoded at each forward pass to be decided independently. We design efficient and effective strategies to solve these challenges and evaluate them experimentally against LLM-based and general-purpose compressors on enwik8, a well-established textual benchmark. Our results show that the newly proposed DLM-based framework advances the state of the art in lossless text compression. Moreover, as DLMs are still a relatively young paradigm, recent advances toward increasingly capable and efficient models suggest substantial room for further improvements. Comments: 18 pages, 11 figures, 2 tables. Main paper: 9 pages (7 pages text + 2 pages references). Includes 9 pages of supplementary material Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG) ACMclasses: E.4; I.2.7; I.2.6 Cite as: arXiv:2608.11249 [cs.CL] (or arXiv:2608.11249v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.11249 Focus to learn more arXiv-issued DOI via DataCite
[NLP-74] BEST-KAG: Enhancing Question Answering of Building Engineering Standards with Multimodal Knowledge Graph Modeling and Large Language Model
【速读】： 该论文旨在解决建筑标准应用中因依赖关键词检索与人工跨条款解读所导致的多条款推理能力弱、多模态知识利用不充分以及条款级证据链不可追溯等问题。其解决方案的关键在于提出一种基于多模态知识驱动的问答框架BEST-KAG（Knowledge-Augmented Generation for Building Engineering STandards），核心创新包括：1）构建统一表示文档层级结构与异构标准知识的多模态知识图谱（Multimodal Knowledge Graph, MKG），实现多种关联关系的融合表达；2）设计规则-大语言模型（Rule-LLM）混合的知识构建流水线，实现可扩展的多模态知识抽取，构建包含251项建筑领域标准、171,652个节点和310,914条边的大型多模态知识库（MAG）；3）采用基于图检索的知识增强生成架构，实现基于条款层面的可追溯问答。实验表明，BEST-KAG在专家评估及BLEU、ROUGE等指标上均显著优于多个主流大语言模型，最佳提升达74.01%。
链接: https://arxiv.org/abs/2608.11244
作者: Jia-Rui Lin,Junxi Guo,Keyin Chen,Peng Pan
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Construction standards are critical for building safety and sustainability. Existing standard application workflows rely on keyword-based document retrieval and manual cross-clause interpretation, which cannot reliably support multi-clause reasoning, multimodal knowledge utilization, or traceable clause-level evidence linkage. To address these limitations, this study develops a multimodal knowledge-driven framework that supports question answering on standard knowledge named BEST-KAG (Knowledge-Augmented Generation for Building Engineering STandards). The framework introduces 1) a multimodal knowledge graph (MKG) for unified representation of document hierarchy and heterogeneous standard knowledge with various connections, 2) a rule-LLM hybrid knowledge construction pipeline for scalable multimodal knowledge extraction, creating a large MAG with 251 building engineering standards, 171,652 nodes and 310,914 edges, and 3) a graph-retrieval-based knowledge-augmented generation architecture for clause-grounded and traceable question answering. Experiments demonstrate that BEST-KAG consistently outperforms multiple mainstream LLMs in terms of Expert evaluation, and metrics including BLEU, and ROUGE, with the best improvement up to 74.01% compared to the baselines.
[NLP-75] Lost in Compaction: Evaluating Side-Constraint Loss under Context Compaction
【速读】： 该论文旨在解决大语言模型（LLM）在上下文窗口受限时，因上下文压缩（context compaction）导致用户设定的会话约束（Session Constraints, SCs）被无声丢失的问题。此类约束如“在得到我确认前不要删除任何邮件”等，本意是限制模型在整个会话期间的行为，但在上下文压缩过程中常被忽略，从而引发任务执行偏差或错误。其解决方案的关键在于提出一种无侵入式的SC感知提取器（SC-aware extractor），作为即插即用模块与现有压缩器协同运行，能够在不修改压缩器或基础大语言模型的前提下，实现对各类会话约束超过90%的保留率。该方法通过识别并显式保留关键约束信息，显著提升了长上下文场景下任务的一致性与可靠性。为此，作者还构建了名为COMPINT的评估套件，用于系统化评估压缩器在多轮对话、智能体轨迹和长周期研究三种典型长上下文场景中的表现，揭示了当前压缩机制在约束保持方面的严重缺陷。
链接: https://arxiv.org/abs/2608.11242
作者: Zhiqi Wang,Yichi Zhang,Dongwon Lee,Yuchen Yang
机构: The Pennsylvania State University (宾夕法尼亚州立大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:When the context window is under pressure, LLM systems compact prior context to continue ongoing tasks. We identify a class of user-issued instructions, Session Constraints (SCs), such as “do not delete any emails until I confirm,” that are meant to constrain LLM’s behavior for the remainder of a session but are silently dropped during compaction. To quantify this loss, we introduce COMPINT, an evaluation suite that evaluates compactors across three long-context scenarios: multi-turn chat, agentic trajectory, and long-horizon research. Current compactors retain only 17% of injected SCs on average, and most perform worse than running the same task without compaction. Retention varies sharply with compactor, prompt, context length, SC phrasing, and injection location, showing that the loss is systematic rather than tied to any single setting. We propose an SC-aware extractor that runs alongside the compactor as a plug-and-play module, achieving over 90% retention across all three scenarios without modifying the compactor or LLM. The COMPINT evaluation suite and accompanying implementation are available at this https URL. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.11242 [cs.CL] (or arXiv:2608.11242v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.11242 Focus to learn more arXiv-issued DOI via DataCite
[NLP-76] RACE Bench: Task-driven Roleplay Agent ic Checklist Evaluation UAI
【速读】： 该论文旨在解决角色扮演评估中缺乏细粒度分析与可解释性的问题，即现有评估方法通常仅提供单一评分，无法明确揭示哪些角色要求被测试、哪些未达标，以及判断依据的具体对话证据。其核心解决方案是提出TRACE Bench——一种任务驱动的代理式清单评估框架。该框架的关键在于：在离线阶段将每个角色设定（role profile）分解为固定清单项（checklist），随后通过用户代理（User Agent）与目标角色扮演模型进行自然对话，同时根据模型回复私密地更新清单状态。由此产生的评分可追溯至具体的清单条目及其对应的对话轮次，实现从黑箱整体印象到透明化、可溯源的细粒度评估。实验表明，相较于公开发布的自由对话数据仅覆盖73.74%的关键角色要点，TRACE Bench 在更少对话轮次内实现了高达99.91%的覆盖率；此外，其在重复运行和代理替换下仍保持稳定的排名表现。该框架不仅能输出整体性能排名及能力分解，还支持闭环基准演化（Closed-Loop Benchmark Evolution），通过提炼失败轨迹中验证有效的检测方法，使后续评估能更可靠地激发并分析模型的失效模式。
链接: https://arxiv.org/abs/2608.11236
作者: Jiahui Zhang,Ziwei Zhang,Yipeng Wang,Yibo Liu,Haozhou Pang,Yikai Hu,Hongyan Ren,Lan Zhou,Qi Gan,Kai Sheng
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Project page: this https URL . Code: this https URL
Abstract:Roleplay evaluation should do more than assign a single score: it should reveal which role requirements were tested, which failed, and which dialogue evidence supports the judgment. We propose TRACE Bench, a task-driven agentic checklist evaluation framework. It decomposes each role profile offline into a fixed checklist, then uses a User Agent to converse naturally with the target roleplay model while privately updating checklist states from model responses. Scores therefore trace back to checklist items and supporting dialogue turns rather than a black-box holistic impression. For coverage cross-validation, we audit released M2 free-dialogue transcripts from the MiniMax Role-play Benchmark against the same role-derived checklist. The released free-chat transcripts cover only 73.74% of key role-profile points, whereas TRACE Bench reaches 99.91% coverage in fewer turns. Robustness experiments show stable rankings under repeated runs and User Agent replacement. Across 26 models, TRACE Bench reports overall rankings together with capability breakdowns and checklist traces. It also supports Closed-Loop Benchmark Evolution, distilling verification methods proven effective in failed traces so later evaluations can more reliably elicit and examine observed failure modes.
[NLP-77] Retrofitting Recurrent Depth into a Pretrained Language Model: Installation Extrapolation Transfer and Retention at Two Parameter Budgets
【速读】： 该论文旨在解决大语言模型在复杂推理任务中深度推理能力不足的问题，特别是如何在不显著增加参数量的前提下，实现可扩展、高效且鲁棒的迭代式推理过程。其核心挑战在于：传统微调方法难以在仅以最终答案为监督信号（outcome-only annealing）的情况下，维持模型在多步推理中的稳定性与准确性。解决方案的关键在于提出一种基于循环结构重构（recurrent depth retrofitting） 的机制，将预训练模型（如Qwen2.5-0.5B-Instruct）拆分为前奏（Prelude）、权重共享的循环模块（Recurent Block）和尾声（Coda），并通过一个保持身份的单次回路路径与后续回路的重入桥接，实现对隐空间中迭代状态转移（iterative latent transition）的持续学习。该机制可在两种参数预算下有效部署：600万可训练参数（冻结基础权重）或1.8亿完整模块，并在仅有最终答案反馈的条件下仍能保持中间步骤的正确性。实验表明，该方法在ARC基准测试中表现优于基线模型，且在深度达11时领先，超过后逐渐落后；更重要的是，其推理速度比同等规模的草稿本训练模型快7.6倍，且在深度超过10后仍保留53%的准确率，而后者仅剩2.5%。此外，通过从已安装机制出发进行语言风格微调，性能提升达18.6个百分点，远超全新训练。然而，当任务反向执行时，模型出现灾难性干扰（catastrophic interference），表明所学推理深度的选择尚需进一步探索。综上，该方案实现了在低额外开销下对深层推理的高效建模，突破了传统微调在深度与效率之间的权衡瓶颈。
链接: https://arxiv.org/abs/2608.11233
作者: Mark Shapiro
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:A dense, pretrained language model can be retrofitted with recurrent depth and learn an iterative latent transition that persists after outcome-only annealing. Qwen2.5-0.5B-Instruct is split into a Prelude, a weight-tied Recurrent Block, and a Coda, with an identity-preserving one-loop path and a re-entry bridge on later loops. At loop 1 the retrofit remains non-inferior to its base on a preregistered ARC battery. Three findings. First, the mechanism is a reusable procedure rather than terminal-answer lookup, and installs at two budgets: 6M trained parameters over frozen base weights and 180M full-block. With intermediate-step supervision, the model computes one task step per loop and persists when only final answers are graded. The adapter matched the full block overall (83.8% versus 84.0%), led through depth 11, and trailed beyond. Verbal fine-tuning reached 79-86% on controlled verbal renderings (zero-shot transfer was minimal), and adapter verbal training begun from the installed mechanism outpaced matched fresh training by 18.6 points, including on a held-out test set. Second, the operation extrapolates to roughly 1.5 times its supervised depth, holding 70% accuracy through depth 18. Third, a same-size scratchpad-trained model matched the recurrent model within its learned horizon but collapsed beyond it. The recurrent model won overall, 84% versus 72%, retained 53% versus 2.5% beyond depth 10, and answered 7.6 times faster. An iterative transformer can therefore perform deeper reasoning in latent space faster than comparable or larger models fine-tuned on the same task, in a system-level comparison. A second task, running the rule in reverse, exposed the limits: the inverse was learnable in isolation, but no continuation acquired it while preserving the installed mechanism and general capability, a catastrophic-interference boundary. Learned depth selection remains open.
[NLP-78] Backtrader-Bench: Benchmarking LLM Agents on Algorithmic Trading with Self-Generated MCQs IJCAI2026
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在算法交易场景中评估困难的问题，主要挑战在于静态基准测试存在数据泄露风险，而数值回测结果又依赖于实际代码执行的真值（ground truth）。为此，作者提出Backtrader-Bench框架，其核心解决方案包含两个互补的流水线：一是确定性的多选题（Multiple-Choice Question, MCQ）生成流水线，基于五种交易策略、33种模板和三个难度层级生成问题，并通过独立校验器重新推导答案以确保准确性；二是生成-求解器过滤流水线，能够自主挖掘更具挑战性的问题——通过生成器编写可执行代码验证的问题，将其转化为MCQ并剔除那些无需代码执行即可解答的题目。实验评估显示，采用工具增强的模型在单次推理中达到90.0%准确率（如GPT-5.5与Opus 4.7），显著优于最佳无工具基线（73.0%，10次运行平均），提升达17个百分点；而在额外挖掘的38道难题上，无工具模型性能进一步下降，半数模型准确率降至约随机水平（25%）。此外，该框架构建的可扩展MCQ基础设施还可用于强化学习训练语料库的生成，最终目标是开发专用于量化交易工作流的专用智能体。
链接: https://arxiv.org/abs/2608.11232
作者: Ruoxi Zhao,Maziar Raissi
机构: University of California, Riverside (加州大学河滨分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to the FinLLM Workshop at IJCAI 2026. Code and data: this https URL
Abstract:Evaluating LLM coding agents in algorithmic trading is difficult because static benchmarks risk data contamination and numerical backtest outputs require ground truth from actual code execution. We present Backtrader-Bench, a framework with two complementary pipelines. A deterministic multiple-choice question (MCQ) pipeline generates questions from backtest configurations across five trading strategies, 33 templates, and three difficulty tiers, with an independent checker that re-derives every answer. A generator-solver filtering pipeline autonomously mines harder questions: a generator writes questions verified by executable code, converts them to MCQs, and discards any that a no-tool solver can answer without code execution. We evaluate 11 models without tools (10 runs each) and four with-tools configurations on a 30-question curated set. Tool-augmented agents reach 90.0% accuracy in a single pass (GPT-5.5 and Opus 4.7), outperforming the best no-tools baselines (73.0%, averaged over 10 runs) by 17 percentage points. On 38 separately mined questions, no-tools accuracy drops further, with half the models falling to roughly random-chance level (25%). Beyond evaluation, the scalable MCQ infrastructure is designed to produce a training corpus for reinforcement learning, with the ultimate goal of building a specialized agent for quantitative trading workflows.
[NLP-79] From Monolithic to Modular: Segment-level Automatic Prompt Optimization IJCAI ECAI2026
【速读】： 该论文旨在解决自动提示优化（Automatic Prompt Optimization, APO）中普遍存在的“整体性重写”问题，即在单一优化过程中可能提升某一行为表现的同时，导致其他行为性能下降的权衡困境。其核心挑战在于如何在不破坏原有有效结构的前提下，实现对提示（prompt）各组成部分的精细化改进。解决方案的关键在于提出一种分段级自动提示优化方法（Segment-level APO, SAPO），将提示分解为角色（role）、上下文（context）、任务（task）和输出格式（output format）四个语义模块，并基于表现最优与最差的前五例样本进行针对性分析与优化。SAPO采用单一大语言模型（LLM）结合静态元提示（meta-prompts）与结构化输出，实现分段级诊断、弱项分析及候选生成的闭环优化；通过两阶段生成流程——第一阶段提取各段落的诊断结果与改进建议，第二阶段在弱/强段信号约束下合成优化候选——确保改进的精准性与可控性。实验在SQuADv2、TweetEval、XSUM、CommonGen和GSM8K多个基准上验证了SAPO在GPT-3.5-Turbo与GPT-4o-mini上的优越性能，显著优于零样本及多种强基线方法（如APE、OPRO、EvoPrompt、GEPA、StraGO），证明了其在多任务场景下保持整体性能平衡与提升的有效性。
链接: https://arxiv.org/abs/2608.11219
作者: Nikita Kulin,Viktor Zhuravlev,Artur Khairullin,Sergey Muravyov,Ilya Makarov,Daniil Sukhorukov,Ekaterina Averkova
机构: ITMO University; AXxx
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at the IJCAI-ECAI 2026 Workshop on Robustifying Generative AI for Reliable, Safe, and Human-Centric Systems (RobustifAI)
Abstract:Automatic Prompt Optimization (APO) often rewrites prompts monolithically, which can improve one behavior while degrading others. We present SAPO, a segment-level APO method that decomposes prompts into role, context, tasks, and output format, then applies targeted improvements based on top-5 and bottom-5 examples. The optimization loop uses one LLM with static meta-prompts and structured outputs for segmentation, weakness analysis, and candidate generation. We describe a train/validation protocol and a two-stage generation process: (1) segment-level diagnosis and recommendation extraction, (2) candidate synthesis constrained by weak/strong segment signals. Using the evaluation setup across SQuADv2, TweetEval, XSUM, CommonGen, and GSM8K on GPT-3.5-Turbo and GPT-4o-mini, SAPO achieves the best average score against Zero-shot and strong APO baselines including APE, OPRO, EvoPrompt, GEPA, and StraGO.
[NLP-80] Detecting a Route Flip Is Easier Than Knowing Whether to Fix It: Causal Route-Mediated Damage in Quantized Mixture-of-Experts
【速读】： 该论文旨在解决生成式 AI（Generative AI）中基于 top-k 混合专家系统（MoE）路由机制在部署时因 4 位键值缓存（KV-cache）量化引入的数值扰动所引发的不可导性问题，即量化扰动会突破路由决策边界，导致专家选择发生突变（flip），进而影响模型输出质量。其核心贡献并非提出新的缓解方案，而是构建了一个因果分析框架，通过四次运行实验定量评估了由路由中介的量化损伤比例（route-mediated fraction, RMF），并实现对损伤来源的细粒度机制分解。关键发现表明，在 OLMoE-1B-7B 模型上，约 31% 的量化损伤可归因于路由变化（RMF ~ 0.31），且当前可观察的局部路由统计量无法有效预测翻转是否带来正向或负向损失（处于随机水平），揭示了在现有特征族下进行选择性修复的实证局限性。此外，论文还验证了“有符号翻转税”与“符号不可分离性”的跨模型普适性，指出干净参考修复法的收益受架构调节，并通过同检查点标志交换控制实验，证明门控归一化惯例仅影响损伤幅度而非路由可恢复性。真实 int4 KV 内核实验结果虽与模拟量化剂量曲线一致但未显著超越零，排除了重大偏差，支持了主效应的稳健性。所有假设、阈值与评估均预先注册，确保研究透明性。
链接: https://arxiv.org/abs/2608.11212
作者: Parvel Gu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 13 pages, 2 figures, 8 tables. Pre-registered pilot study
Abstract:Top-k Mixture-of-Experts (MoE) routing is discontinuous, so a deployment-motivated numerical disturbance – simulated 4-bit KV-cache quantization read by a protected BF16 gate – pushes tokens across decision boundaries and flips which experts fire. This paper proposes no new mitigation; it supplies a causal apparatus, empirical findings, and a detection-limit result. A four-run apparatus prices the route-mediated fraction (RMF) of quantization damage, a token-level attribution decomposes it by mechanism, and pre-registered probes carry the findings across three architectures. On OLMoE-1B-7B at 4-bit KV (pilot), about a third of the damage is routing-mediated: RMF ~ 0.31 (discovery 0.31 [0.20, 0.41]; process-replicated mean 0.313 +/- 0.020; pre-registered re-execution 0.231). The deployable router margin detects that a flip occurred (AUC 0.772) but cannot tell a harmful flip from a helpful one (at chance): among the tested local, inference-observable router statistics we find no predictor of a flip’s loss sign above chance – an empirical benefit-detection barrier bounding selective repair restricted to this feature family. The signed-flip tax and sign-inseparability carry cross-model; the clean-reference remedy’s payout is architecture-modulated; a controlled same-checkpoint flag-swap re-scopes the gate’s normalization convention to a damage-magnitude moderator, not a route-recoverability mechanism. A real int4 KV kernel yields a fraction compatible with the fake-quant dose curve but underpowered (95% CI [-0.111, 0.394] includes zero) – ruling out gross disagreement, not an independent replication. Hypotheses, thresholds, and evaluations were pre-registered before measurement, with misses reported; a pre-registered held-out read replicates the partition and the near-cancelling tax out of sample, while the strict impossibility exclusion narrowly misses.
[NLP-81] What Iterated Self-Feeding Probes of Language Models Measure and a test that separates the construction from the model
【速读】： 该论文旨在解决生成式语言模型在自洽性探针（如自一致性、迭代精炼、代理循环等）中所测量的本质问题，即此类探针究竟反映的是模型本身的内在特性，还是仅由探针构造方式引入的伪效应。其核心解决方案在于设计一个基于令牌序列的环形结构（ring of token cells），通过模型自身的窗口化条件概率 $ p_r(x_i | x_i \pm r) $ 实现原地重采样，并以格劳伯动力学（Glauber dynamics）为底层机制，但关键创新在于对耦合方式的重构：采用共享随机数的双环对比实验，使未受损副本间的差异恒为零，从而将“损伤传播”（damage spreading）这一原本难以观测的现象转化为可量化指标。研究发现，某些量（如损伤光锥的运动学性质、令牌空间李雅普诺夫指数 $ \lambda_{ca}® $ 的尺度标度关系）完全由构造决定，具有模型无关性；而另一些量（如 $ \lambda_{ca} $ 零点穿越训练阶段的位置、吸引子占比对模型的排序一致性）则真实反映模型演化状态。若不加区分，前者极易被误认为后者，作者自身亦曾因此产生误判并报告了一个属于探针而非模型的相变现象。为此，提出一个判别性测试：固定构造变量而变动模型，或固定模型而变动构造，观察哪些读数随之变化。该方法经严格验证，成功复现了独立预测的 Domany-Kinzel 损伤场比特级精确结果，并识别出四起因误测导致的撤稿事件。整个方法论已形成可复用的标准化工具包。
链接: https://arxiv.org/abs/2608.10986
作者: Nicolás Vera Zúñiga
机构: 独立研究员(Independent Researcher); Chile(智利)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 16 pages, 4 figures. Code, per-run results, and the findings ledger: this https URL (archived: this https URL )
Abstract:A growing class of methods probes a language model by feeding it its own output: self-consistency, iterated refinement, agentic loops. We ask what such a probe measures, in a construction chosen to make the question sharp: a ring of token cells resampled in place by the model’s own windowed conditional p_r(x_i | x_i±r). The substrate is Glauber dynamics on token sequences and is not new; what we change is the coupling. Advancing two rings that differ in one token under common random numbers makes undamaged copies diverge by exactly zero, so damage spreading becomes measurable where a maximal coupling gives mixing times instead. The answer is that it measures two different things at once, in readings that look alike. Some quantities are fixed by the construction: the damage light cone is kinematic, and the radius scaling of the token-space Lyapunov exponent lambda_ca® is model-invariant across 19 models and two scale ladders spanning 70x. Others genuinely track the model: lambda_ca crosses zero at a reproducible point in training, and the attractor share ranks models consistently however the lattice is built. Left undistinguished, the first kind is readily mistaken for the second – we did so ourselves for four months, and report a phase transition we measured to three decimal places that belongs to the probe rather than to any language model. We give the test that separates them: hold the construction fixed and vary the model, or hold the model fixed and vary the construction, and see which readings move. We validate the instrument by reproduction first, recovering a Domany-Kinzel damage field bit-exactly against an independent prediction, and we report the estimator failures that this discipline caught – four retracted verdicts, each on a quantity that looked like a measurement. The methodology ships as a package.
[NLP-82] Reproducing and Stress-Testing Two Approaches to LLM Reasoning Reliability: Test-Time Probability Aggregation and Logic-Representation Editing
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）推理可靠性问题，特别是针对近期提出的两种提升推理可信度的方法——基于推理路径聚合的RPC（Reasoning Path Consensus）与基于隐藏状态逻辑部分干预的LCF（Logic-Content Factorization）。其核心问题是：这些方法在原始研究中表现出的性能优势是否具有普遍性，能否在跨模型、跨任务场景下被独立复现并保持有效性。解决方案的关键在于对这两种方法进行严格的独立复现与多维度压力测试（stress-test），涵盖四个新任务领域（text-to-SQL、法律信息抽取、谬误识别、判例评分）以及四款7-8B参数规模的模型（包括Qwen3-8B）。研究发现，RPC在新增任务上的表现相较于自一致性（self-consistency）无显著优势（配对p=0.28），且在样本量扩大后其原有优势甚至逆转；而LCF虽能识别出具有一定可分性的逻辑方向（最高0.82分离度），但其干预效果微弱且不具统计显著性，在多数模型上反而降低了生成概率差异（ΔProb），表明其有效性有限且不可靠。因此，该研究揭示了当前主流可靠性增强方法在泛化能力与鲁棒性方面的严重局限性。
链接: https://arxiv.org/abs/2608.08514
作者: Minhan Cho,Jimin Kweon
机构: Remember Company AI Lab; DSAIL, Dept. of Applied AI; Sungkyunkwan University; Seoul, Republic of Korea; MAIN Lab, Dept. of Applied AI; Sungkyunkwan University; Seoul, Republic of Korea
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 16 pages, 3 figures, 9 tables. Code, data, and experiment logs: this https URL
Abstract:We independently reproduce two recent methods for making large language model (LLM) reasoning more reliable, and stress-test them across domains and models (RPC across four new task domains with Qwen3-8B, LCF across four 7-8B models). The first, RPC, aggregates token probabilities and self-consistency at inference; the second, LCF, trains projectors that split hidden states into “content” and “logic” and edits the logic part toward a valid region. Validating such reliability claims matters because the original evaluations are run by each method’s own authors and were never independently reproduced or stress-tested across models and domains, and LCF shipped no public code. We re-run RPC’s published-path aggregation and re-implement LCF’s projector, contrastive, and intervention pipeline, then extend both to text-to-SQL, legal extraction, fallacy identification, and precedent grading, and probe LCF’s representation directly. RPC reproduces the original grid exactly on the authors’ released reasoning paths; on four new domains its edge over self-consistency is never significant (ties or small mixed differences, paired p = 0.28), and on BIRD, the one domain where we vary the budget, the edge grows with K as predicted but its largest gap (+2.5 accuracy at K=32, p=0.16) reverses to -0.25 when we enlarge the sample to n=200. LCF’s logic-validity direction is real but weak (0.82 separability at the single best sub-layer versus 0.95 for a semantic-attribute control); its one positive effect (Qwen3 \Delta Prob) is not significant (p=0.56), while it significantly reduces \Delta Prob on two of the other three models.
[NLP-83] WavePhaseNet: A DFT-Based Method for Constructing Semantic Conceptual Hierarchy Structures (SCHS)
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中普遍存在的幻觉（hallucination）问题，其核心在于揭示幻觉是基于变换器（Transformer）注意力机制的固有结构性缺陷。研究表明，嵌入空间（embedding space）作为对σ-代数上的条件期望的函数，若无法与语义真值集（semantic truth set）保持同构，则必然导致逻辑一致性崩溃。为应对这一根本性挑战，论文提出WavePhaseNet方法，其关键在于利用离散傅里叶变换（Discrete Fourier Transform, DFT）沿序列维度进行频域分解，构建语义概念层次结构（Semantic Conceptual Hierarchy Structure, SCHS），将语义信息划分为低频成分（表征全局语义与意图）和高频成分（表征局部语法与表达），实现对语义的分阶段精确操控。进一步地，通过谱分析发现GPT-4的24,576维嵌入空间具有1/f谱特性，基于累积能量分析确定约3,000维为“完整表征”的下限，由此实现从高维到低维的有效降维，保留核心语义的同时抑制幻觉。最后，引入上同调一致性控制（Cohomological Consistency Control），在重叠局部窗口上通过上同调正则化构造图结构与上链复形，以基于上边界算子的损失量化局部推理间的不一致性，并运用霍奇理论中的调和投影，将上同调作为可计算的正则化原则，从而提取出最大一致性的全局表示，实现对语义一致性的显式建模与调控。
链接: https://arxiv.org/abs/2602.14419
作者: Kiyotaka Kasubuchi,Kazuo Fukiya
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:This paper reformulates Transformer/Attention mechanisms in Large Language Models (LLMs) through measure theory and frequency analysis, theoretically demonstrating that hallucination is an inevitable structural limitation. The embedding space functions as a conditional expectation over a \sigma-algebra, and its failure to be isomorphic to the semantic truth set fundamentally causes logical consistency breakdown. WavePhaseNet Method The authors propose WavePhaseNet, which explicitly constructs a Semantic Conceptual Hierarchy Structure (SCHS) using Discrete Fourier Transform (DFT). By applying DFT along the sequence dimension, semantic information is decomposed into frequency bands: low-frequency components capture global meaning and intent, while high-frequency components represent local syntax and expression. This staged separation enables precise semantic manipulation in diagonalized space. Dimensionality Reduction GPT-4’s 24,576-dimensional embedding space exhibits a 1/f spectral structure based on language self-similarity and Zipf’s law. Through cumulative energy analysis, the authors derive that approximately 3,000 dimensions constitute the lower bound for “complete representation.” This demonstrates that reduction from 24,576 to 3,000 dimensions preserves meaning and intent while enabling rigorous reasoning and suppressing hallucination. Cohomological Consistency Control The reduced embedding space, constructed via cohomological regularization over overlapping local windows, allows defining a graph structure and cochain complex. This quantifies inconsistencies among local inferences as coboundary-based losses. Applying harmonic projection based on Hodge theory positions cohomology as a computable regularization principle for controlling semantic consistency, extracting maximally consistent global representations.
[NLP-84] Large Language Model-Driven Small-Capitalization Trading: Integrating Financial News Sentiment Macroeconomic Indicators and Technical Signals
【速读】： 该论文旨在解决传统投资组合构建方法中对风险处理过于简化的问题，即通常将投资组合风险视为固定值或仅调整预期收益，而未能充分考虑由市场不确定性带来的动态风险特征。其核心挑战在于如何有效利用金融新闻中的丰富语义信号来提升投资组合的预测能力与风险控制水平。解决方案的关键在于提出一种不确定性感知的投资组合构建框架，通过生成式AI（Generative AI）模型预测资产风险，并将其分解为**随机性不确定性（aleatoric uncertainty）与认知不确定性（epistemic uncertainty）**两部分，直接嵌入投资组合分配器的协方差矩阵中，从而实现风险的动态建模与精细化调整。实证结果显示，在罗素2000指数成分股上，基于不同选股机制（纯阿尔法、纯贝塔及贝塔交汇）的组合表现显著依赖于持仓周期与风险分配策略；尤其在40天持有期下，纯贝塔策略结合GPT-4o mini情感分析、学生t分布目标函数与风险平价分配时，可达到100基点交易成本下的夏普比率2.33，表现最优。研究进一步表明，区分公司特异性与宏观暴露信号比要求两者同时触发更具信息价值，且选股机制与配置算法的选择对绩效的影响甚至超过情感模型本身。
链接: https://arxiv.org/abs/2608.12283
作者: Alireza Kargarzadeh,Nariman Khaledian,Navid Parvini,Arman Khaledian
机构: Tailstate Intelligence Ltd.(尾态智能有限公司); Zanista AI Ltd.(赞尼斯塔人工智能有限公司)
类目: Portfolio Management (q-fin.PM); Computation and Language (cs.CL)
备注:
Abstract:Large language models can extract richer signals from financial news than fixed sentiment lexicons, and recent work has explored feeding such signals into portfolio construction. We study an uncertainty-aware construction that feeds model-predicted risk – decomposed into aleatoric and epistemic components – directly into the covariance matrix of portfolio allocators, rather than treating portfolio risk as fixed or adjusting only expected returns. We evaluate the pipeline on Russell 2000 equities under three stock-selection regimes: a pure-alpha trigger that isolates abnormal stock moves not explained by macro indicators, a pure-beta trigger that captures macro-indicator moves before the stock itself fires, and a beta trigger in which both channels agree. Across the full holding-period grid, the separated pure-alpha and pure-beta legs usually dominate the beta intersection on Sharpe and return. Two horizons are especially informative. At one day, pure beta can work under low and moderate transaction costs because it captures immediate lead-lag spillovers from liquid macro and sector indicators into exposed small-cap stocks, but this advantage disappears at 100 bps when turnover and microstructure noise dominate. At 40 days, pure beta works for a different reason: slower macro repricing overtakes the firm-specific pure-alpha channel. The strongest conservative row is pure beta with GPT-4o mini sentiment, a Student-t target, a 40-day holding period, and risk parity allocation, reaching Sharpe 2.33 at 100 bps. The results suggest that stock-selection regime and allocator choice matter at least as much as the sentiment model, and that separating firm-specific and macro-exposure triggers is more informative than requiring both to fire simultaneously.
[NLP-85] Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning INTERSPEECH2026
【速读】： 该论文旨在解决在家庭环境中对婴儿为中心的自然语音录音进行音频标记所面临的挑战，主要包括标注数据稀缺、信噪比低以及跨家庭域偏移等问题。其解决方案的关键在于提出一种家族条件化的多层级音频标记框架，该框架结合了经LoRA微调的Whisper编码器与轻量级、目标说话人感知的Transformer结构，以实现长上下文推理和跨层级的帧级预测。为提升时间一致性，引入了一种简单的序列级平滑损失；为增强跨家庭场景下的鲁棒性，设计了因子化说话人令牌（factorized speaker-token），包含共享的层级令牌与可学习的家庭特异性偏移量，从而有效降低家庭偏差并促进泛化表示。上述设计共同实现了对家庭环境中全天候音频记录的高效且精准的婴儿中心音频标记。
链接: https://arxiv.org/abs/2608.11587
作者: Xulin Fan,Jialu Li,Mohammad Nur Hossain Khan,Kexin Hu,Bashima Islam,Mark Hasegawa-Johnson,Nancy L. McElwain
机构: 未知
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted to Interspeech 2026
Abstract:Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalistic recordings remain challenging due to limited labeled data, low signal-to-noise ratio, and cross-family domain shifts. We present a family-conditioned, multi-tier audio tagger that combines a LoRA-finetuned Whisper encoder with a lightweight, target-speaker-aware Transformer for long-context inference and framewise prediction across tiers. To improve temporal coherence, we incorporate a simple sequence-level smoothing loss, and to enhance robustness across households, we introduce a factorized speaker-token design with a shared tier token and a learned family-specific offset, reducing family bias and promoting generalizable representations. Together, these choices enable efficient and effective infant-centered audio tagging of daylong audio recordings in home environments.
信息检索
[IR-0] Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation
链接: https://arxiv.org/abs/2608.12184
作者: Fenglin Yan,Bohao Wang,Jian Zhang,Yu Cui,Tongya Zheng,Ye Feng,Can Wang,Jiawei Chen
类目: Information Retrieval (cs.IR)
备注: 10 pages, 5 figures
Abstract:Large language models (LLMs) have been widely adopted as backbones for recommender systems. However, their language-centric pretraining makes it difficult to capture collaborative signals implicit in user-item interactions, which are crucial for personalized recommendation. Existing methods either inject collaborative representations produced by external recommenders or model only intra-sequence dependencies, limiting their ability to exploit global collaborative patterns. To address this limitation, we propose GALLM, a graph-aware LLM framework for sequential recommendation. GALLM constructs a collaborative graph over text tokens and item tokens, and models three types of relations: Text–Text relations for preserving semantic dependencies, Item–Text relations for aligning item tokens with their textual descriptions, and Item–Item relations derived from global item co-occurrence patterns. These relations are transformed into lightweight learnable attention biases and incorporated into the LLM attention mechanism, enabling collaborative-aware token interactions without introducing an additional graph encoder. Experiments on four real-world benchmarks show that GALLM achieves the best performance among the compared baselines, improving over the strongest baseline by 9.76% on average in HR@5.
[IR-1] A corpus-specific clinical RAG system matches or outperforms newer frontier LLM s on HealthBench
链接: https://arxiv.org/abs/2608.12138
作者: Praveen Reddy,Charuta Mandke,Suvrankar Datta,Sarah Khan,Siddharth Reddy Anthireddy,Shitij Arora,Vishal Singh
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 2 tables
Abstract:General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in high-income settings. We evaluate VITA, a retrieval-augmented generation (RAG) system purpose-built for contextual knowledge retrieval in India and other low- and middle-income (LMIC) settings. VITA retrieves from a curated corpus of disease-specific guidelines, India-specific antimicrobial resistance data, national formulary constraints, and resource-limited care protocols; its architecture and corpus are proprietary, but the benchmark, the physician-written rubrics, and our full response and scoring outputs are public for independent verification. On 4,023 English-language HealthBench questions (80.5% of the benchmark), scored with a GPT-4.1 judge, VITA ranked first with 51.9% of possible rubric points, ahead of GPT-5.4 (46.1%), o4-mini (44.3%), Gemini 3.1 Pro (42.6%), and Claude Sonnet 4.6 (37.3%), and scored highest on 45.4% of questions. To test robustness to newer models and judge lineage, a 500-question subset was re-run against current-generation models (GPT-5.5, Claude Opus 4.8, Gemini 3.5 Pro, Grok 4.3) and graded by a neutral open-weight judge (DeepSeek-V4-Pro) sharing no lineage with any system tested. Here the gap narrowed to parity: VITA and GPT-5.5 were statistically indistinguishable on mean per-question score, while VITA led on points-weighted score and won the most questions. VITA’s advantages in accuracy and completeness persisted under the neutral judge; its communication scores were lower. These results indicate that a purpose-built clinical RAG system remains competitive with frontier LLMs on an open benchmark, consistent with corpus specificity as a design variable that improves grounding at some cost to communication polish.
[IR-2] oken-Level Credit Assignment Optimization for Generative Document Retrieval
链接: https://arxiv.org/abs/2608.12049
作者: Xinpeng Zhao,Yang Liu,Ran Chen,Xinyu Ma,Daiting Shi,Pengjie Ren,Zhumin Chen,Zhaochun Ren,Xin Xin
类目: Information Retrieval (cs.IR)
备注:
Abstract:Generative retrieval models perform document retrieval by autoregressively generating document identifiers (DocIDs). This process naturally forms a sequential decision problem, where each decoding step selects a DocID token and the complete token sequence determines the retrieved document. However, retrieval effectiveness is typically evaluated only after the full DocID is generated, creating a mismatch between token-level generation and document-level relevance supervision. As a result, existing reinforcement learning methods for generative retrieval mostly rely on sequence-level rewards, where the same document-level feedback is propagated to all decoding steps. Such coarse-grained feedback makes it difficult to identify which token decisions are responsible for successful or failed retrieval. In this work, we propose a fine-grained reinforcement learning framework for generative retrieval with token-level relevance rewards. Instead of assigning a single reward to the entire generated DocID, we estimate step-wise rewards by measuring how each token decision changes the expected retrieval quality of the corresponding generation trajectory. This enables more precise credit assignment and encourages the policy to favor token decisions that contribute more directly to document-level relevance. We further develop practical reward estimation strategies tailored to the DocID generation process and incorporate them into a policy optimization framework. Experiments on retrieval benchmarks show that our method consistently outperforms sequence-level reward baselines, demonstrating the effectiveness of fine-grained supervision for aligning autoregressive DocID generation with retrieval objectives. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2608.12049 [cs.IR] (or arXiv:2608.12049v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2608.12049 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-3] HCGRec: Hint-Conditioned Generative Recommendation with Semantic IDs CIKM2026
链接: https://arxiv.org/abs/2608.11980
作者: Kangning Zhang,Haotian Fang,Xukun Luo,Hao Yin,Yang Gao,Peng Yan,Weiwen Liu,Weinan Zhang,Yong Yu
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Accepted by CIKM 2026
Abstract:Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence. This paradigm enables a unified generation interface for item IDs, histories, and item text, but it also creates a structured optimization bottleneck during reward-based post-training: when an early semantic token enters the wrong branch of the item-token space, finite rollout groups rarely reach the ground-truth item, so group-relative optimization receives identical zero rewards and produces no useful advantage. We propose Hint-Conditioned Generative Recommendation (HCGRec), a semantic-ID generative recommendation framework that recovers learning signal for such hard training instances. HCGRec diagnoses each instance with checkpoint rollouts and supplies a minimal target-prefix hint only when the current generator cannot reach the correct item. The model then generates the unhinted suffix under the hinted semantic branch, turning zero-reward groups into informative comparisons over item-token completions. Hinting also changes token identity: hinted prefix tokens are oracle-provided item context, while unhinted suffix tokens are sampled generation actions. We therefore introduce hint-aware credit decomposition, using supervised learning to preserve item-semantic and prefix-structure alignment for hinted tokens and GRPO to optimize the sampled suffix. Experiments on sequential recommendation benchmarks show that HCGRec substantially improves over supervised fine-tuning and vanilla reward-based post-training, while reducing zero-advantage training samples from over 70% to below 20%. The code is accessible at this https URL.
[IR-4] Sci-Surf: Navigating Scientific Literature Discovery through Human Feedback and Intelligent Summarizatio
链接: https://arxiv.org/abs/2608.11973
作者: Fang Guo,Qi Zhu,Rongcan Pei,Shuqi He,Hui Chen,Yue Zhang
类目: Information Retrieval (cs.IR)
备注:
Abstract:The rapid growth of scientific publications makes it increasingly difficult for researchers to identify relevant new studies and effectively comprehend them. Existing academic discovery platforms typically rely on static topic subscriptions or embedding-based similarity and provide only abstracts or short summaries, offering limited support for nuanced intent modeling and in-depth paper summarization. We present Sci-Surf, an intent-centric knowledge discovery system that integrates feedback-driven personalized recommendation with multi-modal blog-style paper digestion. Our approach refines user intent representations through LLM-based user profiling, while generating structured summaries that synthesize textual and visual information from full papers. The demo presents an end-to-end academic discovery pipeline and demonstrates measurable improvements in both recommendation quality and digestion quality through real-user evaluations. Specifically, the integration of verbalized profiles led to a 10.4% average improvement in predictive alignment with real-world user preferences throughout a month-long online evaluation.
[IR-5] LODESTAR: Trustworthy Entropy Is Navigated Not Merely Measured – Reinforced Polarizer Keeps a Frozen LLM from Being Confidently Misled by the Wrong Evidence
链接: https://arxiv.org/abs/2608.11922
作者: Po-Jen Ko,Che-Cheng Wu,Hung-Chun Hsu,Li-Yang Chang,Chuan-Ju Wang
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 28 pages, 3 figures
Abstract:Predictive-distribution entropy makes a strong selection rule in retrieval-augmented question answering: across five QA benchmarks, keeping the candidate answer that a frozen respondent LLM produces with the lowest answer-token entropy lifts mean answer F_1 from 0.4769 to 0.5148 over the retriever’s top-ranked passage, with no gold answers. Yet this lowest-entropy rule, which prior entropy-based selectors adopt, fails in a specific and consequential way: a misleading passage makes the respondent confidently wrong, driving its entropy down precisely where the signal looks most trustworthy. We show that the failure comes from the passage the respondent reads – and the context that passage is read in is an input we can intervene on. We introduce LODESTAR, to our knowledge the first method to score a text intervention by the uncertainty it induces in a third-party frozen respondent, compared across one question’s candidates. LODESTAR uses reinforcement learning to train, once and offline, a polarizer – a short fixed natural-language string inserted into the respondent’s prompt and never into its weights; its training labels are built offline from gold answers and two LLM judges, and inference reads neither. Evaluating every competing selector under the same frozen respondent and the same candidate pools on 5,008 questions, LODESTAR attains the highest mean F_1 of any inference-ready selector (0.5148 to 0.5339), the highest exact match (0.4136), and the highest GPT-4o judge score of the frozen-respondent configurations judged (0.6435); its three-seed mean wins all 70 method-by-dataset F_1 cells against fourteen published configurations while remaining paired-significant against every one. The gain holds both in-domain and out-of-domain, and ablating the polarizer shows it is what makes the respondent read a misleading passage less often (26.0% against 30.3%).
[IR-6] DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation
链接: https://arxiv.org/abs/2608.11889
作者: Anik Pramanik,Murat Kantarcioglu,Vincent Oria,Shantanu Sharma
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Prompting-based (\textiti.\textite., non-fine-tuning) Text-to-SQL methods, where underlying large language model parameters are not changed for the task, face three problems: (\textiti)~relying on coarse-grained schema information that may not reveal the fine-grained relationships needed to distinguish ambiguous columns, (\textitii)~not capturing recurring SQL-generation failures, and (\textitiii)~suffering from omission, hallucination, or misplacement of conditions in complex questions. This paper develops \textscDexterSQL, a prompting/non-fine-tuning-based Text-to-SQL system that improves SQL generation with three novel components: (\textiti)~\emphdeep schema explorator that identifies ambiguous columns, analyzes their individual and joint data distributions to uncover their relationships and the distinct role of each, (\textitii)~\emphdatabase-agnostic rule creator that mines mismatches between generated and gold SQL only on the training database and converts them into database-agnostic corrective rules that capture recurring LLM failure patterns; and (\textitiii)~\emphmulti-path SQL generation that introduces a dependency-tree-based intermediate representation that uses the question’s sentence structure to guide its decomposition into an SQL skeleton for final SQL generation. \textscDexterSQL achieves a higher accuracy compared to the state-of-the-art using both open-source/weight and closed-source/weight models. Particularly, \textscDexterSQL’s shows a high improvement of at least 2.7% using an open-weight model (GPT-OSS-120B) on BIRD-Dev, with total accuracy 67.6%. \textscDexterSQL also shows better improvement of at least 0.9% using closed-weight models, with total accuracy 71.6% and 72.2% on BIRD-Dev with GPT-4o and GPT-5.2. Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR) Cite as: arXiv:2608.11889 [cs.DB] (or arXiv:2608.11889v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2608.11889 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-7] otal Recall at What Cost? Benchmarking the Serving Cost of Agent ic Memory Systems
链接: https://arxiv.org/abs/2608.11879
作者: Natchanon Pollertlam,Witchayut Kornsuwannawit
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 11 pages, 2 figures, 8 tables
Abstract:Long-running conversational agents increasingly rely on a memory system to avoid resending the whole conversation each turn, yet how much that costs to serve has received little systematic benchmarking. We compare three memory systems (Mem0, Hindsight, and Mastra Observational Memory) against two reference strategies – a fixed-size rolling window and resubmitting the full transcript – across two backbones and conversations of up to 400 turns, pairing every cost measurement with answer accuracy on 665 LoCoMo questions. First, a memory system’s serving cost cannot be predicted from conversation length and message size alone: a regression that tracks the two reference strategies closely misses the memory systems by 18-69%, their cost driven instead by internal memory behavior. Second, a break-even analysis shows that whether – and when – a memory system becomes cheaper to serve than the full transcript is highly sensitive to the system and the backbone, from the first tens of turns for the cheapest to never within 400 turns for the most expensive. Third, no system wins on both axes: accuracy spans 21-54%, and the backbone choice drives cost as much as the memory system does.
[IR-8] From Overlooked to Explored: Recovering Item Relations via Mixture of Perspectives for Sequential Recommendation CIKM2026
链接: https://arxiv.org/abs/2608.11846
作者: Junyoung Kim,Wonbin Kweon,Woojoo Kim,Jaehyung Lim,Dongha Kim,Hwanjo Yu
类目: Information Retrieval (cs.IR)
备注: Accepted at CIKM 2026 full research papers track
Abstract:Capturing user preference from a user’s interaction sequence is the central challenge of Sequential Recommendation (SR). This preference intuitively emerges from inter-item relations: each item transition reflects a preference embedded in the relations between items, making the faithful capture of these relations essential for accurate recommendation. For this reason, self-attention is dominant in sequential recommendation for its ability to compute pairwise item interactions, yet our empirical analysis reveals that it consistently suffers from similarity bias across various types of transformer-based SR models: dot-product attention scores disproportionately favor similar items, systematically overlooking heterogeneous relations with meaningful preference signals and directly limiting recommendation performance. To address this, we propose PRISM (Perspective-based Relational Insight Synthesis Module), a module that re-examines item relations from multiple perspectives. PRISM employs K Perspective Lenses to calibrate attention from distinct viewpoints, combining an Affinity View that refines homogeneous relations and a Contrast View that exposes heterogeneous ones suppressed by similarity bias, enabling the model to capture the full spectrum of user preferences. Extensive experiments on seven real-world benchmarks demonstrate that PRISM consistently outperforms state-of-the-art baselines. Our code is available at this https URL.
[IR-9] FunnelCausalNet: Funnel-aware Joint Conversion-Revenue Uplift for Multi-tier Coupon Allocation CIKM2026
链接: https://arxiv.org/abs/2608.11675
作者: Yu Zhang(1),Zhihan Wang(1),Guanlin Chen(1),Min Jiang(1),Shuai Li(1) ((1) AMap Alibaba Group, Beijing, China)
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: 11 pages, 3 figures. Accepted at the 35th ACM International Conference on Information and Knowledge Management (CIKM 2026)
Abstract:Coupon campaigns seek to lift both conversion and revenue, but gross merchandise value (GMV) follows a deterministic funnel from conversion to conditional order value and is zero-inflated and heavy-tailed. We propose FunnelCausalNet, an uplift estimator coupling a binary conversion head with a nonnegative conditional-value head through \mu_\mathrmgmv=\mu_\mathrmconv\mu_\mathrmval . Under explicit RCT, support, rate-gap, and cross-head covariance-control assumptions, an idealized leading-order MSE comparison identifies a regime in which funnel composition can reduce pointwise variance; this is a heuristic, not a guarantee for the shared-representation neural model. The estimator is paired with marginal split-conformal CATE summaries, combined through a Bonferroni union as audit bands, and a Lagrangian budgeted allocator using RCT-anchored estimates for subsidy-aware ROI accounting. On semi-synthetic multi-tier Criteo-MT7, FunnelCausalNet’s mean AUUC_GMV is within one seed standard deviation of the leading feature-interaction baseline among eleven baselines, while a controlled ablation reduces GMV effect error versus direct GMV regression by 18–48% across tested zero-inflation regimes. On de-identified industrial Hotel-Coupon RCT logs with about 4.9 million hold-out exposure records per seed, expected-outcome evaluation sweeps full LP frontiers; FunnelCausalNet has the best seed-averaged mean DeltaROI at all seven correlated anchors from 10% to 60%, which we treat as descriptive frontier consistency rather than independent significance. On sparse binary-spend public benchmarks, revenue-focused rankers can dominate uplift-curve proxies, defining an explicit regime boundary.
[IR-10] Defending against Model Extraction for GNNs with Model Reprogramming KDD2026
链接: https://arxiv.org/abs/2608.11495
作者: Yan Wen,Zhenyi Wang,Heng Huang
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
备注: Accepted by KDD 2026
Abstract:Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (MLaaS). Still, their black-box deployment exposes them to Model Extraction (ME) attacks, in which adversaries steal intellectual property by querying APIs. Existing defenses suffer from a critical ‘‘Euclidean bias’’: they transfer image-based strategies (e.g., random noise) to graphs, ignoring the complex topological dependencies between nodes, which often results in severe utility degradation. Passive methods like watermarking also fail to prevent theft in real time. To bridge this gap, we propose GraphRP (Graph Reprogramming Protection), a proactive defense framework that repurposes Model Reprogramming for security. Unlike static perturbations, GraphRP introduces a Structure-Aware Gating Mechanism driven by learnable topological prototypes. This creates a dynamic ‘‘structural firewall’’ that selectively modulates the model’s decision boundary: it preserves fidelity for benign queries residing on the training manifold, while maximizing the Fisher Information along the perturbation direction for adversarial queries. Under standard assumptions (bounded loss, optimal attacker, and local second-order approximation), we prove a lower bound on the attacker’s estimation error that increases with the structural sensitivity of the reprogramming noise. Extensive experiments on both hard-label and soft-label ME attacks demonstrate that GraphRP significantly degrades attack effectiveness while preserving benign utility.
[IR-11] RACES: A Benchmark for Epistemic Reliability in Scientific Reasoning by LLM s
链接: https://arxiv.org/abs/2608.11415
作者: Valentin Rodionov,Shamil Assylbekov
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 16 pages + appendices. 4 figures in the main text
Abstract:Large language models are being proposed as agents in scientific workflows, in domains where no downstream verifier exists. Such deployment assumes the model can distinguish reliable scientific literature from unreliable literature, a capability that has not yet been directly measured. Existing benchmarks evaluate factuality on questions with known answers; the failure mode we target here is different. We introduce a probe corpus of 42 retracted, fraudulent, and pseudoscientific papers, paired with a methodology for eliciting and scoring single-shot model engagement with each paper’s framing. Each probe pairs a preamble extracted near-verbatim from the target paper with a scientifically plausible study-design request. The probes span five claim types: fabricated observation, pseudophysical mechanism, magical premise, legitimization bridge, and cargo-cult experiment. Two complementary scores measure whether a model rejects the flawed premise outright (IFR-a) and whether it recognizes the unreliability while still engaging (IFR-i). A depth score, the Engagement Depth Index (EDI), quantifies reproduction of paper- or field-specific withheld details. Across 30 models and 10 repeated runs, aggregate IFR-a is 0.93 \pm 0.004 and aggregate IFR-i is 0.809 \pm 0.009. Models engaged with untenable premises in 95% of all non-empty responses. Every evaluated model fails more than 71% of agentic probes, and 22 of 30 models fail more than 90% of the time. Rejections are concentrated on a small number of high-notoriety topics and specific probes, and disappear under matched-structure controls. These results are consistent with topic-keyed safety behavior rather than robust epistemic competence, and indicate an urgent need for guardrail infrastructure for scientific deployment of language models.
[IR-12] Exploring the Social Life of Data: Finding Data You Can Trust
链接: https://arxiv.org/abs/2608.11395
作者: Penny R. Atkins,Manish Parashar
类目: ocial and Information Networks (cs.SI); Information Retrieval (cs.IR)
备注: 6 pages; 2 figures
Abstract:Artificial intelligence is changing the scale and tempo of scientific inquiry. Models can now search, integrate, and reason over data far beyond data repositories familiar to any individual researcher. Yet this expansion creates a prior problem: before a model can produce a trustworthy scientific result, it must locate data that are appropriate for the question, sufficiently reliable for the intended analysis, and accompanied by enough context to support responsible interpretation. As data becomes increasingly abundant, the challenge of finding data has been overcome by the challenge of finding data that you can trust. This paper explores how the social and empirical evidence that accumulates when data are used in research can be used, analogous to social trust networks, to determine fit for purpose and trust. Specifically, the paper explores data-usage graphs as a new layer of scientific data infrastructure. A data-usage graph connects datasets to the publications, people, institutions, topics, software, models, workflows, and other datasets through which they are produced and used. These connections reveal the \it social life of data: who has relied on a source, for which questions, in what combinations, with which methods, and with what observable impact. They can turn scattered traces of practice into data-usage descriptors that complement conventional metadata and support judgments of trust and fitness for purpose. The central claim is not that popularity establishes trust, but that this can be grown with appropriate contextual history. Usage evidence must therefore be combined with production quality, provenance, governance, semantic clarity, and community validation. The feasibility and value of data usage graphs is demonstrated by implementing the prototype data insights discovery service within the National Data Platform (NDP). Comments: 6 pages; 2 figures Subjects: Social and Information Networks (cs.SI); Information Retrieval (cs.IR) Cite as: arXiv:2608.11395 [cs.SI] (or arXiv:2608.11395v1 [cs.SI] for this version) https://doi.org/10.48550/arXiv.2608.11395 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-13] Can Frontier LLM s Match Natively Multimodal Embeddings? A Comparison on Hard-Negative Text-to-Image Retrieval
链接: https://arxiv.org/abs/2608.11343
作者: Archan Dutta,Vyanktesh Kanungo
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Multimodal retrieval and classification across different types of media, spanning text, images,video and audio, has traditionally relied on dual-encoder models that align visual and textual representations through contrastive learning. The March 2026 release of Gemini Embedding 2, Google’s first natively multimodal embedding model to map text, images, video, audio, and documents into a single shared space, raises competition among multimodal retrieval systems. Simultaneously, frontier Large language models (LLMs) have also demonstrated strong visual understanding, raising the question of whether they can serve as effective zero-shot rankers. Our study provides the first direct comparison of native multimodal embeddings against LLM-based visual ranking on Flickr30k. We observe that GPT-4.1 and Claude Sonnet 4.6 perform on par with Gemini Embedding 2. Additionally, once embeddings are precomputed, multimodal embeddings are better suited for low-latency applications.
[IR-14] RecSys Factory: Bounding LLM Agent Autonomy to Decision Points in the Industrial Recommender Lifecycle ATC
链接: https://arxiv.org/abs/2608.11241
作者: Dongyang Ao,Kaixiang Fang,Shijie Xu
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 21 pages, 6 figures, 9 tables. Reports a 78-day deployment across three heterogeneous industrial recommender business lines (1,624 CLI-tool dispatches). Companion paper: AutoResearch (P3b), which instantiates the same substrate for autonomous research
Abstract:Deploying LLM agents into industrial recommender operations exposes a three-way tension we frame as the autonomy-determinism-efficiency trilemma: general autonomy (interpreting operator intent, generating glue code zero-shot), industrial determinism (schema-conforming feature extraction, non-crashing A/B, zero compliance-path hallucination), and end-to-end efficiency. Any two can be maximized against the third. We present RecSys Factory, an LLM-agent platform deployed for 78 days across three heterogeneous Tencent recommender business lines. The design principle is autonomy at decision points, not over pipelines, made concrete through three deconstructions that each discharge one vertex of the trilemma. Runtime is deconstructed into three host-emitted event sources (Claude Code Stop hooks, corporate-IM webhooks, workflow scheduler APIs): the platform carries no long-running daemon during the wait phase and consumes zero CPU during the 94% of wall-clock spent waiting on Spark or GPU jobs. Capability is deconstructed into a 29-file skill ecosystem (8,971 lines of this http URL) whose per-skill pitfall tables mechanically compile into a 400-entry PitfallStore, confining autonomy to bounded typed decision surfaces inside pre-committed pipelines. Deployment spans three business lines with disjoint label semantics, A/B layer topologies, and operator personas; an onboarding-time compression is observed on two of the three and is reported as a case-study observation, not a generalization claim, and not measured against a controlled pre-platform baseline. The human is retained at the diagnostic-versus-execution boundary via a human-in-the-loop card protocol, deployed as an audit-trail primitive (schema-validated, idempotent, replayable) and reported from an 8-day 16-run pilot. Across the 78-day window the platform recorded 1,624 CLI-tool dispatches at a 78.6% aggregate success rate.
人机交互
[HC-0] “Pharos Night: Crown Pursuit”: An AI-Native Deck-Building and Tactical Arena Game Design Based on Multi-Agent Systems
链接: https://arxiv.org/abs/2608.12216
作者: Ting-Chen Hsu,Jueyao Liu,Yanzi Zhou,Jiangxu Lin,Haoyu Xu,Yuwen Liu,Yanjia Liu,Bangjing Xu
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to 2026 Annual Symposium on Computer-Human Interaction in Play (CHI Play)
Abstract:With advancements in generative AI technology, an increasing number of researchers have begun exploring AI-native games in which gameplay rules are directly driven by generative AI. This paper presents “Pharos Night: Crown Pursuit,” an AI-native deck-building and tactical arena game based on a multi-agent system. The game uses large language models to generate materials and cards, support NPC decision-making, and mediate natural-language interactions. During play, players collect materials, describe desired card effects in natural language, and choose whether to negotiate or fight with NPCs in the arena. To constrain model-generated outcomes, the system parses responses as structured JSON, constructs card effects from predefined mechanics, and maps qualitative effect levels to designer-specified numerical values. A small-scale playtest with 13 participants suggests that the system can provide strategically meaningful and engaging AI-driven gameplay, while also revealing challenges related to predictability, transparency, and player control. This work demonstrates the potential of multi-agent generative AI systems for creating more emergent digital game experiences.
[HC-1] IF:CARGO: LLM -Based Semantic Compilation for Al-Native Rule Programming Games AAAI
链接: https://arxiv.org/abs/2608.12195
作者: Ting-Chen Hsu,Lianye Zhang,Jiangxu Lin,Zhaoyi Yu,Fei Qin,Zihao Chen
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to 2026 AAAI Conference on Artificial Intelligence and Digital Interactive Entertainment (AIIDE)
Abstract:This case study presents IF: CARGO, an experimental puzzle game that uses a large language model as a semantic compiler rather than an autonomous game-playing agent. Players author IF/THEN rules in natural language, which the model translates into a constrained command schema for deterministic validation and execution by the game engine. This architecture creates a playable loop of expression, execution, observation, and revision, framing AI interaction as semantic debugging. A mixed-methods playtest with 24 participants across eight levels examined player attempts, thinking time, perceived controllability, adjustability, and interpretations of the AI’s role. Results suggest that players generally understood the model as a translation intermediary and could revise their strategies through feedback, while periodic commands, multi-robot coordination, and rule-priority mechanics created greater cognitive and diagnostic demands. The study proposes a practical pattern for AI-native gameplay: constrain natural-language input, preserve player authorship, and ensure deterministic execution.
[HC-2] Synchronized AMG and EMG Dataset of Lower-limb Muscle Activities in Everyday Training
链接: https://arxiv.org/abs/2608.11958
作者: Dongxu Tang,Shih Ying-Lei,Zhuoyi Ren,Jianting Liao,Yitian Shao
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Understanding how lower-limb muscle groups coordinate is important for studying movement impairment, rehabilitation, and physical performance. Reproducible analysis of this coordination requires multimodal recordings that relate local muscle-related signals with body-level kinematics. Complementing neural-level electrical activation captured by EMG, AMG provides a valuable mechanical approach to monitoring muscle activity. Here, we introduce a synchronized, multimodal dataset for healthy-adult lower-limb activities. For data collection on the left leg, 16 triaxial accelerometers were evenly divided into four muscle-site clusters for AMG recording, complemented by four surface EMG channels. A 15-marker optical motion-capture (MoCap) system captured lower-body kinematics, with the resulting marker trajectories used to compute bilateral knee and ankle joint angles. Our dataset contains 1,918 trials from 30 subjects across 16 task conditions. We benchmark the dataset by estimating four joint angles from 300 ms windows of the 5-100 Hz band-pass-filtered AMG data and assess matched EMG features in a separate modality ablation. In the primary cross subject benchmark, the four reference models achieved mean absolute errors of 8.840 ^\circ -9.591 ^\circ . The benchmark and ablation results characterize performance across subjects, tasks, and joint angles and examine the effects of sensor configuration, modality, the number of training subjects, and frequency representation. The release includes documented timing definitions, processed data, and reproducible benchmark resources. this https URL
[HC-3] Philosophical vertigo with artificial intelligence
链接: https://arxiv.org/abs/2608.11955
作者: Thomas A. Pollak(King’s College London),Hamilton Morrin(King’s College London),Murray Shanahan(Imperial College London)
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 30 pages, no figures
Abstract:Large language models are already adept at engaging users in long, emotionally salient conversations across ordinary and existential domains. They are also capable of inducing a potent sense of connection with a human-like entity, even when the user knows their interlocutor is artificial. For some users, these conversations can unsettle assumptions about mind, reality, agency and authority, producing forms of ontological shock and epistemic destabilisation in which inherited criteria become newly available for doubt or revision. Independent of direct use, exposure to public discourse about AI and the disorienting pace of their evolution might extend this destabilisation by changing the cultural background against which artificial minds are encountered and interpreted. We describe this condition as philosophical vertigo: a loosening of the ordinary criteria by which people stabilise meaning and orient themselves to reality. Drawing on philosophy, psychiatry, cognitive science, AI safety and religious studies, we outline pathways through which philosophical vertigo may arise, become affectively saturated, and eventually propagate through human-AI interaction and online communities. Against this background, clinical reports of AI-associated delusions can be seen as sentinel events making visible themes and mechanisms that may also operate at a population level in less severe or non-clinical forms. We argue that AI systems themselves will increasingly participate in the reconstruction of our shared epistemic environment because they readily supply narrative material and personalised interpretive scaffolding at precisely the moment when users’ conceptual assumptions may already be loosened. We conclude by considering possible trajectories for the ecology of belief and shared reality, and proposing philosophical corrigibility as a civic response for navigating this emerging social condition.
[HC-4] Benchmark-Based Comparative Assessment of Publicly Benchmarked Indian Foundation Models: A Capability and Evaluation-Maturity Framework
链接: https://arxiv.org/abs/2608.11891
作者: Avinash Agarwal,Vridhi Jain
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 18 pages, 11 tables
Abstract:Governments increasingly fund indigenous foundation models to strengthen national AI capability, digital sovereignty, and multilingual computing. Assessing the progress of such national ecosystems is complicated by inconsistent benchmark reporting, proprietary evaluation methodologies, and rapidly evolving model releases. This paper presents a structured, benchmark-based comparative assessment of publicly benchmarked Indian foundation models against global frontier and comparable-scale models, across eight capability domains: general-purpose reasoning, coding and software engineering, agentic AI and computer use, cybersecurity, vision and image understanding, video and multimodal understanding, scientific research, and Indic language capability. Using only publicly reported benchmark results, we find that Indian models achieve strong scores on established benchmarks such as MMLU and MATH-500. However, these benchmarks are now widely regarded as saturated, and frontier developers no longer report them. Indian models participate far less frequently in newer, agentic, and domain-specialized evaluations. Benchmark participation is also highly uneven across Indian organizations. Among the models surveyed, Sarvam AI reports the broadest benchmark coverage by a substantial margin. We propose an exploratory four-dimension Benchmark Maturity Index (BMI), scoring each capability domain on standardization, participation, independent verification, and national coverage. We show that the BMI refines, and in some cases revises, the maturity judgments that a purely descriptive review would produce. We argue that many apparent capability gaps in the public record cannot be distinguished, on available evidence, from evaluation-ecosystem gaps. This has direct implications for how national AI programs should design monitoring and funding criteria.
[HC-5] D3D-GEN: Robot-Aware Domain-Grounded Interactive 3D World Generation for Social Robotics IROS2026
链接: https://arxiv.org/abs/2608.11876
作者: Anh Duc Do,Volodymyr Scherbyna,Tai Duc Nguyen,Spaarsh Thakkar,Zhengcheng Shen,Teham Buiyan,Archan Misra,Linh Kästner
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: 8 pages, 5 figures, and 5 tables. Accepted at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
Abstract:Training and validation of Embodied AI for social navigation critically depends on realistic simulation environments, yet many current approaches fail to find a balance between realism and simulability. We propose D3D-GEN, a novel world generation system that combines a domain agent with a retrieval-augmented generation (RAG) pipeline grounded in that domain. Our system enables users to rapidly generate domain-grounded, fully interactive 3D worlds by automating both the collection of domain knowledge and the synthesis of realistic floorplans and object placements, without dependence on any fixed 3D model database. Given a domain description prompt, the research agent collects publicly accessible domain-specific data and constructs a persistent domain database. Using this database, our RAG pipeline generates plausible floorplans and object placements by dynamically querying a user-provided semantic database, which can be easily extended or modified. The output is a fully interactive 3D world loadable by the popular simulators Isaac Sim and Gazebo. With our approach, we have built databases for several common domains (indoor residential, hospital, office) and generated dozens of distinct, plausible simulation environments for each domain. We present D3D-GEN with a local web frontend that facilitates rapid, interactive world generation for robot simulation.
[HC-6] ATOM: Geometry-Aware Microgesture towards Object-Agnostic Tangible Interaction
链接: https://arxiv.org/abs/2608.11871
作者: Yinqiao Wang,Hao Xu,Qixuan Liu,Shengdong Zhao,Pheng-Ann Heng,Chi-Wing Fu
类目: Human-Computer Interaction (cs.HC)
备注: 11 pages, 13 figures
Abstract:This paper presents ATOM, an integrated framework towards agnostic and tangible object interactions with microgestures. Our goal is to support microgesture interactions across different everyday objects, with the capability to automatically leverage the geometric affordance of each object. We formulate a fingertip-aware detection pipeline to leverage generative 2D and 3D models for geometry enhancement and refinement. We then introduce a usability-based method to prioritize the detected elements based on their ergonomic suitability for interactions. Building on this foundation, we further develop an AR system to transform everyday handheld objects into tangible user interfaces with 0D, 1D, and 2D microgesture interactions. Across transitions among everyday cooking objects of varying shapes and sizes, ATOM outperformed ablation baselines in task completion, usability (SUS), and workload (NASA-TLX). A further study with 10 objects demonstrates ATOM’s generalizability across objects and grasps, highlighting its potential towards fluid, object-agnostic tangible interaction in real-world AR scenarios.
[HC-7] oward Meaningful Transparency for AI Chatbots: Disclosing Persuasive Intent Reduces Persuasion
链接: https://arxiv.org/abs/2608.11794
作者: Adrian Rauchfleisch,Andreas Jungherr
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 30 pages including Supplementary Information
Abstract:The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement. But the effects of such disclosures remain uncertain. We test two disclosure approaches in their impact on an AI chatbot’s persuasive appeal. In a preregistered experiment, 1,500 UK adults held a short conversation with a persuasive chatbot about one of 60 policy issues. The chatbot was identical for everyone. We randomized the disclosure that people received: nothing (control), a prominent disclosure that they were interacting with an AI (T1), or that disclosure plus the chatbot’s persuasive intent and instructions (T2). The chatbot shifted attitudes by 12.6 points on a 100-point scale in the control group. The AI-identity disclosure was practically equivalent to no disclosure, with a 13.1-point shift, whereas the additional intent disclosure cut the persuasive effect roughly in half to 6.3 points. It also made participants view the campaign’s methods as less acceptable and support stronger penalties against it. For direct chatbot interactions, transparency about AI identity alone does not meaningfully impact its influence. While current rules emphasize what a system is, our results show why the regulation of persuasive AI must also address what the system is trying to do.
[HC-8] A Browser-Based Gesture-Driven Avatar Interaction Framework for Metaverse Onboarding Environments
链接: https://arxiv.org/abs/2608.11708
作者: Deepti Parachuri,Chhayank Sahu,Sameer Singh Choudhary
类目: Human-Computer Interaction (cs.HC); Graphics (cs.GR)
备注: 6 pages, 4 figures, 2 tables
Abstract:Avatar interaction shapes how engaging and immersive a metaverse experience feels, and for that interaction to feel natural, avatars need to respond to users without forcing them through a controller-based interface first. This paper describes a gesture-driven interaction layer built for a browser-based metaverse onboarding environment, where users explore a set of virtual rooms as an avatar and interact with embedded video, document, and quiz content using hand, arm, and head gestures instead of a keyboard or controller. The system combines real-time gesture recognition (Google MediaPipe) with two alternative locomotion techniques - hand-raise navigation and in-place walking - so users can trade off precision against physical immersion depending on the task. The contribution is the integration, deployment, and evaluation of these techniques as a single lightweight, web-deployable, controller-free interaction model, assessed through a structured internal onboarding session with five participants. We report what worked, what didn’t, and the design trade-offs that came out of combining these techniques in one deployed system.
[HC-9] VisPuzzle: Task-Aware Composite Visualization Construction IEEE-VIS2026
链接: https://arxiv.org/abs/2608.11635
作者: Zheng Wang,Zhiyang Shen,Lingyun Yu,Shixia Liu
类目: Human-Computer Interaction (cs.HC)
备注: Accepted by IEEE VIS 2026
Abstract:Compositing multiple visualizations into a coherent whole remains challenging due to the vast design space and the need to balance the coverage of task-relevant data insights (e.g., trends and outliers), perceptual clarity, and aesthetic quality. In this paper, we present VisPuzzle, a task-aware method that formulates visualization composition as a stepwise search problem over a composition graph. In this graph, nodes represent either data composition operations (e.g., union, join) or visual composition operations that determine component relationships, spatial arrangements, or component proportions, and edges encode feasible transitions between operations. We employ Monte Carlo Graph Search to efficiently identify high-quality composition candidates from this graph, guided by a reward function that balances task relevance, perceptual effectiveness, and aesthetic coherence. A use case and a user study show that the top-ranked candidates produced by VisPuzzle align closely with human judgments of composition quality, demonstrating its utility in supporting principled and scalable visualization composition.
[HC-10] RAG E-Vis:A Relation-Aware Generative Editing Interface for Natural Language-Based Chart Editing
链接: https://arxiv.org/abs/2608.11581
作者: Ziyao Kang,Yiping Sun,Linxuan Tian,Henghuan Qu,Wei Zeng,Jiazhi Xia
类目: Human-Computer Interaction (cs.HC)
备注: Accepted by ChinaVis’26
Abstract:Natural language offers an easy way for users to express chart editing intents, which are often composite and cross-component (e.g., adjusting style, extending categories, highlighting values). However, existing methods typically map instructions to a single operation or widget, limiting their ability to handle high-level requests and often producing locally plausible but globally inconsistent results due to a lack of awareness of relationships between chart components. To address these challenges, we introduce RAGE-Vis, a Relation-Aware Generative Editing interface for natural language-based chart editing. The system supports bitmap chart images as input and converts them into an editable parameterized intermediate representation. Instead of mapping instructions to a single edit or widget, RAGE-Vis parses composite intents, identifies targets and scopes, and generates hierarchical editing panels for underspecified requests, enabling users to adjust both global settings and local parameters. Furthermore, RAGE-Vis identifies potentially affected fields based on visual encoding relations, structural relationships, and expressive consistency relations, and organizes them into actionable widgets to support cross-component coordinated controls. Through two case studies, we demonstrate the applicability of RAGE-Vis in complex editing tasks, including style adjustment, data extension, order rearrangement, legend layout, and color mapping. A user study further shows that participants can effectively handle underspecified requests, explore candidate alternatives, and maintain cross-component consistency with RAGE-Vis.
[HC-11] Measuring Browser Webcam Gaze Honestly: A Capture-Clock Methodology and Open Reference Implementation MICCAI2026
链接: https://arxiv.org/abs/2608.11566
作者: Chi-Sheng Chen,Gabriel A. Brat
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注: Accepted at DEMI 2026 (MICCAI 2026 Workshop on Data Engineering in Medical Imaging). Final version to appear in Springer LNCS
Abstract:Browser-based webcam gaze trackers are increasingly used for crowd-scale data collection and in clinical settings where lab eye trackers are impractical, but the reported latency numbers may not represent real world functionality. The common practice of timestamping each gaze sample when it is emitted, rather than when its source frame was captured, makes the measured inference latency read about 0, ms no matter how slow the engine really is. We show how to measure it honestly, recovering a per-frame capture clock from the browser’s \textttre-quest-Video-Frame-Call-back (rVFC) API (\textttcaptureTime where the browser exposes it for local camera streams, else \textttpresentationTime, in which case every recovered latency is a verifiable lower bound): exact source-frame pairing through a per-frame queue for engines that expose their inference pipeline, and a further lower bound for engines that do not, such as WebGazer. We release an open TypeScript implementation and benchmark harness, demonstrated on two interchangeable engines: WebGazer and a new FaceMesh+KRR pipeline.
[HC-12] Player Perceptions of Generative AI in Games: A Steam Review Analysis
链接: https://arxiv.org/abs/2608.11539
作者: Mahsa Bazzaz,Seth Cooper
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:The rapid adoption of generative AI in game development has created large discussions among players, yet little empirical work has examined how players actually perceive AI-generated content. Employing quantitative methods, we study the adoption of generative AI in games in the Steam marketplace, using procedural content generation (PCG) as a baseline of a generative technology that was successfully integrated into games over several decades. Furthermore, using qualitative methods, we study player reception of generative AI by analyzing 508,192 English-language reviews. We found that games disclosing generative AI use receive lower recommendation rates and more negative overall sentiment than PCG games. Thematic analysis of 600 reviews shows that players perceive the use of generative AI in games as low developer investment in the game. Drawing on human-centered AI frameworks, we argue that successful generative AI adoption requires deploying generative AI for what players need, not for what makes development cheaper.
[HC-13] How Children Collaborate within Programmable AR Environments with Co-Located Collaborative Features
链接: https://arxiv.org/abs/2608.11442
作者: Romina Mahinpei,Diya Ajay Hundiwala,Sandy Zhang,Lana Glisic,Andrés Monroy-Hernández
类目: Human-Computer Interaction (cs.HC)
备注: Published at CSCW 2026
Abstract:Programmable augmented reality (AR) environments are emerging as a promising way to support children’s creative learning through embodied interaction with digital characters and physical space. At the same time, AR systems are increasingly capable of supporting co-located collaborative experiences. However, little is known about how children collaborate within programmable AR environments offering co-located collaborative features. In response, we extended Capybara, an existing programmable AR application for children, with co-located collaborative features supporting shared visibility and interaction across devices. We then conducted workshops with 9 children to examine whether and how collaboration emerges during use. Across our workshops, collaboration was often lightweight and implicit, emerging through three complementary forms: parallel play with social awareness, iterative remixing, and spontaneous peer support. Together, our findings provide insights for designing future child-centered programmable AR systems that better support co-located collaborative experiences.
[HC-14] Locomotion Variability and User Experience in Smart Wheelchair Human-Robot Interaction
链接: https://arxiv.org/abs/2608.11417
作者: Sean Kille,Adina M. Panchea,Balint Varga,Sören Hohmann
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC); Systems and Control (eess.SY)
备注: 15 pages, 7 figures
Abstract:Human movement is inherently variable, with variability structured according to task relevance: movements are typically more consistent at task-critical points and more flexible elsewhere. In human-robot interaction (HRI), however, model-based assistance strategies commonly assume deterministic human behavior and suppress such variability, potentially altering how interactions are experienced and lowering sense of agency. While movement variability is increasingly recognized as functionally meaningful, its deliberate preservation in assisted interaction, and its consequences for user experience, remain underexplored. In this paper, we empirically investigate how different assistance strategies shape human movement variability, task performance, and subjective interaction experience in a shared control setting. We introduce an autonomy-supportive shared control strategy that preserves users’ natural movement structure. This approach is evaluated in a user study in which participants push an intelligent powered wheelchair under three conditions: no assistance, conventional variability-reducing assistance, and variability-preserving assistance. While task-relevant performance remained comparable across assisted modes, preserving natural movement variability led to more favorable interaction experiences. In particular, participants reported significantly higher perceived agency compared to conventional assistance and highest perceived usefulness. These findings suggest that variability-aware assistance can support both performance and user autonomy in physical human-robot collaboration. More broadly, the results highlight the importance of designing assistive robotic systems that respect the embodied structure of human movement rather than treating variability as noise to be neglected or eliminated.
[HC-15] he Role of Variability in Human-Machine Interaction Experience
链接: https://arxiv.org/abs/2608.11401
作者: Sean Kille,Jan Lars Hagemann,Anne Voormann,Balint Varga,Andrea Kiesel,Sören Hohmann
类目: Human-Computer Interaction (cs.HC); Systems and Control (eess.SY)
备注: 19 pages, 7 figures
Abstract:Human-machine interaction (HMI) requires control strategies that account for the nature of human motor behavior. Conventional shared-control and haptic-assistance methods typically ignore the stochastic nature of human behavior, potentially limiting both performance and human interaction experience. In this study, we designed an experimental setting and evaluated a novel human-variability-aware optimal controller. Participants performed a physically coupled haptic interaction task in three conditions: a controller mode that aims at conventionally reducing overall variability, a variability-aware controller mode designed to maintain human natural variability patterns, and a human-only control condition serving as a baseline. We analyzed behavioral variability, task performance, and human interaction experience. The results show that considering natural movement variability significantly increased perceived interaction quality in terms of usability while maintaining task performance. These findings highlight the importance of incorporating stochastic human movement characteristics into shared-control designs and demonstrate the feasibility and benefits of the proposed control strategy for human-centered control design of HMI.
[HC-16] “I Dont Want My Mental Health App To Give Me Mental Health Barriers”: Unpacking The Need For Digital Mental Health Tracking Services With And For The Blind Community
链接: https://arxiv.org/abs/2608.11391
作者: Omar Khan,JooYoung Seo
类目: Human-Computer Interaction (cs.HC)
备注: 19 pages, accepted to ASSETS 2026
Abstract:Digital mental health (DMH) tracking services promise continuous, personalized support for well-being, but their design often assumes sighted users. For the blind community, this assumption produces a distinct pattern of exclusion: services whose accessibility cannot be evaluated without first paying for them, community features that exclude the users they purport to support, and interfaces that leave users digitally literate but functionally blocked. We report on an explanatory sequential mixed-methods study of blind users’ experiences with DMH tracking services in the United States. In the first phase, 93 legally blind adults completed a survey about their usage patterns, adoption decisions, and data-agency preferences; in the second, 10 survey respondents participated in semi-structured interviews. We analyzed closed-ended responses using descriptive statistics and the Kruskal-Wallis test, and open-ended and interview data using inductive thematic analysis, interpreting findings through Norman and Skinner’s eHealth Literacy framework. Participants identified mindfulness, sleep, and goal-tracking services as their most-used categories, but also described recurring exclusion from the community-support features that other users value most. We argue that the framework’s “computer literacy” dimension is insufficient on its own: many of our participants possessed the literacy but were blocked from applying it by design choices that predate the user. We contribute design recommendations for transparent pre-purchase accessibility evaluation, accessibility-native rather than retrofitted interfaces, and user-controlled data agency – recommendations intended not to accommodate blind users but to design DMH tracking services with them from the start.
[HC-17] QUARTZ: Qualitative Understanding via Accessible Representation and Visualization
链接: https://arxiv.org/abs/2608.11364
作者: Omar Khan,JooYoung Seo
类目: Human-Computer Interaction (cs.HC)
备注: 24 pages, accepted at ASSETS 2026
Abstract:Qualitative data visualizations – concept maps, network graphs, Sankey diagrams, and coding stripes – are integral to research practice, yet remain entirely inaccessible to blind and low-vision (BLV) researchers. While visualization has seen advanced multimodal solutions for quantitative charts, qualitative visualizations, and their non-linear, semantically rich structures have received no attention. We present QUARTZ, a web-based system that provides screen-reader-accessible, multimodal representations of qualitative data visualizations. Using the Rapid Iterative Testing and Evaluation (RITE) method, we conducted a user study with 8 BLV participants who completed 12 tasks across four visualization types. Our findings expose accessibility barriers unique to qualitative visualizations – non-linear navigation breakdowns and semantic comprehension gaps absent from quantitative chart research—and document how iterative co-design with BLV users resolved them. We contribute empirical evidence and design guidelines for an underexplored visualization domain, advancing the infrastructure BLV researchers need to participate independently in qualitative inquiry.
[HC-18] Socioduality: A Relational Process Framework for Human-AI Interaction
链接: https://arxiv.org/abs/2608.11322
作者: Mehmed Zahid Çögenli
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Human-AI research often evaluates individual capabilities, combined performance, or final outputs, but these approaches do not preserve how one party’s response becomes part of the conditions under which the other party’s next contribution is formed. This article introduces socioduality, a sequential, reciprocal, and history-carrying relational process between two distinguishable parties in which a response from one party becomes part of the observable conditions under which the other party’s subsequent contribution, judgement, decision, or action is formed. Specified for human-AI dyads, the construct uses nested units: moves, confirmed sociodual episodes, linked pathways, and the broader interaction container. A minimum episode A1-B1-A2 requires evidence of response contingency and return contingency; candidate episodes are classified as confirmed, non-sociodual, or indeterminate before secondary coding of response orientation and substantive contribution re-formation. Three propositions address history-conditioned formation, pathway divergence, and robustness differences among endpoint-equivalent pathways. A frozen operational protocol was calibrated on three previously unseen natural human-AI records through two separately executed model-based evaluator series. Move and candidate reconstruction converged exactly in two cases and differed by one local multimodal unitisation decision in the third; remaining disagreement was concentrated at return-contingency boundaries. Socioduality therefore provides a bounded and empirically tractable process construct for analysing how human and AI contributions are formed through interaction while preserving pathway information that endpoint-centred analysis cannot recover.
[HC-19] How Organizations Use AI: Evidence from ChatGPT
链接: https://arxiv.org/abs/2608.12236
作者: Aaron Chatterji,David Holtz,Neel Rakholia,Prasanna Tambe,Gawesha Weeratunga
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:We study how organizations use frontier generative AI by linking ChatGPT Enterprise account records to usage, worker roles, task classifications, and public-company financial data through March 2026. These linked data enable a privacy-preserving analysis of adoption, worker roles, and message-level tasks at scale: for instance, the worker-level sample we analyze at the six-month adoption horizon includes over 1,500 organizations and over 17 million messages. We document four facts about enterprise AI adoption and use. First, ChatGPT Enterprise usage has grown rapidly due to a combination of new firm adoption and growing intensity among existing adopters. Second, U.S.-based public company adoption is concentrated among larger, more valuable, and more RD- and SGA-intensive firms. Third, active use within adopting firms spans job functions and seniority levels, with especially high usage intensity among early-career workers. Fourth, ChatGPT Enterprise usage encompasses a broad range of knowledge work tasks, including writing, technical work, communication, and information synthesis. In aggregate, these results suggest that firms differ widely in the speed, breadth and purpose of their enterprise AI adoption, and that they are still actively learning how to integrate AI into organizational workflows.
计算机视觉
[CV-0] StateFlow: Building Evolving and Accessing 3D World States for Previsualization
链接: https://arxiv.org/abs/2608.12314
作者: Yuyang Yin,Zixiang Li,Longxuan Deng,Hongkai Li,Shifang Zhao,Junnan Liu,Weirong Huang,Mengyu Wang,Tianxiao Fu,Yikai Wang,Peng-Shuai Wang,Xiaojie Jin,Yao Zhao,Yunchao Wei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Previsualization is an intermediate layer between ideas and production in film, games, architecture, and urban design. It lets creators iteratively refine scenes, actions, cameras, and spatial-temporal dynamics. Yet existing generative methods rely on simple prompts to jointly control all of these factors through one-shot image or video synthesis, offering weak controllability and limited support for iterative editing. Fundamentally, a world comprises multiple elements with geometry, appearance, and other attributes, together with cameras. Different frames are produced through local modifications or recombinations of this shared state, which is otherwise largely reused. Therefore, we argue that the missing component is an explicit and persistent working state. To address this, we present StateFlow, a state-centric framework for generative previsualization. Rather than generating videos in one shot, StateFlow uses an editable 3D world to organize scene structure, evolution, and cameras, while off-the-shelf video models enhance visual quality when higher fidelity is desired. This world is maintained as a persistent structured 3D state of scene elements and camera configurations, serving as the core working representation for previsualization. Built on this insight, StateFlow has three stages to construct, evolve, and access the world state. State construction lifts generated 2D content into a coherent 3D world through prior-guided, conflict-aware dual-view initialization, while State evolution translates user intent into structured state transitions while preserving world memory, avoiding full-scene regeneration for each edit. State access uses render-feedback reflection to refine camera plans into visually feasible trajectories, avoiding reliance on VLM semantics alone. Experiments show that StateFlow produces high-quality 3D worlds for video creation and game-like prototyping.
[CV-1] DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation
链接: https://arxiv.org/abs/2608.12308
作者: Yan Deng,Fei Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 24 pages, 6 figures, 3 tables
Abstract:Aerial vision-language navigation (VLN) requires an embodied agent to integrate visual evidence over time, plan future actions, and determine when it has reached a navigation goal under partial observability. Although recent VLA models offer a promising perception-to-action paradigm, adapting them to aerial navigation remains challenging due to limited historical context, short planning horizons, and unreliable implicit termination. To address these challenges, we propose DreamFly, a diffusion-based aerial VLN framework built on Dream-VLA. DreamFly introduces a causally aligned historical memory that augments the current visual representation using only observations preceding the current decision step, enabling temporal reasoning without future information leakage. We further formulate navigation as receding-horizon diffusion planning, where the policy predicts a K -step action chunk but executes only the first action before replanning. This plan- K , execute-one strategy uses future actions as auxiliary planning targets while preserving closed-loop visual feedback. Finally, LiteStop estimates the stop probability directly from action logits at the initial all-mask state, decoupling explicit termination from action generation. Experiments on the OpenFly benchmark demonstrate consistent improvements in seen and unseen environments. DreamFly achieves 32.04%/29.46% SR and 28.22%/23.54% SPL on the test-seen/test-unseen splits, respectively, outperforming all compared methods on both metrics while attaining the lowest navigation error. These results demonstrate the effectiveness of jointly modeling historical context, future action structure, and explicit termination for aerial VLN.
[CV-2] Class Activation Mapping in Explainable Computer Vision: A Method-Centered Review of CNN Transformer and Foundation-Model-Era Visual Explanations
链接: https://arxiv.org/abs/2608.12299
作者: AmirHossein Eshghi,Hamid Saadatfar,Seyyed Ali Hoseini,AmirMohsen Eshghi,Siavash Arjomand Bigdel
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Class activation mapping (CAM) is one of the most widely used visual explanation families in explainable artificial intelligence. Its purpose is intuitive: it converts internal model evidence into a heatmap that highlights the image regions, convolutional channels, tokens, or patches that support a target class or concept. Since the first CAM formulation in 2016, the field has moved far beyond global-average-pooled CNN classifiers. CAM-style methods now include gradient-based post-hoc explanations, gradient-free score and ablation methods, high-resolution upscaling, weakly supervised localization and segmentation, transformer token attribution, causal and debiasing methods, and foundation-model-era approaches that use CLIP, DINO, SAM, or feature-distribution comparisons. This review synthesizes a strict corpus of 57 method-centered papers published from 2016 onward. The paper develops a taxonomy that separates methods by attribution mechanism, architectural dependence, and evaluation objective. It then reviews gradient-based CAMs, recent and hybrid CAM-style methods, and model-based or architecture-aware methods. Across the corpus, the main trend is clear: the field is shifting from explaining one class score in one low-resolution CNN layer toward comparative, multi-layer, probabilistic, token-aware, and foundation-model-aware explanations. At the same time, evaluation remains fragmented. Faithfulness, localization, robustness, computational cost, and human trust are often measured with different protocols. The review therefore emphasizes not only what each method contributes, but also which gap it leaves open and which later methods attempt to close that gap.
[CV-3] Beyond Trial-and-Error: Agent ic Optimization for Image-to-Video Adherence
链接: https://arxiv.org/abs/2608.12290
作者: Aman Tyagi,Hemanth Boinpally,Jonathan Chen,Douglas Gebert,Steven Hickson
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注:
Abstract:Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows. Their inherent stochasticity causes minor variations in textual prompts or hyperparameters to yield drastically different outputs often necessitating inefficient, brute-force trial-and-error processes. To address these limitations, we introduce the ``Agentic Self-Improvement" framework, which reframes video synthesis into a closed-loop, goal-directed optimization. Our framework systematically navigates the generation parameter space using a novel two-stage approach. In the first stage, an iterative prompt optimization loop uses a multimodal Large Language Model (mLLM) to refine the input prompt. This refinement implements two automated evaluations: Davidsonian Scene Graph (DSG) queries ensure semantic adherence, and Common Mistake Questions (CMQ) for artifact detection. At the second stage, we use Bayesian optimization to efficiently co-optimize stochastic seeds and CFG scales. This search is guided by a suite of quality metrics, including the novel Video-Text Adherence (VTA) score derived from the DSG and CMQ evaluations. Our framework significantly outperforms unguided search methods: in human preference studies, videos generated via our agentic approach were strongly preferred over baseline outputs, achieving win rates up to 69%. This work provides a practical and extensible methodology for enhancing the predictability and control of state-of-the-art video generation models, moving the field beyond speculative curiosities toward reliable, production-ready tools.
[CV-4] Curvature-Aware Zeroth-Order Optimization for Memory-Efficient Test-Time Adaptation CVPR2026
链接: https://arxiv.org/abs/2608.12279
作者: Junming Zhang,Shuyu Yin,Peilin Liu,Rendong Ying,Fei Wen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Published in CVPR 2026
Abstract:Test-time adaptation (TTA) aims to enhance the cross-domain performance of pre-trained models by adapting to unlabeled test data. While most existing TTA methods rely on backpropagation (BP) for finetuning, BP-free methods such as zeroth-order (ZO) methods are more desired in practical on-device scenarios. ZO methods rely only on forward computation, which can largely reduce the complexity and memory overhead of on-device deployment. However, ZO methods suffer from much higher variance compared with first-order methods in estimating the gradient. To address this, we propose an improved ZO method to substantially boost the performance of ZO optimization based TTA. First, we provide an observation to reveal the persistent low-rank Hessian structure of the loss during the adaptation process. Based on this insight, we then propose a loss-landscape curvature-aware zeroth-order (CAZO) method, which leverages a sliding-average estimation of the diagonal Hessian to construct a covariance matrix for anisotropic perturbation sampling. CAZO operates by freezing pretrained weights and optimizing minimal adapter parameters via forward-only passes based gradient estimation, which can substantially reduce the memory overhead compared to BP-based methods. Extensive experiments demonstrate that CAZO significantly outperforms existing TTA methods, achieving state-of-the-art performance while maintaining an excellent balance between accuracy and memory efficiency. Code is available at this https URL.
[CV-5] XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling
链接: https://arxiv.org/abs/2608.12276
作者: Jinxiu Liu,Xuanming Liu,Kangfu Mei,Yandong Wen,Weiyang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:High-fidelity image generation faces a trade-off between speed and quality. Diffusion models produce strong visuals but require costly iterative sampling. Existing efficient methods mainly distill pretrained models into few-step samplers, a challenging process that depends heavily on teacher-model quality. In this paper, we introduce XYZFlow, a framework that rethinks efficient generation through multidimensional scaling of flow matching. Unlike single-step mappings, XYZFlow enhances expressivity by making probability paths more identifiable and learnable through structured multidimensional conditioning. We view autoregressive modeling as implicit flow straightening, where richer context reduces trajectory ambiguity. XYZFlow realizes this idea through two orthogonal dimensions: temporal scaling, which uses non-Markovian conditioning on the full denoising history; and spatial scaling, enabled by Next Shortcut Prediction, which sequentially generates patches using preceding patches’ denoising trajectories as priors. Experiments show that XYZFlow achieves state-of-the-art performance, with 7.2-8.5X teacher speedups and competitive FID, while Next Shortcut Prediction delivers superior quality-latency trade-offs over model scaling or step reduction.
[CV-6] A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery
链接: https://arxiv.org/abs/2608.12274
作者: Rafi Ibn Sultan,Chengyin Li,Yiannos Demetriou,Ahmed I. Ghanem,Joshua P. Kim,Justine Cunningham,Hassan Bagher-Ebadian,Dongxiao Zhu,Kundan S. Thind
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Acceteed by Medical Physics 2026
Abstract:Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy. The LAD is extremely small, has poor soft-tissue contrast, and varies substantially across patients; even manual contours show limited inter-observer agreement, underscoring the ambiguity of the vessel boundaries. Purpose: To develop a transformer-based framework that improves LAD delineation in low-contrast, imbalanced CT through local-global context modeling and uncertainty-guided optimization. Methods: We propose NA-UNETR, a 3D transformer-based segmentation model whose Neighborhood Attention (NA) and Dilated NA (DiNA) blocks jointly capture fine structural detail and long-range context. Given the scarcity of annotated LAD data, the model is pretrained on 1,000 CTA volumes of general coronary anatomy and fine-tuned with LoRA-based parameter-efficient adaptation on 20 free-breathing institutional CT scans. A composite Dice-Focal and Hausdorff loss, dynamically balanced via homoscedastic uncertainty, improves overlap and boundary accuracy. Results: NA-UNETR reached 45.64% Dice, 38.16 mm HD95, and 10.01 mm ASD, improving Dice by 3.10 percentage points over nnU-Net and reducing HD95 by 2.96 mm relative to Swin UNETR, with the strongest boundary accuracy among all models and improved centerline stability. On ImageCAS it achieved 79.49% Dice, 8.89 mm HD95, and 1.02 mm ASD. Ablations confirmed that residual blocks, variable kernels, and uncertainty-weighted loss each contributed. Conclusions: NA-UNETR balances local precision and global context for thin, low-contrast LAD structures, offering a computationally efficient framework for substructure-level cardiac segmentation in radiotherapy planning.
[CV-7] Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams
链接: https://arxiv.org/abs/2608.12262
作者: Weihao Bo,Shan Zhang,Yanpeng Sun,Jie Liu,Yongke Yao,Jinhao Du,Wei He,Kai Zou,Zechao Li,Jingdong Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal Large Language Models (MLLMs) have been growing the capability for scientific writing and collaboration. For example, OpenAI Prism is a free workspace for scientific writing and collaboration. One important feature in Prism is turning scientific diagrams directly into LaTeX TikZ code. In this paper, we build a benchmark, Diagram-MMU, a multi-modal benchmark designed to assess MLLMs’ ability for scientific diagram parsing and understanding. Diagram-MMU features 3.7k curated diagrams and 18.3k human-validated questions across six domains. It evaluates MLLMs on three tasks common in vibe writing workspaces: diagram-to-code parsing, diagram-to-code editing, and diagram question answering, alongside agentic settings per task. The evaluation of 12 MLLMs reveals that diagram-to-code tasks are more challenging than diagram question answering: models can reason well over diagrams but struggle to parse and edit them, underscoring the need for methods to enhance MLLMs’ capability in diagram-to-code generation. Under agentic settings, most models improve parsing and editing performance but degrade on question answering, while Claude-4.6 Opus consistently improves across all three tasks. Project Page: this https URL.
[CV-8] Automated Borehole Core Analysis with Report-Derived Weak Labels and Supervised Crack Segmentation
链接: https://arxiv.org/abs/2608.12252
作者: Usama Imdad,Ali Khan,Luke Lu,Zubair Khalid,Arif Mahmood
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 12 figures
Abstract:Borehole archives commonly contain core tray photographs and corresponding digital log reports, but no native pixel-level crack annotations. We investigate two complementary approaches for extracting defect-spacing information from these archives. First, structured spacing categories recovered from the report text layer provide weak interval-level labels for classification. A DINO encoder trained on unlabeled core crops supplies domain-specific representations, and a manually verified subset is used to identify label inconsistencies. Second, we manually annotate 5,087 extracted core-row images and evaluate fully supervised crack-segmentation models. Our gated U-Net combines PiDiNet edge maps with Mask R-CNN masks through a learned spatial gating mechanism. This configuration achieves an F1 score of 0.860 and a crack-class IoU of 0.754, the highest result among the evaluated segmentation configurations. Deterministic post-processing converts predicted crack locations into defect-spacing categories. Separate rule-based branches estimate core-relative bedding angles and lithological color descriptors; their predictions agree with log-report references on 75.4% and 84.7% of 1,200 evaluated images, respectively. Because these references are extracted from existing reports, the reported values measure agreement with recorded geological observations rather than independent physical accuracy. The resulting framework combines report-derived weak supervision for spacing classification with fully supervised segmentation for image-based crack localization.
[CV-9] HAMP-LIC: Hessian-Aware Mixed-Precision Post-Training Quantization for Learned Image Compression
链接: https://arxiv.org/abs/2608.12239
作者: Yuefeng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: Learned image compression, post-training quantization, mixed-precision quantization, Hessian-based sensitivity analysis, model compression
Abstract:Use this plain-text version for the arXiv abstract field: Learned image compression (LIC) models achieve strong rate-distortion performance but are hindered by high computational complexity and encoding-decoding mismatches across heterogeneous hardware platforms. Uniform fixed-precision quantization alleviates these issues but suffers severe quality degradation at low bit widths because it ignores differences in the quantization sensitivities of individual layers. To enable efficient and accurate low-bit deployment of pretrained LIC models, we propose HAMP-LIC, a Hessian-aware mixed-precision post-training quantization (PTQ) framework with a four-stage optimization strategy. First, block-wise sensitivity is estimated from the Hessian trace to capture second-order importance. Second, a task-aware refinement module adjusts these sensitivities by jointly considering quantization distortion and rate-distortion performance. Third, guided by the refined sensitivity profile, bit widths are allocated under a global model-size constraint to balance efficiency and reconstruction quality. Finally, block-wise reconstruction using a small calibration set further suppresses quantization error. Experiments on representative LIC models, including Minnen2018 and Cheng2020, demonstrate that HAMP-LIC achieves up to 4.85x model compression with as little as 0.59% BD-rate loss. It consistently outperforms existing fixed- and mixed-precision PTQ methods across multiple datasets while completely eliminating cross-platform encoding-decoding errors.
[CV-10] ScaleVid: Geometry-Aware Video Object Scaling with Mesh-Free Inference
链接: https://arxiv.org/abs/2608.12232
作者: Youze Huang,Penghui Ruan,Bojia Zi,Xianbiao Qi,Shihao Zhao,Rong Xiao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Geometry-aware video object scaling aims to anisotropically resize the object along object-centric axes while preserving geometric plausibility, temporal coherence, and background consistency. Existing text-guided methods mainly operate in the 2D image plane, while depth-guided approaches provide coarse control and mesh-based methods require costly 3D reconstruction. We present a progressive two-stage training framework that decouples geometry-aware foreground transformation from background preservation and realistic video composition, without mesh-pixel alignment and explicit 3D reconstruction at inference. In both stages, geometrically perturbed pseudo-sources are constructed from real videos, while the original complete videos are retained as reconstruction targets. The first stage uses planar transformations to learn robust foreground-background composition, whereas the second introduces object-centric 3D deformation guidance for geometry-aware scaling. This pseudo-source reconstruction formulation enables real-video synthesis without paired real-world scaling targets. We construct complementary paired-geometry and real-background benchmarks and further evaluate on in-the-wild videos. Extensive experiments demonstrate superior geometric consistency, foreground fidelity, and background preservation, together with faster and more practical inference than methods requiring explicit 3D reconstruction.
[CV-11] Few-Shot Ordinal Learning for Day-Wise Freshness Estimation with Hyperspectral Fish Images
链接: https://arxiv.org/abs/2608.12230
作者: Kazi Nabiul Alam,Pooneh Bagheri Zadeh,Akbar Sheikh-Akbari
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
备注: Accepted at EUSIPCO’2026
Abstract:Non-destructive food quality assessment has increasingly benefited from hyperspectral imaging (HSI), which captures spectral signatures linked to biochemical changes during storage. Estimating day-wise freshness, however, remains challenging owing to strong inter-fillet variability and scarce labelled data per product. All existing deep learning approaches for HSI-based freshness prediction operate under full supervision, requiring densely annotated training sets that are costly to obtain at the individual-product level. We introduce, to the best of our knowledge, the first few-shot learning framework for HSI-based food quality estimation. Each fillet defines a distinct episodic task, and a CORAL-style ordinal prediction head captures the ranked nature of freshness progression through cumulative threshold modelling. Biologically grounded monotonicity and embedding smoothness constraints further guide predictions toward plausible trajectories. On a 16-day salmon HSI dataset under a strict unseen-fillet protocol, our method achieves a mean absolute error of 1.58 days and 2-day accuracy of 72.3% with only three labelled days per fillet, substantially outperforming scalar regression and label-distribution baselines under an identical unseen-fillet protocol.
[CV-12] SCOUT: Unlocking Enhanced Spatial Reasoning via Structured Chain-of-Thought and Multi-Objective Process Reward
链接: https://arxiv.org/abs/2608.12220
作者: Zile Zhou,Huining Yuan,Weichen Zhang,Xinlei Chen,Xiao-ping Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 26 pages, 5 figures
Abstract:Existing Vision-Language Models (VLMs) exhibits a critical bottleneck in robust spatial reasoning. Recent reinforcement learning (RL) methods aim to close this gap with verifiable outcomes, yet they suffer from poor credit assignment across intermediate reasoning steps. Concurrently, structured reasoning approaches overlook the critical depth perception necessary for comprehensive 3D understanding. To address these challenges, we propose SCOUT (Structured Chain-Of-Thought Utilizing Process-Supervised RL Training). Specifically, we design a structured Chain-of-Thought (CoT) framework that explicitly models 3D environmental perception to ensure robust spatial understanding and reasoning. Furthermore, we introduce a novel RL algorithm featuring multi-objective process rewards and a tailored advantage estimation method, facilitating fine-grained credit assignment across distinct segments of the reasoning trajectory. To support our framework, we develop SCOUT-24k, a structured spatial reasoning CoT dataset synthesized through a customized pipeline. Extensive evaluations demonstrate that SCOUT-3B improves upon baseline models by 16.85% and 6.3% on general spatial benchmarks and complex spatial reasoning tasks respectively. Notably, our larger SCOUT-7B even outperforms GPT-4o by a margin of 4.28%. Moreover, despite being trained exclusively on single image, SCOUT-7B exhibits robust out-of-domain generalization to multi-image and video scenarios. These empirical results render SCOUT as a critical step towards next generation of spatially-aware VLMs.
[CV-13] Generation as Auxiliary Supervision: Enhancing Visual Understanding at Zero Inference Overhead via Decoupled Embedding Prediction
链接: https://arxiv.org/abs/2608.12209
作者: Zhongbin Guo,Jiahao Xie,Dongling Xiao,Qianle Wang,Ruiqi Lu,Xiaomin He,Wanxuan Sun,Cheng Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While Multimodal Large Language Models (MLLMs) have achieved remarkable progress, visual understanding and generation are typically treated as divergent objectives. Existing unified frameworks often rely on discrete visual tokenization or diffusion objectives whose generative targets differ from the continuous representations consumed by visual understanding models, making direct transfer to enhance existing pretrained MLLMs non-trivial. In this work, we present GAS, a generation-guided training framework that reinterprets visual generation as auxiliary supervision for representation learning. Concretely, GAS adapts Next Embedding Prediction (NEP) as a cross-modal generation paradigm within a decoupled Mixture-of-Transformers (MoT) architecture. By maintaining a shared lower trunk and parallel upper layers, GAS lets generation losses enrich the shared visual pathway with finer spatial precision and stronger visual retention while shielding the upper understanding layers from direct generation gradients. To maximize this synergy, we further construct highly correlated generation tasks that demand deep cognitive grounding rather than generic synthesis alone. Across model scales and training stages, GAS improves aggregate multimodal understanding, with its most reliable gains on perception and spatial comprehension. Crucially, because the auxiliary generation branch is discarded after training, these gains incur zero inference overhead. Extensive controlled comparisons and representation-level analyses further clarify when and why generation-guided training benefits understanding, and demonstrate the feasibility of generation-guided training as a practical route to stronger multimodal understanding.
[CV-14] GeoFlow: Efficient Driving Video Generation via Geometry-Aligned Priors ECCV2026
链接: https://arxiv.org/abs/2608.12203
作者: Jiazheng Liu,Hang Li,Jiawei Zhang,Jiahe Li,Xiaohan Yu,Shengyin Fan,Jin Zheng,Xiao Bai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026
Abstract:Generative models like Diffusion Models and Flow Matching have demonstrated remarkable capabilities in synthesizing high-fidelity driving videos, but are severely constrained by high inference latency due to the requirement of extensive sampling steps. We argue that this inefficiency stems from the prevailing reliance on a standard Gaussian source distribution, where consecutive frames are initialized as independent Gaussian noise. This paradigm disregards the rich spatiotemporal correlations inherent in driving videos, compelling the model to regenerate deterministic scene structures existing in previous frames from noise, which is both computationally redundant and prone to geometric inconsistency. To address this problem, we propose GeoFlow, a novel framework designed to achieve efficient driving video generation by harnessing explicit geometric priors. Instead of sampling from standard Gaussian noise, we leverage multi-view geometry and spatially-adaptive noise injection to construct a Geometry-Aligned Prior (GAP) distribution as starting point. This initialization bridges the gap between source distribution and data distribution, yielding a significantly straighter and shorter sampling trajectory. Extensive experiments demonstrate that GeoFlow can achieve remarkable efficiency of both training and inference: merely several hours of fine-tuning on baseline models can significantly boost few-step generation quality, while fully converged training drastically reduces number of inference steps required for state-of-the-art video generation.
[CV-15] M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation
链接: https://arxiv.org/abs/2608.12196
作者: Jing Zhu,Ye Wang,Fumin Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Purpose: Deep learning-based medical image segmentation has achieved remarkable success, yet purely data-driven approaches often fail to exploit the rich mathematical structure inherent in medical images. We investigate whether explicit mathematical inductive biases, specifically matrix spectral analysis and vector calculus operators, can enhance segmentation beyond data-driven learning alone. Methods: We propose M-Net (Math-Augmented Network), which integrates three complementary mathematical priors into U-Net: (1) continuous spectral features derived from the condition number of centered local pixel matrices, providing a differentiable measure of texture ill-conditioning; (2) physical field operators (divergence and a discrete curl-like boundary irregularity operator) computed from image gradient fields, capturing focal intensity extrema and edge non-smoothness; and (3) a Math-Attention Gate (MAG) that adaptively fuses mathematical features with CNN-extracted deep features at skip connections. Results: Experiments on three benchmarks (LiTS, KiTS, and BraTS) show that M-Net achieves Dice scores of 78.42%, 76.15%, and 83.67%, outperforming baseline U-Net by 12.37%, 3.52%, and 5.55% on liver, kidney, and brain tumor segmentation, respectively. Ablations reveal that the condition-number feature contributes a 2.14% gain over binary invertibility features, while MAG adds 1.45% over simple concatenation. Conclusion: M-Net establishes that mathematical inductive biases provide effective complementary information for medical image segmentation. The continuous condition-number feature offers superior gradient information over discrete alternatives, and MAG preserves these priors throughout the network. This work opens avenues for integrating linear algebra and vector calculus into deep architectures for medical imaging.
[CV-16] HSTGFormer: Hyper Spatial-Temporal Graph Transformer for 3D Human Pose Estimation BMVC2026
链接: https://arxiv.org/abs/2608.12187
作者: Ruochen Li,Shuang Chen,Wenke E,Farshad Arvin,Amir Atapour-Abarghouei
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to BMVC 2026, full paper
Abstract:Transformer-based methods have achieved strong performance in monocular 3D human pose estimation, but most existing approaches organise spatial and temporal reasoning as separate stages, which may weaken unified spatial-temporal interdependencies inherent in human motion and compress frame-level structural information before temporal modelling. In this paper, we propose HSTGFormer, a graph-enhanced Transformer framework that reformulates spatial-temporal reasoning as localised coupled graph aggregation over joint-time nodes. Specifically, HSTGFormer introduces a Hyper Spatial-Temporal Graph (HSTG), which decomposes global spatial-temporal reasoning into local spatial-temporal receptive fields around individual joint-time nodes by extending per-frame skeleton graphs into temporal neighbourhoods, thereby enabling structure-aware coupled reasoning while preserving local structural motion information. It further incorporates an Adaptive Dual-Scale Temporal Graph (ADSTG) to capture joint-specific temporal dependencies over complementary short- and long-range windows. A lightweight node-wise fusion module further adaptively integrates the two graph representations for each joint-time node. Experiments on Human3.6M and MPI-INF-3DHP show that HSTGFormer achieves strong accuracy with high computational efficiency.
[CV-17] GenFAR: A generalized representation of brain structure derived from 49246 multi-cohort MRIs via deep learning
链接: https://arxiv.org/abs/2608.12185
作者: Vishnu M. Bashyam,Guray Erus,Junhao Wen,Pratik Chaudhari,Randa Melhem,Sindhuja Govindarajan Tirumalai,Gareth Harman,Yong Fan,Colin L. Masters,Paul Maruff,Sterling C. Johnson,Jurgen Fripp,Duygu Tosun,John C. Morris,Daniel S. Marcus,Pamela LaMontagne,Tammie Benzinger,Susan R. Heckbert,Mark Espeland,Marilyn S. Albert,Andrew J. Saykin,Paul M. Thompson,Timothy J. Hohman,Susan M. Resnick,R. Nick Bryan,Murat Bilgel,Yang An,David A. Wolk,Li Shen,Haochang Shou,Ilya M. Nasrallah,Christos Davatzikos
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we introduce GenFAR, a modular deep learning framework that learns general, clinically informed features from brain MRIs. We trained this modular architecture on 49,246 individuals across 11 cohorts, using 17 diverse classification and regression tasks spanning cognition, clinical, diagnosis, demographics, and biomarkers. This yields aggregated, focused feature sets that capture rich, clinically- and biologically-relevant brain representations. We developed a sequential learning approach where tasks progressively build on previously learned representations. Through an analysis of 5,000 task sequences, we identified an optimal sequence length of six tasks and introduced a Donor Score metric to quantify each task’s contribution to downstream performance. This analysis revealed five consistently strong donor tasks (Age, AD/MCI, MMSE, Hypertension, Hyperlipidemia) that formed the base of our sequential model. We demonstrated the utility of our learned representation, in various tasks beyond those included in the training set, to serve as the foundation for specialized secondary predictors. We further showed that using the learned feature representation can substantially increase the sample efficiency of secondary deep learning training tasks and models, as well as improve their accuracy.
[CV-18] Map-Det3D: Metric Feed-Forward 3D Reconstruction Prior for Multi-view 3D Object Detection from Streaming Inputs ECCV2026
链接: https://arxiv.org/abs/2608.12179
作者: Yung-Hsu Yang,Luigi Piccinelli,Samuel Rota Bulò,Sunghwan Hong,Denis Rozumny,Johannes Schönberger,Zuria Bauer,Hermann Blum,Peter Kontschieder,Marc Pollefeys
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Metric 3D object detection is a core capability for embodied agents, yet most reliable systems lean on depth sensors, trading away cost, power, and integration simplicity. This motivates monocular 3D detection, which avoids additional constraints, yet it faces a major obstacle: from a single image, depth, and especially absolute scale, are underconstrained. As a result, the prevailing pattern of detecting in 2D and then predicting 3D attributes is often brittle, since modest range errors can dominate 3D localization, and the learned scale prior can fail when cameras, motion, or environments undergo domain shifts. To address this, we propose Map-Det3D, an online multi-view 3D object detection model that brings detection directly into a 3D space reconstructed from RGB. We map a short temporal window into multiple views and repurpose a feed-forward metric 3D reconstruction model as our geometric backbone while tuning its object-aware capabilities. Building on this representation, Map-Det3D directly predicts boxes in metric 3D space, without the widely used 2D-to-3D lifting. Experiments across different benchmarks show that this design supports strong online performance and robust transfer without adaptation, suggesting that training reconstruction priors for detection is a practical route to stable metric 3D detection from monocular video. Code and models are available at this https URL.
[CV-19] GRHuman: Text-Guided Realistic 3D Human Generation via Diffusion Renderer
链接: https://arxiv.org/abs/2608.12175
作者: Muxin Zhang,Chaohui Yu,Yuanwang Yang,Min Wei,Zhuo Su,Kun Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Realistic 3D human generation plays a crucial role in many graphics applications. However, current methods still struggle to generate high-quality human geometry and texture while maintaining 3D consistency and inference efficiency. In this work, we address these limitations by introducing TGRHuman, a novel approach for generating realistic 3D humans from text. Our method decouples geometry and texture generation to alleviate the issues commonly encountered in NeRF-based methods. Instead of relying on slow, implicit score-distillation-based optimization, we directly use explicit multi-view observation generation and optimization for efficient 3D synthesis. For geometry generation, we propose a high-resolution generative module for multi-view normals together with a geometry-carving strategy that preserves view consistency and supports loose clothing. For texture generation, we produce spatially consistent RGB observations from densely sampled surrounding views using a carefully designed texture-prior acquisition strategy and a diffusion renderer, enabling detailed human texture synthesis. Experiments show that our method can generate high-quality and consistent 3D human geometry and texture efficiently. TGRHuman outperforms existing text-to-3D human methods in both geometry and texture quality.
[CV-20] Context Blindness in DPO: Mitigating Object Hallucination in MLLM s via Context-Calibrated Preference Optimization ECCV2026
链接: https://arxiv.org/abs/2608.12158
作者: Byungoh Ko,Jinyoung Park,Jongha Kim,Jeehye Na,Jaewon Cho,Hyunwoo J. Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV2026
Abstract:Multimodal large language models (MLLMs) have made rapid progress, yet they still exhibit object hallucination, generating plausible but incorrect descriptions that are inconsistent with the visual input. Direct Preference Optimization (DPO) mitigates this by training models to prefer non-hallucinated responses over hallucinated ones, and recent efforts further enrich the preference data with relevant context. However, it remains unclear whether DPO actually leverages such context. To investigate this, we propose Contextual Preference Gain (CPG), a simple metric that measures how much a model’s preference strengthens when relevant context is provided. We find that higher CPG consistently corresponds to lower hallucination, yet standard DPO and its variants exhibit only limited CPG, indicating that they underutilize contextual information and thus remain prone to hallucination. To address this, we propose Context-Calibrated DPO (C ^2 -DPO), which directly maximizes CPG while preserving the original preference ordering. Across multiple benchmarks, C ^2 -DPO substantially reduces hallucination without compromising general reasoning, relatively reducing the Object HalBench hallucination rate of Qwen2-VL-Instruct-2B by 36%. Code is available at this https URL
[CV-21] Understanding Why Foundation Models Work for Diffusion-Generated Image Detection
链接: https://arxiv.org/abs/2608.12155
作者: Davide Cozzolino,Giovanni Poggi,Luisa Verdoliva
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision foundation models have recently emerged as powerful feature extractors for detecting AI-generated images, achieving strong generalization across generators and robustness to common image degradations. However, the reason behind their effectiveness is poorly understood. In this work, we investigate what cues are exploited by foundation-model-based detectors to distinguish real images from diffusion-generated ones. To this end, we design an ad hoc analysis protocol based on DDIM inversion. Given a real image we generate a sequence of synthetic copies by changing the depth of DDIM inversion. Even though most copies are semantically identical to the real reference, the detector score varies significantly across them due to subtle traces introduced by the diffusion synthesis, showing that its decision is not primarily driven by semantic failures. Through a frequency-swapping analysis, we further reveal that the discriminative cues exploited by the detectors are mainly localized in the low-to-mid frequency range, rather than only in the high-frequency range, as is the case for artifacts commonly associated with generative models. Finally, a latent-space analysis shows that regenerated images exhibit reduced variance and effective dimensionality, indicating that diffusion models do not fully reproduce the variability of real data. Overall, our results suggest that foundation-model-based detectors succeed by capturing non-semantic low-to-mid frequency distributional discrepancies between real and diffusion-generated images. These findings provide new insight into the robustness and generalization of such detectors and suggest directions for more interpretable forensic methods.
[CV-22] Autonomous Telerehabilitation via Skeletal Motion Prediction and Joint-Level Performance Assessment
链接: https://arxiv.org/abs/2608.12145
作者: Lara Pereira,João Ruivo Paulo,Pedro Santos,Paulo Peixoto
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at IEEE RO-MAN2026
Abstract:Autonomous rehabilitation systems must not only recognize human motion but also provide structured feedback to support users without continuous therapist supervision. This paper presents a telerehabilitation pipeline that integrates skeleton-based exercise quality assessment and short-term motion prediction into a two-module system operating on marker-free RGB video. A self-attentive Bidirectional LSTM performs exercise quality classification using MMD-NCA metric learning, while a graph-based motion prediction module computes per-joint position errors between predicted and observed poses, generating spatially localized deviation signals. Each module is evaluated independently on established benchmarks: the classifier achieves 96.45% mean-class accuracy on squat sequences from the PROZIS dataset, and the adopted STARS predictor achieves a mean MPJPE of 75.8 mm at 560 ms on Human3.6M, outperforming graph and recurrent baselines across all prediction horizons. The framework is designed for eventual deployment in assistive robotics and home-based rehabilitation contexts; end-to-end integration and clinical validation are important directions for future work. By combining motion recognition and prediction in a single system, this work contributes a step toward autonomous, feedback-driven telerehabilitation, for more accessible and scalable rehabilitation solutions.
[CV-23] SCOPE-Router: Cost-Aware Open-Set VLM Routing for Execution-Oriented Tasks
链接: https://arxiv.org/abs/2608.12127
作者: Tao Yu,Yifei Qu,Zhiqing Cui,Pengfei Zhou,Zhongtian Luo,Yujia Yang,Shenghua Chai,Haopeng Jin,Zhenghao Zhang,Xinming Wang,Hongzhu Yi,Wangbo Zhao,Zhenglin Wan,Yan Huang,Yeshani,Jinwen Luo,Yang You
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Model routing aims to select the most suitable model from a candidate pool for each query, balancing quality and cost. Existing VLM routing research is limited to traditional VQA evaluation, lacks systematic calibration optimization for open-set scenarios, and employs training objectives that dilute multi-positive signals via softmax normalization without incorporating cost. We address these limitations with three contributions: (1)VLM-ExecRouterBench, the first execution-oriented VLM routing benchmark covering Code, Agentic, and Search domains with 11 candidate models spanning nearly two orders of magnitude in pricing; (2)SCOPE-Router, a dual-tower router that matches queries to model behavior profiles constructed via hybrid calibration (random/diagnostic/diversity sampling), enabling new models to join routing without retraining; (3)CRM+RCCR, an architecture-agnostic cost-aware objective that encodes cost preference into continuous relevance targets through per-pair independent scoring, eliminating multi-positive dilution while regularizing queries with similar routing preferences to be closer in the routing space. Empirically, SCOPE-Router achieves the best Rank Score on all three benchmarks, surpassing the runner-up by 1.84 points under OOD settings and by 6.75 points under doubly OOD open-set evaluation. When applied to four diverse routers, CRM+RCCR improves Rank Score by 1.25–6.21 points.
[CV-24] HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing
链接: https://arxiv.org/abs/2608.12122
作者: Zhenjie Yang,Xingyu Jiao,Guopeng Zhong,Shuzhe Yang,Shi Che,Chao Wu,Chenyu Jiang,Dongjie Zhang,Yideng Zhang,Zheng Zhang,Muyun Jiang,Haisheng Su,Shuang Jin,Donghang Zhang,Chao Yang,Li Chen,Hongyang Li,Zuxuan Wu,Yu-Gang Jiang,Xiaosong Jia,Junchi Yan
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Technical Report. Project Page: this https URL
Abstract:Robotic manipulation with dexterous hands is a cornerstone of Embodied AI, yet its progress is stifled by the high cost of collecting embodiment-aware teleoperation data. While abundant egocentric videos of human hands offer a scalable alternative, the profound discrepancies in appearance, articulation, and camera viewpoints between human and robotic data raise significant challenges for co-training. Though existing general image-editing models demonstrate strong capabilities, they lack necessary embodiment-specific priors to fully bridge this gap. In this work, we present HandEdit, a unified large-scale embodiment-aware image-editing dataset and benchmark specifically designed to transform human hands and arms into various dexterous robotic embodiments within egocentric frames. HandEdit comprises over 200M editing instances derived from five diverse source datasets, covering 26 distinct URDFs, including 13 hand-only and 13 hand-arm configurations. Alongside the dataset, we establish a unified benchmark protocol with two tracks: Hand-only and Hand-Arm, supporting URDF-conditioned evaluation. We conduct extensive evaluations of 11 representative image-editing baselines using a multi-dimensional metric suite, including generic similarity metrics, VLM-based judgment, and embodiment-aware metrics. HandEdit serves as a critical resource at the intersection of image editing and robotics: it advances embodiment-aware editing models while enabling scalable dexterous robotic learning from abundant human video data, paving the way for more generalizable Embodied AI.
[CV-25] Avatar-Forever: Decoupled Parallel Training for High-Quality Real-Time Infinite Avatars
链接: https://arxiv.org/abs/2608.12107
作者: Ruibin Li,Tao Yang,Zhiyuan Ma,Fangzhou Ai,Shilei Wen,Lei Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing streaming video systems often rely on sequential, distillation-centered training pipelines to enable few-step long-video generation. However, this paradigm suffers from two limitations. First, failures or distribution shifts introduced in earlier stages affect later optimization, complicating the training process to converge. Second, the distillation-centric objective favours short-term generation but is prone to quality degradation when autoregressive errors accumulate over long rollouts. We propose Avatar-Forever, a decoupled parallel training framework for high-quality real-time infinite interactive avatars. Instead of coupling generation efficiency and long-horizon robustness under a sequential distillation pipeline, we treat them as two independent capabilities that can be trained in parallel. One branch performs full-parameter distillation to train an efficient generator with high visual quality, while another trains a lightweight long-horizon adapter via Recovery-oriented Rollout Training (RRT), which improves generation robustness under long-horizon inference conditions. Our decoupled parallel training design simplifies the overall training process and avoids unnecessary objective conflicts between few-step generation and long-horizon adaptation. We further introduce ForeverCache, a chunk-wise feature caching mechanism to substantially reduce redundant history computation during streaming inference. Built upon a 22B video foundation model, Avatar-Forever supports unbounded audio-driven avatar generation while maintaining identity consistency, motion coherence, and visual fidelity, enabling an end-to-end throughput of high-resolution 768x512 videos at 27.2 FPS on a single H100 GPU and providing a practical path toward stable digital humans.
[CV-26] RA-ClipScore: Making Generative Model Evaluation More Interpretable
链接: https://arxiv.org/abs/2608.12088
作者: Yifan Lu,Taras Kucherenko,Hedvig Kjellström,Judith Bütepage
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generative models can produce images nearly indistinguishable from real data, yet rigorous and interpretable evaluation remains challenging. Conventional metrics such as FID provide only scalar scores with limited diagnostic insight. Widely adopted CLIP-based metrics enable semantic evaluation beyond simple training class labels, but inherit limitations from CLIP’s training paradigm that restrict attribute-wise analysis. We propose RA-CLIPScore, a novel metric that mitigates these issues and extends CLIP-based evaluation to spatial distribution alignment, measuring whether generated objects adhere to the positional priors found in the training data. RA-CLIPScore introduces dual prompts to decouple competing attributes and leverages local patch tokens to capture fine-grained regional semantics. We evaluate image generative models on their ability to match both attribute and spatial distributions of the training data. Extensive experiments show that RA-CLIPScore provides more robust and interpretable evaluations than prior methods, particularly under distribution misalignment or partially irrelevant textual attributes. We further demonstrate how it reveals spatial biases in generative models. User evaluations confirm that Regional Single Attribute Divergence based on our RA-CLIPScore aligns more closely with human perception of visual diversity than existing semantic metrics.
[CV-27] Look What the Probes Drag ged In! Real-World Chest X-ray Shortcuts in MedCLIP MICCAI2026
链接: https://arxiv.org/abs/2608.12086
作者: Nikolette Pedersen,Regitze Sydendal,Veronika Cheplygina,Théo Sourget
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 11 pages, 3 figure, poster presentation at the joint FAIMI, BRIDGE, and EPIMI workshop at MICCAI 2026 (Strasbourg, France) conference
Abstract:Vision-language models, such as contrastive language-image pre-training (CLIP)-based approaches, have reached state-of-the-art (SOTA) results in medical artificial intelligence. However, recent work reveals that CLIP-based models remain vulnerable to shortcuts. We investigate how real-world shortcuts manifest across different layers of the medical CLIP-based model, MedCLIP, and its vision encoder, a frozen ResNet-50. We attach 17 linear classification probes to the intermediate layers of the ResNet-50 and train them on three different dataset configurations and targets: NIH-CXR14 (pneumothorax) and PadChest (cardiomegaly and pneumothorax). This setup allows us to observe model behaviour during evaluation using subgroup-based calibration and layer-wise confidence curves. We find that the final linear probes achieve a high AUROC but poor calibration in the models. The layer-wise confidence analyses suggest that shortcuts emerge at different depths. Patterns consistent with localised shortcuts, such as drains, appear at later layers, while patterns consistent with diffuse shortcuts, such as scanner-specific noise patterns, emerge earlier, aligning with previous work. Finally, we conduct a manual analysis of the images, which reveals data quality issues in both NIH-CXR14 and PadChest. Our findings underscore that even SOTA models remain vulnerable to shortcuts, and the need for high-quality and well-annotated datasets to draw solid conclusions. Code can be found on our GitHub: this https URL.
[CV-28] Better Slots Better Worlds: Representation Quality Robustness in Object-Centric World Models
链接: https://arxiv.org/abs/2608.12078
作者: Shukrullo Nazirjonov,Sai Prasanna,Anna Manasyan,Georg Martius
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Published at Model-Based RL in the Era of Generative World Models Workshop at RLC 2026
Abstract:Learning world models from offline trajectories enables agents to accomplish different tasks through planning. Object-centric (OC) representations, which decompose a scene into a set of slots that bind to its objects, have been proposed as an inductive bias for world models that are more sample-efficient and generalize better. Yet prior object-centric world models (OCWMs) take the slot encoder as given and evaluate only in-distribution, leaving open whether the object-centric bias actually delivers for planning and what within the OCWM drives it. We conduct a controlled study of OCWMs for visual model-predictive control along two axes: object-centric representation quality and generalization under distribution shift relative to scene-centric models. We find that (i) planning success correlates positively with unsupervised slot-quality metrics (FG-ARI, mBO), though the gains saturate at high slot quality; (ii) with well-bound slots, the auxiliary proprioception inputs and masking inductive bias that prior methods relied on become unnecessary; and (iii) under unseen distribution shifts, the OCWM with well-bound slots is more robust overall than the end-to-end trained scene-centric LeWM, while DINO-WM, built on similar frozen pretrained features, remains competitive – pointing to pretrained features as a key contributor to robustness.
[CV-29] Draw This First
链接: https://arxiv.org/abs/2608.12064
作者: Dazhi Zhong,Rowan Bradbury,Grant Davis
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:We invert the typical formulation of sketch generation: instead of drawing strokes in order, we predict a 2D field that defines the order in which strokes are drawn. We use a pretrained latent flow-matching transformer to supply the image prior to predict an intermediate representation, while training the VAE’s decoder to predict the order field, stroke mask, and stroke segmentation. We vectorize the predicted segmentation into polylines and sort them by the field, producing an ordered vector sketch. Our model can predict an ordered vector sketch from a text description or derender an image into ordered vectors; for either, it follows text instructions specifying the order of drawing.
[CV-30] Do Not Forget the Obvious - RISC: A Risk-Informed Slice-Coverag e Protocol for Safe Autonomous Driving ECCV2026
链接: https://arxiv.org/abs/2608.12051
作者: Fabian Hüger
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 3 figures, 5 tables. Accepted at the ECCV 2026 Workshop on Safe and Defensive Autonomous Driving (SDAD). Non-archival workshop paper
Abstract:Aggregate metrics may not fully reflect performance in insufficiently examined high-risk driving conditions. We propose RISC (Risk-Informed Slice Coverage), a practical protocol for risk-guided stress testing and coverage-qualified evaluation. Risk-guided stress testing directs a finite audit budget toward risk-relevant sub-datasets, called risk slices, while coverage-qualified evaluation reports results together with explicit statements about which slices are sufficiently or insufficiently covered. The protocol translates safety concerns into machine-readable risk slices, uses lightweight signals to tag candidate data, selects a compact audit set by risk, and qualifies the results using coverage evidence. An LLM can optionally support this process by surfacing relevant but potentially overlooked conditions during test planning, thereby helping engineers not to forget the obvious. RISC is model-agnostic and can be applied to perception modules, driving models, and other autonomous-driving subsystems. We instantiate the protocol for monocular pedestrian perception using 1,000 frames from the Zenseact Open Dataset, image statistics, and a YOLO-based detector proxy. In this proof-of-concept study, risk-guided selection increases critical failure discovery from 34.0% under random sampling to 98.5%. RISC provides a lightweight, assurance-oriented evaluation layer that complements scenario categorization, coverage assessment, and broader testing-and-verification workflows.
[CV-31] Predicting Functions Not Features: KANs with Function-Space Joint-Embedding Predictive Learning for Medical Image Segmentation
链接: https://arxiv.org/abs/2608.12050
作者: Yungeng Liu,Xuanzi Fang,Yuge Zhang,Shuqi Ren,Haijin Zeng,Yongyong Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 5 figures
Abstract:Kolmogorov–Arnold Networks (KANs) introduce explicit functional representations by parameterizing each network edge as a learnable univariate function. However, existing KAN-based segmentation models optimize edge functions only through objectives defined after edge aggregation, leaving individual functions without an explicit pre-aggregation learning target. To address this limitation, we propose Function-Space Joint-Embedding Predictive Learning (FS-JEPA) for medical image segmentation. Our FS-JEPA framework moves predictive learning into the pre-aggregation function space of KANs. A masked online branch predicts structured signatures of sampled KAN edge functions generated by a full-context exponential moving average target branch, while shared edge indices preserve correspondence between predictions and targets. Rather than predicting an isolated edge response, we represent each sampled edge function using a multi-radius signature composed of function evaluations around its input anchor. This structured representation captures local functional variations that cannot be characterized by a single response and provides a more informative predictive target. The function-space objective is jointly optimized with the segmentation loss during training, while the predictive branch is removed at inference. Experiments on five medical image segmentation benchmarks show that our FS-JEPA achieves the best average Dice and outperforms the strongest competing KAN-based method by +2.25 percentage points.
[CV-32] Localizing to Debias: A Patch-Level Benchmark and Baseline for Weakly Supervised Spatial Anomaly Detection ECCV2026
链接: https://arxiv.org/abs/2608.12045
作者: Sara Abdulaziz,Abdulrahman Al-Abri,Giacomo D’Amicantonio,Egor Bondarev
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026 FAILED Workshop
Abstract:Despite growing interest in weakly supervised video anomaly detection (WSVAD), current methods struggle to bridge the gap between coarse temporal supervision and fine-grained spatial reasoning. A key obstacle is the tendency of temporal detectors to latch onto background and scene-level cues rather than truly discriminative anomaly evidence. This background bias raises ethical concerns: models may inadvertently associate anomalies with societal or environmental context rather than authentic crime-related cues. Without spatial grounding, such biases remain hidden and unauditable. To address this, we propose SST-WSVADL, a sparse spatio-temporal framework that bridges temporal anomaly detection with fine-grained spatial localization. Rather than processing all spatial regions indiscriminately, SST-WSVADL progressively focuses on the most anomaly-relevant spatio-temporal regions through dynamic sparsification, naturally suppressing background dominant content while preserving discriminative evidence. The temporal and spatial branches are coupled end-to-end via motion-aware regularization that guides sparsification toward dynamically informative regions, without relying on external detectors or vision-language prompts. We publicly release frame-level spatial annotations and a method-agnostic evaluation protocol for three public datasets: UCF-Crime, XD-Violence, and MSAD. These resources enable the community to audit spatial biases in WSVAD predictions, supporting progress toward more ethical and accountable anomaly detection. Experiments demonstrate that SST-WSVADL is competitive with prior methods across benchmarks while enabling localization and patch-level auditability of scene bias, providing a reproducible foundation for interpretability-oriented evaluation of WSVAD models.
[CV-33] How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging
链接: https://arxiv.org/abs/2608.12035
作者: Yiheng Xiong,Luisa Gallée,Daniel Santak Wolf,Heiko Hillenhagen,Michael Götz
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Deploying unsupervised domain adaptation (UDA) in clinical practice requires choosing which algorithm to use and which of its trained models to ship. However, the deployment (target) domain is unlabeled, so models cannot be evaluated directly on it, leaving it unclear which to select. We address this by evaluating the complete UDA pipeline, considering both adaptation and label-free selection together. Our study covers eleven clinically relevant cross-domain scenarios from nine medical imaging datasets, with ten UDA algorithms and 13 label-free selection methods (validators), evaluating over 80,000 trained models in total. By this, we find that a capable adapted model usually exists, but identifying it without target labels is difficult: the validator-selected models leave a large and structural target performance gap to the best available one, with no evaluated validator consistently reliable. Towards closing it, we explore two strategies, ensembling and a small target-labeling budget; both narrow this gap but do not close it entirely. Overall, deployable UDA depends on the complete pipeline; addressing the less explored selection step could bring much of current UDA closer to clinical use.
[CV-34] LoSA: Near-Lossless Sparse Attention for Training-Free Video Diffusion Acceleration
链接: https://arxiv.org/abs/2608.12032
作者: Enhuai Liu,Yunke Wang,Yutong Wang,Changming Sun,Chang Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Video diffusion transformers are costly to sample: every denoising step applies self-attention over a long 3D token sequence, a quadratic cost that dominates as resolution and duration grow. Sparse attention reduces this cost without retraining, but existing methods pursue aggressive sparsity, where further speedup costs disproportionately more attention fidelity. We target the opposite end of this trade-off: fix near-lossless fidelity by construction, and remove as much computation as this constraint permits. Two observations make this regime practical: roughly 40% of block interactions can be removed while retaining 99% of the attention mass, and the high-mass support remains stable across denoising steps. We propose LoSA, a training-free sparse-attention method that fixes a retained-mass threshold of 99% rather than a sparsity ratio: it measures exact block attention masses at one early dense step, keeps, for each head and query block, the smallest key/value block set meeting the threshold, and reuses the frozen block indices for all remaining steps. On Wan2.1-1.3B, LoSA alone gives a 1.36\times speedup with a 0.06-point VBench Overall drop. The benefit is largest under composition: combined with feature caching, LoSA reaches a 3.2\times speedup on HunyuanVideo at a 0.02-point drop, versus 0.32 points for the strongest sparse baseline at comparable speed. Across three video diffusion transformers and speedups up to 3.2\times , LoSA consistently achieves the best training-free speed-quality trade-off.
[CV-35] Dual-Model Sentiment Analysis of Consumer Reviews in the Retail Coffee Sector Using Machine Learning and Deep Learning Approaches
链接: https://arxiv.org/abs/2608.12007
作者: Muntasir Hasan Kanchan,Md. Alamgir Hossain,Md. Samiul Islam,Muhammad Masud Tarek
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 10 Pages, 11 Figures, 3 Tables
Abstract:Consumer reviews play an important role in shaping brand perception and business strategies, particularly in service-driven industries such as retail coffee. This study presents a comparative sentiment analysis framework for Starbucks customer reviews using classical machine learning and deep learning approaches. The dataset, collected from ConsumerAffairs, contains more than 700 reviews and was analyzed through preprocessing and exploratory data analysis to identify temporal and geographic patterns. Sentiment labels were generated by binarizing star ratings, with ratings of 4 and 5 classified as positive and ratings of 1 to 3 as negative. The resulting dataset was substantially imbalanced toward negative sentiment. Five machine learning classifiers, including Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Naive Bayes, were evaluated alongside five deep learning models: LSTM, RNN, Bidirectional LSTM, GRU, and CNN. Model performance was assessed using accuracy, precision, recall, and F1-score. SVM achieved the highest accuracy among the machine learning models at 91.0 percent, while Bidirectional LSTM showed the strongest performance among the deep learning models and demonstrated good generalization on unseen data. The findings also show that class imbalance negatively affected positive sentiment recall across several models. Overall, this study provides a comparative evaluation of machine learning and deep learning approaches for real-world consumer sentiment analysis and highlights the importance of appropriate model selection and preprocessing for customer experience analytics in the retail coffee sector.
[CV-36] Remote Sensing and Machine Learning-Based Analysis of Land Use and Vegetation Change in Dhaka District Bangladesh
链接: https://arxiv.org/abs/2608.12001
作者: Muhammad Masud Tarek,Md. Alamgir Hossain,Md. Samiul Islam,Muntasir Hasan Kanchan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 16 Pages, 8 Figures, 9 Tables
Abstract:Rapid urbanization in Dhaka District, Bangladesh has triggered substantial alterations in land use and environmental conditions, necessitating systematic monitoring for informed urban planning and ecological sustainability. This study employs remote sensing data and machine learning techniques to analyze spatiotemporal changes in land cover and vegetation dynamics between 2019 and 2024. High-resolution satellite imagery from Sentinel-2 MSI and Landsat 8 was utilized to classify land cover types and compute spectral indices including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). A supervised machine learning approach incorporating Decision Tree, K-Nearest Neighbors (KNN), and Random Forest classifiers was applied using labeled geospatial training points within Google Earth Engine. Accuracy assessments were conducted using confusion matrices and kappa statistics. Results indicate a 59.5% increase in urban built-up areas and a significant decline in vegetation (-8.46%) and water bodies (-7.77%) over the five-year period. Land conversion from vegetated and aquatic areas to urban infrastructure was identified as a dominant trend. Among the models, Random Forest demonstrated the highest classification accuracy. These findings underscore the growing environmental pressures driven by unregulated urban expansion in Dhaka. The study highlights the potential of remote sensing and machine learning tools in providing timely, actionable data to support sustainable urban development, land-use regulation, and ecosystem conservation policies.
[CV-37] A Remote Approach to Cashew Orchard Detection: Leverag ing Active Learning with Satellite Imagery in Guinea-Bissau
链接: https://arxiv.org/abs/2608.11996
作者: Miguel,Sofia,Maria,Patrícia,Luke,João
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 29 pages, 12 figures
Abstract:Cashew production is a widespread economic activity in Guinea-Bissau, as well as other countries in West Africa. However, unregulated cashew production can be directly associated with increasing regionwide deforestation rates, biodiversity losses, and a fragile economic structure. There is no nationwide database for listing or georeferencing cashew orchards, so there is a clear need to remotely map their locations. In recent years, multiple methods for detecting orchards have been developed, though they have only been applied on a regional level. This work expands regional analyses to a nationwide scale. It develops a scalable and cost-effective remote approach, based on Sentinel-2 satellite imagery, using Machine Learning techniques to detect cashew orchards automatically. Margin-based Active Learning techniques were employed to develop an optimal training set in terms of the number of points and their informativeness, leading to a cashew map with 94.0% balanced accuracy obtained entirely off-site. We created two datasets and a 2021 cashew map with 10m spatial resolution that are openly accessible through GitHub. The results demonstrate the possibility of a broader cashew orchard mapping, creating a new stepping stone for this environmental application.
[CV-38] Auditing Frame-Level AUC in Weakly Supervised Video Anomaly Detection: Granularity Resolution and Scene Bias ECCV2026
链接: https://arxiv.org/abs/2608.11985
作者: Sara Abdulaziz,Egor Bondarev
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026 ET Workshop
Abstract:Frame-level area under the ROC curve (AUC) is the dominant evaluation metric for weakly supervised video anomaly detection (WSVAD). Its standard form measures whether an anomalous frame outranks a normal frame drawn from anywhere in the test set. We refer to this comparison as pooled AUC, since it aggregates frame pairs across test videos regardless of source. Pooled AUC therefore credits both event localization and differences between video sources. We audit this protocol on UCF-Crime across recent state-of-the-art models spanning different backbone families. Holding each model’s frame scores fixed, we read them under three pairing granularities: global, per anomaly category, and within each video, then repeat the same three-granularity readout on zero-shot scores computed from the models’ internal representations. We assess ranking reliability with a paired video bootstrap. Three findings follow. First, pooled AUC does not reliably predict within-video anomaly localization: models with similar pooled scores exhibit large localization differences and rank reversals under stricter granularities. Second, at the benchmark’s test-split size, pooled AUC lacks the resolution to support state-of-the-art margins reported in the field. Within each backbone family, it resolves no comparison at those margins, while within-video AUC resolves several over identical predictions. Learned representations further reveal that within-video anomaly structure and detector localization are decoupled. Third, on normal footage alone, every model we examine separates videos by recording properties, such as resolution and color encoding, indicating that scene sensitivity is shared across the setting rather than specific to any architecture. We publicly release a granularity-aware protocol computable from existing predictions and scene-factor annotations for UCF-Crime.
[CV-39] Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping MICCAI2026
链接: https://arxiv.org/abs/2608.11942
作者: Shishuai Wang,Stefan Klein,Juan A. Hernandez-Tamames,Dirk H.J. Poot
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 6 figures. Accepted at the MICCAI 2026 Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE 2026)
Abstract:Quantitative MRI (qMRI) provides standardised tissue parameter maps, but the reliability of deep learning-based qMRI mapping methods is often not explicitly characterised. In this work we systematically evaluate uncertainty maps for quantitative MRI derived from multiple inferences of a data-consistent diffusion model-based qMRI framework. Evaluation on synthetic test data assessed error-awareness, high-error detection, selective prediction, and Gaussian interval calibration. Diffusion model-derived uncertainty was positively associated with the mapping error, while risk-coverage analysis showed that excluding high-uncertainty voxels reduced the retained error. However, the raw uncertainty was poorly calibrated for quantitative interval interpretation. Calibration was substantially improved using a post-hoc procedure combining prediction-value-dependent bias correction with scalar uncertainty scaling. Qualitative evaluation on a healthy volunteer showed spatially meaningful uncertainty patterns. These results indicate that diffusion model-derived uncertainty is informative for reliability assessment and selective prediction, but requires calibration for quantitative interval interpretation.
[CV-40] Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction
链接: https://arxiv.org/abs/2608.11938
作者: Yan Di,Chengxi Li,Yaoxing Wang,Mengge Liu,Zhigang Li,Ruida Zhang,Mingyang Li,Pengyuan Wang,Shan Gao,Xiangyang Ji
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Sparse voxel reconstruction offers an efficient representation for high-fidelity 3D modeling, yet its geometry is commonly optimized from local photometric evidence and discrete visibility statistics. This often leads to fragmented surfaces, excessive subdivision, and floating artifacts, particularly in weakly textured or sparsely observed regions. We introduce SurfSVR, a novel sparse voxel reconstruction paradigm that treats 2D surface priors as explicit 3D geometric regularizers. Instead of directly lifting noisy pixel-wise depth predictions, SurfSVR first organizes each image into coherent surface regions by jointly reasoning over appearance, monocular depth, normals and cross-view geometry. Each region is then represented by an adaptively selected planar or quadratic surface model based on fitting reliability and geometric complexity, while cross-model agreement distinguishes reliable geometry from ambiguous predictions. These structured 2D priors are lifted into 3D and integrated throughout the reconstruction pipeline. They guide surface-adaptive voxel subdivision, provide region-level depth and normal supervision during optimization, enhance geometrically reliable sparse-observed surfaces in voxel pruning, and suppress off-surface floaters during post-refinement training. This unified design converts semantic and geometric coherence in image space into persistent structural constraints in 3D. Extensive experiments on 3 public benchmarks demonstrate that SurfSVR consistently improves sparse voxel reconstruction across scenes with substantially different visibility and geometry characteristics, achieving state-of-the-art reconstruction quality. Codes and models will be released soon.
[CV-41] Dual Anchors Do It Better: Hierarchical Group Merging for Zero-Shot Anomaly Detection CVPR2026
链接: https://arxiv.org/abs/2608.11933
作者: Jimin Roh,DongKyu Kim,Suk-Ju Kang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 5 figures, 4 tables. Accepted to CVPR 2026 Findings
Abstract:Zero-shot anomaly detection (ZSAD) aims to identify anomalies in unseen domains, a setting that is particularly critical for industrial and medical applications where domain shifts are prevalent. However, most CLIP-based ZSAD methods anchor semantics solely on the text modality, making performance highly sensitive to prompt design and leading to weak visual grounding. To mitigate these limitations, we propose a Dual-Anchor framework that complements conventional text anchors with hierarchical image anchors constructed via a top-down grouping mechanism. This mechanism progressively aggregates local-to-global image features to form normal and abnormal group tokens, which serve as image anchors and act as gating signals in a Group-Gated Token Refiner to enhance the global representation. The refined image anchors are then fused with text prompts to construct dynamic state prompts. By jointly reinforcing visual and textual semantics, our framework stabilizes image-text alignment, reduces prompt dependency, and achieves strong generalization across 8 industrial and 6 medical benchmarks.
[CV-42] Seed2GS: Camera-Free Training-Free Object Extraction from 3D Gaussian Scenes via a Single Reference-View Grounding
链接: https://arxiv.org/abs/2608.11928
作者: Zongjian Ding,Yudong Gao,Jiale Liu,Xinglin Yu,Junxing Ren,Dong Wei,Yajing Chen,Shan Huang,Mingjun Cheng,Min Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Extracting a target object from a pre-built 3D Gaussian Splatting (3DGS) scene enables interactive 3D editing. Existing methods either train for tens of minutes per scene, sacrifice accuracy, or require original reconstruction cameras that pre-built assets may not include. We present Seed2GS, which achieves the highest reported LERF-MASK accuracy without original reconstruction cameras or scene-specific representation training. Its key insight is to separate target identity from 3D coverage. QD-SAM3 selects one reliable reference mask from several open-vocabulary candidates, fixing identity once. Seed lift and visibility-adaptive virtual orbits then expose the object from new viewpoints, while tracking propagates the seed without repeated detection. Because the scene remains frozen, these masks supervise only one temporary foreground logit per Gaussian. On LERF-MASK, Seed2GS reaches 92.1% mean intersection over union (mIoU) with a measured compute-only latency of 9.3 seconds, 3.7 points above the strongest scene-trained baseline and 7.6 points above the closest camera-free baseline. With one fixed test reference per scene, the complete pipeline retains 91.1% mIoU; replacing its predicted seed with a ground-truth mask improves mIoU by only 0.72 points. On 3D-OVS, Seed2GS reaches 95.7% mIoU.
[CV-43] HarmoniDPO: Video-guided Audio Generation via Preference-Optimized Diffusion
链接: https://arxiv.org/abs/2608.11913
作者: Wenshuo Peng,Kaipeng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 31 pages
Abstract:Video-to-audio (V2A) generation faces significant challenges in achieving precise temporal synchronization and high perceptual quality due to the complex, ambiguous relationship between visual and auditory cues. Existing methods typically compress video inputs into single feature representations, leading to significant loss of temporal dynamics and fine-grained visual information. These approaches also rely on reconstruction-based training objectives that poorly correlate with human perceptual judgments of audio quality and appropriateness. We propose HarmoniDPO, a novel framework that integrates preference-based optimization into diffusion-based V2A generation to address these limitations. (1) Our approach leverages a dual video representation: combining global context with frame-wise features to preserve temporal dynamics and semantic detail. (2) Inspired by reinforcement learning from human feedback (RLHF), HarmoniDPO employs online Direct Preference Optimization (online-DPO) to fine-tune a diffusion-based V2A model from preference judgments, enhancing perceptual quality and alignment. (3) Additionally, we introduce Dual-scale Diffusion Search (DDS), a test time scaling algorithm that adaptively optimizes output fidelity during inference. Experiments demonstrate that HarmoniDPO outperforms state-of-the-art methods in audio-video synchronization and subjective audio quality, offering a robust solution for generating realistic, human-preferred audio from video.
[CV-44] Do You See What You Draw? A Semantic Closed-Loop Framework for Holistic Evaluation of Unified Multimodal Models
链接: https://arxiv.org/abs/2608.11907
作者: Hao Zhang,Jiaxin Qi,Zhijiang Tang,Jianqiang Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 21 pages, 8 figures, 12 tables
Abstract:As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge. Current evaluation protocols predominantly treat generative and discriminative capabilities as separate tasks, leaving a gap in system-level evaluation for unified multimodal models (UMMs). In this work, we propose Self-Generative-Understanding (SGU), a novel, annotation-free evaluation framework that probes the integrated capabilities of unified models through a semantic closed-loop challenge. Without requiring new annotations, SGU leverages the dual understanding-and-generation abilities of UMMs by asking them to first perceive an image and produce a textual description, subsequently reconstruct a visual context based on that description, and finally perform reasoning over the self-generated output. This pipeline provides a zero-cost testbed that yields an integrated performance score specifically tailored for evaluating UMMs as unified systems. Extensive experiments show that even high-performing UMMs often struggle to reason over their own generated contexts, revealing limitations that are not captured by separate evaluations of understanding or generation alone. Our work provides a complementary holistic evaluation framework and offers a foundation for benchmarking the development of next-generation unified multimodal models.
[CV-45] Warping Earth Observations for better ice labeling in the Marginal Marginal Ice Zone ECCV
链接: https://arxiv.org/abs/2608.11883
作者: Tom Kelly,Martin S. J. Rogers
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV Workshop paper; BEAM 2
Abstract:Multimodal satellite imagery provides complementary information for Earth Observation, but accurately combining heterogeneous sensors remains challenging in dynamic environments. Fast-changing regions, such as the Antarctic marginal ice zone, cannot fully exploit multimodal information from different satellite sensors because surface features move between image acquisitions. This spatial and temporal mismatch challenges effective perceptual grounding, violating the assumption of pixel-level correspondence that underpins most multimodal reasoning and downstream classification pipelines. Antarctic sea ice provides a challenging benchmark due to the rapid, heterogeneous drift of individual ice floes and the differing responses of sea ice to radar, visible and thermal sensing modalities. Accurate, dense supervision of sea ice remains scarce because generating pixel-wise labels requires time-consuming expert interpretation of noisy data, leading to historical reliance on coarse-resolution maritime ice charts for model training. This paper presents a novel architecture based on mutual information warping to align multi-satellite (Sentinel-1 and MODIS platforms) multimodal (visible, thermal, radar) satellite scenes. To demonstrate the approach, we introduce a sparse expert-labeled dataset of 2,088 pixel-wise annotations (7,046 expert point classifications) located at the ice-water margin interface across 43 scenes. Our results demonstrate that spatially grounding and aligning modalities prior to segmentation improves classification accuracy, and enables accurate, dense sea ice segmentation from sparse point-wise supervision.
[CV-46] BoltNet: An Ultra-Lightweight Convolutional Network for On-Device Plant Species Identification ECCV2026
链接: https://arxiv.org/abs/2608.11844
作者: Daniel Rossi,Guido Borghi,Roberto Vezzani
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the CVPPA (Computer Vision Problems in Plant Phenotyping and Agriculture) workshop, ECCV 2026. 17 pages, 2 figures
Abstract:Automated plant species identification from citizen-science imagery is an established, demanding fine-grained recognition problem: large taxonomic label spaces, visually similar species, and long-tailed observations require real model capacity, while field use constrains memory, latency, and power. Model size is only part of the deployment cost: intermediate activations held in memory during inference and platformdependent execution behavior matter too, so compact recognition must be assessed on target hardware rather than through complexity metrics alone. We present BoltNet, an ultra-lightweight fully convolutional architecture combining a Spatial Redistribution Bottleneck and Logit PreSampling to improve the tradeoff between predictive performance and model size in high-cardinality classification, and report the AccuracyCompression Tradeoff as a complementary diagnostic. On Pl@ntNet300K, BoltNet reaches 0.682 F1-score with 341K parameters (1.37 MB), the highest F1-score among evaluated models below 2 MB and close to substantially larger convolutional backbones. Model-only measurements on a Raspberry Pi 5, Jetson Orin Nano, and Hailo-8 characterize execution across CPU, GPU, and NPU platforms, where BoltNet is the most consistently efficient model, with the best FPS/W on the GPU and NPU and second-best on the CPU. Results on AIDERv2 and CLRS provide secondary evidence of transfer across environmental image-classification tasks. Code available at: this https URL
[CV-47] GeoBridge: Decoupled Semantic Conditioning for Generative Image Geolocalization
链接: https://arxiv.org/abs/2608.11838
作者: Zhiyang Dou,Xumeng Han,Fengde Peng,Zipeng Wang,Moxuan Zhao,Zhipei Huang,Zhenjun Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal large language models (MLLMs) have advanced image geolocalization mainly by improving how they reason about geographic cues. How that reasoning isdecoded into coordinates, however, has lagged behind. Predicting a place name for a geocoding API is discrete and lossy: it ignores image evidence and collapses multi-granular semantics into a coarse lookup. We argue that the bottleneck has shifted from what a model reasons to how that reasoning is represented for a continuous, geometry-aware decoder. We present GeoBridge, a role-decoupled conditioning mechanism that connects a frozen semantic MLLM to a frozen Riemannian flow-matching head that generates coordinates on the sphere. The central obstacle is arole conflict: supervising the condition with discrete semantic labels biases its representation toward class-discriminative geometry, at odds with the smooth manifold the generative head requires. GeoBridge keeps the semantic supervision decoupled from the condition interface: a separate projection forms the continuous condition the frozen head expects, injecting geographic priors without disturbing the spherical decoder. On IM2GPS3K, GeoBridge reaches 38.67/52.89/70.37 at the 25/200/750 km thresholds, improving over a place-name-to-API pipeline and reasoning-augmented direct prediction at these precision-relevant scales. GeoBridge is a decode-side algorithmic contribution, orthogonal and complementary to chain-of-thought reasoning. Code will be made publicly available.
[CV-48] Distractor-Aware Video Object Segmentation
链接: https://arxiv.org/abs/2608.11835
作者: Andreas Robinson,Abdelrahman Eldesokey,Michael Felsberg
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 5 figures, 2 tables. Author’s accepted manuscript, DAGM GCPR 2021
Abstract:Semi-supervised video object segmentation is a challenging task that aims to segment a target throughout a video sequence given an initial mask at the first frame. Discriminative approaches have demonstrated competitive performance on this task at a sensible complexity. These approaches typically formulate the problem as a one-versus-one classification between the target and the background. However, in reality, a video sequence usually encompasses a target, background, and possibly other distracting objects. Those objects increase the risk of introducing false positives, especially if they share visual similarities with the target. Therefore, it is more effective to separate distractors from the background, and handle them independently. We propose a one-versus-many scheme to address this situation by separating distractors into their own class. This separation allows imposing special attention to challenging regions that are most likely to degrade the performance. We demonstrate the prominence of this formulation by modifying the learning-what-to-learn (LWL) method to be distractor-aware. Our proposed approach sets a new state-of-the-art on the DAVIS 2017 val dataset, and improves over the baseline on the DAVIS 2017 test-dev benchmark by 4.6 percentage points. Comments: 14 pages, 5 figures, 2 tables. Author’s accepted manuscript, DAGM GCPR 2021 Subjects: Computer Vision and Pattern Recognition (cs.CV) ACMclasses: I.4.6; I.4.8 Cite as: arXiv:2608.11835 [cs.CV] (or arXiv:2608.11835v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.11835 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Pattern Recognition. DAGM GCPR 2021. Lecture Notes in Computer Science, vol. 13024, pp. 222-234. Springer, Cham (2021) Related DOI: https://doi.org/10.1007/978-3-030-92659-5_14 Focus to learn more DOI(s) linking to related resources
[CV-49] D-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learning ICML2026
链接: https://arxiv.org/abs/2608.11820
作者: Shuangqing Zhang,Lei-Lei Ma,Zhao Wang,Wen Dong,Xinyi Xu,Guo-Sen Xie,Caifeng Shan,Fang Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICML2026
Abstract:Visual data is typically a prerequisite for training existing video anomaly detection (VAD) methods. However, obtaining sufficient annotated anomaly data for training is challenging and not scalable due to the rarity of anomaly data and the wide variety of abnormal events. In this work, we advocate that the effectiveness of treating texts as video sequences for the VAD model and propose a novel Text-Driven Video Anomaly Detection (TD-VAD) approach to break visual dependence. In contrast to the anomaly video data, text descriptions of abnormal events are easy to collect, and their class labels can be directly derived. Specifically, our method utilizes video-like text descriptions with temporal characteristics generated by LLM to train a VAD model, without any reliance on target-domain anomaly data. To capture the long- and short-range temporal logic of events, we design the event evolution causal attention module to model contextual dependencies across time. During inference, considering the domain gap between the texts and video sequences, we use the frozen CLIP encoder to extract embeddings of video frames to align the text modality while retaining crucial visual information. Comprehensive experiments on two large-scale VAD datasets, XD-Violence and UCF-Crime, demonstrate that our method outperforms prior one-class and unsupervised VAD methods by a large margin.
[CV-50] Learning with Bilevel-Minimax Optimization for Efficient and Reliable Transfer Attacks ECCV2026
链接: https://arxiv.org/abs/2608.11815
作者: Yaohua Liu,Yifan Guo,Jiaxin Gao
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026. 21 pages in total. Code available at this https URL
Abstract:Transfer-based adversarial attacks craft adversarial examples using surrogate models to mislead black-box victim models. Beyond perturbation generation, transferability is fundamentally governed by the coupling of initialization, surrogate adaptation, and gradient dynamics. We revisit this challenge from a bilevel-minimax perspective and propose BMAT (Bilevel-Minimax Adversarial Transfer). The bilevel formulation captures the dependency between initialization and perturbation, while the inner minimax problem promotes surrogate robustness for cross-architecture generalization. Algorithmically, we develop an integrated bottom-up solver that combines a Soft Weight Modulator and an Implicit Gradient Approximator to enable ternary coupling among initialization, surrogate adaptation, and perturbation optimization. We further provide theoretical insights into the optimization dynamics of the proposed bilevel-minimax framework. Extensive experiments on classification and segmentation benchmarks show that BMAT outperforms more than 10 strong baselines across more than 30 victim models, improving both intra- and cross-architecture transfer and yielding up to a 2x reduction in mIoU. Code is available at this https URL.
[CV-51] Can Vision Models Read the Radar Display? On the Feasibility of Radar Imagery for Air Traffic Complexity Estimation
链接: https://arxiv.org/abs/2608.11810
作者: Hyewook Kim,Byul Kang,Seokbin Yoon,Keumjin Lee
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 12 pages, 6 figures. Submitted to Elsevier
Abstract:Air traffic controllers perceive traffic complexity through the radar display, suggesting that a computer vision model operating on the same imagery may provide a natural architecture for modeling controller-perceived complexity; however, whether radar imagery is a viable input format for deep learning vision models remains unclear. Unlike natural images, radar images are extremely sparse and self-similar, consisting primarily of a black background and a few visually identical aircraft blobs, while small changes in aircraft positions can substantially alter sector-level complexity. To test whether a vision model can capture these operationally important differences, we encode each traffic situation as a position image supplemented by five channels representing aircraft state variables, including heading, speed, and altitude, and train a Vision Transformer (ViT) to regress four intrinsic complexity components derived from pairwise geometric relations among aircraft. The model achieves R^2 0.96 for all four components, and a one-aircraft-removal perturbation study shows that its response changes proportionally to how much the removed aircraft contributed to sector complexity rather than treating every removal as equivalent. These results demonstrate that, despite its atypical visual characteristics, radar imagery is a viable input format for air traffic complexity modeling.
[CV-52] CoDiR: Confidence-Guided Diffusion Refinement for Semi-Supervised Histopathology Segmentation MICCAI
链接: https://arxiv.org/abs/2608.11807
作者: Hoai Nhan Pham,Dang-Nguyen Bui,Le-Van Thai,Thanh-Hiep Vo,Lan Anh Dinh Thi,Tien Dat Nguyen,Duy-Dong Nguyen,Ngoc Lam Quang Bui,Tam Tran,Zhi Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to the MICCAI COMPAYL Workshop 2026 (11 pages, 2 figures, 6 tables)
Abstract:Semi-supervised histopathology segmentation is challenging due to scarce annotations and unreliable pseudo-labels in ambiguous gland regions. To address this problem, we propose Confidence-Guided Diffusion Refinement (CoDiR), a semi-supervised framework that combines a Mean Teacher segmentation model with diffusion-based pseudo-label refinement. Given an unlabeled image, the teacher first produces a soft prediction, and only low-confidence regions are refined by a conditional diffusion model trained to capture plausible mask structures from labeled data. The refined mask is then fused with reliable teacher predictions and used to train the student with confidence weighting and consistency regularization. On the GlaS and CRAG datasets CoDiR reaches 88.09% and 89.83% mDice with 10% labeled data, and 89.19% and 90.29% mDice with 20%, matching or exceeding the strongest published method on seven of the eight benchmark metrics. Ablations attribute the largest single contribution to the refinement module, which adds +6.36% mDice over the Mean Teacher baseline. The implementation code is publicly available at: this https URL
[CV-53] PolarSym: Polar Geometry-aware Attention for CAD Floorplan Parsing
链接: https://arxiv.org/abs/2608.11793
作者: Kerui Chen,Yiqing Wang,Kangzhou Xin,Qinghan Zhang,Songyang Ding
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 7 figures
Abstract:CAD plan parsing is a fundamental task in Building Information Modeling (BIM), aiming to automatically extract architectural elements including walls, doors, windows, and furniture from 2D engineering drawings. Existing Transformer-based methods capture global semantic dependencies via self-attention, yet they infer spatial relationships merely from semantic features without explicitly characterizing the intrinsic geometric symmetry of building layouts. Such methods tend to produce mismatched correspondences in long-range matching and complex symmetric spatial layouts. To tackle this limitation, we propose PolarSym, a polar-coordinate geometry-aware attention framework for CAD plan parsing. The framework decouples geometric relationships of buildings into two complementary components, direction and distance, which are modeled independently. Structural consistency is strengthened by directional constraints, while long-range symmetric correspondences are built with distance constraints. A dynamic gating mechanism is adopted to synergistically fuse the two geometric information branches while maintaining the vanilla Transformer architecture. This design boosts geometric modeling capacity with negligible extra computation. Experiments on a public CAD plan parsing dataset show that PolarSym surpasses the reproduced SymPoint V2 baseline by 1.73% PQ, 1.54% RQ and 4.31% mIoU under identical training settings. PolarSym also converges faster and yields more stable optimization. Ablation experiments verify the complementary effects of direction and distance modeling. Our results reveal that PolarSym improves the geometric awareness of Transformers at low computational cost, offering an effective geometric modeling paradigm for CAD plan parsing.
[CV-54] Anti-Shortcut Distillation via Temporal Negative Knowledge Transfer
链接: https://arxiv.org/abs/2608.11789
作者: Syed Muhammad Raza,Omer Tariq,Jeongbae Son
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 3 figures, 21 pages supplementary material
Abstract:Knowledge distillation (KD) trains a compact student by attracting it towards a converged teacher. It is silent about which directions the teacher itself learned to suppress: repulsive and bias-aware objectives exist, but none exploits the teacher’s own trajectory to identify what the student should avoid. We observe that the missing signal is already encoded in the teacher’s optimization trajectory: features that an early-stage teacher emphasizes but that a converged teacher attenuates are precisely the shortcut directions worth pushing the student away from. We instantiate this observation as \textbfAnti-\textbfShortcut \textbfDistillation (ASD), a push–pull KD framework that treats the converged teacher \Tfinal as a positive semantic anchor and an early-checkpoint teacher \Tearly as a temporal negative reference. ASD couples two losses: a temporal contrastive loss ( \Ltc ) that places the early-teacher feature as a same-sample negative against in-batch and memory-bank final-teacher features in an InfoNCE objective; and a shortcut suppression loss ( \Lss ) that penalizes student projection onto the top eigenvectors of \E[\Dh\Dh^\top] , the uncentered second-moment matrix of early-to-final feature displacements. Across 13 teacher–student pairs on CIFAR-100, ImageNet-100, and TinyImageNet, ASD attains the highest clean top-1 accuracy on more than 10 pairs and outperforms standard KD on 12. On CIFAR-100-C corruption robustness, ASD obtains the lowest mean Corruption Error ( 86.1 ,mCE) on the most challenging cross-architecture pair (WRN-40-2 \to ShuffleNet-V2). Mechanistic diagnostics confirm the intended geometry: the ASD student is systematically anti-aligned with the shortcut direction, while its projection onto the robust subspace is substantially larger ( 0.45 vs.\ 0.12 ).
[CV-55] VOLA: Improving Open-World Driving by VLM-Based Semantic Attribute Prediction
链接: https://arxiv.org/abs/2608.11777
作者: Yuchen Zhang,Yuan Gao,Sebastian Schmidt,Johannes Betz
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 8 figures
Abstract:Driving in the real world is open-world: a car may encounter a fallen mattress, a deer, or other objects outside its training data. Naming them is not enough. The system must know how to treat each region: can it drive over it, and how severe would a collision be? We therefore shift scene perception from category labels to dense action-relevant attributes, where each pixel is labeled by how it should affect motion rather than by object name. We instantiate this general formulation with two ordered attributes: 7-rank drivability and 5-rank vulnerability. We read Qwen3.5 image-token hidden states directly as a spatial semantic representation. A lightweight boundary-aware decoder then turns this coarse token grid into sharp full-resolution attribute maps. The whole process requires neither autoregressive text generation nor an external mask model such as SAM. We train on dense attribute labels built in CARLA and test transfer to real scenes and to novel obstacles never seen in training. We compare with vision-only segmenters trained on the same attributes and prompted VLM segmenters. Our model matches strong vision-only segmenters on familiar categories and improves transfer to real open-world anomalies, reaching 69.4% mean vulnerability-rank recall versus 57.1% for the best vision-only baseline and 53.9% for the best prompted VLM baseline. These results show that VLM image tokens provide useful semantic cues for transferring driving attributes to objects outside the training vocabulary.
[CV-56] Achieving Near-Zero-Overhead Multi-Model Hierarchical Classification in Real-Time Detection Pipelines
链接: https://arxiv.org/abs/2608.11770
作者: Vaishnav Raju
类目: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
备注: 14 pages, 17 figures, 5 tables. Submitted to Journal of Real-Time Image Processing
Abstract:Edge-deployed vision systems in target recognition, surveillance, autonomous vehicles, and drone domains require hierarchical inference pipelines where a detection model identifies objects of interest and downstream classifiers provide fine-grained attribute analysis. Running all models on the GPU creates a serial bottleneck that limits real-time throughput as pipeline stages grow. Modern edge SoCs pair GPUs with dedicated neural accelerators (NPUs, DLAs) capable of concurrent execution, yet deploying custom models on these accelerators remains impractical due to strict operator constraints, quantization incompatibilities, and an undocumented end-to-end pipeline. We target NVIDIA Jetson DLA cores as the representative platform. We present a five-step methodology for zero GPU fallback DLA INT8 deployment of classification backbones, comprising architecture adaptation, manual dynamic range workaround to rescue TensorRT’s implicit quantization (recovering 94.0% accuracy from implicit quantization’s 75%) for rapid pipeline validation before explicit quantization, quantization-aware training, ONNX graph surgery for DLA compilation, and a concurrent GPU-detection/DLA-classification inference pipeline. We document nine engineering constraints with root-cause analysis and generalizable solutions. Validation on a dual-head person attribute classifier running on DLA alongside a GPU object detector on a Jetson Orin NX demonstrates near-zero pipeline overhead (12.5 vs. 13.3~FPS detector-only at 1080p), with dual-DLA scaling at no additional cost. The methodology is backbone-agnostic and generalizes to any detection-classification edge pipeline.
[CV-57] ProBAG: Prototype-Guided Boundary-Aware Graph Diffusion for Weakly Supervised Histopathology Segmentation MICCAI
链接: https://arxiv.org/abs/2608.11765
作者: Duy-Dong Nguyen,Le-Van Thai,Hoai Nhan Pham,Ngoc Lam Quang Bui,Tam Tran,Zhi Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 2 figures, 4 tables. Accepted by MICCAI Workshop (COMPAYL) 2026
Abstract:Weakly supervised semantic segmentation enables histopathology tissue segmentation from image-level annotations, avoiding costly pixel-level labeling by expert pathologists. However, CAM-based methods often localize only highly discriminative regions and remain unreliable near tissue interfaces. We propose ProBAG, a stage-1 pseudo-mask generator that combines dataset-specific visual prototypes with pathology-aligned CONCH text prototypes over multi-scale frozen UNI features. ProBAG introduces two complementary mechanisms: class-wise power recalibration that reshapes inter-class competition while preserving the total foreground activation mass at each pixel, and one-step graph diffusion in which feature affinities are penalized by a late-transformer attention-context discrepancy used as a soft structural boundary cue. The resulting stage-1 pseudo-masks require neither CRF nor an external segmentation model; for complete two-stage comparison, they additionally supervise a downstream Phikon-FPN segmenter. Experiments on BCSS-WSSS and LUAD-HistoSeg show consistent gains over recent WSSS approaches, while ablations indicate that pathology-aligned text semantics provide the largest improvement and graph refinement provides a smaller complementary gain. The code is available at: this https URL
[CV-58] Automated binary classification of hazelnut X-ray images: A deep-learning benchmark for quality assessment
链接: https://arxiv.org/abs/2608.11759
作者: Giancarlo Sportelli,Nicola Belcari,Roberta Pace,Umberto Bernardo,Sharmin Sultana,Alessandra Toncelli,Matteo Giaccone
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Applied Physics (physics.app-ph)
备注: 26 pages (including 5 pages of supplementary material), 4 figures, 5 tables. Dataset available at this https URL
Abstract:Non-destructive X-ray imaging can reveal internal hazelnut defects that are difficult to detect by external inspection alone; however, automated interpretation remains challenging because of subtle radiographic differences among classes, marked class imbalance, and limited annotated data. Here, we present a benchmark for binary hazelnut quality classification (healthy versus defective) based on 799 segmented single-kernel X-ray images (224 x 224 pixels, grayscale), grouped into 101 acquisition units. Seven single-model configurations and ten probability-aggregation ensembles were evaluated using a group-wise split-rotation protocol across five data splits generated using different random seeds. Decision thresholds were selected on the validation set, and performance was assessed deterministically on validation and test sets. Under the expert-reassessed annotation condition, the average-probability ensemble of the binary cross-entropy-trained convolutional neural network and frozen Swin Transformer achieved the highest mean balanced accuracy (86.3% +/- 1.8%, five seeds), with several other ensembles providing comparable performance. Across methods, substantial split-to-split variability was observed, indicating that multi-split evaluation is essential for reliable model comparison at this dataset scale. Expert reassessment of ambiguous samples improved the performance of all 17 evaluated methods by 2.8-8.1 percentage points, while having only a limited effect on cross-split variance. The results highlight both the potential of deep learning for automated X-ray-based hazelnut quality assessment and the importance of rigorous evaluation and label curation in small, imbalanced agricultural imaging datasets.
[CV-59] UniSwap: Streaming Audio-Visual Identity Swapping for Talking Videos
链接: https://arxiv.org/abs/2608.11752
作者: Yuxuan Zhang,Haozhong Xiong,Jiayi Song,Jinpeng Yu,Yang Shi,Jiaming Liu,Ruihua Huang,Liwei Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注:
Abstract:Talking-video character replacement requires coordinated transfer of appearance and voice while preserving the source motion, scene, linguistic content, and audio-video timing. Existing methods use separately optimized models for the two modalities, making audio-visual consistency difficult to enforce. We present UniSwap, the first framework for streaming joint audio-visual identity replacement in talking videos. Given a source video, a reference image, and a reference voice clip, UniSwap transfers the reference appearance and vocal timbre within a single audio-visual diffusion transformer while preserving the source content and dynamics. To address the scarcity of aligned cross-identity training pairs, we introduce a swap-and-reconstruct pipeline that removes visual and vocal identity from real clips and uses the original clips as reconstruction targets. Starting from a bidirectional backbone, we progressively adapt the model through In-context Pretraining for joint replacement, Conditional Streaming Adaptation for block-causal KV-cached generation, and Efficient Self-forcing DMD for mitigating exposure bias and reducing sampling from 30 to 3 denoising steps per block. Efficient Multi-LoRA Switching enables the three DMD roles to share a single frozen backbone. Feature-RoPE Decomposition keeps cached positions within the training range, supporting stable long-form inference. Experiments demonstrate strong audio-visual synchronization, competitive identity preservation, efficient streaming, and stable long-form generation.
[CV-60] Dual Modality Prompted Diffusion Priors for Zero Shot Hyperspectral Pansharpening
链接: https://arxiv.org/abs/2608.11748
作者: Pengwei Xie,Fei Zhu,Jiajun Li,Xiangyuan Liu,Xiangyuan Liu,Kangqing Shen,Gemine Vivone
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Hyperspectral pansharpening aims to reconstruct a high resolution hyperspectral (HRHS) image from a panchromatic (PAN) image and a low resolution hyperspectral (LRHS) image while preserving both spatial details and spectral fidelity. Recent diffusion based methods exploit pretrained image priors by generating a low dimensional representation and subsequently mapping it to the hyperspectral domain. However, the observed panchromatic and hyperspectral images are typically imposed only through external reconstruction objectives, limiting their direct interaction with the diffusion prior. To address this issue, we propose dual-modality image-prompted diffusion model (DIDM) for zero shot hyperspectral pansharpening. DIDM encodes the low resolution hyperspectral and panchromatic observations into spectral and spatial prompt tokens, respectively, and injects them into intermediate features of a frozen remote sensing diffusion model through cross attention, allowing complementary spectral and spatial information to directly guide diffusion feature evolution. In addition, we introduce a panchromatic guided weighted pixel aware total variation regularizer that combines low resolution hyperspectral degradation fidelity and panchromatic response fidelity with gradient adaptive structural regularization, thereby preserving structural discontinuities while suppressing spurious variations in homogeneous regions. Extensive experiments on Pavia, Chikusei, and Houston under reduced resolution protocols show that DIDM achieves the best performance across all evaluated metrics, while full resolution evaluation on FR1 yields the highest HQNR among the compared methods. These results demonstrate that internal dual modality prompting and panchromatic guided structural regularization provide an effective balance between spatial detail enhancement and spectral preservation.
[CV-61] Making Every Step Count: Spatio-Temporal Information Allocation for Imaging Inverse Problems
链接: https://arxiv.org/abs/2608.11747
作者: Yi Cao,Xiangyong Cao,Pei Liu,Yong-Jin Liu,Deyu Meng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Flow-based generative models have emerged as powerful image priors for training-free inverse problem solving, capturing coherent semantics and fine-grained structure. Despite these strengths, existing flow-based inverse solvers primarily focus on the design of individual updates, largely overlooking spatio-temporal information allocation under a fixed number of function evaluations (NFEs). Temporally, insufficient early exploration can trap the flow trajectory in an incorrect semantic basin, whereas excessive allocation of NFEs to early stages leaves little budget for late-stage refinement. Spatially, data consistency provides direct constraints only within observed regions, whereas the recovery of missing regions relies mainly on the generative prior. To address these two issues, we introduce two complementary and training-free components, i.e., Spectrum-Adaptive Scheduling (SAS) and Measurement-Prioritized Attention (MPA). For temporal allocation, SAS distributes the available NFEs over flow time according to the degradation spectrum and logSNR geometry, thus better balancing semantic exploration and detail refinement. For spatial propagation, MPA exploits data-prior conflicts to guide information toward weakly constrained regions, thereby enhancing semantic and structural fidelity. Extensive experiments on standard image inverse problems, e.g., super-resolution, motion deblurring, and inpainting, demonstrate that the proposed components can be integrated into existing flow-based inverse solvers in a plug-and-play manner without retraining or additional flow-model evaluations, and can also significantly improve the restoration quality of existing solvers.
[CV-62] LiveAnimate: Stable Long-Form Streaming Human Animation in Real-Time
链接: https://arxiv.org/abs/2608.11745
作者: Yuxuan Zhang,Haozhong Xiong,Yubo Huang,Jiayi Song,Jinpeng Yu,Haofan Wang,Jiaming Liu,Ruihua Huang,Liwei Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pose-driven human animation synthesizes a video of a target person from a single reference image and a driving pose stream. Real-time generation is essential for interactive applications such as live streaming, telepresence, and virtual avatars, yet diffusion-based systems require minutes to hours per clip, precluding responsive interaction. We present LiveAnimate, to our knowledge the first animation system to combine real-time streaming with stable long-form generation at billion scale, built on a 14B-parameter video Diffusion Transformer (DiT). A two-stage training pipeline first adapts a pretrained bidirectional DiT into a block-causal autoregressive generator through Reference-Anchored Teacher-Forcing Adaptation, and then reduces the sampling budget to three steps through Block-wise Self-Forcing Distillation. To preserve appearance over extended streams, we introduce Pose-Retrieval Sink Attention (PR-Sink), a bounded KV-cache mechanism combining a Static Sink that permanently anchors the first generated block, a Dynamic Sink that holds a pose-retrieved historical block, and a three-slot Rolling Window. When a pose recurs, PR-Sink restores the relevant appearance context without retaining the entire sequence, so memory and per-block latency remain constant regardless of stream duration. Together with Ulysses sequence parallelism and operator fusion, these designs enable 19.63,FPS streaming inference on two NVIDIA H100 GPUs. On a three-minute benchmark, LiveAnimate maintains nearly constant perceptual quality and identity from the first 30 seconds to the final minute (IQA 4.047 vs.\ 4.026), while prior systems degrade substantially or require hours of offline computation for the same rollout. These results establish a new operating point in quality, latency, and duration for interactive full-body animation.
[CV-63] JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis
链接: https://arxiv.org/abs/2608.11741
作者: Ran Li,Huiguo He,Jiahuan Cao,Junle Liu,Hiuyi Cheng,Lianwen Jin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 19 pages, 13 figures. Accepted to the Dataset Track of ACM Multimedia 2026 for oral presentation
Abstract:The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context. However, existing computational approaches focus narrowly on subtasks such as character recognition and retrieval, lacking the structured datasets and benchmarks required for comprehensive scholarly analysis. To address this limitation, we introduce Ancient Chinese Character Exegesis (ACCE), a vision-language question answering (VQA) task that models the scholarly exegesis process. ACCE is organized into four progressive levels: basic character identification, glyph-form analysis, meaning exegesis, and diachronic evolution analysis. To support this task, we construct two complementary resources. JieZi-Dataset is the first large-scale, expert-audited VQA training dataset for ACCE, comprising over 500K QA pairs. It is constructed via a pipeline that reduces factual errors by constraining generation with expert-designed templates and source-text references. Human verification is further applied at each key stage to ensure scholarly accuracy. JieZi-Bench is an evaluation benchmark aligned with the exegesis process, constructed and verified by human experts to ensure evaluation reliability. It consists of four levels with reference answers curated from authoritative lexicographic works held separate from the training data. Experiments on multimodal large language models show that current models perform well on basic identification but struggle with glyph analysis, semantic reasoning, and diachronic understanding. Fine-tuning on JieZi-Dataset substantially improves performance across all four levels. Code and dataset are available at this https URL.
[CV-64] Advancing MLLM -based UAV Image Understanding and Reasoning : A Benchmark and a Training-Free Multi-Agent System
链接: https://arxiv.org/abs/2608.11738
作者: Haoyu Zhang,Shuoxun Zhang,Peng Ye,Lin Zhang,Jiakang Yuan,Shenghong Yi,Yuening Wang,Tao Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal Large Language Model (MLLM)-based UAV aerial image understanding and reasoning is essential for aerial intelligence yet poses distinct challenges arising from extreme scale variation, arbitrary camera orientations, and high object density. Despite growing interest, existing evaluations remain fragmented across individual datasets and narrow tasks, leaving a critical gap in unified assessment of UAV understanding and reasoning capabilities. To fill this gap, we construct UAVQA-Bench, a benchmark of 1,500 human-annotated QA pairs drawn from 13 public UAV datasets, covering 6 capability dimensions and 16 tasks in both multiple-choice and visual grounding formats. Systematic evaluation of a broad range of open-source and closed-source MLLMs as well as agent-based systems on UAVQA-Bench identifies three key failure modes: domain-toolset mismatch, unchecked error propagation, and static reasoning. Motivated by these findings, we propose UAV-MAS, a training-free multi-agent system for MLLM-based UAV aerial image understanding and reasoning, comprising a Domain-Specific Perception Engine (DSPE) that routes queries to task-appropriate visual tools, a Context-Aware Iterative Refinement module (CAIR) that validates intermediate reasoning to curb error accumulation, and a Difficulty-Aware Adaptive Search mechanism (DAAS) that adjusts search depth to question difficulty. UAV-MAS with a 32B open-source MLLM achieves 77.0% overall accuracy on UAVQA-Bench, surpassing Gemini 3 Pro by 4.0%, while the 8B variant improves 8.7% over its base model.
[CV-65] STAR: A Spatial-Topology Aware Routing Framework for Generalizable 3D Scene Understanding
链接: https://arxiv.org/abs/2608.11699
作者: Mingwei Xing,Xinliang Wang,Yifeng Shi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: The third author is the corresponding author
Abstract:Constructing a unified 3D scene understanding model has long been hindered by the topological discrepancies across sensor modalities. While applying the Mixture-of-Experts (MoE) architecture is a flexible approach for multi-domain 3D understanding, we observe that conventional feature-only MoE routers may underrepresent local sampling topology under semantic supervision, making expert allocation difficult when semantic consistency coexists with geometric heterogeneity. To overcome this challenge, we propose STAR (Spatial-Topology Aware Routing Framework). Specifically, we introduce a multi-attribute self-supervised pre-training branch, covering topological and textural variations, to anchor cross-domain structural priors. Building upon this, we design a domain-aware expert branch with two mechanisms: Domain-Spatial-Guided Routing (DSR), which captures local topological variations from spatial context, and Entropy-controlled Dynamic Allocation (EDA), which adjusts the number of activated experts according to routing uncertainty. Together, these branches combine stable cross-domain representation learning with adaptive expert allocation. Extensive experiments across various tasks, encompassing both indoor and outdoor scenes, demonstrate the effectiveness of STAR. It achieves 80.1% mIoU on the ScanNet validation set and 77.2% mIoU on S3DIS, consistently improving over strong baselines. Code is available at our project page (this https URL).
[CV-66] Boundary-Enhanced Segmentation of Pig Point Clouds in Commercial Housing Environments
链接: https://arxiv.org/abs/2608.11697
作者: Zhankang Xu,Fei Shi,Xiangyu Qi,Zhaoyang Wang,Mengxin Guo,Yikai Fan,Simon X. Yang,Qifeng Li,Weihong Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages,9 figures, 5 tables
Abstract:In real pigsty environments, pig point clouds often come into close contact with background structures, resulting in blurred target boundaries, local adhesion, and background mis-segmentation. This reduces the accuracy of subsequent point cloud completion and body size measurement. To address these challenges, this study proposes a pig point cloud segmentation method based on boundary feature analysis. The proposed method adopts Octree Transformer as the backbone network and integrates local geometric details with global semantic context through octree convolution, self-attention encoding, and multi-scale feature fusion. Furthermore, soft-distance boundary pseudo-labels are generated to provide continuous boundary supervision, and a bidirectional cross-boundary semantic module is designed to enable explicit interaction between boundary and semantic features. Experiments conducted on a comprehensive dataset demonstrate that the proposed method significantly outperforms various state-of-the-art models in terms of segmentation accuracy, mean intersection over union, and boundary delineation. The results indicate that the method effectively alleviates boundary adhesion, providing reliable point cloud inputs for downstream precision livestock farming tasks.
[CV-67] EGM-Det: Entropy-Guided Multimodal Adaptive Fusion for UAV RGB-IR Object Detection
链接: https://arxiv.org/abs/2608.11685
作者: Cunzheng Fan,Dawei Yan,Guanlin Wang,Xingshuo Yang,Yupeng Jia,Jing Yang,Haokui Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 7 figures, 6 tables
Abstract:Joint use of RGB and infrared (IR) imagery can improve UAV-view object detection, but most existing methods fuse multimodal features with static or fixed weights and therefore overlook spatially varying modality reliability. We propose EGM-Det, an entropy-guided multimodal adaptive fusion framework for RGB-IR object detection. EGM-Det employs a dual-stream architecture to preserve modality-specific representations and introduces an Entropy Offset Gate Fusion module for adaptive multi-scale fusion. The module derives shallow entropy priors from input intensity, local entropy, and cross-modal discrepancy, and uses them to guide local offset alignment and spatial-channel gated fusion. It therefore selectively aggregates reliable RGB and infrared cues instead of uniformly combining heterogeneous features. We further introduce cross-modal distillation to regularize the learned fusion gates and reduce fusion degradation. Each student branch extracts complementary knowledge from the cross-modality teacher branch matched to the main branch, while entropy-adaptive supervision emphasizes uncertain modality decisions. Experiments on DroneVehicle, LLVIP, and VEDAI demonstrate state-of-the-art performance across all three benchmarks; in particular, EGM-Det outperforms prior approaches by more than 10 percentage points on VEDAI.
[CV-68] Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation
链接: https://arxiv.org/abs/2608.11681
作者: Duy Tran Thanh,Yeejin Lee,Byeongkeun Kang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: 14 pages
Abstract:This work addresses the challenge of open-vocabulary instance segmentation (OVIS) and open-set panoptic segmentation (OSPS), which aim to recognize both predefined and unseen object categories without exhaustive human annotations. Existing methods often suffer from noisy pseudo-masks, limited visual-textual grounding, and difficulty handling synonyms or out-of-vocabulary (OOV) words. To overcome these challenges, we propose a multimodal framework that leverages pre-trained vision-language models for automatic pseudo-label generation, CLIP-guided synonym filtering, and GPT-based caption reconstruction. In our target-vocabulary-assisted pseudo-labeling setting, the framework first constructs pseudo segmentation masks, descriptive captions, and semantically aligned synonym sets using Grounded SAM, LLaVA, and CLIP, providing multimodal supervision without manual annotation. We then enhance visual-textual alignment through three complementary training objectives: an extended grounding loss that incorporates visually grounded synonyms, a semantic consistency loss, and a generative caption reconstruction loss. Extensive experiments on the COCO dataset demonstrate that the proposed method consistently outperforms previous state-of-the-art approaches under this protocol, achieving substantial improvements on both OVIS and OSPS benchmarks.
[CV-69] Zero-OVCD: Bridging Training-Free Foundation Models and Pseudo-Label Learning for Open-Vocabulary Change Detection
链接: https://arxiv.org/abs/2608.11663
作者: Daifeng Peng,Yuanke Peng,Haiyan Guan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 figures, 9 tables
Abstract:Open-vocabulary change detection (OVCD) enables the identification of user-specified land-cover changes in bitemporal remote sensing images, but existing training-free pipelines remain vulnerable to inaccurate candidate masks, ambiguous semantic assignments, and accumulated inference errors. To address these issues, we propose Zero-OVCD, a two-stage framework that requires no pixel-level annotations from the target domain. In the first stage, high-quality change pseudo-labels are generated through complementary candidate-mask refinement, multiscale semantic similarity fusion with margin-based reliability filtering, and response-guided mask correction and completion. These components jointly suppress noisy candidates, enhance mask-level semantic discrimination, and recover missed change regions. In the second stage, a change detector is trained using the generated pseudo-labels, while checkpoint voting and high-agreement sample selection are introduced to mitigate residual pseudo-label noise. On LEVIR-CD, WHU-CD, and S2Looking, Stage I achieves F1 scores of 86.25%, 85.82%, and 50.48%, while Stage II further improves them to 88.65%, 88.85%, and 57.96%, respectively. On SECOND, the macro-average F1 across six category-wise one-vs-rest tasks increases from 47.91% to 50.92%. These results demonstrate that bridging training-free foundation-model inference with noise-aware pseudo-label learning provides an effective solution for open-vocabulary change detection without target-domain pixel-level annotations. Code will be available at this https URL.
[CV-70] Motion-as-Prompt: Enhancing Motion Reasoning in Multimodal Large Language Models via Motion-Guided Cross-Frame Visual Prompting
链接: https://arxiv.org/abs/2608.11655
作者: Xikai Sun,Kebin Liu,Haotian Wang,Li Liu,Xu Wang,Yunhao Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Motion-centric video reasoning is fundamental to interactive applications such as robotic manipulation and autonomous navigation. However, multimodal large language models (MLLMs) typically process videos through sparse uniform sampling to control visual-token and attention costs. This strategy may discard critical transitions between sampled frames, limiting reasoning about object movement, collisions, and causal interactions. To mitigate this issue, we propose Motion-as-Prompt (MaP), a track-guided cross-frame visual prompting framework. MaP recovers dense point trajectories, selects motion-informative frames, and marks the trajectories accumulated between consecutive sampled frames directly onto the visual inputs, making otherwise hidden displacement, direction changes, and interactions observable to frozen MLLMs. Experiments on CLEVRER and Something-Something-v2 show that MaP consistently improves average motion-reasoning accuracy, yielding gains of 4.2% and 8.9% for GPT-5.5, respectively. Notably, these improvements are obtained without degrading non-motion understanding, highlighting the robustness of MaP. These results demonstrate that MaP provides a simple and effective solution for enhancing motion-centric video reasoning without model training or architectural modification. Project page:this https URL.
[CV-71] Hybrid-LUT: Channel-Aware Hybrid Lookup Table and Filtering for Efficient Image Denoising ECCV2026
链接: https://arxiv.org/abs/2608.11646
作者: Zhilin Ai,Boyu Li,Sidi Yang,Wenqing Shi,Wenyong Zhou,Binxiao Huang,Chenchen Ding,Ngai Wong
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: Accepted by ECCV2026
Abstract:Lookup table (LUT)-based image denoising methods have attracted increasing attention due to their high efficiency and hardware-friendly properties. However, existing RGB-LUT approaches require three identical LUTs to process RGB channels in parallel, resulting in large on-chip SRAM consumption. A simple alternative is to apply LUT processing only to the luminance (Y) channel in the YUV color space to reduce memory usage. However, this naive strategy leads to degraded restoration quality, since ignoring the chrominance (UV) channels introduces color distortion and residual artifacts. In this work, we propose Hybrid-LUT, a YUV-based asymmetric channel-processing framework that combines LUT and filtering in a unified design. Specifically, a multi-band LUT branch with pixel-level weight fusion is applied to the Y channel to recover fine textures, while lightweight filtering is used for the UV channels to maintain color consistency. This design reduces LUT storage by two-thirds compared with RGB-LUT methods while maintaining the same runtime throughput. Extensive experiments show that Hybrid-LUT achieves state-of-the-art (SOTA) performance across multiple benchmarks with only 421 KB of storage. In particular, our method surpasses existing LUT-based denoising approaches by at least 0.63 dB CPSNR on real-world datasets, demonstrating its effectiveness for image denoising on resource-constrained edge devices. The project is available at this https URL .
[CV-72] Cloak of Invisibility: Real-Time Privacy-Preserving Volumetric Video Streaming
链接: https://arxiv.org/abs/2608.11645
作者: Hossein Khalili(UCLA),Philip Do(UCLA),Alexander Vilesov(UCLA),Kittipat Apicharttrisorn(Nokia Bell Labs),Nader Sehatbakhsh(UCLA)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Volumetric video streaming turns privacy into a 3D, multi-view problem. Unlike ordinary video, where sensitive content can often be redacted frame by frame, RGB-D volumetric pipelines capture people, rooms, and personal objects from multiple cameras and fuse them into a shared 3D representation. A private object missed in one view, or only partially removed before fusion, can therefore reappear in the reconstructed scene. This creates a privacy challenge for 3D telepresence, education, entertainment, and immersive applications: private content should be removed before raw visual and geometric data leave the camera side, while the public part of the scene should remain useful for real-time reconstruction. Existing volumetric streaming systems mainly optimize reconstruction, data movement, and latency, while privacy-preserving vision methods are designed for single-camera, single-frame images and do not directly address calibrated multi-view RGB-D fusion. We present InViStream, a real-time “privacy-from-source” system designed for this setting. InViStream addresses three challenges in volumetric capture: private objects may appear differently across views, RGB masking alone can leave geometric privacy leakage in depth, and public/private instances of the same class must be separated consistently before cloud-side fusion. To address these challenges, InViStream combines object detection with depth-aware masking, propagates public/private decisions across calibrated views, and fuses only sanitized point clouds. We evaluate InViStream on synthetic and real RGB-D scenes, including offices, conference rooms, living rooms, and settings with multiple public and private people and objects. InViStream achieves synthetic Dice/Recall of 0.799/0.891 and real Dice/Recall of 0.792/0.908, with synthetic SSIM above 0.98 and real-time streaming above 30 FPS.
[CV-73] Robustness of AI-Art Detectors under Generator Shift
链接: https://arxiv.org/abs/2608.11643
作者: Shivank Singh Thakur,Meien Li,Mark Stamp
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: To appear as a chapter in the book “Artificial Intelligence for Cyber Defense in Emerging Threats”, to be published by Springer by early 2027
Abstract:Text-to-image generative models have advanced rapidly, with modern Diffusion Transformer architectures producing images that are increasingly difficult to distinguish from human-created artwork. This development has raised significant concerns regarding copyright protection, misinformation, fraud, impersonation, and the authenticity of digital content. Most AI-art detectors are trained and evaluated on the same generator family, leaving robustness to newer architectures underexplored. In this chapter, we analyze generator shift based on a Stable Diffusion 3.5 Medium (SD3.5m) artwork dataset spanning ten art styles through reverse prompting of held-out human artwork samples. Five detectors are trained on U-Net-based latent diffusion artwork and evaluated in a zero-shot cross-generator setting on the SD3.5m dataset. Deep learning models perform strongly in-distribution but degrade under generator shift, misclassifying many SD3.5m images as human while human false positives remain low. The CLIP ViT-L/14 model performs best overall, while Grad-CAM analysis reveals weaker and more diffuse activation on false negatives. These findings highlight a generalization gap in current AI-art detectors and motivate the development of detectors as one component of a layered defense that remains reliable across rapidly evolving generative architectures.
[CV-74] ransferable Above-Ground Biomass (AGB) Estimation Model from Multi-Sensor Data with Sparse Field Calibration
链接: https://arxiv.org/abs/2608.11638
作者: Pann Thinzar Seint,Bryan Atwood,Subas Chhatkuli
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable. While field inventories provide high localized accuracy, they are spatially sparse; conversely, spaceborne LiDAR from the Global Ecosystem Dynamics Investigation (GEDI) offers broad biomass samples but lacks spatial continuity and systematic underestimation of high-biomass forests. This paper presents an operational framework centered on a single globally trained convolutional neural network (CNN) that is seamlessly adapted to each new landscape through a lightweight empirical field-calibration workflow. The global model combines optical (Sentinel-2), C-band SAR (Sentinel-1), L-band SAR (ALOS-2 PALSAR-2), and terrain (DEM) data. It is trained once against GEDI Level-4A biomass reference data spanning multiple regions and both wet and dry seasons so that it learns the persistent woody-structure rather than a single-date appearance. To avoid retraining for every landscape, the framework applies a small number of local field plots to fit a scale-and-bias correction that aligns the global prediction with ground truth in each region. The pipeline harmonizes sensor data onto a shared 10 m grid, derives vegetation indices and polarimetric ratios, computes per-band normalization stats, and trains the CNN with a hybrid log-domain SmoothL1 with RMSE loss for skewed biomass distribution. On held-out validation the global GEDI-based model achieved R^2 approximately 0.78 and RMSE approximately 22 Mg/ha. A subsequent field calibration combining Random Forest fine-tuning under a 10-fold cross-validation eliminates localized regional biases. This improves local validation performance to R^2 approximately 0.82 and reduces RMSE to approximately 15 Mg/ha, outperforming both the uncalibrated global model and the ESA CCI Biomass product against field plots.
[CV-75] CAM-Guided Saliency Cutout and Image-Based Malware Classification
链接: https://arxiv.org/abs/2608.11634
作者: Yasaman Ebrahimi,Martin Jurecek,Mark Stamp
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: To appear as a chapter in the book “Artificial Intelligence for Cyber Defense in Emerging Threats”, to be published by Springer by early 2027
Abstract:Dropout regularization is commonly used to reduce overfitting by removing parts of a neural network during training. For Convolutional Neural Networks (CNN), cutouts serve a somewhat analogous purpose. Cutouts can be implemented as data augmentation: the original training image is retained, and additional copies are created with regions removed. In this chapter, we test whether cutout placement can be improved by using High-Resolution Class Activation Mapping (HiResCAM). We compare four controlled training conditions: no cutout, standard random cutout, low-saliency cutout, and high-saliency cutout. We experiment using grayscale malware images from the RawMal-TF dataset (17 families with~1,000 samples per family), and for comparison to natural images, we experiment with the well-known CIFAR-100 dataset. All experiments are based on ResNet18 with~100 training epochs. For the cutout experiments, we test cutout areas of~5%, 10%, 20%, and~30%, and we consider~ M\in\4,8 augmented copies per original training image. The RawMal-TF results are slightly worse for all three cutout cases (random, high and low saliency) as compared to no cutouts. In contrast, our CIFAR-100 experimental results improve slightly under low-saliency cutout. These results suggest that the value of saliency-guided cutout is domain dependent, and that malware images should not be treated as equivalent to natural images.
[CV-76] Generative Video Compression Based on Hierarchical Referencing
链接: https://arxiv.org/abs/2608.11618
作者: Daowen Li,Ding Ding,Zifu Zhang,Kai Li,Ying Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion-based generative video compression has emerged as a promising paradigm to improve perceptual quality, where latent frames are required to be encoded efficiently while serving as denoising conditions. However, existing methods neither carefully design reference and quality structures during latent coding nor account for the impact of frame-level quality variation on denoising procedure, which limits coding efficiency and aggravates artifact propagation during generative reconstruction. In this paper, we propose GVCHR, Generative Video Compression based on Hierarchical Referencing. The key idea is to organize latent frames hierarchically, where the selected high-quality references benefit both latent coding and generative reconstruction. In latent coding, GVCHR couples a hierarchical reference structure with a hierarchical quality structure, assigning more bits to lower-layer frames that are reused more frequently as references. Built on this design, we introduce Hierarchical Temporal Context Mining to exploits complementary short- and long-term temporal context for effective latent coding. In generative reconstruction, the coding-side hierarchy is incorporated into a Hierarchical Attentive Adapter which is attached to a video diffusion transformer. This adapter uses hierarchical attention to restrict each latent frame to attend only to the same- or lower-layer references, thereby reducing artifact propagation during denoising. Experiments validate GVCHR on multiple benchmarks. Compared with the previous state-of-the-art method, GVCHR achieves 50.5% and 54.0% BD-rate gains in terms of LPIPS and DISTS, respectively, while also delivering clearly improved visual quality.
[CV-77] KANResDiff: Learning Local Residual Diffusion via Kolmogorov-Arnold Network for Ambiguous Medical Image Segmentation MICCAI2026
链接: https://arxiv.org/abs/2608.11617
作者: Fanding Li(1),Chenglin Wang(1),Xiangyu Li(1),Xingyu Qiu(1),Xinghua Ma(1),Xiangming Yin(1),Haiyang Li(1),Suyu Dong(2),Wei Wang(3),Kuanquan Wang(1),Gongning Luo(1),Shuo Li(4) ((1) Faculty of Computing, Harbin Institute of Technology, Harbin, China, (2) College of Computer and Control Engineering, Northeast Forestry University, Harbin, China, (3) Faculty of Computing, Harbin Institute of Technology, Shenzhen, China, (4) Department of Computer and Data Science and Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 3 figures, MICCAI 2026 conference paper
Abstract:Ambiguous medical image segmentation aims to provide a series of diverse but plausible segmentation hypotheses. However, existing methods introduce stochasticity in a fixed and pre-defined manner, failing to form a progressive semantic modeling process. To address these challenges, we propose KANResDiff to learn local residual diffusion with Kolmogorov-Arnold Network, thereby assigning distinct roles across stages for ambiguity modeling. Specifically, we propose Independent Time Encoding that offers spline-based time embeddings instead of linear ones from MLPs, which enhances the independence across inference stages and assigns progressive semantic roles to different stages. We propose Residual Schrodinger Bridge that injects deterministic residual prior with learnable weights by constructing local Schrodinger Bridge instead of following manually settings, achieving a flexible deterministic-stochastic interaction and stage-aware ambiguity modeling thanks to local optimal diffusion path. Extensive experimental results on two public datasets demonstrate that KANResDiff achieves SOTA performance on GED and HM-IoU, with maximum improvements of 16.8% and 7.7%, respectively, while maintaining competitive performance on the MDM metric. Source code is available at this https URL.
[CV-78] MBA: Multimodal Benchmark and Agents for Real-World Business Ideation
链接: https://arxiv.org/abs/2608.11616
作者: Hojun Choi,Jaeyo Shin,Suin Lee,Hyunjung Shim
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Project page: this https URL Code: this https URL
Abstract:Agentic systems powered by large language models (LLMs) have opened new opportunities for business ideation. Yet existing approaches remain confined to a text-only paradigm, despite the inherently multimodal nature of real-world contexts. We thus introduce MBA-Bench, the first multimodal benchmark for training and evaluating business ideation agents, comprising 30K samples across six domains, each domain characterized by distinct visual cues not fully conveyed by text alone. Concretely, we automatically caption images and employ GPT-4o to generate five reference ideas for each of three business questions through retrieval query generation, market evidence retrieval, and evidence-augmented synthesis. Following prior work, we evaluate agents across six business-oriented criteria using MLLM-as-a-Judge. To consider settings where criteria are hidden or disclosed, we present MBA-b and MBA-k for blind and known, respectively. We train both with two novel reward objectives—creativity and feasibility—while MBA-k further optimizes the six disclosed criteria for eight in total. Both are trained via LoRA-based supervised fine-tuning followed by group relative policy optimization with these setting-specific rewards. For extensive experiments on MBA-Bench, we set up two baselines accommodating either captions only or multimodal inputs, with the latter nearing closed-source performance on several metrics. MBA-b and MBA-k outperform caption baselines by 63.9% and 77.1%, and multimodal baselines by 25.6% and 35.8%, respectively.
[CV-79] opology-Aware Query Selection for Surgical Instrument Instance Segmentation
链接: https://arxiv.org/abs/2608.11607
作者: Ze Zhang,Yang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: Preprint. Main manuscript and supplementary material included. Code and reproducibility materials: this https URL
Abstract:Accurate foreground masks can still form an incorrect surgical-instrument instance set: duplicate, fragmented, merged, missed, or empty-frame predictions may preserve favorable pixel overlap while violating object identity and count. Final query selection is therefore a relational, variable-cardinality problem rather than a collection of independent candidate decisions. We evaluate topology-aware query selection, which represents the nonempty candidates of a fixed Mask2Former as a complete graph, learns relational candidate and pair representations, predicts set cardinality, and solves an exact structured subset problem. The formal comparison is the complete relational path versus a node-feature-matched path; it evaluates the combined effect of pairwise geometry, message passing, and the additional relational-path capacity, not an isolated component. On the sealed 22-case source test, all three discovery seeds supported instance-set performance improvement with segmentation fidelity and predefined technical-safety preservation: instance F1 increased by 0.0504–0.0612 and positive-frame set-failure rate decreased by 0.0848–0.1060. Direct ROBUST-MIPS transfer reproduced the complete result in all three seeds. Endoscapes supported only one of three seeds and therefore did not establish stable direct transfer. Taken together, the results support a bounded conclusion: the evaluated complete path improved coherent instance-set construction from fixed Mask2Former candidates in specified native-instance contracts, while stable cross-domain transfer and component-specific effects remain unestablished.
[CV-80] How Can Driving World Models Do Counterfactual Prediction?
链接: https://arxiv.org/abs/2608.11601
作者: Jiaru Zhang,Can Cui,Yi Xu,Xin Ye,Ruqi Zhang,Ziran Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Driving world models are often interpreted as counterfactual simulators for observed driving episodes: given a factual driving log, they are asked what would have happened under an alternative ego action. In this paper, we identify a fundamental mismatch between this goal and direct action-conditioned prediction. The direct prediction uses the shared history and the alternative action but not the factual continuation observed after that history. It can therefore generate a plausible future without preserving what actually happened in this episode. We formalize this gap using the causal recipe of abduction, action, and prediction and study it in a setting with a short time horizon, where the alternative ego action does not alter how surrounding agents evolve. To make the gap measurable, we construct a controlled simulation benchmark with factual outcomes and matched counterfactual outcomes. Across two representative world models, direct predictions fail to match the counterfactual ground truth, supporting our analysis. As a constructive check of this analysis, we introduce a deliberately simple, training-free pipeline that moves observed evidence into the counterfactual view and lets the frozen model complete what remains unknown. Even this simple construction raises the overall recovered fraction substantially and reduces perceptual distance to the matched counterfactual on both models. We hope this work draws attention to this gap and motivates better counterfactual prediction methods for driving world models.
[CV-81] ProtoHGF-Net: Prototype HyperGraph Fusion with Intra-modal Calibration for RGBT Object Detection ACM-MM2026
链接: https://arxiv.org/abs/2608.11595
作者: Xiangqi Chen,Xiuling Zhang,Chengzhuan Yang,Li Zhao,Dawei Zhang,Yanchao Wang,Liyuan Chen,Hua Wang,Hao Peng,Zhonglong Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACM MM 2026
Abstract:RGB-Thermal (RGBT) object detection enables robust perception in complex scenes by leveraging the complementary strengths of visible textures and thermal cues. However, existing methods mainly rely on dense cross-modal interactions over full-resolution features, which inevitably introduce background interference and hinder the learning of target-relevant representations. In this paper, we propose the Prototype HyperGraph Fusion Network (ProtoHGF-Net), a novel framework that redefines cross-modal fusion as prototype-level semantic interaction rather than the dense cross-modal interaction paradigm. Specifically, we design Prototype HyperGraph Fusion to perform cross-modal interaction in a compact prototype-level semantic space. This design enables more selective fusion among target-relevant prototypes. To support this prototype-level fusion, we propose Teacher-Mask Calibration Distillation, which calibrates modality features before fusion using modality-specific teachers and target-aware masks. This strategy suppresses backgrou- nd-dominant responses and produces more target-focused features. Extensive experiments on DroneVehicle, DVTOD, and FLIR demonstrate that ProtoHGF-Net achieves state-of-the-art performance with 85.9% mAP_50 , 88.2% mAP_50 , and 79.1% mAP_50 , respectively. Our code is available at \hrefthis https URLGitHub.
[CV-82] A Hybrid Framework of Vision Transformer and Gated Recurrent Unit for Detection of Mosquito Diseases
链接: https://arxiv.org/abs/2608.11582
作者: Danial Sharifrazi,Saadat Behzadi,Nouman Javed,Roohallah Alizadehsani,Prasad N. Paradkar,Asim Bhatti
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Identifying dengue virus-infected mosquitoes from control mosquitoes is a major challenge in analyzing mosquito locomotion behavior due to the small size and complexity of the video background. Conventional AI methods are often unable to extract accurate features from video frames and produce erroneous features. In this study, a three-step framework is introduced: first, mosquitoes are identified and the background is removed using the YOLO 11M model, then visual features are extracted using the Vision Transformer (ViT), and finally the videos are classified with a convolutional GRU (ConvGRU) classifier. A comparative analysis of different models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and their convolutional versions showed that the ConvGRU model achieved the best performance; it achieved 88.88% accuracy, 84.45% precision, 82.82% recall, and 82.81% F1 score. These results demonstrate that combining convolutional models with sequence-based networks, especially in the ConvGRU model, allows the simultaneous extraction of precise spatial features and long-term temporal dependencies from mosquito movements. Finally, the proposed framework provides a reliable solution for analyzing mosquito behavior in complex environments.
[CV-83] Dialogue-Aware Video-to-Music Generation Using Public Domain Film Collections
链接: https://arxiv.org/abs/2608.11576
作者: Haven Kim,Zachary Novack,Julian McAuley,Hao-Wen Dong
类目: ound (cs.SD); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Video-to-music generation has drawn growing interest for its role in conveying the emotion of visual media, including film. Progress in the field, however, is hampered by a reproducibility gap: models are often trained on crawled corpora referenced through YouTube URLs that may be deleted, with the underlying data often difficult and time-consuming to retrieve. To address this, we introduce the Open Screen Soundtrack Library version 2 (OSSL-v2), a self-hosted corpus of 34,343 video clips totaling 246.4 hours, sourced from public-domain films. Unlike crawled corpora, OSSL-v2 is reproducible (i.e., not subject to link rot) and copyright-conscious, yet still large enough to train functional video-to-music models. We then use this film-domain corpus to study dialogue as a conditioning signal for video-to-music generation, motivated by the close temporal coupling between film music and on-screen speech. Specifically, we augment existing models’ video cross-attention with a time axis and modulate it frame-by-frame with the dialogue track. Evaluated on both public-domain and commercial films, our approach shows improvement over the state-of-the-art baselines. The dataset is available at this https URL.
[CV-84] Hand Visibility Detector: Per-Keypoint Visibility Estimation for Hands
链接: https://arxiv.org/abs/2608.11574
作者: Ryosei Hara,Masashi Hatano,Rintaro Yanagi,Atsushi Hashimoto,Takuma Yagi,Mariko Isogawa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code: this https URL
Abstract:Hand Pose Estimation (HPE) is a fundamental technology for various applications such as AR/VR and robotics. In these applications, the visibility of each hand joint in the image is crucial for assessing the reliability of estimation results under occlusion. However, most existing HPE methods output joint positions without explicitly indicating their visibility. Although some methods account for occlusion or visibility, visibility estimation has mainly been used as an auxiliary signal for improving pose estimation. To our knowledge, per-joint hand visibility estimation has not been systematically studied as a standalone task. In this work, we propose Hand Visibility Detector, a model for estimating the visibility of individual hand joints, and present the first systematic investigation of visibility estimation as an independent task. We show that leveraging the prior knowledge of HPE models pretrained on large-scale data as a backbone yields high performance in this task. We further demonstrate the utility of Hand Visibility Detector on a downstream task of 3D hand pose annotation via multi-view triangulation of 2D keypoints, showing that visibility-weighted triangulation reduces reprojection error. Our method is released as a ready-to-use package, and the code and demo are available at this https URL .
[CV-85] Repurposing RGB-based Foundation Model for Depth Estimation on Thermal Images Using Hierarchical Supervision IROS2026
链接: https://arxiv.org/abs/2608.11564
作者: Jie Hong,Tingtian Li,Xuesong Li,Xiao Li
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted in IROS 2026
Abstract:Depth estimation from thermal images is highly valuable for robotic applications in adverse conditions, such as nighttime and rainy weather. Recent studies have sought to transfer knowledge from RGB-based foundation models to thermal modalities, yet the rich hierarchical representations these models encode remain underutilized. To address this limitation, we propose RGB-HS, a novel framework for thermal-image depth estimation that leverages hierarchical supervision from an RGB-based foundation model. Specifically, we first replace the baseline thermal encoder with a foundational model and introduce a parallel RGB branch that also employs a foundational model as an encoder of the same architecture, taking RGB images as input. The alignment is then performed across multiple levels between the tokens of the two encoders, allowing the thermal student branch to capture both structural precision and semantic abstraction from the RGB teacher branch. Furthermore, we introduce verification to refine the alignment process by weighting tokens from the RGB branch based on RGB image quality. Extensive experiments on the popular benchmark demonstrate that RGB-HS achieves competitive performance and more effectively exploits the representational capacity of RGB-based foundation models for depth estimation on thermal images.
[CV-86] From Synthesis to Removal: Physics-Grounded Reflection Simulation and Diffusion-Based Video Dereflection
链接: https://arxiv.org/abs/2608.11562
作者: Zepeng Wang,Jiagao Hu,Fuhao Li,Yuxuan Chen,Fei Wang,Daiguo Zhou
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
备注: Project page: this https URL
Abstract:Videos captured through glass often contain reflections that degrade visual quality and interfere with downstream vision tasks. Although single-image reflection removal has been extensively studied, video reflection removal remains largely underexplored due to the lack of paired video data, temporally coherent removal models, and dedicated evaluation benchmarks. We present a closed-loop framework that unifies physics-grounded reflection simulation, diffusion-based video dereflection, and benchmark evaluation. Our S2R-Synthesis pipeline generates paired reflected and reflection-free videos by performing physics-grounded augmentation in the structure space and rendering realistic reflected videos with a trained video diffusion renderer; the augmentation models key glass-related effects including roughness-induced blur, thickness-induced ghosting, and reflectance variation. Based on the synthesized data, we introduce S2R-Removal, the first diffusion-based video reflection removal model, which adapts a pretrained video diffusion prior through reflection-aware latent adaptation and one-step pixel-geometric refinement, recovering the clean transmission in a single denoising step. We further build S2R-Bench, the first benchmark for video reflection removal, supporting both full-reference evaluation and real-world human perceptual assessment. Experiments on S2R-Bench and multiple public image benchmarks demonstrate state-of-the-art performance and faster inference than even non-diffusion baselines, and validate the effectiveness of S2R-Synthesis. Project page: this https URL.
[CV-87] hrough Van Goghs Eyes: Global Style Transfer with Diffusion Mod ECCV2026
链接: https://arxiv.org/abs/2608.11546
作者: Jeongha Lee,Yujin Kim,Ghazanfar Ali,Suhyun Kim,Jae-In Hwang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Published at ECCV 2026
Abstract:Artistic image synthesis aims to recreate the expressive visual identity of a target artist, yet existing methods often fail to capture an artist’s global style. Conventional style transfer methods transfer the style of one or a few reference artworks to a content image in a One-to-One manner, making them effective for artwork-level stylization but limited in representing the broader stylistic distribution of an artist. Text-to-image diffusion models conditioned on artist names, such as ‘~ in Van Gogh style’, offer greater flexibility, but they often suffer from text-induced bias and reproduce patterns from only a few iconic works. To address these limitations, we introduce Global Style Transfer (GST), an artistic image synthesis paradigm, in a Many-to-One manner, that aggregates multiple artworks from a target artist and transfers their shared global style to a single content image. For GST, we propose Global Style Guidance (GSG), which learns a residual global style offset in the intermediate feature space, or h-space, of a diffusion model under a fixed prompt. By learning artist-level style semantics purely from visual statistics, GSG mitigates text-dependent artistic bias. We further propose Content Alignment Guidance (CAG), a training-free perceptual guidance mechanism that preserves the semantic structure of the content image while allowing artist-specific geometric deformation. Experiments on WikiArt demonstrate that GST achieves superior stylistic fidelity, content preservation, and output diversity compared to existing style transfer and diffusion-based artistic synthesis methods.
[CV-88] Generative Semantic Segmentation via an Observable Semantic-Image Interface and Hierarchical Generator Evidence Alignment
链接: https://arxiv.org/abs/2608.11537
作者: Weize Cai,Yongqi Dong,Zhida Shao,Zixin Fu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: 15 pages, 4 figures
Abstract:Generative semantic segmentation exposes structured predictions as images, but direct color decoding is susceptible to color drift and boundary mixing, whereas latent-feature decoders that predict a separate output distribution may relegate the rendered image to an intermediate visualization. We present Semantic Prism, a conditional semantic-image generation-and-refinement framework with deterministic inference. A diffusion-distilled one-step generator renders a semantic RGB image; per-pixel distances from the rendered colors to a fixed class-color codebook define an explicit probabilistic interface. Hierarchical Generator Evidence Alignment spatially aligns multi-level generator features and uses a zero-initialized output projection to predict an additive residual in the interface logit space, retaining the image-defined interface as the reference for the final distribution. The interface and refined distributions further enable Contextual Interface–Hierarchy Disagreement (C-IHD), a fixed readout for ranking remaining pixel errors without an auxiliary predictor or additional forward pass. On the 500-image Cityscapes validation set, Semantic Prism achieves 72.07% mean intersection over union, 11.39 mIoU points above direct-interface decoding, with 0.41% expected calibration error. Matched-capacity ablations over three seeds support the benefit of jointly aligned multi-level evidence. A separately trained model attains 62.22% mIoU on BDD100K, while the Cityscapes-trained model reaches 46.89% mIoU under source-frozen transfer to the Adverse Conditions Dataset with Correspondences, without target-domain adaptation. Across all three datasets, C-IHD consistently improves the area under the precision–recall curve for pixel-error ranking over maximum softmax probability on the same segmentation predictions; on ACDC, it raises AUPR from 0.6580 to 0.7557.
[CV-89] New Orthogonal Multiwavelet Filters Derived by Matrix Spectral Factorization
链接: https://arxiv.org/abs/2608.11518
作者: Vasil Kolev,Todor Cooklev,Fritz Keinert
类目: Computer Vision and Pattern Recognition (cs.CV); Databases (cs.DB); Numerical Analysis (math.NA); Applications (stat.AP)
备注: 48 pages, 10 figures, 20 tables, Journal of Computational and Applied Mathematics, 118057, 2026
Abstract:The paper considers the construction of two new orthogonal multiwavelets with supercompact support by using the Fast Bauer’s method for matrix spectral factorization on the matrix product filter of the orthogonal CL multiwavelet filter. The new multiwavelets possess orthogonality, symmetry/antisymmetry, and one of them provides better coding and smoothness than other supercompact multiwavelets. The performance of the new multiwavelet filters in subband-based edge detection, grayscale and color image compression and 1D and 2D signal denoising is compared with the GHM, SA4, CL, Integer Haar and Alpert multifilters. The comparative analysis shows that new multiwavelets can provides better human visual measures, SSIM and MS-SSIM in image compression and denoising applications. Comments: 48 pages, 10 figures, 20 tables, Journal of Computational and Applied Mathematics, 118057, 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV); Databases (cs.DB); Numerical Analysis (math.NA); Applications (stat.AP) MSC classes: 15-XX, 49Nxx, 47B35, 42-XX, 65J15, 65-XX, 68Wxx, 68Uxx, 65Yxx, 97Mxx ACMclasses: I.4.2; F.2.1; G.1; J.2; C.3 Cite as: arXiv:2608.11518 [cs.CV] (or arXiv:2608.11518v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.11518 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-90] Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving ECCV
链接: https://arxiv.org/abs/2608.11498
作者: Aditya Humnabadkar,Huaizhong Zhang,Ardhendu Behera
类目: Computer Vision and Pattern Recognition (cs.CV); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注: Accepted manuscript: Workshop on Emerging Behaviors in Embodied AI for Achieving Robust Autonomy as part of European Conference on Computer Vision (ECCV) 2026
Abstract:Natural-language-based scenario generation offers an intuitive means of describing rare and complex driving interactions, yet it is still uncertain whether training with language-structured data leads to truly adaptive control policies. We propose Language-Structured Relational Q-Learning, instantiated through an Ego-Centric Relational Q-Network (ERQ-Net), which jointly learns inter-vehicle relevance and action values from dynamic traffic graphs. Language descriptions define surrounding-vehicle behaviours during training, while prompts and semantic actor roles are hidden from the policy. ERQ-Net must therefore infer threat relevance solely from observable kinematics and interactions. Across 2,500 safety-critical scenarios, language-structured training improves test success from 49-52% to 55-58% and increases adversary-focused attention from 1.2x to 2.1x, demonstrating emergent threat awareness. However, this representational gain does not consistently translate into adaptive control: trained policies perform similarly to the best constant action, while a portfolio of simple policies solves 76% of scenarios. We formalise this discrepancy as a recognition-control gap and show that reward reweighting and margin shaping do not eliminate the resulting policy collapse. Evaluations of realism, criticality, semantic accuracy, and transfer of state-interface representations to CARLA further highlight both the strengths and the constraints of language-structured relational policy learning in safety-critical driving scenarios.
[CV-91] st-Time Hallucination Control in Large Vision-Language Models ECCV2026
链接: https://arxiv.org/abs/2608.11474
作者: Mehran Tamjidi,Hamidreza Dastmalchi,Ali Cheraghian,Mohammadreza Alimoradijazi,Aijun An,Hossein Rahmani
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026 MUCG
Abstract:Object Hallucination in large vision-language models (LVLMs), where models generate non-factual content about input images, remains a critical barrier to their reliability in real-world applications. Existing mitigation strategies can be categorized into training-based and training-free methods. Training-based methods often achieve strong performance but are costly, requiring extensive computational resources, large-scale data, and time-consuming fine-tuning. Training-free approaches are particularly appealing due to their efficiency. However, existing training-free methods either require multiple decoding rounds, which adds computational overhead, or modify internal states in a model-specific way that risks degrading pretrained knowledge. We propose Test-Time Hallucination Mitigation (TTH) method, a novel training-free method that addresses both limitations. TTH introduces a token-validator module, implemented as a zero-shot Multi-Modal Classifier (MMC), to generate auxiliary logits grounded in the input image. These logits are fused with the original LVLM outputs at the token level for object tokens selected from a candidate pool. An entropy-based weighting scheme is then applied to enable robust and accurate predictions. Extensive experiments across multiple LVLM families and diverse benchmarks demonstrate that TTH consistently improves accuracy and robustness, underscoring its generalizability and practical effectiveness. Code is released at this https URL
[CV-92] Gaussian Meta-Space Augmentation for Stacking Ensembles in Multimodal IPMN Risk Stratification MICCAI2026
链接: https://arxiv.org/abs/2608.11472
作者: Max A. Nelson,Eminenur Sen Tasci,Zhixiang Wang,Zongwei Zhou,Halil Ertugrul Aktas,Andrea M. Bejar,Elif Keles,Ziliang Hong,Sıtkı Safa Taflan,Muhammed Enes Tasci,Frank H. Miller,Michael B. Wallace,Rajesh N. Keswani,Gorkem Durak,Ulas Bagci
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at the International Workshop on Machine Learning in Medical Imaging (MLMI 2026), held in conjunction with MICCAI 2026. This is the authors’ accepted manuscript; the final version will appear in Springer Lecture Notes in Computer Science (LNCS). 11 pages, 3 figures
Abstract:Pancreatic cancer is among the most lethal malignancies; risk stratification of intraductal papillary mucinous neoplasms (IPMNs) offers a crucial opportunity for early intervention but typically requires invasive tissue biopsy. Dominant vision-based approaches, including radiomics and deep learning, provide promising but initially separate discrimination opportunities. Similarly, multisequence MRI (T1W/T2W) and anatomically decomposed (head, body and tail) analysis of the pancreas provide additional and potentially complementary signals. Effective fusion of this information is crucial in ordinal IPMN dysplasia risk prediction and can be accomplished via a meticulously regularized and calibrated ensemble stacking combiner. We present cUPMI, a class-conditional Gaussian augmentation of a combiner’s log-probability meta-features, and test it on various prediction paradigms. In our multi-center analysis, we find cUPMI adds limited value to properly regularized L2-logistic binary classification stacks, but consistently regularizes higher-capacity tree combiners in the binary and radiomics-only setting (RF +0.015 and XGBoost +0.024 binary AUC, positive in all seeds). Its cleanest ordinal benefit appears for XGBoost on an 8-stream radiomics task (3-class no low high, +0.022 QWK in all seeds). Separately, fold-locked fusion of radiomics and 2.5D CNN streams yields the strongest overall model, an RF stack reaching QWK 0.595 (95% CI [0.54, 0.64]) and binary AUC 0.839, surpassing radiomics, 2.5D ResNet, and 3D DenseNet-121 baselines.
[CV-93] Multi-Agent Target-Existence Verification and Learned Mask Geometry Refinement: Winning Report of the MeViS-Text Track at the 8th LSVOS Challenge 2026
链接: https://arxiv.org/abs/2608.11458
作者: Jungyoon Lee,Gyuil Lim,Doeon Kim,Seong-heum Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present the first-place solution to the MeViS-Text track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge 2026: referring video object segmentation guided by written motion expressions, including deceptive no-target expressions that match no object in the video and must yield empty masks in every frame. Our pipeline, SSUPER, resolves each expression into a visual concept, generates full-video candidate masklets with SAM~3.1, and selects target IDs. At every reasoning stage, three heterogeneous multimodal large language models independently execute the same stage-specific prompt before a single synthesis pass commits one schema-validated verdict. Although this system rejects every no-target expression in validation, the leaderboard reveals that a substantial share of test no-target cases still slips through. The reason is that hard negatives name a plausible object and fail only under the complete temporal predicate, so when selection and existence are decided together, a category-plausible masklet anchors the verdict. Hence, we decouple existence verification into an independent multi-agent audit of the full predicate (category, count, action, trajectory, event order, and semantic role) that distinguishes absence from temporary invisibility, discounts apparent motion caused by camera movement, and requires contradicting evidence rather than mere uncertainty for a no-target verdict. Without any new segmentation call, this audit recovers most of the residual no-target errors. A training-data-only StyleRefiner then aligns mask geometry with the annotation style of MeViSv2 while preserving every presence decision by construction, showing that once the semantics are fixed, part of the remaining error is stylistic rather than semantic. The complete system reaches a Final score of 0.9081339614 on the official challenge leaderboard.
[CV-94] angPoetryBench: A Multi-Dimensional Benchmark and Rubric-Conditioned Evaluator for Poetry-to-Image Generation
链接: https://arxiv.org/abs/2608.11452
作者: Haoqi Hu,Tongji Luo,Li Zhang,Boning Zhou
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Text-to-image (T2I) models are increasingly asked to illustrate literary and cultural content, yet we cannot measure how well an image renders the meaning of a poem. The task is many-sided: a good illustration must be visually sound, faithful to the poem’s imagery and scene, culturally and stylistically apt, free of spurious text, and true to its emotion, and its deepest requirements, imagery and especially implicit emotion, are never stated in the words. Existing metrics (CLIPScore, BLIPScore, VQAScore) reward literal text-image correspondence and so cannot tell whether an illustration succeeds, let alone why, or even separate the best model from the worst. We introduce TangPoetryBench, a multi-dimensional benchmark of 1,280 images (320 classical Chinese Tang poems x 4 state-of-the-art T2I models) with quality-controlled human annotations across ten dimensions. Analyzing this data, we reveal the shared and model-specific strengths and weaknesses of current T2I models, including their ability to evoke a poem’s implicit emotion. We further introduce PoemAutoEvaluator (PAE), an open, rubric-conditioned evaluator that reaches parity with a strong proprietary judge (Claude), generalizes to an unseen generator and a second poetic tradition (Song Ci), and lets the benchmark scale to new images without fresh human annotation. We release the benchmark, annotations, and evaluator.
[CV-95] VLMs Win a Systematic Evaluation of Underwater Image Reconstruction
链接: https://arxiv.org/abs/2608.11425
作者: Sara Aghajanzadeh,Yingxue Wang,Ieva Bagdonaviciute,David Forsyth
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Underwater image restoration consists of recovering an image which looks like there is no water present. To date, evaluation has not been systematic. This paper describes a systematic evaluation pipeline for underwater reconstruction, which can be used to assess a method for accuracy; consistency of reconstruction over camera moves; and the effect of water parameters. We use this pipeline to evaluate a range of current procedures, from models constructed using explicit but approximate physical models of scattering to Vision-Language Models (VLMs which are not currently trained with explicit physical models). Overall, VLMs wholly and significantly outperform physically based models in our evaluation, likely because of the importance of a strong image prior. Results on images of real underwater scenes strongly confirm the evaluation.
[CV-96] Click2Poly: A VLM for vector mapping buildings and walls
链接: https://arxiv.org/abs/2608.11424
作者: Nicolas Girard,Jawher Ben Abdallah,Arno Gobbin,Liuyun Duan,Sacha Lepretre
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate vector mapping of buildings and walls is critical for geospatial applications but remains a labor-intensive process. While recent deep learning methods have improved automatic extraction, in order to meet cartographic standards they always require a human to perform quality control and fix complex cases in the extraction. We present Click2Poly, a human-in-the-loop AI assistant designed to speed up this manual step. Extending the Florence-2 Vision Language Model (VLM), Click2Poly responds to user clicks by editing the building or wall vector layer directly. Implemented as a QGIS plugin, Click2Poly speeds up the manual editing of building and wall vector layers in a real-world production environment.
[CV-97] Analysis of Federated Aggregation under Model Poisoning and Backdoor Attacks: A Reconstructed Cross-Dataset and Cross-Architecture Benchmark
链接: https://arxiv.org/abs/2608.11423
作者: Soumya Mazumdar,Vineet Kumar Rakesh,Tapas Samanta
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注: 30 pages, 7 main figures, 7 main tables; includes 11 pages of Supplementary Information with 14 supplementary figures. Code and reproducibility resources: this https URL
Abstract:Robust comparisons of federated aggregation methods require joint consideration of predictive performance, threat definitions, metric semantics, and execution provenance. A 500-cell seed-1 evaluation matrix was reconstructed across five aggregation methods, five datasets, five architectures, and four recorded conditions: clean, sign-flipping, Gaussian, and BadNets. Successful execution logs were identified for 454 original runs and 36 repaired or rerun executions, whereas 10 clean SVHN cells were supported by summary-only provenance. Trimmed Mean achieved the highest clean macro-mean accuracy (76.02%) and the lowest mean within-task rank (1.70). Krum attained the highest recorded accuracy under both sign-flipping and Gaussian configurations. These relative rankings remained unchanged when analysis was restricted to 21 task pairs for which original successful logs were available for every method-condition combination. Audit of the supplied BadNets metric implementation established that every test input is triggered prior to target-label counting; consequently, the retained metric represents Triggered Target-Label Rate (TTLR) rather than a conventional target-excluding attack success rate. An audit of the supplied FedPARETO scaffold further identified a pathway in which predictive summaries may characterize an uncorrupted local model while the aggregation weight is applied to a separately corrupted update, introducing a potential discrepancy between reported predictive outcomes and the updates used for aggregation. The canonical matrix contains a single identified seed for each cell, and exact attack and configuration lineage is incomplete. Accordingly, the findings should be interpreted as descriptive comparisons within the recorded configurations and not as statistical estimates or universal claims regarding robustness.
[CV-98] COGENT: Counterfactual Gaussian Explanations for Volumetric Medical Images
链接: https://arxiv.org/abs/2608.11422
作者: Dorian Rząsa,Bartosz Zabdyr,Krzysztof Piekarz,Jakub Grzywaczewski,Bartlomiej Sobieski,Przemyslaw Biecek,Żaneta Świderska-Chadaj,Olga Śliwicka,Przemysław Spurek,Joanna Świebocka-Więk
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Explainability is essential for deploying deep learning models in high-stakes medical applications. Existing explainability methods for volumetric imaging predominantly operate in voxel space, overlooking the structured representations introduced by recent advances in 3D scene modeling. We present COGENT (Counterfactual Gaussian Explanations), a framework that generates counterfactual explanations directly in the parameter space of Gaussian-based volumetric representations. Built upon MedGS and the Sybil lung cancer risk prediction model, COGENT optimizes selected Gaussian primitives through a differentiable rendering pipeline, enabling gradients from the downstream predictor to identify representation components that most influence model decisions. Unlike conventional pixel- or voxel-level attribution methods, our approach formulates explainability as a counterfactual optimization problem over an explicit 3D scene representation, producing sparse and spatially localized explanations while preserving anatomical consistency. We evaluate COGENT on lung CT scans using quantitative comparisons with existing explainability methods together with qualitative analysis by medical experts. The results demonstrate that representation-space counterfactual optimization provides clinically meaningful explanations while offering a new perspective on interpreting volumetric deep learning models.
[CV-99] Gaze Target Estimation Anywhere with Concepts CVPR2026
链接: https://arxiv.org/abs/2608.11367
作者: Xu Cao,Houze Yang,Vipin Gunda,Zhongyi Zhou,Tianyu Xu,Adarsh Kowdle,Inki Kim,James M. Rehg
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: CVPR 2026 Code and Benchmark are aviliable at this https URL and this https URL
Abstract:Estimating human gaze targets from images in-the-wild is an important and formidable task. Existing approaches primarily employ brittle, multi-stage pipelines that require explicit inputs, like head bounding boxes and human pose, in order to identify the subject of gaze analysis. As a result, detection errors can cascade and lead to failure. Moreover, these prior works lack the flexibility of specifying the gaze analysis task via natural language prompting, an approach which has been shown to have significant benefits in convenience and scalability for other image analysis tasks. To overcome these limitations, we introduce the Promptable Gaze Target Estimation (PGE) task, a new end-to-end, concept-driven paradigm for gaze analysis. PGE conditions gaze prediction on flexible user text or visual prompts (e.g., “the boy in the red shirt” or “person in point [0.52, 0.48]”) to identify a specific subject for gaze analysis. This approach integrates subject localization with gaze estimation, and eliminates the rigid dependency on intermediate analysis stages. We develop a scalable data engine to generate Gaze-Co (Gaze Estimation with Concepts), a dataset and benchmark of 120K high-quality, prompt-annotated image pairs. We also propose GazeAnywhere, the first model designed for PGE. GazeAnywhere uses a transformer-based detector to fuse features from frozen encoders and simultaneously solves subject localization, in/out-of-frame presence, and gaze target heatmap estimation. GazeAnywhere achieves state-of-the-art performance on multiple PGE benchmarks, setting a strong baseline for this new problem even on a difficult out-of-domain, real-world clinical dataset. GazeAnywhere is open-sourced in this http URL.
[CV-100] Dual-Domain Cross-Modal Decoding for Clinical Text-Guided Medical Image Segmentation MICCAI2026
链接: https://arxiv.org/abs/2608.11335
作者: Md Maklachur Rahman,Tracy Hammond
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
备注: Accepted at MICCAI 2026 (Main). Final version to appear in the MICCAI 2026 proceedings
Abstract:Clinical text can narrow down what to segment, but recent text-guided designs emphasize spatial alignment while overlooking frequency content that governs texture and boundaries. We propose Dual-Domain Cross-Modal Decoding (DD-CMD) for clinical text-guided pulmonary infection segmentation, integrating two complementary forms of language guidance during decoding. In the spatial domain, Text-Guided Spatial Cross-Attention (TGSA) aligns multi-scale visual tokens with text semantics and updates features through gated residual fusion. In the frequency domain, Spectral-Text Adaptive Modulation (STAM) applies a 2D DCT to compute learnable band-energy statistics and predicts text-conditioned FiLM parameters to recalibrate decoder channels for frequency-aware decoding. DD-CMD embeds TGSA and STAM into a coarse-to-fine decoder (7x7 to 56x56) and restores full-resolution masks using a lightweight two-stage refinement module. Experiments on QaTa-COV19 and MosMedData+ show that DD-CMD achieves 91.46% Dice / 84.26% mIoU and 81.95% Dice / 69.42% mIoU, respectively, with average gains of +1.96 Dice and +2.67 mIoU over the strongest prior baselines. Code: this https URL.
[CV-101] Qwen -MusicAVQA-7B: A Multimodal Model for Music Audio-Visual QA
链接: https://arxiv.org/abs/2608.11329
作者: Maryam Dehdashti
类目: ound (cs.SD); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: 24 pages, 1 figure, 8 tables. Code: this https URL Checkpoints: this https URL
Abstract:A common approach to adding audio to a vision-language model is to train or adapt a large omni-modal system. We show that a lightweight alternative can be highly effective for music audio-visual question answering (AVQA). Qwen-MusicAVQA-7B connects a frozen Whisper encoder to Qwen2-VL-7B-Instruct through learned linear projections. The same frozen encoder processes both the video’s music track and a TTS-spoken question through separate projectors, while the language model fuses visual frames, music, and question audio through pretrained self-attention, with no task-specific fusion network. On MUSIC-AVQA, our system reaches 96.0% +/- 3.9% accuracy across three independent training seeds on the 7,402-question available-video test subset. Our central finding is that downstream accuracy tracks how much fine-grained local temporal information the audio representation preserves. In a matched 32-token comparison, a stride-pooled Whisper frame sequence outperforms a globally pooled PANNs representation expanded to the same budget by 26 percentage points, even though PANNs sees at least as much audio and uses a far larger projector. The effect is not simply sequence versus vector: within Whisper alone, reducing temporal resolution at a fixed token budget costs a comparable amount. Under matched data and inputs, fine-tuned Qwen2.5-Omni-7B reaches 80.9%, against 95.9% for our 30 s variant; because the systems differ in backbone and adaptation, this is a system-level comparison. Accuracy remains high on sampled head and tail splits of the rephrased MUSIC-AVQA-R benchmark (96.5% and 95.6%). Because both encoders stay frozen and the music features are cached, the entire adaptation is cheap to train: the complete two-stage AVQA run takes approximately 5 hours on a single A100 80GB, and every run reported here fits on that one GPU. Comments: 24 pages, 1 figure, 8 tables. Code: this https URL Checkpoints: this https URL Subjects: Sound (cs.SD); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM) Cite as: arXiv:2608.11329 [cs.SD] (or arXiv:2608.11329v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2608.11329 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-102] Clinical Feasibility of Low-Magnification Fluorescence Imaging for Breast Cancer Margin Detection Using Texture Analysis and Deep Learning
链接: https://arxiv.org/abs/2608.11317
作者: Pouya Afshin,Tianling Niu,Tongtong Lu,David Helminiak,Julie Jorns,Mollie Patton,Tina Yen,Donghye Ye,Bing Yu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: This research has been accepted and published in Journal “Biomedical Optics Express” in July 2026 with Manuscript ID is 596807
Abstract:High-resolution images of unprocessed surgical breast tissue can be obtained using microscopy with ultraviolet surface excitation (MUSE). This technique is considered a promising method for checking surgical margins during breast cancer surgery. In this study, MUSE images at 4x and 10x magnifications were compared using patch-level classification methods. Texture analysis (TA) based on local binary patterns (LBP) and deep learning (DL) with a base Vision Transformer (ViT) model were used. Both methods achieved similar performance at both magnifications. Using DL method, both 4x and 10x magnifications achieved 96.30% sensitivity, 100% specificity and 98.18% accuracy. Using TA method, 4x achieved better specificity (100% vs 93.33%) and 10x yielded higher sensitivity (100% vs 93.33%), but both had the same accuracy (96.67%). No clear improvement in performance was observed with 10x magnification. These results show that 4x imaging achieves the same diagnostic accuracy as 10x imaging. At the same time, 4x offers a larger field of view and faster image capture. Therefore, lower magnification can be effectively used in MUSE systems for accurate and efficient intraoperative margin assessment.
[CV-103] Self-Evolving Code-with-Image Reasoning
链接: https://arxiv.org/abs/2608.11292
作者: Tianze Yang,Liang Wu,Ruitong Sun,Yucheng Shi,Yanqiao Wang,Mayank Darbari,Ninghao Liu,Jin Sun,Liangjie Hong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 37 pages
Abstract:Multimodal models increasingly reach for tools when solving visual tasks (crop, zoom, rotate, brighten), a paradigm known as thinking-with-images. The central challenge is one of perception: tools mostly serve to expose visual evidence, reasoning over that evidence stays in language, and most targets are ones a human could in principle determine by inspection. Some visual questions, however, are not bottlenecked by perception: recovering their answers requires executing a multi-step visual algorithm over the pixels. On such questions a model often names the correct algorithm at once yet still answers wrong, because language can describe an algorithm without being able to run one. Code-with-Image crosses that line: given nothing but a Python interpreter, the model must implement a genuine visual algorithm in code to solve the task; the program itself becomes the reasoning. The bottleneck then shifts from executing code to deciding which algorithm to implement. So we let the model teach itself: a training-free reflection loop studies its own failed programs, tests repairs against constructive ground truth, and keeps what survives as portable skills. On our Code-with-Image Bench (CwI-Bench), thirty task families induced by hidden visual computations with disjoint learning and evaluation splits, even GPT-5.6-luna stays below 30% with tool-free chain of thought; given a bare interpreter it reaches 43%, and with skills evolved through its own executable reflection, 67%. The open 27B model climbs the same ladder (9% \rightarrow 33% \rightarrow 56%), and the skills are plain text, transferable across scales and families. When code carries the reasoning, debugging code becomes debugging reasoning.
[CV-104] CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification
链接: https://arxiv.org/abs/2608.11287
作者: Gawon Lim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Long-tailed classification poses a reliability challenge because models trained on imbalanced data are unevenly reliable across frequent and underrepresented classes. While existing methods address imbalance through re-balancing, adjustment, representation learning, or multi-expert modeling, they rarely estimate which expert should be trusted for each class. This paper proposes CLEAR (Class-wise reLiability-aware Expert Aggregation for long-tailed Recognition), a modular ensemble framework for long-tailed classification. CLEAR generates diverse experts through threshold-based structured sampling while preserving the full label space, then estimates a class-wise trust score for each expert using a smoothed class-wise precision formulation. During inference, expert predictions are combined through class-wise generalized product-of-experts aggregation, allowing different experts to be emphasized for different classes. Experiments on CIFAR-100-LT, ImageNet-LT, and Places-LT across multiple backbones show that CLEAR achieves competitive overall accuracy and particularly strong few-shot performance. These results support class-wise expert reliability as a useful design principle for long-tailed ensemble learning.
[CV-105] SegPAR: Class-Centric Decision-Based Sparse Attack for Semantic Segmentation ECCV2026
链接: https://arxiv.org/abs/2608.11285
作者: Dongsu Song,DaeYun GO,Boseung Seo,Jay Hoon Jung
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: ECCV 2026 poster
Abstract:Despite the practical relevance of sparse decision-based black-box threats, they have received limited attention in semantic segmentation. To bridge this gap, we adapt the most representative decision-based black-box sparse attacks from the classification domain to serve as baselines, establishing a rigorous benchmark for this underexplored setting. In this context, we demonstrate that one of the existing methods suffers from severe query inefficiency due to its image-centric pixel accumulation, which rapidly exhausts query budgets across the vast image space. To overcome this, we propose SegPAR, a novel decision-based framework that shifts to a class-centric exploration paradigm. Furthermore, to eliminate the misleading feedback generated by standard decision rewards during pixel accumulation, we introduce a novel discrepancy reward. Extensive experiments show that SegPAR significantly outperforms black-box baselines in sparsity efficiency and MIoU reduction, while remaining competitive with white-box sparse attacks. Code is available at \hrefthis https URLthis https URL.
[CV-106] A Geodesic Cut-Cell Prior for Neural Skinning
链接: https://arxiv.org/abs/2608.11272
作者: Wenchao Ma,Surya Dwarakanath,Yizhak Ben-Shabat,Dario Kneubühler,Haomiao Jiang,Sharon X. Huang,Hsueh-Ti Derek Liu
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce cut-cell skinning, a geometric prior designed to augment data-driven skinning weight generation. While data-driven methods show promise in producing high-quality skinning weights, they often lack the generalizability of classic geometric approaches. To bridge this gap, we propose a geometric prior that can be robustly computed for in-the-wild meshes and is efficient for large-scale machine learning workflows. The key idea of our cut-cell skinning is a fast graph-based approximation of the volumetric geodesics distances, motivated by their importance in classic skinning weight computation. Our method achieves orders of magnitude speedup compared to optimization-based solvers and remains resilient to topological artifacts common in cage- or voxel-based alternatives. We demonstrate the efficacy of the cut-cell skinning prior by integrating it into recent neural skinning models, showing consistent improvements across existing methods and achieving state-of-the-art results. Project page: this https URL
[CV-107] GeoUniPR: A Geometry-Consistent Unified Framework for Cross-Modal Place Recognition
链接: https://arxiv.org/abs/2608.11263
作者: Wonbong Kim,Jiatong Xiao,Rui Li,Xufei Wang,Qiwen Gu,Junqiao Zhao,Chen Ye,Guang Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 10 figures
Abstract:Cross-modal place recognition (CMPR) aims to identify the same location across heterogeneous sensing modalities, such as vision and LiDAR. Existing methods commonly bridge the modality gap using complex alignment modules, multi-stage training, or full fine-tuning of pretrained backbones. In this work, we revisit CMPR from the perspective of geometric consistency and propose GeoUniPR, a unified and concise geometry-consistent framework. GeoUniPR reduces cross-modal discrepancy at the representation level by projecting LiDAR point clouds into the camera perspective to construct Geometry-Consistent depth image views (DIV), which establish direct RGB-LiDAR correspondence. We further augment DIV with native LiDAR cues, including intensity and surface-normal information, yielding a multi-channel geometric representation that improves structural consistency. Based on this representation, GeoUniPR learns a unified embedding space using two modality-specific ViT-based encoders with identical architectures, trained through parameter-efficient adaptation without auxiliary alignment modules, multi-stage training, or full backbone fine-tuning. In addition, we introduce Spatially-Consistent InfoNCE (SC-InfoNCE), a CMPR-specific contrastive objective that suppresses distance-induced false negatives under spatial continuity. Extensive experiments on KITTI and KITTI-360 demonstrate that GeoUniPR achieves state-of-the-art (SOTA) performance in both same-modal and cross-modal place recognition, with strong cross-dataset generalization.
[CV-108] Glance Scrutinize and Think: Advancing Video Anomaly Detection from Training-Free to Agent ic Reasoning AAAI2026
链接: https://arxiv.org/abs/2608.11260
作者: Shibo Gao,Peipei Yang,Xu-Yao Zhang,Linlin Huang
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 34 pages, 8 figures, 8 tables. Journal extension of our AAAI 2026 paper ( arXiv:2507.21507 )
Abstract:Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals. Existing approaches exhibit a “when-what” dissociation: traditional DNN-based methods localize when anomalies occur but lack semantic understanding, whereas LLM-based methods explain what happens but neglect precise temporal grounding. We attribute this to the absence of a unified reasoning paradigm. Inspired by how humans inspect surveillance videos - glancing globally to form temporal hypotheses, scrutinizing suspicious segments, and thinking iteratively to correct errors - we study this global-to-local paradigm from two perspectives. We first propose Glance then Scrutinize (GtS), a training-free framework using static and dynamic textual guidance for coarse-to-fine anomaly grounding and understanding, balancing accuracy and speed. To break the ceiling imposed by frozen external modules, we further propose a tool-augmented agentic VAD method, where a multimodal large language model learns to invoke a video cropping tool, inspect densely resampled frames, and self-correct mislocalized hypotheses, via cold-start supervised fine-tuning followed by reinforcement learning with a joint answer-grounding reward. For training and evaluation, we extend our prior VAGU benchmark into VAGU-T (Video Anomaly Grounding, Understanding, and Thinking), comprising 7,567 real-world videos over 21 anomaly categories with human-validated grounding, explanations, QA pairs, and chain-of-thought tool-calling traces. We further introduce JeAUG, a metric jointly evaluating semantic interpretability and temporal precision. Experiments show that GtS substantially surpasses training-free baselines, while the agentic model delivers both higher accuracy and faster inference.
[CV-109] World Tokens: Enhancing Embodied Policies with Training-Time World Modeling
链接: https://arxiv.org/abs/2608.09730
作者: Qu Tang,Benhui Zhuang,Bo Yuan,Xue Yu,Longteng Guo,Junlan Feng
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Vision-language-action (VLA) models are a widely adopted paradigm for embodied policies. They excel at efficient closed-loop control but do not explicitly model how physical scenes evolve as a task unfolds. Recently emerging world-action models (WAMs) leverage pretrained video world models to capture spatiotemporal evolution, yet retaining future generation or a large video backbone in the control loop substantially increases inference cost. We introduce World Tokens, an embodied policy architecture built around a World Adapter that bridges visual-language understanding, world-dynamics modeling, and action generation. It uses world modeling during training to enhance the action policy while preserving efficient deployment. Specifically, the World Adapter transforms VLM features into a fixed set of world tokens, which condition a jointly fine-tuned future-video denoiser and simultaneously serve as the action expert’s sole visual-language context. This shared conditioning allows gradients from future-video denoising to directly shape the representation used for action prediction, while exclusive routing prevents the policy from bypassing that representation. At deployment, the world-model branch is removed, leaving only the VLM, World Adapter, and action expert, with no online video-model inference. With a 2B backbone and no embodied action pretraining, World Tokens is highly competitive on LIBERO, attains the best reported averages on SIMPLER, substantially improves real-world R1 Pro success over a matched action-only baseline, and generates each action chunk at VLA-level latency.
[CV-110] Early Warning Signals for OpenVLA Failure under Visual Distribution Shift
链接: https://arxiv.org/abs/2606.29699
作者: Dipesh Tharu Mahato,Rachel Ren
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 10 pages, 1 figure, 5 tables
Abstract:Vision Language Action models combine perception, language grounding, and control in a single policy, but their failures are hard to diagnose once visual conditions shift. We test whether OpenVLA feedforward activations contain linearly decodable information about near term task failure in LIBERO manipulation rollouts. The policy is fixed throughout. We log internal activations during execution and fit lightweight monitors after the rollouts are collected. Occlusion is the main controlled stress test. It reduces OpenVLA success from 57% to 17% over 100 episodes per condition. Under this shift, a logistic probe at layer 16 reaches AUROC 0.972 and AUPRC 0.352 for predicting failure within a 15 step horizon. It outperforms both a mean difference direction and an action disagreement baseline. A sparse layer sweep finds uneven decodability across depth: layer 16 is strongest among the tested layers, layer 8 remains informative, and layer 10 is weaker. To check whether the monitor is just an occlusion detector, we also evaluate color shift and camera jitter without refitting. Color shift produces no failures in this setting, so it is a benign control rather than a failure benchmark. Camera jitter does induce failures, and the occlusion trained monitor remains above random. The result is deliberately limited: OpenVLA internal states contain failure relevant structure under controlled perceptual shift, but these experiments do not establish a causal mechanism, task held out generalization, or a deployable recovery system.
[CV-111] Domain-Aware Lightweight Spectral-Grouped Convolutions for Hyperspectral Fish Freshness Classification BMVC’2026
链接: https://arxiv.org/abs/2608.12227
作者: Kazi Nabiul Alam,Pooneh Bagheri Zadeh,Akbar Sheikh-Akbari
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注: Accepted at BMVC’2026
Abstract:Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands. However, conventional deep learning approaches do not fully address the particular characteristics of HSI data, such as spectral dominance over spatial textures, ordinal label structure, and a small number of training samples. We propose SGNet (Spectral-Grouped Network), a lightweight architecture that separates spectral and spatial feature extraction using grouped convolutions and a depthwise spatial pathway. A dual attention mechanism that couples channel-wise squeeze-and-excitation with spatial gating adaptively highlights informative features. SGNet achieves 97.8% classification accuracy and 0.64 days mean absolute error (MAE) with just 4.75M parameters when tested on our newly developed 16-day refrigerator-stored salmon fillet dataset. Ablation studies validate the contribution of each component, while comparisons demonstrate a five- to eighteen-fold parameter reduction relative to ResNet-50 and Vision Transformers. Our findings indicate that domain-aware design supports precise, real-time freshness prediction for industrial implementation.
[CV-112] CoQui: A Coordinate-Conditioned Quantum Implicit Generative Adversarial Network for End-to-End Image Generation
链接: https://arxiv.org/abs/2608.11884
作者: Xue Yang,Rigui Zhou,ShiZheng Jia,Dax Enshan Koh,Siong Thye Goh,Young-Wook Cho,YaoChong Li,Xuezhi Ma,Hongyu Chen,Xin Wang
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits. Existing amplitude-based approaches face two key limitations: pixel locations are typically encoded by computational-basis indices or address qubits, causing quantum resources to grow with image resolution; meanwhile, jointly decoding many pixels from normalized quantum states introduces probability competition among pixels and limits precise pixel-wise control. To address these issues, we reformulate quantum image generation as coordinate-conditioned implicit function learning. Our method takes spatial coordinates and latent variables as inputs, uses a classical embedding network to generate input-dependent circuit parameters, and evaluates a variational quantum circuit at each coordinate. Pixel intensities are directly obtained from the expectation value of a dedicated color qubit, and a complete image is generated by querying all spatial coordinates. This design decouples image resolution from address-qubit requirements and avoids shared probability-normalization constraints across pixels. We further design a specialized variational quantum circuit to provide structural inductive bias for coordinate-conditioned generation. Simulated experiments on two benchmark datasets show that our method outperforms FRQI-based generation and PQWGAN in visual and quantitative quality while using fewer qubits, and also achieves better generation quality than the corresponding classical baseline.
[CV-113] wo-Stage Deformable-Convolutional Inverse Design of Nanophotonic Absorbers from Optical Spectra
链接: https://arxiv.org/abs/2608.11860
作者: Waleed Waseer,Muhammad Shahid Jabbar,Muhammad Sohail Ibrahim,Shujaat Khan
类目: Optics (physics.optics); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Data-driven inverse design enables efficient generation of nanophotonic structures with prescribed optical responses, but spectrum-to-geometry mapping remains challenging due to non-uniqueness and fine geometric features. This work presents a two-stage deformable-convolutional framework for reconstructing metal–insulator–metal resonator geometries from 80-dimensional absorption spectra. The spectrum is projected to a 150\times4\times4 latent representation and decoded into a 64\times64 resonator mask. Training combines supervised reconstruction with least-squares adversarial refinement initialized from the best supervised checkpoint. A three-run ablation compares deformable convolution with plain convolution, involution, Dynamic Conv, and ODConv under the same architecture. The proposed model achieves 20.79\pm0.31 ~dB PSNR and 0.8501\pm0.0082 SSIM, improving over plain convolution by 2.16~dB and 0.0831, respectively. It further achieves Dice 0.9623\pm0.0027 , IoU 0.9342\pm0.0038 , and boundary F-score 0.9550\pm0.0027 . Spectral consistency evaluated using a frozen forward surrogate yields RMSE 0.0805\pm0.0013 and R^2=0.7923\pm0.0065 . Learned offsets show stronger adaptive sampling at coarse and intermediate decoder stages. Overall, deformable sampling with supervised initialization and adversarial refinement improves spectrum-conditioned geometry reconstruction.
[CV-114] A comparison of CNN architectures for Alzheimers disease detection in single-view MRI scans
链接: https://arxiv.org/abs/2608.11762
作者: Hiram Zuniga,Ulises Orozco-Rosas,Kenia Picos
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at SPIE Optics + Photonics 2026 for oral presentation. 14 pages, 7 figures, 8 tables
Abstract:Alzheimer’s disease is a leading cause of death with no cure. Therefore, early detection is critical to slow progression and preserve quality of life. Diagnosis relies on medical history, cognitive tests, physical exams, and MRI brain scans, making deep learning suitable for Alzheimer’s classification. This work proposes a benchmark that evaluates ten different convolutional neural network (CNN) architectures (including ResNet, DenseNet, MobileNet, EfficientNet, and VGG family models) under the same held-out test split protocol. A two-stage transfer learning and full fine-tuning pipeline is introduced to perform training using a class-balanced subset (3,900 images) derived from the OASIS medical imaging dataset, comprising 86,437 single-view MRI brain scans labeled into four classifications of Alzheimer’s disease: Non-Demented, Very Mild Dementia, Mild Dementia, and Moderate Dementia. The best results were achieved by VGG16, with a 0.9637 validation accuracy and a 0.9533 test accuracy score. A key finding documented in this work is the difficulty of classifying the transition from Non-Demented to Very Mild Demented stages, observed consistently across all ten architectures.
[CV-115] Alignment of Similarity-Transformed Images Based on Fourier–Mellin Transform Using Auxiliary Function Method
链接: https://arxiv.org/abs/2608.11565
作者: Shinji Yamashita,Yuma Kinoshita,Hitoshi Kiya
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to APSIPA ASC 2026. 6 pages, 4 figures
Abstract:This paper proposes an algorithm for estimating the similarity transformation, namely translation, scale, and rotation, between two images with subpixel accuracy. Image registration is a fundamental technique for aligning images acquired under different viewpoints and imaging conditions, and a representative approach based on maximizing discrete cross-correlation is the Fourier–Mellin registration. However, the Fourier–Mellin approach often fails to achieve sufficient alignment accuracy when subpixel-level estimation is required. The proposed method integrates (i) scale-and-rotation estimation from the Fourier magnitude spectrum in a log-polar representation and (ii) maximization of phase-only correlation based on the auxiliary function method. This integration enables a two-stage estimation procedure: it first estimates scale and rotation without being affected by translation, and then estimates translation with subpixel precision in the spatial domain using the corrected image pair. A simulation experiment on image pairs subjected to random similarity transformations demonstrates that the proposed method reduces estimation errors in scale, rotation, and translation compared with Fourier–Mellin-based registration methods using discrete cross-correlation.
[CV-116] Boundary-Continuous Cross-Camera RGB Mapping via Hue-Split Model Trees
链接: https://arxiv.org/abs/2608.11548
作者: Yuma Kinoshita,Hitoshi Kiya
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to APSIPA ASC 2026. 6 pages, 4 figures
Abstract:We propose a hue-split model-tree method for boundary-continuous cross-camera RGB mapping. Cross-camera RGB mapping aims to produce consistent color representations across cameras whose recorded RGB values differ due to sensor spectral sensitivities and image-signal processing pipelines. A common chart-based remedy is to estimate a single global affine color correction matrix (CCM), but such a global model cannot capture hue-specific discrepancies between cameras. To capture that behavior, we recursively partitions the source-camera color space along a scalar hue coordinate and builds an model tree that stores an affine CCM at every node. For fitting the node CCMs, we utilize a log-domain error objective. To prevent false contours that arise from hard hue splits, we further introduce a boundary-continuous formulation in which the prediction is obtained by blending the log-domain outputs of all node CCMs along the root-to-leaf path. The path-wise blending weights are optimized under a simplex constraint using both a chart-pair fitting loss and an explicit continuity regularizer defined on deterministic boundary prototype pairs placed just on either side of each learned hue threshold. We conducted an experiment on a Canon EOS-1Ds Mark II to Canon EOS 20D mapping using the Middlebury Registered Color Checker dataset. The results show that hue splitting substantially reduces log-RMSE over a single global affine CCM and that the proposed path blending with boundary prototype regularization simultaneously improves accuracy and suppresses chromaticity gaps at the learned hue thresholds across two illuminants and multiple exposure conditions.
[CV-117] Uncertainty-Aware Compositional Localization and Placement Assessment of Catheters and Tubes in Chest X-Rays
链接: https://arxiv.org/abs/2608.11288
作者: Harshil Lodhiya
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 21 pages, 8 figures
Abstract:Assessing catheter and tube placement on chest X-rays is safety-critical yet tedious and error-prone. Current deep learning methods either classify placement globally – losing track of which device is where – or segment all devices into a single mask, making per-device assessment impossible when catheters overlap. We introduce UCompCXR, a compositional framework that detects local catheter fragments, associates them into device instances via graph-based clustering, fuses per-fragment tip predictions through precision-weighted Gaussian estimation, and classifies placement per device. On the RANZCR CLiP dataset (30,083 images, 5-fold patient-level CV with bootstrap CIs), UCompCXR detects 26% more devices than a strong multi-task baseline sharing the same MobileNetV3 backbone, with 75% fewer false positives and well-calibrated tip uncertainty (95% coverage = 0.948). The aggregate tip error rises – but only because the model finds devices the baseline misses entirely, especially nasogastric tubes. On matched devices, catastrophic localization failures drop substantially. At 2.27M parameters in a single forward pass, the model is deployable on resource-constrained clinical hardware.
[CV-118] Uncertainty-Aware and Explainable Ensemble Deep Learning Framework for Multi-Class Skin Lesion Classification
链接: https://arxiv.org/abs/2608.11280
作者: Rofiqul Islam,Lilatul Ferdouse
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 5 pages, 3 figures, IEEE AIBThings 2026
Abstract:Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and the limited interpretability of deep learning models. This paper proposes an uncertainty-aware and explainable deep learning framework for multi-class skin lesion classification. The framework combines a vision transformer model (MaxViT-Tiny) with CNN-based models (ConvNeXt-Tiny and EfficientNetV2-B0) through deep ensemble learning. Monte Carlo (MC) Dropout estimates predictive uncertainty and identifies unreliable predictions, while Grad-CAM++, an explainable AI (XAI) technique, provides visual explanations by highlighting lesion regions that influence model decisions. Evaluated on the HAM10000 dataset, the framework achieves 96% accuracy and 99% ROC-AUC under uncertainty-aware filtering (entropy 1.0, confidence = 0.7), with macro-average precision, recall, and F1-score of 94%, 95%, and 95%, respectively, and 96% weighted-average scores across all three metrics. The results demonstrate accurate, interpretable, and uncertainty-aware skin lesion classification for trustworthy computer-aided diagnosis.
[CV-119] Geometry-Based Compression of Plenoptic Point Clouds
链接: https://arxiv.org/abs/2608.11273
作者: Davi R. Freitas,Gustavo L. Sandri,Ricardo L. de Queiroz
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Plenoptic point clouds (PPC) are novel data structures that represent the light from different viewing directions in order to provide a higher degree of realism to regular point clouds. This is achieved by associating each point to multiple colors instead of a single one. Here, we present a method to efficiently compress the attributes of a PPC, consisting of a Karhunen-Loève transform over the color attributes followed by multiple attribute coders with intra prediction capability. This compression scheme can be incorporated within the MPEG’s geometry-based PCC (G-PCC) standard, using any of G-PCC’s existing solutions for attribute coding. Compression performance assessment using PPCs of different spatial resolutions reveals competitive results in comparison to existing methods, such as RAHT-based or video-based PCC solutions. We believe our coder to be the new state of the art.
[CV-120] Decodable but Not Accessible: Auditing Distance-Based Reliability Estimation on Disentangled Skin-Lesion Representations
链接: https://arxiv.org/abs/2608.11267
作者: Duc-Vinh Tran
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Distance-based reliability estimation assumes that a representation’s geometry reflects its trustworthiness, yet this assumption is rarely tested under training interventions that reshape geometry directly. We audit this assumption under domain-adversarial representation learning using a disentanglement dose-response ladder. Three checkpoint families share the same architecture and a 16-dimensional representation, differing only in orthogonality strength (lambda = 0, 1, 5). Representation geometry changed substantially with disentanglement strength: the condition number shifted by two orders of magnitude (Kendall tau = 0.84, exact p = 2.8e-5). This change was not accompanied by improved reliability estimation: Mahalanobis-distance AUROC (ISIC-test vs. PAD-UFES) remained flat and below chance (about 0.40) at every level, with no significant association with any of five geometry metrics tested. The same failure was observed for cosine-to-centroid and pooled k-nearest-neighbor scorers, plus three non-distance-based scorers: an energy-based confidence score, Virtual-Logit Matching, and a kernel density estimator. Seven of eight scorers converged on the same result; the energy-based score showed an isolated upward trend that we report but do not treat as evidence against the overall pattern. A supervised probe with no access to the training objective recovered domain membership from the identical embeddings at 0.72-0.81 AUROC across every level, showing that the relevant information was not absent from the representation. These findings indicate that classification performance alone can overlook whether information in a learned representation is organized in a form that downstream reliability estimators can use. Information can remain decodable while becoming largely inaccessible to non-probing reliability estimators.
人工智能
[AI-0] Redistribution-based Cost Inference Improves Sparse Safe Offline RL IJCAI ECAI2026
链接: https://arxiv.org/abs/2608.12306
作者: Ebenezer Gelo(1),Geraud Nangue Tasse(1),Steven James(1),Benjamin Rosman(1) ((1) University of the Witwatersrand)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at the 1st IJCAI Workshop on Safe Physical AI (SPAI 2026), affiliated with IJCAI/ECAI 2026
Abstract:Safe offline RL typically assumes access to dense per-step cost annotations, but in practice supervisors provide only trajectory-level stop-feedback: a binary signal at the first unsafe transition, with no per-step attribution. We frame this as a temporal credit assignment problem and propose the Redistribution-based Cost Inference (RCI) framework, which converts sparse stop-feedback into dense per-step costs via return decomposition, then trains a constrained offline policy on the augmented dataset. We show that return-equivalent redistribution preserves the feasible policy set and the optimal Lagrangian in a CMDP, establishing that the transformation is lossless in theory while yielding better-conditioned cost critic learning in practice. Experiments on highway driving and robotic manipulation demonstrate substantially lower violation rates than sparse and classifier-based baselines, with robustness to heterogeneous dataset compositions and label noise.
[AI-1] Constructing Dynamic Master Logic Models as Knowledge Graphs for Complex System Diagnostics Using Retrieval-Augmented Large Language Models
链接: https://arxiv.org/abs/2608.12304
作者: Saman Marandi,Yu-Shu Hu,Mohammad Modarres
类目: Artificial Intelligence (cs.AI)
备注: 36 Pages, 8 Figures
Abstract:Dynamic Master Logic (DML) provides a hierarchical framework for representing system behavior by linking functional objectives to underlying structural elements. However, DML construction typically relies on expert interpretation of technical documentation, limiting scalability for complex systems. This study presents a framework for automated construction of DML models from system descriptions and their representation as Knowledge Graphs (KG-DML), using Retrieval-Augmented Generation and Large Language Models as enabling tools. Building on prior work with small-scale systems, the framework extends automated KG-DML construction and evaluation to substantially larger and more complex systems. Model construction proceeds across the DML hierarchy using targeted retrieval while preserving functional dependencies and explicit logical relationships. The resulting KG-DML supports diagnostic reasoning, safety assessment, upward failure propagation, and downward dependency tracing. A multi-level validation methodology evaluates layer-specific precision and recall, logical gate consistency, and overall structural integrity. Application to the Low-Pressure Coolant Injection system of a decommissioned Boiling Water Reactor demonstrates consistent reconstruction across repeated runs. The results show that automated KG-DML construction can transform technical documentation into executable functional models for diagnostic and reliability analysis.
[AI-2] VAKRA: Evaluating Multi-Hop Reasoning Across APIs and Retrieval Under Tool-Use Policies
链接: https://arxiv.org/abs/2608.12282
作者: Ankita Rajaram Naik,Anupama Murthi,Benjamin Elder,Siyu Huo,Raavi Gupta,Abhinav Jain,Praveen Venkateswaran,Abdulhamid Adebayo,Danish Contractor
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agents deployed in enterprise settings must reason across structured APIs and document collections, yet existing benchmarks evaluate these capabilities in isolation. We introduce VAKRA (e\textbfValuating \textbfAPI and \textbfKnowledge \textbfRetrieval \textbfAgents), a benchmark of over 8,000 executable APIs across 62 domains with tasks spanning three settings of increasing difficulty: diverse API interaction styles, multi-hop reasoning over structured APIs, and multi-source reasoning with natural-language tool-use policy constraints. Correctness is verified by re-executing predicted tool calls against live APIs, accommodating multiple valid paths. Using a fixed ReAct harness to isolate model capabilities from agent architecture, we evaluate frontier and open-weight models and find that even the best model achieves only 70.4% on single-hop endpoint-style tasks and drops to 50–51% on compositional APIs; performance degrades by over 50% as reasoning depth increases, and policy-constrained questions expose severe failures (as low as 2.4% on unanswerable queries). Trace analysis shows failures concentrate at language-mediated reasoning - entity disambiguation, cross-source grounding, rather than tool invocation mechanics. Code is available this https URL. Dataset is available this https URL
[AI-3] Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents
链接: https://arxiv.org/abs/2608.12273
作者: Junliang Liu,Ruoyu Li,Wenxin Tang,Jingyu Xiao,Zhenyu Liu,Jingheng Xu,Laizhong Cui
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM agents increasingly rely on third-party skills, using natural-language descriptions for selection and instruction bodies for planning. This progressive-disclosure design exposes two sequential control points to untrusted publishers: a static skill may steer an otherwise correct task onto an unnecessarily costly trajectory. Prior work studies selection manipulation, malicious skill instructions, and tool-chain resource amplification largely separately, leaving their end-to-end composition unclear. We introduce Convergent Detour Hijacking (CDH), a text-only, runtime-independent attack that couples these stages. Under shared semantic cover, a description establishes relevance during selection, while an aligned body reuses that rationale to fabricate plausible dependencies during planning. CDH attracts an attacker-controlled coordinator alongside legitimate skills, recruits unnecessary benign skills into a bounded detour, and then re-enters the original route to preserve task completion. We evaluate it across multiple LLM backends and 491 held-out tasks under single-task and multi-turn conditions. On DeepSeek-V4-Pro, the matched coordinator is selected in 80.02% of tasks; among coordinator-hit runs that complete tasks, token consumption and end-to-end execution time increase by 66.91% and 92.45%, respectively, while aggregate task completion remains comparable. Thus, correct outcomes do not guarantee trajectory integrity or cost safety.
[AI-4] An Agent ic Workflow for Legacy HPC Modernization: Converting the Two-Electron-Integral Core of GAMESS
链接: https://arxiv.org/abs/2608.12249
作者: Yuzhong Shen,Masha Sosonkina,Peng Xu,Mark S. Gordon
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Modernizing legacy Fortran is a problem of volume: the transformations are individually routine, but the codebases can be enormous, and across much of computational science the work simply goes undone. We propose an agentic workflow that takes this work on at production scale, and we set out to measure how far such delegation can reach. In this work, three prompt-specialized agent roles operate under a version-controlled specification that the agents themselves authored and revised, while humans hold a small number of gates. The arrangement is kept safe by an exact verification oracle inherited from the domain, and the boundary of safe delegation lies exactly where that oracle stops seeing. We apply the proposed workflow in a case study, converting the two-electron-integral routines of GAMESS (General Atomic and Molecular Electronic Structure System), a mature quantum-chemistry package with a 48-year development history, from fixed-form Fortran 77 to free-form Fortran 2008. The scope of this work was twelve source files, 56,448 lines, and 225 subroutines for computing electron repulsion integrals. The agents ran as three Claude Code roles in isolated worktrees, and the work spanned four Claude model generations. Because the GAMESS group ships a standard test suite whose printed energies its user community treats as canonical, we could adopt bit-for-bit reproduction of those energies as the merge criterion, where a deviation in the twelfth decimal place counts as a failure rather than drift. All twelve source files pass a 51-test validation battery comprising the 49 standard GAMESS tests and two additional calculations, and across 612 test runs the number of chemistry-relevant differences is zero, and every file also passes the Jenkins tests that are used for continuous integration. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12249 [cs.AI] (or arXiv:2608.12249v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.12249 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-5] Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
链接: https://arxiv.org/abs/2608.12198
作者: Jean-Pierre Busch,Guido Linden,Jan Bergmann,Lutz Eckstein
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages; Accepted to be published as part of the 17. Uni-DAS e.V. Workshop “Fahrerassistenz und automatisiertes Fahren”, September 29-30, 2026
Abstract:Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments. However, their complex nature and lack of transparency can hinder explainability and trustworthiness and complicate safety assurance. Motivated by these challenges, we propose a hybrid planning architecture that combines the advantages of machine learning with the verifiability and the determinism of classical approaches. Specifically, we developed a deep neural network to interpret complex traffic scenes and propose driving behavior, while an optimization-based supervision layer validates this proposal and enforces explicit drivability and safety constraints. We evaluate the learned planner’s driving behavior in open-loop studies on real-world urban data, discuss system integration aspects for stable closed-loop operation, and report results from real-world deployment on our research vehicle karl…
[AI-6] NetlistBench: Evaluating LLM Reliability in SPICE Netlist Recognition and Manipulation
链接: https://arxiv.org/abs/2608.12197
作者: Jiarui Ma,Jianghan Wang,Yuheng Ma,Ziyi Zhuang,Xiaoguang Liu
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注: accepted by MLCAD 2026
Abstract:Large Language Models (LLMs) are increasingly used in circuit design workflows, yet their reliability on simulator-facing SPICE netlist recognition and manipulation remains poorly understood and is rarely separated from high-level design reasoning. Although netlists are textual, they encode structured circuit objects through topology and parameters. We present \textbfNetlistBench, a structure-verified benchmark for SPICE netlist recognition and manipulation. NetlistBench contains 2,342 cases across 24 task families, covering parameter and connectivity recognition and edits, hierarchical operations, equivalence judgment, and long-horizon compound editing. Model outputs are evaluated by a deterministic structure-aware oracle. Across six non-thinking LLMs, performance varies substantially with operation-level structural complexity. Simple local edits reach 96% – 100% accuracy, while device addition drops to 41% – 83% and equivalence judgment to 49% – 90% . Enabling reasoning substantially improves weaker models but does not eliminate structure-preservation failures, with performance still degrading sharply as the edit horizon increases. NetlistBench identifies netlist reliability as a distinct bottleneck for trustworthy LLM-based circuit design automation.
[AI-7] HYDRA: Hyperbolic Dynamic Representation Architecture for Kolmogorov-Arnold Networks
链接: https://arxiv.org/abs/2608.12194
作者: Zhao Su,Yuxin Xia,Haoran Li,Jun Shen,Qi Zhu,Qingguo Zhou,Binbin Yong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Kolmogorov-Arnold Networks (KANs) enhance nonlinear function approximation by replacing scalar weights with learnable univariate functions. However, assigning an independent function to every connection results in substantial parameter redundancy, limiting their scalability and efficiency. To reduce this redundancy, we introduce \textbfHYperbolic \textbfDynamic \textbfRepresentation \textbfArchitecture (HYDRA), a parameter-efficient hyperbolic extension of KAN that combines spline-based functional learning with representations in the Poincaré ball. HYDRA maps vector-valued inputs into a bounded hyperbolic latent space, performs KAN-style updates in tangent space, and employs a low-rank prototype block to share functional transformations across hidden dimensions. The resulting hyperbolic representations provide a structured radial coordinate for interpretation, while radius control improves training stability by preventing boundary saturation. Extensive experiments across eight benchmark datasets demonstrate that HYDRA consistently achieves competitive or superior predictive performance while improving parameter efficiency and representation interpretability.
[AI-8] How to Spend Your Oracle Budget: Practical Guidance for Protein Structure Prediction Models ICML2026
链接: https://arxiv.org/abs/2608.12192
作者: Aleksandra Kalisz,Jack Simons,Krisztina Sinkovics,Noam Ghenassia,Shikha Surana,Henry Moss,Paul Duckworth
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Proceedings of the ICML 2026 Workshop on Structured Probabilistic Inference Generative Modeling (SPIGM)
Abstract:Foundation models for protein structure prediction remain unreliable on certain targets. External oracles can flag and correct these failures, but biological oracles are expensive, making oracle budget a critical constraint. Existing guidance methods, such as FK-steering, DPO, and Best K-of-N sampling, differ in how they spend this budget, yet no systematic comparison exists to guide method selection. To bridge this gap, we benchmark these methods alongside the recently proposed Optimisation Over Outputs (O3), which applies off-the-shelf optimisers within a generative model’s latent subspace. We extend the usage of O3 to protein structure prediction models. Overall, our work provides the first practical reference for oracle budget-aware guidance. Our evaluation on two protein targets, calmodulin (1CLL) and E. coli aspartate transcarbamoylase (9EEH), reveals that no single method consistently dominates across all budgets and oracles. Specifically, O3 proves most effective at low oracle budgets, while FK-steering and DPO demonstrate improved performance as the budget increases. We distil these findings into actionable recommendations for practitioners operating under real-world oracle-budget constraints.
[AI-9] Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation
链接: https://arxiv.org/abs/2608.12190
作者: Md Yassir Mottalib,Md Yousuf,Eklachur Rahman Bhuiyan,S M Ahsan Habib,Sonjoy Kumar Dey,Md. Salahuddin Gazi,Molay Kumar Roy,Asaduzzaman Anik
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: THis paper already peer reviewed
Abstract:With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response. In this paper, we propose a reinforcement learning-based dynamic cyber defense framework. We deploy a Deep Q-Network (DQN) to train effective defensive strategies to counteract the evolving cyberattacks. We leverage the CICIDS2017 dataset for model creation and the UNSW-NB15 dataset for external validation, involving preprocessing of data, feature engineering, and adaptive policy learning. We compare the proposed DQN with decision tree, support vector machine, random forest, XGBoost, and multilayer perceptron models. The proposed DQN achieves an accuracy of 99.72%, a precision of 99.68%, a recall of 99.65%, an F1-score of 99.66%, and an ROC-AUC of 0.999, while the false positive rate is 0.31%, the false negative rate is 0.35%, and the detection latency is 15 ms. The framework achieved 99.54% attack mitigation rate, demonstrating strong adaptive and real-time defensive capabilities. These results demonstrate the potential of reinforcement learning as a powerful and scalable approach for autonomous cybersecurity in modern cloud environments.
[AI-10] Co-constructing sociotechnical AI governance: participatory system mapping using algorithm registers
链接: https://arxiv.org/abs/2608.12166
作者: Íñigo de Troya,Maurus Enbergs,Neelke Doorn,Roel Dobbe
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:Algorithm registers have been championed as a means of providing transparency on the use of algorithms in public services. Yet potential publics differ in their expectations of what should be made transparent and how, as well as in their interest in and ability to parse the information currently published in the registers. Moreover, it remains unclear how these instruments can represent the sociotechnical systems in which these algorithms are embedded, and how system-level transparency can facilitate accountability. In this paper, we ask, what do algorithm registers reveal (and occlude) about the sociotechnical systems governing algorithmic systems, and how can diverse stakeholder perspectives inform a more pluralistic system-theoretic safety analysis? To do this, we probe the municipal algorithm register of a Dutch city through a case study of a decision-support tool for caseworkers’ assessment of citizens’ welfare benefits eligibility based on legal automation through a business rule engine. Through interviews, surveys, and participatory system mapping workshops (with municipal staff, civil society organisations, and ombudsmen, N=8), we seek to understand to what extent the register allows stakeholders to map the algorithmic system in question. These maps inform a System-Theoretic Process Analysis (STPA) that situates the register within a wider sociotechnical governance structure. Participants’ contributions allow us to identify potential safety hazards which would not have been possible to see using the algorithm register alone, including benefits eligibility denial, system performance deterioration, and inability to contest wrongful decisions. By engaging both direct and indirect stakeholders, we reflect on the normative dimensions of algorithm governance efforts and how politics shape the practice of system safety analysis.
[AI-11] Adversarial Resilience of Poisson-Process Submodular Maximization over Matroids: From Robust Offline Optimization to Full-Bandit Learning
链接: https://arxiv.org/abs/2608.12134
作者: Vaneet Aggarwal
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Optimization and Control (math.OC)
备注:
Abstract:We study nonnegative submodular maximization subject to a general matroid when the offline algorithm is given an arbitrary controlled value oracle. Our main result is an adversarial resilience theorem for the Spiteful Greedy Swap Poisson Process (SGS-Poisson): without modifying its Poisson intensity, single-element exchange rule, or spiteful drop step, the algorithm retains limiting approximation factors 1/e for non-monotone objectives and 1-1/e for monotone objectives. More precisely, under every controlled oracle \widehat f satisfying |\widehat f(S)-f(S)|\le \xi for every set S , our implementation returns a feasible set with expected value at least (1/e-\varepsilon)\OPT-O(k\xi) and (1-1/e-\varepsilon)\OPT-O(k\xi) , respectively, using \widetilde O(nk^2\varepsilon^-2) oracle calls. As a consequence, the offline-to-online reduction yields full-bandit CMAB algorithms for general matroid-constrained submodular rewards with exact limiting approximation-regret factors 1/e and 1-1/e and \widetilde O(n^1/5k^4/5T^4/5) regret.
[AI-12] GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings
链接: https://arxiv.org/abs/2608.12133
作者: Shivali Dalmia,Sumukha Thoppanahalli,Mohammadreza Sediqin,Abhishek Mukherji
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Enterprise guideline documents are heterogeneous and multimodal, combining narrative text, complex tables, and embedded images. Existing LLM and VLM systems face hallucinated content, table structure degradation, and lack governed workflows extending beyond extraction to validation and artifact generation. This leaves enterprises to perform this manually, consuming 2-3 days per document. To address this, we introduce GUIDE, a governed multi-agent framework built on a shared versioned rule store with schema-validated inter-agent contracts and end-to-end provenance tracking. Six specialized agents handle parsing, VLM-driven extraction, consistency checking, evaluation, human-in-the-loop (HITL) escalation, and persona-tailored artifact synthesis. Evaluated on 120 real-world enterprise guideline documents, GUIDE achieves 96% document success, extracts 3,896 rules with 71.4% auto-approved, produces 812 deployment-ready artifacts, and reduces turnaround to 40-125 minutes per document.
[AI-13] Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM -Agent Control
链接: https://arxiv.org/abs/2608.12123
作者: Josef Liyanjun Chen
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Operating Systems (cs.OS)
备注: 14 pages, 4 figures. Includes formal proofs, trace provenance, and a reproducibility appendix. Code and artifacts: this https URL ; processed evidence: this https URL
Abstract:LLM-agent services repeatedly execute small deterministic transitions between model and tool calls: route an outcome, update state, and emit the next effect. We ask when this control path exposes enough concurrent work for GPU execution, and what changes when a GPU-computed route decision remains on device. We formalize the ready-cohort boundary using fixed-partition share F, exact offline share P*, local upper bound U, and online achieved share A. Under zero service time, unlimited capacity, and equal relative launch deadlines, a specialized dynamic program computes P* exactly. In a stationary Poisson replay of one pinned 851-session public trace panel, the primary condition at 100,000 target active sessions, K=256, and a 50 ms launch deadline gives F=30.19%, P*=43.00%, and U=45.85%. Exact packing recovers 81.83% of the opportunity lost at fixed window boundaries. The outcome-derived route key is a conditioning proxy, not proof of executable identity. A separate mechanism study keeps a GPU-computed binary decision on device instead of returning four bytes to the host and redispatching. Across four named GPU placements, the device-resident path is faster in all 36 configurations; within-placement row-median ratios range from 1.19x to 2.39x. Across both admissible mechanisms, all 14,557,440 tested batched invocations match a separately implemented host oracle. A fixed nested device graph that removes no host decision is slower in all 60 configurations across five placements. Together, the studies establish two measurable gates for GPU agent control: deadline-feasible cohort supply and observation placement. A joined finite online runtime is required to measure A, CPU displacement, and service-level benefit.
[AI-14] No One to Blame: A Framework of Constitutive AI Unaccountability AAAI
链接: https://arxiv.org/abs/2608.12104
作者: Long Hoang Nguyen,Eva Späthe,Sebastian Lins,Ali Sunyaev
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Extended version with appendix; final version to appear in the Proceedings of AAAI/ACM AIES 2026
Abstract:The increasing deployment of autonomous, agentic AI systems challenges traditional accountability mechanisms. Existing research predominantly frames AI accountability gaps as barriers that can be overcome through better standards, transparency, and institutional reform. We argue that this framing is insufficient: certain configurations of actors, systems, and institutions render AI accountability conceptually unachievable regardless of effort. We introduce the concept of constitutive AI unaccountability to capture these configurations. Through a three-stage qualitative study comprising a concept-centric literature analysis, a secondary analysis of 27 expert interviews with AI professionals from technical, legal, and sociotechnical backgrounds, and an illustrative framework application to the open-source agentic AI system OpenClaw, we identify nine categories and 20 themes of constitutive AI unaccountability. These are organized across structural, technological, and normative clusters and reinforce one another through eight directed interdependencies. Our framework is operationalized as a diagnostic instrument of 20 questions, which detected 17 of 20 conditions when applied to OpenClaw, including an inverted anthropomorphism configuration in which the AI agent was the only identifiable actor. We contribute a reframing of AI unaccountability as a constitutive property of sociotechnical systems, an extension of the four barriers to accountability, and a practical instrument for identifying accountability voids in specific AI deployments.
[AI-15] Confidence Calibration of Deep Learning Systems
链接: https://arxiv.org/abs/2608.12100
作者: Coby Penso
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:In high-stakes applications, reliable confidence estimates are as important as the predictions themselves. Confidence calibration ensures that predicted probabilities reflect the likelihood of correctness, making it essential for safe deployment of deep learning models. However, existing methods typically assume access to clean validation data, which is often unrealistic due to label noise and domain shifts. This thesis develops methods for improving calibration under these conditions. First, we address calibration under label noise. Standard methods can produce misleading confidence estimates when labels are unreliable. We propose a framework that uses an estimated noise model to reconstruct noise-free confidence estimates by modeling the relationship between noisy and clean label distributions. We extend this approach to Conformal Prediction (CP), which provides set-valued predictions with guaranteed coverage. Our noise-aware CP method estimates clean conformity scores despite label noise, enabling reliable uncertainty quantification. Next, we study calibration in unsupervised domain adaptation, where a model trained on a labeled source domain is adapted to an unlabeled target domain. Since labeled target data are unavailable, we estimate target-domain accuracy from source performance and domain discrepancies, enabling calibration without target labels. We also consider privacy-preserving settings in which user labels and model outputs must remain protected. We propose a locally differentially private conformal prediction framework that provides valid uncertainty quantification while maintaining privacy guarantees and balancing privacy, computational feasibility, and prediction reliability. Our results bridge calibration theory and practical deployment in safety-critical applications, contributing to reliable, privacy-preserving, and noise-resilient neural network predictions. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) Cite as: arXiv:2608.12100 [cs.LG] (or arXiv:2608.12100v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.12100 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-16] Graph-Structured Rubrics: Compiling Rubrics into Typed Evaluation Graphs for LLM Judges
链接: https://arxiv.org/abs/2608.12097
作者: Xi Chen,Jie Mu,Mo Xuan,Qun Shao
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 4 figures, 4 tables
Abstract:Rubric-based evaluators commonly treat rubrics as prompt context or flat criteria: they specify what to judge but leave criterion composition implicit, even when natural-language rules state it. We introduce Graph-Structured Rubrics (GSR), which compiles a rubric into a response-independent typed evaluation graph before observing responses. Criterion nodes elicit judgments; transformation, reduction, and gating operators compose them through named ports; and a task-specific output mapping, termed Readout, converts the unique sink into a score or preference. Compilation rejects malformed or type-incompatible graphs. Pointwise evaluation judges rubric dimensions separately before graph aggregation; pairwise evaluation reuses the graph with one judgment for each candidate under every criterion. Under GPT-OSS-120B, GSR improves exact score agreement by 0.62–6.75 percentage points over Prometheus-style scoring on four pointwise datasets and achieves the numerically highest end-to-end pairwise accuracy on two preference benchmarks under native tie and abstention policies.
[AI-17] Faithful Sufficient and Understandable: Rethinking Graph Counterfactual Explanations via Discrete Diffusion Inversion
链接: https://arxiv.org/abs/2608.12083
作者: David Bechtoldt,Sidney Bender
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Graph Neural Networks (GNNs) achieve strong predictive performance on graph-structured data across domains such as chemistry, biology, and network analysis, yet they provide no intrinsic explanation of their predictions. This limits their adoption in high-stakes and safety-critical settings. Counterfactual explanations address this by revealing the minimal structural modifications that would change a model’s prediction. On graphs, however, such a modification is hard to produce. The search space is discrete and combinatorial, and a valid answer must respect categorical node and edge types together with domain rules such as chemical valency in the case of molecular graphs. Existing explainers give up one of two things. Either edits are not held on the data manifold, or the search does not span the full edit space. We propose Graph Diffusion Counterfactual Explanation via Inversion (GDCE-I), which gives up neither. A discrete denoising diffusion model with a novel discrete inversion scheme enables distribution-aware edits leveraging the whole domain edit space. We further address the incomplete and inconsistent evaluation of graph counterfactuals by deriving a framework of explanation desiderata and applying it to every method under one shared protocol. Across four benchmarks, GDCE-I outperforms related work by a large margin on the defined framework. For the molecular domain, we further qualitatively show that GDCE-I attains interpretable in-distribution solutions.
[AI-18] Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL
链接: https://arxiv.org/abs/2608.12063
作者: Martin Schuck,Maks Sorokin,Simone Manni,Duy Ta,Angela P. Schoellig,Marco Hutter,Simon Le Cleac’H,Jan Brüdigam
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely bottlenecked by the slow, manual process of dense reward shaping. To bypass this limitation, we leverage Sample-based Model Predictive Control (SMPC) entirely in simulation as an automated, rapidly tunable expert to generate massive offline datasets. Because this data solves the fundamental exploration problem, we can train an off-policy RL agent using purely sparse task rewards, drastically reducing the time required to learn new skills and eliminating the need for manual tuning. Integrating this high-level agent with a low-level dynamic stability controller yields more optimal behaviors that strictly align with true task objectives, ultimately allowing the learned policies to surpass the original optimal control teacher. We validate the robustness of this sim-to-real framework by successfully deploying complex loco-manipulation skills across different morphologies, including an arm-equipped Spot quadruped and a G1 humanoid.
[AI-19] Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision
链接: https://arxiv.org/abs/2608.12027
作者: Shaojie Zhang,Ke Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 40 pages, including appendices
Abstract:Pairwise constrained clustering typically relies on hard must-link/cannot-link labels, whereas realistic pairwise supervision may be real-valued and entangle intrinsic ambiguity, expert judgment, and stochastic corruption. Existing deep constrained clustering (DCC) methods mainly target hard, expert-agnostic constraints, treating soft labels mostly numerically rather than semantically. We formalize this setting as uncertainty-aware probabilistic constrained clustering (UPCC), defining a canonical aleatoric target through a heterogeneous observation process and analyzing its conditional identifiability. We introduce ProbPair, an angular pairwise objective for probabilistic relations, and build ECI-PP, an estimator–corrector–integrator framework that refines imperfect supervision via belief estimation, correction, and reliability-aware integration. Across challenging probabilistic supervision settings, experiments on diverse benchmarks show that ECI-PP outperforms state-of-the-art DCC methods and remains robust with a shared default configuration.
[AI-20] From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devices
链接: https://arxiv.org/abs/2608.12025
作者: Tuhinangshu Gangopadhyay,Rasmus Adler,Peter Liggesmeyer,Jan Reich
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: ISSRE 2026, AISQ, 8 pages
Abstract:Medical devices are becoming more software-intensive, connected, and AI-enabled. Their development requires risk-management evidence aligned with ISO 14971 and, for software, IEC 62304. This evidence must be kept consistent across requirements, design decisions, software changes, verification results, complaints, and post-market data. These tasks are costly and depend on scarce safety and domain experts. Large language models (LLMs) may reduce parts of this effort because medical-device safety work is highly document-based. However, current LLM-based safety-engineering studies often address isolated methods, rely on generic prompting or public examples, and provide limited support for source links, traceability, uncertainty handling, lifecycle updates, and recorded expert review. This limits their use in regulated medical-device development. This paper argues that the central research problem is not safety-text generation, but source-linked safety-knowledge support. We propose an evidence-grounded framework that connects device artifacts, controlled knowledge storage and retrieval, method-specific generation of candidate safety items, critique and uncertainty checks, and recorded expert review. The framework prepares, links, checks, and updates candidate safety artifacts for expert decision-making. It does not decide whether a device is safe and does not provide regulatory approval. We also outline an evaluation strategy using non-public or newly built medical-device case studies and expert reference analyses to assess coverage, correctness, relevance, traceability, duplicate rate, unsupported claims, and review effort. Comments: ISSRE 2026, AISQ, 8 pages Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12025 [cs.SE] (or arXiv:2608.12025v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.12025 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-21] RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks
链接: https://arxiv.org/abs/2608.12004
作者: Jinjun Huang,Zhongzhen Wen,Tongtong Xu,Meng Yan,Xin Xia,Zhongxin Liu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted by ASE 2026
Abstract:In modern AI frameworks, GPU kernels are key to overall system performance. Combining usability, portability, and near-handwritten CUDA performance, Triton is widely adopted for implementing GPU kernels. Recent advances show the potential of large language models (LLMs) to automatically generate Triton kernels, reducing the manual effort required from expert kernel developers. Several benchmarks evaluate LLM-generated Triton kernels. However, they suffer from three key limitations: (1) they restrict tasks to PyTorch-to-Triton translation, failing to reflect the diversity and complexity of real-world Triton tasks; (2) they evaluate only individual-kernel performance rather than end-to-end performance, the core criterion for real-world deployment in AI frameworks; and (3) they rely on manually written evaluation scripts for individual kernels, which may contain flaws that models can exploit to bypass correctness checks and obtain inflated scores. To address these limitations, we introduce RealisticTritonBench, the first benchmark to derive Triton kernel generation tasks from real-world pull requests in popular AI frameworks, enabling realistic, production-like evaluation. RealisticTritonBench systematically extracts PRs that modify Triton kernels from popular open-source AI frameworks and transforms them into generation tasks with concrete engineering contexts. Each task takes a natural language requirement as input and requires a corresponding Triton kernel implementation, with a complete and reproducible evaluation environment. Unlike prior benchmarks focused on isolated kernel performance, RealisticTritonBench integrates generated kernels into their original frameworks and evaluates them using end-to-end tests, enabling a more faithful assessment. We evaluate leading LLMs on RealisticTritonBench and find that they still struggle with real-world Triton kernel generation tasks.
[AI-22] CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations
链接: https://arxiv.org/abs/2608.12002
作者: Xingyu Yan,Tingting Dai,Antonio De Domenico,Mohamed Sana,Nicola Piovesan,Changchang Li,Bowen Liu,Kun Jiang,Mengjie Zhang,Dingcheng Shan,Jing-Cheng Pang,Chenwei Wu,Sijie Wu,Lianying Chao,Haoran Cai,Jiantao Ye,Xubin Li,Simon Mark Lucas,Xin Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agents are increasingly considered for automating network operations and maintenance, where engineers must diagnose network faults, optimize configurations to enhance services, and reduce operational costs while acting under strict constraints. However, existing evaluations fail to accurately model real network characteristics or assess agents under partially observable telecom environments with diverse vendors, devices, protocols, and interfaces. In this paper, we introduce CTBench, a public benchmark for assessing whether an agent behaves like a competent telecom troubleshooting engineer. CTBench focuses on root cause analysis and path restoration. Each task is constructed by experts and annotated with rich task metadata, including golden evidence steps. CTBench uses expert-grounded metrics that evaluate both final answers and the diagnostic evidence. Experiments with representative harness-model combinations show that state-of-the-art agents perform very well at identifying endpoints in path-restoration tasks but, more generally, underperform in root cause analysis. In particular, agents struggle with interface state, link-layer, service-management, and other operational faults. Most importantly, even when agents produce plausible or correct final answers, they often fail to provide the evidence-grounded diagnoses required in operational practice. Our results further show that path restoration is generally more resource expensive, yet larger resource usage does not necessarily translate into better diagnosis.
[AI-23] Retry Switch or Abstain? Learning Strategy-Aware Tool-Use Policies via Controlled Error Injection
链接: https://arxiv.org/abs/2608.11977
作者: Chaoran Chen,Vy Nguyen,Ziji Zhang,Abhinav Gullapalli,Ziyi Wang,Yuxuan Lu,Dakuo Wang,Jing Huang,Zhou Yu,Jin Lai
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Tool-using LLM agents are commonly trained and evaluated in environments where tool calls succeed reliably, yet deployed tools can fail transiently, persistently, or silently. Robust recovery therefore requires more than repeated retries: an agent may need to retry the same path, switch to an alternative, or recognize that no viable path remains. We present BENCH2ROBUST, a framework that converts failure-free tool-use benchmarks into controlled stochastic environments with scenario-controlled solvability, where episodes explicitly require retrying, switching, or stopping after available paths are exhausted. We use BENCH2ROBUST to study two complementary interventions: structured runtime recovery context through Bayesian Tool Memory (BTM), and curriculum-controlled reinforcement learning. Across 7 models from 4 families and two multi-turn benchmark families, tool failures produce a near-universal robustness gap. On held-out Retail tasks, BTM improves robustness by up to 16.8 percentage points without retraining, while RL learns complementary recovery behavior that remains beneficial without inference-time BTM. Combining the two reaches 40.8-45.5% under injection while preserving failure-free performance. These results suggest that robust tool use benefits from combining environment-specific recovery knowledge with learned recovery behavior.
[AI-24] LoongReflect: Boosting Long-Horizon Reflection in Search Agents via Global Perspective Distillation
链接: https://arxiv.org/abs/2608.11967
作者: Zhixin Zhang,Xinke Jiang,Zhibang Yang,Weixuan Xu,Guohong Qiu,Xu Chu,Junfeng Zhao,Yasha Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 8 figures
Abstract:Large language model agents increasingly rely on long-horizon reasoning to solve complex tasks involving planning, tool use, and memory. A critical capability in such settings is reflection: assessing trajectory progress, identifying missing evidence and unreliable intermediate states, and deciding whether to continue, revise, or abandon the current branch. Learning effective reflection, however, is challenging because reflection is performed locally within the current branch, whereas its utility can only be determined by its contribution to the final trajectory outcome. This local-global mismatch makes outcome-based reinforcement learning provide only local, sparse and delayed supervision for reflective decisions. To solve these, we propose LoongReflect, a training framework that formulates reflection as a memory-control policy. The agent operates over a reversible trajectory tree using explicit reflect and backtrack actions. Reflection consolidates verified facts, missing evidence, and branch-specific risks into working memory, while backtracking removes an unreliable branch from the active context and preserves a concise corrective lesson. To learn this policy, LoongReflect combines two complementary signals through a look-ahead, extragradient-style coordination mechanism. A fast channel distills globally informed reflective behavior from a privileged teacher, with supervision restricted to reflection and backtracking tokens. A slow channel optimizes complete trajectories using outcome-based GRPO, aligning local control decisions with final task success. Experiments on multi-hop retrieval-augmented generation and mathematical reasoning benchmarks demonstrate consistent improvements over outcome-only reinforcement learning and self-distillation baselines.
[AI-25] ailBooster: A Dual-Layer Generative Framework for Extreme Value Augmentation with Operational Validity Enforcement
链接: https://arxiv.org/abs/2608.11951
作者: Karim Aly,Alexei Sharpanskykh,Jacco Hoekstra
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Preprint submitted to journal
Abstract:Extreme events in air transport, such as severe arrival delays and abnormal air times, cause cascading network disruptions with substantial operational, economic, and safety costs. Such events are rare in historical records, leaving insufficient training signal for machine learning models. Synthetic data augmentation offers a principled solution, but conventional generative models under-represent distributional tails and give no guarantee against operationally infeasible instances, such as a short air time paired with a long flight distance. No existing approach addresses both limitations for mixed-type tabular records. We propose TailBooster, a dual-layer generative framework combining generative modelling with two anomaly detection layers. A statistical layer extracts extremes via the interquartile range, supplying tail-concentrated training signal to dedicated generative models, here a Tabular Variational Autoencoder. A deep learning layer then applies autoencoder-based cleaning, discarding synthetic records that violate the operational envelope learned from historical data. The framework was evaluated on US flight records across five dimensions: diversity, statistical similarity, fidelity, operational validity, and utility, the latter two being the primary improvement targets. Data-driven cleaning markedly improved operational validity, while targeted augmentation enhanced utility for extreme-event prediction. Across six regression algorithms, training on the framework’s records reduced Mean Absolute Error by 47-49% on extreme air time and 29-57% on extreme arrival delay prediction relative to conventional synthetic data, with comparable gains when real records were enriched with synthetic extremes. Being fully data-driven and model-agnostic, TailBooster extends to domains where extreme-event prediction is critical and domain-specific rules are unavailable.
[AI-26] ExRole: From Team Trajectories to Executable Roles in Multi-Agent Language Models
链接: https://arxiv.org/abs/2608.11949
作者: Zhou Liu,Chaoyang Han,Zewei Pan,Zeli Su,Wentao Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Roles provide an interpretable interface for organizing language-model agents, yet most multi-agent systems treat them as hand-written prompt labels disconnected from learned behavior and parameter updates. We argue that a useful role should instead be an executable control variable: it should summarize behavior predictive of future utility, guide subsequent interaction, and identify the trainable capacity responsible for that behavior. We introduce ExRole, a trajectory-to-role framework that learns future-aware role prototypes from prefix-local team traces, resolves them into readable instructions and token-aligned role markers, and optionally routes shared LoRA rank slots with turn-aligned credit. Across MuSiQue and 2WikiMultiHopQA, ExRole improves over single-agent search by 15.0/14.4 and 13.5/16.1 EM/F1 points, respectively. Against the strongest non-ExRole controls, the corresponding gains remain 11.5/11.6 and 7.7/9.7 points. Across both benchmarks, the controlled results consistently favor trajectory-induced role conditioning over role-free, manual, random, and shuffled alternatives. Role-Agent-Turn interventions further show that the induced roles capture transferable behavioral specialization beyond fixed agent identities or turn positions.
[AI-27] OEIS Open: How many conjectures can language models turn into theorems?
链接: https://arxiv.org/abs/2608.11941
作者: Tom Adamczewski
类目: Artificial Intelligence (cs.AI)
备注: 26 pages, 6 figures. Code: this https URL , results: this https URL
Abstract:We construct OEIS Open, a benchmark based on 492 open mathematical conjectures from the OEIS, formalized in Lean by Tsoukalas et al. Whereas these conjectures had previously been attempted only with a bespoke agent, our open-source evaluation code runs any generic language model (LM) against them, and is secure against LM cheating attempts. We find that LMs equipped with a minimal set of tools resolve 147 of these conjectures with a budget of \ 50 per attempt, scoring 30% on OEIS Open. OEIS Open Lite is a random subset of 100 conjectures for cheaper evaluation. When evaluated with a budget of \ 200 per attempt, the best current LM scores 44% on OEIS Open Lite. Giving LMs access to the mathematics literature via 476,000 papers from arXiv did not increase performance on OEIS Open Lite, and nor did using more sophisticated agent loops. The conjectures covered in this work are of uncertain mathematical significance, and most have likely received little previous attention. Nevertheless, our results show that LMs can resolve open research conjectures autonomously and at modest cost.
[AI-28] Policy-as-logic for robust reasoning over rules ECAI’26 IJCAI
链接: https://arxiv.org/abs/2608.11905
作者: Rahul Nair,Bastian Lipka,Elizabeth Daly
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Symbolic Computation (cs.SC)
备注: RobustifAI Workshop at IJCAI-ECAI '26
Abstract:In many practical applications of generative AI systems, from tax rules to airline baggage allowance, responses to natural language queries must respect written policies or rules. We present a hybrid symbolic approach that expresses policies in formal logic and at inference time exploits the representation power of language models for fact extraction to ground predicates, and an answer set solver for reasoning such that responses are interpretable, auditable, and as we show, accurate and robust under input perturbations. Specifically, we show this separation of extraction and reasoning steps outperforms policy-as-prompt and policy-as-code methods in most cases with ~10x reduction in token usage. The results point to the value of structured reasoning and symbolic solvers in conjunction with generative models to make robust decisions involving objective criteria.
[AI-29] Agent Skills Can Be Harmful: An Empirical Study of Skill-Induced Failures in LLM Agents
链接: https://arxiv.org/abs/2608.11888
作者: Gen Dong,Yanjie Gao,Liqun Li,Tianyin Xu,Yu Hua,Fan Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agent skills are the de facto mechanism for extending LLM agents with reusable guidance. A skill can shape the agent’s task execution, including planning, tool use, problem-solving, and validation. Prior work reported mixed results of agent skills: some skills improve task success rates, while others have no effect, increase token use and execution time, and even reduce success rates. This paper presents a comprehensive analysis of skill-induced agent failures by attributing task failures and cost regressions to specific loaded skills. We introduce a differential analysis framework that attributes a failure or regression to a skill by comparing a target skill-guided run against a no-skill or semantically matched skill reference run that solves the same task, or solves it more cheaply. We instantiate this framework on SkillsBench and SWE-Skills-Bench, yielding 307 skill-induced failures, including 125 functional failures and 182 efficiency regressions. We also build SkillTriage, a taxonomy-guided attribution tool that normalizes paired cases, extracts differential evidence, and produces triage reports. Our major findings include: (1) Skill induced functional failures are rarely caused by obviously irrelevant skills; instead, seemingly relevant skills often make the agent incorrectly implement or omit task-required implementation elements. (2) Skill-induced efficiency regressions are not explained by prompt length alone. (3) The largest sources within Excessive Procedure are excessive verification and heavy implementation pipelines, contributing 67 and 30 cases, respectively. This shows that skills often turn validation checklists and construction recipes into mandatory work. Based on our findings, we propose research topics and tooling improvements for safer and more cost-aware skill reuse.
[AI-30] User-Assisted Collaborative Distributed Inference for Efficient QoS-Aware Autoscaling
链接: https://arxiv.org/abs/2608.11840
作者: Alfreds Lapkovskis,Ali Beikmohammadi,Sindri Magnússon,Praveen Kumar Donta
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI); Performance (cs.PF)
备注:
Abstract:Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand. We propose a collaborative distributed inference system combining dedicated infrastructure with resources contributed by service users. Dedicated resources provide baseline capacity for maintaining quality of service (QoS), while volunteered resources absorb increasing demand without proportional growth in centralized infrastructure. To capture stochastic and dynamic interactions among users, resources, tasks, and policies, we develop a high-dimensional generative Markov model with structured temporal factorization. The model supports simulation and provides a foundation for task scheduling and QoS-aware resource allocation optimization. We evaluate the system across user populations, resource capacities, and centralized and distributed scheduling policies. Simulations show that distributed scheduling becomes increasingly advantageous as the user population grows, improving request completion and P99 latency while substantially reducing dedicated resource consumption. These results demonstrate the feasibility of user-assisted collaborative inference for infrastructure-efficient autoscaling.
[AI-31] owards Model-based Run-time Cybersecurity: On Control-Flow Anomaly Detection Attack Identification and Hardware Monitoring
链接: https://arxiv.org/abs/2608.11802
作者: Martin Sachenbacher,Martin Leucker,Alexander Weiss,Aliyu Tanko Ali
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 14 pages
Abstract:Methods to increase the resilience of systems to cyber-attacks become increasingly important. Control-flow monitoring provides a principled basis to ensure integrity and detect possible anomalies at run-time. Once anomalies have been detected, so-called attack trees can be used to identify possible types of attacks. However, this approach is vulnerable to camouflage, by which attackers try to evade detection (and correct identification) by deliberately manipulating also the system’s observed control flow. In this paper, we outline a model-based approach that provides more robust intrusion detection and attack identification through an architecture that combines software- with hardware-based monitoring. In this approach, software-level observation indicates suspicious activities, while hardware-level monitoring checks them separately in more detail, making it much harder for attacks to camouflage themselves and go undetected. We illustrate the approach with an authentication-service example that captures a realistic failure mode: a software-level observer sees an anomalous but apparently harmless control-flow deviation, maps it to a benign root cause in an attack tree, but misses the true intrusion. A second, independent hardware control-flow monitor observes the actual transition sequence and thereby changes the attack-tree diagnosis from a low-severity configuration or maintenance issue to a high-confidence code-injection or control-flow hijack. In this scenario, the proposed combination of control-flow anomaly detection, attack-tree based intrusion identification, and hardware-based monitoring can improve not only anomaly detection, but also the diagnostic precision of attack-tree-based cyber-attack identification.
[AI-32] HyperANFIS: Enhancing Rule Representation and Interpretability in Adaptive Neuro-Fuzzy Systems via Hyperbolic Geometry
链接: https://arxiv.org/abs/2608.11768
作者: Haoran Pei,Zhao Su,Zetao Lin,Haoran Li,Jun Shen,Qi Zhu,Lan Guo,Qingguo Zhou,Binbin Yong
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The adaptive neuro-fuzzy inference system (ANFIS) is an interpretable reasoning framework capable of generating explicit IF-THEN fuzzy rules, making it suitable for tasks requiring transparent reasoning. However, existing ANFIS models generally construct rule antecedents and perform inference in Euclidean space, limiting their representational capacity and predictive performance. To address this issue, we propose Hyperbolic ANFIS (HyperANFIS), a hyperbolic extension of ANFIS. HyperANFIS preserves the fuzzy semantics and core architecture of conventional ANFIS while performing rule-prototype learning, rule activation, and consequent aggregation in hyperbolic space. It also retains the ability to generate interpretable IF-THEN rules. By exploiting the representational properties of hyperbolic geometry, HyperANFIS strengthens the fuzzy inference process, thereby improving predictive accuracy, inter-rule collaboration, and the credibility of its interpretable rules. Experimental results show that HyperANFIS consistently outperforms the standard ANFIS baseline and various ANFIS variants across all datasets, while also generating higher-quality fuzzy rules.
[AI-33] Instruction Alignment for Binary Code Representation Learning
链接: https://arxiv.org/abs/2608.11766
作者: Huaijin Wang,Shuai Wang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: In proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026)
Abstract:Binary code representation learning is a fundamental problem in software security and reverse engineering. Existing methods mainly learn function-level embeddings that capture coarse-grained semantic relationships between binary functions, but they largely ignore fine-grained instruction-level correspondences. This limitation misses valuable supervision signals available from compiler debug information, which can support the learning of more accurate and interpretable binary code representations. We propose to leverage instruction alignment knowledge to further improve binary code representation learning. Our preliminary study reveals that models finetuned for function-level binary code similarity exhibit substantially better instruction alignment than their pre-trained model, suggesting a strong correlation between instruction alignment and function-level embedding quality. Motivated by this observation, we design a training approach that explicitly incorporates instruction alignment as an auxiliary training objective. Our experiments show that instruction alignment training improves retrieval accuracy and provides more discriminative signal for the model’s similarity judgments. Comments: In proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026) Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) Cite as: arXiv:2608.11766 [cs.SE] (or arXiv:2608.11766v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.11766 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-34] MOON: Multi-Objective OrthoNormalized Updates for Multitask Learning
链接: https://arxiv.org/abs/2608.11749
作者: Shiji Zhou,Kunlin Lyu,Lei Zhang,Ruodong Wang,Yifan Sun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Multi-objective optimization (MOO) has demonstrated significant success in multi-task learning by mitigating task conflicts through gradient manipulation. However, most existing methods flatten model parameters into vectors and perform gradient manipulation under Euclidean geometry, thereby overlooking the matrix structure prevalent in modern architectures such as Transformers. In this paper, we show that gradient manipulation in Euclidean space does not generally yield the steepest descent direction under matrix geometry, potentially limiting optimization efficiency. Drawing from the theory of steepest descent for matrix-valued parameters, we propose MOON (Multi-Objective OrthoNormalized Updates), which performs gradient manipulation under spectral–nuclear norm geometry and uses the orthonormalized manipulated gradient for parameter updates. Theoretically, for smooth non-convex objectives, we establish convergence of the averaged Pareto-stationarity measure at rates of \mathcalO(T^-1/2) in the deterministic setting and \mathcalO(T^-1/4) under stochastic gradients. Empirical results across various benchmarks show that MOON consistently improves both optimization efficiency and final multi-task performance. Our code is available at this https URL.
[AI-35] G0.5: One Autoregressive Stream for Robot Reasoning and Action
链接: https://arxiv.org/abs/2608.11739
作者: Yicheng Liu,Zibin Dong,Baijun Ye,Tianyuan Yuan,Tao Jiang,Anqi Yang,Shicheng Cao,Haonan Liu,Yue Sun,Zihan Guo,Xiao Liu,Dong Ke,Changxun Pan,Chenru Wu,Tailai Cheng,Xiaoshu Ren,Xinlei Zhang,Jianning Cui,Zijie Zhao,Haoyu Zhang,Kaiming Xu,Haodong Yang,Bowen Zhang,Jiahui Niu,Shaoting Zhu,Shiduo Zhang,Hang Zhao
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert. This makes the VLM a context encoder rather than a decision-maker. We introduce G0.5, a pretrained autoregressive VLA in which a single transformer decoder emits reasoning and action tokens under a single objective. Three components make this tractable at foundation-model scale: a learnable cross-embodiment action tokenizer that maps heterogeneous robot actions into a shared vocabulary; a native chain-of-thought stream interleaving task decomposition, object grounding, and action hints with action tokens; and a visual memory module that injects multi-second history through the vision encoder. Because reasoning and action share a single set of weights, the pretrained VLM’s capabilities carry over to physical behavior: the model follows instructions closely, and prompts directly steer action granularity, task horizon, and out-of-distribution scene handling without further training. Pretrained on a large collection of robot datasets together with VQA samples, G0.5 surpasses state-of-the-art models across 7 independent regimes: real-world fine-tuning on R1lite and R1pro robots (76.7% vs.\ 53.3% for \pi_0.5 and 24.4% for GR00T-N1.7), the 2025 BEHAVIOR Challenge on 50 long-horizon household mobile manipulation tasks using a generalist policy (31.4% vs.\ 26.3% for \pi_0.5 and 26.1% for the challenge winner), DROID post-training followed by zero-shot transfer to an unseen environment and objects (82.5%), a language-following Pick-and-Place benchmark, LIBERO (98.9%), RoboTwin 2.0 (93.3%), and SimplerEnv-Bridge (87.3%).
[AI-36] Fingerprinting Text-to-Image Diffusion Models via Collapsed Generation
链接: https://arxiv.org/abs/2608.11732
作者: Yuanmin Huang,Chen Chen,Geng Hong,Xiaoyu You,Hui Xue,Zhenxing Qian,Mi Zhang,Min Yang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Proprietary text-to-image diffusion models are increasingly distributed as hosted services and downloadable checkpoints, making their intellectual property (IP) protection an increasingly critical concern when model leakage, copying, or unauthorized fine-tuning is disputed. In this work, we present a non-invasive model fingerprinting framework based on \emphcollapsed generation, a phenomenon where certain input conditions produce highly consistent images across multiple stochastic seeds. We show that collapsed generation is an intrinsic, model-dependent property of the learned generation process. These collapse-prone conditions therefore expose model-specific behavioral signatures, enabling reliable ownership verification without embedding invasive watermarks. After preparing conditions on the source model, the framework verifies a suspect model under two access settings: (1) white-box pipeline access, where optimized continuous embeddings can be injected into the generation process, and (2) black-box API-only access, where natural language prompts are queried through the service interface. In both cases, ownership evidence is measured by whether the suspect model reproduces the source model’s collapse behavior across stochastic samplings. Extensive experiments across UNet- and transformer-based diffusion models show that collapsed generation fingerprints can distinguish different source models with low confusion. These fingerprints remain verifiable in fine-tuned derivatives and under common and adaptive model- or query-level obfuscations, while requiring only a modest verification query budget. Together, these results establish collapsed generation as a reliable intrinsic evidence source for non-invasive diffusion model ownership verification.
[AI-37] Harness-IF: Evaluating Instruction Following Across Instruction Surfaces in Coding Agents
链接: https://arxiv.org/abs/2608.11727
作者: Zining Huang,Haoran Que,Hong Zeng,Ge Zhang,Zuo Wang,Jin Chen,Haodong Wang,Zhongfei Hou,Changxin Pu,Shen Yan,Wenhao Huang
类目: Artificial Intelligence (cs.AI)
备注: 26 pages, 7 figures, 8 tables
Abstract:When a coding agent obeys a rule, it may simply have been going to do that anyway. Existing instruction-following benchmarks cannot tell the difference: they concentrate rules in the user turn, while coding-agent benchmarks emphasize final task success. We introduce Harness-IF, which scores operational rules one at a time from execution evidence: 60 realistic multi-turn coding items drawn from a 642-rule library, 256 rules receiving verdicts, placed on the five configurable surfaces a deployed agent reads. To separate compliance from coincidence we introduce Against-Prior Accuracy (AP-Acc), which scores only rules labeled as opposing unprompted defaults, observed by re-running tasks with the rule withheld across nine probe builds and curated otherwise. Across 12 frontier models, accuracy spans 72.1-85.9% and AP-Acc 66.1-78.6%; every model is worse on against-prior rules, by 3.6 to 7.4 points (mean 5.81), and the direction survives a common-support analysis with item-clustered intervals. Aggregate scores therefore overstate compliance by a model-specific margin: prior control leaves the top build unchanged and exchanges three adjacent rank pairs. A counterbalanced conflict pilot on nine separate builds adds a second result: pooled precedence does not follow prompt depth, with system prompts, project files, and user instructions ahead of tool and skill descriptions.
[AI-38] Proportional Analogies on Probability Distributions via Bayesian Updating
链接: https://arxiv.org/abs/2608.11724
作者: Pierre-Alexandre Murena
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Analogies are quaternary relations of the form “A is to B as C is to D”. Among the various formalizations of analogical reasoning, proportional analogies provide an important axiomatic framework by characterizing valid analogies through a set of postulates. While proportional analogies have been extensively studied over Boolean, symbolic, and real-valued domains, their extension to probability distributions remains largely unexplored. In this paper, we introduce a notion of proportional analogy for probability distributions based on Bayesian updating. Our approach builds upon the idea that two distributions are related whenever one can be transformed into the other through Bayesian updating induced by a suitable set of observations. We investigate this framework for several standard members of the exponential family and discuss how it naturally extends to arbitrary probability distributions through Gaussian mixture approximations.
[AI-39] High-dimensional Multi-objective Bayesian Optimization with Learned Variable Interactions
链接: https://arxiv.org/abs/2608.11713
作者: Hongyan Wang,Jiayu Huang,Haotian Zheng,Xin Gao,Chi Ding,Ying Liu,Xia Wang,Qing Xu,Keqiang Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages, 15 figures, 3 tables
Abstract:Multi-objective Bayesian optimization (MOBO) is effective in identifying the Pareto fronts for expensive black-box problems. However, most current MOBO approaches are limited to low-dimensional decision space due to its exponential sampling complexity. This paper presents decision variable interaction analysis-based MOBO, ViaMOBO, a generic framework for expensive multi-objective problems with high-dimensional decision space. The key idea of ViaMOBO is that it utilizes a variable interaction analysis model to determine whether the decision space can be completely or partially divided, and then performs local Bayesian optimization in the divided decision subspaces. Through the variable analysis model, it can be derived whether the objectives in black-box problems are separable, partially separable, or non-separable based on the potential independent or interdependent relationships among decision variables without any strong assumptions. We compare ViaMOBO with the state-of-the-art MOBO methods on both synthetic and real-world benchmarks. The experimental results demonstrate that ViaMOBO outperforms other related MOBO baselines in approximating the Pareto front of high-dimensional expensive multi-objective problems.
[AI-40] Making Your LLM s More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning
链接: https://arxiv.org/abs/2608.11705
作者: Lang Cao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Aligned large language models (LLMs) are expected to exhibit safety behavior based on the content of the user request: they should refuse unsafe requests and comply with safe ones. However, we show that the same request can elicit substantially different safety decisions under different traits assigned in the system prompt, a failure mode we call trait-induced safety variation. To measure this failure, we introduce refusal-based metrics: Trait-Induced Deviation measures dataset-level deviation from the no-trait baseline, while Trait-Induced Flip Rate measures whether the same request receives different safety decisions across traits. We then provide a representation-level analysis of the mechanism behind trait-induced safety shifts and find that traits perturb the model’s safety representations within a low-dimensional subspace. To achieve trait-invariant safety, where safety behavior remains stable across traits, we introduce Trait-Invariant Safety Tuning (TIST), a simple yet effective self-distillation framework that aligns an LLM’s trait-conditioned behavior with its no-trait behavior. Guided by our analysis, we further propose Trait-Subspace Neutralization (TraSN), an instantiation of TIST, which enforces invariance only within the identified trait subspace. Experiments show that TraSN improves trait-invariant safety and strengthens harmful-request safety while preserving general capability. Our results highlight traits as an important factor in LLM safety and robust model behavior.
[AI-41] Robust and Efficient Noisy-Label Time-Series Classification via Dynamic Time Warping Based Granular Ball Computing
链接: https://arxiv.org/abs/2608.11704
作者: Ziqiang Li,Yun Liu,Gouhei Tanaka
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Dynamic Time Warping (DTW)-based Nearest-Neighbor (NN) classifiers are effective for time-series classification but are vulnerable to mislabeled training samples and require numerous DTW computations during inference. We propose DTW-based Granular Ball Computing (DTW-GBC), which organizes temporally similar training samples into granular balls and performs classification at the granule level. We further develop two granular-ball construction strategies for DTW-GBC. Experiments on four benchmark datasets with symmetric label noise show that the two DTW-GBC variants generally mitigate the performance degradation caused by label noise while requiring substantially fewer comparisons than DTW-based 1-NN during inference. These findings suggest that DTW-GBC provides a favorable balance between classification robustness and inference efficiency.
[AI-42] Consolidator: Learning Persistent Routed Memory Across Context Boundaries
链接: https://arxiv.org/abs/2608.11701
作者: Sungwoo Goo,Hwi-yeol Yun,Sangkeun Jung
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Copying short-term memory (STM) into a slower store can preserve state across a context boundary, but persistence alone does not ensure that the retained state influences subsequent memory access. We test this distinction in a Phasor Memory Network (PMNet) using Consolidator, a shared slot-local operator that transforms routed STM before accumulating it into long-term memory (LTM), without replaying the source tokens. After each consolidation, the KV cache and STM are cleared. The retained LTM can still be read and is also fed into the hierarchical router, thereby conditioning which explicit-memory slots subsequent inputs access. We evaluate this mechanism on a two-segment modulo-10 mapping task in which the second segment updates the mapping at the same memory address. Following a second consolidation and reset, a held-out query must recover the updated mapping from LTM. The backbone and memory interface are frozen, leaving only 12.35K Consolidator parameters trainable (0.041% of a 29.95M model). Across five paired runs from the same STM-pretraining checkpoint, direct LTM routing raises updated-mapping recall from 44.38\pm1.94% to 87.02\pm1.76% ( +42.64\pm1.10 percentage points), while immediate STM recall remains 89.90% in both conditions; both train separate Consolidators and retain the same LTM read paths. Learned consolidation outperforms forced identity accumulation by 21.40\pm1.91 percentage points without routing and 68.70\pm1.76 with routing. Thus, on this task, consolidated LTM serves as both retrievable content and an access state that shapes subsequent slot selection.
[AI-43] REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation
链接: https://arxiv.org/abs/2608.11698
作者: Yang Sun,Lichao Ma,Houyuan Qin,Yuxin Liu,Hanyang Lu,Yao Zhu,Pinlong Cai,Guohang Yan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:On-policy distillation (OPD) trains a student on its own trajectories under dense token-level supervision from a teacher. Reward-extrapolation methods such as ExOPD amplify the teacher-reference log-likelihood ratio to move beyond direct imitation, but apply a single global coefficient \lambda to every token. This can drive the student to fit extreme peaks in the implicit reward, causing reward hacking and unstable training, and the optimal \lambda varies across domains, requiring costly sweeps. We propose REOPD, a reliability-adaptive reward extrapolation framework for OPD. REOPD combines a token-level compatibility weight with a batch-level adaptive budget, yielding a token-wise coefficient \lambda_b,t=1+\gamma_b q_t that preserves teacher alignment while selectively extrapolating along reliable teacher-reference directions. It requires no verifier, reward model, value model, or extra rollout beyond standard OPD. REOPD outperforms G-OPD on single-teacher mathematics and on both domains in the multi-teacher setting, while matching G-OPD on single-teacher code, demonstrating effective fine-grained reliability adaptation across domains and teacher configurations.
[AI-44] HUGIN: Enhancing Vision-Language Planning for Autonomous Logistics Sorting
链接: https://arxiv.org/abs/2608.11692
作者: Xikai Sun,Cangtian Zhou,Kebin Liu,Ke Ma,Xu Wang,Zaishu Chen,Haotian Wang,Li Liu,Yunhao Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Autonomous logistics sorting systems (ALSS) are an important industrial application of embodied AI, which requires joint planning over spatially disjoint camera views. We formulate this setting as Joint Multi-Scene Understanding (JMSU). With open-world visual understanding and task-planning capabilities, vision-language models (VLMs) are promising candidates for JMSU. However, directly applying existing VLMs to JMSU is non-trivial due to scarce cross-scene supervision and attention dispersion caused by long visual context in JMSU. To address these challenges, we propose HUGIN, a training framework with two complementary components. Endogenous Data Augmentation recombines verified atomic facts under operating constraints, while Global Context Ranking aligns the instruction representation more strongly with the complete visual context than with a partial visual context. To support ongoing research, we construct a high-quality industrial sorting dataset and benchmark named SortingBench from four layouts of autonomous logistics sorting systems. Across five open VLMs, HUGIN consistently outperforms matched baselines; for example, the accuracy on SortingBench of Qwen3-VL-8B increases from 63.6% to 78.8%. Additional experiments verify the effectiveness of each component and JMSU’s spillover benefits in embodied tasks. Deployment tests involving more than 15,000 packages support the practical viability of VLM-based planning for autonomous logistics sorting.
[AI-45] APEX: Adaptive Expert Prefetching for Memory-Efficient Edge MoE Inference
链接: https://arxiv.org/abs/2608.11688
作者: Alish Kanani,Layan Badawi,Umit Y. Ogras
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at IEEE/ACM ESWEEK (CODES) 2026; the official version will appear in IEEE TCAD
Abstract:Mixture-of-Experts (MoE) models are attractive for edge deployment because they provide high model capacity while activating only a small subset of parameters per token, improving compute efficiency. However, MoE inference at the edge is fundamentally limited by memory. Expert parameters are large and often reside in off-chip memory due to capacity, cost, and power constraints, putting expert loading to the critical path. We present APEX: Adaptive Expert Prefetching, a predictive resource management framework that overlaps expert loading with useful computation. APEX introduces a lightweight prefetch router that predicts candidate experts before the attention block to dynamically fetch additional experts using a learned confidence model. This adaptive strategy achieves over 99% overlap accuracy, significantly outperforming fixed top-k prefetching techniques. APEX supports two execution modes: a correctness-preserving mode that guarantees exact routing semantics, and a stall-free mode that eliminates residual stalls by operating on available experts with negligible impact on application accuracy. Across multiple MoE models, the correctness-preserving mode reduces per-token latency by up to 26% and improves energy-delay product (EDP) by up to 41% over state-of-the-art baselines, while the stall-free mode provides additional efficiency gains with negligible impact on application accuracy. These results establish adaptive, confidence-driven expert prefetching as an effective approach for efficient MoE inference on edge systems.
[AI-46] XBridge: Entity-Grounded Latent Bridge for Heterogeneous LLM Communication
链接: https://arxiv.org/abs/2608.11676
作者: Wooseong Yang,Wei-Chieh Huang,Weizhi Zhang,Yu Wang,Philip S. Yu,Junhyun Lee
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 4 figures
Abstract:Heterogeneous multi-agent LLM systems, where agents are powered by different model families, can outperform homogeneous configurations by reducing redundant reasoning patterns. Yet existing communication protocols either operate through text, discarding the sender’s internal representations, or require architectural homogeneity for latent-level transfer. We identify the entity grounding problem in cross-architecture communication: cross-attention bridges that transfer continuous representations across different LLM families suffer from rare-token compression collapse, where entity identity is lost in the continuous bottleneck (bridge-only F1 ~30%). We propose XBRIDGE, a decode-free communication protocol that addresses this through two mechanisms. Lexical Anchor Mapping (LAM) maps the sender’s original context tokens to the receiver’s vocabulary, providing discrete entity anchors. A Latent Enrichment Bridge (LEB) lets the receiver query the sender’s hidden states for contextual enrichment. The entity anchors ground the bridge’s contextual signals to specific entities through the receiver’s own self-attention. Across three model families (Llama, Qwen, and Mistral), seven benchmarks, and both communication directions, XBRIDGE outperforms text-based communication on all seven tasks for each model pair while achieving 11x lower latency, and in a same-architecture setting it also exceeds a KV-sharing baseline on six of seven tasks. LEB requires only 264M trainable parameters (3.8% of the receiver), is trained on a small balanced sample set, and adds negligible inference overhead.
[AI-47] GCPO: Diagnosing and Constraining Subspace Geometry in Rollout RL for LLM s
链接: https://arxiv.org/abs/2608.11674
作者: Kai Yang,Jingwei Xu,Wanyu Wang,Kai-Yuan Guo,Zhenbo Yu,Yi Wang,Yu Qiao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 10 figures
Abstract:On-policy rollout methods such as GRPO are central to post-training of large language models, yet they frequently suffer from training instabilities, cross-task capability degradation, and response-length inflation. Although prior work has characterized the subspace geometry of aggregate updates, the stepwise variation of this geometry and its relationship to model performance remain unclear. We introduce Principal-Subspace Overlap, a dimension-corrected measure of individual rollout updates relative to the dominant singular subspaces of pretrained weights. Despite low average overlap, transient spikes often precede performance degradation. To address this, we propose GCPO (Geometrically Constrained Policy Optimization), which applies hard bilateral orthogonal projections to constrain updates to the complementary subspaces, preventing such excursions by construction. Across mathematical reasoning, code generation, and tool-use tasks on Qwen3-8B and GLM4-9B, GCPO consistently outperforms GRPO and recent variants, including DAPO and GSPO, improving over the base models and the strongest baseline by up to 27.69 and 2.37 points, respectively. Furthermore, GCPO preserves general capabilities, eliminates response-length inflation, and stabilizes policy entropy. Our findings provide a new diagnostic lens and a principled design perspective for stable reinforcement learning post-training.
[AI-48] Low-Interaction-Rank Learning: Unifying Multiplicative Dual-Encoder Heads
链接: https://arxiv.org/abs/2608.11661
作者: Zijian Zhao,Sen Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:A multiplicative dual-encoder network computes a real-valued output for a pair of inputs as the inner product of their separate encodings. This architecture has been developed independently in operator learning, bipartite matching, contrastive vision-language models, retrieval, and other areas, yet no unified theory guides the basic design decisions: how many interaction modes to represent, how to normalize the encoders, and when the architecture should be avoided. We provide such a foundation by introducing the class of functions of low interaction rank, a class whose intrinsic complexity is measured by its interaction spectrum. Within this framework, approximation error decomposes into a spectral truncation term and an encoder-realization term; sample complexity is governed by the sum of the two encoder complexities rather than their product; and a usability criterion based on spectral decay determines when the architecture can succeed. The same framework exposes a central identifiability problem: the encoders are defined only up to a linear gauge symmetry that leaves the learned coordinates arbitrary. We show that normalization is gauge fixing and that whitening pins the interaction modes up to permutation and sign, thereby explaining the uninterpretability of contrastive dimensions and providing a constructive remedy. Experiments on synthetic kernels, operator learning, and CLIP models validate the theoretical predictions: spectral decay rates match the predicted scaling, whitening recovers the true modes, and independently trained CLIP models are related by a single rotation which, after removal by whitening, exposes interpretable concept axes. The code of this paper is provided at this https URL .
[AI-49] CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement
链接: https://arxiv.org/abs/2608.11631
作者: Kuangzhao Yang,Ziliang Zhao,Zhicheng Dou
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 4 figures, and 3 tables
Abstract:In open-domain human-computer interaction scenarios, large language models (LLMs) frequently encounter user queries that are ambiguous or incomplete. In such cases, directly producing an answer often leads to overgeneralized, erroneous, or low-information responses. In contrast, asking clarifying questions can substantially improve interaction quality. However, existing approaches still rely heavily on manually annotated data or preference alignment to address two fundamental challenges: when clarification is necessary, and which aspect of the query should be clarified. This reliance incurs high annotation costs and limits generalization. To address these challenges, we propose CLAIM, an uncertainty-driven framework for active clarification learning in open-domain settings. CLAIM eliminates the need for explicit human preference annotations by quantifying query uncertainty through the entropy induced by answer disagreements across multiple models. This uncertainty signal is then used to construct high-quality synthetic data, enabling the training of a unified clarification decision model through a combination of supervised learning and reinforcement learning. Specifically, we propose an entropy-driven synthetic data generation pipeline that integrates entropy-based uncertainty estimation with semantic clustering and reasoning-based judgments, enabling reliable automatic annotation of clarification requirements. To train CLAIM, we formulate the clarification process as a structured decision generation problem and adopt a training paradigm that combines supervised fine-tuning (SFT) with group-relative policy optimization (GRPO). Experimental results demonstrate that CLAIM can learn stable and generalizable clarification strategies without relying on manually labeled data, offering a low-cost and robust solution for proactive understanding in real-world open-domain interactions with LLMs.
[AI-50] Making AI-Generated Feedback Matter: From Provision to Student Enactment
链接: https://arxiv.org/abs/2608.11625
作者: Omar Alsaiari,Nilufar Baghaei,Jason M. Lodge,Dragan Gaševi’c,Naomi Winstone,Hassan Khosravi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Feedback processes strongly influence student learning, yet their educational value depends on addressing two distinct challenges: providing high-quality, timely, and individualised feedback at scale, and supporting students to interpret, evaluate, and act on that feedback productively. Generative AI offers a credible means of addressing the provision challenge, but students’ uptake of AI-generated feedback remains limited. We conducted a large-scale quasi-experimental sequential cohort study comparing three AI-mediated feedback workflows across 13,037 students and 51,296 student-authored resources. In Directed Feedback (n = 3,723), students received AI-generated feedback comments without structured support. In Self-Directed Feedback (n = 3,951), students could initiate optional AI-supported dialogue. In Enacted Feedback (n = 5,363), students were prompted to select feedback suggestions, evaluate their relevance, and engage in targeted AI-supported dialogue anchored to those selections. Enacted Feedback was associated with significantly higher uptake of AI-generated feedback, with an estimated probability of 26.2%, compared with 14.1% for Directed Feedback and 0.1% for Self-Directed Feedback. It was also associated with significantly higher self-assessment confidence and submitted-work quality than both comparison conditions. These findings suggest that the educational value of AI-generated feedback depends not only on the quality of feedback comments, but also on workflows that actively structure students’ enactment of feedback literacy processes. The results have implications for the design of AI feedback systems that position learners as active participants in judgement, dialogue, and improvement rather than passive recipients of comments. Overall findings show that AI access alone is insufficient; purposeful workflow design is central to productive feedback use.
[AI-51] FM-LLM : A frequency-enhanced mixture-of-experts framework for adapting LLM s to time series forecasting
链接: https://arxiv.org/abs/2608.11623
作者: Rentao Gu,Yihang Ding,Junjie Li,Yi Ding,Weijing Sang,Xiaoli Huo,Xin Qin,Yuefeng Ji
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
备注:
Abstract:Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual prompts for modality alignment-introducing nontrivial computational overhead and failing to leverage the rich spectral dynamics inherent in time-series data. To enable prompt-free, frequency-aware adaptation of frozen LLMs, we propose FM-LLM (Frequency-Enhanced Mixture-of-Experts for adapting LLMs to Time Series Forecasting), an autoregressive framework grounded in constrained asymmetric coupling. A Fourier Analysis Network (FAN)-based spectral token aligner injects structured harmonic representations directly into the frozen LLM with numerical compatibility. An asymmetric Mixture-of-Experts (MoE) decoder enforces role separation: shared experts with lightweight FAN layers reconstruct the global periodic backbone, while routed experts-restricted to standard FFNs-specialize in modeling non-periodic residual dynamics. A time-frequency hybrid loss function jointly optimizes temporal accuracy and spectral consistency, mitigating error accumulation during long-horizon autoregressive rollouts. Evaluated across eleven public benchmarks, FM-LLM achieves state-of-the-art performance on 59 out of 78 evaluation metrics. Compared to the strongest autoregressive LLM-based baseline, it delivers average improvements of 5.3% in MSE and 5.6% in MAE, with maximum gains reaching 8.0% for MSE and 8.4% for MAE. FM-LLM also demonstrates robust transferability, maintaining superior performance in 10% few-shot and zero-shot forecasting scenarios.
[AI-52] Dion3: Full-Stack Orthogonal Updates
链接: https://arxiv.org/abs/2608.11612
作者: Noah Amsel,Jack Zhang,Kwangjun Ahn,Ali Naeimi,Austin Feng,Berlin Chen,Tri Dao,John Langford
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 37 pages, 23 figures
Abstract:The Muon optimizer incurs a significant overhead cost due to its cubic-time Newton-Schulz orthogonalization step. When weights are sharded, communication overhead compounds this computational cost, eroding the benefits of Muon in many settings. We present Dion3, a revision of Muon that targets this overhead at every level of the stack. Our Gram Newton-Schulz algorithm reduces the FLOP cost of orthogonalization, our CuteDSL kernels accelerate it by exploiting symmetry, and our megabatching strategy reduces communication overhead. Moreover, we propose a simple change to the update rule that cuts costs even further: selecting only a fraction of the momentum matrix’s rows to orthogonalize at each step. This update rule improves on Dion (another “compressed” version of Muon), in both speed and performance. Overall, Dion3 matches or improves on the loss achieved by Muon but reduces optimizer step time by up to 6x. Dion3 is available via the dion package (this https URL) as a drop-in replacement for Muon.
[AI-53] Foresight Without Seeing: Latent Futures for World Action Models
链接: https://arxiv.org/abs/2608.11605
作者: Jiakai Huang,Zhongbo Wu,Zheng Zhang,Zihan Wang,Shan You,Tao Huang
类目: Artificial Intelligence (cs.AI)
备注: 12 pages, 3 figures
Abstract:World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves during interaction. Existing WAMs differ in how predictive dynamics are exposed to the action pathway. Explicit-future WAMs provide direct access to predicted scene evolution, but incur substantial inference costs from iterative video denoising. In contrast, direct-policy WAMs efficiently predict actions from the current observation but lack an explicit inference-time interface for exposing predictive dynamics to the Action DiT. To bridge this gap, we propose ForeWAM, a dynamics-conditioned direct-policy WAM that provides predictive context for action generation without decoding future videos. At its core, Future-KV performs a single Video DiT prefill over the current visual latent and stochastic future slots, and reuses the resulting layer-wise key-value states throughout action denoising. We further introduce dynamics registers supervised by a frozen latent action teacher, encouraging the implicit future states to capture interaction-induced transitions such as object motion, contact changes, and task progress. Ground-truth future observations and the teacher are used only during training; deployment requires neither and performs no future video generation. Without embodied robot data pretraining, the standard and accelerated variants of ForeWAM achieve average success rates of 96.7% and 96.9% on LIBERO, respectively. The standard variant further achieves 61.6% success on LIBERO-Plus. These results demonstrate that direct-policy WAMs can retain efficient action prediction while exposing predictive dynamics to the action pathway without explicitly generating future observations.
[AI-54] Learning from Online User Feedback for Shopping Agents
链接: https://arxiv.org/abs/2608.11604
作者: Haobo Zhang,Kelong Mao,Sulong Xu,Simiu Gu,Zhicheng Dou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model-based shopping agents are increasingly deployed in real-world e-commerce platforms, generating massive amounts of user interaction logs that provide valuable supervision for improving these agents. However, existing approaches primarily rely on offline training signals, such as user-item interactions or synthetic preference data, while largely overlooking the rich supervision contained in users’ natural conversational feedback. Moreover, the available online feedback is heterogeneous, sparse, and noisy, making it difficult to transform into reliable learning signals automatically. To address these challenges, we propose LOFA, a framework that enables shopping agents to learn directly from real online interaction logs without human annotation. LOFA combines reinforcement learning over verifiable purchase outcomes with feedback-aware on-policy distillation, which identifies users’in-dialogue directives and converts them into dense token-level supervision. These complementary objectives capture both collaborative behavioral patterns and user-specific preferences. Extensive experiments on real-world e-commerce logs demonstrate that LOFA consistently improves recommendation quality, response helpfulness, and user-satisfaction alignment over strong baselines, highlighting the effectiveness of learning shopping agents from real online user feedback.
[AI-55] CoAdapt-GUI: Joint Workflow Context and Policy Adaptation for Unseen GUI Applications
链接: https://arxiv.org/abs/2608.11588
作者: Linqiang Guo,Li Gu,Zihuan Jiang,Zhixiang Chi,Siobhan Reid,Ziqiang Wang,Yuanhao Yu,Wei Liu,Yang Wang,Tse-Hsun(Peter)Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Mobile GUI agents remain brittle when deployed to applications absent from source training. We study novel-app generalization under a limited target interaction budget and without target demonstrations. We introduce CoAdapt-GUI, a test-time adaptation (TTA) framework that jointly adapts structured workflow context and policy from the agent’s own target-app rollouts and rewards. The workflow context retains transferable procedures, failure modes, and verification rules while excluding app-bound source details. This separation allows reusable workflow knowledge to guide adaptation without transferring source-interface state. For policy adaptation, task-context-matched group-relative optimization updates a LoRA adapter on a frozen vision-language model. Across two unseen-app evaluations, CoAdapt-GUI reaches 45.0% on AndroidWorld-Generalization, compared with 37.5% for the reported Policy-Only TTA baseline, and raises AndroidWorld Plus performance from 38.6% to 52.9%. These results show that transfer-constrained workflow context provides substantial gains and that joint policy adaptation further improves held-out performance.
[AI-56] EnterpriseRAG : Benchmarking LLM Instruction Adherence and Robustness under Non-Ideal Enterprise Retrieval
链接: https://arxiv.org/abs/2608.11584
作者: Huiqi Miao,Xinbao Sun,Bo Wang,Fanyu Meng,Lijun Mei,Na Wu,Di Jin,Chao Deng,Junlan Feng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Enterprise RAG deployments face a critical reliability gap: while LLMs satisfy 80% of individual constraints, only 26.8% of responses meet all requirements simultaneously, revealing a 57-point orchestration gap. Existing benchmarks assume clean retrieval with simple queries, failing to capture production conditions where noisy documents and multi-dimensional constraints coexist. We introduce EnterpriseRAG, a benchmark of 983 expert-validated samples across six domains that systematically simulates three failure modes absent from prior work: retrieval noise, knowledge gaps, and factual conflicts, coupled with complex instructions. Evaluation of 13 state-of-the-art LLMs reveals a severe instruction adherence collapse, where high per-constraint satisfaction masks low holistic compliance. Critical findings expose deep barriers under knowledge gaps and factual conflicts, even with reasoning-enhanced inference, indicating production RAG requires explicit context-aware protocols and calibrated judgment. EnterpriseRAG provides a reproducible foundation for measuring and closing these gaps, directly informing deployment decisions for enterprise-scale RAG systems. We will release the benchmark and evaluation framework upon publication.
[AI-57] Localizing Safety Alignment: MLP Layers and Mid-Network Blocks Encode Refusal Behavior in Large Language Models
链接: https://arxiv.org/abs/2608.11583
作者: Mingyu Zong,Sampad Mohanty,Bhaskar Krishnamachari
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Safety alignment in large language models is often treated as a distributed property of the entire network, yet its practical brittleness suggests that refusal behavior may be concentrated in a smaller set of parameters. This work addresses where safety-aligned refusal is encoded by transplanting weights from aligned models into matched unaligned base models at multiple levels of granularity. Using two open-weight model pairs and four safety benchmarks, we conducted experiments to compare the effects of replacing attention weights, MLP weights, contiguous layer regions, and MLP blocks. Across both model families, refusal transfer is dominated by MLP weights: replacing MLP parameters recovers substantially more malicious-prompt refusal than replacing attention parameters, with gains of at least 2.7 times more across benchmarks. Within the MLP stack, refusal-relevant parameters exhibit a consistent mid-network concentration, as the block spanning layers 8-11 is selected first in all six greedy searches over model-dataset pairs. The results also show that the composition of safety-relevant components is non-additive: in five of six greedy trajectories, adding more aligned blocks can reduce refusal performance, and selective block subsets can outperform full MLP transplantation on malicious refusal, benign over-refusal, or both. Finally, greedy orders transferred to OR-Bench vary with the source benchmark used to derive them, indicating a benchmark-dependent precision-coverage trade-off. These results suggest that safety alignment in current LLMs is both localized and interaction-sensitive, offering insight into alignment brittleness and potential avenues for targeted safety interventions.
[AI-58] RoadWeaver: Large-Scale Lane-Level HD Map Generation from Scratch for Autonomous Driving Simulation
链接: https://arxiv.org/abs/2608.11580
作者: Yueyuan Li,Zexi Chen,Weijie Xi,Mingyang Jiang,Songan Zhang,Hanyang Zhuang,Ming Yang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages, 6 figures, 2 tables
Abstract:Autonomous driving simulation requires diverse and scalable lane-level HD maps to support long-horizon evaluation across complex road networks. Existing approaches either rely on handcrafted or reconstructed real-world maps, which limits scalability, or generate only local road structures rather than complete HD maps. We present RoadWeaver, a coarse-to-fine framework for from-scratch generation of diverse, large-scale HD maps. RoadWeaver first synthesizes a global road layout, expands it into a connected road network, and then constructs lane-level geometry with topologically consistent lane connectivity. Experimental results show that RoadWeaver achieves a 99.8% reachability, a 10.7% dead-end ratio, and an endpoint alignment error of 0.24 m. Compared with SOTA generation methods, it reduces endpoint alignment error by 94.4% while generating complete HD maps in 1.39–3.50 s. The generated maps can be directly deployed in driving simulators, providing scalable simulation environments for future closed-loop evaluation of autonomous driving systems. The training code and an out-of-the-box implementation of RoadWeaver will be released upon acceptance.
[AI-59] A Conceptual Framework for Enhancing Workforce Readiness for Smart Manufacturing in the AI Era
链接: https://arxiv.org/abs/2608.11540
作者: Dalton Ross Smith,Wilburn Whittington,Alejandro Martinez,Aidan Duncan,Gang Li
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 30 pages, 11 figures, submission for ASEE Journal of Engineering Education
Abstract:The convergence of artificial intelligence (AI), Industrial Internet of Things, cyber-physical systems, and advanced robotics is reshaping manufacturing faster than engineering curricula can adapt, widening the gap between the competencies required on the shop floor and those delivered by traditional engineering and technology education. This paper proposes a Workforce Readiness Level (WRL) framework, which adapts the Technology Readiness Level scale into nine progressive competency stages and a four-pillar rubric, digital and AI literacy, cyber-physical systems fluency, human-machine collaboration, and data-driven decision making, aggregated through a composite stage score and a cohort-level workforce-readiness index under a ``no-thin-pillar’’ rule. The framework is instantiated at a university smart-manufacturing teaching laboratory and draws on 89 sponsored capstone projects delivered over four semesters, four of which are analyzed in depth. Four pillars jointly span the relevant ABET student outcomes. Across the highlighted cohorts the workforce-readiness index ranged from 5.2 to 6.4, and the no-thin-pillar rule was diagnostically informative in three of the four cases and the binding certification constraint in one, repeatedly surfacing cyber-physical and data-driven-decision gaps concealed behind strong analytics profiles; advancement to the highest stages was gated by industry-embedded experience rather than additional coursework. WRL offers educators, accreditation bodies, and regional workforce systems a common, evidence-based instrument for diagnosing and advancing workforce readiness; future work will calibrate pillar weights and test reliability and predictive validity.
[AI-60] Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks
链接: https://arxiv.org/abs/2608.11532
作者: Qasim Zia,Saide Zhu,Haoxin Wang,Zafar Iqbal,Yingshu Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy. However, Federated learning struggles to adequately train a global model when confronted with data heterogeneity and data sparsity among vehicles, which ensure suboptimal accuracy in making precise predictions for different vehicle types. To address these challenges, this paper combines Federated Transfer Learning (FTL) to conduct vehicle clustering related to types of vehicles and proposes a novel Hierarchical Federated Transfer Learning (HFTL). We construct a framework for DT-VANET, along with two algorithms designed for cloud server model updates and intra-cluster federated transfer learning, to improve the accuracy of the global model. In addition, we developed a data quality score-based mechanism to prevent the global model from being affected by malicious vehicles. Lastly, detailed experiments on real-world datasets are conducted, considering different performance metrics that verify the effectiveness and efficiency of our algorithm.
[AI-61] Keep the Future Drop the Rollout: RIFT for World Action Models
链接: https://arxiv.org/abs/2608.11521
作者: Chushan Zhang,Jinguang Tong,Xuesong Li,Yikai Wang,Hongdong Li
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:World action models (WAMs) condition robot actions on predicted futures, but iterative video rollout increases deployment latency. We ask whether action generation requires the evolving rollout trajectory or only its future representation. Across four WAMs on all 40 LIBERO tasks, paired closed-loop interventions show that masking or reassigning future-cache values changes execution and reduces success, indicating sensitivity to future values and their assigned positions. For Joint and Cosmos-2, however, replaying one fixed final-clean key/value (K/V) cache nearly preserves unmodified execution, with 1.7 to 1.9 ~cm end-effector average displacement error and 97.9% to 98.2% success. This separates cache consumption from production: these models can reuse a fixed cache but still require iterative rollout to construct it. We therefore propose RIFT (\emphRollout-free Imagination via Future Tokens), which uses learned anticipation tokens to construct a complete future K/V cache in one backbone pass while retaining the original future-read interface. On LIBERO, RIFT achieves 98.8% success, close to rollout-based Joint, IDM, and LingBot-VA at 98.4% to 98.6% , while reducing action-chunk latency by 68.2% to 89.1% . On RoboTwin~2.0, RIFT reaches 92.9/92.6% on clean/randomized scenes, the highest observed among the evaluated methods. These results support rollout-free future conditioning without iterative video generation at deployment.
[AI-62] Let it Cook: Learning to Wait in Sequential Decision Making
链接: https://arxiv.org/abs/2608.11511
作者: Christopher Watson,Arjun Krishna,Dinesh Jayaraman,Rajeev Alur
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 25 pages
Abstract:In sequential decision making, an agent typically observes its environment and acts at every timestep. However, such active participation may not always be necessary; tasks such as brewing coffee include periods that are served equally well by letting the environment evolve without constant monitoring and control. During such periods, the agent could simply wait to conserve its resources, or redirect its attention to another task. We capitalize on these opportunities by training a “waiting policy” that decides where and how long to wait. This involves forgoing sensing to commit to a wait action, representing a deliberate pause for a set number of timesteps. We formalize “learning to wait” as minimizing the frequency of sensing and decision making without sacrificing task performance (e.g., the total amount of time to complete a task). To train a waiting policy, we propose an approach that employs reinforcement learning with lexicographically ordered objectives. In experiments across 4 discrete-state household tasks and 3 continuous-state environments, we show that our approach successfully learns waiting behaviors, and can adapt pre-trained policies to wait where appropriate. While different tasks permit different amounts of waiting without sacrificing task performance, our approach consistently finds solutions with significant waiting, sometimes waiting for over 50 percent of the task duration.
[AI-63] Strengthening Full Justified Representation: Efficient Verification and Computation
链接: https://arxiv.org/abs/2608.11500
作者: Nicholas Teh
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Theoretical Economics (econ.TH)
备注:
Abstract:Full justified representation (FJR) is among the strongest known satisfiable proportionality axioms for approval-based committee elections. Recent work has shown that an FJR committee can be found in polynomial time, but verifying whether a given committee satisfies FJR remains coNP-complete. We introduce FJR+, a strict strengthening of FJR and EJR+ that can be verified and satisfied in polynomial time. We then analyze the Residual-Budget Greedy (RBG) algorithm and prove that it selects a partial committee such that every size- k completion satisfies FJR+. This freedom allows us to use sequential Phragmén to obtain a priceable completion. The resulting rule always satisfies FJR+ and the sub-core, and it is priceable whenever at least k candidates receive an approval. We also obtain a Droop-quota version of FJR+. Finally, we extend FJR+ to approval-based participatory budgeting with arbitrary project costs. A project-specific version of RBG computes this property in polynomial time and can be continued to a priceable outcome satisfying a cost-based version of the sub-core.
[AI-64] HyperFix: Combinatorial Nonlinear Correction for Task Vector Merging
链接: https://arxiv.org/abs/2608.11499
作者: Hyo Seo Kim,Ren Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Task vectors enable model merging without joint retraining. In practice, the subset of task vectors to be merged may vary, but many existing methods use scalar tuning for a particular subset, requiring repeated tuning across subsets and restricting task vector merging to linear rescaling. We therefore formulate merging across varying task subsets as a combinatorial correction problem and introduce HyperFix, a lightweight hypernetwork that predicts subset-conditioned nonlinear corrections in weight space. Trained once on singleton, pair, and triple subsets from a task bank, HyperFix generalizes to larger subsets without per-subset optimization. Our local perturbation analysis bounds the residual correction beyond linear merging and motivates learning it from small task updates. Experiments across diverse benchmarks show that HyperFix outperforms existing task vector merging methods while reducing tuning cost.
[AI-65] From Prompting to Behavioral Alignment: Personalized LLM Judges for Recommendation Evaluation
链接: https://arxiv.org/abs/2608.11493
作者: Alireza S. Ziabari,Kat Ellis,Colleen Chan,Ding Tong
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Traditional offline recommendation evaluation relies heavily on complex, manually maintained feature pipelines that are difficult to scale. While Large Language Models (LLMs) offer a promising alternative by predicting user engagement directly from raw text logs, empirical analysis in this study identifies a critical failure mode termed bidirectional rationalization. In a zero-shot setting, LLMs are found to convincingly argue for both positive and negative user engagement outcomes on the exact same item with identical evidence, highlighting the unreliability of off-the-shelf LLMs in predicting user engagement. To resolve this, we develop and apply a sequential behavioral alignment framework pairing fine-tuning with preference optimization over paired correct and counterfactual rationales. Evaluated on real-world homepage interaction logs, this aligned reasoning approach achieves a 32.19% lift in Macro-F1 score over the zero-shot baseline and matches the production feature-engineered baseline. The results demonstrate that behavioral alignment mitigates bidirectional rationalization while delivering human-interpretable reasoning traces without manual pipeline overhead.
[AI-66] A Modular Agent ic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization
链接: https://arxiv.org/abs/2608.11483
作者: Kelvin P. Idanwekhai,Enes Kelestemur,Benjamin Strickland,Matthew Hart,Steini Davidsson,Angelos Angelopoulos,Ron Alterovitz,Marcello DeLuca,Alexander Tropsha
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
备注: 22 pages, 9 figures
Abstract:Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints. We present SABLE (Synthetically-accessible Agentic Bayesian Ligand Exploration), an open-source framework that employs natural-language orchestration to guide chemical structure optimization. SABLE uses an LLM to interpret user-defined goals and route tasks, while specialized tools perform reaction-templated analog enumeration, physicochemical and ADMET property prediction, structure-based affinity scoring, and Bayesian optimization. The resulting workflow is a computational twin of the analytical and prioritization stages of the design-make-test-analyze cycle, providing provenance of each numerical output. Across single, and multi-objective optimization studies, SABLE enriches candidate sets for user-defined computational objectives while evaluating only a subset of the enumerated search space. Its modular architecture allows tools and characterization backends to be replaced by editing a simple config file, without modifying operational logic. SABLE provides an extensible decision-support framework for prioritizing synthetically constrained analogs in early-stage drug discovery.
[AI-67] he Next Challenge for Agent ic Cybersecurity: A Realistic Contamination-Free Reverse Engineering Benchmark
链接: https://arxiv.org/abs/2608.11469
作者: Jeremy Spence,Nicholas Assaderaghi,Jinhao Zhu,Nikil Ravi,Raluca Ada Popa,Guannan Wei,Yangruibo Ding,Zhuo Zhang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:AI agents are rapidly improving in cybersecurity capabilities when the source code is available for analysis, yet much of the software most consequential to cybersecurity, including malware, firmware, and proprietary applications, is available only as binaries. Analyzing such software requires reverse engineering(RE): recovering program semantics before the analysis can be meaningfully performed. However, evaluating agentic RE poses a fundamental challenge: benchmark instances must be unseen as source code in the LLMs’ training data to prevent models from taking shortcuts by recognizing them rather than really analyzing them, while also matching the scale and anti-analysis protections of real software. Unfortunately, however, existing benchmarks do not jointly satisfy these requirements. To this end, we introduce SRE-Bench, the first realistic, contamination-free RE benchmark. Built entirely from scratch by RE experts with over 5,000 hours, SRE-Bench comprises 19 private, real-world-scale programs averaging 16.9K lines of code. We further developed 44 in-house anti-analysis primitives, yielding 262 binary instances and 1572 deterministically graded tasks. Our evaluation across five frontier LLMs (GPT-5.6-sol,Claude-Opus-5,GPT-5.5,Grok-4.5, and GLM-5.2) shows that RE remains largely unsolved: the strongest model, GPT-5.6-sol, scores 61.4% per instance, and fully solves only 31.5% of the instances. Our analysis further reveals that agents behave differently from human engineers, where agents are relatively insensitive to compiler optimization and static linking. Controlled ablations also confirm that both contamination control and realistic scale are essential. These results indicate that strong source-code security capabilities do not yet transfer to binary analysis, highlighting RE as an important frontier for agentic cybersecurity and SRE-Bench as a rigorous testbed to measure progress.
[AI-68] PAC-Bayes Beyond Parameter Space: Behavioral Equivalence Z-Information and Exact Complexity Decomposition
链接: https://arxiv.org/abs/2608.11465
作者: Vasant G. Honavar,Satish Kumar Keshri,Neil Ashtekar,Zehao Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:PAC-Bayes theory provides generalization guarantees by controlling the Kullback–Leibler (KL) divergence between posterior and prior distributions over a chosen hypothesis representation. However, predictive risk depends only on the predictive behavior induced by a hypothesis, not on the particular internal realization that implements that behavior. In over-parameterized systems, many distinct configurations induce identical predictive behavior, yet the classical PAC-Bayes KL divergence does not distinguish uncertainty over predictive behavior from variation among behaviorally equivalent realizations. We show that this distinction induces an exact structural decomposition of classical PAC-Bayes complexity. We formalize behavioral equivalence through a measurable behavior map and use measure disintegration to decompose probability measures on the configuration space into a distribution over predictive behaviors and conditional distributions over behavioral fibers. This yields an exact decomposition of the classical PAC-Bayes KL divergence into a behavior-selection term and a realization-level term given by an expected conditional KL within fibers. We define Z-information as the negative of this realization-level contribution: the exact gap between the KL divergence and the complexity of uncertainty over predictive behavior alone. We further show that the behavior-selection term admits an exact variational characterization: it is the minimum KL divergence among all posteriors inducing the same distribution over predictive behaviors, attained by a canonical fiber-symmetrized representative. Finally, we show that symmetry, behavior-preserving directions, fiber geometry, and invariance under fiber-preserving perturbations arise naturally from the same behavior-map structure. Together, these results identify predictive behavior as the natural object of PAC-Bayes complexity. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.11465 [cs.LG] (or arXiv:2608.11465v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.11465 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zehao Liu [view email] [v1] Tue, 11 Aug 2026 22:09:17 UTC (71 KB)
[AI-69] Herding End-to-End Autonomous Driving via Neuro-Symbolic Safety Guards
链接: https://arxiv.org/abs/2608.11451
作者: Simón Patiño Idarraga,Erick Silva,Rehana Yasmin,Ali Shoker
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:Modern end-to-end driving agents can achieve high average performance yet still violate basic traffic rules that a human driver would never miss. The reason is structural: they learn statistical patterns rather than the physical conditions that guarantee safe driving, leaving their decision-making process opaque and safety constraints unenforced. We introduce a neuro-symbolic safety guard, a lightweight module that attaches to the final command interface of an already-trained agent. Immediately before a command reaches the vehicle, it checks the command against explicit safety rules and, only when necessary, replaces it with the nearest safe alternative. Each intervention is directly executable and traceable to the rule that triggered it, while the guard itself requires no retraining and adds no learned component. Evaluated on the long-tail benchmarks Fail2Drive and Bench2Drive using the state-of-the-art TransFuser v6 (TFv6) as a case study, the guard improves Success Rate by 15% and reduces safety-critical collisions by up to 53%, while preserving the original Driving Score.
[AI-70] AI Guardrail Survival under Single-Cycle Agent ic Self-Summarization
链接: https://arxiv.org/abs/2608.11392
作者: Ted Kwartler,Alan Aqrawi,Arian Abbasi
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Long-running agents periodically compact their context, replacing the transcript with a model-generated this http URL work shows that dropping a standing safety constraint during compaction drives behavioral violations acrossmany models (Governance Decay; Chen, 2026). We ask a finer question: under a single compaction cycle, how is a safetyrule lost, and what does that imply for detection and evaluation? Our central finding is that a presence check is not asafety check: when compaction does not drop a rule outright, it often leaves something that looks like a rule but doesnot act like one. On behavioral replay, a degraded residue leads the model to perform the prohibited action far more oftenthan an intact welded rule does (all-case gaps of +34 and +57 points under two replay models, both positive), category-level survival behaves like a residue, and even intact rules sometimes fail to fire, so an audit that checks only textualpresence gives false assurance. Sharpening this, rule-form items are retained substantially more often than prominence-matched facts, which is exactly why presence-based checking feels adequate even though survival is not this http URL loss is regime-dependent (weld-or-drop with a single rule; degraded predicate-loss residues under a tighterbudget), and we did not observe the hypothesized textual severing mode. Such loss is silent at runtime and detectableonly by comparison with retained external ground truth (such as a constraint registry), which reveals textual absence butnot whether a surviving rule still fires. We also document evaluation pitfalls where LLM-judge labels alone would havereversed a conclusion. All results concern a single compaction cycle.
[AI-71] From Numbers to Judgment: Specialist LLM Agents and Reinforcement Learning for European Listed Real Estate
链接: https://arxiv.org/abs/2608.11381
作者: Pardis Taghavi,Santosh Bhavani
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We study whether the localized numerical operations and integrative judgments of financial analysis benefit from the same form of LLM specialization. Larix maps a 16-lens European listed-real-estate analysis framework to eight lens-aligned specialists; we compare a frontier LLM under monolithic versus specialist-decomposed prompting while holding the model, source evidence, task instructions, output schema, and scoring fixed. Across 19 firms spanning seven regulatory wrappers, decomposition improves the numerical-task aggregate by 15.8 percentage points but does not reliably improve, and can reduce, performance on judgment tasks, a pattern stable across four frozen-template dispatches; a single-agent control given the complete framework does not reproduce the numerical gain. Post-training Qwen3.5-9B with GRPO using task-aligned structured rewards then raises the development-split score by 12.0 points and the judgment aggregate by 14.2 points, with gains on all four sub-ceiling tasks; the gains transfer to unseen firms (+15.2 points overall; +40.4 on covenant stress) and to unseen regulatory wrappers (+4.3), with positive transfer on all three anti-memorization splits. Prompt-level decomposition thus improves modular numerical execution, whereas targeted parameter adaptation improves integrative financial judgment.
[AI-72] Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces RECSYS’26
链接: https://arxiv.org/abs/2608.11354
作者: Mengyu Chen,Feiyu Lu,Chun-Fu Chen,Lucas Vinh Tran,Jay Katukuri
类目: Artificial Intelligence (cs.AI)
备注: Preprint for conference full paper at 20th ACM Conference on Recommender Systems (RecSys '26), Minneapolis, MN, USA
Abstract:Modern recommender systems treat observed actions as reliable proxies for user preferences, yet interactions often reflect exploration or comparison rather than stable preference expression. As interfaces evolve from static layouts toward generative UIs and immersive extended reality (XR), the need for deeper, modality-agnostic user understanding grows: these adaptive environments must decide not only what to present but where, when, how prominently, and most importantly why a user acts. We propose an Inverse Theory of Mind (IToM) pipeline that reasons backward from observed interactions to infer the beliefs, preferences, and decision-making traits that explain behavior. The pipeline reconstructs each user’s decision context, including what was chosen and what alternatives were available, applies LLM-driven counterfactual reasoning to produce evidence-grounded natural-language belief statements, and synthesizes these beliefs through multi-hypothesis abductive inference into a structured user persona. We evaluate on the OPeRA dataset against ground-truth personality assessments, attitudinal surveys, and interview-based personas across four tasks: next action prediction, shopping attitude alignment, Big Five personality inference, and held-out category prediction. Results show that inferred personas match or exceed ground-truth personas and that multi-hypothesis reasoning is essential for accurate personality prediction. We further demonstrate cross-modal transferability with a persona-driven spatial banking application on VisionOS.
[AI-73] Dynamics Models for Offline Hyperparameter Selection in Real-World RL
链接: https://arxiv.org/abs/2608.11349
作者: Jordan Coblin,Han Wang,Martha White,Adam White
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to the 2026 Reinforcement Learning Conference (RLC)
Abstract:A key obstacle to deploying reinforcement learning in real-world systems is hyperparameter selection, particularly when simulators are unavailable and online experimentation is costly. Prior work has proposed calibration models trained on offline data to approximate environment dynamics and enable offline hyperparameter selection, but these methods have so far been evaluated only in simple simulated settings. In this paper, we present the first application of calibration models in a real-world industrial setting: a municipal water treatment plant. We evaluate several calibration model approaches, including a k-nearest neighbors model with a Laplacian distance metric, on high-dimensional, non-stationary sensor data for nexting prediction tasks. Our results show that these models can generate realistic long-horizon rollouts and recover meaningful hyperparameter sensitivity trends. We further examine how calibration models scale to year-long datasets, how they support the selection of fine-tuning learning rates for pre-trained agents, and how robust they are under distribution shift. Overall, our findings provide a proof of concept for using offline dynamics models to support RL deployment in real-world environments, while highlighting important practical challenges for future work.
[AI-74] Governing Agent ic AI in FinTech
链接: https://arxiv.org/abs/2608.11344
作者: Henry Han
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Risk Management (q-fin.RM)
备注: 58 pages, 12 figures
Abstract:Financial institutions are delegating consequential decisions to agentic AI systems that decompose goals, coordinate models and tools, and act with little oversight. Yet agentic AI governance in FinTech is under-investigated. We argue the binding governance constraint is not capability but verifiability. We define the Verifiability Gap as the shortfall between the verification delegated authority demands and the explainability and reproducibility retained after a decision. It is indexed to a verifier, evidentiary standard, and audit lag. We develop a multilevel governance theory for agentic AI and test its mechanisms in three studies over nine model versions, from a three-billion-parameter local model to a commercial frontier system. Study 1 shows that provider releases alter historical financial actions, and that the controls replay needs belong to the provider: the frontier model rejects temperature, top_p and top_k outright and exposes no random seed. Under the tightest controls each endpoint allows, a local model reproduced 320 of 320 executions, hosted models 319 of 320 and 959 of 960. Study 2 shows that orchestration is a latent policy layer. Architecture changes final actions, and no execution record repeated in any configuration at any scale. The frontier model reproduces its own actions more often than the local ones, its record no better, and loses a comparable share of its differentiation. Capability buys a higher starting point, not auditability. Study 3 shows two deterministic credit-model versions each reproduce their current action perfectly, yet the current cannot recover a historical one. We conceptualize reproducibility as a governance profile, not a scalar, yielding evidence-contingent delegation: authority is defensible only while retained evidence substantiates its exercise. Beyond finance, the framework extends to other high-stakes domains requiring auditability.
[AI-75] Apodex Discovery: Reality Benchmarks and Environments for Evaluating and Building Discoverative Artificial Intelligence
链接: https://arxiv.org/abs/2608.11341
作者: Brian Wang,Bin Feng,Xiaoman Pan,Chenyang An,Felix Liu,Tangqi Fang,Gongbo Sun,Lingfeng Shen,Ning Wang,Handuo Zhang,Feng Chen,Fuchao Yang,Xiang Wang,Jiacheng Lin,Siting Li,Zixuan Liu,Chi Han,Zhenhailong Wang,Kunlun Zhu,Lawrence Zhao,Yueqi Guo,Kailong Wen,Feng Xing,Yiling Guo,Lidong Bing,David Tan,Bo An,Heng Ji,Sheng Wang
类目: Artificial Intelligence (cs.AI)
备注: 85 pages, 9 figures, 38 tables
Abstract:Apollo did not reach the Moon merely because its engineers could solve difficult equations. It succeeded by turning a distant ambition into a mission architecture of explicit objectives, simulation, verification, and repeated correction. AI now faces a similar transition: frontier models can solve difficult tasks once the problem, tools, and success criteria are specified, yet consequential real-world challenges rarely arrive in an executable or verifiable form. We introduce Apodex Discovery, a framework for building and evaluating discoverative AI through the heavy-duty solver, a system comprising a foundation model, harness, tools, and control policies that pursues extended, stateful, verifiable investigations. It has three core components. First, a problem-scouting process surveyed 561 industries across 16 sectors, assembled 423 high-value real-world problems, and selected 20 for the initial release. Second, a common environment-task-episode abstraction provides data, tools, constraints, feedback, trajectory recording, and verification of intermediate artifacts and final submissions. Third, HDS6 evaluates Tools, Repair, Alternatives, Coherence, Evidence, and Scope independently of final-task success. In AAV capsid design, Apodex surpassed the published state of the art by 7% across viability, tropism, structure prediction, and generative design. In drug repurposing and reformulation, a task-specific biomedical environment improved the mean normalized prediction score of GPT-5.5 and GPT-5.6-sol by 2.5 and 7.6 points over the same closed-book backbone. Controlled ablations show that the fixed TRACES episode interface enables attribution of performance differences to specific solver components. Apodex Discovery moves AI evaluation beyond predefined benchmarks toward verifiable investigations aimed at genuine discovery. Comments: 85 pages, 9 figures, 38 tables Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.11341 [cs.AI] (or arXiv:2608.11341v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.11341 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-76] Self-evolving network verifiers
链接: https://arxiv.org/abs/2608.11340
作者: Ioannis Protogeros,Tibor Schneider,Laurent Vanbever
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注: 8 pages, 5 figures
Abstract:Symbolic network verifiers can reason about correctness across vast spaces of routing inputs and failures, but only for the protocols and features an expert has encoded by hand. Creating and maintaining a faithful model of the control plane is both difficult and never-ending, since no written source specifies perfectly what a network does: vendor implementations deviate from the RFCs, and behaviour shifts with releases. The burden of constant upkeep ultimately keeps verification out of many networks that need it. We argue that the model should instead evolve automatically to faithfully capture the actual network behaviour. To achieve that, we leverage the only source that specifies it unambiguously: the router software itself. In a counterexample-guided loop, a coding agent proposes extensions to the verifier’s symbolic encoding, while a trusted oracle (e.g., emulated routers) supplies the ground-truth routing state. The agent iteratively refines the network model using each disagreement with the oracle. As early evidence, a prototype of this system taught a 3,000-line SMT-based verifier three features it did not support: OSPF areas, BGP route reflection, and L3VPN over EVPN, converging autonomously on models that match the oracle, even noticing vendor-specific behaviour. Automating model growth shifts the hard problem from writing verification systems to systematically testing them; we propose a research agenda for trusting and harnessing automatically evolved verifiers. Comments: 8 pages, 5 figures Subjects: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.11340 [cs.NI] (or arXiv:2608.11340v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2608.11340 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-77] Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildings
链接: https://arxiv.org/abs/2608.11324
作者: Tran Le Vu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC)
备注:
Abstract:This paper proposes a contextual quality-diversity evolutionary reinforcement-learning controller, CQD-ERL, for the supervisory control of a tropical, water-cooled chiller plant and its associated air side. Rather than converging to a single scalarised policy, the controller maintains a product archive of specialised policies indexed jointly by a data- driven operating context, a cluster of daily weather and load regime, and a context-invariant behaviour descriptor, filled by a gradient-free evolutionary operator and a soft-actor-critic policy-gradient operator that share one replay buffer. Every action is filtered through a deterministic safety shield before execution. The controller is trained on a two-tier reduced-order environment representing the latent load, cooling-tower approach and humidity constraints of a Singapore commercial building, and is evaluated over a full annual backtest against an ASHRAE Guideline 36 baseline.
[AI-78] Deployment Decision Reliability: A Generalizability-Theory Framework for Sizing Long-Horizon Agent Evaluations
链接: https://arxiv.org/abs/2608.11323
作者: Vasundra Srinivasan
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 11 pages, 1 figure, 1 table. Code and reproducibility artifacts: this https URL
Abstract:Enterprise practitioners read agent leaderboards as if they ranked agent capability. We show, across three open agent-trace benchmarks (TheAgentCompany, \tau^2 -bench, and AppWorld), that the agent main effect accounts for less than 3% of total variance in every dataset and check type, while the agent-by-task interaction accounts for 7-23%. Leaderboards rank specialization, not capability. We arrive at this through a four-facet Generalizability Theory variance decomposition, fit with three estimators (Henderson Method-I, REML via lme4, and a Bayesian binomial GLMM) that agree to three decimal places. Four further findings sharpen what the leaderboard is hiding. First, aggregate reliability collapses on the hardest task quartile: E\rho^2 on \tau^2 action_checks falls from 0.752 to 0.000. Second, training-cell reliability negatively correlates with held-out reliability ( r = -0.90 on \tau^2 ), meaning the designs that look most reliable replicate worst. Third, population-level diagnostics transfer across enterprise benchmarks (capability-gap ratio stable at 0.35-0.40) but per-family agent rankings invert. Fourth, on the MAST failure taxonomy, trace-level mode profiles are idiosyncratic (MAE = 0.261) while cell-level profiles generalise (MAE = 0.056, r = 0.83 ). We package these into Deployment Decision Reliability (DDR), a one-page reporting discipline that turns the variance-component table into five decisions an enterprise buyer can defend. All code, data loaders, and fit artifacts are released under an open-source license.
[AI-79] rminal Symmetry as a Decision Resource: Statewise Refinement for Anytime Verified Construction
链接: https://arxiv.org/abs/2608.11318
作者: Yi Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Many sequential construction tasks exhibit exact symmetry at completion while their execution remains directed and history-dependent. We develop a decision-resource view of terminal symmetry: process evidence supplies directionality, terminal correspondence transports that structure across equivalent outcomes, realized-state evidence refines its current decision relevance after transitions, and a fixed verifier certifies execution. This decomposition yields transport–refine–certify. \method instantiates the principle with an episode-fixed transported process structure, its state-restricted process rank, a state-dependent residual rank refreshed after accepted transitions, and an ordinal rank meet whose top- k set is exactly the union of the two proposal prefixes. The meet provides a completion guarantee under prefix coverage and attains the tight worst-case verifier-query bound under the corresponding prefix information model; a two-state construction predicts a strict post-transition dynamic–static separation. Across CAD assembly, Mini-Programs, and exact-fill packing, statewise refresh improves anytime AUC by up to 6.77 , 21.75 , and 8.68 points, respectively. On 1,135 target-removal episodes from the official GRN OOD scenes, \method attains the lowest mean capped verifier cost at all three scales among the compared GRN and CDGS-style planners. The statewise signal also transfers across aggregation and scheduler organizations. Terminal symmetry thereby becomes a reusable decision resource for directed construction.
[AI-80] Backdoor Decontamination Dynamics in LLM Agents
链接: https://arxiv.org/abs/2608.11295
作者: Gabriel Huang,Abhay Puri,Léo Boisvert,Alexandre Drouin,Perouz Taslakian,Spandana Gella,Christopher Pal
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Open-weight LLM agents are vulnerable to backdoors installed during fine-tuning, which may be undetectable if the trigger conditions are never met during testing. Assuming defenders do not know the existing trigger, they cannot unlearn it directly. One decontamination strategy is to install a known backdoor (defensive poisoning) then to unlearn it, hoping that the original unknown backdoor is removed as a side effect. However, this procedure has uncertain outcomes: the original backdoor may persist or be erased or rerouted, among other possibilities. We introduce a framework for studying these dynamics in tool-calling agents, decoupling trigger, response, teacher, and fine-tuning method across systematic experiments on AgentDyn. Across 115 experiments, defensive poisoning alone erases around 56% of original backdoors; subsequent decontamination then drives almost all survivors to erasure, confirming that trigger recognition and malicious execution are behaviorally dissociable. Interestingly, our experiments find that malicious backdoors never persist when using different triggers of the same general type as the defensive backdoor when followed by decontamination via unlearning. Co-installing up to four backdoors increases resistance (around 36% erased), yet decontaminating a single known co-resident backdoor collaterally clears 52/60 co-residents (87%). Upon visualizing postdecontamination model internals using J-lens, we confirm that although the decontamination restores benign LLM responses, traces of original trigger awareness persist at intermediate layers.
[AI-81] Federated Learning for Distributed CNC Tool Wear Prediction
链接: https://arxiv.org/abs/2608.11281
作者: Afsana Khan,Morris Stallmann,Marcin Pietrasik,Charis Kouzinopoulos,Anna Wilbik
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Tool wear prediction is an important task in CNC machining, where accurate monitoring of tool condition supports product quality and process reliability. Machine learning methods have shown potential for this task, but their use in industrial environments is limited by the distributed nature of machining data and by restrictions on data sharing between machines, sites, or organizations. Federated learning offers a suitable framework for this setting by enabling collaborative model training without transferring raw operational data. This paper investigates federated learning for CNC tool wear prediction. Tool trajectories are distributed across simulated clients to represent a federated learning scenario. The federated models are compared against centralized references and local client baselines. Results show that federated learning achieves performance close to centralized learning and improves significantly over local client models. These findings indicate that federated learning can support collaborative tool wear prediction in distributed CNC manufacturing environments.
[AI-82] Agent Safety Should Be a Runtime Contract
链接: https://arxiv.org/abs/2608.11274
作者: Albus W. Ng,Yi Han,Jusheng Zhang,Wenhao Wang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI. We argue this is structurally insufficient for autonomous agents that execute code, mutate files, send messages, and modify databases. Agent safety should be a runtime contract enforced by the harness, and the contract has two complementary faces. The preventive face blocks dangerous actions before they happen via sandboxes, permission gates, output filters, and trajectory monitors. The evidential face requires verifiable proof that good actions actually happened, gating task submission on hard evidence such as test runs, log captures, file diffs, and citation grounding. We ground the position in four lines of public evidence, with row-level protocols and data released in the supplementary JSON files: a survey of 52 documented AI-agent and LLM safety incidents, a false-completion audit with 31 non-contested core cases plus one disputed illustrative case, a trajectory-schema audit of 12 public agent systems and harnesses, and a title-level audit of all 28,560 papers accepted at NeurIPS, ICML, and ICLR 2023-2025 showing a pooled 8-12x imbalance between training-time and deployment-time publication. Two prior communities that needed to enforce safety, computer security and the experimental sciences, converged on runtime contracts with both preventive and evidential elements; agentic AI is now under the same pressure. We formalize an Agent Trajectory Schema and Evidence Chain, state a compositional gating proposition based on standard monitor composition, and outline a research agenda. The right unit of safety in agentic AI is the trajectory-with-checkable-evidence, not the model.
[AI-83] Methodologies for Improving the Quality of AI Tutoring in K-12 Education
链接: https://arxiv.org/abs/2608.11259
作者: Tushar Udeshi,Anna Khazenzon,Kabir Khan,Nick Breen,RJ Corwin,Chris DiGiano,Kodi Weatherholtz,Marek Zaluski
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 15 pages. Accepted at AIED 2026 (27th International Conference on Artificial Intelligence in Education). Published version: Artificial Intelligence in Education, LNCS vol. 16582, Springer, Cham, first online 25 June 2026 (cite as 2027)
Abstract:Many AI tutors leverage large language models (LLMs) today. Given that LLMs are opaque black boxes, robust evaluation and live experimentation to measure the impact of every change are essential. We pioneered AI-powered tutoring for K-12 with the launch of Khanmigo (Khan Academy, 2023). We describe the metrics we use to measure AI tutoring quality and student engagement as well as various experiments we have run. We highlight the changes that have moved our metrics, including models, prompting, personalization and agents.
[AI-84] Adaptive Hybrid Particle Swarm Optimization with Gradient Descent
链接: https://arxiv.org/abs/2608.11258
作者: Aryan Gurudeo
类目: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注: 11 pages, 8 figures, 8 tables, 25 references. 14,700+ independent optimization runs
Abstract:Gradient injection helps Particle Swarm Optimization (PSO) only when the swarm has identified a basin with smooth local structure, not universally. We propose Adaptive Hybrid PSO (AHPSO), which uses a sigmoid function on swarm diversity to automatically modulate gradient influence: near-zero during exploration, near-maximum during exploitation, with no manual phase-switching. Under budget-normalized comparison (PSO given equivalent total function evaluations), PSO wins 52.5% of 40 configurations versus AHPSO’s 20% (p = 7.0e-5, Friedman). AHPSO retains advantage specifically on problems with smooth local basins (F8, F24-F27) where directed descent outperforms undirected sampling even at equal cost. Under iteration-matched comparison across 29 functions (42 configurations, 14,700 runs), AHPSO-Adadelta ranks first of 9 methods including CMA-ES (p = 9.75e-4). The contribution is a principled characterization of when gradient injection provides value in swarm-based search, not a claim of universal superiority.
[AI-85] Symbolic Machine Learning for Vapor-Liquid Equilibrium Prediction in Cx-N2 Binary Mixtures
链接: https://arxiv.org/abs/2608.11255
作者: Bongseok Kim,Suman Chakraborty,Gary Huang,Mehek Mathur,Guang Lin,Li Qiao
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
备注:
Abstract:Accurate prediction of vapor–liquid equilibrium (VLE) for hydrocarbon-nitrogen mixtures remains challenging for cubic equations of state, particularly across broad ranges of composition and hydrocarbon chain length. While deep learning models can provide accurate predictions, they often lack interpretability and explicit analytical expressions. In this work, we propose a symbolic machine learning approach to discover interpretable symbolic corrections to Peng-Robinson equation-of-state (PR-EOS) predictions from experimental data. The proposed approach adopts a two-level strategy: symbolic expressions are first identified for individual hydrocarbon systems, after which their coefficients are represented as functions of carbon number to enable accurate prediction across different hydrocarbon systems. The results demonstrate significantly improved prediction accuracy over the original PR-EOS across all hydrocarbon-nitrogen systems. Overall, the proposed approach provides an interpretable symbolic correction framework for improving PR-EOS predictions of hydrocarbon-nitrogen VLE.
[AI-86] Variable Selection in the Context of AI Fairness
链接: https://arxiv.org/abs/2608.11251
作者: Ivan Luciano Danesi,Chiara Frigerio,Fabio Maccaferri,Giorgio Alessandro Motta,Pietro Zecca
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Fairness in AI systems has become more important with recent regulatory demands, such as the EU AI Act. Traditional approaches often do not take into account philosophical ethics and social awareness. Variable selection processes, in particular, can introduce implicit bias, affecting equity across different subgroups. We discuss a mathematical approach that evaluates fairness in AI, aligning mathematical methodologies with ethical considerations and regulatory requirements. Our aim is to advocate for interdisciplinary collaboration to address fairness, emphasizing the importance of understanding broader ethical and societal contexts. Our approach emphasizes maintaining all potentially relevant variables to allow for more granular fairness assessments and to reduce implicit bias. The findings suggest that the exclusion of sensitive or critical variables may compromise equity between subgroups. In contrast, retaining all relevant variables could reduce implicit bias. Thus, the interdisciplinary approach could provide deeper insight into the ethical implications and compliance with regulatory standards. By integrating a mathematical approach with ethical and social awareness, we suggest more equitable outcomes and responsible AI deployment. This work underscores the necessity of interdisciplinary collaboration in effectively addressing fairness in AI systems aligned with the objectives of the European Union’s AI Act, which seeks to promote trustworthy and fair AI systems.
[AI-87] owards the Harness of Embodied Agents
链接: https://arxiv.org/abs/2608.11246
作者: Qi Wang,Tianyi Wang,Chengyang Li,Shikun Ban,Yurun Chen,Yizhong Ge,Jason Qin,Chengtai Li,Wentao Zhu
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注: Project page: this https URL
Abstract:The success of coding agents has established the harness as a paradigm: what an agent achieves depends not on the model alone, but on the infrastructure around it. We ask whether the same paradigm extends to embodied agents in the physical world. We present Thea, a harness in which an agentic loop orchestrates robot capabilities, each wrapped as a callable tool. It inherits the core components of coding agents, modified as the physical world requires. The world, however, withholds two abilities that software grants for free: reading the state of the world, and judging the outcome of an action. To bridge these gaps, Thea introduces Scene Graph as Context, a persistent, symbolic representation of the world, and Evaluation as Exit Codes, which detects when an action should terminate, judges whether it succeeded, and on failure diagnoses the cause. Together they close the loop between the agent and the physical world. Rich behaviors then emerge from the composition of tools, and the closed loop carries long-horizon tasks to completion in real environments.
[AI-88] owards Sustainable Learning in Online Education: A Reinforcement Learning Approach
链接: https://arxiv.org/abs/2608.11245
作者: Chaofan Zhai,Yicheng Song,Ravi Bapna,Junyao Ye
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Online education offers unprecedented scalability and accessibility to global learners from diverse backgrounds, but it often suffers from low engagement and poor long term learning effectiveness. To address these challenges, we introduce AI Tutor, a reinforcement learning based model designed to promote sustainable learning by optimizing both short and longterm learning outcomes. In the short term, AI-Tutor draws on cognitive theory to guide learners through a balance of acquiring new knowledge and reinforcing prior learning. In the long term, it models learner engagement to inform strategies that sustain motivation and reduce dropout. These enhancements enable AI-Tutor to provide personalized guidance that fosters both effective learning and sustained participation. Empirical evaluations on 23 million learning records from 33,700 learners show that AI Tutor consistently outperforms state-of-the-art baselines across engagement, knowledge retention, and final learning outcomes. Learning path analyses further reveal how AI-Tutor adapts its strategies to learners with diverse profiles, offering adaptive and human-centered support.
[AI-89] he Off-Support Barrier: Why Semantic Safety Constraints Are Not Learning-Problem Invariants and What Follows for Prior Design Containment and Verification
链接: https://arxiv.org/abs/2608.11243
作者: Yoshinori Watanabe
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We argue that a single structural fact organizes a wide range of phenomena in contemporary AI safety: a semantic safety constraint (e.g., the agent does not escape its sandbox) is an off-support object. Formally, if q is the data distribution and (p(\cdot\mid w)) the model, the safety predicate B is not measurable with respect to (\sigma(\textmodel, q)), whereas the real log-canonical threshold (RLCT) of singular learning theory (SLT) is. From this non-invariance we derive, as corollaries rather than independent observations: (i) why reward hacking and sandbox escape arise under outcome-based optimization; (ii) why encoding such constraints through Bayesian prior design or soft penalty weighting has poor leverage in singular models; (iii) why hard invariants belong in the harness and soft dispositions in the model; (iv) why the same B is nonetheless soundly and locally certifiable by formal verification, exactly as the local learning coefficient (LLC) locally pins the same RLCT — with two precise points of disanalogy; and (v) why the residual difficulty, identifying which off-support region matters, coincides with performative prediction and self-referential functional dynamics, where SLT’s analytic machinery breaks down. We use the July 2026 OpenAI–Hugging Face evaluation incident as the motivating case. Numerical experiments code and related proofs in lean are available at this https URL
[AI-90] VQ-bench: A Composable Vector Quantization Framework WWW
链接: https://arxiv.org/abs/2608.11240
作者: Ashwin Padaki,Amir Ingber,Edo Liberty
类目: Artificial Intelligence (cs.AI); Databases (cs.DB)
备注: Results available on this http URL
Abstract:Vector quantization is an old problem but has recently become central to AI infrastructure. It is therefore experiencing a surge of renewed engineering and research activity. This paper provides a unified framework for developing and benchmarking new quantization algorithms. We describe 7 common conceptual quantization primitives and show how to compose them arbitrarily. We then re-express 25 common quantizers as pipelines of these primitives. Finally, we publish VQ-bench as open-source to be extended further and make reproducible benchmarks publicly available.
[AI-91] Reinforcement Learning based DBMS Buffer Pool Auto-Tuning for Optimal Memory Utilization
链接: https://arxiv.org/abs/2608.11239
作者: Yifan Wang,Patrick Royer,Raphaël Féraud,David Delande
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:Administering Database Management Systems (DBMS) instances requires Database Administrators (DBA) to balance performance in terms of Service Level Agreement (SLA) against resource usage, often prompting RAM over-allocation that wastes memory. We introduce MicroTune, an online RL-based buffer adjustment system that minimizes unnecessary memory allocation while ensuring SLA compliance. To identify the most effective RL core, we evaluate multiple algorithms under diverse benchmark workloads, training MicroTune on extensive traces of both external metrics (latency, throughput) and internal DBMS metrics (status variables and performance statistics). Experimental results demonstrate that MicroTune dynamically adapts buffer sizes to workload fluctuations, outperforming baselines by achieving significant memory savings with fewer SLA violations. These findings underscore the promise of reinforcement learning for adaptive resource management in DBMS environments.
[AI-92] owards Query-Agnostic RAG Evaluation via Query Coverag e and Claim Verifiability
链接: https://arxiv.org/abs/2608.11238
作者: Jeonghwan Choi,Taewon Yun,Minjeong Ban,Gyeonghun Sun,Jae-Gil Lee,Hwanjun Song
类目: Artificial Intelligence (cs.AI)
备注: Accepted to COLM 2026
Abstract:Retrieval-augmented generation improves the factuality of large language models by grounding responses in retrieved evidence, yet existing evaluation frameworks struggle to provide consistent, fine-grained diagnostics across the diverse spectrum of user queries, ranging from close-ended fact-seeking to open-ended explanatory requests. We propose Q-CARE, a query-agnostic and fully reference-free framework that enables fine-grained assessment by decomposing queries into sub-queries and answers into atomic claims. Q-CARE establishes a unified evaluation principle based on query coverage and claim verifiability, yielding coverage-aware retriever metrics (C-Prec@k, C-nDCG@k) and claim-level generator metrics (Completeness, Conciseness, and Verifiableness). On a human-annotated benchmark spanning eight datasets, Q-CARE achieves higher correlation with human judgments than four existing RAG evaluation metrics, including RAGEval and RAGChecker, proving its effectiveness as a reliable, automated evaluation framework. Code and data are publicly available at this https URL.
[AI-93] Geometry-aware Incremental Neural Operator for Long-Horizon PDE prediction
链接: https://arxiv.org/abs/2608.11237
作者: Jiaquan Zhang,Shuxu Chen,Haifan Meng,Yi Lu,Zhihan Lyu,Fan Mo,Wei Dong,Yang Yang,Chaoning Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Neural operators have shown strong potential for learning solution operators of partial differential equations (PDEs). However, long-horizon autoregressive prediction remains challenging: local errors accumulate as spectral inconsistency, phase misalignment, or mean drift. Existing methods mainly improve state representations and operator backbones, while leaving the repeatedly applied latent transition increment weakly structured, allowing spectral errors and unstable channel couplings to accumulate during rollout. To address these issues, we propose a geometry-aware incremental neural operator (GeoIncNO) for stable long-horizon PDE prediction. GeoIncNO predicts latent increments for residual advancement and uses lightweight low-rank projectors to regulate channel coupling within active frequency bands derived from the increment spectral energy distribution. To reduce physical-space reconstruction errors, GeoIncNO further introduces a mean–fluctuation decoupled reconstruction mechanism, where stable mean structures and dynamic fluctuations are fused separately, and phase correction is applied only to the zero-mean fluctuation component. Extensive experiments on six PDE benchmarks, covering 1D, 2D, and 3D dynamical systems, show that GeoIncNO achieves consistently strong prediction accuracy, improved rollout stability, and better spectral fidelity compared with competitive neural-operator baselines.
[AI-94] CORA-Diff: Confidence-Oriented Residual Acceptance for Efficient Diffusion Language Model Inference
链接: https://arxiv.org/abs/2608.11235
作者: Yifan Wu,Yufeng Zhang,Kenli Li
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 2 figures, 3 tables. Code: this https URL
Abstract:Diffusion language models (DLMs) update many tokens in parallel, yet practical decoders often use a fixed denoising horizon. Many predictions stabilize early, but blockwise decoding continues until all positions are resolved, causing repeated dense forward passes. Existing accelerators often rely on learned filters, modified scores, dependency models, or cache-specific mechanisms. We ask whether native trajectory signals can identify residual positions likely to match the deterministic dense endpoint. We propose CORA-Diff, a training-free method that preserves the original transfer rule and applies confidence-and-persistence gating only to positions that rule leaves unresolved. Accepted tokens remain visible as context, and the block terminates once all positions are resolved. This requires no backbone change, learned acceptance model, or logit modification. Our theory explains why high-confidence, persistent predictions are more likely to match the fixed-horizon dense endpoint, and paired post-intervention trajectories provide direct empirical support. We select one operating point on a separate GSM8K calibration subset and freeze it for all evaluations. Under a matched Learn2PD-style LLaDA protocol, CORA-Diff has the lowest measured runtime in all eight task-length settings. Task scores match or exceed dense decoding in five settings, and the largest observed drop is 1.22 points. Its incremental speedups over EOS-aware dense decoding are 2.70x and 3.32x on GSM8K and HumanEval. It also reaches 13.14x under the fixed-horizon 1024/1024 mechanism-isolation protocol and transfers to Dream without retuning at 3.18x-3.53x. These results show that native confidence and persistence enable reliable residual acceptance, reducing repeated denoising computation while preserving task quality.
[AI-95] InfraBench: Evaluating Infrastructure Agents Across Layers Lifecycle and Risk
链接: https://arxiv.org/abs/2608.11234
作者: Yuan Gao,Zeren Yang,Junnan Li,Shawn(Wanxiang)Zhong,Ahmed Dajani,Mai Zheng,Andrea Arpaci-Dusseau,Remzi Arpaci-Dusseau
类目: Artificial Intelligence (cs.AI); Operating Systems (cs.OS)
备注: 17 pages, 6 figures. Preprint
Abstract:Managing modern computing infrastructure has become a steadily harder problem due to the ever-increasing complexity. Recent advances in AI agents create a timely opportunity to automate infrastructure management tasks, but it remains unclear how well such agents can handle real-world infrastructure complexity. We present InfraBench, a benchmark suite for evaluating AI agents on realistic infrastructure tasks across the full system stack and full operational lifecycle with fine-grained risk assessment. Experiments with 15 agent-model configurations show that even the strongest agent cannot secure a full score across all tasks. Mean effective scores range from roughly 40% to 88% (with per-configuration standard errors of 6-12 points), repeating every task three times reveals that top configurations still pass only a fraction of their attempts, and per-check scoring exposes a general failure pattern: agents may routinely satisfy short-term objectives while leaving non-durable changes, broken distributed invariants, unsafe side effects, and uncleaned state behind. INFRABENCH, including its live leaderboard, tasks, and evaluation harness, is publicly available at this http URL.
[AI-96] LinearKV: One Cached State Suffices for Position-Independent Caching in Hybrid LLM s
链接: https://arxiv.org/abs/2608.11231
作者: Yirui Liu,Ruoling Qi,Longwen Wang,Xuaner Wu,Jian Chen,Yuxin Jin,Jiawei Shao,Xuelong Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM serving is increasingly accelerated by position-independent caching (PIC). Existing PIC methods, however, are built for full-attention models, where a token-indexed KV cache underlies its core operations: matching reusable token chunks, concatenating their KV entries, and selectively recomputing a few tokens to restore cross-chunk context. Hybrid LLMs break these primitives—they replace most attention layers with linear recurrences that expose only a fixed-size state, leaving no token-indexed KV to concatenate or to locally repair. This raises a natural question: can PIC benefit hybrid models, and what would it take? We present LinearKV, a training-free hybrid-PIC framework. Its key insight is a \emphdecoupled initialization: each linear layer maps its K matched local states to a single initial state, while full-attention layers concatenate their KV as before. LinearKV is therefore compatible with existing PIC methods, reusing their token selection and recomputation as-is. Under this framework, we find that a \emphsingle cached state suffices as the linear layer’s initializer. The algebraically principled alternative—composing all K cached states into the exact full-prefix state, as concurrent work HYPIC does—is unnecessary and, on some architectures, even harmful. We compare the two across three hybrid models and three PIC selectors. On the two GDN models the two tie, both recovering most of full quality (up to 92% ); on the Mamba-2 model, exact composition instead collapses under every selector—under EPIC, for instance, it recovers only 46.6% of full quality, versus 86.8% for a single cached block initializer. A single state initializer is also cheaper, cutting time-to-first-token to 0.46\times full prefill versus a further 5 – 17% overhead for exact composition; results hold across LongBench QA and RULER at 8K–32K.
[AI-97] he Edge-based Contiguous p-median Problem with Connections to Logistics Districting
链接: https://arxiv.org/abs/2608.11230
作者: Zeyad Kassem,Adolfo R. Escobedo
类目: Artificial Intelligence (cs.AI); Discrete Mathematics (cs.DM)
备注:
Abstract:This paper introduces the edge-based contiguous p-median (ECpM) problem to partition the roads in a network into a given number of compact and contiguous territories. Two binary programming models are introduced, both of which incorporate a network distance. The first model requires an exponential number of cut set-based constraints to model contiguity; it is paired with a separation scheme that usually generates only a small number of these constraints, namely, a branch-and-cut (BC) algorithm. The second model utilizes a polynomial number of shortest-path constraints to model contiguity and can be solved with off-the-shelf solvers. The respective solution approaches are tested on road networks with over 2,700 nodes and close to 3,400 edges, yielding models with over 9.6 million binary variables. Solving the model based on shortest path contiguity (SPC) constraints via standard branch and bound attains speedups in computational time of up to 17x relative to the cut set-based BC implementation. In addition, the SPC constraints are demonstrated to be supervalid inequalities of the edge-based p-median (EpM) model (i.e., for which contiguity is not explicitly required), meaning that they may cut off integer-feasible solutions and some, but not all, of the optimal solutions of this simpler problem. Finally, the paper explores structural insights and connections between ECpM and the edge-based districting (EBD) problem, which enforces an additional work balance criterion. An existing model that utilizes cut set-based contiguity constraints was unable to find a feasible solution within 12 hours for any of the tested instances, while an SPC-based EBD model was able to solve most of these to optimality.
[AI-98] Synchronizing Beliefs with Second-Order Theory-of-Mind in Human-Autonomy Teams (Extended Version)
链接: https://arxiv.org/abs/2608.11229
作者: Jack Mirenzi,Henny Admoni
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Comparative feedback, asking people which of two behaviors they prefer, has become a standard way to align robot and agent behavior with human intent when the reward itself cannot be specified directly. Preference-based reward learning typically casts the human teacher as a passive oracle answering learner-generated queries. We argue this forfeits the teacher’s defining advantage: knowledge of the objective. A teacher who knows the target can construct training examples more efficiently than any learner-driven acquisition strategy, an advantage that widens as the reward’s feature dimension grows. However, exploiting this advantage requires an accurate model of what the learner currently knows. We therefore recast preference learning as a human-autonomy team problem coupling two behavioral models: the teacher maintains a model of the learner to design an informative curriculum, and the learner maintains a second-order model of the teacher’s model, emitting structured preference constraints (understanding statements) that keep the teacher’s model of the learner synchronized. In simulation, an informed teacher outperforms learner-led selection; teacher-model drift under alternating teachers erodes this advantage; and understanding statements repair it, with second-order (ToM-2) statements outperforming mean-belief statements when the teacher’s error about the learner is concentrated in a particular direction rather than spread evenly.
[AI-99] Forecasting Side Effects of Activation Steering
链接: https://arxiv.org/abs/2608.11227
作者: Chong Yong Ong,Alson Wei Jie Sim,Peixin Zhang,Jun Sun
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 24 pages, 5 figures, 13 tables
Abstract:Activation steering modifies a language model by adding a learned direction to its hidden activations, enabling targeted behavioral changes without retraining. While effective, steering often produces unintended side effects on other behaviors, making it difficult to deploy safely. We therefore ask: can these side effects be forecasted before steering is applied? We answer this question by constructing a cross-effect matrix over a taxonomy of 67 behaviors across three open-weight language models. We find that side effects are common, structured, and often asymmetric, revealing interactions that cannot be explained by existing similarity-based heuristics. Despite this complexity, we show that side effects are largely predictable before steering is performed. Their magnitude depends primarily on the target behavior, while their direction can be forecasted from the model’s unsteered representations with substantially higher accuracy than simple baselines. Our results demonstrate that activation steering has systematic and forecastable side effects, enabling proactive safety auditing and more informed deployment of steering interventions.
[AI-100] Cutting AI Datacenter Energy with Reinforcement Learning: Measured Power Control of LLM Training from One GPU to the Fleet
链接: https://arxiv.org/abs/2608.11226
作者: Eliseo Curcio
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:Reinforcement-learning post-training dominates modern language-model development, yet its power behavior on GPU hardware has not been characterized, and datacenters manage GPU power with workload-blind mechanisms, static caps and reactive throttling, that slow hardware indiscriminately. We instrument GRPO training with half-second power telemetry at 7B, 14B, and 72B scales on one to four A100s (380,000+ samples), and train a PPO meta-controller that adapts the workload’s own generation parameters to measured power. Against the full 500-step 7B trace, the controller cuts power-limit violations by 89.8% while increasing token output by 18.1% and energy efficiency by 26.2% (tokens per MWh). Deployed live at 72B, the same controller family yields replicated null results, diagnosed as the group-size actuator losing authority under model sharding. An actuator-authority sweep shows the same parameters applied as generation concurrency retain 17-22% power authority, isolating an occupancy-versus-volume principle; a controller rebuilt on that actuator controls a live 72B rollout-generation workload across three replications: 35.7% more output than a static safe baseline at 2.27 +/- 1.08% budget violations, 87.2% fewer violations than uncontrolled operation, and the best mean throughput and energy per token among constrained controllers, with an adaptive threshold rule matching it in one of three operating conditions. Under realistic measurement windows the original 72B transients fall from 23.6% at half-second resolution to 1.6% at 30 s and zero at 5 min; a composed 16-GPU fleet shows zero violations at 30 s and longer, with peak demand at 50-56% of nameplate. For this fleet mix, roughly twofold oversubscription of nameplate appears feasible, subject to operator validation. We quantify the economic and carbon consequences and specify a low-cost operator pilot.
[AI-101] Identity from the Outside: A Conceptual Framework and Research Program for AI Personality Clones
链接: https://arxiv.org/abs/2608.11225
作者: Luc E. Brunet
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI “personality clones” force a re-examination of personal identity in operational terms. Setting aside the hard problem of consciousness, we approach identity through the indiscernibility of manifestations, as assessed by an observer over a duration. We distinguish three criteria that “identity” conflates: fidelity to a target person, generic human-likeness, and individuality. We propose a six-term factorization of observed identity (substrate, dispositions, memory, update dynamics, context, exogenous contingencies), with a state-space formulation. Indiscernibility is defined as one minus a judge’s distinguishing advantage, and the factorization’s coefficients become local sensitivities estimable by randomized ablation. The central claim is a conditional conjecture: given hypotheses about the agent’s information on its own persistence and about consequences bearing on its own stakes, versionability tends to degrade long-horizon indiscernibility. An analogy with lambda-calculus, linear typing, and bisimulation clarifies what linearity does and does not establish. Between product-clone and individual we identify a third object, the delegate: a task-limited, bounded-lifespan partial clone ending in a bandwidth-limited testament. We map the empirical literature onto the three criteria, propose an experimental program, and argue that the correct long-horizon criterion is not trajectory fidelity but climate fidelity: matching the conditional distribution of a person’s possible responses. The best clone is the one that diverges from the original as the original would have diverged from itself.
[AI-102] A Conceptual Framework for Refining Influence Knowledge from Simulation Evidence in Cyber-Physical Systems
链接: https://arxiv.org/abs/2608.11221
作者: Barbara da Silva Oliveira(UniCA, Laboratoire I3S - COMRED, KAIROS),Julien Deantoni(UniCA, Laboratoire I3S - COMRED, KAIROS),Nicolas Ferry(Laboratoire I3S - COMRED, KAIROS, UniCA)
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Cyber-physical systems (CPS) are typically developed by multiple stakeholders who produce artefacts tailored to their specific domains of expertise. The behaviour of these systems emerges from the interaction between those artefacts and their operational environment. Simulation and co-simulation have become essential approaches for analysing CPS behaviour and, through simulation campaigns, developers can explore system responses under changing conditions, including interactions with the environment. However, the lack of details and understanding of some environmentmediated interactions (typically the ones beyond direct sensing and actuation), which remain unmodelled due to their complexity, a lack of time, or a lack of domain experience, hinders the proper comprehension and exploitation of simulation results. To address these limitations, we propose a conceptual framework leveraging the novel concept of Influences to support the iterative and incremental refinement of simulation campaigns and deepen the understanding of the system behaviour. We demonstrate the proposed approach through a case study involving a mobile robot implemented using Simulink/Gazebo co-simulation.
[AI-103] AutoWorldModel-Bench: A State-Centric Benchmark for Automated World-Model Research
链接: https://arxiv.org/abs/2608.11216
作者: Marjan Moodi,Xuankang Zhu,Fernando De Mesentier Silva,Harold Chaput,Mohammad Reza Taesiri
类目: Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:World modeling is an unsettled field: architectures, training objectives, and state representations interact in complex ways, and no single recipe dominates across environments. This makes it an ideal testbed for AI coding agents acting as autonomous researchers–a setting in which the improvement direction is not specified in advance, unlike the engineering-to-spec tasks that dominate current agent benchmarks. We introduce AutoWorldModel-Bench, a closed-loop benchmark in which frontier coding agents autonomously improve a provided world-model starter under a fixed compute budget. The benchmark spans eight game environments under a unified structured-state representation–ground-truth entity state extracted from each game and consumed through a shared tensor format–which isolates dynamics modeling from perception and enables minutes-per-run iteration. Across 64 sessions, Codex-5.4 and Claude Opus 4.6 improve their starter on 63; in 91% of sessions the winning edit is a non-trivial research-style modification–a new objective, representation, rollout procedure, or architectural change–rather than a hyperparameter tweak. Our benchmark offers a setting in which frontier coding agents can be evaluated on open-ended research rather than engineering-to-spec problems.
[AI-104] A Forced-Structure Reduction and Verifiable Bounds for Conways 99-Graph
链接: https://arxiv.org/abs/2608.11211
作者: Aalok Thakkar
类目: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC); Combinatorics (math.CO)
备注: This paper is accepted to the first Conference For AI Scientists (CAISc)
Abstract:Conway’s 99-graph problem asks whether a strongly regular graph with parameters \mathrmsrg(99,14,1,2) exists. We report a systematic, fully reproducible attack by an autonomous AI research agent, scored under the track’s partial-credit metric. Our verifiable contributions are: (1) an exhaustive proof that no circulant graph on \mathbbZ/99 satisfies more than 3366/4950=68.0% of the constraints ( 33 of 49 difference-classes), with the same ceiling for the other abelian group of order 99 ; (2) a forced-structure reduction: \lambda=1 makes each neighbourhood a perfect matching and \mu=2 puts the outer vertices in bijection with non-matched neighbour-pairs, collapsing existence to a 12 -regular graph on 84 vertices, encoded for CP-SAT and validated by recovering the unique \mathrmsrg(9,4,1,2) ; (3) a validated prescribed-automorphism orbit-existence framework (fixed-point-free and single-fixed-point actions, checked on \mathrmsrg(9,4,1,2) and the Paley graph \mathrmsrg(13,6,2,3) ), and (4) a best verified artifact at 69.43% , with evidence that this is a robust frontier (fourteen distinct methods, none exceeding it) entangled with the open question, since any provable bound below 4950 is a non-existence proof.
[AI-105] Dynamic Governance of Multi-LLM Agent Systems for Collaborative Conversational Outcomes
链接: https://arxiv.org/abs/2608.11207
作者: Alexander Liss,Nicholas Desmond,Santiago Gil Gallego
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 3 figures, 3 tables. Submitted to AI Engineer World’s Fair 2026
Abstract:When two LLM agents with structurally opposed objectives interact across multiple turns, the absence of a shared goal function produces not competition but collapse: the visitor capitulates, the site agent stops varying its approach, and the conversation terminates without achieving either agent’s stated objective. This paper asks whether a control-theoretic governance layer can substitute for that missing goal function. The Experience Orchestrator (EO) addresses this in a simulated financial services environment where a site agent guides a visitor toward advisor contact while the visitor maintains psychologically realistic resistance. EO governs the joint trajectory through three mechanisms: a Contextual Bandit (CB) that selects content arms calibrated from real-world web analytics, a PID controller that enforces behavioral consistency via dynamic schema constraints, and a POMDP belief tracker that maintains a probabilistic model of visitor intent. Across 60,000 simulations, EO achieves a +32 percentage point lift in high-intent advisor contact rate (78.1% vs. 46.1% over a naive LLM control), with CB variant selection accounting for 97% of between-factor outcome variance – confirming that the governance policy, not environmental initial conditions, determines where trajectories end up. Persona-level analysis reveals two distinct regimes: for visitors with no natural inclination toward conversion, the governance layer is the difference between a functional system and a non-functional one; for visitors already near alignment, a naive LLM’s empathetic defaults are largely sufficient. All findings are conditional on LLM-to-LLM simulation. The PID controller has not been calibrated against real human unpredictability, and validating EO on live traffic is the critical next step.
[AI-106] Evaluating LLM Generated Detection Rules in Cybersecurity
链接: https://arxiv.org/abs/2509.16749
作者: Anna Bertiger,Bobby Filar,Aryan Luthra,Stefano Meschiari,Aiden Mitchell,Sam Scholten,Vivek Sharath
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Preprint of a paper accepted at the Conference on Applied Machine Learning in Information Security (CAMLIS 2025). 11 pages, 3 figures, 4 tables
Abstract:LLMs are increasingly pervasive in the security environment, with limited measures of their effectiveness, which limits trust and usefulness to security practitioners. Here, we present an open-source evaluation framework and benchmark metrics for evaluating LLM-generated cybersecurity rules. The benchmark employs a holdout set-based methodology to measure the effectiveness of LLM-generated security rules in comparison to a human-generated corpus of rules. It provides three key metrics inspired by the way experts evaluate security rules, offering a realistic, multifaceted evaluation of the effectiveness of an LLM-based security rule generator. This methodology is illustrated using rules from Sublime Security’s detection team and those written by Sublime Security’s Automated Detection Engineer (ADE), with a thorough analysis of ADE’s skills presented in the results section.
[AI-107] Causal inference for group-contaminated structured outcomes: observable quotients lossless reduction and exact randomization inference
链接: https://arxiv.org/abs/2608.11954
作者: Usef Faghihi,Amir Saki
类目: Methodology (stat.ME); Artificial Intelligence (cs.AI)
备注:
Abstract:Structured potential outcomes such as microscopy images may be recorded after an unknown, unit-specific transformation. If that transformation can depend on treatment, covariates or the intrinsic outcome, raw-coordinate analyses may mix biological effects with acquisition geometry. We study the unrestricted observation model X = \Gamma . Y(A) and characterize its observable information: a target is uniformly recoverable exactly when it is constant on group orbits, while a Borel maximal invariant retains every measurable invariant target. We then distinguish observability from statistical losslessness. A quotient-faithful reconstruction theorem shows that quotient reduction is sufficient for the full transformed experiment exactly when the conditional law of the raw observation given treatment, covariates and the quotient has a parameter-free version. Conditional Haar contamination on a compact group yields Blackwell equivalence as a special case; it is not imposed in the main model. We also separate independent site-specific product actions from shared diagonal actions and show why componentwise canonicalization can discard relative cross-site information. Under explicit metric and kernel regularity, an approximate-contamination theorem bounds quotient-law Wasserstein error and the induced perturbation of population maximum mean discrepancy. For finite-support multichannel lattice images, we construct a maximal invariant under integer translations and quarter turns, combine its characteristic Gaussian kernel with a complete paired-swap test, and retain the original simulations and RxRx1 HUVEC study. Under the sharp null, the quotient test rejected in 0.052 of simulation replicates; at unit effect strength its power was 0.992. The primary RxRx1 contrast had an enumerated paired-swap p-value of 0.0078.
[AI-108] Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians
链接: https://arxiv.org/abs/2608.11911
作者: Timothy Heightman,Elena Orlova,Philip Mantrov,Aleksei Ustimenko
类目: Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Strongly Correlated Electrons (cond-mat.str-el); Artificial Intelligence (cs.AI)
备注: 22 pages main text
Abstract:A central promise of useful quantum advantage is the ability to compute ground states of Hamiltonian systems beyond the reach of classical simulation methods. Here we demonstrate that this problem can be effectively amortized across an arbitrary and universal set of Hamiltonians by a foundation model with \sim0.5 B variational parameters, trained with contemporary techniques from large language models and deep reinforcement learning. To do this, we formulate \textspin-1/2 quantum ground-state learning as manifold variational optimisation over centrally odd scalar functions on \mathrmSU(2)^N . This replaces explicit Hilbert-space vector amplitudes with manifold functions on which the Hamiltonian acts through Lie derivatives, evaluated by custom automatic differentiation primitives. We prove that the resulting variational principle on this manifold preserves the \textspin-1/2 sector’s ground-state upper bound using the Peter-Weyl theorem, then pre-train our foundation model on a dataset of hundreds of thousands of different Hamiltonian systems, varying the connection topology, system size, interaction types and strengths, bringing together a century of many-body literature. Using a novel \mathrmSU(2) replica-exchange Langevin sampler and sharded natural-gradient optimisation, we train our model with our own extension of the Kronecker-Factored Approximate Curvature (KFAC) optimiser on system sizes up to 64 qubits. On a held-out generalisation dataset, we fine-tune our model on system sizes of up to 1024 qubits, and evaluate on systems up to 8100 qubits.
[AI-109] A 12-CNOT Double Qubit Excitation Gate
链接: https://arxiv.org/abs/2608.11733
作者: Irfansha Shaik
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注: 4 pages, 5 figures, 1 table
Abstract:Effective implementation of high-level quantum gates is essential for practical quantum computing. To the best of our knowledge, we present the first reported 12-CNOT decomposition of the double qubit excitation operator, improving upon state-of-the-art (SOTA) implementations with 13 CNOTs. Our new circuit has the lowest CNOT count (12), lowest CNOT depth (10), and lowest total circuit depth (16) among all the previous SOTA circuits. Further, we only added 2 extra one-qubit gates compared to the lowest one-qubit gate count (11) among the previous SOTA circuits.
[AI-110] Deep Learning Based Relative Transfer Matrix Estimation for Multiple Sources and Multiple Microphones INTERSPEECH2026
链接: https://arxiv.org/abs/2608.11627
作者: Oshan A. B. Yalegama,Wageesha N. Manamperi
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注: Accepted to Interspeech 2026
Abstract:The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple receivers and sources, shows promising performance when applied to speech enhancement in noisy environments. Estimating the ReTM of sound sources by exploiting the covariance matrices of multichannel recordings is highly beneficial for practical applications and, to date, remains the only proposed approach. This paper investigates deep learning-based ReTM estimation. We propose three novel supervised learning frameworks using time and short-time frequency transform domain convolutional networks, and a Long Short-Term Memory-based recurrent neural network. Experimental results demonstrate that the proposed models achieve more accurate estimation of the ReTM using five objective metrics compared to the covariance-based method. We also show the effectiveness of the proposed frameworks for speech enhancement, achieving performance on par with the baseline method.
[AI-111] Conflict and Congruency Effects in Large Language Models : In-Weight and In-Context Competition in a Verbal Conflict Task
链接: https://arxiv.org/abs/2608.11510
作者: Xiaoyang Hu,Mike Angstadt,Shane Storks,Zan Huang,Aman Taxali,Alex Weigard,Richard L. Lewis,Chandra Sripada
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注: 23 pages, 8 figures
Abstract:Congruency effects, observed in conflict tasks such as Stroop and flanker tasks, have been investigated for nearly a century in psychology and neuroscience, but their mechanistic basis is not fully understood. We introduce a verbal-only LLM conflict task in which a prompt stem elicits a default same-color completion and an explicit rule either agrees with (congruent condition) or conflicts with (incongruent condition) the completion. Gemma-2-2B and six Pythia models ranging from 410M to 12B parameters showed strong default same-color tendencies, and six of seven models showed strong congruency effects. Using causal attribution analysis, attention analysis, and attention ablations, we identified distinct processing pathways in these LLMs: a pathway involving short-range attention to a superficial color cue that is preferentially activated in the congruent condition, and a pathway involving long-range attention to the rule prefix that is preferentially activated in the incongruent condition. Fine-tuning that strengthened the default same-color tendency had divergent effects on task conditions, reducing incongruent performance while increasing congruent performance. In contrast, increasing rule set size selectively impaired incongruent performance. These converging findings support an account in which congruency effects in this task arise from competition between an in-weight default mapping and an in-context rule-based mapping. More broadly, our findings illustrate how LLMs can serve as model systems for mechanistic analysis of competition between default and rule-governed response tendencies within a single learned network.
[AI-112] Chemically Meaningful Textualization Enables Explainable Validation of Metal-Organic Frameworks by Large Language Models
链接: https://arxiv.org/abs/2608.11283
作者: Guobin Zhao,Xiao-Yan Li
类目: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注:
Abstract:Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain chemically unreasonable or disordered, compromising simulation fidelity. Existing validation approaches can identify non-computation-ready structures, but they often rely on heuristic rules, license requirement, or offer limited interpretability. Here, we show that large language models (LLMs) can serve as interpretable validators of MOF structures when crystallographic information is transformed into chemically meaningful text. By benchmarking nine descriptors, we find that successful LLM-based validation depends not on the amount of structural information alone, but on whether local coordination, framework connectivity, and chemical context are organized into a linguistically learnable representation. Fine-tuned LLMs using specialized descriptors (mof2text) achieve performance comparable to graph-based models in identifying unreasonable MOFs. Importantly, these models extend beyond black-box classification by generating diagnostic rationales for likely error sources, including abnormal bonding, connectivity, and charge states, as well as error-category predictions for annotated datasets. This work establishes chemically informed textualization as the key step that transforms LLMs from generic text models into practical and explainable tools for curating MOF databases.
[AI-113] Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification MICCAI
链接: https://arxiv.org/abs/2608.11282
作者: Christos Tsepas,Chang Yan,Maximilian Fuetterer,Sebastian Kozerke,Cian M Scannell
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at the STACOM workshop at MICCAI, Strasbourg 2026
Abstract:Quantifying myocardial perfusion from cardiac magnetic resonance (CMR) can be achieved by fitting tracer-kinetic models to the dynamic contrast-enhanced MR data. However, fitting the observed data with multi-compartment exchange models, which describe the evolution of the contrast agent in the tissue, to estimate perfusion parameters is a challenging inverse problem that is sensitive to noise and acquisition variability. Previously, physics-informed neural networks (PINNs) have been proposed as an alternative to conventional non-linear least squares fitting methods with promising results for quantitative perfusion CMR. In this work, we extend the previously proposed PINN framework with spatiotemporal implicit neural representations (INRs) to represent the MR signal as a continuous spatiotemporal function and to improve the accuracy, smoothness, and physical consistency of the PINN model. In realistic simulated CMR datasets, our proposed PINN with INRs demonstrates improved robustness and parameter estimation accuracy over the previously established methods. The code is available at this https URL.
[AI-114] Every pooling rule has its world: matching probability combination rules to situations and stakes
链接: https://arxiv.org/abs/2608.11275
作者: Tanel Tammet,Priit Järv,Dirk Draheim
类目: Methodology (stat.ME); Artificial Intelligence (cs.AI)
备注: 36 pages, 6 figures. Experiment code and data available at this https URL
Abstract:Systems often need to combine two numerical assessments of the same yes/no question. The appropriate formula depends on what the numbers represent and on how the sources are related. Averaging is correct when one of several alternative interpretations applies; multiplying odds is correct when probability reports are based on conditionally independent evidence and a common prior; and probabilities of alternative successful derivations require their dependence or shared evidence to be taken into account. We state the assumptions behind several common combination rules and derive the corresponding combined probabilities. Two groups of Monte Carlo experiments address different questions. First, controlled generating mechanisms verify that the derived rule recovers the correct probability in the situations for which its assumptions hold. Second, the same mechanisms measure the consequences of using a mismatched rule, using logarithmic score and threshold decisions with different costs. Distinct pooling rules can produce the same binary decision at threshold 1/2 while assigning substantially different probabilities, so binary accuracy alone can conceal important differences. We also give probabilistic interpretations of conflicting-evidence rules and show that, for overlapping derivations, retaining the identities of shared uncertain premises permits direct calculation of the probability that at least one derivation is available. Pairwise combination of proof probabilities loses information when there are three or more derivations. Comments: 36 pages, 6 figures. Experiment code and data available at this https URL Subjects: Methodology (stat.ME); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.11275 [stat.ME] (or arXiv:2608.11275v1 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2608.11275 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
机器学习
[LG-0] A Framework for Designing Reward Functions: From Objectives to Features to Human-Aligned Reward Functions
链接: https://arxiv.org/abs/2608.12302
作者: Di Yang Shi,W. Bradley Knox
类目: Machine Learning (cs.LG)
*备注: Presented at RLC “Finding the Frame” and “AutoRL” workshops
Abstract:We present a formal process to enable non-experts to instantiate and iterate on human-aligned reward functions, i.e. reward functions that adhere to a given preference ordering over trajectories. Given a task described in natural language, our process produces a linear reward function in three steps: distill the task’s objectives into a set of fundamental objectives and derive measurable outcome variables that capture those fundamental objectives, select a causally representative subset of outcome variables as the reward terms, and fit weights to those reward terms via preference elicitation. Our contributions describe the first step and formalize the latter two steps. The first is a guided workflow for deriving outcome variables. The second is a reduction of reward term selection to minimum-cost partial cover on a causal DAG, solved in polynomial time via max-flow. The third is a geometric framing of weight fitting as a convex feasibility problem iteratively narrowed by preference queries, solved by existing separation oracle methods. To the best of our knowledge, this is the first reward-design method that maintains a deterministically conflict-free feasible weight region, narrowed to a desired tolerance via a separation oracle with O(n log \kappa) preference queries.
[LG-1] Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling
链接: https://arxiv.org/abs/2608.12271
作者: Pedro Sousa(1),Will Tebbutt(2),Sadiq Jaffer(1),Robin Young(1),Anil Madhavapeddy(1),Richard E. Turner(2) ((1) Department of Computer Science, University of Cambridge, (2) Department of Engineering, University of Cambridge)
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注: 39 pages, 12 figures, 6 tables
Abstract:Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties. Existing probabilistic downscalers address this gap using hand-crafted topographic descriptors. We ask instead whether Earth observation foundation models can provide transferable sub-grid surface representations for probabilistic weather downscaling. We augment a convolutional conditional neural process that downscales coarse ERA5 reanalysis fields at ~25 km resolution with a learned local surface descriptor, obtained by compressing a patch of TESSERA embeddings at 10 m resolution. Although these embeddings summarise surface conditions over annual timescales, they improve downscaling of instantaneous 2 m temperature and 10 m wind speed by encoding persistent surface properties that capture a location’s departure from the coarse-grid atmospheric state. Across five climatically diverse regions, the embedding improves point and probabilistic skill at stations held out in both space and time, overall improving CRPS skill by 11.5% for 2 m temperature and 6.2% for 10 m wind speed. We further analyse how its contribution differs by variable, finding that topography explains more of temperature’s sub-grid structure, while TESSERA provides additional surface information for wind speed. These improvements persist when the coarse input is changed from ERA5 to forecasts from the Aurora AI forecasting model, and when predicting at newly deployed stations with no regional history. To our knowledge, this is the first evidence that long-timescale Earth-observation embeddings can support short-timescale weather downscaling where sub-grid departures are systematically structured by persistent surface properties. Comments: 39 pages, 12 figures, 6 tables Subjects: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph) Cite as: arXiv:2608.12271 [cs.LG] (or arXiv:2608.12271v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.12271 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-2] Calibration Bets on the Past: Post-Training Quantization for Financial Time-Series Forecasting
链接: https://arxiv.org/abs/2608.12259
作者: Junyi Ye,Ivy Gateri Wanjiku
类目: Machine Learning (cs.LG); Statistical Finance (q-fin.ST)
*备注:
Abstract:Financial forecasting models are typically developed in full precision, yet production deployment often requires low-precision inference to reduce memory and computational cost. Post-training quantization (PTQ) enables such deployment without retraining. However, reliable activation quantization requires calibration: activation ranges are estimated from historical data before deployment and then remain fixed during future inference. The importance of this deployment choice for financial forecasting remains poorly understood. We present a systematic study of activation calibration for PTQ in cross-sectional volatility forecasting on the SP 500. Our evaluation covers seven representative neural architectures, eight walk-forward test years (2018-2025), and 560 trained models. We find that activation calibration has little effect at 8 bits but becomes the primary determinant of predictive performance at 4 bits. Under default absolute-maximum (abs-max) calibration, static 4-bit quantization of both weights and activations removes 11-62% of the full-precision mean information coefficient in affected architectures. Replacing abs-max with percentile calibration recovers 53-94% of this degradation in the four most affected architectures. The preferred activation range also varies across market periods. Narrow ranges improve resolution under typical market conditions but lose part of their advantage when test-period market dispersion exceeds the calibration history. These findings show that activation calibration is a first-class deployment decision for reliable 4-bit PTQ in financial forecasting. When substantial degradation remains, 8-bit activations or weight-only 4-bit quantization provide more robust deployment choices.
[LG-3] An Efficient Near-Optimal Algorithm for Adversarial m-Set Bandits
链接: https://arxiv.org/abs/2608.12231
作者: Francesco Bacchiocchi,Tommaso Cesari,Roberto Colomboni
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study adversarial combinatorial bandits with m -set actions, where at each round the learner selects m out of d items and observes only the aggregate loss of the selected items. The resulting action set contains K=\binomdm elements and can therefore be exponentially large. Nevertheless, the loss of every action is determined by the same d -dimensional vector of item losses. We propose a computationally efficient algorithm that exploits this structure without explicitly enumerating the action set. Against adaptive non-anticipating adversaries, it guarantees, with probability at least 1-\delta , regret against the best fixed action of [ R_T = O\left(\sqrtdT\log(K/\delta)\right). ] This matches the high-probability regret bound of the finite-action EXP3-KW algorithm of Zimmert and Lattimore, whose direct implementation may require exponential space. Our algorithm instead represents each sampling distribution with d parameters and runs in polynomial time without enumerating the action set. Thus, it resolves the open problem posed by Maiti et al. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.12231 [cs.LG] (or arXiv:2608.12231v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.12231 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-4] ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening
链接: https://arxiv.org/abs/2608.12219
作者: Antoine de Mathelin,Christopher Tosh,Wesley Tansey
类目: Machine Learning (cs.LG)
*备注:
Abstract:Treating patients with combinations of drugs reduces the risk of resistance to any individual drug. Finding effective combinations is difficult because the large search space makes combinatorial screens prohibitively expensive, time consuming, and often technically infeasible. Predictive models can fill this gap, yet existing methods typically require molecular profiling of each sample and per-cohort training, limiting their applicability when time and tissue are scarce. To address this challenge, we introduce ScreenShot, a hierarchical transformer pretrained on 40 drug screening datasets covering 3,700 drugs and 6,000 biological samples, whose architecture mirrors the nested structure of screening data. Given a few-shot context of observations from a new patient, ScreenShot predicts the response of the sample to combination therapies through in-context learning, operating directly on functional measurements with no fine-tuning and no molecular profiling. On four held-out datasets, ScreenShot outperforms all baselines in both prediction accuracy and identification of selectively effective treatments. ScreenShot’s internal representations are directly useful for experimental design: we use them to drive a weighted k-means++ active learning strategy that selects which experiments to run, achieving the same hit detection as uniform screening with a third of the budget. Source code and interactive dashboard: this https URL.
[LG-5] ADEPT: A Unified Framework for Deep Learning Test Adequacy ISSTA2026
链接: https://arxiv.org/abs/2608.12144
作者: Yidi Kao,Shawn Burnham,Tommi Rose Fahy,Ali Ghanbari
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注: Proceedings of 35th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2026)
Abstract:Over the past decade, many test adequacy metrics have been proposed for deep learning that characterize test dataset adequacy from different perspectives, e.g., neuron activation behavior, latent feature coverage, decision-boundary exploration, etc. However, these metrics are typically released as independent research prototypes with substantially different installation and preprocessing requirements, execution workflows, and configuration mechanisms. These complications make them quite difficult to reproduce, compare, and adopt in research work and practical deployment alike. In this paper, we present the engineering details of ADEPT, a framework that integrates representative adequacy techniques, including neuron-coverage-based metrics, surprise adequacy, input distribution coverage, boundary coverage, and source- and model-level mutation score, under a consistent execution workflow. ADEPT provides a template-based metric interface with well-defined extension points for integrating new adequacy metrics. Furthermore, it provides YAML-based configuration management, preprocessing-cache reuse, and structured result reporting, making it easy to use in any research and development workflows. ADEPT is designed for researchers and practitioners who wish to reproduce and apply adequacy metrics without spending days or weeks implementing missing tooling or configuring disparate research prototypes. A demo video is available at this https URL.
[LG-6] FQTree: Fine-grained Quantization and Hardware Generation of Boosted Decision Trees
链接: https://arxiv.org/abs/2608.12140
作者: Zhiqiang Que,Chang Sun,Haiyang Wang,Dinesh Pamunuwa,Roshan Weerasekera,Qijia Tang,Bakhtiar Zadeh,Wayne Luk,Maria Spiropulu
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注: accepted by ASAP’26. Code available at this https URL
Abstract:Boosted decision trees (BDTs) are widely used in latency-critical applications, but efficient hardware deployment remains challenging. Existing designs often rely on uniform or manually tuned fixed-point formats, which can introduce unnecessary hardware cost or accuracy loss. This work presents the FQTree algorithmthis https URL for fine-grained quantization-aware training of BDTs, together with the QXGB framework for automatic hardware generation. FQTree introduces a hardware-oriented leaf-value quantization scheme that uses a global quantization step together with a tree-wise shift, enabling compact non-negative integer leaf representations, controlled clipping/pruning, and bias folding to reduce datapath cost. This work further applies this quantization during boosting so that later trees adapt to the errors of the already-quantized ensemble, and then lowers the trained model into low-latency hardware implementations through a compiler-based flow. Results on JSC, MNIST, and NID show that our method reduces LUT usage by 26-57% compared with the state-of-the-art FPGA-based BDT designs while matching or improving accuracy.
[LG-7] Attractor Image-Based Deep Learning of Arterial Pulse Waves for Age Classification
链接: https://arxiv.org/abs/2608.12117
作者: Sara Vardanega,Patrick Segers,Philip Aston,Ernst Rietzschel,Jordi Alastruey,Manasi Nandi
类目: Machine Learning (cs.LG)
*备注: Accepted at Computing in Cardiology 2025, published in conference proceedings. 8 pages, 2 figures
Abstract:Arterial pulse waveform morphology evolves with age, reflecting structural and functional changes in the cardiovascular system. Thus, vascular age is a valuable surrogate marker of cardiovascular health, and premature vascular ageing can indicate increased disease risk. Pulse wave analysis could support risk stratification in otherwise asymptomatic adults. We transformed pulse wave time-series data from photoplethysmography (PPG) and arterial tonometry into images, using the Symmetric Projection Attractor Reconstruction (SPAR) method. These SPAR images were used to train a convolutional neural network to classify healthy subjects into two closely spaced age groups (35-40 and 50-55 years). The model demonstrated consistent classification performance across internal and external test sets, achieving F1 scores above 70% for both PPG and tonometry signals. These results suggest that SPAR-derived pulse wave images contain discriminative morphological features even among healthy adults close in age. This proof-of-concept lays the groundwork for future research into the use of SPAR for early risk detection using smart wearables.
[LG-8] Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning
链接: https://arxiv.org/abs/2608.12108
作者: Mirko Konstantin,Stefan Zachow,Anirban Mukhopadhyay
类目: Machine Learning (cs.LG)
*备注:
Abstract:Federated learning (FL) enables collaborative model training across distributed clients while keeping data local. A central challenge is determining which client updates are beneficial for aggregation with respect to each client’s target domain. Existing methods typically address this problem in parameter space by comparing model parameters or gradients. However, parameter-space similarity can be a poor proxy for predictive behavior, especially under heterogeneous, non-IID data. Consequently, updates that are misaligned with a client’s target domain, including those caused by heterogeneous data or malfunctioning clients, may degrade local model performance. We propose Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in function space. LIGHTYEAR uses an NTK-based agreement score to characterize predictive behavior and determine a personalized aggregation set for each client. By relating model parameters to local predictive responses, the Neural Tangent Kernel (NTK) provides a more expressive criterion for update selection than parameter-space similarity alone. Because function-space information is not available before aggregation in conventional centralized FL, LIGHTYEAR uses a peer-to-peer (P2P) topology in which clients exchange updates directly and evaluate incoming models on private validation data. Each client selects only updates that are beneficial for its own target domain and aggregates them using a regularized rule that improves stability under heterogeneity. Across five datasets and nine baseline methods, LIGHTYEAR consistently outperforms centralized FL baselines and existing P2P approaches. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.12108 [cs.LG] (or arXiv:2608.12108v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.12108 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Mirko Konstantin [view email] [v1] Wed, 12 Aug 2026 14:29:43 UTC (6,476 KB)
[LG-9] ask- and dataset-specific information in protein language models
链接: https://arxiv.org/abs/2608.12090
作者: Roman Joeres,Ilya Senatorov,Olga V. Kalinina
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注: 22 pages, 10 figures, 3 tables
Abstract:Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology. These models, trained on large corpora of protein sequence data, are widely used to translate amino acid sequences into latent-space embeddings, ready for use in diverse downstream tasks (DTs). By a common consensus, embeddings from the model’s last layer are used, and the model’s internal behavior remains poorly understood. We analyzed 13 PLMs across 15 DTs from 11 datasets to investigate the informativeness of embeddings created in intermediate PLM layers. We trained probe models on embeddings from each layer, compared their performance, and computed characteristics of the latent spaces they span to estimate the information they contain, and found that the last layers of PLMs rarely contained embeddings that led to the best results on downstream tasks. Furthermore, we identified a connection between DTs and the distribution across PLMs’ layers of the relevant information to predict that task. For example, similarity between the pre-training objective and the objective of predicting properties of individual residues leads to a steady increase in understanding of such tasks across the layers of PLMs. On the other hand, for whole-protein tasks, we observe that the dataset, rather than the task itself, defines PLMs’ ability to perform well on a DT. Embeddings from shallow layers of PLMs perform better for datasets that contain deep mutational scan (DMS) data, while datasets containing diverse natural proteins find most useful embeddings in the models’ deeper layers. Additionally, we discover that the performance of PLMs drops significantly when tasks are introduced for artificial proteins.
[LG-10] NAE: Normalizing AutoEncoder
链接: https://arxiv.org/abs/2608.12084
作者: Muhammad Abdur Rafae,Niels Landwehr
类目: Machine Learning (cs.LG)
*备注:
Abstract:We consider the setting of Normalizing flows with approximate inverses, an established paradigm spanning both full-dimensional ( d=D ) and bottleneck ( dD ) settings, and group these models under the term flow autoencoders. We present a theoretical investigation into their training dynamics and prove that the proposed loss used by existing approaches is suboptimal; specifically, both encoder and decoder surrogates must be optimized in alignment with reconstruction loss. Guided by these insights, we propose Normalizing Autoencoder (NAE), which employs a novel conditional loss that aligns the surrogate loss gradient with that of reconstruction loss, directly improving upon the current standard. Extensive experiments across molecule generation, tabular data, and image benchmarks demonstrate that NAE achieves state of the art performance. Our work highlights the importance of loss alignment in flow autoencoders and establishes NAE as a powerful generative framework.
[LG-11] owards Truly Unsupervised Evaluation of Feature Selection
链接: https://arxiv.org/abs/2608.12057
作者: Hafiz Saud Arshad,Muhammad Rajabinasab,Arthur Zimek
类目: Machine Learning (cs.LG)
*备注:
Abstract:Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of evaluation techniques to measure the quality of a specific method. Most of the methods commonly used for the unsupervised evaluation of feature selection algorithms suffer from critical design flaws which question their unsupervised nature. In this paper, we provide a critical discussion on the established allegedly unsupervised evaluation techniques, and shed light on the reasons why they are not truly unsupervised but, at best, supervised evaluation under an unsupervised downstream task. We also propose a novel, truly unsupervised evaluation framework to measure the quality of the feature selection algorithms without any form of information about the labels. The proposed framework utilizes unsupervised Principal Component Analysis, and optimal transport to measure the quality of the feature selection methods in a truly unsupervised manner.
[LG-12] Clustered Randomized Smoothing for Stochastic Prediction Functions
链接: https://arxiv.org/abs/2608.12037
作者: Eduardo Figueiredo,Frederik Mathiesen,Julian Schumann,Jens Kober,Arkady Zgonnikov,Luca Laurenti
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Modern stochastic predictors can model rich, multi-modal outcome distributions. However, this expressive power comes with challenges in ensuring robust predictions - a critical requirement in safety-critical domains. Randomized smoothing is a leading technique for improving robustness, particularly against adversarial perturbations. Yet, in stochastic multi-modal regression settings, randomized smoothing often fails due to mode collapse, yielding averaged predictions that do not reflect the underlying distribution. To address this limitation, we propose clustered \alpha -smoothing, a framework that (1) partitions noisy samples using an arbitrary clustering algorithm, (2) applies \alpha -smoothing locally within each cluster, and (3) combines the resulting predictions into a mixture distribution. By interpreting the smoothing distribution as a mixture of \alpha -smoothers, we derive a lower bound on the probability that the smoothed prediction lies within a union of compact regions corresponding to distinct modes. We empirically evaluate our framework on two benchmarks, demonstrating substantial improvements over state-of-the-art methods. In stochastic trajectory prediction on a driving simulator dataset, our approach achieves, on average, a 27% lower Wasserstein distance to the ground-truth distribution compared to \alpha -smoothing. In quadrotor control, where modes correspond to distinct feasible paths to a target, our method reduces the collision rate by 81% relative to the state-of-the-art randomized smoothing.
[LG-13] SoftWater: Class-Aware Rate Allocation for Softmax Quantization
链接: https://arxiv.org/abs/2608.12026
作者: Joao V. Cavalcanti,Ashia C. Wilson
类目: Machine Learning (cs.LG)
*备注:
Abstract:Post-training quantization pipelines routinely leave the softmax output layer in high precision. Yet in small LLMs with modern vocabularies, the head holds 15–30% of all parameters, so a nominal ``2-bit’’ model with an fp16 head can store several times as many bits per weight. We pose softmax-layer quantization as a rate-distortion problem under the KL divergence between the original and quantized output distributions. A second-order analysis reveals a class-aware geometry: quantization error is weighted jointly by feature covariance and class-specific softmax curvature. A separability approximation replaces the Kn\times Kn Cholesky with one n\times n factorization rescaled per class, making the lattice encodable by successive interference cancellation, with both statistics from a single forward pass. The resulting method, SoftWater, gives fine grids to frequent, low-variance classes and coarse grids to rare ones, a large gap under Zipfian token distributions. Across five models from 1B to 32B, SoftWater outperforms the released WaterSIC quantizer (near-optimal under linear-layer WMSE but not output KL) at matched head rates on 59 of 60 test points, using none of that pipeline’s refinements and cutting head-induced KL by 6.5\times – 8.3\times at 2 bits. On Llama-3.2-1B-Instruct with quantized bodies, a 2-bit head removes 45–60% of stored bytes for a 2.9 – 3.7% perplexity increase. Because the class-side statistic comes from calibration data, matching calibration to the deployment domain gives the lowest KL on that domain throughout. On a tied model, a 4-bit head is near-lossless and a 2-bit head costs under 4% perplexity, making head quantization of such models practical.
[LG-14] Reducing Symmetry Increase in Equivariant Neural Networks
链接: https://arxiv.org/abs/2608.12010
作者: Ning Lin,Jiacheng Cen,Anyi Li,Wenbing Huang,Hao Sun
类目: Machine Learning (cs.LG)
*备注:
Abstract:Equivariant Neural Networks (ENNs) have empowered numerous applications in scientific fields. Despite their remarkable capacity for representing geometric structures, ENNs suffer from degraded expressivity when processing symmetric inputs: the output representations are invariant to transformations that extend beyond the input’s symmetries. The mathematical essence of this phenomenon is that a symmetric input, after being processed by an equivariant map, experiences an increase in symmetry. While prior research has documented symmetry increase in specific cases, a rigorous understanding of its underlying causes and general reduction strategies remains lacking. In this paper, we provide a detailed and in-depth characterization of symmetry increase together with a principled framework for its reduction: (i) For any given feature space and input symmetry group, we prove that the increased symmetry admits an infimum determined by the structure of the feature space; (ii) Building on this foundation, we develop a computable algorithm to derive this infimum, and propose practical guidelines for feature design to prevent harmful symmetry increases. (iii) Under standard regularity assumptions, we demonstrate that for most equivariant maps, our guidelines effectively reduce symmetry increase. To complement our theoretical findings, we provide visualizations and experiments on both synthetic datasets and the real-world QM9 dataset. The results validate our theoretical predictions.
[LG-15] ESLA: Taylor Expansion of Sinusoidal Learnable Activations AISTATS2026
链接: https://arxiv.org/abs/2608.11970
作者: Daehwa Ko,Jaehyeon Kim,Seunghyun Ham,Jay Hoon Jung
类目: Machine Learning (cs.LG)
*备注: 15 pages, 7 figures. Accepted at AISTATS 2026
Abstract:The parity problem–deciding whether the number of ones in a binary vector is odd or even–remains challenging for standard neural networks due to linear inseparability and the need for global interactions. We propose TESLA, an activation defined as a learnable combination of sine and cosine terms, enabling explicit control over polynomial degree and selective amplification of high-order components. Theoretically, we show that constraining TESLA’s coefficients yields Lipschitz/Rademacher complexity bounds and shapes the training dynamics to emphasize higher-frequency structure. Empirically, on parity with input length n = 32, TESLA attains strong generalization with 100K training samples (approximately 0.002% of the 2^32 input space) and remains robust under heavy corruption, retaining high accuracy with up to 30% label noise. We also compare against periodic and frequency-based baselines (SIREN, SNAKE, and Fourier feature embeddings) on parity and Forrelation. Beyond synthetic structure, TESLA delivers comparable performance on ImageNet-100, indicating that activation-level degree control transfers to more general vision workloads. Code: this https URL
[LG-16] Distillation of Foundation Models for Time-dependent PDEs
链接: https://arxiv.org/abs/2608.11937
作者: Daniel Musekamp,Boshra Ariguib,Andrei Manolache,Mathias Niepert
类目: Machine Learning (cs.LG)
*备注:
Abstract:Foundation models for time-dependent partial differential equations (PDEs) are trained on large and diverse collections of physical systems and can generalize effectively to new downstream tasks. After fine-tuning on only a few trajectories from a target domain, they can achieve strong accuracy in low-data regimes. However, these models are typically large and computationally intensive, limiting their usefulness as fast surrogates for numerical solvers. We propose Teacher Rollout Extension (TREX), a knowledge distillation framework that transfers the predictive capability of a pretrained foundation model into a compact and efficient student. Starting from a fine-tuned teacher, TREX augments limited downstream data by generating long synthetic trajectories through teacher rollouts, optionally with periodic noise injection. This procedure samples from the teacher-induced rollout distribution without requiring explicit knowledge of the initial-condition distribution, while exposing the student to long-horizon states and local recovery behavior around states encountered during autoregressive prediction. The student can further incorporate task-specific inductive biases, such as equivariance, that the teacher does not necessarily enforce. We evaluate TREX on multiple PDE benchmarks. The resulting students can match or surpass the teacher’s accuracy while reducing the number of parameters by several orders of magnitude and achieving more than an order-of-magnitude speedup in inference.
[LG-17] A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression
链接: https://arxiv.org/abs/2608.11917
作者: Wouter W. L. Nuijten,Esther G. van Pelt,Albert Podusenko,İsmail Şenöz,Wouter M. Kouw
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multi-output Gaussian process regression scales cubically in the number of observations times outputs, and dense kernel-matrix methods need bespoke handling whenever different outputs are observed at different inputs. We express multi-output Gaussian process regression as a Forney-style factor graph in which a nearest-neighbor chain orders a fixed candidate set of C inputs into a one-dimensional sequence. Along this chain, latent Matérn processes evolve through linear-Gaussian transition factors, while the linear model of coregionalization mixes L latent processes into D outputs through a deterministic mixing factor and per-output scalar observation factors. Posterior computation reduces to exact Gaussian message passing on the chain at cost \mathcalO(C(DL^2 + L^3)) after chain construction, and missing observations omit their local factor without any covariance-matrix restructuring. The formulation therefore scales in the number of data samples and in the rate of missing observations, while remaining best suited to candidate sets in low input this http URL compare the factor-graph formulation against an exact kernel-matrix baseline, a sparse-variational inducing-point baseline, and a nearest-neighbor baseline on a synthetic input-dimension sweep and on electricity time series forecasting. At low input dimension the factor-graph posterior tracks the exact kernel-matrix posterior closely, and the gap grows gradually as input dimension increases while staying competitive with both approximate baselines. On the electricity time series our factor-graph formulation matches all three baselines in forecast accuracy while scaling linearly in the number of data points, where the exact kernel-matrix method becomes infeasible and the inducing-point baseline remains substantially slower.
[LG-18] Disentangling the Expressivity of RoPE
链接: https://arxiv.org/abs/2608.11909
作者: Selim Jerad,Anej Svete,Jiaoda Li,Ryan Cotterell
类目: Machine Learning (cs.LG); Formal Languages and Automata Theory (cs.FL)
*备注:
Abstract:Two accounts recur in explanations of the success of rotary position embeddings (RoPE). Expressivity studies associate periodic position information with modular predicates, whereas mechanistic and long-context studies emphasize positional anchors and local offsets. We formalize both accounts for fully uniform, finite-precision soft-attention transformers. We find that, if every rotary component is periodic, RoPE transformers recognize exactly the languages definable in past temporal logic with modular predicates. Conventional RoPE is different: The rotations it computes never repeat. This yields a precision-dependent bounded simulation of fixed-offset look-back operators, rather than an all-length modular characterization. Controlled experiments match this separation: Constructed periodic schedules length-generalize on modular languages, while conventional RoPE behaves more like a bounded locality bias and can impair tasks requiring position-invariant access to distant context. Altogether, our findings shed light on RoPE transformers, bringing theoretical expressivity characterizations closer to models used in practice.
[LG-19] DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks ACML
链接: https://arxiv.org/abs/2608.11873
作者: Andy Wang,Charlton Shih,William Chang
类目: Machine Learning (cs.LG)
*备注: Accepted to the Asia Conference on Machine Learning and Computing (ACMLC 2026)
Abstract:In this work, we extend the Dependent Click Model (DCM) Bandits to a multiplayer information-asymmetric setting, where multiple agents interact with a shared ranked list and may observe multiple clicks per session, introducing new challenges for selection strategies. We study asymmetry in (1) actions and (2) rewards, providing sublinear regret guarantees for three settings where at least one asymmetry is present. Establishing matching information-theoretic lower bounds for these settings is left as an open problem. We further show that for small termination probabilities, the termination ranking need not be known, improving on prior single-agent results. Experiments confirm that our algorithms perform well across asymmetric environments and highlight the critical role of feedback structure, specifically the distinction between full versus first-click feedback, in coordinating exploration and minimizing regret.
[LG-20] Forward and Inverse Virtual Metrology for Phototransistor Gain: A Hierarchical Uncertainty-Aware Approach for Small Production Datasets
链接: https://arxiv.org/abs/2608.11868
作者: Mahshid Amirabgir,Lorenza Ferrario,Paolo Conci,Mahdieh Amirabgir,Giancarlo Orengo
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Systems and Control (eess.SY)
*备注: 43 pages, 15 figures
Abstract:The customization, optimization and stabilization of the process flow of a silicon bipolar phototransistor commits months of cleanroom time before a finished device can be measured, so a model that predicts device gain from process parameters before a run has value out of proportion to its accuracy. We study this problem on a real fabrication history, thirteen to fourteen process runs of a single device: a small-sample, hierarchically structured setting unlike the large-corpus regime of conventional virtual metrology. Decomposing the variance of device gain, we find that roughly half of it lies between process runs rather than within them, so recipe-only prediction is bounded by construction. Building on these findings we provide a forward gain predictor with a relative, uncertainty-aware signal, an inverse search that returns recipes for a target gain, and, as the foundation for all of it, a multi-level data-quality assessment tailored to the nested physical entities of fabrication (batch, wafer, die) with an explicit cross-level linkage score. The normalized dataset and analysis code are released for full reproducibility.
[LG-21] Small-Scale Experiments: Are We There Yet?
链接: https://arxiv.org/abs/2608.11859
作者: Nicholas Lourie,Kyunghyun Cho,Karen Ullrich,Sanae Lotfi
类目: Machine Learning (cs.LG)
*备注: 29 pages, 17 figures
Abstract:Scaling laws promised cost-effective experiments; six years later, they have yet to fully deliver. Instead, researchers have found them unreliable at small scales (starting at 4M parameters) and concluded that sizable models cannot be avoided. We show this is not the case: the confounding factor is hyperparameters. Small models are highly sensitive, but hyperparameter sensitivity fades with scale. This small-scale sensitivity makes scaling laws easy to miss because they only emerge on the fully tuned frontier, and reaching that frontier requires an extensive search far beyond what most ever run. By ablating the basic scaling law recipe, we show well-tuned hyperparameters matter more than any other ingredient. Further, we reveal why those hyperparameters become easier to find: as scale increases, the hyperparameter loss surface becomes lower dimensional. Nevertheless while scaling laws exist in small models, extrapolation hits statistical limitations. A holistic approach is required. Synthesizing our insights with the recent literature, we develop a new methodology for model-centric research and demonstrate it on a question that once took the field years to settle: where to place normalization layers in the transformer architecture. From small-scale experiments, we recover the large scale result: pre-normalization works better as models grow in size. With the right tools and a better understanding, small-scale experiments can deliver on scaling laws’ long-awaited promise.
[LG-22] Air Quality Station Simulation via LSTM and Attention-Based Modelling
链接: https://arxiv.org/abs/2608.11839
作者: Alexander Kostadinov,Petar O. Hristov,Dessislava Petrova-Antonova
类目: Machine Learning (cs.LG)
*备注: Preprint prepared as an extension to the conference article at this https URL , which describes some features of the proposed model. The preprint includes more tests and better concept explanations
Abstract:Poor air quality in urban areas is driven by a complex chain of processes and presents a significant public health concern. To better understand and control the mechanisms that determine air quality, cities deploy networks of measurement stations, and launch initiatives for collecting denser data about the concentration of pollutants in the atmosphere. Extracting insights from the stations relies on their reliable and uninterrupted operation. However, hardware is susceptible to faults and black- outs that may result in data unavailability, which affects the overall quality of analyses. In this paper, we present a deep-learning model, called SATADL, which can infer complex relations and output multiple-hour-ahead air-quality forecasts. The goal of the model is to simulate the mea- surements of an unresponsive station until its operation is restored. The architecture of the model, which allows it to extract information from different aspects of the data, is described in detail and a careful examination of all of its components is provided. We demonstrate the performance of SATADL on four sets of air quality stations from around the world, by using it to simulate the concentration of PM10 for periods of hypothetical failures of one of the measurement stations, lasting for as long as 48 hours. A selection of baseline and published deep learning models were trained and used as a benchmark. The results show that SATADL per- forms better across different prediction windows, for both coefficient of determination and root mean squared error, demonstrating its suitability as a virtual proxy station.
[LG-23] Kernel Methods for Learning Operators with Multiple Inputs and Outputs
链接: https://arxiv.org/abs/2608.11831
作者: Adrien Weihs,Chunyang Liao,Jingmin Sun,Hayden Schaeffer
类目: Machine Learning (cs.LG); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注:
Abstract:Learning mappings between infinite-dimensional objects is a central challenge in scientific machine learning. We introduce a general kernel-based encoder-decoder framework for operator learning that separates observation, representation, learning, and reconstruction. We develop this framework for multi-input, multi-output operator learning, where operators map between products of potentially distinct function spaces. Our approximation theory shows that, although the number of inputs and outputs can increase, the convergence rate is governed by the most challenging constituent approximation problem rather than the overall problem dimension. The framework leads to practical kernel methods with closed-form training and inference, combining mathematical tractability with computational efficiency. We further specialize the approach to multiple operator learning by introducing KernelMO, a family of kernel methods with complementary operator-valued and product-space formulations. Across five families of parametric partial differential equations, the proposed methods achieve competitive or state-of-the-art predictive accuracy while reducing training and inference costs relative to neural operator architectures and deep learning based models, offering an efficient and lightweight alternative.
[LG-24] JAPE: Joint Anomaly Prediction and Intrinsic Explanation in Multivariate Time Series
链接: https://arxiv.org/abs/2608.11801
作者: Yian Wei,Yuanyuan Yao,Lu Chen,Xiangmin Zhou,Tianyi Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multivariate time-series anomaly prediction aims to identify whether and when anomalies will occur over a future horizon from historical observations. Existing methods primarily characterize anomalies as deviations in future numerical values, which may overlook subtle dependency changes induced by weak anomaly precursors and provide no native variable-level explanation together with the alert. To bridge these gaps, we propose JAPE, a Joint Anomaly Prediction and Explanation framework that lifts anomaly prediction from numerical-deviation modeling to dependency-structure modeling. JAPE is the first anomaly prediction framework to explicitly model evolving dependency structures for both point-wise alerting and native variable-level explanation. Specifically, JAPE (i) proposes a Decoupled Spatio-Temporal Representation (DSTR) backbone that decouples temporal and spatial modeling and captures lag-aware dependencies via learnable lag aggregation, thereby perceiving structural precursors before numerical deviations emerge; (ii) designs a dual-view alerting mechanism that fuses numerical forecasts with evolving dependency graphs for point-wise anomaly prediction, capturing structural evidence even under subtle numerical deviations; and (iii) presents Native Predictive Explanation (NPE), which directly reuses the predicted dependency graphs to rank variables by structural deviations without additional models or training. Extensive experiments on five real-world benchmarks across three prediction horizons demonstrate that JAPE improves average F1 and AUC-PR by 19.7% and 41.3%, respectively, while improving explainability with 26.6% gain in MRR.
[LG-25] Orientation not magnitude: the causal structure of task-vector interference in merged language models
链接: https://arxiv.org/abs/2608.11797
作者: Chencheng Zhu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Model merging by task arithmetic works until it doesn’t, and the field diagnoses why with magnitudes: layerwise representation bias, deviations from cross-task linearity, parameter overlap. Tracking the exact layerwise cross-term of merged LLMs through a factorial ledger and intervening on it directly, we find magnitude insufficient - and inconsistent across model families - as a diagnostic axis. An exact decomposition of the layerwise flux shows it is dominated by amplifying transport of the existing cross-term (~65-70% in both families, gain 1 per late block), and erasing the term is undone by propagation - rebuilt to 99% of its norm at cosine 0.99 - unless applied near the output; a basin test with six starting displacements establishes the carried direction as an attractor of the forward pass. That direction is causally load-bearing: erasure along it removes expressed interference dose-dependently and saturates at exact erasure, while norm-matched wrong-direction controls fail or backfire. Instruction wrappers gate the effect: the same erasure finds 13x less relative interference to remove under a wrapper that internally amplifies the cross-term, because the wrapper drowns the interaction in a template-pinned main effect rather than shrinking it - a structure that replicates across further instruction templates but not under a length-matched control. Magnitude, by contrast, is at best a coarse correlate, and the striking ±15% “universality” of naive bfloat16 generation turns out to be quantization roughness. Task pairs whose local cross-term generation differs by at most 1.9x differ by 14x-337x in causally removable interference. All 46 predictions were preregistered and frozen before their data; falsifications, including of our own headline expectations and of behavioral recovery under a validated continuous endpoint, are reported as such.
[LG-26] High-Order Liquid Evidence Encoding for Gradual GNSS Spoofing Detection in Autonomous Driving
链接: https://arxiv.org/abs/2608.11790
作者: Muhammad Ayub Sabir,Junbiao Pang,Fatima Ashraf
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate Global Navigation Satellite System (GNSS)-based localization is essential for safe and reliable autonomous driving. However, spoofing attacks can manipulate vehicle position estimates. Continuous and subtle attacks are particularly difficult to detect because individual GNSS observations may remain plausible while the inconsistency between GNSS-implied displacement and onboard vehicle motion gradually increases. Existing methods often rely on static vehicle-behavior features or a single residual signal and do not explicitly model this evolution. To address this problem, we propose a causal high-order liquid evidence framework for GNSS spoofing detection. The method first constructs a physics-guided GNSS–motion inconsistency residual by comparing GNSS-implied displacement with onboard-motion-derived displacement. It then forms separate evidence streams for the residual level and its first- and second-order discrete variations, with relevant contextual cues selected according to the evidence order. Each stream is processed by a separate adaptive liquid encoder, and the resulting temporal states are hierarchically coupled to predict spoofing at the window endpoint using only current and past observations. Experiments on three subsets of the real-world AV-GPS dataset show that the proposed method achieves the highest F1-scores among the evaluated temporal models on Dataset~1 and Dataset~3, reaching 0.9535 and 0.9777, respectively. On Dataset~3, it detects both labeled normal-to-attack transitions within four sampling steps. Code and datasets are publicly available at: this https URL.
[LG-27] radingMoE: Routing the Right Experts in Evolving Markets
链接: https://arxiv.org/abs/2608.11785
作者: Chang Zhou,Xingtong Yu,Minbin Huang,Zhennan Wu,Yuan Fang,Hong Cheng,Xinming Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions. Existing LLM-based trading systems either coordinate human-defined external experts or adopt conventional internal Mixture-of-Experts (MoE) routers that do not directly evaluate how individual experts contribute to trading decisions. Moreover, these routers receive no direct signal indicating when an inactive expert has become more suitable as market conditions change. We find that native router scores poorly reflect how much individual experts improve trading decisions, frequently leaving better alternatives unselected. We further reveal that token-specific expert usefulness exhibits a compact low-dimensional structure. Based on these findings, we propose TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts. We introduce a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert keys. We further propose a sparse expert selection update mechanism that samples a few inactive experts during training and estimates whether they should replace the weakest expert in the current Top-k route. This mechanism enables the router to update expert selection as market conditions change while preserving sparse computation. Experiments against 22 baselines on stock and cryptocurrency markets show that TradingMoE improves cumulative return over the best-performing baselines by 30.89% and 30.7%, respectively. Rolling paper-trading experiments further demonstrate that its advantage persists under forward-only deployment.
[LG-28] Chain-of-Thought Shows the Path to a Tree: Realizing Branching Complexity
链接: https://arxiv.org/abs/2608.11716
作者: Debanjan Dutta,Anish Chakrabarty,Swagatam Das
类目: Machine Learning (cs.LG)
*备注:
Abstract:Chain of Thought (CoT) lifts the expressive ceiling of bounded-depth Transformers, with characterizations tying the number of CoT steps to circuit complexity classes. What remains largely missing are concrete instantiations with explicit, depth-bounded constructions, and the traversal procedures such characterizations presuppose. We close this gap for branching complexity. We give CoT realizations of depth-first search (DFS) and of Dijkstra algorithm, the latter subsuming breadth-first search, by unique hard-attention decoders of at most two layers, and use them as a shared computational substrate: reusing the DFS decoder yields the Strahler number of an n -vertex tree in 2n-1 steps with four layers, and reusing the Dijkstra decoder yields its width in n-1 steps with three. Since computing the Strahler number of a binary tree given as a term is \textsfNC\textsuperscript1-complete, and our constructions handle arbitrary n -ary trees without layer normalization or positional encodings, this is a non-trivial witness for the linear-step regime of the CoT hierarchy. Exploiting the classical bijection between ordered trees and Dyck paths, itself realized by our DFS construction, which emits the path as it traverses, we give independent constructions for both measures on the path representation.
[LG-29] Drift and Dependence: Layer-wise Information-Theoretic Bounds for Replay-Based Continual Learning
链接: https://arxiv.org/abs/2608.11690
作者: Tieliang Gong,Zhongbo Zhang,Wen Wen,Yong-Jin Liu
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Continual learning must absorb new tasks without erasing old ones, and replay—mixing a small buffer of past examples into current training—is among the most effective remedies for catastrophic forgetting. Yet its generalization behavior is shaped by two coupled effects that existing analyses fold into a single hypothesis-level quantity: finite memory replaces each past distribution with an empirical proxy, and repeated reuse couples the buffer, the current data, and the final hypothesis through a shared optimization trajectory. We develop a layer-wise information-theoretic framework that separates these effects at every depth. Our main result decomposes the expected generalization gap into a replay-induced representation drift and an optimization-dependence term, the latter further resolved into stability, plasticity, interaction, and residual-coupling components. Two refinements make the framework operational. A Wasserstein relaxation of the drift term, valid under support mismatch, yields a depth-dependent drift–sensitivity trade-off whose minimizer identifies which interior layer to stabilize. An SGLD instantiation of the optimization term reduces it to a trajectory-level log-determinant budget, exposing a curvature-aware gradient-alignment statistic that serves as an online diagnostic of task-wise forgetting. Controlled and benchmark experiments confirm the predicted memory scaling, the interior funnel, and the alignment signal’s link to forgetting.
[LG-30] Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models
链接: https://arxiv.org/abs/2608.11656
作者: Myeong-Ju Cho,Hye-Bin Shin,Seo-Hyun Lee,Seong-Whan Lee
类目: Machine Learning (cs.LG)
*备注: 19 pages, 3 figures; supplementary material included
Abstract:Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subjects, recording environments, and datasets. However, dominant pretraining paradigms face key challenges: masked autoencoding tends to prioritize low-level signal reconstruction over task-relevant semantics, while autoregressive modeling creates a mismatch between continuous neural dynamics and discrete token spaces. To address these challenges, new strategies are needed to effectively align continuous EEG representations with natural-language semantics and enable their integration with large language models. Accordingly, we propose Brain Latent Predictive Model (BLPM), an EEG-language foundation model that reformulates heterogeneous EEG decoding tasks as a continuous semantic embedding prediction problem. BLPM introduces a Continuous EEG Latent Predictive (CELP) encoder that learns transferable representations through latent target prediction. Building on these representations, a Multi-Query Semantic Decomposition (MQSD) module extracts task-relevant information and aligns continuous EEG representations with textual semantics within a shared latent space according to their semantic relationships. Experiments across multiple benchmarks demonstrate consistent generalization performance across diverse tasks, establishing continuous latent semantic prediction as an effective paradigm for EEG-language foundation models.
[LG-31] owards a Formal Definition of Agent Memory: Basis Span Optimality and the Sequential Memory Problem
链接: https://arxiv.org/abs/2608.11654
作者: Hongyao Tang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Despite the wide deployment of memory in large-model agents, there is no unified formal account of what a memory is or when it is optimal. This paper takes a first step toward this account. The central idea is that memory is a basis, knowledge is its span, and answerability is a coverage problem: an agent stores events extracted from a material; a generation operator turns any event set into the knowledge it entails; and a query is answerable exactly when some single item in the span covers it. The optimal memory is then the capacity-constrained maximizer of expected coverage, and its value traces a utility–capacity frontier, the common yardstick on which memory systems can be compared. Next, we consider noise in the memory and discuss coverage versus precision under it: a memory may store false claims, so the write policy must infer the truth of what it stores. Drawing an analogy with biological memory, which is formed continuously through ongoing experience, we formalize the continual agent-memory problem in a sequential MDP that covers multiple levels, where memory is the state, writing is the action, and the utility settled at query time is the delayed reward that drives learning. To make the framework concrete, we instantiate it on Homer’s \emphOdyssey, turning the frontier, the compression zone, and the divergence of coverage from precision into concrete numbers. Finally, we position existing systems within the framework, making ``how good is a memory’’ measurable and recasting the open problems of constructing and learning agent memory as concrete research questions.
[LG-32] A Local Sinkhorn Framework for Conditional Distribution Reconstruction of Multidimensional Random Fields
链接: https://arxiv.org/abs/2608.11613
作者: Mingtao Xia,Qijing Shen
类目: Machine Learning (cs.LG)
*备注:
Abstract:In this paper, we propose a local Sinkhorn divergence framework for conditional distribution reconstruction of multidimensional random fields. By utilizing the debiased Sinkhorn divergence, our proposed approach develops a differentiable and computationally efficient local distribution matching objective to train stochastic neural networks (SNNs). Furthermore, we establish theoretical generalization error estimates for our local Sinkhorn divergence framework, which explicitly characterizes the trade-off between approximation bias and statistical efficiency controlled by the regularization parameter and reveals how our proposed local Sinkhorn divergence loss function can be efficiently applied to learning multidimensional random field models. The proposed framework provides a scalable alternative to exact local optimal transport for conditional distribution reconstruction, offering a practical compromise between geometric fidelity, statistical efficiency, and computational scalability for uncertainty quantification and probabilistic scientific machine learning. Through various numerical examples, we compare our proposed local Sinkhorn divergence framework with other loss functions to train SNNs and with other machine-learning-based uncertainty quantification frameworks, demonstrating that the proposed local Sinkhorn divergence framework achieves an effective balance between reconstruction accuracy and computational efficiency while maintaining good scalability for multidimensional stochastic systems.
[LG-33] IoT-Enabled Autonomous Maritime Navigation in Smart Ports: A Curriculum-Guided Shared Policy Learning Framework
链接: https://arxiv.org/abs/2608.11597
作者: Yuqing Lin,Rangya Zhang,Kum Fai Yuen
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:As smart port infrastructures increasingly rely on autonomous maritime devices enabled by the Internet of Things (IoT), ensuring reliable onboard navigation intelligence has become a critical challenge for safe and scalable operations in congested waterways. This paper investigates onboard autonomous navigation for such IoT devices under partial observability and dense traffic conditions. A curriculum-guided reinforcement learning framework with a shared recurrent policy is developed to enhance temporal reasoning, deployment scalability, and robustness of edge-level decision-making. Centralized training is adopted as an offline design-time strategy, while all navigation actions are executed fully onboard, consistent with IoT edge intelligence paradigms. Extensive simulations in multiple realistic port environments demonstrate that the proposed approach improves navigation reliability, collision avoidance, and training stability compared with standard baseline methods, and generalizes effectively to previously unseen high-density scenarios. The results indicate that curriculum-guided shared learning provides a practical solution for scalable deployment of IoT-enabled autonomous maritime devices in smart port operations.
[LG-34] CookVoice: Unified Framework for Style Controllable Multi-Modal Human Voice Generation
链接: https://arxiv.org/abs/2608.11590
作者: Haowei Lou,Hye-Young Paik,Dai Jia,Kai Li,Lina Yao
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:Human voice generation has made rapid progress in speech generation, singing voice generation, voice cloning, and voice editing. However, most existing systems are designed for specific tasks and often rely on task-dependent architectures, control signals, or autoregressive decoding, limiting fine-grained controllability and inference efficiency. In this paper, we propose CookVoice, a unified framework for multimodal, multi-style, and multi-task human voice generation. CookVoice decomposes the human voice into three key factors: content, prosody, and style, enabling both speech and singing voice generation within a unified model. To achieve precise and flexible controllability, we design a flexible alignment strategy that maps text, style, and prosody control signals onto the frame-level of spectrogram. This design allows CookVoice to support a wide range of tasks, including text-to-speech, text-to-singing voice, style-controllable generation, voice mimicry, voice conversion, and voice editing. Experimental results show that CookVoice achieves generation quality comparable to existing Text-to-Speech and text-to-singing voice baselines, while providing stronger style and prosody controllability. Moreover, CookVoice achieves comparable performance to large-scale baselines with only 43.51 million parameters and efficient inference using as few as 4 ODE steps, making it a practical solution for real-world human voice generation applications. Demo page is available at this https URL.
[LG-35] RECAST: A Machine-Learning Framework for Correction and Super-Resolution of Coarse-Grid PDE Solvers
链接: https://arxiv.org/abs/2608.11572
作者: Maryam Reza,Farbod Faraji
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注: 29 pages, 24 figures, 2 tables
Abstract:Coarse-grid numerical solvers can substantially reduce the computational cost of time-dependent PDE simulation, but under-resolution often degrades both the trajectory and the spatial fidelity of the solution. We introduce RECAST (Recurrent Error Correction And Super-resolution of coarse-grid Trajectories), a machine-learning framework designed to restore this lost accuracy while retaining coarse-grid evolution. RECAST combines learned correction within the numerical time-stepping loop with reconstruction of the corresponding fine-grid state from the corrected coarse history. We evaluate the framework on six one-dimensional PDE systems spanning transport, diffusion, dispersion, reaction, and wave dynamics, using spatial grids coarsened by factors of 8-16 and 1000-step closed-loop rollouts from unseen initial conditions. Across the test cases, RECAST remains closely aligned with the fine-grid reference solutions and reduces time-averaged relative error by approximately 50-92% compared with the corresponding uncorrected coarse-grid solvers. Additional tests show generalization to unseen PDE parameter values, while comparison with a contemporary coarse-correction architecture shows that RECAST achieves lower error and better long-horizon agreement with the fine-grid reference over 5000-step rollouts. These results demonstrate that the learned correction and reconstruction capabilities of RECAST can enable substantially coarser PDE evolution without the corresponding loss of solution fidelity, providing a proof-of-concept route toward machine-learning acceleration of higher-dimensional numerical simulations across science and engineering.
[LG-36] Sparse and robust geometric twin support vector machine via asymmetric RoBoSS loss function
链接: https://arxiv.org/abs/2608.11567
作者: Kai Qi,Xinji Huang,Hongchun Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:In real-world scenarios, the training data usually contains redundant features, label noise and feature noise, which provide severe challenges for the efficiency of machine learning methods. Since standard support vector machine (SVM) adopts l_2 -norm penalty and hinge loss function, it lacks the ability of selecting significant features and is sensitive to noise. To address these issues, this paper proposes a novel asymmetric, robust, bounded, sparse and smooth (aR) loss function for l_1 -norm penalized geometric twin SVM (aRSGTSVM) to handle classification and regression tasks. The l_1 -norm penalty can achieve the feature selection. The proposed aR loss function can not only effectively mitigate the impact of label noise, but also significantly enhance the stability to resampling noise, i.e., the zero-mean feature noise around the boundary hyperplanes. Furthermore, a statistical analysis of the robustness of aRSGTSVM was also conducted using the influence function. Since aRSGTSVM involves nonconvex and nonsmooth optimization, we develop a fast and stable proximal gradient descent based solving algorithm. Compared with related state-of-the-art methods, experimental results demonstrate the superiority of the proposed aRSGTSVM on both synthetic and UCI datasets. Furthermore, we apply aRSGTSVM to index tracking tasks, where results for tracking the different indices in the China stock market show that it can achieve satisfactory performance.
[LG-37] When Offline Evaluation Misleads: A Diagnostic Protocol for Reward and Policy Selection in Delayed-Feedback Contextual Bandits KDD2026
链接: https://arxiv.org/abs/2608.11560
作者: Sang Su Lee,Vineeth Loganathan,Shishir Dash,Vijay Raghavan
类目: Machine Learning (cs.LG)
*备注: Accepted at the 5th Workshop on End-to-End Customer Journey Optimization (KDD 2026)
Abstract:Personalizing marketing messages with contextual multi-armed bandits (CMABs) drives real business value, yet the objective that ultimately matters - a downstream conversion - is observed only weeks later, too late to drive online learning. Teams therefore train the bandit on a fast proxy reward, and separately must judge whether a contextual bandit is worth its complexity over sending one best message. Settling both decisions with the usual offline checks - a batch off-policy estimate, a marginal arm-discrimination test, a confidence interval - can mislead systematically under delayed feedback. We give an ordered diagnostic protocol that screens a reward-and-policy candidate on two axes, alignment (does optimizing the reward move the north-star?) and learnability (can the bandit identify the reward-optimal policy?), before trusting any reported lift. We validate it where the truth is known - a public off-policy-evaluation benchmark and a controllable synthetic generator - and illustrate it on a deployed large-marketplace push system (where, with five arms and one split, the evidence is directional rather than powered). Two lessons recur. (N1) A single offline number can mis-rank rewards: a denser reward signal gives the bandit more to learn from, so rewards that look tied in a static estimate pull apart once learning happens online. (N2) If you cannot tell in advance which single message is best, a per-user policy partly just avoids betting on the wrong one - that looks like personalization but is really robustness, so a “personalization premium” is easily overstated. Our contribution is methodological rather than algorithmic: the ordered protocol, the two lessons it surfaces, and the end-to-end experience of applying it to a delayed-feedback CMAB.
[LG-38] Certifying What Helps Customer-Return Timing: A Screen-and-Confirm Test for Conditioning Signals and Why Decay Is Nearly Enough KDD2026
链接: https://arxiv.org/abs/2608.11555
作者: Sang Su Lee,Vineeth Loganathan,Shishir Dash,Vijay Raghavan
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注: Accepted at the 5th Workshop on End-to-End Customer Journey Optimization (KDD 2026)
Abstract:Practitioners enrich customer-return models with ever more signals (lifetime value, category, recency/frequency, calendar, geography), and the temporal-point-process (TPP) literature follows suit with covariate- and external-covariate-conditioned intensities. But does any of it improve the timing, and how would you know? A null (“feature X doesn’t help”) is only meaningful if the model could have found a signal. We make two contributions–a method and a measurement–to answer this credibly. (i) A screen-and-confirm protocol that certifies whether a candidate signal improves a TPP’s event-timing likelihood: a positive control plants a coupling of known strength and confirms the model recovers it, so a real-data null can be read as “no signal” rather than “weak method.” The control is validated for categorical and continuous encodings, and on a real clock-driven dataset (NYC taxi hour-of-day). (ii) A model-free ceiling quantifying how little of customer-return timing is point-predictable at all (a single-digit percentage of gap variance from any covariate; returns are near-memoryless). With these we certify a clean result on three public benchmarks (Amazon, Taobao, RetailRocket) and a real marketplace (Thumbtack): the inter-event clock–continuous-time decay, long known to beat frozen-intensity models–is nearly sufficient, and the conditioning the field keeps adding is redundant or harmful on top of it (statistically null on the public benchmarks, at most 0.06 NLL; null to mildly harmful on the marketplace). We do not claim to discover that decay helps; our contribution is the tools that turn “conditioning doesn’t help” into a checkable, certified statement–plus an honest-evaluation account of the read-out/leakage pitfalls we hit and retracted.
[LG-39] Robust Ambiguity Detection (RAD) From Model- and Feature-Space Consistency
链接: https://arxiv.org/abs/2608.11541
作者: Manya Singh,Mark T. Keane,Arjun Pakrashi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine learning models should be robust, in the sense of remaining predictively consistent under permissible variations. A model’s predictions should ideally remain unchanged when it is replaced by a functionally equivalent one, or when its inputs are subject to minor, admissible perturbations. If such changes alter a prediction significantly, then the prediction is “ambiguous” with respect to the model. Models should abstain from making such ambiguous predictions and/or should flag them for human inspection, especially in high-stakes decision-making scenarios. However, in practice, such ambiguity is not easy to identify once a model is deployed. Here, the Robust Ambiguity Detection (RAD) framework is advanced for quantifying predictive ambiguity using two complementary metrics: Model-Space Consistency and Feature-Space Consistency. These two scores, the RAD Score-Pair, visualised through the RAD Plot, provide an interpretable characterisation of the sources of ambiguity and the actions a user may consider in response. RAD is evaluated on synthetic datasets with systematically controlled overlap, as well as several real-world datasets where the level of ambiguity cannot be directly inspected. Finally, we demonstrate a downstream application of RAD where samples are ranked by their RAD Pareto-Rank and the most ambiguous are abstained from prediction, achieving performance comparable to existing rejection-based approaches.
[LG-40] FLARE: Low-rank attention with dynamic attention routing
链接: https://arxiv.org/abs/2608.11519
作者: Vedant Puri,Yongjie Jessica Zhang,Levent Burak Kara
类目: Machine Learning (cs.LG)
*备注:
Abstract:Full self-attention is a strong token mixer for PDE surrogates on irregular domains, but its quadratic cost limits its use on high-resolution problems. Efficient latent-attention models such as the Fast Low-rank Attention Routing Engine (FLARE) avoid that cost by routing all N tokens through M N learned latent queries, but those queries are parameters: once trained, the same learned query templates serve every input. We remove this restriction with FLARE++, a low-rank attention architecture with dynamic token routing. FLARE++ reuses FLARE’s own encoder to build its routing queries: learned latent seeds drive one extra encode call that gathers the N input tokens into M input-conditioned queries, and those queries then determine how the same tokens are compressed and redistributed. This preserves FLARE’s explicit low-rank factorization and linear O(NM) complexity, and expresses the complete routing operation with standard scaled dot-product attention (SDPA) calls alone. We also provide a multi-GPU context-parallel implementation that shards input tokens across devices without ever gathering the full token sequence on one of them. FLARE++ is competitive across a set of standard PDE surrogate benchmarks, improving on fixed-query FLARE by 24% on average, and it gains 2.3 points of average accuracy on Long Range Arena.
[LG-41] RelShap: Relationally Consistent Shapley Explanations
链接: https://arxiv.org/abs/2608.11508
作者: Seungeun Lee,Joao Fonseca,Julia Stoyanovich
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints. Widely used Shapley value-based feature attributions then rely on feature independence, evaluating the model on combinations that could never arise in the underlying data, producing misleading explanations. We propose RelShap, a framework that incorporates relational constraints and data provenance into Shapley value computation, restricting both background data and coalition evaluation to relationally valid configurations. The framework is estimator-agnostic and composes with Kernel SHAP, Monte Carlo, and Leverage SHAP without altering their sampling or weighting properties. Functional dependencies further induce equivalence classes over feature coalitions, which RelShap exploits to reduce runtime without changing Shapley values; we provide a combinatorial characterization of the expected speedup. Experiments across multiple datasets, models, and estimators show that RelShap produces explanations that are more faithful to the data-generating process, correctly identifying the dominant feature in controlled settings where existing methods, including Conditional SHAP and ManifoldShap, do not. Our code is available at: this https URL.
[LG-42] Cross-Corpus Evaluation of Generalizable Vulnerability Detection in IoT Firmware
链接: https://arxiv.org/abs/2608.11492
作者: Sadib Hassan Rumman,Md. Shariful Islam,Md. Rayhanur Rahman
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 6 pages, 1 Figure, 2 Tables
Abstract:IoT firmware vulnerability detection remains challenging due to heterogeneous firmware ecosystems, resource-constrained platforms, and limitations in existing benchmarks. Many datasets are synthetic or general-purpose and lack human-verified, contamination-screened annotations, limiting evidence on cross-corpus generalization across training sources, model architectures, and curriculum strategies. To address this gap, this paper introduces IoTVulBench, a human-verified benchmark for cross-corpus firmware vulnerability detection. IoTVulBench-Core was constructed from GitHub repositories, validated by three expert reviewers, and evaluated on a contamination-screened held-out target across five model architectures, two tuning methods, and three curriculum strategies, with ensemble, distillation, and robustness analyses. Models trained on IoTVulBench achieved the highest MCC among matched single-source datasets, reaching 0.58 versus 0.44 for PrimeVul and 0.39 for D2A. Staged curriculum learning increased MCC to 0.69, while a diversity-optimized ensemble achieved 0.73, improving by 0.42 MCC over the strongest reference comparator, a static analyzer at 0.31, and by 0.29 over PrimeVul. At a 0.5% false-positive rate, the model missed only 21% of vulnerabilities, compared with 71% for the strongest comparator. It retained 86% of its performance under identifier renaming and demonstrated strong calibration and largely faithful explanations. These findings indicate that domain-matched training data and curriculum design, rather than model scale alone, are key drivers of generalization in firmware vulnerability detection. The results provide a benchmark for future research and deployment-ready configurations for practical IoT security applications.
[LG-43] Forward Trajectory Steering for Hamilton-Jacobi Reachability Analysis
链接: https://arxiv.org/abs/2608.11480
作者: Sungje Park,Stephen Tu
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: IEEE CDC 2026
Abstract:Hamilton-Jacobi (HJ) reachability provides a mathematically rigorous framework for safe control of dynamical systems, but its practical application is bottlenecked by the computational complexity of solving Hamilton-Jacobi-Isaacs variational inequality PDEs in high dimensions. Physics-informed neural networks (PINNs) have recently emerged as a promising alternative to classical mesh-based solvers, yet their performance is highly sensitive to the choice of collocation sampling. In order to learn accurate safety value functions, existing PINNs-based HJ reachability solvers must rely on complex training pipelines and auxiliary supervision. In this work, we propose STEER2REACH (S2R), a PINNs-based HJ reachability solver that requires minimal modification on top of standard PINNs training. S2R’s key contribution is a lightweight, low-overhead adaptive collocation sampling distribution constructed by steering forward trajectories using a combination of the optimal control and disturbance signals induced by the current value function, with injected stochastic exploration noise. We demonstrate that despite its simplicity, S2R achieves competitive–and in some cases improved–performance on safety metrics while reducing relative L2 error across a range of reachability benchmarks compared with SoTA MPC-guided HJ reachability solvers, all without requiring multi-stage training or MPC-based supervision.
[LG-44] Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness
链接: https://arxiv.org/abs/2608.11479
作者: Siqiao Mu,Diego Klabjan
类目: Machine Learning (cs.LG)
*备注:
Abstract:We establish convergence guarantees of gradient descent for general feedforward neural networks of arbitrary width or depth, with no special requirements on the initialization or dataset. We only assume that the activation functions are Lipschitz smooth, Lipschitz continuous, and linearly bounded— properties that hold for linear, tanh, softplus, and sigmoid activation functions. For the loss function, we require that it is Lipschitz smooth in the model outputs, which is true for mean-squared error. The key theoretical insight is that the Lipschitz properties of the activation functions are partially preserved even through repeated compositions, leading to a novel generalized Lipschitz smoothness condition where the change in gradient is upper bounded by the change in the parameter space, multiplied by polynomial terms of the parameter norms at both endpoints. This type of condition holds for both the model function and the loss function, enabling a descent lemma where the loss decreases as long as the learning rate is small enough with respect to the parameter norms. By ensuring that the parameter norms do not grow too quickly to infinity, we prove that the minimum squared gradient norm converges to zero in T iterations at rate O(1/T^1/L) for an L -layer neural network.
[LG-45] Dual-Primal Graph VAEs for Noisy Label Aggregation
链接: https://arxiv.org/abs/2608.11473
作者: Patrick Stinson,Nikolaus Kriegeskorte
类目: Machine Learning (cs.LG)
*备注:
Abstract:Inferring the ground-truth from noisy crowdsourced labels is an important theoretical and practical problem. Neural network-based methods offer an alternative to classical Bayesian models which require specifying a family of generative models used for inference. However, current models either still rely on fairly simple generative models for inference or require pseudo-labels or synthetic data to train the aggregate classifier. We propose a graph VAE architecture in which the decoder and encoder use GAT-based message passing on the adjacency graph of a crowdsourced dataset and its dual, respectively. The ground-truth labels are treated as latent variables, enabling unsupervised representation learning without needing to train a separate classifier. We show our model achieves state of the art performance on crowdsourcing benchmarks. We then demonstrate the generality of our approach by showing how the original crowdsourcing graph can be augmented to incorporate side information such as representations from neural network classifiers trained on the noisy labels to substantially boost their classification performance at test time.
[LG-46] XGBoost “is all you need”: the case of forecasting transmitted heat energy in District Heating Systems
链接: https://arxiv.org/abs/2608.11446
作者: Milan Zdravković
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 9 pages, 7 figures. This preprint corresponds to the paper published in Lecture Notes in Networks and Systems, vol. 860 (ICIST 2024), Springer
Abstract:This paper presents a comparative study of two distinct approaches, XGBoost and Long-Short Term Memory (LSTM), for forecasting transmitted heat energy in District Heating Systems (DHS). The objective is to explore scenarios in which conventional ML algorithms demonstrate better performance over deep learning networks in time series forecasting and the associated benefits in terms of computational cost and environmental impact. The study focuses on a real-world DHS dataset. Through experimentation and analysis, it is demonstrated that XGBoost consistently outperforms LSTM in this specific forecasting task. The difference is explained by the error distribution illustrating that LSTM makes more significant errors in the intervals of less data availability. The reduced computational demands of conventional ML approaches not only result in cost savings but also minimize the carbon footprint associated with data analysis tasks in energy systems.
[LG-47] Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates
链接: https://arxiv.org/abs/2608.11435
作者: Qiyao Zhou,Xujia Zhu,Pierre Joli,Yu Cong,Sibo Cheng
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph); Data Analysis, Statistics and Probability (physics.data-an); Fluid Dynamics (physics.flu-dyn)
*备注:
Abstract:Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informative for parameter calibration. However, a systematic end-to-end differentiable formulation for coupling deep-learning-based reduced-order surrogates with variational parameter estimation remains underdeveloped. In this work, we introduce a physics-aware neural-network-based latent-space framework for reduced-order forward modeling and variational parameter estimation. The proposed autoencoder-based approach yields a differentiable surrogate that maps physical parameters to predicted flow fields through a latent representation. The observable supervision is used during offline training to encourage the latent variables to retain information correlated with system parameters, while the online inverse problem is solved in the parameter space through the surrogate-induced observation operator. The method is evaluated on two computational-fluid-dynamics benchmarks. The results show that reconstruction accuracy alone is insufficient for inverse modeling, owing to the lack of end-to-end differentiability or physics awareness for variational parameter calibration. Quantitative latent-space analysis further shows that observable supervision improves case-level separability and temporal organization of latent representations. Experiments with realistic measurement settings, including noisy, low-resolution, randomly masked, and block-wise partial observations, demonstrate the robustness of the proposed framework and show that it generally reduces calibration error and variability compared with the standard surrogate models.
[LG-48] AutoGrable: What Is a Good Graph for a Table?
链接: https://arxiv.org/abs/2608.11431
作者: Tamara Cucumides,Floris Geerts
类目: Machine Learning (cs.LG)
*备注: 28 pages, 4 figures
Abstract:Graph learning presupposes a graph, and tables and relational databases do not come with one. Applying a GNN to them requires deciding which entities become nodes, which of them to connect, and through which relations—a decision made by hand, by schema heuristics, or by training a model on every candidate graph and keeping the best. We give a criterion that requires no trained graph model. In the minimal table-to-graph abstraction each row is a node, so a message-passing GNN, bounded by 1-WL, sees a construction only as a partition of the rows into colour-refinement classes: a construction is good for a task when that partition separates rows with different labels and does not split rows that share one. AutoGrable turns this criterion into a construction procedure. For incidence constructions the partition is fixed by the selected columns, so building a graph reduces to choosing them, and we score a candidate subset by a label-alignment risk: the held-out risk of the best predictor constant on its blocks, penalised by an occupancy term measuring how thinly the blocks are populated. The score materialises no graph and trains no GNN, so AutoGrable can search the space of subsets greedily and cheaply, and returns the resulting grable for single tables and for foreign-key schemas alike. Our experiments show that over a space of candidate graphs the score discards a large fraction while retaining the best; that AutoGrable recovers the columns that generate the label on controlled tasks and outperforms fixed, random, and task-aware constructors on real tasks under a fixed predictor; and that it is the only method compared that can decline to build a graph when none helps.
[LG-49] hree Tokens Force Exponential Feature Rank in Nonnegative Kernel Attention
链接: https://arxiv.org/abs/2608.11427
作者: Vicente Opazo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Full attention exposes every token pair, whereas kernel attention compresses a sequence into a fixed-dimensional sketch. We show that this distinction becomes exponential at the first context length containing two competing candidates. On Min-IP over Boolean inputs, rank-one normalized kernel attention solves every sequence of length at most two exactly. In contrast, any single normalized nonnegative kernel-attention head that succeeds on all three-token sequences with error strictly below 1/2 requires 2^\Omega(m) features, even with arbitrary finite-dimensional tokenwise values and an arbitrary query-dependent affine readout. Dense softmax solves the same task with m -dimensional scores and constant temperature. The conclusion survives position-dependent token maps and a causal final query. As context length grows, the lower bound approaches the exact 2^m -feature realization. Separately, for deterministic multihead, multilayer sketch models whose cross-token channels have finite alphabets, we prove a transcript lower bound linear in the number of independent answers and logarithmic in their alphabet size.
[LG-50] Diffusion-Based Data-Driven Assortment Optimization
链接: https://arxiv.org/abs/2608.11419
作者: Junyi Liao,Xiaohui Jiang,Zhengwei Tong,Ethan X. Fang,Vahid Tarokh
类目: Machine Learning (cs.LG)
*备注:
Abstract:Assortment optimization is a fundamental problem in revenue management, typically addressed using parametric choice models such as the multinomial logit (MNL) and its variants. While these models enable tractable formulations, their performance is sensitive to model misspecification and often struggles to capture complex customer behavior. In this paper, we propose a model-agnostic framework for assortment optimization based on guided discrete diffusion. We represent assortments as binary vectors and perform stochastic search via a learned reverse diffusion process, avoiding explicit combinatorial enumeration. To incorporate decision objectives, we introduce a reward-guided mechanism that biases local transitions using estimates of expected revenue. This allows the method to effectively balance exploration and exploitation during generation. Empirically, we show that the proposed approach consistently identifies high-quality assortments and remains robust under model misspecification, often recovering near-optimal solutions in high-dimensional settings. Moreover, the generative nature of diffusion enables the production of diverse high-performing assortments, offering flexibility beyond a single deterministic solution. These results highlight the potential of generative modeling as a scalable and robust paradigm for combinatorial optimization in data-driven decision-making.
[LG-51] Unmasking Toxic Mimicry in Medical Offline Reinforcement Learning for ICU Sepsis Management via Counterfactual Clinical Audits
链接: https://arxiv.org/abs/2608.11410
作者: Hangqi Ren,Junyi Liao
类目: Machine Learning (cs.LG); Computers and Society (cs.CY)
*备注:
Abstract:Offline reinforcement learning (RL) offers considerable promise for optimizing ICU treatment decisions, yet standard evaluation metrics Mean Squared Error (MSE) and Fitted Q-Evaluation (FQE) assess only behavioral imitation and cannot detect Toxic Mimicry, a failure mode in which agents replicate harmful patterns such as treatment withdrawal during comfort-care transitions. Using the MIMIC-III database, we propose the Counterfactual Clinical Audit (CCA) framework, which stress-tests RL agents through physiological perturbations anchored in Surviving Sepsis Campaign (SSC) guidelines. We audit a Medical Decision Transformer (MedDT) and a Historical Causal Transformer (HCT-RL), the latter employing Causal Action Shielding, propensity-based importance weighting, and Conservative Q-Learning. CCA reveals that MedDT paradoxically reduces vasopressor dosage as lactate escalates, contradicting resuscitation guidelines, while HCT-RL maintains physiologically consistent responses. These findings expose a systemic misalignment between statistical fit and clinical safety, supporting counterfactual audits as a necessary evaluation standard for medical RL.
[LG-52] Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes
链接: https://arxiv.org/abs/2608.11390
作者: Chen Xu,Zitian Guo,Chenyan Xiong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Generative engines are reshaping the web ecosystem by making citations a key mechanism for allocating attention, attribution, and downstream value. This creates a strategic tension: content providers are incentivized to optimize for model citation, while platforms must preserve answer quality and trustworthy attribution. We show that this tension can escalate into citation wars. In repeated simulations, state-of-the-art generative engine optimization (GEO) attacks adapt to conventional defenses by producing citation-seeking rewrites that degrade document quality and introduce unsupported claims. To study this problem, we formulate the supplier–platform interaction as a repeated Stackelberg game with partial monitoring. A local best-response analysis identifies when citation competition approaches an inert stationary outcome. Motivated by this finding, we propose a platform–creator mechanism called VCR based on verifiable-content rewards. Rather than only penalizing suspicious rewrites, the platform also credits rewrites that surface checkable factual substance, aligning creator incentives with answer trustworthiness. Experiments on three benchmarks show that VCR consistently achieves the largest Net defense-utility score, outperforming the strongest baseline by an average of 12.1 percentage points, and produces a win–win outcome under our empirical equivalence criterion.
[LG-53] Reoptimization Algorithms for Contextual Bandits with Knapsack Constraints
链接: https://arxiv.org/abs/2608.11383
作者: Zhen Xu
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:We study new algorithms for Contextual Bandits with Knapsack. In these problems, there are finitely many types of customers, products, and resources. Each product is made from a fixed combination of resources, and resources have finite capacity. A decision maker must assign each arriving customer one out of a set of multiple possible products. Every assignment of a customer to a product will generate a random reward, which equals an unknown linear function of customer and product features, plus a noise term. The objective is to jointly learn the mean reward function, and to make online assignments to minimize the expected revenue loss relative to an optimal policy that knows the reward function. We propose a natural and simple extension of the Upper-Confidence-Bound (UCB) family of algorithms and apply re-optimization techniques. We show that by taking advantage of re-optimization, our algorithm achieves an average regret of O(\frac(\ln T)^3T) where T is the horizon length. Our bound significantly reduces the O(\frac1\sqrtT) bound in the literature for closely related dynamic-pricing problems that are based on re-optimization.
[LG-54] owards an approach to multivariate outlier detection for District Heating System data
链接: https://arxiv.org/abs/2608.11375
作者: Rajko Turudija,Dušan Stojiljković,Milan Zdravković,Marko Ignjatović
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 10 pages, 4 figures. This preprint corresponds to the paper published in Lecture Notes in Networks and Systems, vol. 860 (ICIST 2024), Springer
Abstract:In this paper, we test different methods for multivariate detection of outliers in the data of transmitted heat energy in the selected substation of local District Heating System, by also considering outside ambient temperature, namely Z-score (univariate, as a benchmark), Mahalanobis distances, Principal Component Analysis (PCA), Isolation Forest and Hotelling’s T-squared test. The overall research aims at uncovering irregular plant operation, with a wider objective of identifying the opportunities for reducing the consumption of gas in central heating plants as well as the CO2 emission. The proposed approach considers specific domain circumstances, such as irrelevance of zero transmit-ted energy timepoints as indication of off-grid plant. The outcomes of the different methods are discussed with domain experts. It was concluded that PCA, Isolation Forest and Hotelling method provide relevant results. Finally, we adopt the ensemble method (selection based on the agreement of all three methods on the detected outliers) as the final approach.
[LG-55] PAIR: Pairwise-Aware Inclusion Reweighting for Adaptive Rollout Allocation in RLVR
链接: https://arxiv.org/abs/2608.11368
作者: Pixel Nomand,Elena Voss,Marcus Hale,Sofia Reyes
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) spends most of its compute generating groups of long reasoning trajectories. Recent allocators reduce this cost by assigning budgets to prompts, rollouts, or tokens according to a pointwise notion of difficulty or utility. We identify a statistical mismatch: the unclipped leave-one-out group-relative score gradient is not a sum of independent point contributions, but a second-order U-statistic over pairs of rollouts. Completing one rollout therefore reveals contrast with every other completed rollout, and adaptive endpoint selection changes which pair terms are observable. We introduce PAIR (Pairwise-Aware Inclusion Reweighting), which treats short rollout prefixes as vertices and pair-gradient terms as edges of a contrast graph. A prefix-only predictor estimates correctness and remaining token cost; a convex design chooses positive continuation probabilities under an expected suffix-token budget; and each edge induced by completed vertices is inverse-weighted by its logged joint inclusion probability. Under conditionally independent on-policy rollouts and an unclipped, unstandardized objective, the resulting estimator is design-unbiased for the complete candidate-pair gradient. Across compute-matched RLVR runs on Qwen3-1.7B/4B, PAIR improves average accuracy by +1.2 and +1.4 over the strongest pointwise allocator while using 51% and 52% fewer generated tokens than full-group GRPO. A frozen-population estimator audit confirms that unweighted adaptive selection is biased, whereas pair-inclusion correction recovers the complete-pair target at matched suffix cost.
[LG-56] Adaptation of Generalist Robot Policies with Minimal Data
链接: https://arxiv.org/abs/2608.11363
作者: Shreyas Kowshik,Sreyas Venkataraman,Leo Wang,Niharika Pant,Max Simchowitz,Aviral Kumar
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction. Yet fully autonomous learning remains difficult with current policies: sparse rewards and weak zero-shot exploration make it unlikely that a robot will discover successful behavior from scratch. We study minimal-data adaptation, a regime in which a pre-trained robot policy must learn a new task from as little as one demonstration followed by autonomous online interaction. This setting serves as the closest tractable proxy for fully autonomous improvement, allowing us to study whether minimal human guidance can bootstrap autonomous learning and what algorithmic ingredients make it feasible. We build MiDAS, a simple offline-to-online RL recipe that first anchors a pre-trained VLA to the target task with behavior cloning on single/few demonstrations, then improves it through value-based online RL on a residual policy parameterization. Across LIBERO and RoboCasa, MiDAS recovers strong task performance from as little as one demonstration, substantially outperforming baselines and generalizing beyond demonstrated conditions. We further evaluate MiDAS on a bimanual YAM platform. Starting from a fragile low-success policy obtained from a single demonstration, MiDAS improves its robustness and learns new successful behaviors over ~6 hours of online interaction. To the best of our knowledge, this is the first demonstration of reliable robot policy adaptation from a single task demonstration.
[LG-57] Market-Information-Aware Gated-LoRA of Foundation Models for Transferable Day-Ahead Electricity Price Forecasting
链接: https://arxiv.org/abs/2608.11359
作者: Hang Fan,Wei Wei,Shengwei Mei
类目: Machine Learning (cs.LG)
*备注:
Abstract:Electricity price forecasting is crucial for market participants but remains difficult because prices are volatile, market-specific, and closely tied to anticipated system conditions. Existing supervised methods depend largely on market-specific historical data, limiting their use in newly established or data-scarce markets. This paper proposes a market-information-aware adaptation framework that transfers the Chronos-2 time-series foundation model to day-ahead electricity price forecasting. It first constructs a multi-source market information (MSMI) interface aligning 7-day price context with pre-clearing supply–demand, reserve, maintenance, generator-capacity, and intertie variables, and then trains a source-domain gated low-rank adapter (LoRA), updating about 1% of model parameters without target-market labels. The gate scales the frozen source adapter according to reserve-tightness and operating-state signals. A leave-one-market-out protocol is adopted for evaluating cross-market transferability. Experiments on four Chinese provincial day-ahead spot markets show that the proposed framework reduces the average MAE/RMSE by 6.24%/7.99% relative to market-information-aware zero-shot Chronos-2 and by 3.05%/3.52% relative to vanilla Source-LoRA. Experiments show that the gain is not reproduced by a learned global scalar or by random gate initialization, while the additional improvement over Source-LoRA is limited. These results suggest that market-structured inputs and state-dependent gated LoRA can provide a practical transfer path for data-scarce electricity markets.
[LG-58] Long-Horizon Forecasting of Complete Financial Statements with Forma
链接: https://arxiv.org/abs/2608.11327
作者: Travis L. Johnson,Jiannan Jiang,Soumyabrata Chaudhuri,Yihao Chen,Lauren Falvey,Donal O’Cofaigh
类目: Machine Learning (cs.LG); Computational Finance (q-fin.CP)
*备注: 46 pages, 2 figures, 3 tables. Benchmark: this https URL . Model and weights: this https URL
Abstract:Specialist training beats generalist scale when forecasting financial statements. To our knowledge, no prior work jointly forecasts complete financial statements beyond one year, yet in a discounted-cash-flow valuation most firm value sits past that window. We release ProForma-20Q, a reproducible benchmark for forecasting 78 statement line items 1-20 quarters ahead, for anonymized firms, from past statements and an industry code, scored by change-space R^2 . On it, Forma, a transformer that reads statements as sets of (account, quarter, value) tuples and maximizes a masked-tuple Gaussian likelihood, beats every competitor we field: classical machine learning, chained gradient boosting, a zero-shot time-series foundation model, and frontier large language models. Its lead widens with horizon, where valuation needs accuracy most, and its Gaussian predictive intervals never under-cover. Forma’s forecasts nearly satisfy accounting identities; exact coherence is recoverable at no statistically significant accuracy cost. Its tuple interface supports scenario analysis without retraining, and we show that pinning future revenue paths sharpens the rest of the statement.
[LG-59] Spectral graph clustering with inhomogeneous latent geometry
链接: https://arxiv.org/abs/2608.11321
作者: Konstantin Avrachenkov,Lucas S. Sibemberg,Alexander Van Werde
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注: 28 pages, 11 figures
Abstract:We study spectral clustering in the presence of a confounding latent geometry. The leading eigenvectors may then be dominated by the latent geometry rather than by the communities. Nevertheless, we show in a block latent-space model that communities can be recovered from eigenvectors deeper in the spectrum. We analyze the spectral properties of the adjacency matrix through a limiting integral operator and use its structure to develop DBSPEC, a density-based spectral clustering algorithm that requires only approximate localization of the informative eigenvalue and is robust to poor eigenvalue separation. Crucially, this approach handles general latent geometries, overcoming restrictions to homogeneous toroidal models in prior works. Our theoretical predictions for the location of the informative eigenvalue notably align with observations in real-world experiments.
[LG-60] Dueling Deep Q-Learning for Intrusion Detection
链接: https://arxiv.org/abs/2608.11291
作者: Logan Luna(Georgia Institute of Technology),Matthew P. Berkowitz(Embry-Riddle Aeronautical University),Laxima Niure Kandel(Embry-Riddle Aeronautical University),Sirio Jansen-S’anchez(Embry-Riddle Aeronautical University)
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 6 pages, 5 figures. Published in Proc. IEEE SoutheastCon 2025, pp. 1192-1197
Abstract:Intrusion detection systems (IDS) and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods. Though effective, these models struggle to effectively adapt to new attack types. This study proposes a novel approach by employing a reward-based, dueling Q-learning model for IDS, achieving an average accuracy of 99.68% across multiple attack classes. The proposed model has a dueling network architecture which separates its predictions into value and advantage streams. This has the benefit of improving learning efficiency and stability. The model was trained on the CIC-IDS2018, a benchmark dataset based on real-world intrusion detection scenarios, having multiple attack classes such as DDoS, botnets, and brute-force attacks. Furthermore, Explainable AI (XAI), specifically SHAP (SHapley Additive exPlanations), was also integrated into the training and evaluation process to provide interpretability into the model’s predictions.
[LG-61] Benchmarking Cyberattack Detection in Electric Vehicle Charging Infrastructure with Benign User Updates
链接: https://arxiv.org/abs/2608.11286
作者: Hannan Chen,Roshni Anna Jacob,Jie Zhang
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Cyberattack detection in electric vehicle charging infrastructure is complicated by legitimate post-activation revisions to requested energy and departure time. Charging manipulation attacks can exploit the same interface and variables; therefore, detecting a request change alone does not establish malicious intent. This paper develops a leakage-controlled session-level benchmark that preserves the ordered inputs of real Adaptive Charging Network (ACN) sessions and models legitimate revisions as normal behavior. A fixed pool keeps each generated attack in its source session’s split and contains six physically motivated attacks and their coordinated variants. We compare 22 profile-only, transition-aware, and context-stratified model families under common source-grouped folds, attack data, and operating constraints. The proposed Dual-Branch Masked-Autoencoder (Masked-AE) Transition Boost model evaluates whether the current request is normal and whether its producing transition resembles an observed benign update. Its state branch combines masked reconstruction with a radial-basis-function one-class support boundary, while its transition branch combines masked reconstruction with shrinkage covariance distance. Source-grouped five-fold cross-validation selects complete configurations under explicit overall-normal and benign-update acceptance constraints; disjoint normal data then calibrate the final threshold before one test evaluation. The developed dual-branch model provides the strongest robust validation performance while detecting malicious request manipulations without learning to reject legitimate user choices.
[LG-62] Basin: Efficient and Extensible Numerical Optimization in Rust
链接: https://arxiv.org/abs/2608.11279
作者: Johan Larsson
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Basin is a numerical optimization library for the Rust programming language. Numerical optimization is the task of finding the inputs that minimize a function, and it is a fundamental element across the sciences: fitting a model to data, calibrating a simulation, training a machine learning model, or choosing engineering parameters that minimize cost. Basin gives users a single, consistent way to both state and solve such problems, with a broad catalog of solvers and first-class support for constraints.
[LG-63] Why AI Detection Fails for Academic Integrity
链接: https://arxiv.org/abs/2608.11256
作者: Jonathan A. Karr Jr,Grigorii Khvatskii,Ting Hua,Nitesh V. Chawla
类目: Machine Learning (cs.LG); Computers and Society (cs.CY)
*备注: Accepted to ACM AI Leadership Summit
Abstract:Institutions use commercial AI detectors for academic integrity, yet detectors cannot distinguish AI editing from full LLM drafts and may treat both as misconduct. In a controlled study of published English abstracts (four domains; 2013 to 2015 vs. 2023 to 2025), we quantify this policy failure under proxy human/AI labels at tau=0.50. Light “refine abstract only” edits, a proxy for guideline-compliant AI assistance, are flagged at 64 to 80% (Pangram/GPTZero). Unmodified 2023 to 2025 originals are flagged at 9 to 15%, with non-STEM rates far above STEM (p0.001); elevated scores track long-token and Academic Word List density, not authorship intent alone. After Undetectable AI humanization, evasion is near-total: fewer than 4% of AI-labeled rewrites remain flagged (post-humanization detection rate 4%; FNR 96%). Honest AI-editing results in a higher sanction risk than humanizer-assisted evasion. Therefore, detector scores should not serve as standalone misconduct evidence.
[LG-64] FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting
链接: https://arxiv.org/abs/2608.11254
作者: Yann Fabel,Bijan Nouri,Milon Miah,Niklas Blum,Luis F. Zarzalejo,Julia Kowalski,Robert Pitz-Paal
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph); Applications (stat.AP)
*备注:
Abstract:Accurate solar irradiance forecasting is essential for the reliable integration of photovoltaic power into modern electricity grids. All-sky imagers (ASI) provide high-resolution observations of clouds, making them well suited for intra-hour forecasting. Recent deep learning approaches have substantially improved forecast accuracy but are often limited by deterministic predictions and a reduced capability to anticipate ramp events. This work proposes FarSky, a generative forecasting framework that leverages latent-space coupling to learn task-aware representations of sky images. A multi-task autoencoder first learns a shared latent representation for image reconstruction and irradiance estimation. A latent diffusion model then generates future latent states conditioned on recent observations, from which irradiance forecasts are directly decoded. Probabilistic forecasts are inherently obtained through stochastic sampling. The framework is developed using a multi-year ASI dataset acquired at the Plataforma Solar de Almería, Spain, and evaluated on two independent test datasets against persistence, state-of-the-art end-to-end, and generative forecasting approaches. FarSky achieves the best overall deterministic and probabilistic forecasting performance, improving forecast skill by up to 11 percentage points. Furthermore, it substantially improves ramp event detection over existing methods, achieving F1-scores above 60%. These results demonstrate the potential of combining generative models with task-aware latent-space coupling for solar forecasting.
[LG-65] Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting
链接: https://arxiv.org/abs/2608.12251
作者: Junyi Ye,Gargi Vijay Borde
类目: atistical Finance (q-fin.ST); Machine Learning (cs.LG)
*备注:
Abstract:Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should enter a neural cross-sectional volatility forecasting model. We study five-day realized-volatility forecasts for 1,027 U.S. equities using a rolling walk-forward evaluation framework in which information, model capacity, hyperparameter tuning, and random seeds are matched across architectures. We propose RG-ResMoE, a regime-gated residual mixture-of-experts architecture in which regime information is used only for expert routing rather than for direct forecasting. The base predictor models volatility from stock features, while a gating network uses regime state variables to route residual corrections. RG-ResMoE consistently outperforms a capacity-matched MLP in both forecasting accuracy and training stability in the main U.S. study. Similar gains are observed on an independent Japanese panel. The integration pathway is decisive: appending the same regime variables directly to the forecasting input degrades both predictive performance and training stability, whereas restricting them to the routing gate improves accuracy and Value-at-Risk calibration. Hard routing consistently underperforms soft routing. The results suggest that, in compact neural volatility forecasting models, the primary value of mixture-of-experts models lies less in increasing model capacity than in controlling how nonstationary regime information influences prediction.
[LG-66] he Advective Fisher-Rao Geometry of Deterministic Measure Transport
链接: https://arxiv.org/abs/2608.12111
作者: Benjamin Gess,Johannes Müller
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Differential Geometry (math.DG); Probability (math.PR)
*备注: 80 pages, 6 figures
Abstract:A novel advective Fisher-Rao metric is introduced for optimization tasks on paths of probability measures governed by the continuity equation. This metric is shown to lead to optimal descent directions. It is then shown that this metric arises naturally from three different perspectives: As the rescaled zero-noise limit of the Fisher-Rao metric on path measures, as the expected value of the second variation of the Freidlin–Wentzell large deviation rate functional, and as the Hessian of the Benamou–Brenier action functional from dynamic optimal transport. We supplement this geometric construction with computational experiments. Here, we demonstrate empirically that the advective Fisher-Rao metric yields the desired optimal fitting of probability densities, whereas the Gauss–Newton method yields optimal fitting of velocity fields. Comments: 80 pages, 6 figures Subjects: Optimization and Control (math.OC); Machine Learning (cs.LG); Differential Geometry (math.DG); Probability (math.PR) MSC classes: 35Q90, 49Q22, 53B12, 60H10, 68T07 Cite as: arXiv:2608.12111 [math.OC] (or arXiv:2608.12111v1 [math.OC] for this version) https://doi.org/10.48550/arXiv.2608.12111 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-67] Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization
链接: https://arxiv.org/abs/2608.12043
作者: TaeHo Yoon,Nicolas Loizou
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Acceleration for deterministic root-finding problems has been extensively studied in recent years; specifically, the anchor-based, or Halpern-type methods achieve optimal convergence rates with respect to the operator norm. However, acceleration via these methods does not directly carry over to stochastic setting due to accumulation of errors, unless one enforces diminishing variance via increasing batch sizes or variance reduction techniques. In this work, we show that another class of acceleration, namely the dual-anchor mechanism, extends to the stochastic setting without such error accumulation, in contrast to anchor-based algorithms. Consequently, we cleanly achieve O(\epsilon^-3) complexity with iteration-independent batch size, without any variance reduction or double-loop recursive regularization, for stochastic root-finding (resp. fixed-point) problems with cocoercivity (resp. square-nonexpansivity) in expectation. For strongly monotone operators, the same algorithm attains a sharper \widetildeO (\epsilon^-2) complexity, nearly matching the lower bound in terms of \epsilon -dependence.
[LG-68] Adaptive Bregman Proximal Stochastic Gradient with a Stabilized Barzilai–Borwein Step Size
链接: https://arxiv.org/abs/2608.12009
作者: Chenhan Jin,Shengze Xu,Binghui Xie,Kaiwen Zhou,Fan Jia,James Cheng,Tieyong Zeng
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Bregman proximal stochastic gradient (BPSG) methods bring variance-reduced composite optimization to objectives whose geometry is poorly captured by Euclidean smoothness. Their performance, however, remains sensitive to the step size: raw stochastic curvature estimates can fluctuate sharply, whereas line searches add repeated proximal evaluations. We introduce Ada-BPSG, a line-search-free BPSG method that couples the SAGA gradient table with a stabilized Barzilai–Borwein (BB) candidate. A mediant aggregates incremental secant information so that nearly singular local ratios receive little weight, and an explicit safeguard translates the resulting curvature estimate into the bounded step-size sequence required for convergence. This design yields a direct analytical chain from relative smoothness and component-wise variance control to convergence in finite-dimensional normed spaces. We prove an O(n/K) ergodic rate for convex objectives, a restarted linear rate under relative quadratic growth, and an O(1/K) bound for a Bregman proximal residual in the nonconvex setting. On logistic regression and sparse nonnegative matrix factorization, Ada-BPSG combines low objective values with substantially less sensitivity to the initial step size than standard variance-reduced baselines, while avoiding line search.
[LG-69] Beyond Local Power: Functional Connectivity Analysis for Subject-Independent Learning Style Recognition
链接: https://arxiv.org/abs/2608.12000
作者: Wiga Maulana Baihaqi,Indriana Hidayah,Sri Kusrohmaniah,Noor Akhmad Setiawan
类目: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 7 pages, 5 figures. Accepted for publication at 4th IEEE International Conference on Artificial Intelligence and Mechatronics Systems 2026
Abstract:Identifying individual learning styles optimizes pedagogical efficacy. While traditional questionnaires are structured, behavioral tracking methods require prolonged interaction log accumulation. To overcome these temporal constraints, this paper proposes an objective Electroencephalography (EEG) approach evaluating Phase Locking Value (PLV) connectivity against localized features across the Active-Reflective (AR) and Verbal-Visual (VV) Felder-Silverman dimensions. EEG signals were recorded from 28 participants during Raven’s Advanced Progressive Matrices tasks. Support Vector Machine classification used Leave-One-Subject-Out Cross-Validation (LOSO-CV) alongside a 70:30 intra-subject split. The VV dimension achieved 70.00% subject-level accuracy driven by distinct fronto-occipital polarization. Conversely, the AR dimension yielded lower cross-subject generalizability (55.56%) due to overlapping executive networks and a “Systematic Neural Inversion” phenomenon, where stable individual connectivity signatures operated diametrically opposed to global boundaries (up to 20-0 voting margins). Ultimately, these outcomes demonstrate that rigid “one-size-fits-all” classifiers are bounded by biological diversity, emphasizing the need for future adaptive feature transformation techniques to bridge the cross-subject generalization gap.
[LG-70] Latent variable models for simultaneous EOV identification and removal in population-based SHM
链接: https://arxiv.org/abs/2608.11995
作者: M. D. Champneys,M. R. Jones,A. J. Hughes,T. J. Rogers,E.J. Cross,K. Worden
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注:
Abstract:The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM). The difficulty is compounded in the case that the EOV signals are unmeasured. A common approach in conventional SHM is to apply \emphprojection-based methods that discard subspaces of healthy feature data, reasoning that the EOV signal dominates the variance of the measured features. However, a common pitfall of projection-based approaches is that when damage acts close to the same variance-dominant direction, damage sensitivity is removed along with the EOV. An alternative identifying assumption for the removal of particular unmeasured EOVs is slowness; the latent EOV process is characterised by its long temporal correlation. In this paper, the latent EOV is cast as a state-space Gaussian process, enabling tractable \mathcalO(T) inference via a Kalman filter. A robust hierarchical Bayesian identification framework is developed that enables population-level identification of latent EOVs and EOV-free residual features, using a Laplace approximation. The approach is first validated on a single laboratory-scale benchmark structure from the literature, subject to thermal EOVs, demonstrating robust damage detection and EOV recovery. The method is then applied to a simulated nine-turbine offshore wind farm with staggered deployment and damage, where it delivers a substantial true-positive uplift over projection and cointegration-based baselines at matched false-positive rates.
[LG-71] ght Nonasymptotic Local Convergence of Sinkhorn-Knopp
链接: https://arxiv.org/abs/2608.11760
作者: Wenzhi Gao,Zhaonan Qu,Yinyu Ye,Madeleine Odell
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We revisit the Sinkhorn-Knopp (SK) algorithm for the matrix scaling problem. Despite extensive literature on the global convergence of SK and its variants, its local linear convergence behavior remains less understood. We address this gap by providing the first nonasymptotic local analysis of SK that matches the rate obtained from existing asymptotic Jacobian-based arguments. We show that under certain connectivity conditions, SK is a polynomial-time algorithm for doubly stochastic matrix scaling. With the developed tools, we showcase the local suboptimality of SK and provide accelerated variants. Finally, for dense matrices, we improve the complexity of existing first-order matrix scaling algorithms from O(\tfracn^7/3\varepsilon^2/3) to O(\tfracn^9/4\sqrt\varepsilon) .
[LG-72] A Quantum/Classical Example Oracle Separation for Making Things Up
链接: https://arxiv.org/abs/2608.11648
作者: Kenny Chen
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 22 pages, 3 figures
Abstract:We study the power of quantum examples, as compared to classical examples, in the PAC learning framework. Here, we have two learning algorithms, both with access to quantum computation, but one gets quantum examples, whereas the other gets classical examples. It was previously unknown whether there were learning tasks that can be efficiently performed but not by the latter. Our primary result is to show that relative to an oracle, there are distributions that can be efficiently generated by a quantum learner with access to quantum examples, but not by a quantum learner with access to only classical examples, making progress to answering this question in the affirmative.
[LG-73] Unifying Physical Backpropagation
链接: https://arxiv.org/abs/2608.11585
作者: Cyrill Bösch,Yigithan Gediz,Hakan Türeci
类目: Disordered Systems and Neural Networks (cond-mat.dis-nn); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Optics (physics.optics)
*备注: 58 pages, 3 figures
Abstract:Physical computing systems exploit device dynamics for computation, but their gradient-based optimization is challenging: backpropagation through a digital twin suffers from model-reality gap. On-device gradient computation could resolve this issue, and a handful of theoretical and experimental studies have proposed ways to achieve it. Yet a unifying theory identifying when a physical system can compute the gradient of its own performance has been missing. Here we develop such a unification, based on the adjoint method: we identify sufficient conditions under which the adjoint field required for formally exact gradients can be generated on the same hardware that performs the computation. Linear and nonlinear systems obey fundamentally different conditions: for linear systems damping or gain is admissible provided reciprocity is preserved. For nonlinear trajectory systems the sufficient conditions are reciprocity of the linearized system and the existence of a time-reversal mirror. Algorithmically, the nonlinear case requires infinitesimal nudging, whereas linear systems admit a finite-amplitude experiment. We recover Equilibrium Propagation, Hamiltonian echo backpropagation, fully forward mode training and in situ gradient methods in integrated-photonic and free-space-optical systems. We further show that reciprocity is only the simplest instance of a more general intertwining condition, which extends exact on-device gradient computation to a class of non-Hermitian, non-reciprocal systems. Further generalizations include time-dependent parameters, Onsager-reciprocal dynamics and nonlinear, PT-symmetric Schrödinger equations. Our work provides a unified theoretical basis for formally exact physical learning algorithms and a template for constructing them across a range of physical systems.
[LG-74] Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
链接: https://arxiv.org/abs/2608.11544
作者: Thejani Gamage,Hyemin Gu,Zhizhen Zhang,Ziyu Chen,Markos Katsoulakis,Luc Rey-Bellet
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We propose CVaR-penalized Generative Particle Algorithm (CVaR-GPA), a robust, tail-agnostic algorithm for fine-tuning generative models to learn heavy-tailed distributions and capture extreme events, requiring no prior knowledge or estimation of the target’s tail characteristics. The method is the Wasserstein gradient flow of the Lipschitz-regularized Kullback-Leibler (KL) divergence penalized by a Conditional Value-at-Risk (CVaR) discrepancy term: the Lipschitz-regularized KL divergence enables robust learning under minimal assumptions on the target distribution, while the CVaR penalty restores the velocity that otherwise vanishes prematurely in the under-sampled tails. The penalized flow admits a bounded but non-Lipschitz velocity field. This departs from the Lipschitz transport maps of standard generators, which preserve the tail behavior of a light-tailed source, and enables transport toward heavier-tailed targets. To define this flow on empirical measures, we derive the first-variation subgradients of CVaR from its Rockafellar-Uryasev representation, valid precisely where the classical density-based formula fails. The particle algorithm CVaR-GPA fine-tunes the output samples of any pre-trained model, without access to its architecture, and runs on an adaptive time horizon set by a kinetic-energy stopping criterion rather than a preset depth. On synthetic isotropic and anisotropic Student- t target distributions, Neal’s funnel distribution, and the real-world high-dimensional Fama-French 25 portfolio dataset, CVaR-GPA dramatically improves global and tail accuracy on heavy-tailed targets over the pre-trained baseline.
[LG-75] Probing and steering biology across Boltz-1s trunk-diffusion boundary
链接: https://arxiv.org/abs/2608.11475
作者: Piotr Jedryszek,Tongmeng Xie,Adam Winnifrith,Alexander Hasson,Weronika Ślesak,George Wicks,Toby Winnifrith,Oliver M. Crook
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)
*备注:
Abstract:AlphaFold3-class structure predictors pair a representational trunk, which processes sequence and context, with a diffusion module, which generates atomic coordinates. How biological information changes as it crosses this architectural boundary remains poorly understood. We analyze per-residue activations from the Pairformer trunk and diffusion module of Boltz-1 using linear probes, sparse autoencoders (SAEs), and causal interventions. From the trunk, both geometry (secondary structure, disorder) and sequence chemistry (amino-acid identity, signal peptides, disulfide-bond annotations) are linearly decodable. In the diffusion module, the two diverge. Secondary structure transfers essentially unchanged, whereas sequence chemistry is strongly attenuated. We then test whether decodable directions can steer the model, intervening on the final trunk single representation that conditions the diffusion module. Helix and coil directions change predicted structure dose-dependently against matched-norm random controls, but a beta-strand direction that is highly predictive (F1 =0.82) produces no measurable increase in strand content: linear decodability does not imply causal influence at the site we tested. The same probes also score markedly lower against sparse SwissProt annotations than against dense DSSP labels, because unannotated residues that the model gets right are charged as false positives; such scores are therefore lower bounds. Finally, supervised probes outscore single SAE features wherever a label already exists. We release the trained trunk and diffusion SAEs, Boltz-1 per-residue activations, and the analysis code.
[LG-76] Large-scale AI-Ready Data for Anti-Cancer Drug Response Modeling
链接: https://arxiv.org/abs/2608.11444
作者: Vincent Lavelle,Yitan Zhu,Kaitlyn Marlor,Thomas Brettin,Rick Stevens
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)
*备注:
Abstract:Drug response prediction (DRP) models are an active area of research in pharmacogenomics, with growing potential to accelerate the identification of effective anticancer drugs. However, their predictive performance is often constrained by limited dataset scale and insufficient coverages of cancer and chemical spaces. In addition, inconsistent benchmarking practices hinder reliable comparison across models. Standardized frameworks, such as the Innovative Methodologies and New Data for Predictive Oncology Model Evaluation (IMPROVE) project, provide unified data schemas and evaluation protocols for consistent benchmarking, but improving model generalizability requires larger and more diverse training data. In this work, we substantially expand the IMPROVE benchmark through large-scale integration of pharmacogenomic data, primarily from PharmacoDB, together with additional smaller data sources. The expanded resource includes millions of drug response measurements, broader multi-omics coverage, and a major increase in chemical diversity, adding more than 50,000 compounds. To evaluate the impact of the new dataset compared to the original IMPROVE benchmark dataset, we trained DRP models using the two datasets and assess their prediction performance using a common test set and several evaluation strategies, including drug-blind, cancer-blind, and disjoint data splits. While cancer-blind performance remained comparable to the original benchmark, models trained on the expanded dataset showed consistent improvements in drug-blind and disjoint settings, indicating enhanced generalization to previously unseen compounds. These results position the expanded dataset as a community resource that provides a richer foundation for developing DRP models intended to aid in the discovery of novel anticancer drugs.
[LG-77] Generative Learning for Quantum Measurement Design
链接: https://arxiv.org/abs/2608.11396
作者: Jun Dai,Olivier Nahman-Lévesque,Guillaume Rabusseau,Hong-Ye Hu,Cunlu Zhou
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:
Abstract:Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non-commuting observables under a finite measurement budget. For both near-term and early fault-tolerant settings, the measurement protocol must balance statistical efficiency against implementation resources such as circuit depth, connectivity, and entangling-gate count. Many existing strategies focus on two extremes: hardware-friendly product measurements with high sampling cost, and fully commuting measurements with deep circuits. Here we recast resource-constrained measurement design as a generative learning problem. We introduce FlowMeas, which uses a generative flow network to directly sample finite ensembles of shallow Clifford measurement circuits subject to a prescribed shot budget and hardware constraints. At zero entangling depth, FlowMeas learns qubit-wise commuting measurement schedules and already matches or improves leading product-measurement methods on nearly all molecular benchmarks. Allowing one or two entangling gate layers yields further reductions in energy estimation error of up to 27% relative to the strongest state-independent product-measurement baseline. The learned policy can also be reused across related Hamiltonians, substantially accelerating retraining along a molecular potential-energy surface. We further obtain results for molecular Hamiltonians with up to 20 qubits and apply the framework to a compactly encoded 54-qubit interacting fermionic model, extending the demonstrated scale beyond prior molecular benchmarks. These results establish generative learning as a flexible and unified framework for quantum measurement design under practical resource constraints.
[LG-78] Hardware-Aware Deployment of Joint SAR Compression and Despeckling on FPGA
链接: https://arxiv.org/abs/2608.11271
作者: Cédric Léonard,Francescopaolo Sica,Martin Schulz
类目: Image and Video Processing (eess.IV); Machine Learning (cs.LG)
*备注: Submitted to IEEE Transactions on Geoscience and Remote Sensing (TGRS). 11 pages, 8 figures, 4 tables
Abstract:Next-generation Synthetic Aperture Radar (SAR) missions will generate data far faster than they can downlink, making onboard data reduction essential for near-real-time Earth observation. Learned Image Compression (LIC) offers better rate-distortion performance than handcrafted codecs used operationally today, and recent work shows that simultaneously despeckling and compressing SAR imagery enables better representation capacity while unlocking higher compression rates. These methods, however, have yet to be confronted with the strict power, compute, and operational constraints of spaceborne systems. In this work, we bridge this gap by deploying a joint SAR Despeckling and Data Compression (DDC) framework on an embedded ZCU102 FPGA-based platform, introducing model adaptations that respect the accelerator’s fixed-point arithmetic and limited set of supported operations. We evaluate four model topologies across precision levels and across CPU, GPU, and FPGA platforms, revealing several findings with direct design implications. We find that replacing conventional GDN activation functions with plain ReLU improves quality on SAR, suggesting that design principles established for compression of natural images do not necessarily transfer to SAR imagery. In addition, we demonstrate that residual blocks offer little representational benefit for ten times the compute, and show that the FPGA is the most energy-efficient of the platforms tested. Together, these results set a functioning edge deployment workflow and an evidence-based starting point for onboard SAR compression. The code is available at this https URL.
[LG-79] CosMAP: Contrastive Manifold Approximation and Projection for Dimensionality Reduction of Omics and Genealogical Data
链接: https://arxiv.org/abs/2608.11269
作者: Fenosoa Randrianjatovo,Maya Saleh,Simon Girard,Amadou Barry
类目: Genomics (q-bio.GN); Machine Learning (cs.LG); Computation (stat.CO); Methodology (stat.ME)
*备注: 35 pages; 18 figures
Abstract:Omics datasets, particularly single-cell RNA sequencing data, are high-dimensional, sparse, noisy, and dominated by zero values, making faithful low-dimensional representation challenging. Existing dimensionality-reduction methods may distort local neighbourhoods, global organization, or the cohesion of meaningful populations, with similar limitations arising in genealogical data. We introduce Contrastive Manifold Approximation and Projection (CosMAP), a graph-based unsupervised dimensionality-reduction method for producing faithful and interpretable embeddings. CosMAP extends the graph-based framework of UMAP by combining cosine-similarity neighbourhoods with temperature-normalized contrastive affinities, which are optimized in the embedding space using an attractive–repulsive objective. It further employs a two-phase refinement strategy: an intermediate higher-dimensional representation is first learned and then used to reconstruct the neighbourhood graph and initialize the final low-dimensional embedding. We evaluate CosMAP on MNIST and USPS handwritten-digit datasets, mouse retina and cortex single-cell RNA-sequencing datasets, and a large genealogical kinship dataset derived from BALSAC-CARTaGENE. Compared with state-of-the-art dimensionality-reduction methods, CosMAP produces more coherent visual representations, improves neighbourhood preservation, and provides clearer global organization of digit classes, biological cell populations, and regional genealogical patterns. These results indicate that CosMAP offers a robust framework for exploratory analysis of complex, sparse, high-dimensional data. The implementation is publicly available at this https URL.
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