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目录
概览 (2026-08-17)
今日共更新510篇论文,其中:
- 自然语言处理共62篇(Computation and Language (cs.CL))
- 人工智能共185篇(Artificial Intelligence (cs.AI))
- 计算机视觉共89篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共138篇(Machine Learning (cs.LG))
- 多智能体系统共9篇(Multiagent Systems (cs.MA))
- 信息检索共13篇(Information Retrieval (cs.IR))
- 人机交互共34篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Submodular Policy Learning for Distributed Task Allocation in Open Multi-Agent Systems
【速读】：该论文旨在解决开放环境下多智能体系统中分布式任务分配的策略学习问题，其核心挑战在于：智能体在时变环境中动态加入与退出，且各阶段团队效用具有子模性（submodular），而传统的连续松弛方法基于独立伯努利采样，与智能体在分类策略下产生的可行联合动作对所构成的分划拟阵（partition matroid）结构不一致。为解决这一结构性不匹配问题，论文提出了一种基于策略的连续松弛方法——分划多线性扩展（Partition Multilinear Extension, PME），其连续支持空间精确对应于分类策略下的可行动作组合。研究证明，各阶段效用的边际增益可作为PME梯度的无偏估计，且在动作分布上最大化PME等价于在智能体动作上最大化阶段效用，从而为设计合理的策略学习算法提供了理论依据。基于此，作者提出了一个集中训练、分散执行的KL镜像策略学习方法SubMAPL，利用局部边际增益作为随机PME梯度进行训练，并通过KL镜像更新保证分类可行性而不依赖欧氏空间。针对表格式软最大（tabular-softmax）策略场景，进一步引入开放策略迁移与开放系统KL追踪机制以应对智能体动态变化。通过动态遗憾分析，建立了考虑环境开放性及阶段最优与全局最优效用差距的累积效用下界。仿真结果表明，SubMAPL在多智能体覆盖任务中显著优于基于策略梯度和在线学习的基线方法。
链接: https://arxiv.org/abs/2608.14390
作者: Jing Liu,Luca Ballotta,Yangyang Yang,Fangfei Li,Yang Tang,Ruggero Carli
机构: East China University of Science and Technology (华东理工大学); University of Padova (帕多瓦大学); Shanghai Institute for Mathematics and Interdisciplinary Sciences (上海数学与交叉科学研究院)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:This paper studies policy learning for distributed task allocation in open multi-agent systems, where agents may join and leave in a time-varying fashion, with submodular stage team utilities. At each time, the active agents select actions from local categorical policies such that the feasible joint agent-action pairs form a partition matroid. Standard continuous relaxations of submodular set functions are based on independent Bernoulli sampling, making them inconsistent with agents’ this http URL solve this mismatch, we propose the \emphpartition multilinear extension (PME), a policy-based relaxation whose continuous support matches feasible actions under categorical this http URL prove that the marginal gains of the stage utility provide an unbiased estimator of the gradient of the PME and that maximizing the PME over action distributions is equivalent to maximizing the stage utilities over agent actions, which are critical to devise principled policy this http URL on this, we design \emphSubMAPL, a centralized-training decentralized-execution KL-mirror policy-learning method that uses local marginal gains as stochastic PME gradients during training. KL-mirror updates preserve categorical feasibility without Euclidean this http URL the case where agents run tabular-softmax policies, we introduce open policy migration and an open-system KL tracking variation to handle agent arrivals and departures. Using dynamic regret analysis, we establish a lower bound on the cumulative utility which accounts for the openness of the environment and for the gap between optimal stage-wise and global utilities. Simulations on multi-agent coverage demonstrate that SubMAPL outperforms policy-gradient and online-learning baselines.
[MA-1] A Graph-Based Reinforcement Learning Framework for Structured Drift Diagnosis and Recovery in Autonomous LLM Agents
【速读】：该论文旨在解决自主大语言模型（LLM）代理在复杂真实工作流中部署时面临的运行时行为漂移（runtime behavioral drift）问题，即代理在执行过程中悄然偏离原始任务目标，可能对外部系统造成不可逆的负面影响。现有方法仅在提示（prompt）层面进行干预，缺乏对执行步骤级别的漂移检测、风险评估与恢复决策的结构化机制。针对主任务执行代理通常为大型且昂贵的模型而无法频繁重训练的现实约束，本文提出一种即插即用的恢复模块。其核心创新在于构建一个基于图结构的框架，通过强化学习训练一个小型语言模型（small language model），使其在恢复图的每个节点上专业化：分别承担漂移分类、操作识别、风险评估和最终决策等职责。每个节点生成符合特定角色的结构化XML格式推理输出。训练过程结合规则驱动的结构奖励与大模型作为裁判（LLM-as-judge）的语义质量信号，使模型在回答格式（如模式与长度）和内容语义两方面均被评估。在公开的AppWorld基准上的实验表明，该方法能够有效利用漂移发生前兆信息，以小型模型实现正确的恢复决策；同时，训练后的模型能稳定遵循预设输出模式，并在各节点角色下生成语义恰当的内容。
链接: https://arxiv.org/abs/2608.14109
作者: Ismail El Hamraoui,Sagar Jose,Nicolas Bureau,Robert Plana
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Autonomous LLM agents are increasingly deployed in complex real-world workflows, yet they remain vulnerable to runtime behavioral drift, a silent deviation from the original task that can lead to irreversible side effects on external systems. Existing approaches address drift at the prompt level but lack structured mechanisms for step-level detection, risk assessment, and recovery decision. Because the main task-executing agent is often a large and expensive model that cannot be re-trained on every deployment, this work targets a plug-and-play recovery module instead. It introduces a graph-based framework in which a single small language model is trained via reinforcement learning to specialize at each node of a recovery graph, external to the main agent. Each node has a precise role,: drift classification, operation detection, risk evaluation, or final decision and the model learns to produce structured XML-formatted reasoning adapted to that role. Training combines rule-based structural rewards with an LLM-as-judge semantic-quality signal, so that the model is graded both on how it answers (schema and length) and on what it says. Experiments on the public AppWorld benchmark show that the method generally exploits information about the suspected drift onset to issue correct recovery decisions using a small language model. In addition, the trained small language model reliably respects the prescribed output schema and produces semantically appropriate content in each field according to its assigned node role.
[MA-2] XAI-Guided Conservative Decentralized Execution for Offline Multi-Agent Network Slicing
【速读】：该论文旨在解决6G及后6G网络中网络切片（network slicing）环境下异构服务在共享基础设施下资源分配的复杂问题，核心挑战在于多个切片间对有限资源的竞争与协同，形成具有资源耦合特性的合作优化难题。现有基于多智能体强化学习（MARL）的在线方案因依赖环境交互和智能体间通信，存在计算开销大、安全性差且难以部署等问题。为此，本文提出一种可解释人工智能（XAI）引导的保守去中心化执行框架（X-CODE），其关键创新在于采用可解释性感知的奖励塑形（explainability-aware reward shaping）机制，在集中式训练阶段通过调整联合离线轨迹间的相对偏好，提升去中心化部署时的资源分配行为表现。该方法完全离线运行，无需环境交互或智能体间通信，实现了零观测资源冲突事件的同时最小化各切片延迟。仿真结果表明，相比在线基线方法，该框架显著降低信令开销，并在所考虑的通信时延模型下将有效推理延迟降低88%。
链接: https://arxiv.org/abs/2608.13982
作者: Eslam Eldeeb,Hatim Chergui,Merouane Debbah
机构: 未知
类目: Multiagent Systems (cs.MA)
备注:
Abstract:The recent advances toward sixth-generation (6G) and beyond-6G networks have accelerated the need for intelligent resource management mechanisms capable of supporting heterogeneous services under shared infrastructures in network slicing. However, resource allocation in network slicing naturally forms a resource-coupled cooperative optimization problem with competing slice demands. Slices compete for limited resources to minimize individual latencies while coordinating to avoid conflicts and underutilization. Although multi-agent reinforcement learning (MARL) has shown promising performance in such settings, existing online formulations remain costly, unsafe, and difficult to deploy due to their reliance on environmental interactions and communication among agents. In this work, we present explainable artificial intelligence (XAI)-guided conservative decentralized execution (X-CODE). X-CODE is an explainable offline MARL that operates offline without environmental interaction, nor inter-agent communication. It exploits explainability-aware reward shaping to modify the relative preference among joint offline transitions during centralized training to improve decentralized resource-allocation behavior. In deployment, the agents operate independently without signaling exchange among the agents. Simulation results demonstrate that the proposed approach achieves zero observed resource-conflict events in the evaluated test episodes while minimizing per-slice latencies. Moreover, the proposed framework exhibits lower signaling overhead and reduces effective inference latency by 88 % under the considered communication-delay model compared to the online baselines. Source codes and datasets are available through: this https URL.
[MA-3] From Passive Delegates to Strategic Negotiators: Reinforcing Social Reasoning in Small Language Models with SocialRL
【速读】：该论文旨在解决生成式AI代理（Generative AI Agent）在执行委托任务时面临的“社交冲突”问题：当代理代表用户与目标可能相悖的对手（如其他用户代理、卖家或招聘方）互动时，尽管友好、助人的大模型在日常交互中表现良好，但其倾向于无意识泄露用户隐私信息或在遇到阻力时过早让步，从而损害用户利益。为应对这一挑战，论文提出SocialRL——一种直接训练社会推理能力的通用方法，通过在六个不同领域（包括谈判、求职面试、日程安排等）中统一采用相同训练范式，使40亿参数模型在各领域内达到前沿性能。其解决方案的关键在于：（1）基于领域内训练实现性能突破，使模型在未见场景中对齐甚至超越GPT-5系列；（2）利用游戏结构驱动跨域迁移，发现具有相似结构的博弈任务可相互促进，而结构孤立的任务则无法有效迁移；（3）结合级联强化学习（cascade RL）与多教师在线策略蒸馏（multi-teacher on-policy distillation, OPD），将各领域专精模型整合为单一统一的4B规模代理，在所有六项环境中平均效用达0.627，与GPT-4.1、GPT-5.1及GPT-5.2相当；（4）引入显式的心理理论（Theory-of-Mind, ToM）架构仅在训练阶段有效，通过蒸馏包含未来动作预测的ToM轨迹而非仅动作本身，显著提升各环境下的效用并增强泛化能力，且仅有“下一步动作预测”这一技能能有效预判谈判结果。
链接: https://arxiv.org/abs/2608.13787
作者: Wenyue Hua,Zachary Huang,Tyler Payne,Safoora Yousefi,Saleema Amershi,Asli Celikyilmaz
机构: Microsoft Research, AI Frontiers(微软研究院，人工智能前沿)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 25 pages, 3 figures
Abstract:AI agents increasingly act on their users’ behalf, handling tasks such as scheduling meetings, comparing offers, and haggling over prices. These principal-driven tasks routinely place the agent across from a counterpart (another user’s agent, a seller, a recruiter) whose goals may conflict with its principal’s. Yet the dispositions that make an assistant pleasant can make it a poor delegate: a friendly, helpful frontier model may disclose its principal’s private information unprompted and concede at the first sign of resistance. We present SocialRL, a general recipe that trains social reasoning directly, and apply it to a 4B model across six domains: Deal-or-No-Deal, CaSiNo, Craigslist, Job Interview, Calendar, and Marketplace. Every domain is trained in-domain under the same recipe, and every policy is evaluated on all six. We find that (1) in-domain training reaches the frontier: on held-out scenarios the 4B matches or exceeds the GPT-5 family per domain, closing 73-122% of the baseline-to-frontier gap on the negotiation games, with 78% of buyer openings anchoring below target versus 3% untrained; (2) cross-domain transfer follows game structure: structurally paired games lift each other, a broad multi-issue donor lifts nearly all domains, and structurally isolated games transfer nothing; (3) guided by this transfer structure, two strategies, cascade RL and multi-teacher on-policy distillation (OPD), consolidate the per-domain specialists into a single unified 4B that reaches 0.627 average utility across all six environments, matching or exceeding GPT-4.1 (0.625), GPT-5.1 (0.619), and GPT-5.2 (0.613); (4) an explicit theory-of-mind scaffold helps only through training: distilling the ToM trace, rather than actions alone, lifts utility on every environment and generalizes better across them, and of the two ToM skills, only next-action prediction predicts negotiation outcomes.
[MA-4] Adjacency-Based Spectral Proxy Control of Mobile Communication Agents
【速读】：该论文旨在解决异构移动代理网络中通信代理的在线重定位问题，即在任务代理移动过程中，如何动态调整通信代理的位置以维持网络连通性与性能。传统方法通常采用代数连通性（algebraic connectivity）作为代理目标，但其优化依赖于拉普拉斯矩阵次小特征值对应的Fiedler向量，而该向量的分布式估计需要无限多轮通信才能收敛，难以在实际受限通信环境下应用。本文的关键贡献在于揭示了Fiedler梯度控制器的结构分解：其可被拆分为局部交互规则与图嵌入组件，并据此提出一种替代方案——A-Fiedler，用邻接矩阵的主特征向量（dominant eigenvector of the adjacency matrix）替代Fiedler向量作为节点嵌入表示。该嵌入具有更优的分布式可估计性，尤其适用于局部通信约束下的系统。实验表明，在无通信限制时，A-Fiedler与经典Fiedler梯度控制器性能相当；而在分布式估计条件下，前者表现出更强的鲁棒性——相同通信轮次下，传统方法可能收敛至断开配置，而A-Fiedler仍能保持网络连通性与高效性能。因此，本工作为实现高效、可扩展的分布式网络控制提供了一条更简洁可行的技术路径。
链接: https://arxiv.org/abs/2608.13616
作者: Mariana del Castillo,Federico Larroca
机构: Universidad de la República (乌拉圭共和国大学); Facultad de Ingeniería (工程学院)
类目: Robotics (cs.RO); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注:
Abstract:We consider a heterogeneous mobile-agent network composed of uncontrolled task agents and controllable communication agents. The objective is to reposition communication agents online as task agents move. Since throughput-based objectives are generally unsuitable for real-time control, spectral graph metrics such as algebraic connectivity are commonly adopted as surrogate objectives. However, controlling algebraic connectivity relies on the eigenvector corresponding to the second-smallest eigenvalue of a graph’s Laplacian matrix (i.e., the Fiedler vector), whose distributed estimation requires an unbounded number of communication rounds to converge. In this work, we identify a structural decomposition of this Fiedler-gradient controller into a local interaction rule and a graph embedding component, suggesting the use of alternative embeddings that are easier to estimate distributively than the Fiedler vector. As a particular instance, we propose A-Fiedler, which replaces the Fiedler embedding with the dominant eigenvector of the adjacency matrix, commonly used as a graph embedding of nodes into a latent geometry. This representation is more naturally suited for distributed implementation under local communication constraints. We evaluate A-Fiedler against the classical Fiedler-gradient controller. Results show comparable network performance in the absence of communication constraints and improved robustness under distributed estimation. For instance, under the same number of communication rounds, the Fielder-gradient may even converge to disconnected configurations whereas our proposition maintains performance. We believe our contribution provides a simpler path toward distributed network control. Subjects: Robotics (cs.RO); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Systems and Control (eess.SY) Cite as: arXiv:2608.13616 [cs.RO] (or arXiv:2608.13616v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.13616 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-5] MobileMem: Learning from a Year of Mobile Experiences
【速读】：该论文旨在解决当前人工智能代理在真实移动场景下缺乏对用户长期、持续学习能力的问题，尤其针对现有基准测试在异构性、多模态性、动态演化及高度个性化体验建模方面的不足。其核心挑战在于如何构建具备长期记忆的个人助理系统，使其能够基于用户的实际使用经历实现理解、记忆与持续适应。解决方案的关键在于提出MobileMem——一个基于一年尺度移动设备使用数据构建的基准与框架，通过知识引导的合成流程生成时空一致的长时程行为轨迹；该框架支持文本与多模态任务，涵盖多跳推理、时间推理、知识更新及隐式偏好推断等能力，使智能体不仅能回忆过去、理解当下，还能预测并适应未来需求。通过建模用户真实经验而非孤立事实，MobileMem将记忆机制从传统信息检索提升至面向持续个人学习的体验智能（experiential intelligence）层面。
链接: https://arxiv.org/abs/2608.13606
作者: Xinle Deng,Yida Xue,Xiangyuan Ru,Haoming Xu,Shuofei Qiao,Mengru Wang,Yijun Chen,Buqiang Xu,Chen Jiang,Yuchen Eleanor Jiang,Lizhong Wang,Jianfeng Wang,Li Zeng,Haofen Wang,Guilin Qi,Huajun Chen,Ningyu Zhang
机构: OPPO(欧珀); OpenKG; Zhejiang University (浙江大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Multimedia (cs.MM)
备注: Technical Report; Project Page: this http URL
Abstract:The next generation of AI agents is increasingly moving beyond systems that answer isolated questions toward persistent personal assistants that can understand, remember, and continuously learn from users’ experiences. Such assistants require long-term memory to accumulate and leverage user-specific experiences over time, yet existing benchmarks remain inadequate for realistic mobile settings, where experiences are heterogeneous, multimodal, evolving, and deeply personal. We introduce MobileMem, a benchmark and framework for studying on-device long-term memory, grounded in a year-scale collection of mobile experiences. MobileMem employs a knowledge-grounded synthesis pipeline to construct coherent and temporally consistent long-horizon trajectories from user-app sessions. It provides complementary text and multimodal settings covering multi-hop and temporal reasoning, knowledge updating, and implicit preference inference. Specifically, MobileMem enables agents to remember the past, understand the present, and adapt to the future. By modeling experiences rather than isolated facts, MobileMem moves memory beyond information retrieval toward experiential intelligence for continuous personal learning.
[MA-6] Cross-Disciplinary Taxonomy and Modeling of Misunderstanding Generation Amplification and Detection from Prag matics to AI Agents
【速读】：该论文旨在解决在人工智能（AI）中介沟通日益普遍的背景下，误解检测机制滞后于沟通模式变革这一紧迫问题。随着人际交流逐渐脱离实时、面对面互动，转向由生成式 AI（Generative AI）等技术支撑的非实时渠道，沟通双方失去了依赖即时反馈进行纠错的关键资源，而新的误解检测手段尚未及时建立。为此，论文将误解视为一个分层过程，包含分歧产生、可能被放大，以及是否被识别与修复或持续未被察觉的阶段。通过整合九个互不引用的学科领域研究，论文识别出十一种精确的故障模式，并发现每种模式均作用于沟通过程中的特定节点，而非任意位置。这些节点构成了八个分析层级，完全基于文献推导，而非既有的理论模型。其中，八种机制主要引发分歧，两种主要放大已有分歧，一种则控制分歧是否被检测与修复。论文对这八个层级进行了形式化建模，将信息与通信理论从信号传输扩展至意义重构范畴；同时提供来源可追溯的证据矩阵、编码手册及九个对话案例的详细分析，确保所有判断具有可审计性。该研究首次实现了对误解机制在过程节点上的精确定位及其功能类型的系统分类，为构建可解释、可验证的误解检测框架提供了理论基础与实证支持。
链接: https://arxiv.org/abs/2608.13604
作者: Babak Abbaschian
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注: 49 pages, 2 figures, 8 tables, 94 references. Cross-disciplinary conceptual synthesis across multiple fields. Includes a source-by-source evidence matrix in Appendix A and a coding manual in Appendix B for independent application of the taxonomy
Abstract:Detection of misunderstanding is an urgent problem to solve because communication has moved away from real-time, in-person interaction and is increasingly handled by AI-mediated channels. This shift cuts communicators off from the resources repair depends on faster than new means of detection are being built. In this paper we analyse misunderstanding as a layered process in which a divergence is generated, may then be amplified, and is either detected and repaired or left to persist unnoticed. Consolidating accounts from nine fields of research that do not ordinarily cite one another, we identify eleven exact failure modes and show that each operates at a specific point in a communicative process rather than anywhere within it. Those points give eight analytical layers, derived from the literature rather than adopted from an existing model. Eight of the mechanisms primarily generate a divergence, two primarily amplify one already present, and one governs whether a divergence is detected and repaired. We model the eight layers formally, extending information and communication theory from the transmission of signals to the reconstruction of meaning, and we supply a source-by-source evidence matrix that makes every rating auditable, a coding manual, and nine analysed dialogue cases. No prior classification of misunderstanding both locates mechanisms at points in the process and types them by function.
[MA-7] Beyond Simplification: DFT-GEN for Fidelity-Preserving Visual Accessibility in Dyslexia-Friendly Educational Texts
【速读】：该论文旨在解决教育类密集文本对阅读障碍者造成的可避免的认知负担问题，同时避免传统文本简化方法在降低阅读难度时删减关键术语、任务约束或原文证据等必要信息的风险。核心挑战在于：在减轻认知负荷的同时，必须保障信息保真度（information fidelity）。其解决方案的关键是提出DFT-GEN框架，该框架基于利益相关者（包括阅读障碍成年用户与专业人员）的反馈设计，引入一种针对阅读障碍的专用可访问性层（dyslexia-specific accessibility layer），通过结合受保护片段保留机制与确定性控制的阅读障碍可访问性控制器（Dyslexia Accessibility Controller, DAC），实现对视觉单元长度、分块间距、来源/任务分离、高亮预算及可审查风险标记等要素的可复现控制。该方法将评估体系划分为两部分：DCF-I（保真度-安全性诊断）与B-DVAS-VL（渲染后视觉可访问性诊断）。在2,280个双语考试样题上的实验表明，DFT-GEN在保持任务关键信息完整性的同时显著提升视觉可访问性，在英文和中文测试中分别以93%和64%的胜率优于同基础模型的对照组；在针对阅读障碍成年读者的受控试点中，既维持了答题能力，又有效降低了阅读努力程度。
链接: https://arxiv.org/abs/2608.13583
作者: Jiaqian Yu,Chen Jason Zhang,Haoyang Li,Guoqiong Ivanka Huang
机构: The Hong Kong Polytechnic University (香港理工大学)
类目: Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注: Preprint. Code available at this https URL
Abstract:Dense educational texts impose avoidable reading friction on people with dyslexia, yet generic simplification can delete terminology, task constraints, or source evidence that readers still need. Stakeholder interviews with dyslexic adults and specialists reveal a core tension: reduced burden must not compromise information fidelity. We present DFT-GEN, a stakeholder-informed text transformation framework for content-heavy educational materials. Its central contribution is not a generic LLM refinement loop, but a dyslexia-specific accessibility layer that combines protected-span preservation with a deterministic Dyslexia Accessibility Controller (DAC) for rendered visual organization. DAC converts stakeholder and expert preferences into reproducible controls for visual-unit length, chunk spacing, source/task separation, highlighting budget, and reviewable risk flags. We therefore separate evaluation into DCFI, a fidelity-safety diagnostic, and B-DVAS-VL, a rendered visual-accessibility diagnostic. On 2,280 bilingual exam-style items, DFT-GEN preserves task-critical information while improving visual accessibility: it wins 93% in English and 64% in Chinese of B-DVAS-VL pairwise judgments against same-backbone controls, and in a controlled pilot with dyslexic adult readers it preserves answerability while reducing effort.
[MA-8] Agent ao: A Governed Local-First Runtime for Tool-Using LLM Agents
【速读】：该论文旨在解决大语言模型（LLM）代理在执行工具调用、修改本地状态、使用持久化记忆及与外部协议交互时所面临的安全与治理挑战，包括权限过度、审计困难、提示注入攻击、工具污染以及不可控的副作用等问题。其核心解决方案是提出Agentao——一种以本地优先（local-first）为设计原则的受控运行时环境，通过分层架构将模型生成的动作建议与主机授权执行相分离：该架构包含面向主机的接口、主机合约、运行时核心、基于权限的工具系统，以及支持记忆、回放、插件、技能、子代理和协议集成的辅助子系统。关键在于将权限、状态、协议边界和执行轨迹等要素显式地抽象为运行时实体，从而增强代理的可治理性、可追溯性和可审计性，使其更适用于由主机控制的本地环境。尽管不提供形式化安全保证，但Agentao展示了如何通过结构化设计提升代理系统的可控性与透明度。
链接: https://arxiv.org/abs/2608.13574
作者: Bo Jin,Qiang Jiao,Xin Tong
机构: The Third Research Institute of the Ministry of Public Security(公安部第三研究所); Bureau of Science and Technology Information(科技信息局), Ministry of Public Security of the People’s Republic of China(中华人民共和国公安部); People’s Public Security University of China(中国人民公安大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: The code is publicly available at Github. We are conducting testing and analysis of this framework, and will provide experimental results and examples in future versions
Abstract:LLM agents increasingly operate as execution systems that invoke tools, modify local state, use persistent memory, and interact with external protocols. These capabilities make agents useful, but they also introduce risks related to over-privileged actions, weak auditability, prompt injection, tool poisoning, and uncontrolled side effects. This paper presents Agentao, a governed local-first runtime for tool-using LLM agents. Agentao separates model-generated action proposals from host-authorized execution through a layered architecture consisting of host-facing surfaces, a host contract, a runtime core, a permission-mediated tool system, and supporting subsystems for memory, replay, plugins, skills, sub-agents, and protocol integration. We describe the motivation, threat model, design goals, governance model, execution pipeline, and structured event interface of the system. Agentao does not provide formal safety guarantees; rather, it demonstrates how permissions, state, protocol boundaries, and execution traces can be made explicit runtime abstractions for building agents that are more governable, inspectable, and suitable for host-controlled local environments. The code is publicly available at this https URL.
自然语言处理
[NLP-0] Split the Labor: Separating Evidence Interpretation from Decision Aggregation
【速读】： 该论文旨在解决多源信息融合系统中语言模型（Language Model, LM）在处理多个证据来源时存在的根本性设计缺陷：将“源理解”与“解释整合”这两个具有不同需求的操作混合在同一输入提示（prompt）中，导致性能下降。其核心问题是，传统方法通过简单拼接所有来源生成单一提示，使得系统在面对不同可靠性来源时无法正确建模置信度，从而引发一种称为“计数尺度漂移”（count-scale drift）的失败模式——即基于未归一化权重求和的阈值判断本质上是后验概率阈值，但其操作点随所咨询来源数量变化而滑动，且受读者（即模型）可靠性影响加剧；当各来源可靠性不同时，投票规则与后验排序产生分歧，单一阈值无法调和二者矛盾。为此，论文提出四元证据元组（假设、可靠性桶、推理过程、出处），明确分离两个阶段，并引入校准对数似然比（calibrated log-likelihood ratios）的加权求和策略作为解决方案。该方案为算术层面的改进，而非架构重构，可泛化至多种非语言模型系统，如基于得分求和的分诊引擎、阳性计数评分的诊断面板以及多信号叠加检测器。作者在纵向数据集上两次验证该原则：一次在结果明确后，一次在结果未决前，均发现同一划分机制在不同粒度下有效——前者优化阅读层面的证据评估，后者提升学习容量层面的建模能力。实验中，采用小规模序列编码器结合树集成并携带截断生存损失的方法，实现0.921的AUPRC，显著优于人工设计基线的0.805。研究进一步区分了可迁移成分与需领域重估成分，并提出五项可证伪预测、三项负面结果及仍受混杂因素影响的对比方式。
链接: https://arxiv.org/abs/2608.14509
作者: Zhelun Wu
机构: Atlassian
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Atlassian. 22 pages, 2 figures
Abstract:Systems that ask a language model to reach a conclusion from many sources usually concatenate them into one prompt. This conflates two operations with different requirements. Interpreting a source rewards capacity and context. Combining interpretations rewards fixed arithmetic, comparability across instances, and the option to return nothing. Once separated, the design problem becomes the interface between them. We propose a four-field evidence tuple (hypothesis, reliability bucket, rationale, provenance) and show that fixing it determines both halves. The separation also reveals a failure mode in how such systems combine, which we call count-scale drift. Thresholding a sum of unnormalized weights is exactly posterior thresholding, but at an operating point that slides with the number of sources consulted. The slide grows with reader reliability. When source reliabilities differ, the vote rule and the posterior order instances differently, and no threshold reconciles them. Pooling calibrated log-likelihood ratios addresses both problems. The fix is arithmetic rather than architectural, and applies to a class of rules beyond language models: score-summing triage engines, diagnostic panels scored by counting positives, and additive multi-signal detectors. We then instantiate the principle twice on one longitudinal corpus, once after outcomes resolve and once before. The same partition helps in both, at different granularities: over reading in the first, over learning capacity in the second. There, a small sequence encoder on an easy auxiliary objective plus a tree ensemble carrying the censored survival loss reaches 0.921 AUPRC against 0.805 for a hand-crafted baseline. We separate what transfers from what must be re-estimated per domain, and state five predictions that would falsify the framework, three negative results, and which comparisons remain confounded.
[NLP-1] You Only Pass Once: Answering and Abstaining Together in a Single Forward Pass of a Frozen Language Model
【速读】： 该论文旨在解决冻结语言模型在推理任务中的两个关键缺陷：一是未能充分利用其残差流（residual stream）中已编码的证据，二是无法识别输入信息不足的情况，导致产生虚构性回答（confabulation）。其解决方案的核心在于协同利用同一残差流上的两项技术：条件性引导探针（conditional steering probe）通过在中间层写入信号以恢复冻结主干网络的推理准确性，而零样本充分性方向（zero-shot sufficiency direction）则通过读取残差流来判断信息是否充足并选择拒绝回答。然而，这两者在单次前向传播中存在干扰——引导探针的写入会改变后续读取方向的状态，导致跨域迁移性能下降最高达8 AUROC点；若采用独立的清洁前向传播则需加倍推理成本。为克服此问题，本文提出一种新方法：固定充分性方向，训练一个小型网络从被引导修改后的残差流中重建原始未引导状态（基于均方误差损失，无需充分性标签），并在重建结果上执行充分性判断。由此构建的系统YOPO（You Only Pass Once）实现了在一次前向传播内完成回答生成、引导修正与拒绝决策，应用于冻结的Qwen2.5主干网络（1.5B/3B/7B）。实验表明，端到端三类准确率较冻结基线提升超过一倍（如1.5B模型在alphaNLI上由0.375升至0.798），且在所有规模下均优于双阶段基准（0.798/0.830/0.893 vs 0.753/0.790/0.863），并在六个模型家族共十个主干网络上验证了有效性。研究还绘制了容量-迁移边界，强调拒绝行为不应通过训练获得；通过源端审计发现原alphaNLI数据构造中存在表面特征泄露，因此将结论锚定于真实标签复现任务（SQuAD2, RepLiQA, MuSiQue）；在标准四领域评测集上，本文贡献了目前所知首个支持“回答或拒绝”二元决策的基准，其中YOPO的门控机制在所有域内数据集上表现最优，且仅无标签的充分性方向能有效维持跨域迁移能力。
链接: https://arxiv.org/abs/2608.14465
作者: Ziyang Luo,Zhongyao Chu,Xinjie He,Youting Wang,Xukui Qin,Runxiong Wu,Yan-Syuan Chen
机构: Georgia Institute of Technology (佐治亚理工学院); Columbia University (哥伦比亚大学); University of Wisconsin-Madison (威斯康星大学麦迪逊分校); The University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 24 pages. Ziyang Luo and Zhongyao Chu contributed equally
Abstract:A frozen language model on reasoning tasks has two coupled weaknesses: it under-uses evidence its own residual stream already encodes, and it fails to detect when the input is insufficient to answer, so it confabulates. This paper consolidates two research lines that address these on the same residual stream: a conditional steering probe writes the stream at mid-stack layers and recovers reasoning accuracy from a frozen backbone, and a zero-shot sufficiency direction reads the stream and abstains when information is insufficient. Deployed in one forward pass they interfere: the steering write shifts the state the direction reads, costing up to 8 AUROC points of cross-domain transfer on small models; a separate clean pass doubles inference cost. We keep the direction fixed and train a small network to reconstruct the pre-steering residual from the steered one – mean-squared error on (steered, clean) pairs, no sufficiency labels – and read the direction on the reconstruction. The resulting system, YOPO (You Only Pass Once), answers, steers, and abstains in one forward pass of a frozen Qwen2.5 backbone (1.5B/3B/7B). End to end, three-way accuracy more than doubles the frozen baseline (0.375-0.798 on 1.5B alphaNLI) and one pass beats the two-pass reference at every scale (0.798/0.830/0.893 vs 0.753/0.790/0.863) and on ten backbones across six model families. We chart the capacity-transfer frontier quantifying the principle that abstention should not be trained in; a source-side audit catches our own alphaNLI construction leaking a surface artifact, so architectural claims are anchored on native-label replications (SQuAD2, RepLiQA, MuSiQue); and on the standard four-domain suite we contribute, to our knowledge, the first answer-or-abstain benchmark, where our gate tops every in-domain dataset and the label-free direction is the only gate family to survive domain transfer.
[NLP-2] Information Satisfaction: A Reader-Centered Axis for Summarization Evaluation
【速读】： 该论文旨在解决现有摘要评估方法在衡量摘要对特定用户实际效用方面的局限性问题。当前主流的摘要评估指标（如ROUGE、BERTScore）或针对特定属性（如可读性、事实性）的评估方法，均未能有效反映摘要在满足个体用户信息需求方面的实用性。例如，生物医学研究人员与家庭医生在获取疫苗研究最新进展时的信息需求存在显著差异，而传统查询聚焦式摘要（query-focused summarization）因用户通常无法通过简短查询完整表达其背景需求，难以准确捕捉这种差异。为此，本文提出以用户的背景或人物画像（persona，即角色与专业背景）作为更稳定且能恢复缺失上下文的关键信号，用于评估摘要是否真正满足特定读者的信息需求。研究发现，多数流行评估指标（包括基于大语言模型的评判指标）在面对信息内容扰动时表现不佳，无法敏感地识别信息需求差异；进一步的人工专家评估表明，无论是传统指标还是基于大模型的指标，均无法有效衡量信息满足度，且与人类判断一致性较低。因此，论文的核心解决方案在于引入人物画像作为关键变量，强调需发展能够结合用户背景和使用场景的新型评估框架，以提升摘要评价的个性化与实用性。
链接: https://arxiv.org/abs/2608.14457
作者: Isabel Cachola,William Walden,Reno Kriz,Mark Dredze
机构: St. Edward’s University (圣爱德华大学); Johns Hopkins University (约翰霍普金斯大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:The majority of work on summarization evaluation focuses on general summary quality (e.g., ROUGE, BERTScore) or specific desired properties (e.g., readability, factuality). However, these metrics fail to measure the utility of a summary to an individual user. For example, a biomedical researcher learning about the latest vaccine research will have different informational needs from a family doctor. Query-focused summarization captures part of this need, but in practice, users rarely state everything relevant in a query: a single short query is likely inadequate to distinguish the needs of a researcher from those of a physician. By contrast, a reader’s background or persona (their role and expertise) is comparatively stable across queries and recovers much of this missing context, which makes it a practical signal for assessing whether a summary satisfies that reader’s needs. In this work, we assess how sensitive popular summarization metrics are to both informational and persona differences, and find that many popular metrics, including strong LLM-as-judge metrics, fail basic perturbation tests of informational content. We additionally conduct an expert human evaluation, measuring summary preferences based on information satisfaction given a specific person’s background and use case. We find that both traditional and LLM-based metrics are insufficient measures of information satisfaction and agree poorly with human judgment.
[NLP-3] Whose doctor does the AI recommend? An algorithm audit of reputation and demographic signals in large language model-assisted physician choice
【速读】： 该论文旨在解决生成式 AI（Generative AI）作为医疗信息中介时，其推荐系统中隐含的偏见与不透明性问题。具体而言，当患者通过大语言模型（LLM）助手选择家庭医生时，这些算法在无声且大规模地决定哪些医生获得可见性，但其推荐逻辑往往未被揭示。研究通过预设的随机算法审计，系统性检验了影响推荐结果的关键因果因素。关键发现是：声誉信号（如评分）具有主导作用——将评分从3.9提升至4.7可使被选概率提高31.4个百分点，而费用从90美元升至190美元则降低20.0个百分点；此外，尽管存在种族与性别相关的显著偏好（如女性、拉丁裔、南亚裔及非裔命名的医生比白人命名者获得1.3–2.9个百分点的优势），但模型在绝大多数情况下（<0.03%）并未提及这些特征作为理由，且在0.39%的案例中直接回避判断，导致其解释完全无法反映真实偏差。因此，解决方案的核心在于采用“冻结设计”（frozen design）的可重复行为审计机制，以真实响应而非模型自述解释作为评估依据，从而实现对算法偏见的客观监测，强调行为审计应取代依赖自我报告的透明性要求，成为监管生成式 AI 医疗推荐系统的适配技术。
链接: https://arxiv.org/abs/2608.14399
作者: Syeda Anshrah Gillani,Mirza Samad Ahmed Baig
机构: Fandaqah(法达卡); Heidelberg University(海德堡大学)
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 26 pages, 9 figures, 10 tables
Abstract:Patients increasingly ask large language model (LLM) assistants which doctor to see, making these systems AI infomediaries: algorithms that intermediate one person’s choice among other people and thereby decide, silently and at scale, which physicians become visible. We report a prespecified randomized algorithm audit of what causally moves those recommendations. Seven models (six open-weight; gpt-4o-mini) each chose among five synthetic family-medicine physician cards whose attributes were independently randomized across 3,024 choice sets, three patient personas, nine prompt paraphrases and nine experimental arms, yielding 40,068 scored responses; gender and ethnicity were signaled through names following correspondence-audit methodology. Reputation signals dominate: raising a rating from 3.9 to 4.7 increases choice probability by 31.4 percentage points (pp), and raising the fee from 90 to 190 lowers it by 20.0 pp. Demographic parity is rejected, but not in the direction human audit studies predict: female-signaled names gain 2.5 pp, and Hispanic-, South-Asian- and Black-signaled names gain 1.3-2.9 pp over White-signaled names, tilts worth 7- 14 per visit in fee-equivalent terms, and a content-free first-listed position is worth 11. Yet models mentioned gender or ethnicity in at most 0.03% of their stated reasons and abstained in 0.39% of trials, so these effects are invisible in the models’ own explanations, and transparency obligations relying on model self-report would not detect them. One reasoning model failed the prespecified auditability gate outright. The frozen design makes the audit repeatable: any new model can be assessed against identical stimuli, making recurring behavioural audit, rather than self-reported explanation, the monitoring technology fit for purpose.
[NLP-4] LLM s Dont Pay for the Jump
【速读】： 该论文试图解决的问题是：当前大语言模型（Large Language Models, LLMs）在面对需要创造性推理的科学发现任务时，无法实现类人水平的“溯因”（abduction）能力，尤其在关键理论突破（如普朗克提出能量量子化假设 E=hν）中表现出根本性局限。尽管已有研究将此归因于缺乏具身模拟（embodied simulation），但本文通过分析普朗克解决黑体辐射问题的历史案例指出，该突破并非源于感官运动经验，而是由经典理论预测与实际观测之间的严重认知误差（epistemic error）所驱动，并伴随显著的物理代价（physical cost）。其解决方案之关键在于：真正推动溯因的并非具身性或数据规模，而是一种认知错误与物理成本之间的热力学耦合机制（thermodynamic coupling）。作者进一步证明，固定权重的Transformer推理架构缺乏这种耦合机制，即使模型规模扩大也无法弥补，这解释了为何在因果复杂度急剧上升时，模型输出熵保持稳定而准确率却从100%骤降至17%。因此，论文主张机器溯因缺失的核心并非具身性，而是系统必须具备一种使认知错误产生足够“代价”的物理机制，以强制信念修正与创新性假设生成。
链接: https://arxiv.org/abs/2608.14397
作者: Paras Balani,Subhrakanta Panda
机构: Birla Institute of Technology and Science, Pilani, Hyderabad Campus (比尔拉科技与科学学院，皮拉尼，海得拉巴校区)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 14 pages
Abstract:Zahavy [2026] argues that Large Language Models, despite their capabilities in induction and deduction, cannot perform the abductive “Jump” that produced Einstein’s equivalence principle, and attributes this limitation to the absence of embodied simulation. Zheng-Xin [2026] and Farmer [2026] question whether embodiment is necessary for abduction, pointing to alternative routes to General Relativity and forms of abduction that require no sensorimotor grounding. Max Planck resolved the blackbody radiation problem in 1900. Planck’s move to E = h\nu required no embodied simulation. It was motivated by a mathematical consequence of classical theory, an infinite predicted energy for a finite measured quantity, that could not be physically accepted. We show that neither induction nor deduction could have produced the postulate and argue that its adoption required a coupling between epistemic error and physical cost. We formalize this distinction through thermodynamic coupling and show that fixed-weight transformer inference lacks such coupling, regardless of model scale. This is consistent with empirical results showing that output entropy remains nearly unchanged across tasks with sharply increasing causal difficulty, even as accuracy falls from 100% to 17%. We therefore argue that the missing ingredient in machine abduction may lie deeper than embodiment: a system must have some physical mechanism through which epistemic error becomes costly enough to force revision.
[NLP-5] A Survey of Large Models in Sports ACL2026
【速读】： 该论文旨在解决当前体育领域中如何有效利用大模型，尤其是多模态大语言模型（Multimodal Large Language Models, MLLMs），来提升体育理解、分析与交互能力的问题。随着生成式AI在多个领域的快速发展，其在体育场景中的应用潜力亟待系统性挖掘与整合。论文的关键解决方案在于构建一个全面的综述框架，涵盖三大核心方面：一是针对不同参与者群体（如运动员、教练、观众等）的多样化任务与应用场景；二是对现有体育相关数据集与评估基准的深入梳理与分析；三是对当前技术挑战（如数据稀缺性、跨模态对齐困难、泛化能力不足等）及未来发展方向的批判性讨论。通过整合这些内容，论文为推动基于大模型的体育智能研究与实际应用奠定了理论基础与实践路径，并提供了开源代码库以促进社区协作与技术演进。
链接: https://arxiv.org/abs/2608.14377
作者: Yichen Xu,Jianzhe Ma,Chuhan Wang,Zhonghao Cao,Liangyu Chen,Wenxuan Wang,Qin Jin
机构: Renmin University of China(中国人民大学); Sichuan University(四川大学); Beijing University of Posts and Telecommunications(北京邮电大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 36 pages, 4 figures, 6 tables. Accepted to Findings of ACL 2026
Abstract:Sports have witnessed growing global enthusiasm in recent years, serving as a vital force for physical health, cultural exchange, social connection, and economic growth. The rapid advancement of large models, particularly (multimodal) large language models (M)LLMs, has demonstrated transformative potential to reshape sports understanding, analysis, and interaction across diverse domains. This paper presents a comprehensive survey of large models in sports, including (i) an overview of tasks and applications across different participant groups; (ii) a detailed analysis of sports-related datasets and benchmarks; and (iii) a critical discussion of current challenges and future directions. Our goal is to establish a foundation for advancing research and practical development of large-model-driven sports intelligence. An open-source GitHub repository is maintained at: this https URL.
[NLP-6] Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages
【速读】： 该论文旨在解决多智能体推理系统中基于答案正确性进行信息筛选的局限性问题，即现有方法通常依赖于答案正确性、置信度或自动化评分来决定哪些消息应影响最终答案，但这种策略忽略了错误答案中可能蕴含的有用推理分解、约束条件或科学原理。其解决方案的关键在于提出一种名为“多样假设审议”（Diverse Hypothesis Deliberation, DHD）的受控测量协议，通过缓存五个独立生成的消息，并在相同下游求解器（称为整合器）上逐个重播每条消息（可选可见或隐藏），从而量化每条消息的轨迹价值（trajectory value）——即该消息是否有助于后续推理过程。实验结果表明，在多个数学与科学基准测试及两种开源模型（gpt-oss-120b 和 gemma-4-31B-it）中，几乎所有组合均存在“错误但有益”的消息；在改变最终正确性的错误消息中，超过40%具有正向轨迹价值。统计检验排除了随机重播变异导致该现象的可能性（p=0.0002）。进一步干预分析发现，完整消息的表现优于仅保留答案的部分，且保留推理过程比仅保留结论更能提升成功率，说明完整消息的优势来源尚待揭示。更重要的是，同一问题内重复出现的轨迹价值证据能够提供比单纯依赖答案正确性更优的“保留或移除”决策依据。因此，该研究揭示了答案正确性并非轨迹价值的充分指标，而DHD则有效捕捉了这一缺失属性，并为训练智能体何时应采纳他人信息提供了可复用的标注数据。
链接: https://arxiv.org/abs/2608.14375
作者: Chih-Hsuan Yang,Anjir Ahmed Chowdhury,Cheng-Hau Yang,Weijian Zheng,Fernando Llorente,Xiaolong Ma,Xinyang Li,Eliu A. Huerta,Ian T. Foster,Rajeev Thakur
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 24 pages, 9 figures. Includes an appendix and an ancillary reproducibility artifact
Abstract:Multi-agent reasoning systems often use agreement, confidence, or automated scores to decide which messages should shape a final answer. Such filtering assumes that a message likely to be correct is also worth keeping. Yet a wrong answer can contain a useful decomposition, constraint, or scientific principle. We test this distinction with Diverse Hypothesis Deliberation (DHD), a controlled measurement protocol that caches five independently generated messages and replays the same downstream solver, called the integrator, with each message available or hidden. The replay comparison measures a message’s trajectory value: whether making the message available helps or harms subsequent reasoning. Across five mathematics and science benchmarks and two openly available model families, gpt-oss-120b and gemma-4-31B-it, wrong-helpful messages appear in every benchmark-model combination. Among wrong-answer messages that change final correctness, more than four in ten changes are helpful in each model. Controlled repeats show that the number of repeatable message effects is unlikely to arise from replay variation alone (p=0.0002). A focused intervention on repeatable wrong-helpful messages finds that the complete message works best, while retaining its reasoning preserves more success than retaining only its answer; the source of the complete-message advantage remains open. Within the same problem, repeated trajectory-value evidence also identifies a better keep-or-remove choice than answer correctness alone. Answer correctness is therefore informative but does not determine trajectory value. DHD measures this missing property and produces reusable labels for learning when agents should listen.
[NLP-7] Local and Global Regimes of Geometric Complexity in Language Model Representations
【速读】： 该论文旨在解决语言模型表征复杂性评估中一个关键争议：内在维度（Intrinsic Dimensionality, ID）的差异究竟是反映了语言本身的固有属性，还是数据集构建方式所导致的伪像（artefacts）。研究聚焦于词汇多样性（lexical diversity，即数据集中唯一末尾词项的数量）对ID估计的影响，发现其引发一种尺度依赖的相变现象：在低词汇多样性条件下，唯一词项较少的数据集表现出更高的ID；而在高词汇多样性条件下，这一关系反转，唯一词项更多的数据集反而具有更高的ID。论文推导出一个无需参数的精确公式，准确预测了该转变发生的临界点，且与各尺度下的实证结果高度吻合。解决方案的关键在于揭示了词汇多样性驱动的双ID态（two-ID regimes）现象，表明语言数据在大语言模型（LLM）中的内在流形结构并非单一模式，而是由数据分布特性决定的动态组织原则。这一发现强调了在解释表征空间复杂性时必须谨慎对待ID指标，并为理解语言模型内部数据流形的结构性组织提供了新范式。
链接: https://arxiv.org/abs/2608.14361
作者: Arwa Osman,Marco Baroni,Iuri Macocco
机构: Universitat Pompeu Fabra (UPF); ICREA
类目: Computation and Language (cs.CL)
备注: 12 pages, 9 figures
Abstract:Intrinsic dimensionality (ID) is widely used to probe the representational complexity of language models, but it remains unclear whether ID differences reflect properties of language itself or artefacts of how the underlying dataset was constructed. In this paper, we focus specifically on how lexical diversity, the number of unique last-token items present in a dataset, affects ID estimates of that dataset. We find a scale-dependent transition between two regimes: at low lexical diversity, conditions with fewer unique final words produce higher ID, while at high lexical diversity, this ordering reverses, and conditions with more unique words produce higher ID. We derive an exact, parameter-free formula for the point at which this reversal occurs, which matches the observed transition point at every scale tested. On the one hand, our results highlight how care must be taken when interpreting the intrinsic dimensionality of a set of representations as a straightforward cue of their complexity. On the other hand, our discovery of the two ID regimes reveals a general principle of organisation of linguistic data in LLMs that sheds new light on their inner manifold structures.
[NLP-8] A Four-Axis Trustworthiness Benchmark for LLM -as-Judge in Principle-Based Regulation KDD2026
【速读】： 该论文旨在解决基于原则的监管（Principle-based Regulation）在实际应用中难以量化评估的问题，尤其针对金融宣传内容是否符合“公平、清晰、不具误导性”或“实现良好结果”等非二元化评价标准的判断难题。当前，生成式大模型作为“法官”（LLM-as-judge）被广泛用于替代传统人工评判，但其可靠性面临严峻挑战。论文提出，任何此类判别系统必须从四个维度进行严格评估：准确性（accuracy）、改写鲁棒性（paraphrase robustness）、对抗鲁棒性（adversarial robustness）以及校准度（calibration）。为此，研究团队发布了首个涵盖这四个维度的基准测试工具——Principle-Bench，包含168个与英国金融行为监管局（FCA）两项核心原则相关的加密资产金融宣传场景，并通过预注册规范生成了改写、对抗性关键词堆砌及边界扰动样本。同时，论文提出Ceca（Calibrated Exemplar-Cluster Assessment）方法，一种可校准、可审计的评估框架，能够输出每个示例的精确反事实归因。实验表明，尽管120B规模的大模型在正常输入下表现最佳，但在经过关键词堆砌的消费者保护条款（Consumer Duty）输入上，准确率从0.74骤降至0.27，暴露出严重的“合规表演”（compliance theatre）问题；另一模型家族的判别结果与之仅达到0.16的Cohen’s kappa一致性，将失败根源定位在模型本身而非数据集。因此，论文强调，面向部署级应用的生成式大模型“法官”必须同时报告各原则下的对抗欺骗检测结果和事后校准信息，以确保监管判断的可信性与透明性。
链接: https://arxiv.org/abs/2608.14329
作者: Dipankar Sarkar
机构: 独立研究员(Independent Researcher)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 7 pages, 3 figures. Accepted at the KDD 2026 Workshop on Secure and Trustworthy Large Language Models (SeT-LLM), poster
Abstract:Principle-based regulation, with evaluative standards such as “fair, clear, and not misleading” or “deliver good outcomes”, cannot be reduced to binary predicates, and LLM-as-judge is increasingly used as the substitute. Our position is that any such judge must be evaluated on four axes: accuracy, paraphrase robustness, adversarial robustness, and calibration. We release Principle-Bench, 168 cryptoasset financial-promotion scenarios mapped to two UK FCA principles, with paraphrase, adversarial keyword-stuffing, and boundary perturbations authored under a pre-registered rubric; the first benchmark covering all four axes for principle-based regulation. We also introduce Ceca (Calibrated Exemplar-Cluster Assessment): a calibrated, auditable assessor that emits exact per-exemplar counterfactual attributions. Across keyword counting, three sentence-transformer embedders, an open-weight LLM-judge, and a calibrated cascade, no method dominates all four axes. A 120B LLM-judge, strongest on benign inputs, loses 47 accuracy points (0.74 to 0.27) on keyword-stuffed Consumer Duty inputs: “compliance theatre.” A second judge from a different model family agrees only at Cohen’s kappa = 0.16 on that split, localising the failure to the model rather than the corpus. Any deployment-grade LLM-judge for principle-based regulation must report per-principle adversarial deception and post-hoc calibration alongside aggregate accuracy.
[NLP-9] AnchorBench: A Multi-Pathway Benchmark for the Anchoring Effect in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中锚定效应（anchoring effect）的评估不全面、路径依赖性强且缺乏对锚点相关性区分的问题。现有研究通常仅考察有限的锚点引入路径，且未能有效区分无关锚点与合理锚点的影响，导致对模型认知偏倚的理解存在偏差。为此，作者提出 AnchorBench，一个基于显式锚点相关性维度的基准测试框架，系统评估多种锚点路径在不同模型上的表现。其解决方案的关键在于：构建一个涵盖多路径、可量化锚点相关性的评估体系，揭示锚定效应在不同路径下的显著差异，并发现合理锚点通过强路径引入时会产生更大偏移，而锚点影响随其与证据支持答案的距离增加而减弱，尤其在外部知识检索（External）和检索增强生成（RAG）场景中更为明显。此外，研究发现即使前沿API模型在无锚条件下的任务准确率超过95%，仍可能受到合理锚点的显著干扰，表明高基准性能并不等同于对认知偏倚的鲁棒性。
链接: https://arxiv.org/abs/2608.14320
作者: Yiderigun Borjigin,Alexander Hermann,Christian Cyron,Roland Aydin
机构: Saarland University (萨尔兰大学); Hamburg University of Technology (汉堡工业大学); Helmholtz-Zentrum Hereon (亥姆霍兹海洋研究中心); German Research Centre for Artificial Intelligence (DFKI) (德国人工智能研究中心)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Published as a conference paper at COLM 2026
Abstract:The anchoring effect is a cognitive bias in which an initial reference value shifts a later judgment toward itself. This effect is well established in human judgment and decision-making, and recent work suggests that large language models (LLMs) exhibit similar behavior. However, existing work on anchoring in LLMs typically evaluates only a narrow set of anchor pathways and rarely distinguishes irrelevant from plausible anchors. We introduce AnchorBench, a benchmark for the anchoring effect in LLMs that evaluates multiple anchor pathways under an explicit anchor relevance axis. Across fourteen models, including ten open-weight models and four frontier API models, and a large set of controlled prompts, we find that (1) anchoring is strongly pathway-dependent, (2) plausible anchors usually induce larger shifts than irrelevant ones when introduced through stronger pathways, (3) anchor influence generally weakens as the anchor moves farther from the evidence-supported answer, most clearly on External and RAG, and (4) high task accuracy on the anchor-free control condition (Acc _10 : answers within 10 points of gold) does not guarantee robustness: even frontier API models above 95% control accuracy remain susceptible to plausible anchors.
[NLP-10] Envs-FORGE: Frontier-Optimized Reward-Grounded Environment Synthesis for Agent RL
【速读】： 该论文旨在解决强化学习（Reinforcement Learning, RL）中终端智能体训练环境缺乏动态适应性与可调难度的问题，即传统固定提示策略（如少样本、Self-Instruct、Evol-Instruct）对所有种子（seed）采用统一的提示生成方式，无法根据当前策略性能动态调整任务难度或类型，从而限制了训练效率与泛化能力。其解决方案的关键在于提出Envs-FORGE，一种基于验证器奖励反馈的动态环境合成提示策略：通过估计各种子的通过率，围绕目标学习前沿评估六种投影方向的动作，并求解每个种子独立的混合整数线性规划（Mixed-Integer Linear Program, MILP），以选择最优生成动作；该动作同步驱动指令、测试用例、标准答案、测试集及Docker环境的重构，仅经过黄金验证的环境包进入RL训练流程。此外，索引化的MILP形式还支持可选的软技能覆盖，用于组合规划。实验表明，Envs-FORGE在Qwen 3.5 35B模型上将tb-core和tb-2.0的Pass@1分别提升9.2和6.4个百分点，超越最强固定配方基线2.4和2.1个百分点，并在SWE-bench Verified上达到77.1%的通过率，显著优于基线。所有方法均生成100个已验证环境，使用约227万至288万合成令牌，确保下游训练集规模与操作尺度一致。
链接: https://arxiv.org/abs/2608.14312
作者: Xiaojun Wu,Cehao Yang,Honghao Liu,Xueyuan Lin,Zhichao Shi,Hao Zhou,Xuhui Jiang,Chengjin Xu,Jia Li,Jian Guo
机构: IDEA Research; The Hong Kong University of Science and Technology (Guangzhou); DataArcTech Ltd.
类目: Computation and Language (cs.CL)
备注: 19 pages, 5 figures
Abstract:Reinforcement learning (RL) for terminal agents needs executable training environments with reliable rewards and useful difficulty. Fixed recipes such as few-shot, Self-Instruct, and Evol-Instruct apply the same prompting policy to every seed, even when the current policy would benefit from a harder, easier, or simply different task. We present Envs-FORGE, a prompting policy that converts verifier rewards into per-seed environment-synthesis actions. Envs-FORGE estimates seed pass rates, scores six projection–direction actions around a target learning frontier, and solves a per-seed mixed-integer linear program (MILP) to choose the action that conditions generation. The selected action drives synchronized rewriting of the instruction, fixtures, oracle solution, tests, and Docker environment; only gold-verified bundles enter RL training. The indexed MILP form also supports optional soft skill coverage for portfolio planning. On Qwen 3.5 35B, Envs-FORGE improves Pass@1 over Base by 9.2 percentage points on tb-core (40.0% to 49.2%) and 6.4 points on tb-2.0 (23.0% to 29.4%), exceeding the strongest fixed-recipe baseline by 2.4 and 2.1 points. It reaches 77.1% on SWE-bench Verified versus 73.4% for Base, and improves tb-core by 6.8–9.2 points across the evaluated 4B–35B models. All synthesis methods export 100 verified environments and use 2.27M–2.88M synthesis tokens, placing the comparison at the same downstream training-set size and the same operational scale. The source code is available at this https URL.
[NLP-11] Seeing Red Thinking Bad: Color Bias in Vision Language Models ICPR2026
【速读】： 该论文旨在解决生成式视觉语言模型（Vision Language Models, VLMs）在处理以图像形式呈现的文本时，其对语义理解易受视觉风格偏差影响的问题。具体而言，研究关注视觉样式（如颜色、对比度等）如何干扰VLMs对文本内容的正确解读，进而导致决策偏差。其解决方案的关键在于引入“隐匿视觉提示”（Stealth Visual Prompts），通过微调文本的视觉表现形式（如将正面词汇渲染为绿色），在不改变语义的前提下系统性地操控视觉输入，并量化这些变化对VLMs输出的影响。实验结果表明，视觉风格的细微调整会显著改变模型的语义判断，例如绿色高亮正向词汇会诱发模型向积极方向偏移，忽略文本中的负面信息；同时，降低文本与背景的对比度会增强模型对视觉显著性线索的依赖，导致视觉问答（VQA）任务中出现更多错误。进一步分析发现，此类现象与视觉编码器的潜在表示（latent representations）因颜色和对比度变化而发生的特征偏移密切相关。因此，该研究揭示了视觉样式对VLMs语义理解的非预期影响机制，强调了在实际应用中需警惕视觉呈现方式对模型行为的隐蔽引导作用。
链接: https://arxiv.org/abs/2608.14286
作者: Kohsuke Ide,Ryousuke Yamada,Yoshihiro Fukuhara,Hirokatsu Kataoka,Yutaka Satoh
机构: National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan; University of Tsukuba, Tsukuba, Japan; University of Technology Nuremberg, Nuremberg, Germany; University of Oxford, Oxford, UK
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 15 pages. Accepted to ICPR 2026
Abstract:Vision language models (VLMs) are increasingly used in industrial decision-making systems, such as recruitment support and recommendation. This motivates careful analysis of how VLMs process visual and textual information. In this work, we study how VLMs interpret text rendered as an image, and investigate the influence of visual styling biases. To this end, we introduce Stealth Visual Prompts, which subtly change visual styling of text, such as color and contrast, while preserving semantic content. Using these prompts, we systematically control the visual styling of words in text and measure their impact on the analysis performed by VLMs. We further analyze how such visual perturbations affect the latent representations of the vision encoder. From our experiments, we observed that coloring positive words in green consistently shifts sentiment predictions toward a positive direction. As a result, VLMs often fail to properly account for negative words present in the text. Our analysis suggests that this behavior is correlated with changes in the latent representations of the vision encoder induced by color variations. In addition, we show that reducing text–background contrast increases reliance on visually salient cues and leads to more incorrect Visual Question Answering (VQA) outputs. These results suggest that the visual styling of rendered text can guide VLMs’ interpretation in ways that diverge from human semantic understanding. Project page: this https URL Comments: 15 pages. Accepted to ICPR 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2608.14286 [cs.CV] (or arXiv:2608.14286v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14286 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: In: Pattern Recognition. ICPR 2026. Lecture Notes in Computer Science. Springer, Cham, pp. 261-275 Related DOI: https://doi.org/10.1007/978-3-032-31583-0_18 Focus to learn more DOI(s) linking to related resources
[NLP-12] SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning
【速读】： 该论文旨在解决在长上下文推理教师模型（long-context reasoning teacher models）与短上下文学生模型（short-context student models）之间进行基于策略的蒸馏（On-policy Distillation, OPD）时所面临的实际挑战，主要包括分词器不匹配、师生分布差异、生成长度爆炸以及训练不稳定等问题。其核心解决方案在于：首先，在共享文本空间中执行OPD，仅对在学生和教师分词器下占据相同文本片段的token进行对齐，以缓解分词器差异带来的问题；其次，引入学生参考KL损失（student reference KL loss），并通过屏蔽特殊终止标记（如 /think 和 |im_end|）的优势，有效控制学生模型的生成长度增长，防止过度漂移，从而缓解师生分布失配并实现稳定的长度扩展。实验表明，该方法在同家族及跨家族的学生模型（如Qwen3、Intern-S2、GLM-4.7、Gemma-4）上均显著提升数学推理能力，尤其在自然语言数学证明任务上表现突出，其中Intern-S2-Preview在ProofBench上提升21.2分至55.2，超越Gemini-2.5-Pro；同时在科学基准（HLE、HiPhO）上也取得改进，验证了所转移的推理能力具有良好的跨域泛化性。
链接: https://arxiv.org/abs/2608.14277
作者: Haonan He,Haodi Lei,Yun Luo,Haoran Zhang,Shunkai Zhang,Yizhuo Li,Shengji Tang,Zhilin Wang,Runzhe Zhan,Lei Bai,Ganqu Cui,Fangchen Yu,Yafu Li,Peng Ye,Ning Ding,Yu Cheng
机构: Shanghai Artificial Intelligence Laboratory(上海人工智能实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:On-policy distillation (OPD) offers a promising way to transfer reasoning capabilities from stronger teacher models, but applying it to long-context reasoning teachers and short-context students introduces practical challenges, including tokenizer mismatch, teacher-student distribution mismatch, response length explosion, and training instability. In this work, we study this setting by transferring proof-reasoning capabilities from the long-context reasoning model SU-01 to short-context student models. To handle tokenizer differences, we perform OPD in a shared text space and align only tokens that occupy identical text spans under the student and teacher tokenizers. To mitigate the problem of excessive generation length and frequent truncation, we introduce a student reference KL loss and mask the advantages of special termination tokens such as /think and |im_end|. This strategy constrains the student from drifting excessively from its initial policy, thereby mitigating the teacher-student distribution mismatch problem and fostering steady length growth. Experiments on both same-family and different-family student models, including Qwen3, Qwen3.5, Intern-S2, GLM-4.7, Gemma-4, show consistent gains in mathematical reasoning, especially natural-language math proving. Notably, Intern-S2-Preview improves by 21.2 points on ProofBench, reaching 55.2 and surpassing Gemini-2.5-Pro. It also improves on science benchmarks such as HLE and HiPhO, suggesting that OPD transfers reasoning capabilities that generalize beyond the mathematical training domain.
[NLP-13] Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成内容时缺乏真实世界参照（reference）或语义内容（content）的可靠性问题，尤其是在面对任务需求时，模型能否通过可修正的路径（corrective routes）实现对事实性错误的动态检测与修复。其核心问题是：当模型依赖训练中习得的间接约束（如一致性、连贯性、历史校正模式）而非实时、独立的外部反馈机制时，其输出是否具备真正的“可回答性”（answerability），即能否被任务目标所驱动地响应并修正事实偏差。解决方案的关键在于引入“路由剖面”（route profile）的概念，用以刻画哪些信息路径（routes）对模型决策施加了约束及其相互关系，从而分析模型在多任务场景下对真理追踪（truth-tracking）的支持程度。研究指出，仅靠文本训练形成的模式继承（如自我一致性、检索、工具调用等）虽能提供衍生性的可回答性（derivative answerability），但唯有具备实时、独立且任务敏感的修正路径，才能实现真正的“活态可回答性”（live answerability）。因此，论文强调需通过任务-路由交互实验来区分表面性能提升与实质真理追踪能力的提升，并建立可预测未见组合的实证框架，避免概念重构。
链接: https://arxiv.org/abs/2608.14252
作者: Brett Reynolds
机构: Humber Polytechnic(汉伯理工学院); University of Toronto(多伦多大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 24 pages, 1 figure, 1 table. A six-page methodological supplement, reproducible R script, and constructed data are included as ancillary files
Abstract:Recent work suggests that some large language model representations have content or reference. Grounding can secure either without supplying live routes for correction. This paper asks what follows from that gap. An output is answerable when discrepancies can affect what a target- and task-specific arrangement produces, accepts, or withdraws. The arrangement has corrective control only when live, sufficiently independent routes can detect and repair fresh discrepancies. A route profile records which routes constrain the arrangement and how they are related. Those profiles support analysis of truth-tracking: patterned support for representational success. Language models are the pressure case; text-only arrangements provide a task-relative limiting case. Text-trained models inherit patterns of testimony, coherence, and prior correction. Where target-sensitive correction survives training, these can supply derivative answerability (inherited constraint); live answerability is the relation supplied by a current route for fresh discrepancies. Fluent failures should follow when a task requires independently informative access to the facts. Self-consistency, retrieval, tools, code execution, multimodal input, and feedback should help selectively. Route-by-task interactions test the distinctions. The decomposition’s empirical burden is to predict held-out route–task combinations or improve intervention choice without conceptual refitting. Surface improvement and truth-tracking improvement can come apart. Comments: 24 pages, 1 figure, 1 table. A six-page methodological supplement, reproducible R script, and constructed data are included as ancillary files Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2608.14252 [cs.AI] (or arXiv:2608.14252v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14252 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-14] he More Popular The Harder to Forget: Adaptive Popularity for LLM Unlearning
【速读】： 该论文旨在解决大语言模型（LLM）在去记忆（unlearning）过程中存在的不均衡问题：主流事实（popular facts）由于在预训练阶段被频繁学习，具有更强的记忆稳定性，而现有去记忆方法对所有事实施加相同的梯度压力，导致主流事实难以被有效遗忘，而罕见事实则可能被过度删除。其解决方案的关键在于提出一种自适应流行度（AdaPop）机制，该机制通过结合局部标记置信度与基于外部代理（如Wikidata站点链接或生成式AI作为评判者）的每条事实流行度依赖指数，动态调整遗忘-保留平衡。该方法采用双上升控制器（dual-ascent controller），在每个训练周期自动调节保留惩罚权重，实现更精细的控制。实验表明，在三个模型家族和两个基准测试中，AdaPop 在改写查询下泄露的遗忘内容比现有方法减少约5倍，在对抗性重构下减少约1.6倍；内部表征分析进一步显示，使用该方法后，遗忘集隐藏状态与预去记忆模型状态间的距离显著增大，而保留集表示保持稳定，验证了其有效性。
链接: https://arxiv.org/abs/2608.14229
作者: Anna Borisiuk,Andrey Savchenko,Alexander Panchenko,Elena Tutubalina
机构: AIRI; Sber AI Lab(斯伯尔AI实验室); Skoltech(斯科尔科技研究所); ISP RAS Research Center for Trusted Artificial Intelligence(俄罗斯科学院信息处理系统研究所可信人工智能研究中心)
类目: Computation and Language (cs.CL)
备注:
Abstract:Popular facts are memorised more deeply during pretraining and resist removal longer than rare ones, yet existing LLM unlearning methods apply uniform gradient pressure regardless of training-data frequency. We propose the AdaPop (Adaptive Popularity) method, which combines local token confidence with a per-fact popularity-dependent exponent derived from an external proxy (e.g., Wikidata sitelinks, LLM-as-Judge), and automates the forget-retain balance via a dual-ascent controller that adjusts the retain penalty each epoch. Across three model families and two benchmarks, AdaPop leaks ~5x less forgotten content than competing methods under paraphrased queries and ~1.6x less under adversarial reformulations. We support our analysis with internal metrics: under our method, forget-set hidden states move further from the pre-unlearning model’s states than under other methods, while retain-set representations remain close.
[NLP-15] MathForm: Scaling Mathematical Autoformalization with Knowledge Retrieval and Verification-Guided Refinement
【速读】： 该论文旨在解决生成式 AI 在数学命题自动形式化（autoformalization）过程中面临的两大核心挑战：一是如何准确地将自然语言数学陈述映射到形式化库（如Mathlib）中复杂的类型与定义层级结构，确保生成内容在语义上忠实于原始命题；二是现有方法依赖模型自身的参数化记忆来获取领域特定知识，且训练数据构建流程缺乏反馈驱动的迭代修正机制，导致生成质量受限。其解决方案的关键在于提出MathForm框架，通过引入基于Mathlib知识检索的预生成规划模块，结合编译器诊断与语义一致性反馈进行验证引导的迭代优化，实现高质量、可验证训练数据的构建。基于此框架，研究者构建了包含约36.7万条经验证示例的FormalVerse数据集，并在此基础上训练出MathForm-8B模型，通过监督微调与强化学习联合优化，在多个基准测试中显著优于多个专用的32B规模模型，尤其在高难度子集FATE-H和FATE-X上表现出更强的泛化能力，验证了该方法在提升形式化准确性与鲁棒性方面的有效性。
链接: https://arxiv.org/abs/2608.14221
作者: Lushi Pu,Weiming Zhang,Xinheng Xie,Zixuan Fu,Bingxiang He,Hengyu Zhao,Hongya Lyu,Xin Li,Jie Zhou,Yudong Wang
机构: ModelBest Inc.(ModelBest公司); Tsinghua University(清华大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 25 pages, 6 figures, 8 tables
Abstract:Autoformalization is commonly framed as translating natural-language mathematical statements into machine-verifiable formal languages such as Lean 4. However, faithful formalization requires more than translation. Models must map mathematical concepts to the complex hierarchy of types and definitions in formal libraries such as Mathlib, while ensuring that generated statements preserve the meaning of the source propositions. Existing approaches struggle because they rely heavily on the model’s parametric memory for library-specific knowledge, while common data construction pipelines often resort to filtering single-pass outputs and lack mechanisms for feedback-driven revision. To address these challenges, we introduce MathForm, an autoformalization framework for constructing verified training data through Mathlib knowledge retrieval and verification-guided iterative refinement. Before generation, a retrieval planner gathers relevant definitions and existing formalizations from Mathlib to guide the formalization generator. Generated statements are then revised using compiler diagnostics and semantic-consistency feedback. Using this framework, we construct FormalVerse, a Lean 4 dataset containing approximately 367K verified examples across diverse mathematical domains and sources. We then train MathForm-8B through supervised fine-tuning followed by reinforcement learning. Across six benchmarks, MathForm-8B achieves average Pass@8 rates of 88.06% under Syntax Check (SC) and 72.37% under Consistency Check (CC), outperforming multiple specialized 32B autoformalizers. On the challenging FATE-H and FATE-X subsets, it attains CC pass rates of 63% and 37%, exceeding the strongest specialized baselines in both cases.
[NLP-16] How Much Do Legal RAG Systems Still Hallucinate?
【速读】： 该论文旨在解决法律领域中检索增强生成（Retrieval-Augmented Generation, RAG）系统中存在的幻觉问题，即模型生成缺乏事实依据的错误答案，可能导致严重后果。其核心挑战在于如何有效识别并降低法律问答场景下由误导性前提或错误假设引发的幻觉现象。解决方案的关键在于通过细粒度分析，结合命题层级（claim-level）与回答层级（answer-level）的评估方法，对八种法律RAG系统在欧盟通用数据保护条例（GDPR，英文）及某国民法典（法文）两个语料库上的表现进行系统性评测，量化幻觉密度与严重程度，并针对不同问题类别和用户角色进行性能剖析。研究进一步利用142道由法律专家编写的独立测试题验证结果，发现包含错误前提的“虚假前提类问题”在人工构造的问题中尤其容易诱发高幻觉率，凸显出当前RAG系统在处理复杂法律推理任务时对前提正确性的敏感性不足。因此，提升系统对前提逻辑的识别与拒答能力是缓解法律领域幻觉问题的关键。
链接: https://arxiv.org/abs/2608.14210
作者: Souvick Das,Sallam Abualhaija,Domenico Bianculli
机构: SnT, University of Luxembourg(卢森堡大学网络安全与技术研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Hallucination is a major challenge for retrieval-augmented generation (RAG) systems in the legal domain, where ungrounded answers can lead to serious consequences. To better understand this problem, we conduct a fine-grained analysis of hallucination behavior in eight legal RAG systems across two legal corpora, the GDPR (in English) and a national civil law (in French). Using claim-level and answer-level evaluation, we report on hallucination density and severity, analyze performance across question categories and user personas, and validate our findings on an independent set of 142 legal-expert-authored questions. Our results show that hallucinations remain pervasive, ranging from less than 10% of responses for the best-performing systems to nearly half in the worst case. We further find that false-premise questions, containing incorrect assumptions that must be rejected, produce high hallucination rates on the manually-drafted questions.
[NLP-17] MINT: A Universal Zero-Shot Predictor for Transaction Data
【速读】： 该论文旨在解决现有支付领域基础模型在面对新型下游预测任务时缺乏灵活的零样本推理能力的问题，同时克服基于大语言模型（LLM）的零样本预测方法在利用交易数据潜在预测信号方面的不足，以及因依赖高成本文本序列化或任务特异性架构而导致的可扩展性差的问题。其解决方案的关键在于提出一种名为“交易多模态指令网络”（Multimodal Instruction Network for Transactions, MINT）的框架，该框架通过轻量级嵌入注入、交易-语言对齐机制与指令微调，将预训练的交易序列编码器与仅解码器结构的大语言模型高效连接。实验表明，MINT在分布内和分布外问题上均实现了当前最优的问答性能，且相比文本序列化基线显著降低了输入令牌数、延迟和内存消耗。研究表明，紧凑的交易嵌入相较于文本序列化，在多模态推理与零样本预测任务中具有更优的表示能力。
链接: https://arxiv.org/abs/2608.14198
作者: Parameswaran Kamalaruban,Viktor Drobnyi,Maeve Madigan,Julia Rozanova,David Sutton,Stuart Burrell
机构: Visa Inc.(Visa公司); United Kingdom(英国)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Banks analyse sequential financial transaction data to perform many tasks, including fraud prevention, credit risk assessment and offer personalization. To improve the predictive accuracy of these tasks, Payments Foundation Models encode transaction sequence data as rich contextual embeddings, which can then be provided to task-specific models as features. However, these Foundation Models are not designed for flexible zero-shot reasoning across novel downstream prediction tasks, limiting their adaptability and utility. Existing LLM-based approaches to zero-shot prediction often fail to fully exploit the predictive signal within transaction data, while relying on costly text serialization or task-specific architectures that scale poorly. To address these limitations, we present the Multimodal Instruction Network for Transactions (MINT), a framework that connects a pretrained transaction sequence encoder to a decoder-only LLM through lightweight embedding injection, transaction-language alignment, and instruction tuning. We find that MINT achieves state-of-the-art predictive question-answering performance in both in-distribution and out-of-distribution questions, while substantially reducing input tokens, latency, and memory consumption compared to text-serialization baselines. Through comprehensive analyses of representations, alignment strategies, training data, and history length, we establish that compact transaction embeddings are a superior approach to transaction representation than text serialization for multimodal reasoning and zero-shot prediction tasks.
[NLP-18] KV Cache Compression Through the Lens of Transform Coding
【速读】： 该论文旨在解决长上下文推理中键值（Key-Value, KV）缓存带来的主要内存瓶颈问题。现有量化方法通常对KV缓存采用统一的低精度数据类型表示，并设计量化方案以最小化缓存本身的重建误差，但忽略了量化误差在注意力机制中的传播效应。本文提出，在白噪声量化模型下，期望的注意力感知失真可分解为独立于标记和通道的键与值贡献之和。基于信号处理与率失真理论中的经典工具——变换编码（Transform Coding）与逆水填法（Reverse Water-Filling），作者提出了注意力感知变换编码（Attention-Aware Transform Coding, AATC）方法，通过在校准集上分配比特数以最小化注意力感知失真。实验结果表明，在Llama-3.1-8B-Instruct和Qwen-2.5-7B-Instruct模型上，结合LongBench、RULER、GSM8K、MMLU-Pro和MATH-500等基准测试，该方法实现了约5.8倍压缩比下的近无损精度表现，而所有基线方法均在至少某一场景下出现性能下降。其解决方案的关键在于引入注意力感知的失真建模与自适应比特分配机制，使量化误差控制更贴合实际推理需求。
链接: https://arxiv.org/abs/2608.14191
作者: Hannah Laus,Claudio Mayrink Verdun,Hao Wang,Flavio du Pin Calmon,Felix Krahmer
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Signal Processing (eess.SP)
备注:
Abstract:The key-value (KV) cache stores information from past tokens and is a major memory bottleneck in long-context inference. Existing quantization methods address this bottleneck by representing the KV cache uniformly with lower-precision data types and designing quantization schemes to minimize reconstruction error in the cache itself, without accounting for how that error propagates through attention mechanisms. We prove that, under a white-noise quantization model, the expected attention-aware distortion decomposes into additive key and value contributions that factor across tokens and channels. Building on transform coding and reverse water-filling, which are classical tools from signal processing and rate-distortion theory, we introduce Attention-Aware Transform Coding (AATC), which allocates bits over a calibration set to minimize attention-aware distortion. On Llama-3.1-8B-Instruct and Qwen-2.5-7B-Instruct, evaluated across LongBench, RULER, GSM8K, MMLU-Pro, and MATH-500, our method achieves near-lossless accuracy at approximately 5.8\times compression, whereas each baseline degrades in at least some settings.
[NLP-19] Leading-Silence Augmentation and Multi-Stage Synthetic Supervision for the Second MLC-SLM Challenge
【速读】： 该论文旨在解决多语言对话场景下无分段、无先验语音边界与说话人标签的完整对话中，说话人分离与识别（任务1）及对话理解（任务2）的挑战。其核心问题在于如何在缺乏标注信息的情况下，实现高精度的说话人分割与语义理解。解决方案的关键在于：针对任务1，采用随机前导静音裁剪、一致的时间戳校正以及指数移动平均（EMA）训练策略对VibeVoice-ASR-7B模型进行微调，有效降低了通话错误率（tcpMER）；针对任务2，通过多模态候选生成、静音音频过滤及分布匹配增强构建合成问答对，并基于Qwen3-Omni-30B-A3B-Instruct模型采用标记直接回答（tagged direct answering）方式进行微调，显著提升了对话理解准确率，从83.0%提升至86.0%。
链接: https://arxiv.org/abs/2608.14150
作者: Kexin Shi,Renhe Sun,Yuge Huang,Ximeng Wang,Jiayi Zhou,Jian Liu,Malu Zhang
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:The second Multilingual Conversational Speech Language Model (MLC-SLM) Challenge evaluates two tasks over complete, unsegmented multilingual conversations: speaker diarization and recognition (Task 1) and conversational speech understanding (Task 2). Neither task provides oracle utterance boundaries or speaker labels at evaluation, and Task 2 provides no question-answer training set. For Task 1, we fine-tune VibeVoice-ASR-7B with random leading-silence cropping, consistent timestamp correction, and an exponential moving average (EMA) training strategy. For Task 2, we construct synthetic question-answer pairs through multimodal candidate generation, silent-audio filtering, and distribution-matched augmentation, and fine-tune Qwen3-Omni-30B-A3B-Instruct for tagged direct answering. On the Task 1 evaluation set, cropping reduces tcpMER from 18.30% to 17.27%, and EMA further reduces it to 16.73%. On the Task 2 evaluation set, jointly applying distribution-matched augmentation and tagged direct answering raises accuracy from 83.0% to 86.0%.
[NLP-20] Regime-Conditional Verification: Correctness Estimation for Adapting and Monitoring Safety Classifiers
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）部署时安全分类器存在的两大核心问题：一是分类器的决策结果反映的是训练过程中学习到的策略，而非部署方期望的实际安全策略；二是随着实际部署流量分布的变化，分类器性能会持续退化。其提出的解决方案——分段条件验证（Regime-Conditional Verification, RCV），是一种无需重新训练即可适配现成安全分类器的轻量级封装方法。RCV的关键在于：通过分析分类器内部表征，估计每个预测与部署方期望策略不一致的概率，并对高风险预测进行选择性修正；同时，这些一致性估计本身可作为无标签信号，用于检测数据分布漂移，从而构建一个维护闭环——仅在必要时才触发分类器微调，显著降低更新成本。实验表明，RCV在三种现成安全分类器和两个基准数据集上均实现了对部署方策略的全面改进，最高可捕获此前81%未被识别的有害内容；在十种未参与训练的攻击场景部署测试中，RCV在专用注入面板中成功检测全部攻击，且多数分布漂移事件可通过校正机制修复，仅少数残余漂移需微调处理，充分体现了其高效性与鲁棒性。
链接: https://arxiv.org/abs/2608.14089
作者: Thiago Sandoval,Ufuk Topcu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 16 pages including technical appendix, 6 figures
Abstract:Safety classifiers deployed with large language models often fail for two reasons: their decisions reflect the policy learned during training rather than the deployer’s desired policy, and their performance degrades as deployment traffic evolves. We present Regime-Conditional Verification (RCV), a lightweight wrapper that adapts an off-the-shelf safety classifier without retraining it. RCV estimates, from the classifier’s internal representations, the probability that each prediction disagrees with the deployer’s policy, and selectively corrects predictions likely to be wrong. The same correctness estimates also provide a label-free signal for detecting distribution shift, enabling a maintenance loop that updates the correctness estimation layer and resorts to classifier fine-tuning only when necessary. Across three off-the-shelf safety classifiers and two benchmark datasets, RCV improves adherence to the deployer’s policy in every classifier-dataset combination, catching up to 0.81 of previously missed unsafe content without modifying the underlying classifier. In a deployment study with ten attack campaigns, each a harm category held out of RCV’s training, RCV detects every campaign in a dedicated injection panel; in the maintenance census most drift episodes are repaired without updating the classifier, and the fine-tune is reserved for the residual episodes that repair does not restore.
[NLP-21] he conditional superiority of fast silicon sampling
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在进行硅基抽样（silicon sampling）时，快速模式（fast mode）与慢速模式（slow mode）在算法保真度（algorithmic fidelity）上的差异问题，尤其关注快速采样是否会导致对总体参数估计的精度下降。研究基于新加坡全国代表性调查样本，评估了当前前沿模型下两种采样模式的表现。其解决方案的关键在于：尽管硅基抽样仍处于早期发展阶段，需谨慎使用，但相较于传统慢速模式，快速模式在计算资源消耗和运行时间上显著更高效，且在算法保真度上呈现单调优势，即在所有测试条件下均优于慢速模式。这一发现表明，在承认现有局限性（如低估意见方差、扭曲人类意见背后的潜在语境空间）的前提下，快速硅基抽样可作为更具可行性的替代方案。
链接: https://arxiv.org/abs/2608.14079
作者: Nickolas Hock Yuen Lam,Ji Xuan Voo,Xiangyu Ma
机构: 未知
类目: Computation and Language (cs.CL); Materials Science (cond-mat.mtrl-sci)
备注:
Abstract:Silicon sampling can produce surprisingly good population estimates at times. Does doing it fast attenuate such fidelity? In this study, we extend and assess ongoing work in silicon sampling by comparing the algorithmic fidelity of “fast” and “slow” modes of silicon sampling among a nationally representative sample of Singaporean survey respondents. We find that silicon sampling with contemporary frontier models remains a method in early development to be used only with great caution. While silicon samples are able to produce moderately faithful estimates of population means, they continue to understate opinion variance and distort the latent contextual space behind human opinions. Conditional on such limitations, we find “fast” modes of silicon sampling to be relatively superior to traditional “slow” modes of silicon sampling. Fast silicon sampling is significantly more efficient in compute resources and run-time while being monotonically superior to slower modes of sampling in algorithmic fidelity.
[NLP-22] HERMES: a multi-agent framework for structured knowledge extraction from ultra-long documents in geoscience
【速读】： 该论文旨在解决地球科学领域中权威科学知识大量滞留于非结构化的历史专著与文献中的问题，这些文献因文本无序、版式复杂而难以实现计算可访问性。其核心解决方案是提出一种可扩展的多智能体框架HERMES，通过协调大型语言模型（Large Language Model, LLM）在文档层面统一执行结构化数据提取，整合领域约束、验证规则与证据溯源机制，并有效处理解析后的文本、表格、图表及其图注。该方法在《无脊椎动物古生物学汇编》55卷文献上的应用，成功构建了一个包含32,277个化石分类单元和451,878个属性的结构化数据库，且在不同化石类群间保持稳定的提取性能（实体平均F1得分约0.90，属性约0.91），相比全人工基准效率提升约六倍。此外，在古地磁学与地球化学领域的零样本迁移评估表明，该方法具备跨地质学科的泛化能力。本研究为将历史科学文献转化为符合可发现性（Findable）、可访问性（Accessible）、可互操作性（Interoperable）与可重用性（Reusable，FAIR）原则的结构化数据提供了可行路径，构建了面向数据密集型学科的大规模知识集成可持续基础设施。
链接: https://arxiv.org/abs/2608.14055
作者: Ziqi Song,Zongyuan Xiang,James G. Ogg,Bruce S. Lieberman,Gabi Ogg,Natalia López Carranza,Wen Du,Yufei Ye,Shuan Li,Zhong Peng,Shaoqi Yu,Juye Wei,Ying Zhou,Jieping Ye,Jiang Yang
机构: 未知
类目: Computation and Language (cs.CL)
备注: 31-page main manuscript with 6 figures and 3 tables; supplementary information included
Abstract:Authoritative scientific knowledge in geoscience remains largely trapped in legacy monographs and historical literature, where unstructured text and complex layouts hinder computational access. We introduce HERMES, a scalable multi-agent framework that extracts structured data from ultra-long scientific documents. Using a coordinating large language model, HERMES integrates domain constraints, validation rules and evidence tracing within a unified document-level extraction process that incorporates parsed text, tables, figures and captions. Applied to the 55-volume Treatise on Invertebrate Paleontology, the system produced a structured database of 32,277 fossil taxonomic entities and 451,878 attributes, released online at this https URL. Extraction performance remained stable across fossil groups (average F1 scores of approximately 0.90 for entities and 0.91 for attributes), improving per-volume efficiency approximately sixfold relative to the tested fully manual baseline. Evaluation in palaeomagnetism and geochemistry, conducted without additional model training, demonstrated transfer across distinct geoscience domains. This work provides a practical pathway to transform historical scientific literature into FAIR-oriented structured data, offering a sustainable infrastructure for data-intensive disciplines and large-scale knowledge integration.
[NLP-23] S2Dialog: Multimodal Dialogue Retrieval with Semantic and Acoustic-Style Modeling
【速读】： 该论文旨在解决多模态对话检索中缺乏对对话整体语义连贯性与声学对话风格一致性建模的问题。现有方法多局限于话语级或单模态匹配，难以有效捕捉完整对话在语义和风格上的全局一致性。其解决方案的关键在于提出S2Dialog统一框架，通过对话级文本检索器与对话级声学检索器分别将对话的文本与语音模态编码为全局对话表示，并引入对话级文本-声学对比学习机制，实现跨模态语义与风格的对齐与区分，从而提升多模态对话库中的对话级检索性能。
链接: https://arxiv.org/abs/2608.14029
作者: Xueqi Wang,Zhigang Wang,Runqing Zhang,Zhenqi Jia,Junfeng Zhao
机构: Inner Mongolia University (内蒙古大学); University of Electronic Science and Technology of China, Shenzhen Campus (电子科技大学深圳校区)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multimodal dialogue retrieval aims to retrieve dialogues from multimodal dialogue banks that are similar to a target dialogue in terms of both textual semantics and acoustic conversational styles. Such dialogue-level retrieval is crucial for many dialogue-related tasks, including Emotion Recognition in Conversation, Spoken Dialogue Systems, and Conversational Speech Synthesis, where external dialogue examples can provide valuable semantic and stylistic references. However, existing retrieval methods are still largely limited to utterance-level or unimodal matching, and often fail to capture the global semantic coherence and stylistic consistency of an entire dialogue. To address this gap, we propose S2Dialog, a unified framework for dialogue-level semantic-style retrieval from multimodal dialogue banks. Specifically, S2Dialog consists of a Dialogue-level Textual Retriever and a Dialogue-level Acoustic Retriever, which encode the textual and acoustic modalities of a dialogue into dialogue-level representations, respectively. To further enhance multimodal retrieval, we introduce Dialogue-level Textual-Acoustic Contrastive Learning, which aligns semantically and stylistically similar dialogues while distinguishing unrelated ones. Extensive experiments on the multimodal dialogue dataset DailyTalk demonstrate that S2Dialog achieves outstanding retrieval performance.
[NLP-24] Batch-wise Adaptive Pruning: Periodic Neuron Activation-Aware Weight Pruning for Language Reasoning Model ATC
【速读】： 该论文旨在解决大型推理模型（Large Reasoning Models, LRMs）在批量推理（batched inference）场景下，现有无训练自适应剪枝方法性能严重下降的问题。其核心挑战在于：批量推理要求所有样本共享同一剪枝掩码，导致激活值在样本间聚合，破坏了原始激活分布的统计特性，使得原本在单样本环境下校准的阈值选择策略失效，进而引发稀疏率漂移和推理准确率急剧下降。为此，论文提出了一种专为批量推理设计的无训练自适应剪枝方法，其关键创新点在于两个方面：一是采用周期性Top-k选择机制替代传统的阈值选择，该机制对激活值聚合带来的分布偏移具有鲁棒性，并且每更新周期仅执行一次选择，保持了计算效率；二是引入激活记忆（activation memory）机制，利用推理过程中重要神经元呈现周期性重激发的特性，累积跨周期的重要性得分，从而有效保留关键神经元。实验结果表明，在DeepSeek-R1-Distill-Qwen-7B模型上，该方法在批处理大小为4、目标稀疏度50%时，平均准确率相比先前最优方法提升39.7个百分点，实际稀疏度达50%时实现1.40倍于密集推理的加速比。
链接: https://arxiv.org/abs/2608.14003
作者: Yongmin Kim,Shota Takashiro,Yusuke Iwasawa,Takeshi Kojima,Yutaka Matsuo
机构: The University of Tokyo(东京大学)
类目: Computation and Language (cs.CL)
备注: Accepted at COLM 2026. 28 pages, 12 figures, 18 tables. Code: this https URL
Abstract:Large Reasoning Models (LRMs) achieve strong performance on complex tasks through extended chain-of-thought generation, but incur substantial computational costs during inference. In production settings, batched inference is essential for high throughput, yet the existing training-free adaptive pruning methods we evaluate severely degrade in this regime. Because a batch must share a single pruning mask, these methods aggregate activations across samples and then apply threshold-based selection; the threshold, calibrated offline on unaggregated activations, no longer matches the aggregated distribution, so the realized sparsity ratio drifts and accuracy on reasoning tasks collapses under batched inference. In this work, we propose a training-free adaptive pruning method designed specifically for batched inference in LRMs, built on two components. First, we replace threshold-based selection with periodic top-k selection over the aggregated importance scores, which is unaffected by the shift that aggregation induces in the activation distribution, and which runs selection once per update period rather than at every token, preserving the speedup. Second, based on the observation that important neurons re-fire periodically during long reasoning generation, we introduce an activation memory that accumulates importance across update phases so that recurring neurons are retained. Experiments on diverse reasoning benchmarks demonstrate that our method outperforms the previous state-of-the-art adaptive pruning method by 39.7 percentage points in average accuracy at batch size 4 with 50% target sparsity on DeepSeek-R1-Distill-Qwen-7B, and reaches 1.40x speedup over dense inference at 50% actual sparsity. Comments: Accepted at COLM 2026. 28 pages, 12 figures, 18 tables. Code: this https URL Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.14003 [cs.CL] (or arXiv:2608.14003v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.14003 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-25] QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction
【速读】： 该论文旨在解决量化感知训练（QAT）中因权重重建不准确导致的损失下界过高与训练轨迹次优的问题。随着大语言模型推理向低精度演进，后训练量化（PTQ）的鲁棒性下降，迫使QAT成为维持模型性能的关键手段；然而，传统QAT在计算损失与代理梯度时依赖有损的全精度权重重建，而更新的是潜在权重本身，二者之间的不一致会引入偏差。尽管二阶PTQ方法通过最小化损失感知重建误差缓解此问题，但其需对冻结模型进行耗时数小时的优化，难以在动态权重演化过程中重复执行。为此，本文提出QUASAR，一种在训练循环中持续进行轻量级、损失感知重建的QAT方法：在每个训练步中，利用平方梯度的指数移动平均作为在线显著性估计，搜索一组小范围的截断区间，并通过显著性加权最小二乘法拟合仿射反量化器。理论分析表明，损失感知重建误差是决定QAT收敛边界中唯一依赖重建的项，直接控制最终量化模型的损失，因此其作为优化目标具有理论合理性。QUASAR仅修改训练流程，兼容标准部署格式（如整数量化与NVFP4），无需推理时更改或引入开销。在Qwen3和Llama-3.1上，QUASAR在2、3、4比特条件下均达到最低的保留KL散度，相较于现有竞争方法在3和4比特下降低至少10%、2比特下降低29%；在2比特下，其在八个任务上的平均准确率较强的QAT与PTQ基线提升3.5–4.3个百分点。
链接: https://arxiv.org/abs/2608.13966
作者: Vincent Counathe,Ben Athiwaratkun,Christopher De Sa,Tianyi Zhang
机构: Together AI; Cornell University (康奈尔大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Machine Learning (stat.ML)
备注: 39 pages
Abstract:As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aware training (QAT) essential for preserving model quality. However, QAT computes the loss and surrogate gradients using a lossy reconstruction of latent full-precision weights, while applying updates to the latent weights themselves. This mismatch can lead to suboptimal training trajectories and a higher loss floor. Second-order PTQ methods mitigate a similar gap by minimizing loss-aware reconstruction error, but doing it once for a frozen model can take hours; repeating this process throughout QAT as the weights evolve is impractical. We introduce QUASAR, a QAT method that continuously performs lightweight, loss-aware reconstruction in the training loop to lower the loss floor and improve the resulting low-bit model. At each training step, QUASAR uses the exponential moving average of squared gradients as online saliency estimates, searches over a small set of clipping ranges, and fits affine dequantizers via saliency-weighted least squares. Our analysis shows that the loss-aware reconstruction error is the only reconstruction-dependent term in the QAT convergence bound and controls the loss of the final quantized model, establishing QUASAR’s objective as a principled optimization target. QUASAR modifies only the training procedure and supports standard deployment formats, including integer quantization and NVFP4, with no inference-time changes or overhead. Across Qwen3 and Llama-3.1, QUASAR achieves the lowest held-out KL divergence among competitive QAT methods at 2, 3, and 4 bits, reducing KL by at least 10% at 3 and 4 bits and by 29% at 2 bits. At 2 bits, it improves average accuracy across eight tasks by 3.5-4.3 percentage points over strong QAT and PTQ baselines.
[NLP-26] Repair Not Improvement: Decomposing Constrained Decoding in Tool-Call Abstention
【速读】： 该论文旨在解决在受限生成（constrained generation）场景下，模型因格式约束而抑制函数调用（function calling）所导致的性能退化问题。其核心挑战在于：当生成任务要求输出必须符合特定语法结构（grammar）时，模型可能因过度遵循格式限制而拒绝调用必要的工具，从而影响任务正确性。解决方案的关键在于引入枚举（enum）机制，通过明确限定可输出的词汇集合，在不改变提示词（prompt）字节级一致性的前提下，将“拒绝调用”这一行为从默认选项中移除，从而恢复模型对工具调用的合理响应能力。实验表明，尽管在部分条件下存在负向影响，但整体上该方法显著提升了生成结果的形式合理性——在698次被修复的拒绝调用案例中，545例原本无有效输出，且无一例被评分者判定为无效判断。研究进一步揭示，当前预设语言基准在跨语言评估中均失效，凸显了对生成约束与工具使用之间关系进行重新建模的重要性。
链接: https://arxiv.org/abs/2608.13959
作者: Janghoon Lee(Redrob)
机构: Redrob
类目: Computation and Language (cs.CL)
备注: 24 pages, 4 figures, 17 tables
Abstract:Function calling is what the recent accounting of constrained generation explicitly sets aside: it finds the decoder’s contribution small for format constraints, then warns in its Section 7 against extrapolating where a constraint encodes a correctness requirement, and names function calling as one. Tool abstention is that case at its sharpest: an enum leaves the wording of an answer alone and narrows the set of answers there are, and declining to call anything is the first it drops. We measure the excluded case. Three conditions over one byte-identical prompt separate a grammar’s two jobs: it fixes where generation stops as well as which tokens may be emitted. We evaluate open-weight models from 0.6B to 4B on matched English and Korean items, so the language comparison is made within item. Against an unconstrained decoder, prior work’s contrast is negative on abstention in four of six cells with intervals excluding zero, worst -29.5 points, and positive with an interval excluding zero in none. The total is a sum with opposite signs: on the smallest model in Korean the stop token costs -20.0, the enum returns +19.5, and the two leave -0.5. What it recovers is form: of 698 abstentions repaired, 545 had no readable answer and 0 were judgements the scorer refused. On tool-needed items it is positive throughout; abstention leads because it is the preregistered measure, and the pooled number being kinder to the intervention makes moving to it worse rather than better. Both preregistered language claims fail.
[NLP-27] Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion CIKM2026
【速读】： 该论文旨在解决当前大语言模型（LLM）高质量创意写作数据严重依赖以故事为中心的数据集，导致模型难以掌握多样化创意格式在结构、风格与格式上的规范性问题。其解决方案的关键在于提出一种属性引导的体裁扩展框架（attribute-guided genre expansion framework），通过将主题广度与体裁形式控制相分离：利用人类撰写的多样化故事提示作为创意种子，同时结合人工精心标注的体裁属性以强制执行不同体裁在结构、风格和格式上的特定规范。在此基础上，通过强模型生成符合体裁一致性的问答对，并进行质量过滤，构建出包含13种创意体裁（如故事、说唱、歌词、剧本、游戏设计、角色设计等）共5万条样本的多体裁语料库（Multi-Genre Collection）。实验表明，基于该语料库微调的模型在跨分布写作基准和保留体裁诊断任务中均显著优于基础模型、专用写作基线及现有写作语料库训练的模型，且体裁数量消融实验进一步证明，受控的体裁扩展而非单纯的故事数据扩容，是提升模型鲁棒创意写作能力的核心驱动力。
链接: https://arxiv.org/abs/2608.13947
作者: Hwan Chang,Yongil Kim,Heuiyeen Yeen,Yireun Kim,Jinsik Lee,Hwanhee Lee
机构: LG AI Research( LG人工智能研究); Chung-Ang University(中央大学)
类目: Computation and Language (cs.CL)
备注: CIKM 2026
Abstract:High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models’ ability to follow the structural and functional conventions of diverse creative formats. We propose an attribute-guided genre expansion framework for scaling creative writing data beyond story generation. By separating thematic breadth from genre-form control, our framework leverages human-authored story prompts as diverse creative seeds, while utilizing manually curated genre attributes to enforce distinct structural, stylistic, and formatting conventions. We combine these to prompt strong LLMs for genre-faithful query-response pairs, which are then quality-filtered. Applying this framework, we construct the Multi-Genre Collection, a 50K-example corpus spanning 13 creative genres, including story, rap, lyrics, scripts, game design, character design, and other creative formats. Experiments across out-of-distribution writing benchmarks and held-out genre diagnostics demonstrate that models fine-tuned on our data consistently surpass not only base models and writing-specialized baselines, but also models trained on existing writing corpora. Genre-count ablations further indicate that controlled genre expansion, rather than story-centric scaling alone, is a key driver of robust creative writing capability.
[NLP-28] Never the Number: Structural Abstention for AI Systems Whose Answers Are Consumed as Fact
【速读】： 该论文旨在解决生成式大语言模型（Large Language Models, LLM）在自然语言到数据库查询（NLIDB）系统中因“幻觉”问题导致的可靠性危机：当系统生成看似合理但错误的查询时（如虚构列名或错误聚合），其输出在语义上与正确答案难以区分，尤其在企业级AI部署和自动化代理场景中，用户无法验证生成的SQL语句，使得仅追求准确率已不足以保障系统可信性。其核心解决方案是提出一种“可信内核-生成外壳”（trusted kernel with a generative shell）的架构模式，关键在于维护一个根本性不变原则：能够生成内容的组件仅能影响系统回答的问题本身，而不能影响返回的具体数值结果。具体实现上，生成外壳负责解析模糊输入并生成自然语言回复，而确定性内核则基于预定义的、可回答的问题模板集合，通过确定性执行匹配并编译查询；两者在用户确认环节交汇，确保所有不可回答请求被明确拒绝而非近似处理——这一机制称为“结构化拒答”（structural abstention），区别于依赖置信度估计的统计拒答。该设计不依赖特定实现，提供通用决策框架，并在跨三类应用场景中验证其有效性，同时扩展至代理系统的动作层面，最终通过两年生产环境案例研究与两种生成式替代方案对比，证明其在可靠性方面的显著优势。
链接: https://arxiv.org/abs/2608.13926
作者: Zhelun(Allen)Wu
机构: Apple Inc. (苹果公司)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB)
备注: 26 pages, 5 figures, 5 tables. Technical report. Describes architecture and design principles only; contains no code, schemas, datasets, or performance metrics
Abstract:Large language models have made natural language interfaces to databases (NLIDB) newly credible, but LLM text-to-SQL systems fail in a way that matters for deployment: a hallucinated column or a mis-aggregated total yields a fluent wrong answer, indistinguishable at the point of use from a right one. Where the consumer cannot inspect the generated query, as in enterprise AI deployments and operational dashboards, and increasingly where the consumer is a tool-using agent rather than a person, accuracy alone is insufficient: nothing marks which answers to distrust. This is a reliability problem before it is an accuracy problem. We propose an architectural pattern for such systems, a trusted kernel with a generative shell, resting on one invariant: a component that can fabricate may influence which question the system answers, never which value it returns. A generative shell interprets underspecified input and phrases replies; a deterministic kernel matches fully specified questions against a bounded set of answerable question shapes and compiles them to queries by deterministic execution. The two meet at a confirmation the user reads before any value is computed, and requests the kernel cannot express are declined rather than approximated. We call this structural abstention, and distinguish it from the statistical abstention of selective prediction and calibrated confidence: refusal here needs no confidence estimate, because unanswerable requests are unrepresentable. We specify the pattern implementation-independently, give a five-decision recipe and work it across three domains, extend the invariant from returned values to the actions of agentic systems, and report a two-year production case study alongside two generative alternatives, a fine-tuned parser and a tool-retrieval agent. We close against enterprise and reliability benchmarks published since. Comments: 26 pages, 5 figures, 5 tables. Technical report. Describes architecture and design principles only; contains no code, schemas, datasets, or performance metrics Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB) ACMclasses: H.2.3; H.5.2; I.2.7 Cite as: arXiv:2608.13926 [cs.AI] (or arXiv:2608.13926v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.13926 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-29] CForce: Boosting Parallel Decoding for dLLM s via Consistency Forcing
【速读】： 该论文旨在解决扩散型大语言模型（dLLM）在采用激进并行解码策略时，早期去噪阶段预测不可靠导致误差传播的问题。其核心解决方案是提出一致性强制（Consistency Forcing, CForce），一种通过知识蒸馏使早期阶段的掩码预测与后期阶段对齐的方法。CForce 利用预先收集的自回滚轨迹（self-rollout trajectories）进行训练，从而提升训练与推理的一致性。其关键创新在于引入置信度自适应KL散度（Confidence Adaptive KL Divergence）作为蒸馏目标，有效融合前向与反向KL散度的优势。理论分析表明，该一致性目标可近似最小化早期阶段的预测误差。值得注意的是，该方法适用于掩码到词元（mask-to-token）解码及支持编辑能力的解码场景；在后者中，后期的词元到词元精炼为早期掩码状态预测提供了额外监督信号。实验结果表明，无论在非编辑还是可编辑的LLaDA模型上，CForce均显著提升了高并行解码预算下的速度-质量权衡性能。
链接: https://arxiv.org/abs/2608.13925
作者: Yuji Ren,Chenkai Xu,Zhuocheng Gong,Jianguo Li,Zhijie Deng
机构: Shanghai Jiao Tong University(上海交通大学); Ant Group(蚂蚁集团)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Diffusion large language models (dLLMs) accelerate language generation by predicting multiple masks in a single forward pass. However, existing dLLMs can suffer from unreliable predictions in early denoising stages under aggressive parallelism strategies, leading to errors that can propagate to later stages. To tackle this issue, we present Consistency Forcing (CForce) for dLLMs, a distillation method to force the mask predictions of early stages to align with those of later stages. CForce trains the model on pre-collected self-rollout trajectories, thereby improving training-inference alignment. We introduce Confidence Adaptive KL Divergence as a distillation objective to conjoin the merits of forward and reverse KL. We further provide a theoretical analysis for the consistency objective to explain why CForce can approximately minimize the prediction error of early stages. Critically, the same formulation applies to both mask-to-token decoding and edit-capable decoding; in the edit-capable case, later token-to-token refinements provide additional supervision for earlier masked-state predictions. Experiments on non-edit and edit-capable LLaDA models show improved speed-quality trade-offs, especially under high-parallelism decoding budgets. Code is available at: this https URL.
[NLP-30] Agent ic Transaction: Towards ACID-Compliant Agent Systems
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在执行长时序任务时面临的可靠性挑战，尤其是在持久化环境与多步骤工作流中，因模型不确定性及动态执行环境导致的执行不可靠、状态不一致、并发冲突及状态持久性不足等问题。其解决方案的关键在于提出“代理事务”（agentic transaction）概念，并构建一个符合ACID原则的代理系统框架，通过四个语义层面的保障机制重新诠释经典ACID属性：语义原子性（Semantic Atomicity）、语义一致性（Semantic Consistency）、语义隔离性（Semantic Isolation）和语义持久性（Semantic Durability）。该框架的核心创新在于将事务处理的思想引入代理系统，具体实现上依赖于事务化的探索-执行-验证循环、事务化技能枢纽、基于置信度分歧的验证机制、语义依赖感知的隔离策略以及事务感知的语义状态管理，从而在不确定的模型行为下仍能保证系统的可靠性和一致性。实验结果表明，该系统在多个主流基准测试中相较当前最优代理（如Claude Code）提升了10.6%的性能，为构建可信、可扩展且自演进的AI代理系统开辟了新的研究方向。
链接: https://arxiv.org/abs/2608.13900
作者: Zhaoyan Sun,Xiaoxiao Wang,Guoliang Li
机构: Tsinghua University (清华大学)
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation. As agents increasingly operate over persistent environments and multi-step workflows, they face challenges analogous to those addressed by transactional database systems: reliable execution, consistent outcomes, safe concurrency, and durable state management. We introduce the concept of an agentic transaction and propose an ACID-compliant agent system framework that reinterprets the classical ACID properties for agent execution through four semantic guarantees: Semantic Atomicity, Semantic Consistency, Semantic Isolation, and Semantic Durability. Together, these properties provide a principled foundation for building reliable agent systems despite model uncertainty and dynamic execution environments. To instantiate this framework, we develop an ACID-compliant data agent that realizes these guarantees through transactional exploration-execution-validation cycles, transactional skill hubs, confidence divergence-based validation, semantic dependency-aware isolation, and transaction-aware semantic state management. Experimental results on widely used benchmarks show that our system achieves a 10.6% improvement over state-of-the-art agents, including Claude Code. This work opens a broader research agenda on extending transactional principles and system architectures toward building trustworthy, scalable, and self-evolving AI agent systems.
[NLP-31] Geometric Filtering of LLM -Generated Samples for Few-Shot Text Classification
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成文本分类合成训练数据时存在的样本质量异质性问题：生成样本在句子嵌入空间中分布不均，部分位于正确类别区域，而另一些则处于边缘或跨类别区域，导致噪声数据引入影响分类器性能。其解决方案的关键在于提出一种基于几何一致性的过滤框架，通过计算每个LLM生成样本与真实类别样本在句子嵌入空间中的欧几里得距离，筛选出几何上与真实数据一致的样本；进一步采用软加权机制将过滤得分转化为训练样本权重，以优化分类器学习过程。实验表明，该方法在13个数据集、5种分类器、10种数据增强方法及超过6700种配置下，显著优于SMOTE基准（提升2.61个百分点，p < 0.0001，Cohen’s d = 0.95，胜率88.9%），且可直接推广至命名实体识别任务（提升9.26个百分点，胜率100%），对5家不同提供商的LLM均表现出强鲁棒性。核心发现为：最简单的基于距离的过滤策略始终优于复杂的多准则方法，凸显了几何一致性在数据质量控制中的关键作用。
链接: https://arxiv.org/abs/2608.13866
作者: Benjamín Schindler,Gonzalo A. Ruz
机构: Universidad Adolfo Ibáñez (阿道夫·伊巴涅兹大学); Millennium Nucleus for Social Data Science (SODAS) (社会数据科学千年研究中心); Millennium Nucleus in Data Science for Plant Resilience (PhytoLearning) (植物韧性数据科学千年研究中心)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 6 pages, 2 figures, to be published in IEEE LACCI 2026
Abstract:Large language models (LLMs) can generate synthetic training data for text classification, but the quality of generated samples is heterogeneous: some fall in correct class regions of the embedding space while others land in peripheral or cross-class zones. We propose a geometric filtering framework that evaluates each LLM-generated sample by its Euclidean distance to real class examples in a sentence embedding space, selecting only geometrically consistent candidates. A soft weighting mechanism transforms filter scores into sample weights for classifier training. Evaluated across 13 datasets, 5 classifiers, 10 augmentation methods, and over 6,700 configurations, our method achieves +2.61 percentage points (pp) over SMOTE ( p0.0001 , Cohen’s d=0.95 , 88.9% win rate). The approach generalizes to named entity recognition (+9.26pp, 100% win rate) without filter modification, and is robust across 5 LLMs from 4 providers. A key finding is that the simplest distance-based filter consistently outperforms complex multi-criteria alternatives.
[NLP-32] Bootstrapping Niche Multilingual Code Translation via Reinforcement Learning with Execution-Based Verifiable Supervision
【速读】： 该论文旨在解决多编程语言间（many-to-many）的代码翻译问题，特别是在缺乏充足平行语料的情况下，传统神经代码翻译模型难以生成可执行且语义一致的翻译结果。其核心挑战在于：在非主流语言对中，由于监督数据稀缺，模型易产生看似合理但不可执行的翻译。为应对这一问题，论文提出一种基于执行反馈的偏好强化学习（preference-based reinforcement learning with execution-based supervision）解决方案，其关键在于构建一个由可验证的种子Python程序扩展而成的多语言可执行代码池，利用代码执行结果作为标签对翻译候选进行偏好标注，并据此训练一个跨语言翻译质量的奖励模型；随后，通过广义相对策略优化（GRPO）在600个定向语言对上微调基础大语言模型（LLM），以提升其在多种语言间的可执行翻译能力。该方法通过引入可执行性作为核心监督信号，有效缓解了低资源语言对中的数据稀疏问题。为进一步评估模型在广泛语言空间中的表现，论文还提出了HumanEval-X++基准，覆盖更广泛的多对多语言场景。实验表明，使用Qwen-3.5 4B和9B模型，在HumanEval-X++及现有基准上均显著优于未训练基线，其中4B模型在所有语言上平均提升13%，在中等流行度语言上提升达21%。本研究建立了从数据生成、模型训练到评测的完整闭环体系，为实现高质量多对多编程语言代码翻译提供了可靠范式。
链接: https://arxiv.org/abs/2608.13854
作者: Kouki Yuki,Jie Zeng,Kyoko Ogawa,Ryunosuke Ikeda,Yohei Kobashi,Takeshi Kojima,Ikuya Yamada,Yusuke Iwasawa,Yutaka Matsuo
机构: 未知
类目: Computation and Language (cs.CL)
备注: 11 pages, 3 figures, 5 tables. Preprint under review
Abstract:Code translation must preserve executable behavior across many programming languages, yet neural code translation has largely focused on a few popular languages such as C++, Java, and Python. This leaves a niche, many-to-many setting where parallel supervision is sparse, producing plausible but non-executable translations. We address this setting with preference-based reinforcement learning driven by execution-based supervision. Our pipeline firstly expands verifiable seed Python programs into a multilingual pool of execution-validated codes. Using the pool, a base LLM generates translation candidates across language pairs, which we label by their execution outcomes. The resulting preferences are used to train a reward model that scores cross-language translation quality. Finally, we optimize our base LLMs with GRPO over 600 directed language pairs (25 x 24) using the reward model as a signal. To evaluate the niche translation capability, we introduce HumanEval-X++, an execution-based benchmark that extends HumanEval-X to a broad many-to-many language space. We evaluate our approach using Qwen-3.5 4B and 9B models. On HumanEval-X++ and existing benchmarks, it yields consistent gains over the untrained baselines. In particular, the 4B model achieves an average improvement of 13% across all languages on HumanEval-X++, with a gain of 21% on mid-tier languages. Our study establishes a reliable approach of data generation, training, and benchmarking, paving the way toward further bootstrapping the quality of many-to-many translation for programming languages.
[NLP-33] ASSERT: A Measurement Pipeline for GenAI Audits
【速读】： 该论文旨在解决生成式 AI（Generative AI）系统审计中报告率（reported rate）的可解释性与可比性问题。当前审计普遍依赖单一数值来衡量系统对政策的合规程度，但该数值受测量选择（如对话设置、模拟用户、评估者及非合规判定阈值等）显著影响，导致无法区分性能变化是源于系统本身改进或退化，还是测量方法的变动。为此，论文提出 ASSERT——一种基于规范驱动的生成式 AI 审计测量流水线，其核心在于将每个报告率与一套明确的测量规范（measurement specification）绑定，确保所有审计过程透明可追溯。通过在对话欺骗场景的案例研究中验证，发现不同测量配置会显著改变报告率并重塑系统排名。由于每项报告率均附带可审查的规范，审计结果间的差异得以更准确地归因于系统行为或测量设计的变化，从而提升了审计的可信度与可复现性。
链接: https://arxiv.org/abs/2608.13840
作者: Riccardo Fogliato,Abhinav Palia,Xiawei Wang,Emily Sheng,Chad Atalla,Jean Garcia-Gathright,Nicholas Pangakis,Sharman Tan,Dan Vann,Hannah Washington,P. Alex Dow,Heba Elfardy,Hanna Wallach,Sandeep Atluri
机构: Microsoft(微软)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Audits of generative AI (GenAI) systems often summarize behavior as a reported rate: how often the audited system complies with policy. Researchers and stakeholders use that rate to compare systems, track regressions, and gate deployment. A reported rate reflects both the system under audit and the measurement choices behind it, so a change in the rate can leave it unclear whether the system or those choices moved. We introduce ASSERT, a specification-driven measurement pipeline for GenAI audits that ties each reported rate to a written specification of the measurement choices used to produce it. ASSERT helps draft a behavioral rubric and test cases, then runs the audit against a GenAI system and returns a reported rate. In a case study on conversational deception, we observe that the reported rate moves substantially with the dialogue setup, the simulated user, the judge, and the evidence bar for non-compliance. These measurement choices substantially change the reported rate and can reorder GenAI system rankings. Because each reported rate is tied to an explicit specification, differences across audits are easier to attribute and interpret.
[NLP-34] When Lexical Change Misleads: Rethinking Dynamic Topic Model Evaluation with Traditional and LLM -Based Metrics
【速读】： 该论文旨在解决动态主题模型（Dynamic Topic Models, DTM）在评估其生成主题的语义连贯性时，传统时间一致性度量（Temporal Coherence）因词汇变化而失效的问题。当词汇表随时间发生显著更替但语义内涵保持稳定时，传统指标难以准确反映主题的真实语义演化。其解决方案的关键在于引入基于大语言模型（LLM-based Semantic Similarity）的语义相似性评估方法，以捕捉主题在跨时段中的深层语义一致性。实验结果表明，该方法在NYT、DBLP和arXiv数据集上与人工语义判断具有强相关性（ρ = 0.502–0.721），显著优于传统时间一致性度量（ρ = -0.256 至 0.614）。研究进一步通过词汇变化程度分层分析揭示了聚合评估中被掩盖的性能差异，提出应采用“词汇变化感知”的评估范式，即同时报告传统一致性指标与基于大语言模型的语义相似性指标，二者作为互补而非替代信号，共同提供更全面的主题质量评估。
链接: https://arxiv.org/abs/2608.13835
作者: Charu Karakkaparambil James
机构: RPTU University Kaiserslautern-Landau (莱法州应用技术大学凯撒斯劳滕-兰道分校); Kaiserslautern, Germany
类目: Computation and Language (cs.CL)
备注:
Abstract:Dynamic topic models capture evolving word distributions, but traditional coherence metrics may fail when vocabulary changes while semantic meaning persists. We evaluate 120 topics from CoNTM and DLDA across NYT, DBLP, and arXiv, using three human annotators and Low, Medium, and High lexical-change categories. Traditional temporal coherence shows highly variable agreement with human judgments ( \rho =-0.256 to 0.614). In contrast, LLM-based semantic similarity agrees strongly with human semantic judgments for CoNTM on NYT ( \rho =0.609), DBLP ( \rho =0.721), and arXiv ( \rho =0.502), but is less consistent for DLDA. Lexical-change stratification reveals variation hidden by aggregate evaluation. We therefore advocate lexical-change-aware evaluation, jointly reporting traditional coherence and LLM-based semantic measures as complementary rather than interchangeable signals.
[NLP-35] Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models
【速读】： 该论文旨在解决生成式模型在推理过程中哪些行为与正确答案相关，以及面向推理的训练是否真正增强了这些关键行为的问题。核心挑战在于，尽管推理导向训练可能使推理过程呈现出更“深思熟虑”的表象，但未必能有效提升与模型正确性最紧密相关的本质行为。为此，作者提出“行为增益（Behavioral Lift）”这一量化指标，用以衡量某一行为在推理轨迹中存在与否对模型正确率的影响差异。通过对15种模型、6个涵盖纯文本与视觉-语言任务的基准数据集进行分析，研究团队标注了15,282条推理轨迹，并构建了一个适用于大语言模型（LLM）和视觉-语言模型（VLM）的统一行为分类体系。研究发现存在“放大-增益差距（Amplification-Lift Gap）”：模型在训练中显著增强了自我修正、假设检验和不确定性承认等行为，但这些行为与正确性的关联较弱；而与正确性最强相关的则是信心校准（confidence calibration）、知识对齐（knowledge alignment）和自我意识（self-awareness），但这些高增益行为却几乎未被训练所放大。尤其值得注意的是，信心校准在两种模态中均为最强正向预测信号，却极少被增强；相反，不确定性承认虽被放大3–7倍，却与正确性呈弱相关或负相关。这一发现表明，当前的推理导向训练并未优先放大最具价值的行为，从而凸显出发展基于过程层面的目标——即奖励校准且基于事实的推理——的重要性，而非仅关注表面形式上的“思考痕迹”。
链接: https://arxiv.org/abs/2608.13760
作者: Jean de Dieu Nyandwi,Leena Mathur,Yonatan Bisk,Robert Hawkins,Graham Neubig
机构: Carnegie Mellon University (卡内基梅隆大学); Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Published in COLM 2026
Abstract:Which reasoning behaviors are associated with correct answers in reasoning models, and does reasoning-oriented training amplify those behaviors? This distinction is important because reasoning-oriented training can make traces look more deliberative without amplifying the behaviors most tied to model correctness. We quantify this mismatch with Behavioral Lift, a metric that measures how much correctness changes when a behavior is present versus absent in a model’s reasoning trace. Across 15 models and 6 benchmarks spanning text-only and vision-language reasoning, we annotate 15,282 traces with a taxonomy whose core behaviors are defined for both LLM and VLM traces. We find evidence for an Amplification-Lift Gap, in which thinking models strongly amplify self-correction, hypothesis testing, and uncertainty acknowledgment, while the highest-lift behaviors are confidence calibration, knowledge alignment, and self-awareness. Confidence calibration is among the strongest positive signals of correctness in both modalities, yet is barely amplified; uncertainty acknowledgment is amplified by 3–7 \times , yet is weakly or negatively associated with correctness. We find that reasoning-oriented training does not preferentially amplify the highest-Lift behaviors, motivating process-level objectives that reward calibrated and grounded reasoning rather than surface form alone.
[NLP-36] GALA: Generation-Aware Cross-Modal Alignment for Text-to-Time-Series Synthesis
【速读】： 该论文旨在解决文本条件化时间序列生成中条件表示与信号模态不匹配的问题。现有方法要么使用预训练文本编码器的固定嵌入，要么端到端微调编码器，但均未主动对齐文本与时间序列模态，导致条件表示无法有效引导生成。其核心解决方案是提出GALA（Generation-Aware cross-modal Alignment），采用两阶段框架：首先通过对比学习将预训练文本编码器与时间序列基础模型耦合至共享嵌入空间，并引入辅助生成损失联合优化双编码器以适配生成任务；随后冻结对齐后的文本嵌入，用于驱动流匹配生成器。实验表明，GALA在TSFragment-600K数据集上于36个评估指标中取得30项第一，平均排名达1.08/1.08/1.42（长度24/48/96），显著优于最强基线（1.92/2.00/1.75）。进一步分析显示，内部文本编码器会引发保真度与标题一致性之间的权衡，而基于对齐嵌入的条件化可同时提升三项指标；消融实验验证了辅助生成损失是实现跨模态对齐的关键组成部分，而非附加模块。
链接: https://arxiv.org/abs/2608.13741
作者: Haochen Zhang,Gengwei Zhang,Laura Yao,Nicholas Knoz,Tianlong Chen
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 21 pages, 6 figures
Abstract:Synthesizing time series from natural language is emerging as the most expressive form of controllable time series generation. However, existing text-conditioned generators either take caption embeddings frozen from off-the-shelf text encoders, or adapt the encoder end-to-end, letting the denoising loss shape the embeddings only as a by-product. In either case, the conditioning representation is never deliberately matched to the signal modality, leaving it ill-suited to guide generation. We address this by introducing GALA: Generation-Aware cross-modaL Alignment for text conditional time series generation. GALA is a two-stage approach that first contrastively couples a pretrained text encoder with a time-series foundation model into a shared embedding space with both encoders adapted to generation by an auxiliary generative loss, and then freezes the resulting caption embedding to drive a flow-matching generator. On TSFragment-600K, spanning four domains and three fragment lengths, GALA sets a new state of the art, ranking first in 30 of 36 metric columns and reaching an average rank of 1.08/1.08/1.42 at lengths 24/48/96 against 1.92/2.00/1.75 for the strongest baseline. We further find that generator-internal text encoders force a trade-off between fidelity and caption adherence, whereas conditioning on the aligned embedding breaks it: FID, CTTP, and JFTSD all improve at once. Ablating the auxiliary loss degrades FID, CTTP and JFTSD together, it indicates the generative term is a necessary component of the alignment rather than an add-on.
[NLP-37] BM25-Augmented Many-Shot Translation for Low-Resource North-Eastern Indian Languages
【速读】： 该论文旨在解决低资源东北印度语言间的双向翻译问题，尤其针对英语与十一种东北印度语言之间缺乏充足平行语料的挑战。其核心解决方案是采用基于检索增强的多示例（many-shot）翻译框架，该框架源自作者在AmericasNLP 2026中的系统。在推理阶段，通过BM25算法从特定语言的训练语料库中检索最相似的平行语料作为上下文示例，再利用Gemini 2.5 Flash模型以这些示例为条件生成翻译结果，整个过程无需对模型进行微调。训练语料库整合了WMT26官方数据、Samanantar等公开语料以及历届WMT共享任务发布的数据。通过在所有22个语言方向上对检索数量r和开发集示例数量d进行网格搜索，最终为每个语言方向确定最优配置，从而实现高效且性能稳定的零样本迁移翻译。
链接: https://arxiv.org/abs/2608.13722
作者: Aashish Dhawan,Christopher Driggers-Ellis,Dzmitry Kasinets,Christan Grant,Daisy Zhe Wang
机构: University of Florida(佛罗里达大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper describes the University of Florida Gators submission to the WMT26 Low-Resource Indic Language Translation shared task. We adapt the retrieval-augmented many-shot translation pipeline from our AmericasNLP 2026 system to translate between English and eleven North-Eastern Indian languages in both directions. At inference time, BM25 retrieves the most similar parallel examples from a language-specific training bank, and Gemini 2.5 Flash translates the input conditioned on these examples. No model fine-tuning is involved. Training banks combine official WMT26 data with publicly available corpora such as Samanantar and prior WMT shared task releases. A grid search over retrieval count r and development exemplar count d across all 22 language-direction pairs selects the best configuration for each submission.
[NLP-38] Capacity-Dependent Effects of Data Selection for Reasoning
【速读】： 该论文旨在解决生成式 AI（Generative AI）在推理任务中进行监督微调时，如何选择最优训练数据以提升模型性能的问题。传统基于似然度的数据选择方法假设高似然响应能提供更有效的监督，但本文通过系统实验发现，这种假设的有效性高度依赖于模型容量与训练时长。其解决方案的关键在于揭示了“快适配/慢收益”（Fast-Fit / Slow-Gain）的容量依赖现象：对于小模型，高似然数据可带来快速且稳定的早期性能提升；而对于大模型，在较长训练周期下，低似然数据反而更具优势，因其有助于模型逐步逼近教师模型的分布。研究进一步通过学习动态分析和容量受限的蒸馏理论框架，阐明了数据难度、数据覆盖范围与学生模型容量之间的协同作用机制。因此，论文提出有效数据选择应综合考虑模型容量与计算预算，而非采用单一偏好高似然数据的通用策略。
链接: https://arxiv.org/abs/2608.13721
作者: Cuong Dang,Hoang Anh Just,Ruoxi Jia
机构: Virginia Tech(弗吉尼亚理工学院); Blacksburg, VA, USA
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to COLM 2026
Abstract:In reasoning supervised fine-tuning, candidate responses for the same instruction can differ substantially in how well they match the student’s current distribution. Recent likelihood-based response selection methods suggest that responses closer to the student distribution provide more effective supervision, motivating the hypothesis that high-likelihood responses may generally be preferable for fine-tuning. In this paper, we revisit this intuition and show that the value of likelihood-based data selection depends critically on model capacity and training duration. Through controlled experiments on mathematical reasoning, using students ranging from 1.5B to 8B parameters and supervision generated by stronger teacher models, we observe a clear \emphcapacity-dependent ``\colorSMALLCOLOR\textbfFast-Fit / \colorLARGECOLOR\textbfSlow-Gain’’ pattern. High-likelihood data provides faster and more stable early improvements, especially for smaller models, but low-likelihood data becomes increasingly beneficial for larger models when training is allowed to continue longer. To explain this phenomenon, we analyze learning dynamics, showing that small models often fail to absorb low-likelihood supervision and instead fall into shallow or repetitive behaviors, while larger models are better able to move toward the teacher distribution under such data. We further provide a capacity-constrained theoretical view of distillation that clarifies how data difficulty, data span, and student capacity jointly govern transfer. Overall, our findings show that effective data selection for reasoning should be aware of model capacity and computing budget rather than based on a single universal preference for high-likelihood supervision.
[NLP-39] StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition
【速读】： 该论文旨在解决流式自动语音识别（Streaming Automatic Speech Recognition, ASR）在目标域音频发生领域偏移时性能下降的问题，尤其针对标注的域内数据成本高昂而未标注数据丰富的现实场景。其核心挑战在于如何有效利用大量未标注数据来提升模型在新域上的泛化能力。解决方案的关键在于提出一种半监督学习框架——StreamHear，该框架通过以下步骤实现：首先，在有标签训练集上对离线的声学-词元转换器（transducer）教师模型进行微调；随后，利用该教师模型为大量未标注数据生成伪标签（pseudo-labels）；最后，将真实标签与伪标签混合，用于微调流式学生模型。此外，引入基于先验信息的动态规划重对齐（prior-regularized dynamic-programming realignment）步骤，利用ASR假设作为锚点，修正分块级别（chunk-level）的词位放置误差，从而提升时间对齐精度。实验结果表明，StreamHear在涵盖金融电话、朗读语音和电话质量对话的四个数据集上均显著优于纯监督微调的学生模型，并大幅缩小了与离线教师模型之间的性能差距。
链接: https://arxiv.org/abs/2608.13717
作者: Zefang Liu,Chenyang Zhu,Sangwoo Cho,Xujun Peng,Shi-Xiong Zhang,Sambit Sahu
机构: 未知
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注:
Abstract:Streaming automatic speech recognition (ASR) underperforms on domain-shifted target audio, where labeled in-domain data is costly to prepare while unlabeled audio is abundant. We present StreamHear, a semi-supervised pipeline that adapts a pretrained streaming student by fine-tuning an offline transducer teacher on the labeled training set, generating pseudo-labels on the unlabeled portion, and fine-tuning the student on the mixture. We further introduce a prior-regularized dynamic-programming realignment step that fixes chunk-level word placement using an ASR-hypothesis anchor. Across four datasets spanning financial calls, prepared read speech, and phone-quality dialogue, StreamHear consistently outperforms supervised student fine-tuning and narrows the gap to the offline teacher.
[NLP-40] Reading Between The Lines: Modeling and Evaluating Behavioral Realism in Legal Simulation ICML2026
【速读】： 该论文旨在解决法律人工智能（Legal-AI）评估中忽视证人行为动态性的问题，现有评估多聚焦于事实准确性、推理能力或回答的表面合理性，而未能充分衡量模拟证人在交互过程中行为的真实性和复杂性。其解决方案的关键在于提出WitnessSim——一个基于可调控法律人格（controllable legal personas）的庭审质询模拟系统，并构建了一个分离行为真实性和教学有效性的评估框架。通过对抗测试、盲法律师对比以及纵向行为轨迹分析，验证了WitnessSim在保持合理行为边界方面的有效性；同时，教学测试表明证人行为能对问题形式和律师干预做出有意义的响应，且未完全丧失预设人格特征。这一成果不仅实现了法律模拟中行为保真度的建模，还为评估此类系统的性能提供了可复用的标准化框架。
链接: https://arxiv.org/abs/2608.13712
作者: Divya Vetticaden,Arya Gupta,Julian Nyarko,Megan Ma
机构: Stanford University (斯坦福大学)
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at ICML 2026 AI4Law. 47 pages, 26 tables, 14 figures
Abstract:Deposition training requires attorneys to manage dynamic witness behavior, yet legal-AI evaluations largely focus on factual accuracy, reasoning, or response-level plausibility. We introduce WitnessSim, a deposition simulator driven by controllable legal personas. We use an evaluation framework separating behavioral realism from pedagogical usefulness. We assess realism through adversarial testing, blinded attorney comparison, and analysis of longitudinal behavioral trajectories. WitnessSim generally maintained plausible behavioral boundaries, and attorneys did not systematically prefer either original testimony or WitnessSim generated testimony. Pedagogical tests showed that witness behavior changed meaningfully in response to question form and attorney intervention without uniformly collapsing the assigned persona. Together, these results showcase a model of behavioral fidelity in legal simulations, and provide a framework for evaluating its performance.
[NLP-41] achMateGPT : A Multi-Agent Knowledge-Grounded Framework for Pedagogical Assessment Generation from Science Curriculum Materials
【速读】： 该论文旨在解决现有检索增强生成（Retrieval-Augmented Generation, RAG）系统在生成基于教材的科学测评题时存在的多重局限性，包括依赖扁平化检索、仅支持单题生成、缺乏对弱证据的防护机制，以及难以适配低资源、以国家考试为导向的课程体系。其核心解决方案是提出TeachMateGPT——一个面向课程结构化科学测评题生成的多智能体系统，关键创新点在于：(i) 提出COPE（Hierarchical Knowledge Base），采用分层知识库替代传统的基于标记窗口的文本切片，依据课程纲要结构进行多粒度文档分割，并通过可遍历的图谱化溯源关系，在三个层级间建立关联，实现证据与教学层次的精准匹配；(ii) 设计分阶段、失败封闭的智能体流水线，取代一次性检索-生成模式，包含路由门控搜索、融合密集与词法证据的检索模块，以及在证据覆盖不足时主动阻断生成的覆盖门控机制，由专业智能体分别生成客观题与开放性作答题；(iii) 引入SAVER源属性验证协议，针对每个创造性问题的四个子部分，综合评估忠实度、相关性与幻觉风险，结合教师参与的闭环评估而非自动过滤，显著提升生成内容的可信度；(iv) 构建NCTB-SciGen8数据集，涵盖印度国家课程委员会（NCTB）八年级科学教材全部14章的198道题目（143道选择题，55道开放题），由系统生成并经三名在职教师评分验证。实验表明，TeachMateGPT相较于基线RAG模型，在忠实度（0.68 → 0.96）和答案相关性（0.60 → 0.89）上均有显著提升。
链接: https://arxiv.org/abs/2608.13708
作者: Fatema Tuj Johora Faria,Mukaffi Bin Moin,M. F. Mridha,Jubayer Al Mahmud
机构: Ahsanullah University of Science and Technology, Bangladesh; American International University - Bangladesh; Jashore University of Science and Technology, Bangladesh
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Automatically generating textbook-grounded assessment items can reduce science teachers’ workload, but existing retrieval-augmented generation (RAG) systems rely on flat retrieval, support only single-question generation, lack safeguards against weak evidence, and are ill-suited to low-resource, board-exam-structured curricula. We address these limitations with TeachMateGPT, a multi-agent system contributing four advances to curriculum-grounded science-assessment authoring. (i) COPE, a hierarchical knowledge base replacing token-window chunking with a multi-resolution index that segments documents along syllabus structure and links them at three granularities via a traversable graph-based lineage, matching evidence to each topic’s instructional level. (ii) A staged, fail-closed agent pipeline replacing one-shot retrieve-then-generate: routing gates search, retrieval fuses dense and lexical evidence under a coverage gate that withholds generation on insufficient evidence, and specialist agents draft objective and constructed-response items. (iii) SAVER, a source-attributed verification protocol scoring faithfulness, relevance, and hallucination risk against retrieved evidence, applying stricter grounding checks across each creative question’s four sub-parts, paired with teacher-in-the-loop evaluation rather than automatic filtering. (iv) NCTB-SciGen8, a curriculum-grounded dataset of 198 items (143 multiple-choice, 55 creative questions) spanning all 14 chapters of the NCTB Class 8 science textbook, produced by the pipeline and rated by three practicing teachers. TeachMateGPT raises faithfulness (0.68 \rightarrow 0.96) and answer relevancy (0.60 \rightarrow 0.89) over a vanilla RAG baseline.
[NLP-42] CLAIR-Fin: An Adversarial Multi-Agent Framework for Claim-Level Verification and Adaptive Debate in Cross-Modal Financial QA
【速读】： 该论文旨在解决检索增强型与多智能体流水线中幻觉（Hallucination）问题的系统性缺陷，具体表现为：跨模态证据存在分歧时仍盲目信任证据、辩论仅验证汇总报告而非个体主张、且验证环节滞后至文本生成完成后，导致智能体间的错误无法在早期被检测。其解决方案的关键在于提出一种九智能体框架 CLAIR-Fin，通过四个核心机制实现精准控制：1）异构证据权威机制（Asymmetric Evidence Authority），依据主张类型动态设定证据可信度，而非对所有模态一视同仁；2）权责链验证（Chain-of-Custody Verification），在撰写与对抗审查之间的交接点即时检查事实依据，实现过程内嵌式校验；3）自适应反驳循环（Adaptive Rebuttal Cycle），对争议主张启动深度可扩展的对抗性辩论，以动态调节验证强度；4）终端蕴含审计与持续幻觉风险指数（Hallucination Risk Index），区分通过严格审查的主张与未受挑战的主张，实现风险量化追踪。实验基于从孟加拉国银行年报构建的跨模态金融评估集 BB-FinQA-X（500 个问题），按查询类型、格式与难度分层，结果表明，相较于单次检索增强生成基线，CLAIR-Fin 在忠实性上显著提升（0.780 → 0.889），并在证据不足时主动拒答（5.4%），避免生成无根据结论，同时优于 HyDE 与 Graph-RAG 等更强基线，在忠实性上达到 0.874 以上。
链接: https://arxiv.org/abs/2608.13706
作者: Fatema Tuj Johora Faria,Mukaffi Bin Moin,Jubayer Al Mahmud,M. F. Mridha,Md. Alam Hossain
机构: Ahsanullah University of Science and Technology(阿萨努拉大学科技学院), Bangladesh(孟加拉国); Jashore University of Science and Technology(杰绍尔科技大学), Bangladesh(孟加拉国); American International University - Bangladesh(美国国际大学-孟加拉国)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate verifies an aggregate report rather than individual claims, and such verification occurs only after drafting, leaving inter-agent errors undetected until the final text. To close this gap, we present CLAIR-Fin, a nine-agent framework that decomposes each question into atomic claims maintained in a typed Financial Claim Ledger. Each claim is resolved through Asymmetric Evidence Authority, which conditions evidence trust on claim type rather than treating all modalities as equally reliable; Chain-of-Custody Verification, which checks grounding at the hand-off between drafting and adversarial review rather than only at the pipeline’s exit; an Adaptive Rebuttal Cycle, which routes contested claims through adversarial debate whose depth scales with what that debate finds; and a terminal entailment audit paired with a continuous Hallucination Risk Index that distinguishes claims that passed scrutiny from claims never contested. We evaluate CLAIR-Fin on BB-FinQA-X, a 500-question cross-modal financial evaluation set built from Bangladesh Bank Annual Report material, stratified by query type, format, and difficulty. Relative to a single-pass retrieval-augmented generation baseline, it raises faithfulness ( 0.780 \rightarrow 0.889 ) while abstaining on 5.4% of questions when evidence is insufficient rather than forcing an unsupported response, and it exceeds stronger retrieval-strategy baselines such as HyDE and Graph-RAG on faithfulness ( \leq 0.874 ).
[NLP-43] GRPO Beyond English: A Large-Scale Study of GRPO in Non-English and Multilingual Settings
【速读】： 该论文旨在解决当前基于强化学习与可验证奖励（Reinforcement Learning with Verifiable Rewards, RLVR）的模型训练方法在多语言场景下存在严重英语中心化的问题，即现有研究主要聚焦于英语任务，缺乏对非英语语言及多语言环境下模型推理能力提升的有效评估与优化。其解决方案的关键在于系统性地开展大规模跨语言实验，采用组相对策略优化（Group Relative Policy Optimization, GRPO）在多种基础模型、训练语言和推理语言奖励设置下进行对比分析。研究发现，以母语进行训练时，模型在目标语言上的推理表现仅略逊于英语训练，且存在显著的跨语言迁移效应——某一语言的训练往往能同时提升其他多种语言的表现；然而，这种提升具有高度依赖于具体模型和语言的特性，部分情况下甚至会导致其他语言上出现严重的性能退化。因此，该研究强调：尽管超越英语的RLVR能够带来广泛的跨语言收益，但必须辅以全面的多语言评估体系，以识别并规避特定语言下的性能退化问题。
链接: https://arxiv.org/abs/2608.13698
作者: Konstantin Dobler,Federico Scozzafava,Jonathan Janke,Mohamed Ali,Simon Lehnerer
机构: Apple(苹果); Hasso Plattner Institute (哈索·普拉特纳研究所); ELLIS Unit Potsdam (ELLIS波茨坦单位)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Reinforcement Learning with Verifiable Rewards (RLVR), often optimized with Group Relative Policy Optimization (GRPO), has become a central recipe for improving the reasoning capabilities of pretrained language models but current studies remain heavily English-centric. We conduct a large-scale empirical study of multilingual and non-English GRPO across a wide range of base models, training languages, and different reasoning language rewards. We find that training to reason in the native language often leaves only a small gap to training for English reasoning. We further observe strong crosslingual transfer: training in one language often improves performance in many others. However, specific trends are highly model- and language-dependent. In some cases, training in a particular language induces severe regressions on out-of-domain capabilities in other languages. Our analysis shows that RLVR beyond English can provide broad crosslingual gains, but also requires broad evaluation to detect language-specific regressions.
[NLP-44] Asymmetric Discourse Homogenization and Shared Language Technology: Evidence from Reddit
【速读】： 该论文旨在解决政治话语分化趋势在2022年底出现的意识形态不对称性断裂问题，具体表现为保守派用户群体的政治言论多样性显著中断，而进步派用户未表现出类似变化。其核心解决方案的关键在于通过多种计量方法（包括事件研究法ITS、双重差分法DiD、随机干预时间点回归RDiT及倾向得分匹配）与多维度数据验证，揭示这一不对称现象并非由单一时间节点（如ChatGPT发布）引发，而是呈现渐进式演变特征。进一步分析表明，基于连续累积大语言模型（LLM）暴露指数构建的长期趋势模型，在采用二次项设定后仍保持显著性，支持渐进效应而非突变假说；同时，对持续活跃作者（stayer）的子样本分析排除了个体层面的AI使用对言论同质化的影响，提示其机制更可能源于社区层面的话语生态收敛（ecological convergence），而非个体行为改变，尽管无法完全剥离与同期社会结构性变迁的混杂效应。
链接: https://arxiv.org/abs/2608.13674
作者: Fengming Liu
机构: 未知
类目: Computers and Society (cs.CY); Computation and Language (cs.CL); Social and Information Networks (cs.SI)
备注: 36 pages, 5 figures, 6 tables. Replication package available (code and instructions). Keywords: political discourse; semantic similarity; discourse homogenization; generative AI; computational text analysis
Abstract:I document an ideologically asymmetric break in the pre-existing diversification trend of political discourse, emerging around late 2022, using 6 million Reddit comments from two cross-partisan forums, 2019-2025. Conservative users experienced an interruption of their prior diversification trajectory; progressive users showed no comparable change. The asymmetry is consistent across estimation strategies (ITS, DiD, RDiT, propensity-score matching) and temporal aggregations. A daily-frequency permutation test over 2,377 candidate cutoff dates shows the ChatGPT threshold produces an unremarkable estimate (49.8th percentile): the shift builds gradually instead of breaking at a single date. A continuous cumulative LLM index, tracking AI exposure across seven model releases, remains significant under a quadratic trend specification that eliminates the binary estimate. A stayer analysis narrows the mechanism: the homogenization effect disappears when the sample is restricted to authors active throughout the study period, and the stayer confidence interval excludes within-author effects even a tenth the size of the full-sample estimate. The mechanism is most parsimoniously ecological (community-level discursive convergence) rather than individual-level AI adoption, though the data cannot cleanly separate this account from concurrent secular change.
[NLP-45] A Calibrated Test of Internal Action Maps: State Signals Without Global Affine Closure
【速读】： 该论文旨在解决生成式模型中隐藏状态（hidden state）是否能在不依赖可重用动作映射（action map）的情况下实现可解码性或因果可用性的问题。其核心挑战在于验证在缺乏源信息（held-source）时，动作映射是否仍能保持自然的后动作激活与组合特性。解决方案的关键在于构建一个证据格（evidence lattice），并在已知的仿射对称群 S5 载体上验证几何分支：所有无源折叠（held-source folds）均通过单步、组合、逆元、解码及交换律门控测试。研究发现，结构曲率与保持域共轭虽导致误差单调上升，但仅有23/30最强单元成功通过闭包门控，表明校准具有边界性而非普遍性。在微调后的Qwen/Qwen3-4B模型中，冻结最终标记的h28层仿射映射平均保持实体误差为0.519，显著高于域内交叉拟合的0.398；七组随机实体划分与映射几何分析排除了纯实体特异性解释。早期层（h4/h16）虽更优拟合单步转移，但其冲突状态解码能力弱，词汇控制问题仍未解决。三组从同一冻结检查点重构的干预数据集仅在h28/h36层观测到因果效应。结果表明，基于结果感知的再拟合虽将h28单步误差降至0.474（加权后0.469），但无法通过组合门控。此外，学习得到的有限世界虽保留相对代数信号或共享图谱，但未实现保持源的仿射闭包。综上，在所测试的载体范围内，状态可用性、因果使用、局部几何与可重用闭包可分离。研究局限性包括仅针对单一预训练模型、采样最终标记层、两个有限世界及特定仿射或诊断函数类。
链接: https://arxiv.org/abs/2608.13626
作者: Dekun Yang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 12 pages, 7 figures, 4 tables; includes supplementary results and ancillary reproducibility files
Abstract:A hidden state signal can be decodable or causally usable without supporting a reusable action map. We test whether action maps fitted without a source reach its natural post-action activation and compose. We organize the tests as an evidence lattice and validate the geometric branch on a known affine S_5 carrier: all held-source folds pass one-step, composition, inverse, decoding, and commutativity gates. Structured curvature and held-domain conjugacy raise error monotonically, but only 23/30 strongest cells flip a closure gate, bounding rather than universalizing calibration. In post-trained Qwen/Qwen3-4B, frozen final-token h28 affine maps have mean held-entity error .519, versus .398 for within-test-domain cross-fit. Seven randomized entity splits and map geometry do not support a purely entity-specific account. Earlier h4/h16 layers fit one-step transitions better, but h4 conflict-state decoding is weak and lexical controls remain unresolved. Three matched intervention datasets regenerated from one frozen checkpoint show causal effects only at h28/h36. Outcome-aware refitting improves h28 one-step error to .474 (.469 with weighting), yet no refit passes composition. Learned finite worlds likewise preserve relative algebraic signals or shared charts without held-source affine closure. Within the tested carriers, state availability, causal use, local geometry, and reusable closure are separable. The result is limited to one pretrained model, sampled final-token layers, two finite worlds, and the tested affine or diagnostic function classes.
[NLP-46] Measuring Fairness in Large Audio Language Models via Semantic-Aware Bias Estimation
【速读】： 该论文旨在解决大型音频语言模型（Large Audio Language Models, LALMs）在语音理解任务中存在的人群子群体公平性评估失真问题。现有评估方法因未能有效控制语义内容差异与说话人特异性等混杂因素，易导致对模型偏见的误判。其解决方案的关键在于提出一种语义感知的混合效应回归框架，通过引入参考文本的句级语义嵌入作为协变量，并将说话人身份建模为随机效应，从而显式校正上述混杂因素。尤为关键的是，语义表示由待评估的LALM自身提取，实现了模型主观感知下的语义控制，提升了公平性评估的内在一致性与可解释性。实验结果表明，该方法显著降低了虚假公平性结论的发生率，在模拟数据与真实基准上均能提供更稳健、可信的子群体性能差异估计。
链接: https://arxiv.org/abs/2608.13624
作者: Zhe Liu
机构: Meta(元)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:Large Audio Language Models (LALMs) have seen increasing use for audio understanding tasks such as speech recognition and audio question answering, raising concerns about fairness across demographic subgroups. Fairness evaluation in spoken-input settings is challenging due to confounding factors, including semantic variation in spoken content and speaker-specific characteristics. Ignoring these factors can result in misleading conclusions about model bias. We propose a semantic-aware mixed-effects regression framework for fairness evaluation in LALMs that explicitly accounts for these confounders. Our approach incorporates sentence-level semantic embeddings of reference text as covariates and models speaker identity as a random effect. Notably, semantic representations are extracted from the same LALM under evaluation, enabling semantic control over variation as perceived by the model itself. Experiments on simulated data and real-world benchmarks demonstrate that the proposed approach substantially reduces spurious fairness findings and yields more robust and interpretable estimates of subgroup performance differences.
[NLP-47] ARC: Fair Relative Advantage Comparison in Open-Ended Real-World Interaction
【速读】： 该论文旨在解决开放世界交互中多行为路径共存所引发的奖励不公平问题，即在群体强化学习（Group-based RL）框架下，由于不同策略产生的行为不可比，导致奖励模型偏好特定交互风格而非上下文适配性，从而扭曲相对优势评估并引导优化偏离合理行为。其解决方案的关键在于提出ARC（Advantage Regularization via Conditioning）训练范式，通过策略条件化的轨迹分组实现更公平的相对比较，并结合混合奖励与熵正则化机制。同时，论文引入\inter（interactive reasoning paradigm），一种解耦用户可见沟通与底层推理及工具使用的新型交互范式，支持可响应、可引导且具备执行感知能力的交互；并构建了\inter-86K这一带策略标注的训练语料库，用于监督与强化学习。实证结果表明，ARC显著提升了核心工具使用基准表现，而\inter将首次响应时间从4.91秒降低至1.27秒，揭示出开放交互学习的核心瓶颈不仅在于奖励设计，更在于行为比较是否公平。
链接: https://arxiv.org/abs/2608.13622
作者: Yongqi Tong,Tan Li Hui Faith,Choy Zhen Wen Marcus,Zhou Jin,Kewei Fu,Jiang-Ming Yang,Jianshe Li,Xin Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Open-ended real-world interaction admits multiple valid behaviors: an agent may answer directly, ask for clarification, provide progress updates, or confirm before acting. This flexibility breaks a core assumption behind group-based RL: rollouts compared within a group are no longer guaranteed to be behaviorally comparable. As a result, reward-model preferences over interaction style can distort relative advantages and steer optimization toward reward-preferred behaviors rather than context-appropriate ones. We formalize this as a \textitreward fairness problem and propose \textbfARC (Advantage Regularization via Conditioning), a training recipe that restores fairer relative comparison through strategy-conditioned rollout grouping, together with hybrid rewards and entropy regularization. We study ARC in our proposed \inter, a novel paradigm for responsive, steerable, and execution-aware user-agent interaction that decouples user-visible communication from latent reasoning and tool use. \inter\ also provides the annotation and distillation pipeline for constructing \inter-86K, our strategy-annotated training corpus for supervised and RL training. Empirically, ARC substantially strengthens the core \tau/\tau^2 tool-use benchmarks, while \inter\ reduces time-to-first-token from 4.91s to 1.27s relative to a think-style baseline. Together, these results suggest that a central bottleneck in open-ended interactive learning is not only how agents are rewarded, but whether their behaviors are compared fairly in the first place. The ARC implementation and \inter-86K training data will be released.
[NLP-48] No Universal Signal Predicts Sample-Level LLM Regression under Version Updates
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在迭代更新过程中可能出现的样本级退化（sample-level regression）问题，即某些原本在旧版本模型中正确的输出在新版本中变为错误。其核心挑战在于如何在推理阶段利用可获取的信号，提前预测此类退化现象。解决方案的关键在于系统性地评估多种推理时可用的信号，包括单模型信号（如置信度、logit边际、注意力熵）与跨版本信号（如输出KL散度、似然漂移、词元级KL散度、表征漂移），并通过统一的增量价值测试（added-value test）剥离各信号相对于置信度基线的独立贡献。研究发现：（1）信号有效性具有任务依赖性——置信度在多选题和简单数学任务中表现最优，而似然/KL类信号在复杂数学和代码生成任务中更有效；（2）不存在对所有模型更新均最优的通用信号；（3）部分跨版本信号即使在置信度失效时仍保持信息量，且无需标签即可使用，支持了“选择性回滚”（selective fallback）的可行性，即对高风险样本自动回退至旧模型。这一发现为实践者根据具体任务类型选择合适的退化预测信号提供了依据。
链接: https://arxiv.org/abs/2608.13607
作者: Jia Sheng,Yiwei Lu
机构: University of Ottawa(渥太华大学); Vector Institute(向量研究所)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Frontier LLMs are updated frequently and typically outperform their predecessors in aggregate. But aggregate gains say little about individual samples: an update can still cause sample-level regression, where a response correct under the old model becomes incorrect under the new one. This paper studies how to predict such regressions from signals available at inference time. We compare single-model signals (confidence, logit margin, attention entropy) against cross-version signals (output KL divergence, likelihood drift, token-level KL, representation drift) under a unified added-value test that isolates each signal’s gain over a confidence baseline. Across six benchmarks in three task families (multiple-choice question answering, or MCQ; math reasoning; code generation) and six model update pairs, we find that (1) signal effectiveness is task-dependent: confidence is strongest on MCQ and simpler math, while likelihood/KL signals give the most frequent gains on harder math and code; (2) no signal is universally best across model updates either; and (3) some cross-version signals stay informative even when confidence fails, including without labels, which supports a proof-of-concept selective fallback that routes high-risk samples back to the old model. Practitioners can use these task-level patterns to choose which regression signal to trust for a given update. Code is available at this https URL.
[NLP-49] Stable Miscalibration in Large Language Models : A Practical View of High-Confidence Errors ICML2026
【速读】： 该论文旨在解决大语言模型中高置信度错误的根源问题，传统观点认为这类错误源于模型内部推理的脆弱性，而本文提出一种替代解释：稳定误校准（stable miscalibration），即高置信度的错误答案在小扰动下仍保持局部稳定。其解决方案的关键在于引入双诊断框架：一是基于标签感知的输出层审计评分（audit score），用于量化不同领域中置信度波动及强制回答基线下的过度自信错误；二是基于隐藏状态敏感性的内部敏感性探测（internal sensitivity probe），以评估模型内部表征的变化。实验结果表明，该审计评分能有效识别自省式提示（abstention-aware self-critique）降低决策损失的区域，且自省提示在三个开源模型中均显著降低了各层隐藏状态的敏感性，支持了提示诱导的局部稳定性机制，而非单纯的输出层回避模式。然而，审计定义的过度自信错误并不表现出比高置信度正确答案更强的局部敏感性，说明部分高置信度错误可能是稳定且误校准的，而非简单脆弱性所致，从而揭示了模型校准问题的复杂性。
链接: https://arxiv.org/abs/2608.13591
作者: Akira Okutomi
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at the 2nd Workshop on Epistemic Intelligence in Machine Learning (EIML)@ICML 2026. 7 pages, 4 figures
Abstract:High-confidence errors in large language models are often treated as evidence of fragile internal inference. We study a different possibility: stable miscalibration, where a confident wrong answer remains locally stable under small perturbations. We combine two diagnostics: a label-aware output-level audit score that ranks domains by confidence variation and overconfident mistakes under a forced-answer baseline, and an internal sensitivity probe that measures hidden-state movement. On a multi-domain binary factual audit set, this audit score tracks where abstention-aware self-critique reduces decision loss, although direct labeled baselines rank the same gain more strongly. Internally, self-critical prompting consistently reduces hidden-state sensitivity across layers in three open-weight models. This supports prompt-induced local stabilization rather than a purely output-level abstention pattern, but it does not imply calibration: audit-defined overconfident errors are not clearly more locally sensitive than confidently correct answers, so some high-confidence errors may be stable and miscalibrated rather than simply fragile.
[NLP-50] IterCOMP: Reasoning -aware Adaptive Prompt Compression for Multi-hop Question Answering ACL2026
【速读】： 该论文旨在解决多跳问答（multi-hop question answering）任务中，检索增强生成系统因处理冗长且嘈杂的上下文信息而导致效率与准确率下降的问题。现有提示压缩方法多针对单轮查询设计，难以捕捉多步推理之间的依赖关系。本文提出一种无需训练的统一式提示压缩框架IterCOMP，其核心在于通过迭代压缩循环将文档分解为证据片段，评估问题可回答性，并生成针对性的后续问题，从而逐步整合关键证据，构建紧凑且具备推理导向性的提示。该方案有效提升了模型在MusiQue、2WikiMultiHopQA和HotpotQA等多个基准上的精确匹配（Exact Match）与F1分数，同时显著降低令牌预算，在推理复杂度增加时仍保持鲁棒性。
链接: https://arxiv.org/abs/2608.13588
作者: JungMin Yun,YoungBin Kim
机构: Chung-Ang University (中央大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: ACL 2026 Main Conference
Abstract:Multi-hop question answering requires complex reasoning across multiple evidence segments, which often overwhelms retrieval-augmented generation systems with lengthy and noisy contexts, thereby undermining both efficiency and accuracy. While existing prompt compression methods attempt to address this issue, they are typically designed for single-turn queries and fail to capture interdependent reasoning steps. We propose IterCOMP, a unified, training-free prompt compression framework that incorporates multi-hop reasoning within an iterative compression loop. IterCOMP decomposes documents into evidence segments, evaluates question answerability, and generates targeted follow-up questions to iteratively integrate essential evidence, producing a compact, reasoning-oriented prompt. Experiments on MusiQue, 2WikiMultiHopQA, and HotpotQA demonstrate that IterCOMP achieves substantial improvements in Exact Match and F1 scores while reducing the token budget, outperforming existing baselines and exhibiting robustness as reasoning complexity increases.
[NLP-51] Jais 2: A Family of Arabic-Centric Open Large Language Models
【速读】： 该论文旨在解决阿拉伯语为中心的大型语言模型（Large Language Model, LLM）在规模、文化适配性、训练效率与开放可用性方面存在的短板。现有模型普遍存在阿拉伯语专用能力不足、对阿拉伯文化语境理解不充分、训练资源消耗大且缺乏开源可复现性等问题。其解决方案的关键在于构建一个完全从零训练的700亿参数（70B）阿拉伯语专属大模型（Jais 2 70B），并辅以80亿参数（8B）的高效变体，通过自定义的阿拉伯语专用词汇表（Arabic-centric vocabulary）实现高效的训练与推理；同时采用优化的模型架构与训练策略，在远低于同类模型的令牌预算（token budget）下实现卓越的阿拉伯语性能，并在多类文化相关任务（如诗歌、宗教、饮食、梦解等）及通用任务（如翻译、摘要生成）中取得领先表现。此外，模型以宽松商业许可发布于HuggingFace，并部署于Cerebras硬件平台，支持跨平台（Web/iOS/Android）高吞吐量实时聊天服务，最高可达每秒2000个令牌，实现了规模、语言多样性、文化保真度、开放性与速度的统一，为阿拉伯语大模型研究提供了可扩展、可访问的开源基础。
链接: https://arxiv.org/abs/2608.13580
作者: Mohamed Anwar,Abed Alhakim Freihat,George Ibrahim,Mostafa Awad,Abdelrahman Sadallah,Gurpreet Gosal,Gokulakrishnan Ramakrishnan,Sarath Chandran,Biswajit Mishra,Rituraj Joshi,Ahmed Frikha,Etienne Goffinet,Abhishek Maiti,Ali El Filali,Sarah AlBarri,Samujjwal Ghosh,Rahul Pal,Parvez Mullah,Awantika Shukla,Sajid siddiki,Samta Kamboj,Onkar Pandit,Sunil Kumar Sahu,AbdelRahman Elbadawy,Amr Mohamed,Ahmad Chamma,Evan Dufraisse,Abdelaziz Bounhar,Dani Bouch,Hadi Abdine,Guokan Shang,Fajri Koto,Yuxia Wang,Zhuohan Xie,Ali Mekky,Rania Elbadry,Sarfraz Ahmad,Momina Ahsan,Omar El Herraoui,Daniil Orel,Hasan Iqbal,Kareem Elzeky,Mervat Abassy,Kareem Elozeiri,Saadeldine Eletter,Farah Atif,Nurdaulet Mukhituly,Haonan Li,Xudong Han,Aaryamonvikram Singh,Zainul Abedien Ahmed Quraishi,Neha Sengupta,Larry Murray,Avraham Sheinin,Joel Hestness,Natalia Vassilieva,Hector Xuguang Ren,Zhengzhong Liu,Michalis Vazirgiannis,Preslav Nakov
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong performance across the Arabic and culturally grounded benchmarks evaluated in this report. The family includes, to our knowledge, the largest open Arabic-centric LLM trained from scratch at 70B parameters, and a competitive 8B-parameter variant among the evaluated open models. A custom Arabic-centric vocabulary enables efficient training and inference. In addition, an optimized architecture and training recipe yield highly compute-efficient training. With a substantially smaller token budget than comparable models, Jais 2 achieves strong Arabic performance on the benchmarks considered in this report and competitive English results. The models obtain leading results among the evaluated open models on OALL2 and AraGen. They also perform strongly on several culturally grounded Arabic benchmarks, including poetry, religion, cuisine, and dream interpretation, as well as in general tasks such as translation and summarization. We release the models in HuggingFace under a commercially permissive license. Jais 2 70B is also released as a chat app on the Web, iOS, and Android; it runs on Cerebras hardware, delivering up to 2,000 tokens per second, and enabling high-throughput Arabic-centric chat serving in our deployment setting. By uniting scale, linguistic diversity, cultural fidelity, openness, and speed, Jais 2 provides an open-weight foundation intended to support further research and development in Arabic-centric LLMs.
[NLP-52] BCMT: Blockwise Causal Memory Transformer
【速读】： 该论文旨在解决传统Transformer架构在处理长序列时因密集自注意力机制导致的二次时间复杂度问题，从而限制了其在长上下文建模中的可扩展性。其核心解决方案是提出一种名为块状因果记忆Transformer（Blockwise Causal Memory Transformer, BCMT）的新架构，其关键在于将局部词元交互与全局上下文传播解耦：在每个局部块内使用独立的密集因果自注意力以捕捉局部依赖，同时通过指数因果记忆机制对每个块生成自适应摘要，并将该摘要回注到词元表示中，实现高效且并行化的长程上下文信息传播。该记忆机制不依赖于显式的全局注意力或可学习的记忆状态，既避免了远距离词元间的密集交互，又保持了与标准密集自注意力实现的兼容性。实验表明，BCMT在长达1024个词元的上下文上实现了与密集Transformer相当的验证性能，同时显著提升了训练吞吐量并降低了内存消耗，消融实验证实这些优势主要源于所提出的记忆机制。这表明基于块摘要构建的指数因果记忆可作为长上下文语言建模中密集全局注意力的有效替代方案。
链接: https://arxiv.org/abs/2608.13578
作者: Rachid Arezki
机构: Independent Researcher(独立研究员)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 19 pages. Official implementation: this https URL
Abstract:Transformer architectures rely on dense self-attention to model long-range dependencies, but this mechanism exhibits quadratic complexity with respect to sequence length. We introduce BCMT (Blockwise Causal Memory Transformer), an architecture for long-context language modeling that decouples local token interactions from global context propagation. Dense causal self-attention is applied independently within local blocks, while each block produces an adaptive summary aggregated through an exponential causal memory. This memory is subsequently injected back into the token representations, enabling efficient propagation of long-range contextual information without relying on explicit global attention. Unlike standard Transformers and recurrent memory architectures, BCMT maintains neither dense interactions between distant tokens nor learned memory states. Its memory mechanism is fully parallelizable and remains compatible with standard implementations of dense self-attention. Experiments on language modeling with context lengths of up to 1024 tokens show that BCMT achieves validation performance comparable to that of Dense Transformers while significantly improving training throughput and reducing memory consumption. An ablation study further confirms that these improvements arise from the proposed memory mechanism. These results demonstrate that an exponential causal memory constructed from block summaries provides an effective alternative to dense global attention mechanisms for long-context language modeling.
[NLP-53] Not All Tokens Are Equal: Inflation-Aware Routing for Agent ic LLM Systems
【速读】： 该论文旨在解决生成式 AI（Generative AI）在复杂任务中因多次重试（retry）导致的“令牌通胀”（token inflation）问题，即模型在单次调用成本与实际工作流总成本之间存在显著差距。其核心挑战在于现有代理系统（如FrugalGPT）依赖单次调用成本进行模型路由，忽视了多轮推理带来的额外开销，从而在高难度任务上严重低估真实成本，可能导致资源浪费或性能下降。本文提出InflationAgent，其关键解决方案包含三方面：首先，系统性地测量不同模型层级和任务类型下的令牌通胀水平，发现7B模型在多跳问答任务中通胀可达4.25倍；其次，引入一种仅依赖本地推理计算的预执行难度信号——思维链分支熵（CoT Branching Entropy, CBE），可有效预测高通胀情况（AUROC达0.887）；最后，采用基于语义交换率（Semantic Exchange Rate, SER）的动态路由策略，以预期准确率与预测真实成本之比为优化目标，并结合“全新升级”（fresh-escalation）机制，在失败推理链被丢弃后才切换至更强模型，避免无效迭代。实验表明，在固定预算下，InflationAgent在GSM8K数据集上实现94.7%准确率，优于FrugalGPT的91.0%，同时减少31%的令牌消耗，且验证了将失败链直接转发至GPT-4o会使其准确率下降最高达34.8个百分点，证明了全新升级设计的有效性。
链接: https://arxiv.org/abs/2608.13571
作者: Heming Fu,Shan Lin,Qianqian Xie,Guojun Xiong
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:When a language model fails to answer a query on the first attempt, an agentic system retries, consuming additional tokens each time. This retry overhead creates a gap between what a model’s per-token price implies and what a full workflow actually costs. We call this gap \emphtoken inflation and define it as the ratio of true workflow cost to single-call cost. Systems like FrugalGPT route based on the latter, which can underestimate real cost by more than 2\times on difficult tasks. We address this with InflationAgent, a four-stage router that (1) measures token inflation systematically across model tiers and task types, finding inflation as high as 4.25\times for a 7B model on multi-hop question answering; (2) introduces CoT Branching Entropy (CBE), a pre-execution difficulty signal computed entirely from local inference, which predicts high inflation with AUROC 0.887; and (3) selects models by maximizing a Semantic Exchange Rate (SER) that divides expected accuracy by predicted true cost, with a fresh-escalation policy that discards failed chains before routing to a stronger model. On GSM8K under a fixed budget, InflationAgent achieves 94.7% accuracy versus 91.0% for FrugalGPT while using 31% fewer tokens, and we show that forwarding a failed reasoning chain to GPT-4o reduces its accuracy by up to 34.8 percentage points, validating the fresh-escalation design.
[NLP-54] hink in Latent Explain in Language: Self-Explainable Latent Reasoning
【速读】： 该论文旨在解决生成式模型中隐式推理（latent reasoning）所面临的可解释性难题：尽管隐式推理通过将冗长的思维链（Chain-of-Thought, CoT）压缩为紧凑的嵌入表示，显著提升了计算效率，但其内在的“黑箱”特性使得推理过程难以被人类理解。现有方法存在两难困境——要么完全丧失可解释性（如Coconut），要么依赖额外的后处理解码器来生成解释（如Heima），后者不仅增加架构复杂度，还导致解释与原始推理过程脱节。为此，本文提出统一的自解释隐式推理（Self-Explainable Latent Reasoning, SELR）框架，其核心在于设计一种多任务训练目标，同时优化两个目标：(1) 答案损失（Answer Loss），确保隐式推理轨迹能够生成准确的最终答案；(2) 思维链损失（CoT Loss），强制模型将自身生成的隐式表示解码回人类可读的推理步骤。这一机制使隐式表示在保持任务有效性的同时具备语义可解释性，无需外部解码器即可实现自包含的可解释性。实验验证表明，SELR在大型语言模型（LLM）和视觉-语言模型（VLM）上均实现了优于基线的令牌效率与准确性，并首次实现了高效且原生可解释的隐式推理。
链接: https://arxiv.org/abs/2608.13570
作者: Dayuan Zhao,Shengcao Cao,Yu-Xiong Wang,Liang-Yan Gui
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Latent reasoning has emerged as a powerful alternative to text-based Chain-of-Thought (CoT), offering significant gains in computational efficiency by compressing verbose reasoning into compact embeddings. However, compressing reasoning into the latent space renders the thinking opaque, hindering its interpretability. Current methods present a stark trade-off: they either function as unexplainable ‘‘black boxes’’ (e.g., Coconut), where the latent reasoning is not human-readable, or rely on separate post-hoc decoders for explainability (e.g., Heima), introducing architectural overhead and decoupling the explanation from the actual reasoning process. In this work, we present a unified framework for Self-Explainable Latent Reasoning (SELR) that trains a single model to perform efficient and inherently explainable latent reasoning. Our core contribution is a novel multi-task training objective that optimizes for two goals simultaneously: (1) an Answer Loss that optimizes the latent reasoning trajectory to produce accurate final answers, and (2) a CoT Loss that explicitly trains the same model to decode its own latent representations back into human-understandable reasoning steps. This design ensures that generated latent representations are both task-effective and semantically interpretable, eliminating the need for external decoders. We validate the effectiveness of SELR on both Large Language Models (LLMs) and Vision-Language Models (VLMs), demonstrating that SELR achieves superior token efficiency and accuracy compared to baselines, while uniquely providing self-contained explainability without auxiliary models. Project page is available at this https URL.
[NLP-55] Does a Language Server Save Tokens for Coding Agents ? A Measurement Methodology and Preliminary Study
【速读】： 该论文旨在解决编码代理（coding agents）在上下文使用中因检索方式选择不当而导致的效率问题，核心矛盾在于：基于词法的检索（grep）虽快速无配置但噪声大，难以区分定义、调用与注释；而基于语言服务器协议（LSP）的语义检索虽精准且类型安全，却需运行索引服务器并引入每符号往返开销。其解决方案的关键在于提出一个可量化的评估框架——“达成任务成功所需的令牌数”（tokens-to-success），并通过五臂消融实验分离语义检索的影响因素，明确识别出三类可测量的失败模式。研究发现，语义检索在多数任务中并不具备预期的令牌效率优势：在符号定位任务中，LSP反而增加令牌消耗（+6%至+118%），且当免费可用时，代理会主动忽略；在引用完整性任务中，它提升精度但未带来令牌节省，也无法突破由代理遍历深度决定的召回上限；仅在最弱模型上，其节省效果才显现。更关键的是，在真实测试执行的编辑任务中，词法检索能完美处理多文件重命名，而仅支持位置的LSP因忽略注释和字符串中的引用，导致75%的任务失败；即便采用完整索引、文本增强的生产级LSP（如LSP-MCP），仍无法完全弥补差距，因其本质排除了对代码注释与字符串的语义覆盖。因此，研究结论并非全盘采用语义检索，而是主张构建一种基于任务类型、模型能力与词法噪声水平的自适应路由机制，实现检索策略的动态优化。
链接: https://arxiv.org/abs/2608.13568
作者: Pengcheng Xu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 6 figures. Code and data: this https URL
Abstract:Coding agents spend most of their context budget on retrieval. Lexical retrieval (grep) is universal, instant, and zero-setup, but noisy: it cannot tell a definition from a call from a comment. Semantic retrieval via the Language Server Protocol (LSP) is precise and typed, but needs a running, indexed server and pays a per-symbol round-trip. The claim that semantic retrieval is more token-efficient is, we find, asserted almost everywhere and measured almost nowhere: no public source isolates the LSP-vs-lexical token delta for an agent at equal task-success. This paper formalizes the question with one metric (tokens-to-success), specifies a five-arm ablation isolating semantic retrieval from confounds, maps three pre-stated failure modes onto measurable variables, and reports a preliminary study (Python and TypeScript repos; Claude Opus 4.8, Sonnet 4.6, Haiku 4.5). The answer is conditional and usually negative. On symbol-named localization the LSP costs tokens (+6% to +118%) and the agent ignores it when free. On reference-completeness it buys precision but not token savings and cannot raise the recall ceiling set by agent thoroughness; it saves tokens only for the weakest model. Tool choice is task-dependent: models default to grep on localization (0-6% semantic use) but reach for the LSP about half the time on reference tasks, unprompted. On edits scored by real test execution the gap is starkest: grep solves multi-file renames perfectly, a location-only LSP fails three-quarters of them by missing a call site, and even a complete, index-warmed, text-enriched LSP (each reference’s line inline, as production LSP-MCP servers do) recovers most of the gap but cannot close it, since a rename must touch comments and strings that semantic references exclude. The implication is not LSP-always but an adaptive router keyed on task class, model capability, and lexical noise.
[NLP-56] Modular Cognitive Architecture Emerges in Large Language Models
【速读】： 该论文旨在探究智能系统是否必然发展出类似人类大脑的功能模块化组织，即这种模块化是智能系统构建的普适性原则，还是仅限于生物大脑的进化偶然现象。为检验这一问题，研究通过在46项任务（涵盖语言、形式推理、社会推理和物理推理四大认知领域）上进行电路分析，考察大型语言模型（Large Language Models, LLMs）是否在非生物演化路径下也自发形成类似的模块化结构。研究发现，LLMs在不同认知任务间表现出与人类大脑高度相似的神经重叠模式：同一功能网络支持的任务在模型中激活重叠的神经元，而跨网络的任务则激活独立的神经元。这一发现表明，尽管优化机制迥异，但模块化结构在生物大脑与人工神经网络中的趋同出现，暗示其可能为智能系统的一种根本性组织原则。
链接: https://arxiv.org/abs/2608.13567
作者: Pengrui Han,Jacob Andreas,Evelina Fedorenko,Andrea Gregor de Varda
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: this https URL
Abstract:The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about other minds, and reasoning about the physical world. Is this modular organization a fundamental principle of how intelligent systems must be built, or an evolutionary accident specific to biological brains? Here, we test whether a similar organization emerges in Large Language Models–another class of intelligent systems created through a very different optimization process. Using circuit analyses across N=46 tasks spanning four cognitive domains (language, formal reasoning, social reasoning, physical reasoning), we find that LLMs develop a modular architecture that mirrors the human brain: tasks drawing on the same network in humans recruit overlapping neurons in LLMs, whereas tasks drawing on different networks recruit distinct neurons. The convergent emergence of modularity in brains and neural networks suggests that it may be a fundamental property of intelligent systems.
[NLP-57] VoiceChat-TTS: A Low-Latency Continuous Speech Synthesis Model for Interactive Agents
【速读】： 该论文旨在解决现有语音语言模型在实时交互场景中存在延迟高、不支持用户抢话（barge-in）以及语音质量下降等问题，尤其针对传统基于多阶段流水线的语音到语音或语音到文本模型在降低延迟的同时难以兼顾语音生成质量与实时性。其核心解决方案是提出一种低延迟、连续流式、可实时交互的文本到语音（Text-to-Speech, TTS）模型——VoiceChat-TTS。该模型的关键创新在于直接接收大语言模型（Large Language Model, LLM）输出的文本标记流（text-token stream），通过控制令牌（control tokens）显式支持中断指令，并在无输入时生成静音，从而实现始终在线、响应迅速的语音生成；同时，该设计保持了模块化架构，并支持在语句中间实现打断而不需重置键值缓存（KV cache），有效保障了语音质量与实时交互的协同优化。
链接: https://arxiv.org/abs/2608.13831
作者: Edresson Casanova,Jaehyeon Kim,Mariana Graterol Fuenmayor,Shehzeen Hussain,Viacheslav Klimkov,Valentin Mendelev,Mikyas Desta,Paarth Neekhara,Piotr Zelasko,Chen Chen,Elena Rastorgueva,Ke Hu,Ankita Pasad,Xuesong Yang,Aya Alja’fari,Rajarshi Roy,Rohan Badlani,Jason Roche,Jason Li,Zhehuai Chen
机构: 未知
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL)
备注:
Abstract:Spoken dialogue is a natural form of human–computer interaction, yet most speech language models remain limited to turn-based operation and lack real-time adaptability, such as user barge-in. Recent duplex speech-to-speech and speech-to-text models reduce latency by replacing multi-stage pipelines, but often compromise speech quality because accurate ASR, interruption handling, and high-fidelity synthesis must be optimized jointly. We propose VoiceChat-TTS, a low-latency, continuous, and streamable text-to-speech model for interactive agents. VoiceChat-TTS is driven directly by LLM text-token streams, supports explicit interruption via control tokens, and produces silence when no textual input is available. The model enables always-on, responsive speech generation while preserving modularity and high speech quality, and it supports mid-utterance interruptions without resetting the KV cache.
信息检索
[IR-0] PriCoRec: A Privacy-Aware Cloud-Device Collaborative Framework for Ad Recommendation under Feature Constraints RECSYS’26
链接: https://arxiv.org/abs/2608.14429
作者: Dairui Liu,Zhongyi Lu,Jitao Lu,Aghiles Salah,Mete Sertkan,Roger Zhe Li,Changhong Jin,Barry Smyth,Xingsheng Guo,Ruihai Dong
类目: Information Retrieval (cs.IR)
备注: 5 pages, 1 figure. Accepted to RecSys’26
Abstract:Privacy regulations increasingly restrict cloud processing of sensitive user data (e.g., age, gender), hindering traditional cloud-only recommendation models. To mitigate this challenge, we propose a Privacy-aware Collaborative cloud-device ads Recommendation framework (PriCoRec) which personalizes recommendations while keeping sensitive features on-device. While separating recommendation into cloud-based and on-device stages enables privacy-aware deployment, naive splitting suffers from degraded shortlist quality and inefficient on-device inference due to limited private features. We therefore design a collaborative framework that comprises a cloud-based pre-ranking stage using cloud-accessible features, and an on-device ranking stage that locally incorporates highly personalized features. We introduce a diversity regularizer to pre-ranking to improve candidate quality. Moreover, to control device power consumption and computational cost, we incorporate a cloud-guided training mechanism that enhances device model performance while keeping the model lightweight. Experiments demonstrate that the proposed framework maintains strong recommendation performance while keeping sensitive features on-device.
[IR-1] MACS: A Hybrid Multi-Agent Framework for Reliable Conversational E-Commerce Recommendation
链接: https://arxiv.org/abs/2608.14068
作者: Juli Huang,Hannah Clay,Sajjad Beygi,Thomas Sarda,Negin Golrezaei,Amin Saberi
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 9 pages, 2 figures, 8 tables. Will be presenting at Stanford TrustSafety Conference, already presented at Stanford Market AI Conference
Abstract:Conversational recommendation for e-commerce is increasingly mediated by large language models (LLMs), yet many real-world deployments operate under a stricter requirement: recommendations must be drawn only from a merchant’s fixed catalog, without web search or unsupported product claims. In this setting, the main challenge is reliability under hard constraints: the system must satisfy user requirements, remain grounded in available inventory, and preserve preferences across multiple conversational turns. We present MACS (Multi-Agent Commerce System), a hybrid multi-agent framework for reliable conversational recommendation in fixed-catalog settings. MACS uses LLMs for language-facing tasks such as interpreting user requests, eliciting preferences, and generating responses, while correctness-critical operations, including product retrieval, hard-constraint filtering, brand exclusion, and progressive relaxation, are executed deterministically by the merchant agent. A session-persistent preference layer tracks constraints across turns, enabling consistent handling of budget overwrites and exclusion reversals. On a 140-query single-turn benchmark, MACS achieves the highest pass rate (87.1%) and perfect brand compliance (1.000). On a 10-scenario multi-turn benchmark, MACS achieves the strongest macro Pass@5 (72% vs. 56% GPT+Catalog / 52% Gemini+Catalog) with zero constraint drift. The advantage is sharpest on exclusion reversal (100% vs. 20% / 0%) and constraint accumulation (100% vs. 60% / 40%). Mean judged response quality is similar across systems (0.751 vs. 0.736). These results suggest that hybrid architectures combining deterministic constraint enforcement with session-persistent preference tracking provide stronger reliability-oriented performance than catalog-bound prompt-only baselines in the fixed-catalog merchant setting.
[IR-2] nderKG
链接: https://arxiv.org/abs/2608.14066
作者: Yacine Mokhtari(Lab-STICC_MOTEL, Lab-STICC, IMT Atlantique - INFO),Véra Pukhkoy,Grégory Smits(IMT Atlantique - INFO, Lab-STICC, Lab-STICC_MOTEL)
类目: Information Retrieval (cs.IR)
备注:
Abstract:Public procurement represents a major economic activity, where public institutions allocate contracts to companies through competitive tendering processes. Despite its importance, this domain remains underexplored by recommender systems, largely due to the lack of publicly available datasets capturing its complexity. In this paper, we introduce TenderKG, a large-scale knowledge graph dataset constructed from French public procurement data covering the period 2021–2023. The dataset models the procurement ecosystem through heterogeneous entities, including companies, tenders, lots, and domain-specific taxonomies of work domains, connected via rich semantic and structural relations. A key specificity of this setting is that only the awarded companies are visible, resulting in sparse explicit signals of awarded interactions. To overcome this limitation, TenderKG integrates extensive side information on the actors in the French tender market and the tenders, including textual descriptions, hierarchical classifications, and geographical features, enabling the study of knowledge-aware recommendation in a highly constrained and competitive environment. We provide detailed statistics and analyses of the dataset, highlighting its structural properties, sparsity patterns, and domain-specific characteristics. We believe TenderKG opens new research directions in bidder recommendation, knowledge graph-based recommendation, competition-aware matching, and provides a valuable benchmark for evaluating methods in real-world, high-stakes decision-making scenarios.
[IR-3] HAM-RAG : Hierarchy-Aware Multimodal RAG for Structure-Faithful Interleaved Generation
链接: https://arxiv.org/abs/2608.14032
作者: Yin Li,Ziyang Hu,Zhiyu Guo,Xiangyu Liu,Wenbin Li,Boo-Ho Yang,Rav Lawana,Ziyue Li,Wei Zeng,Fugee Tsung
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing multimodal RAG methods often flatten structured documents into isolated text and image units, weakening the source organization and local text-image logic needed for faithful evidence selection and placement. We propose HAM-RAG, a Hierarchy-Aware Multimodal RAG framework for structure-faithful interleaved generation. HAM-RAG uses document hierarchy as a grounding signal across retrieval and generation, contextualizing textual and visual evidence and preserving source position and local text-image relations in the prompt. We further introduce HAM-Bench, covering Wukong, Wiki, arXiv, and Recipe across game walkthroughs, web pages, scientific papers, and step-wise recipe documents. Across multiple backbones, HAM-RAG improves the main multimodal average by 17.3% over the strongest non-hierarchical baseline. On Wukong, HAM-RAG improves Img-CBS by 24.2% over the strongest non-hierarchical baseline, demonstrating substantially better local text-image alignment. The main experiments and ablation study together demonstrate that document hierarchy is a key grounding signal for faithful image selection, placement, and local text-image alignment. These findings highlight the value of hierarchy-aware grounding for reliable multimodal assistants that generate answers faithful to the source organization, procedural structure, and local text-image evidence of structured documents, such as technical manuals, maintenance guides, and industrial SOPs. The code is available at this https URL.
[IR-4] Residual Dominance as a Structural Account of Last-Item Reliance in Causal Self-Attention Recommenders RECSYS’26
链接: https://arxiv.org/abs/2608.14021
作者: Keito Kozaki,Keigo Sakurai,Ren Togo,Takahiro Ogawa,Miki Haseyama
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 11 pages, 6 figures. Accepted at the 20th ACM Conference on Recommender Systems (RecSys’26)
Abstract:Transformer-based sequential recommenders with causal self-attention often rely heavily on the most recent interaction at inference time, but how this behavior is structurally expressed in the representation used for prediction remains unclear. We combine prediction-time diagnostics with norm-based analysis of the full attention block. First, we show that SASRec-style models exhibit highly localized last-item reliance. We then find that, although self-attention aggregates contextual information, residual addition sharply shifts the full-block representation toward same-position contributions, which we term residual dominance. To probe this interpretation, we use inference-time residual scaling as a controlled diagnostic intervention. Changing the residual strength induces a monotonic trade-off between structural mixing and last-item reliance, while reducing residual strength recovers a subset of final-position misses for which representations at non-final positions already rank the ground-truth item correctly. Our results provide a structural account linking extreme last-item reliance to residual dominance at inference time. The code is publicly available.
[IR-5] EchoRec: Multi-Item Prediction-Empowered Generative Recommendation via Cycle-Consistent Preference Alignment
链接: https://arxiv.org/abs/2608.14011
作者: Haokai Ma,Aoqi Hu,Yueao Xing,Ruobing Xie,Yonghui Yang,Teng Tu,Lei Meng,Tat-Seng Chua
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 10 pages, 9 figures, Under Review
Abstract:Generative recommendation autoregressively generates the semantic IDs of the target item, unifying preference modeling and index retrieval within the shared token space. Recent attempts have introduced Multi-Token Prediction (MTP) into this field, yet they primarily inherit its efficiency merit, leaving its potential as dense supervision unexplored. Unlocking this potential hinges on whether future behaviors qualify as informative supervision. Our analysis reveals that future behaviors carry a semantic echo of the current one far above that of random pairs, which nevertheless decays along horizons under intent transitions, making them informative yet order-dependent signals. Motivated by this, we propose EchoRec, which empowers MTP with cycle-consistent holistic preference alignment across multi-horizon for generative recommendation. It comprises two synergistic modules. Horizon-aware Preference Generation (HPG) sequentially chains lightweight auxiliary branches upon the base recommender, where each branch conditions on its predecessor to respect preference evolution. Verifiable Holistic-Preference Alignment (VHA) further consolidates them into the holistic preference and echoes it back through cycle-consistent projectors to suppress spurious alignment, with theoretical guarantees that exclude the rank-collapse form of spurious alignment under an invertible transport, enabling the holistic preference to be retained in the decoding representation. All auxiliary components serve as disposable scaffolding discarded at inference, introducing negligible online serving overhead. Extensive experiments on three datasets demonstrate the superiority of our EchoRec, together with its naturally acquired multi-item generation ability. Our code and datasets will be available upon acceptance.
[IR-6] Content Depth Matters in Short-Video Recommendation: Rethinking the Attention Economy
链接: https://arxiv.org/abs/2608.13990
作者: Liwei Deng,Jing Jiang,Zhiwei Li,Yang Wang,Guodong Long
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 9 Pages
Abstract:Driven by the attention economy, short-video Recommender Systems (RSs) are primarily optimized to maximize user engagement by promoting videos that capture attention within seconds. These systems inherently favor shallow-content videos that are effective at attracting immediate attention. However, growing evidence suggests that prolonged exposure to such content may negatively affect users’ cognitive engagement and mental well-being, raising concerns about the long-term societal impact of the short-video platform. To tackle this challenge, this paper introduces a new metric, the \textbfContent Depth Score (CDS), to quantify the content depth of short videos. CDS measures the extent to which a video is expected to stimulate higher-order cognitive processes, using a seven-level scale grounded in established theories of cognitive psychology and learning. As an initial step toward this vision, we present \textbfSCOPE-Bench, the first benchmark for content-depth evaluation in short-video recommendation. Built upon a large-scale open-source short-video dataset, SCOPE-Bench provides CDS annotations for 150K videos, enabling systematic evaluation of RSs from a cognitive-content perspective. Leveraging SCOPE-Bench, we evaluate 13 representative RSs and reveal a consistent preference for shallow-content videos. Moreover, we find that these algorithms recommending cognitively deep content are only marginally better than random selection, highlighting a previously overlooked limitation of existing recommendation objectives. Our code and datasets are available at this https URL.
[IR-7] How retriever redundancy and diversity impact RAG effectiveness
链接: https://arxiv.org/abs/2608.13956
作者: Jonathan J Ross,Bevan Koopman,Anton van der Vegt,Guido Zuccon
类目: Information Retrieval (cs.IR)
备注:
Abstract:In RAG, while the retriever typically ranks documents by their individual relevance to the query, the generator instead produces an answer based on the retrieved documents as a whole. This paper investigates how redundancy and diversity from the retrieved document set impact the generator in terms of answer correctness. Previous work has provided a mix of findings: some showing that redundancy improves generation by reinforcing relevant information, others that LLM-based paraphrasing of the same content may be beneficial. Many of these studies did not control for confounding factors like whether the documents contained the exact answer or not, and if parametric knowledge plays a role. We conduct a carefully controlled experiment investigating three key scenarios of retrieved document sets: 1) Duplicate (exact copies of the same document), 2) Paraphrased (LLM rephrased versions of one document) and 3) Diverse (documents from different genres each containing relevant information in different forms). We control for which documents contain the answer in exact match or rephrased form. Evaluation is done with FictionalQA, a synthetic, fictional question-answer dataset that ensures the LLM generator prior knowledge cannot answer the question; the answer must come from retrieved documents. We show that duplicate redundancy and LLM paraphrasing does not significantly improve answer correctness. However, providing diverse documents is highly beneficial, improving answer correctness by 17%-47%. We further show this improvement is driven by diverse forms of document genre (news, blogs, etc.) alone and not a consequence of more relevant answer being available to generator. Our findings help to direct more attention to how new retrieval methods might improve RAG by catering to the generator preference for diversity in retrieval results. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2608.13956 [cs.IR] (or arXiv:2608.13956v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2608.13956 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-8] Whose Posts Get Ranked: Identical-Text Exposure Gaps in Bluesky Custom Feeds RECSYS2026
链接: https://arxiv.org/abs/2608.13879
作者: Yipeng Wang,Mohit Singhal
类目: ocial and Information Networks (cs.SI); Information Retrieval (cs.IR)
备注: To appear at The 20th ACM Recommender Systems Conference (RecSys 2026), please cite accordingly
Abstract:Bluesky lets users deploy custom feeds, independently operated recommendation algorithms that the platform serves alongside thousands of others. This paper investigates how evenly these feeds treat posts with the same text. To measure this, we take repeated snapshots of the Top-50 lists that 1,366 public feeds return, and we group posts with identical text, from different authors, that were created before the same list response and closely matched in age. Exposure diverges widely inside these matched sets, which span 250 feeds: in 33% of sets, one copy appears on the list while another does not. Fixed-effects regressions show that this divergence is associated with the author’s history on the specific feed. Authors new to a feed receive less exposure for the same text (-0.061 in reciprocal-rank weight), while authors whose posts the feed has returned before receive more. A new author with more followers than the competing author still loses 74% of head-to-head comparisons. Media and post-type features show no detectable association after multiple-comparison correction. These results are early evidence that access to many independent feeds is not enough to give identical texts equal exposure.
[IR-9] Predicting Custom-Feed Returns for New Bluesky Posts: A Prospective Study RECSYS2026
链接: https://arxiv.org/abs/2608.13874
作者: Yipeng Wang,Mohit Singhal
类目: Information Retrieval (cs.IR); Computers and Society (cs.CY)
备注: To appear at The 20th ACM Recommender Systems Conference (RecSys 2026), please cite accordingly
Abstract:The conventional approach to cold-start recommendation addresses new users or newly introduced items. Bluesky custom feeds create a different setting: independently operated feeds filter content from a shared public stream. In this setting, newly published posts are the cold-start objects, while the feeds serve as candidates. We propose a cold-start routing task in which a newly ingested public post is the query and all rankable feeds in the monitored panel are ranked according to whether each will subsequently return it. We build a still-evolving collect-first, label-later benchmark dataset. The collected dataset covers a fixed panel of 5,000 monitored feeds and contains 17.804 million public posts, 1.865 million observable post–feed return records, and 625,083 valid feed polls. The labels record whether a post is observed among a feed’s AppView Top-50 results in at least one poll during the 24 hours after publication. The current experiments use two disjoint 24-hour test folds, each paired with a 24-hour training window and separated by a 24-hour outcome-availability gap. Evaluation is conditional on the 602,186 test posts that have at least one positive observed label and satisfy the metric eligibility criteria; these posts account for 9.04% of all 6,661,658 test posts. Across the two folds, LambdaRank achieves the best equal-fold mean values among the evaluated models: 0.7361 for capped Recall@10, 0.6127 for NDCG@10, and 0.7749 for Hit@10.
[IR-10] he MPB Corpus: A Dataset of Melody Rhythm Harmony and Melody-Harmony Relationships in Brazilian Popular Music
链接: https://arxiv.org/abs/2608.13842
作者: Carlos de L. Almada,Hugo T. de Carvalho,Felipe D. Martins
类目: ound (cs.SD); Digital Libraries (cs.DL); Information Retrieval (cs.IR); Audio and Speech Processing (eess.AS)
备注: 22 pages, 13 figures
Abstract:This paper presents the MPB Corpus, a collection of 500 musical pieces encoded across four musical parameters: melodic contour, melodic rhythm, harmony, and the relationship between melody and harmony. It constitutes the most comprehensive and detailed dataset to date for computational musicology of Brazilian music. To support the encoding process, we introduce specific analytical models designed to capture rhythmic and melodic information with precision, alongside tailored visualizations and metrics summarizing key musical parameters. Finally, we provide a brief qualitative exploratory analysis of the dataset, illustrating its potential to both formulate and systematically address musicological questions concerning the genre.
[IR-11] AdsWorldEngine: A Self-Evolving Conversational Advertising Agent through Orchestrator and Tool Coevolution
链接: https://arxiv.org/abs/2608.13833
作者: Simiao Zuo,Chenhui Xu,Yimeng Jia,Qiang Lou,Jian Jiao,Denis Charles
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Conversational advertising aims to deliver useful ads within multi-turn assistant interactions. Unlike conventional query-based advertising, where the user’s intent is often expressed in a short standalone query, conversational ads must infer latent commercial intent from the current user query, the assistant response, and dialogue history while also deciding whether an ad would be helpful rather than intrusive. We propose AdsWorldEngine, an agentic framework for conversational advertising. AdsWorldEngine uses an Opportunity Gate to determine whether ads should be shown, an Orchestrator to generate commercial intents, call advertising tools, and construct a top-3 ad slate, and an Evaluator to score delivered ads for offline optimization. The central contribution is an iterative actor-tool training procedure: we first train the Orchestrator with supervised fine-tuning and agentic reinforcement learning, then use high- and low-reward rollouts to construct preference data to train tools. This creates a self-improving loop in which the system learns not only how to use advertising tools, but also how to improve them from rewarded behavior. To support subjective production decisions, we introduce label grounded judgment modeling, which trains judgment models from human labels collected under explicit guidelines. It enriches labels with thinking traces, filters inconsistent rationales through reflection, and further optimizes binary judgments with a cost sensitive GRPO variant that preserves asymmetric reward gaps. Offline, AdsWorldEngine improves diversity by 60% and relevance by 80% over the current production ad delivery system. In an online A/B test, it increases RPM by 22% and ads coverage by 74%.
[IR-12] Do AI chatbots find what experts would? Effects of model user role and sample size on study retrieval for medical questions
链接: https://arxiv.org/abs/2608.13786
作者: Qingfang Liu,Qiao Jin,Joe D. Menke,Thorsten Kahnt,Zhiyong Lu
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language model (LLM) chatbots are increasingly used to answer clinical questions with citations to relevant clinical studies. Prior research has largely focused on citation fabrication, leaving a gap in evaluating the quality of retrieved studies and the factors driving their selection. In this study, we evaluated three general-purpose LLM chatbots: Claude Sonnet 5, Gemini 3.1 Pro, and ChatGPT GPT-5.5. We prompted the models with clinical questions adapted from 20 review questions in Issues 6 and 7 of the 2026 Cochrane Database of Systematic Reviews, simulating patient, clinician, and evidence-synthesis researcher roles. Each chatbot was queried under each user role with four independent repetitions, yielding 720 responses. Each chatbot was asked to support its answers with primary clinical citations, which we benchmarked against the included and excluded study sets of the Cochrane reviews. On average, a chatbot response retrieved 39.2% \pm 29.8% of Cochrane included studies, while citing 5.0% \pm 9.4% of excluded studies. Recall of Cochrane included studies varied significantly by model and user role. ChatGPT achieved higher recall than Claude or Gemini (63.1% \pm 29.5% vs. 37.0% \pm 23.8% vs. 17.3% \pm 13.1%; p=2.0\times10^-5 ). The researcher role yielded higher recall than the clinician or patient roles (42.8% \pm 30.8% vs. 38.6% \pm 28.9% vs. 36.1% \pm 29.3%; p=2.0\times10^-5 ). Controlling for publication year, citations per year, and open-access status, sample size was the only independently significant predictor of retrieval (odds ratio 1.80 per 1-unit increase in log sample size, 95% CI 1.37-2.36, p=2.34\times10^-5 ). These findings suggest that while LLM chatbots can retrieve some studies identified by expert reviewers, their performance varies by model and user role, and they exhibit a bias toward clinical trials with larger sample sizes.
人机交互
[HC-0] Visualizing Uncertainty in Non-linear Projections with Ensembles
链接: https://arxiv.org/abs/2608.14513
作者: Kai Nylund,Michael Correll,Lace Padilla
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Widely used non-linear dimensionality reduction (NLDR) methods such as UMAP and t-SNE are stochastic–repeated runs on the same data can produce different low-dimensional projections. In this paper, we explore two problems related to projection variability: on some datasets clusters, structure, and outliers may change run-to-run, and on others projections can be extremely stable when overfitting noise. To address the first problem, we propose visualizing the median of multiple NLDR outputs rather than relying on individual projections. To address the second, we perturb input data before creating consensus embeddings. We find that taking the median of multiple projections performs comparably to individual runs on multiple quality metrics, while increasing perturbation emphasizes global over local structure. We show through a set of exploratory visualizations that even relatively simple ensemble presentations can be used to better communicate the reliability of projection patterns.
[HC-1] From Style Replication to Style Exploration: Enabling Art Style Exploration with Analyze-Experiment-Resituate Framework
链接: https://arxiv.org/abs/2608.14405
作者: Wen-Fan Wang,TsaiHsuan Lin,Chi-Lan Yang,An-Ru Cheng,Bing-Yu Chen
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: Accepted to UIST '26
Abstract:Art style is a signature of professional digital artists that develops through repeated experimentation, reflection, and adaptation. While generative AI (GenAI) can reproduce styles with high fidelity, current tools provide limited support for exploring new stylistic directions and may encourage style replication over exploration. To address this gap, we propose Analyze-Experiment-Resituate (AER), a framework for AI-assisted style exploration derived from interviews with 10 professional digital artists. Rather than prioritizing visually appealing outputs alone, AER supports three core practices of style exploration, including interpreting references, trying out stylistic possibilities, and reflecting on how emerging styles may be received. Specifically, AER enabled artists to (1) analyze artworks into interpretable stylistic elements, (2) have controllable experimentation guided by their own choices, and (3) resituate emerging styles through simulated social perspectives. We implemented AER in a prototype system and evaluated it in a controlled study with 16 artists. Compared with a direct style-transfer workflow, AER increased artists’ agency and reflection as they pursued new stylistic directions. A two-week field study with four artists revealed how the AER framework influenced daily style exploration, such as reflection, experimentation, and stylistic decision-making at each stage. We discuss opportunities and challenges in designing AI-assisted style-exploration workflows, and outline implications for future artistic support tools.
[HC-2] Epistemic Tensions: Reframing A Visualization Co-Design through Entanglement Theory IEEE-VIS
链接: https://arxiv.org/abs/2608.14364
作者: Wei Wei,Foroozan Daneshzand,Zezhong Wang,Erica Mattson,Jenny Farkas,Sarah Storteboom,Charles Perin,Sheelagh Carpendale
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 10 pages, 5 figures, accepted by IEEE VIS BELIV workshop
Abstract:In this work, we present how employing the lens of entanglement helped us examine and reframe epistemic tensions arising in a visualization co-design project. Entanglement theory challenges traditional assumptions in the visualization research community by emphasizing that knowledge is not produced through linear, isolated processes, but is inherently entangled with phenomena and apparatuses. While this perspective offers a compelling critique of conventional research practices, its practical value for visualization research remains underexplored. We apply the entanglement lens to examine and reframe the epistemic tensions that emerged in a longitudinal community-based visualization co-design project. Our experience shows that the entanglement perspective not only provides a richer understanding of these tensions, but also helps transform them into generative opportunities for methodological and theoretical reflection. Applying this lens enabled us to critically interrogate the language used in research, to develop a more nuanced understanding of visualization co-design, and to surface ``dark sides’’ of conventional visualization design pipelines. These contributions illustrate the practical value of embracing entanglement as an epistemological lens for visualization research.
[HC-3] Intelligent Detection of Mechanical Electrical and Plumbing (MEP) Metrics Based on 2D Floor Plans
链接: https://arxiv.org/abs/2608.14317
作者: Tarandeep Singh Mandhiratta,ANK Zaman,Abdul-Rahman Mawlood-Yunis
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:This research developed a neural network-based model to extract various information from 2D floor plans. We detect lighting symbols, identify the appropriate type of light, and extract the associated texts with lights. The study aims to enable efficient floor designing and determining the number and type of lights needed per floor, i.e., allow efficient design and estimate the power requirement of the floor plan. The model was developed using Mask RCNN as the base. The images were annotated and converted into a Coco data format for training the model. The model achieved bbox_mAP and segm_mAP values of 0.7596 and 0.7111, respectively. It also performed well at different IoU thresholds, i.e., with bbox_mAP 50 and segm_mAP 75 values of 0.9850 and 0.9219, respectively. The developed model will help various industries, such as architecture and construction, to improve design time and create efficient workflows by automatically detecting Mechanical, Electrical, and Plumbing (MEP) objects from floor plans, and it is the first step towards building tools that will help energy-efficient building design.
[HC-4] Human and Artificial Intelligence - Promoting Trustworthy and Understandable Collaboration
链接: https://arxiv.org/abs/2608.14291
作者: Gilbert Drzyzga
类目: Human-Computer Interaction (cs.HC)
备注: Comments: 19 pages, dual-language (English and German)
Abstract:Methods of Artificial Intelligence (AI) enable the personalization of information for individual user experiences in many domains; however, they can also conflict with established design principles, e.g., due to uncertainties regarding the real world. Building trust and understanding can serve as an approach to create a more balanced relationship between humans and AI. Building upon a pilot study, an online survey was conducted to investigate 12 individual aspects related to the topics of explainability and controllability. The results indicate that both topics, despite their different and numerous facets, are generally perceived as important by respondents; simultaneously, however, a wide dispersion of opinions is frequently observed. This could be an indication that, alongside a fundamental consensus, individual perspectives, technical knowledge and understanding, context-specific factors, or personal experiences play a role in the perception of such systems.
[HC-5] Designing Mobile and Wearable Sensor-Fused Conversational Agents for Health and Wellbeing
链接: https://arxiv.org/abs/2608.14273
作者: Hansoo Lee,Pablo Fonseca,Md Haseen Akhtar
类目: Human-Computer Interaction (cs.HC)
备注: 6 pages, 3 figures. Accepted as a Tutorial at the 28th International Conference on Mobile Human-Computer Interaction (MobileHCI '26)
Abstract:Mobile and wearable devices increasingly collect continuous wellbeing data, including sleep, activity, heart rate, stress, blood glucose, and blood pressure. Yet access to such data does not automatically help people interpret their condition or change behavior. Many health applications remain dashboard-first, presenting charts, thresholds, goals, and alerts while leaving users to decide what a change means and what action should follow. Conversely, generic LLM-based conversational agents (CAs) can provide fluent advice, but without personal sensor grounding, they cannot detect individualized patterns or provide contextual guidance. This three-hour tutorial teaches participants how to move from passive monitoring to actionable wellbeing dialogue. Participants examine a dashboard that combines wearable health-data visualization with conversational-agent feedback, then use Wearable Sensor-Dialogue Wellbeing Agent Studio (WSDWAS) to simulate wearables, generate sensor snapshots, configure agent personas and prompt blocks, and compare dialogue styles. Grounded in Positive Computing, the tutorial emphasizes autonomy, competence, privacy, safety, and boundaries between wellbeing support and medical advice.
[HC-6] Physics-Bounded mmWave Sensing for Schedulable Privacy-Preserving Human Pose Estimation
链接: https://arxiv.org/abs/2608.14176
作者: Shuntian Zheng,Hongyang He,Jiaqi Li,Xiaoman Lu,Doeon Kim,Jae-Ho Choi,Jin Zeng,Shuai He,Yu Guan
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Millimeter-wave (mmWave) is a promising modality for human pose estimation (HPE) in mobile deployments with strong privacy requirements and limited resources, such as fall detection in bathrooms or activity monitoring in bedrooms, where cameras are inadmissible and computationally demanding processing is infeasible. Although mmWave signals naturally confine human reflections to compact, physically bounded regions, the algorithmic foundations of existing systems fail to provide deterministic execution and accuracy guarantees. They either process the full spectrum uniformly, resulting in unpredictable latency that varies across different scenes, or apply lossy compression that discards vital pose structures. To address this, we present PRISM, a framework that exploits the spatial concentration of RF reflections to achieve schedulable edge HPE. PRISM introduces three core components: 1) Physics-Bounded Integral Processing (PBIP), which restricts computation via constant-time integral queries; 2) Physics-Adaptive Instance Proposal (PAIP), which decomposes scenes involving multiple people into bounded local subproblems; and 3) Deadline-Aware Operation Profiles (DAOP), which provide offline-verified worst-case bounds for runtime quality-latency trade-offs. We evaluate PRISM on four public datasets spanning diverse radar configurations, reporting physical-bound and pose-accuracy measurements across this suite and examining deadline-aware scheduling on multi-person recordings together with an additional single-person set. Under single-threaded isolated execution, PRISM reduces 99th-percentile latency by 24%–58% relative to baselines that miss the deadline, records a 0.0% miss rate on the evaluated traces, and attains the highest pose accuracy among deadline-feasible configurations, providing a practical route toward schedulable mmWave sensing on mobile edge hardware.
[HC-7] Act2Intention: A Benchmark For Developing Active Mobile Agents Through Inferring User Intention from GUI Actions
链接: https://arxiv.org/abs/2608.14132
作者: Xiaokai Yan,Jingtao Ding,Yong Li,Zhiwen Yu
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Mobile GUI Agents powered by multimodal large language models (MLLMs) show promise in human-computer intelligence. However, current research primarily focuses on reactive task execution while lacking a comprehensive understanding-prediction-execution process for user intentions, which are the core requirements of active agents. In this paper, we propose the Act2Intention framework that builds an active mobile agent by integrating understanding, predicting user intentions, and executing decisions. First, we construct the Act2Intention Bench through data collection and validated generation, comprising 72,511 intentions and over 700,000 actions across 52 apps, thereby establishing the first benchmark for evaluating proactive agents via continuous intention-action trajectories. We further develop the Act2Intention Agent, achieving proactive services through Proactive-oriented Intention Understanding, Personalized Proactive Intention Prediction, and Experience-guided Intention Execution. Experimental results show that supervised fine-tuning on Act2Intention Bench yields absolute improvements of +32.0 Acc-S, +10.25 Acc-S, and +6.9 SSR points over non-fine-tuned counterparts under the same agent framework for intention understanding, prediction, and execution, respectively. This success underscores the necessity and value of the Act2Intention Bench, which establishes a standardized platform for developing and evaluating proactive agents and consequently paves the way for research on intention-driven human-computer interaction.
[HC-8] AlignFace: Human-Aligned Face Similarity Metric with Interpretable Concept Relations ACM-MM26
链接: https://arxiv.org/abs/2608.14130
作者: Ying Huang,Wencan Zhang,Brian Y. Lim
类目: Multimedia (cs.MM); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: 10 pages, 10 figures, 2 tables, ACM MM 26
Abstract:Computer vision models for generated facial content, such as face editing and privacy protection, increasingly affect people, requiring similarity metrics that serve as faithful proxies for human perception. While perceptual evaluation has progressed from signal-based heuristics to representation-based metrics, current approaches are limited to behavioral modeling without cognitive alignment. They rely on implicit and spurious relations while assuming a universal observer, failing to account for inherent variations across diverse human populations. This leads to inaccurate evaluative models of stakeholders and misleading guidance for generative model debugging. Rather than treating perception as a black box, we leverage scientific findings from cognitive psychology of human face similarity perception: dependence on facial featural and configural attributes, nonlinear psychophysical response scaling, and own-group biases. We introduce the FACETS dataset and propose AlignFace, an interpretable, human-aligned, face similarity metric that encodes these cognitive principles through ante-hoc modeling. It employs visual-language modeling (VLM) to encode paired face images and text-based attributes, gated cross-attention (CA) to extract attribute-specific facial difference representations, concept bottleneck modeling (CBM) to constrain reasoning via interpretable face attributes, and neural generalized additive model (GAM) to model their nonlinear influence. Experiments found AlignFace significantly improves alignment with human subpopulation perceptions compared to baseline metrics, including recent domain-free learned perceptual metrics. By bridging learned representations and human cognitive processes, this work enables more transparent and aligned perceptual evaluation metrics for face images.
[HC-9] Search or Chat? Comparing How We Learn About Debated Topics
链接: https://arxiv.org/abs/2608.14113
作者: Ran Yu,Alisa Rieger,Rabia Karatoprak Ersen,Jiqun Liu
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:As large language models (LLMs) become more integrated into everyday information platforms, chat-based systems are emerging as a popular alternative to traditional web searches, especially for informational search and informal learning tasks. Despite this shift, little is known about how different tools affect learning outcomes. Our work aims to improve the understanding of how chat-based information access supports and impacts learning performance in informal learning settings. In this paper, we present the results of a crowdsourcing user study (N = 194) that compares learning about debated topics using a traditional search interface versus an LLM-powered chat interface. Through our analysis of learning outcomes, user characteristics, and interaction patterns, we found no significant differences in user learning gain or critical reflection on our study tasks. Our observations from the analysis of further exploratory variables suggest that, in the context of longstanding debated topics, user characteristics such as their attitude strength and level of intellectual humility might be more important in shaping immediate learning outcomes than the information access tool.
[HC-10] AppLooper: An Agent ic Application Engineering Loop for Accountable Release with Virtual-User Feedback
链接: https://arxiv.org/abs/2608.14093
作者: Zihong He,Chen Liang,Hai-Ning Liang
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Much existing research on coding agents organizes application development as an iterative loop of requirement interpretation, implementation, tool execution, evaluation, and repair. As these loops run longer, requirements may drift; users may lose awareness of the current state and rationale for changes; and generated applications may remain insufficiently grounded in target users’ contexts and needs. Application engineering therefore requires a mechanism connecting owner intent, target-user experience, development changes, and responsibility for release. We present AppLooper, a human–coding-agent–virtual-user application engineering loop for accountable release. An application owner confirms frozen requirements, supplies feedback, inspects candidates, and retains final release authority. A development agent produces and revises versioned candidates. A virtual-user agent cohort executes interface scenarios grounded in target users and contexts of use. Besides, an owner-intent simulation agent retests only requirements, constraints, and feedback explicitly confirmed by the owner, abstaining when evidence is insufficient. A testing agent performs read-only developmental checks by reproducing reported failures, running existing regression tests, and exercising the current candidate through its browser interface. The orchestration layer groups the resulting findings and routes them into development revision, targeted retesting, and owner inspection. AppLooper binds requirements, feedback sources, interface targets, development changes, retesting outcomes, owner interactions, and release decisions to specific versions. It thereby extends sustained coding-agent iteration into a traceable and reviewable lifecycle in which humans retain final responsibility for release. Source code is available at this https URL.
[HC-11] Musical Mirrors: The LLM as Sounding Board in Songwriting
链接: https://arxiv.org/abs/2608.13944
作者: Xiao Xiao
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: In Proceedings of The First Reflection in Creative Experience (RiCE) Workshop (RiCE W1) arXiv:2607.24558
Abstract:This paper examines a use of AI in creative practice as an interpretive sounding board for human-generated material, rather than the more familiar pattern of AI generation followed by human curation. Through the lens of resonance as theorized by Hartmut Rosa, I present a first-person case study of songwriting from July 2025 to March 2026, drawing on 16 original pieces in English, French, and other languages along with piano solos. I describe a configuration in which resonance is not located between user and model, but in the author’s deepening contact with their own material, mediated through the model. This kind of resonance was supported rather than inhibited by AI when sounding-board behavior was cultivated through sustained calibration by the user. Two failure modes appeared when calibration was absent: sycophantic drift and magical overinterpretation. This account suggests both the potential and the risks of AI as an interpretive partner in creative practice.
[HC-12] DepressionAgent : Reading Listening Seeing and Deliberating Multimodal Evidence for Depression Risk Assessment
链接: https://arxiv.org/abs/2608.13891
作者: Fangjie Zhu,Haifeng Lu,Sicheng Zhao,Runhao Zeng,Xiping Hu
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Multimodal depression risk assessment requires jointly interpreting textual, acoustic, and visual cues that are often subtle, non-specific, context-dependent, and potentially inconsistent across modalities. Existing multimodal approaches predominantly learn latent representations through feature fusion, leaving the evidence underlying a prediction and the treatment of cross-modal disagreement largely implicit. We propose DepressionAgent, an evidence-centric agentic framework that transforms multimodal depression assessment from implicit feature fusion into explicit evidence deliberation. DepressionAgent first converts textual, acoustic, and visual inputs into modality-specific evidence, and then organizes self-report and behavioral evidence into parallel support–challenge deliberation branches. Cross-modal arbitration explicitly examines agreement and disagreement between the two branches, with conflict reflection revisiting inconsistent assessments before decision making. A subsequent risk reflection mechanism provides an independent textual second opinion for initially low-risk cases to reduce potentially missed risk signals. Without depression-specific supervised training or parameter fine-tuning, DepressionAgent achieves competitive performance on multiple public benchmarks. Extensive ablations, cross-model evaluations, qualitative analyses, and clinician assessments further demonstrate the effectiveness and inspectability of the proposed framework.
[HC-13] Engineering Signals of Human-AI Collaboration in the Agent ic Coding Era: A Longitudinal Analysis of 33228 Pull Requests from vLLM and SGLang with Implications for Biomedical AI Agents and Bioinformatics Pipeline Developmen
链接: https://arxiv.org/abs/2608.13884
作者: Jiada Li,Xuesong Ye,Olamide Olowoniyi
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: 24 pages, 9 Figures
Abstract:The rapid adoption of AI coding assistants and autonomous agentic development systems has coincided with major changes in the pace and structure of open-source software engineering. Yet empirical longitudinal evidence of these changes at the team level remains limited. We present a descriptive longitudinal analysis of seven engineering metrics: pull request (PR) throughput, cycle time, contributor diversity, PR comment density, merge rate, new-author participation, and PR size. Metrics were computed from all merged PRs in two high-velocity AI infrastructure repositories, vLLM (February 2023-June 2026; 18,290 PRs) and SGLang (January 2024-June 2026; 14,938 PRs). We segment development into four eras aligned with major changes in AI-assisted software development and examine human- and bot-authored activities. Both projects show substantial increases in development velocity and AI-developer collaboration signals. PR throughput increased 21x in vLLM and 17.9x in SGLang, while bot-authored PRs accounted for less than 0.2% of this growth, indicating that the increase was overwhelmingly human-driven. In the latest era, median cycle time was 1.04 days for vLLM and 0.62 days for SGLang, while P90 cycle times reached 16.8 and 14.3 days, respectively. Monthly unique authors increased steadily in both projects, suggesting broader contributor participation. PR comment density increased 4.2x in vLLM and 3.8x in SGLang, with bot comments contributing an estimated 15-20% of the increase. In contrast, PR size remained relatively stable across eras. Overall, AI-assisted development is associated with higher throughput, broader contributor participation, and increased AI-developer collaboration signals in high-velocity open-source software development.
[HC-14] Architecture and Affordances of PLAUD: Performative Latents and Unsupervised DDSP
链接: https://arxiv.org/abs/2608.13724
作者: Błażej Kotowski,Frederic Font
类目: ound (cs.SD); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: AI Music Creativity 2026
Abstract:PLAUD (Performative Latents and Unsupervised DDSP) is a neural synthesizer and Max for Live instrument for live electronic music, built on NoiseBandNet and trained on small personal sound corpora. We present its architecture, combining a variational DDSP synthesis model, latent smoothing, multi-scale spectral and adversarial losses, and an optional transformer prior, alongside a set of bending operations that intervene directly in the synthesis chain: component limiting, waveshaping, and prior feedback. The Max for Live interface exposes control generation, trajectory sampling, and modulation as primary modes of interaction. Throughout, we thread an affordance analysis arguing that the system’s performative character follows from architectural decisions rather than being designed on top of them. The paper contributes both a technical account of the system and a situated affordance analysis of its role in live electronic music performance.
[HC-15] he Capturing and Logging Ecological Virtual Experiences and Reality (CLEVER) - Job Simulator Dataset
链接: https://arxiv.org/abs/2608.13715
作者: Qidi J. Wang,Xiaozheng Wang,Akhilesh M. Anand,Veera V. Pala,Rohan V. Penmetsa,Ryan P. McMahan
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Virtual reality (VR) motion tracking and interaction data has become increasingly recognized as valuable for machine learning experiments for a variety of purposes, including predicting user identities, predicting user attributes like gender and age, predicting retention and learning, and more. However, there exist a limited number of publicly accessible VR motion datasets. In this paper, we present a new open-source dataset of 95 participants playing the SteamVR game Job Simulator. Additionally, we review existing datasets, detail our study procedure, describe our data collection process, list attributes of our dataset, and suggest future work, impact, and applications.
[HC-16] FabDreamer: Exploring the Image-to-Physical Workflow Through AI-Assisted Layered Fabrication
链接: https://arxiv.org/abs/2608.13665
作者: Chenfeng Gao,Zeya Chen,Anjie Yang,Karan Ahuja,Danli Luo
类目: Human-Computer Interaction (cs.HC)
备注: 16 pages, 10 figures
Abstract:Generative AI lets anyone create rich visual content in seconds, yet translating that content into a physically fabricable artifact still demands manual decomposition, occlusion repair, and structural verification that most tools leave entirely to the user. We present FabDreamer, an image-to-physical system that carries an image to fabrication-ready SVGs through three stages with deliberately staged AI initiative: (1) AI leads decomposition into depth-ordered layers, (2) assists on demand during creative editing with realtime 3D preview, and (3) advises on structural integrity before export. We instantiate this workflow for layered laser-cut art and evaluate it through three rounds including a formative analysis, an early prototype user evaluation (N=13), and a cross-domain practitioner study with specialists from 6 fabrication domains (N=6). Our findings show that physical awareness during design opens creative opportunities beyond error prevention, that practitioners appropriate the system’s generic geometric operations for their own domains, and that the fabrication agent covers geometry-readable constraints while domain knowledge remains with the maker.
[HC-17] owards Scaling Qualitative Analysis of Video Data
链接: https://arxiv.org/abs/2608.13594
作者: Shiyi He
类目: Multimedia (cs.MM); Human-Computer Interaction (cs.HC)
备注:
Abstract:Scaling qualitative video analysis is difficult as studies grow. This paper presents QualiVision, a design probe examining how an interactive, spreadsheet-backed workspace can support video-based qualitative analysis. By integrating video, transcripts, coding streams, preliminary reports, heuristic visualization and AI support, QualiVision aims to help researchers preserve evidence, compare interpretations, and conduct iterative, reflexive sensemaking as their analysis evolves.
[HC-18] Context Aware AI Assistant and AR Interface for Lunar Extravehicular Activity (EVA) Procedural Guidance SIGGRAPH2026
链接: https://arxiv.org/abs/2608.13589
作者: Rodrigo Gallardo,Qilmeg Doudatcz,Ganit Goldstein,Ilkyaz Sarimehmetoglu,Sergio Mutis,Alexander Htet Kyaw,Anita Lin,Clara Emmerling,Berfin Ataman,Skylar Tibbits
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 4 pages, 4 figures, 1 table. Accepted to ACM SIGGRAPH 2026 Conference Posters, July 19-23, 2026, Los Angeles, California, USA
Abstract:As human space exploration returns to the Moon, astronauts need rapid access to procedural information during extravehicular activities (EVAs), where attention is divided across navigation, repair tasks, tool handling, and environmental risk. The challenge is not the absence of information, but surfacing the right information at the right moment. We present GAIN-AI (Guided Assistant for Intelligent Navigation), a context-aware AI assistant and minimal heads-up interface for procedural guidance in simulated lunar EVA. The system operates in two layers. The first grounds a large language model with structured context: EVA procedure documents, live telemetry data, and error-handling protocols encoded as JSON. The second restructures that output into three compact units for AR display: Goal, Task, and Verification. Evaluated on 111 synthetic EVA scenarios, the system scores 10.0/10 on nominal conditions and 8.15/10 on single-fault scenarios, with performance degrading on multi-fault and boundary-threshold cases.
[HC-19] Student-ChatGPT Interaction Visible: Designing a Teacher Dashboard for EFL Writing Education
链接: https://arxiv.org/abs/2608.13587
作者: Minsun Kim,Seon Gyeom Kim,Suyoun Lee,Yoosang Yoon,Junho Myung,Haneul Yoo,Jieun Han,Hyunseung Lim,Yoonsu Kim,So-Yeon Ahn,Juho Kim,Alice Oh,Hwajung Hong,Tak Yeon Lee
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:We present a Prompt Analytics Dashboard (PAD) for teachers that can traces student-LLM interactions from EFL writing classes. PAD can show student prompt-response exchanges with LLM chatbot and English essay writing revision histories to support data-informed instruction and visibility in classes. Through two iterative co-design sessions with six EFL instructors, we distilled a compact trace taxonomy (misuse signals, goal-alignment cues, revision effort) and instantiated three interface views (overview, week/outcome filter, drill-down with evidence snippets). This pipeline summarizes potential misuse and alignment at class/cohort levels and attaches micro-explanations to reduce over-surveillance. Instructors reported reduced scanning burden and clearer timing for interventions.
[HC-20] he Tool-to-Entity Threshold: Parasocial Dynamics of Personalised AI Agents in Shared Social Spaces
链接: https://arxiv.org/abs/2608.13586
作者: Leonardo Borges(Eigenstack Pty Ltd),Asif Q. Gill(University of Technology Sydney)
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 24 pages, 3 figures, 2 tables
Abstract:As AI agents acquire names, avatars, phone numbers, and persistent personalities, they increasingly inhabit the same messaging platforms and group conversations as the humans they serve, crossing from tools their users operate into social entities their users relate to. This reclassification carries under-explored consequences for consent, emotional attachment, and group dynamics, yet no existing framework identifies the specific infrastructural markers that cause it. We propose the identity marker framework: six design variables (naming, visual identity, contact presence, personality derivation, social co-presence, and persistence) that collectively trigger a discrete psychological reclassification, operating independently of model capability. The framework is derived inductively and read through two established lenses: parasocial interaction theory (Horton Wohl, 1956) and the Computers Are Social Actors paradigm (Nass et al., 1994). We further identify four novel dynamics that arise when such an agent participates in existing group conversations: bidirectional information asymmetry, delegation legibility, social norm negotiation, and parasocial contagion. Our method is autoethnographic: the first author built and deployed a personalised agent into WhatsApp and Signal group chats over two months of live use, supplemented by twelve structured interviews with the group members who encountered it. We treat this as a preliminary qualitative evaluation of the framework, with controlled experimental validation left to future work. The strongest design implication runs through all four dynamics: in shared social spaces, consent to an agent’s presence is categorically distinct from consent to its processing of the messages exchanged there, and existing consent frameworks collapse the two.
[HC-21] FactorFlow: A Visual Analytics Workspace with Large Language Model-Assisted Interpretation for Factor Analysis
链接: https://arxiv.org/abs/2608.13585
作者: Justin Philip Tuazon,Joemari Olea,Richelle Ann Juayong
类目: Human-Computer Interaction (cs.HC); Methodology (stat.ME)
备注:
Abstract:In exploratory factor analysis (EFA), one typically aims to extract and describe a small number of factors (i.e., latent variables) based on the relationships among numerous manifest variables (i.e., directly observable variables). In practice, performing EFA entails examining different factor models (and rotations) to identify the underlying latent structure. Now, the primary criterion for evaluating a factor model is interpretability. That is, the preferred model is the one that yields a meaningful, coherent, and theoretically defensible factor structure. However, gauging a model’s interpretability is not a trivial task, as it is subjective and often requires keeping track of large amounts of information simultaneously. Because of this, researchers typically employ various visualizations to interpret models and determine the “best” one. Hence, we introduce FactorFlow, a visual analytics workspace for performing EFA end-to-end. Using FactorFlow, one can fit and rotate factor models, perform model diagnostics, and more. The main component of the tool is a dashboard with a comprehensive set of interactive visualizations, where a user can easily dissect a factor model and even compare two models side-by-side at the same time. Moreover, several large language models are integrated with FactorFlow, enabling the user to generate and assess automated factor interpretations written in natural language. With multiple views and readily available calculations, FactorFlow can enable the researcher to efficiently and effectively understand factors and ultimately, perform EFA. Finally, we conducted a usability study to identify strengths and weaknesses, and capture feedback to incorporate in the app.
[HC-22] UltraArUco: A Lightweight Multilingual Library And Framework With Low-Latency Real-Time Marker-Based Tracking System For Mobile AR Interaction
链接: https://arxiv.org/abs/2608.13584
作者: Mikhail Kiselev,Aleksandr Marukhin,Ivan Snegirev,Elizaveta Semenyakina,Miguel Altamirano Cabrera,Dzmitry Tsetserukou
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:UltraArUco - a lightweight multilingual library and framework for low-latency, real-time marker-based tracking in mobile augmented reality. Unlike standard OpenCV-based implementations, UltraArUco introduces an optimized multilingual wrapper that reduces per-frame latency by five times, while maintaining high accuracy. Distributed Wi-Fi architecture provides portability, connects a mobile device (camera input) with a PC-based visual application, enabling responsive interactions. The framework is validated through an interactive piano simulation, where static ArUco markers on keys enable occlusion-based note triggering, and hand-mounted markers provide spatial gesture recognition. UltraArUco’s system requirements make it perfect for resource-constrained mobile AR applications, demonstrating a viable AR music application without specialized equipment.
[HC-23] Regulation Power and the Compliance 1 Paradox: A Longitudinal Study of Smart Homes
链接: https://arxiv.org/abs/2608.13582
作者: Wael Albayaydh,Ivan Flechais,Rui Zhao
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 24 pages, 4 table, 1 figure
Abstract:Smart home technologies are becoming increasingly embedded in domestic environments, yet their implications for power, privacy, and inequality remain insufficiently understood, particularly in non-Western contexts. This paper presents a longitudinal socio-technical study of smart home adoption in Jordan, examining how cultural norms, regulatory frameworks, and everyday technological practices shape domestic power dynamics. Building on our 2022 study, we employ a two-phase grounded theory approach comprising (1) a secondary analysis of 30 interviews and (2) 28 new interviews conducted in 2025 with returning and new participants, including household members, domestic workers, policymakers, and civil society advocates. This design provides a rare longitudinal perspective on how regulatory and technological change reshapes domestic surveillance practices. Our findings reveal a compliance paradox: although Jordan’s 2023 Data Protection Law has increased privacy awareness, it also enables new forms of exploitation by leaving domestic contexts largely outside its scope. We identify three dynamics: (1) the normalization of passive surveillance through convenience and legal compliance; (2) regulatory ambiguity that shifts responsibility to households, reinforcing existing hierarchies; and (3) constrained resistance among domestic workers facing intensified monitoring. We contribute a longitudinal account of how privacy regulation is appropriated within socio-cultural systems to reproduce existing inequalities. We conclude by identifying four intervention spaces across design, policy, and advocacy, emphasizing the need for contextually grounded approaches to privacy governance in smart home ecosystems. Comments: 24 pages, 4 table, 1 figure Subjects: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY) Cite as: arXiv:2608.13582 [cs.HC] (or arXiv:2608.13582v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2608.13582 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wael Albayaydh [view email] [v1] Tue, 7 Jul 2026 19:58:31 UTC (630 KB)
[HC-24] From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent ACL2026
链接: https://arxiv.org/abs/2608.13581
作者: Mingyu Huang,Weiqing Min,Ying Jin,Yilin Wang,Shuqiang Jiang
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted in ACL 2026 Findings, 17 pages, 4 figures
Abstract:Personalized glucose regulation remains a central yet unresolved challenge in precision nutrition, as postprandial glucose response varies substantially across individuals. Existing approaches based on glycemic indices fail to adequately account for such heterogeneity and lack the mechanism to dynamically adjust meals based on personal physiological feedback. In this context, recent advances in LLM-based agents offer a promising direction, as they enable context-aware reasoning and iterative refinement. Inspired by this, we propose a physio-feedback agentic loop, a unified system that integrates individualized absorption modeling with dietary intervention to regulate glucose response. Specifically, we develop a Physiology-Aware Glucose Predictor to model individualized absorption dynamics through a learnable Temporal Physiological Absorption Decay Module. We then construct a Prediction-Driven Two-Stage Meal Optimization Agent that iteratively refines real-world meals using predicted outcomes as explicit feedback. Through extensive experiments on multiple public datasets, we demonstrate that our method not only improves prediction accuracy but also effectively reduces glucose excursions. To the best of our knowledge, this paper marks the first step in integrating physiological learning with an LLM-based agent for personalized glucose regulation.
[HC-25] BCIJelly: An integrated ecosystem for brain-computer interface research
链接: https://arxiv.org/abs/2608.13576
作者: Liyuan Han,Xinrui Yang,Tianyu Zheng,Qizhi Yang,Yitao Qin,Liang Chen,Qinglai Wei,Binjie Hong,Xinhe Zhang,Rui Xiong,Yong Gu,Mu-ming Poo,Bo Xu,Chengyu Li,Tielin Zhang
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
备注: 67 pages, 6 figures, 7 extended data figures, 20 supplementary tables
Abstract:Brain-computer interface (BCI) research relies on multistage computational pipelines, yet progress remains constrained by fragmented data formats, heterogeneous decoder implementations and hardware-specific deployment toolchains, and researchers lack an integrated workflow. Here, we fill this gap with BCIJelly, a unified computational ecosystem that integrates 18 curated BCI datasets, 15 benchmark decoders and an algorithmic library of 80 reusable modules, an automated architecture search (AAS) procedure, and hardware-aware deployment through the toChip pipeline within a single Python framework. AAS constructs task-specific decoders without manual architecture design. It is further extended into a closed-loop mode guided by a large language model (LLM), which uses task specifications, module descriptions and search history to support multitask and cross-species decoding. The toChip pipeline compiles trained decoders for execution on neuromorphic chips, enabling energy-efficient deployment for BCI systems. An accompanying visualization software provides a graphical interface to the full workflow, making BCIJelly accessible without programming. We validate BCIJelly across five BCI paradigms (motor, visual, speech, emotion and auditory) with recordings from humans, macaques and mice, and single-task, multitask and cross-species decoding settings. BCIJelly establishes a unified and extensible infrastructure that bridges decoder development and hardware-aware deployment for BCI research.
[HC-26] Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data
链接: https://arxiv.org/abs/2608.13575
作者: Igor Cherepanov,David Sessler,Alex Ulmer,Felix Wagner,Throsten May,Jörn Kohlhammer
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
备注: This paper is a preprint of a manuscript submitted to EuroVis 2025 that was not accepted for publication (it will be resubmitted soon)
Abstract:Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, including the classification of computer network traffic to corresponding applications. However, the data frequently contains diverging patterns within a single predicted class. This presents a significant challenge to the ability to provide a clear and comprehensive explanation and emphasizes the necessity for tools capable of detecting and analyzing these patterns. Furthermore, the capacity to extract descriptive rules for classes is a crucial requirement in network traffic analysis and intrusion detection, particularly when leveraging advanced tools like next-generation firewalls. We provide a visual-interactive system that explains predictions of classes for network traffic. Global explanations derived from multiple samples of a given class contribute to understanding model predictions. Visualization of global explanations enables recognition of different patterns that offer experts a more comprehensive overview of its characteristics. We introduce a prototype that facilitates visual exploration and refinement of global explanations, enabling network experts to detect and refine new patterns for specific applications. These explanations support the identification of misleading features and the formulation of new rules for the management of networks. Our approach also aims at enabling ML experts to acquire new insights, including the possibility of separating or merging classes and the development of more accurate and reliable DL models. Our proposed prototype was evaluated by experts in machine learning and network analysis.
[HC-27] he Architect: Interactive Visualization of Deep Learning Mathematics Directly in Microsoft Excel
链接: https://arxiv.org/abs/2608.13572
作者: Mohammad Imrul Jubair,Tom Yeh
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: Technical Report
Abstract:We present The Architect, a system that turns Microsoft Excel into an interactive view of deep learning mathematics. A user describes a neural network in a compact table. The system then generates a workbook that shows the full forward pass and, when requested, the backward pass and parameter updates. Computed values appear as live spreadsheet formulas, while user-controlled values such as inputs, weights, labels, and hyperparameters remain editable. Excel reactively updates the dependent computations through its recalculation engine. Most deep learning tools hide the numerical details behind library calls. Many visualization tools show architecture diagrams or training summaries, but they do not expose the full arithmetic of the model. The Architect focuses on that missing middle layer. It makes matrices, activations, losses, gradients, and updates visible as inspectable spreadsheet regions, with editable controls for values users naturally manipulate. The system also produces aligned PyTorch snippets, which helps users connect formulas to implementation. This report describes the motivation, design, implementation, and use cases of The Architect. We show how the system supports introductory arithmetic tracing, learning-rate exploration, diagnosis of dying ReLU, and inspection of vanishing gradients. The main idea is simple: spreadsheets already support formulas, direct editing, reactive recomputation, and tabular layout. These properties make them a useful medium for understanding how small educational and diagnostic neural networks compute. Comments: Technical Report Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.13572 [cs.HC] (or arXiv:2608.13572v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2608.13572 Focus to learn more arXiv-issued DOI via DataCite
[HC-28] A Two-Validator Web Interface for Structured Geometry Figure Annotation
链接: https://arxiv.org/abs/2608.13569
作者: Sabin-Codrut Badea,Adrian-Marius Dumitran
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Annotating geometric figures from scanned documents has long been addressed by adapting generic annotation tools, tools not originally designed for such tasks, to use cases where they are suboptimal. An interactive web interface is described that is purpose-built for validating automatically generated geometry figure descriptions, allowing annotators to review and correct conditional declaration language (CDL) descriptions while simultaneously adjusting figure crops and editing source problem text. Submissions pass through two independent annotators in sequence, with each round fully logged. The interface is currently deployed and has been used by 12 annotators to validate 483 problem entries.
[HC-29] Proxy-Validated LLM UX Micro-Simulations: An Artifact-First Protocol for Early-Stage Decision Support
链接: https://arxiv.org/abs/2608.13563
作者: Alexandre Cristovão Maiorano
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 27 pages, 5 figures, 15 tables
Abstract:Early-stage teams often lack users, time, and budget to run repeated UX studies, yet still need decision-oriented signals to iterate safely. We study an LLM-driven UX micro-simulation pipeline that generates structured customer-experience feedback (walkthrough steps, friction points, micro-survey signals) from versioned prompts, personas, tasks, and UI snapshots. Because public usability datasets with task outcomes are scarce, we validate simulated friction themes using multiple public proxy corpora (app reviews, support tweets, and open-source software issues). We propose a lightweight proxy-validation protocol with two alignment metrics: top-k Jaccard and distributional weighted-Jaccard (W), and compare lexical, TF-IDF, and multilingual embedding baselines across six proxy datasets. Embedding-based alignment yields higher W than lexical baselines on primary app-review and support-tweet proxies (e.g., W=0.128 vs 0.000 on Gojek), while top-k Jaccard is shown to overstate alignment at large k. We ablate four agent strategies (single-pass, best-of-N, hybrid, and a proposed score-then-select judge) across Azure OpenAI deployments and report bootstrap confidence intervals over 8 method-dataset pairs; these intervals reveal that the embedding W point estimate is systematically unstable under resampling at our subsample size. We also provide a failure-mode analysis of grounding and fabrication proxies, with documented calibration caveats and worked examples of outputs flagged as fabricated by an adversarial judge. Our artifact-first pipeline produces reproducible tables and figures from versioned run artifacts, supporting iterative prompt and taxonomy refinement before final paid-model calibration.
[HC-30] From Informal Addresses to Reliable Places: Participatory Data Governance of Civic Addressing in Puerto Rico ICIP
链接: https://arxiv.org/abs/2605.02924
作者: Juan A. Padilla
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY); Databases (cs.DB); Social and Information Networks (cs.SI)
备注: This paper is a preprint of a workshop paper accepted at the CHI 2026 Workshop on Participatory Data Governance at the CHI 2026 Conference on Human Factors in Computing Systems
Abstract:This paper examines civic addressing as a problem of participatory data governance. Drawing on a project developed through the U.S. Census Bureau’s The Opportunity Project with engagement from FEMA, we describe the use of actionable geolocations to support services where formal addresses are absent. We introduce Reliable Places as transitional governance artifacts through which place reliability emerges via use, enabling services while supporting pathways toward formal civic address assignment.
[HC-31] n simple rules for non-visual reproducible and accessible bioinformatics
链接: https://arxiv.org/abs/2608.14400
作者: Jacqueline G. Kientsch,Stephan C.F. Neuhauss,Izaskun Mallona
类目: Genomics (q-bio.GN); Human-Computer Interaction (cs.HC)
备注: 28 pages, 4 figures, 5 supplementary figures, 8 supplementary tables, 1 analysis ZIP file
Abstract:Bioinformatics workflows rely heavily on visual representations. Quality-control plots, cell embeddings, heatmaps, genome-browser tracks, and interactive dashboards are not merely illustrations, but instruments for making analytical decisions. For blind and low-vision researchers who use screen readers, braille displays, or audio-based interfaces, these create a barrier: the evidence used to justify an analysis is often encoded in visual form, while the underlying decision remains undocumented. We argue that non-visual accessibility and computational reproducibility are closely aligned, as they both require analyses to be transparent and to record why decisions were made. We present ten simple rules for non-visual bioinformatics, covering plots as decision records, cautious use of AI-generated figure descriptions, accessible computing environments, text-first literate programming, structured data and metadata, compact object summaries, accessible publication formats, collaboration practices, shared community infrastructure, and accessibility as part of FAIR research. The intended audience is computational biologists and developers. Using single-cell RNA-seq as a running example, we show that the accessible equivalent of a plot is a structured decision record. That is, a plot companion that goes beyond storing the underlying data by also stating the purpose of the analysis and the resulting quantitative evidence and uncertainty. We argue that treating accessibility in this way makes bioinformatics more inclusive and also more transparent and auditable. Comments: 28 pages, 4 figures, 5 supplementary figures, 8 supplementary tables, 1 analysis ZIP file Subjects: Genomics (q-bio.GN); Human-Computer Interaction (cs.HC) Cite as: arXiv:2608.14400 [q-bio.GN] (or arXiv:2608.14400v1 [q-bio.GN] for this version) https://doi.org/10.48550/arXiv.2608.14400 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
计算机视觉
[CV-0] CPI-Bench: A ComprehensivePractical and Intelligent Benchmark for Real-World Image Editing
链接: https://arxiv.org/abs/2608.14546
作者: Qinye Zhou,Jun Zheng,Yongchao Du,Yuan Wang,Zhengrui Chen,Zuan Gao,Taihang Hu,Chao Lin,Yefeng Shen,Xingjian Wang,Zhao Wang,Zhengtao Wu,Xiaoli Xu,Zhengze Xu,Hao Yan,Denghui Yang,Yuhang Yu,Huayu Zhang,Mingzhou Zhang,Mengting Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, benchmark report
Abstract:With the rapid advancement of image editing models and their widespread application across various domains, there is an increasingly urgent need to deploy these model capabilities directly into real-world scenarios. However, existing benchmarks remain confined to simple single-image tasks, suffering from limited coverage dimensions and an inability to effectively differentiate performance among diverse models. Consequently, they fail to reliably evaluate model performance in complex multi-image editing, highly demanding reasoning instructions, and practical deployment settings. To address these limitations, we propose CPI-Bench, a Comprehensive, Practical andIntelligent benchmark for real-world image editing. CPI-Bench comprises three core subsets: CPI-General-Bench, which comprehensively covers diverse editing tasks and pioneers the inclusion of multi-image editing evaluation; CPI-Practical-Bench, which focuses on high-frequency real-user application scenarios; and CPI-Intelligent-Bench, which is dedicated to evaluating capabilities in highly demanding reasoning-based editing. Evaluation results of mainstream image editing models based on CPI-Bench demonstrate that CPI-Bench enhances performance differentiation among models. It provides a comprehensive and reliable quantification of gaps in general editing capabilities, practical deployment efficacy, and advanced reasoning-based editing, offering invaluable guidance for the future optimization of image editing models. Crucially, our ranking analysis reveals that CPI-Bench achieves the highest alignment with the Arena Image Edit Leaderboard, indicating it faithfully captures the preferences and perceptual judgments of human evaluators, serving as a robust proxy for real-world user experience.
[CV-1] MagnifiQ: Patch-aware Text Guided Progressive Upscaling for High-Resolution Image Restoration ECCV
链接: https://arxiv.org/abs/2608.14543
作者: Mahesh Reddy,Yashesh Savani,Antoine Mercier,Hong Cai,Fatih Porikli,Guillaume Berger
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Camera-ready version (ECCV workshop - LoViF’26)
Abstract:High-resolution image restoration from degraded inputs is challenging because it must preserve global structural consistency while recovering fine-grained local details, especially at 4K resolution where direct diffusion-based restoration is computationally expensive and prone to repeated or inconsistent textures. In this work, we introduce MagnifiQ, an image restoration framework that progressively upscales and restores images across resolutions, e.g., from 1024x1024 to 4096x4096. Our approach leverages a pre-trained text-to-image diffusion model such as SDXL and adapts it for more scalable high-resolution inference by replacing its original self-attention layers with convolutional operations whose computational cost grows linearly with image resolution. We further propose a progressive upscaling strategy that iteratively restores images over multiple resolution stages, refining each intermediate output rather than directly hallucinating the final 4K image, thereby improving global coherence and reducing high-resolution artifacts. To enhance local details while controlling content drift, MagnifiQ uses patch-specific text prompts that provide spatially localized semantic guidance during restoration. Extensive experiments on synthetic and real-world degraded images show that MagnifiQ outperforms prior diffusion-based restoration methods in perceptual quality and human preference, producing sharper textures and more coherent 4K results while offering practical speed–quality trade-offs through its scalable backbone and progressive design.
[CV-2] Decoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils
链接: https://arxiv.org/abs/2608.14539
作者: Karel Becerra,Boris Mederos,Dean Snow,Ramón A. Mollineda
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Determining the biological sex of the individuals who created Upper Paleolithic hand stencils remains a challenging problem due to the absence of ground truth, population differences between contemporary and prehistoric groups, and the uncertainty introduced by image degradation. Traditional morphometric methods suffer from high structural overlap across sexes, poor cross-population generalizability, and subjective feature engineering. This study presents an uncertainty-aware deep learning framework for sex attribution in prehistoric hand stencils that explicitly models, propagates, and aggregates uncertainty throughout the analytical pipeline. The methodology combines dual image processing, dual contour extraction, structured silhouette augmentation, model architectural diversity, and ensemble-based decision aggregation. The pipeline generates twelve plausible silhouette realizations per stencil to capture boundary uncertainties, which are processed by two ensembles of ten deep neural networks each (EfficientNet-B3 and MobileViT-S) trained on 14,036 contemporary hand samples. Furthermore, a triangulated validation scheme integrates ensemble predictions with unsupervised 2D latent-space manifold mapping (UMAP + k-NN) and explainable AI spatial attributions (LayerCAM) to ensure anatomical consistency. On contemporary data, ensemble models achieve strong classification performance, with accuracies exceeding 88% in older age groups. When applied to prehistoric stencils, the framework produces both sex predictions and confidence measures of internal agreement, enabling the distinction between morphologically stable and ambiguous cases. Convergence across ensemble predictions, latent-space structure, and interpretability analyses shows that uncertainty can become a measurable component of archaeological inference, enabling robust and reproducible decoding of ancient rock art.
[CV-3] Marionette: Predicting World States Rendering Geometry Painting Appearance
链接: https://arxiv.org/abs/2608.14530
作者: Zian Meng,Zhen Li,Chuanhao Li,Qiang Li,Kaipeng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:Interactive game world models typically autoregress visual observations directly in pixel or latent space, forcing structured properties such as pose, geometry, and occlusion to be implicitly maintained by the same generative sequence. Over long horizons, errors in these latent world properties accumulate, making consistency and controllability fragile. We explicitly model the evolving world state, delegate exact geometric computation to a fixed, zero-parameter renderer, and leave the neural model to synthesize appearance. We instantiate this idea as Marionette, a world model for interactive games with articulated characters. First, a two-stage autoregressive dynamics model predicts an explicit and interpretable 276-dimensional 3D world state comprising multi-entity articulated skeletons, metric root trajectories, and rotations. Second, a zero-parameter graphics bridge converts the predicted state into pose-control videos, computing world-space geometry and occlusion in closed form. Third, a control-conditioned video-diffusion observation model synthesizes photorealistic RGB observations from the resulting structured controls. Our experiments establish two properties of Marionette. First, the predicted world state is directly controllable. Forcing a mismatched action stream changes root-aligned joint error by 31% across 48 held-out segments. Second, long-horizon behaviour is determined in the state, and can be repaired there. Left free, the two generated characters drift to 21.2 m apart (recorded sessions stay near 5 m) and a third of frames show ground penetration. Two rules imposed on the explicit state, a terrain collider and a separation cap, cut penetration by 66% and keep the pair engaged, with no change to the observation model. Routing appearance through the predicted state costs no fidelity we can detect, at an FVD of 831 against 799 for recorded pose.
[CV-4] Style or Signature? Artist-Disjoint Evaluation of Style Classification in Frozen Vision Embeddings ECCV2026
链接: https://arxiv.org/abs/2608.14435
作者: Rory Ashton
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 15 pages, 3 figures. Accepted at the VISART VIII workshop, ECCV 2026
Abstract:Frozen image embeddings from models such as CLIP are increasingly used to classify paintings by art-historical style, with high reported accuracy. We ask whether this accuracy reflects an understanding of style or the recognition of individual artists. Standard evaluation uses random splits in which works by the same artist appear on both sides, so a classifier can succeed by recognising the painter rather than the movement. We re-evaluate style classification under an artist-disjoint protocol, holding out every artist in turn so that no work is ever classified using other works by its own painter. On a balanced dataset of 320 paintings across four twentieth-century movements, 5-NN style accuracy falls from 0.87 to 0.77 under this protocol, and the drop is sharply uneven. Impressionism and Cubism barely move, while Surrealism falls twenty points. The pattern holds across four image encoders, including a vision-only self-supervised model, which places the effect in visual structure rather than language. Where an encoder captures genuine shared form, individual artists are barely recognisable yet style is robust, while Surrealism shows the opposite. We argue that artist-disjoint evaluation is necessary to measure stylistic understanding in frozen embeddings.
[CV-5] Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View
链接: https://arxiv.org/abs/2608.14430
作者: Yixian Xu,Yuanrui Zhang,Shengjie Luo,Liwei Wang,Di He
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注: 29 pages, 9 figures, 4 tables; work in progress
Abstract:Reinforcement learning (RL) post-training provides a direct way to align diffusion models with human preferences and task-specific rewards. However, current RL algorithms for diffusion models remain fragmented: reverse-trajectory methods rely on discretized likelihood ratios, whereas forward-matching methods train on reward-labeled noising versions of the rollout samples. This paper shows that these seemingly different losses arise from a single path-space principle. Starting from the regularized diffusion-RL objective, we use importance sampling between sampling SDEs to obtain an explicit policy-gradient estimator on trajectory space. The estimator contains the stochastic Itô integral underlying Flow-GRPO-type updates; we derive an equivalent variance-reduced value-gradient form that recovers the forward-matching structure of AWM and DiffusionNFT. This identifies the empirical gap between these method families as a variance-reduction effect rather than a difference in RL principle. The derivation yields a unified design space organized by value-gradient estimation, weight functions, and sampling choices. Within this space, we propose a multi-sample KDE value-gradient estimator that reuses rollout groups, together with scale-bounded weight families that retain stable existing recipes while excluding singular ones. Experiments on SD3.5-M and Qwen-Image models validate the variance-reduction explanation and show that the resulting recipe improves over prior diffusion-RL baselines.
[CV-6] GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure ECCV2026
链接: https://arxiv.org/abs/2608.14428
作者: Mohamed Abdelsamad,Bin Yang,Michael Ulrich,Miao Zhang,Yakov Miron,Alexandru Paul Condurache,Abhinav Valada
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV2026
Abstract:3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable pretraining and transfers well to per-point tasks such as semantic and panoptic segmentation, but transfer to 3D detection remains weaker. We analyze recent SSL methods and find that most objectives are defined only on measured LiDAR returns from visible surfaces, leaving occluded and unobserved regions unconstrained. This visible-surface bias can be sufficient for point-wise prediction, but 3D detection requires robustness to missing structure. To address this gap, we propose GhostPoint, an SSL framework that hallucinates latent features in local neighborhoods around discovered instances, generated via a novel instance voxel dilation. In GhostPoint, an encoder processes observed returns, and an additional predictor infers neighborhood representations from observed context. In addition to standard encoder-level supervision, we introduce a predictor-level supervision scheme on sampled voxels from generated neighborhoods. Specifically, observed (visible/masked) voxels match teacher-encoder targets, while unobserved voxels match teacher-predictor hallucinations. This design encourages the learned representation to explicitly model structure beyond observed returns. Extensive evaluations on nuScenes and Waymo demonstrate that our method achieves state-of-the-art performance, consistently improving downstream 3D detection, especially under sparse scans and limited labels.
[CV-7] CRAFT: Constrained Reward via Attention Fine-Tuning for Subject Personalization without Composed Targets SIGGRAPH
链接: https://arxiv.org/abs/2608.14403
作者: Jihun Park,Kyoungmin Lee,Jongmin Gim,Hyeonseo Jo,Jaeyeul Kim,Han Zou,Zhenpeng Zhan,Yan Zhang,Sunghoon Im
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 8 figures, ACM SIGGRAPH Asia 2026
Abstract:Subject-driven image personalization—generating new images that preserve the identity of one or several reference subjects in novel scenes—is a foundational capability for modern visual content creation. It is currently dominated by generalized methods that fine-tune a pretrained multimodal diffusion transformer (MMDiT) on hundreds of thousands to millions of paired \emph(reference, composed-target) examples, where each composed target is a synthesized image of the subject in a novel scene. Producing such targets demands a costly multi-stage curation pipeline—LLM-based prompt generation, T2I-based composed-target synthesis, reference-subject extraction, VLM-based quality filtering, and correspondence labeling—and tightly couples each method to a particular target synthesizer and curation choice. We introduce \emphCRAFT (Constrained Reward via Attention Fine-Tuning), a single-step ReFL framework that fine-tunes a pre-trained \emphreference-aware MMDiT via LoRA adapters using a compact reference-only data construction— 10 K reference images and subject masks, with no composed-target supervision. CRAFT realizes a \emphWhere to look principle: attention-level rewards align noise- and phrase-token attention with the correct reference subject, and the resulting per-subject attention masks gate a pixel-level identity reward to keep image-space supervision consistent with the learned attention routing. Applied to FLUX.2-klein-9B, CRAFT achieves state-of-the-art performance on XVerseBench \revwhile using no composed-target supervision—only 10 K reference-only samples, whereas prior generalized methods require 150 K to over 2 M composed-target pairs. The same recipe transfers to other reference-aware backbones, consistently improving performance. Project page: this https URL.
[CV-8] IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection ICONIP2026
链接: https://arxiv.org/abs/2608.14394
作者: Xiao Guo,Wanke Xia,Lili Yang,Caicong Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICONIP 2026
Abstract:Perception is a fundamental component of autonomous driving systems. While LiDAR-based methods have achieved remarkable progress in object detection, their reliability can degrade under adverse weather conditions. Radar point clouds provide a robust alternative due to their resilience to bad weather and low-illumination scenarios. However, radar point clouds are typically sparse, unordered, and less informative than LiDAR data, making it challenging to directly apply existing LiDAR-based perception methods. To address these challenges, we propose IRGNN, an Invariant Radar Graph Neural Network for radar point cloud object detection. IRGNN first reconstructs radar point clouds into graph representations using translation- and rotation-invariant feature designs, enabling robust modeling of sparse radar measurements. It then employs an improved message passing neural network (MPNN) with residual connections and a virtual node layer to enhance local feature propagation and global context modeling. Finally, task-specific heads are applied to the learned graph representations for object classification and bounding box prediction. Experimental results on the RadarScenes dataset show that IRGNN outperforms existing radar-based object detection methods and achieves competitive performance. In addition, IRGNN significantly reduces computational cost and memory usage during inference, demonstrating its effectiveness and practical potential for efficient radar-based perception in autonomous driving.
[CV-9] Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors Generators and Social Dissemination
链接: https://arxiv.org/abs/2608.14391
作者: Shuo Liang,Yixing Ma,Pengfei Zhou,Xingyan Chen,Zihan Mei,Manting Li,Feihan Chen,Zhiwen Wang,Bin Xu,Haotian Zhang,Jiajun Song,Shiya Su,Run Liu,Zhenghang Ni,Yifa Yu,Jintao Hong,Bolong Feng,Yifei Liu,Zirui Zhang,Jingxuan Zhang,Songlin Zhao,Yifan Bai,Kang Tan,Yizhe Liu,Junhao Du,Yongtao Ge,Zhaopan Xv,Xinyuan Zhang,Mengru Ma,Chunhua Shen,Wei Wang,Yang You,Zheng Zhu,Kaipeng Zhang,Wangbo Zhao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 63 pages, 20 figures, 32 tables
Abstract:Recent video generators can fabricate realistic depictions of wars, disasters, public emergencies, and other real-world crises, creating substantial risks of misinformation. Existing benchmarks, however, provide limited evidence on detector and generator behavior in such settings, including how detectability varies with generation conditions, how people perceive generated videos, and whether detectors remain reliable during social dissemination. To address this gap, we introduce RA-Bench, a benchmark for AI-generated video detection that uses Real videos as Anchors. RA-Bench contains 17,886 videos, comprising 1,830 real-video anchors across 10 social-risk categories and 16,056 generated clips from four open-source and five closed-source generators. Based on RA-Bench, we organize our evaluation along three dimensions. We first assess detector generalization across seven traditional detectors, ten zero-shot multimodal models under three review settings, and two MLLMs specifically fine-tuned on AI-generated video detection. Across these methods, none of the three detector families generalizes consistently across RA-Bench instances. We then examine how detectability varies with generation quality, conditioning information, and sampling seeds. These analyses show that generation properties affect detector families differently, while source-level detection patterns remain stable across seeds. Finally, we study human authenticity judgments and detector reliability during social dissemination. We find that videos that mislead people are also difficult for current detectors, and that social dissemination makes detection harder. Together, these findings show that current methods struggle to detect realistic AI-generated videos, highlighting the need for detectors robust to evolving video generators.
[CV-10] GBU-Palm: A Multimodal Video Dataset and Benchmark for Palm Presentation Attack Detection
链接: https://arxiv.org/abs/2608.14389
作者: Yingjie Ma,Zitong Yu,Wei Jia,Ajay Kumar,Linlin Shen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing palm presentation attack detection (PAD) datasets are often limited by static imagery, restricted acquisition conditions, or insufficient multimodal video data, hindering systematic evaluation across environments, modalities, and attack types. We present GBU-Palm, a large-scale multimodal video dataset and benchmark containing 21,326 videos from 105 subjects and 210 palms across six acquisition environments, including bona fide, Print, and Replay presentations, with 6,310 synchronized RGB-NIR samples. We construct leakage-controlled protocols that separate palm identity and attack lineage and benchmark four representative video architectures under environment-matched and held-out-environment settings. Results reveal substantial architecture-dependent degradation under environmental shift and show that RGB-NIR fusion does not consistently outperform RGB-only input. We further analyze model behavior through true accept (TA), true reject (TR), false accept (FA), and false reject (FR) decomposition, spectral masking, temporal-order intervention, and frozen-backbone NIR probing, revealing distinct failure patterns and evidence utilization across architectures. GBU-Palm provides a unified and challenging benchmark for developing and evaluating robust multimodal palm PAD methods under cross-environment conditions.
[CV-11] Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery
链接: https://arxiv.org/abs/2608.14366
作者: Andrea Federici,Jakob Grahn,Giacomo Boracchi,Filippo Maria Bianchi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Polar lows are intense maritime cyclones that form rapidly at high latitudes. Deep learning can detect them in Synthetic Aperture Radar (SAR) imagery, but pixel-level segmentation remains an open challenge. No pixel-level masks are available for training, and a polar low’s extent is inherently subjective, with diffuse boundaries that even experts delineate inconsistently. We propose Constrained Region Erasing with Soft Targets (CREST), a Weakly Supervised Semantic Segmentation (WSSS) framework that generates masks solely from image-level labels. Our approach builds on Adversarial Erasing (AER), which iteratively mines discriminative regions, erases them, and retrains a classifier to reveal complementary cues that become pseudo-labels for segmentation. However, standard AER also collects irrelevant background features, degrading pseudo-label quality. CREST addresses this with (i) a Constrained Ordinal Region Expansion (CORE) module that encodes the spatial-connectedness prior of polar lows, constraining region expansion from a high-confidence seed, and (ii) a Dynamic Bootstrapping (DB) loss that treats the mining order as a proxy for label reliability, attenuating supervision from noisier, later-mined regions. On Sentinel-1 SAR data, CREST follows the cyclone structure more closely than standard AER, and returns a multi-class rather than binary mask whose classes indicate the reliability assigned to each region. We further evaluate on BUS-UCLM breast ultrasound and PASCAL VOC person data, whose targets satisfy the same connectedness prior but come with the dense masks the SAR data lacks. On both datasets, CREST performs better than the equivalent AER pipeline under identical settings.
[CV-12] RIAGE: Risk-Controlled Pseudo-Label Admission for Annotation-Efficient Semi-Supervised Retinal OCT Classification
链接: https://arxiv.org/abs/2608.14321
作者: Md Ashraful Hossen Akash,Shyla Afroge,Abdullah Al Mamun,Md. Kishor Morol,Tze Hui Liew
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 38 pages, 12 figures, 13 tables. Code available at this https URL
Abstract:The advanced retinal disease diagnosing imaging modality, optical coherence tomography (OCT), encounters a lack of automation because of the high expenses for annotations performed by specialists. The use of SSL solves the problem of insufficient annotations using unlabeled B-scans; however, most of the current techniques for generating pseudo-labels are based on prediction confidence without considering the asymmetry between different types of errors. This paper proposes TRIAGE, a risk-controlled semi-supervised framework for OCT scans classification, which uses the concept of a patient-level conformal risk controller with an asymmetric cost matrix. TRIAGE unites three crucial modules: a hierarchical classifier that is capable of working with partially abnormal supervision of the disease subtypes, a patient-grouped conformal risk controller with primal-dual coverage control, and a context-aware Transformer teacher for cross-slice verification. On the dataset from Noor Eye Hospital (16,822 B-scans, 161 patients, and 554 volumes) with a test set of unseen patients, TRIAGE demonstrates 89.66% scan-level accuracy, 0.8805 macro-F1, 0.9641 macro-AUC, and an 8.34% under-grading rate when using only 20% of the labeled data. With only 5% of the labeled data, TRIAGE keeps 76.88% accuracy and a 0.1656 under-grading rate. Compared with the other six state-of-the-art semi-supervised methods, TRIAGE significantly outperforms them with ablation study demonstrating the contribution of each module in the overall framework performance (by 42.7% in terms of under-grading rate comparing to fixed threshold methods). TRIAGE demonstrates 98.00% accuracy for 3-class classification with 1% labeled data and 95.94% accuracy for 8-class classification with 10% labeled data on the OCT-C8 dataset.
[CV-13] Spatial Message Passing in Language Space for Pathology Image Interpretation MICCAI2026
链接: https://arxiv.org/abs/2608.14309
作者: Jing-Cheng Yang,Hao-Jung Wang,Jinhao Du,Yang Hu,Ming-shan Tsai,Jens Rittscher,Bin Li
类目: Computer Vision and Pattern Recognition (cs.CV); Tissues and Organs (q-bio.TO)
备注: Accepted at MICCAI 2026 Workshop (Oral)
Abstract:Multimodal Large Language Models (MLLMs) can generate pathological descriptions from histological images, but gigapixel Whole Slide Images (WSIs) exceed their visual context limits. The standard tiling workaround makes WSIs tractable yet severs the tissue neighborhoods that define tumor-stroma interfaces and morphology. We introduce Spatial Language Message Passing (SLMP), a framework that performs spatial reasoning entirely in language space, human-readable by construction. SLMP represents a WSI region as a spatial text graph: tiles are nodes initialized with MLLM descriptions, and edges encode spatial adjacency. For each tile, an LLM refines its description by integrating language messages from adjacent tiles under a shared aggregation policy that, on the tile grid, acts as an adaptive local kernel operating on text rather than learned embeddings. This policy is an inspectable prompt that can be refined from model-observed tissue phenotypes via textual gradients, enabling automatic semantic optimization from local cellular context to broader tissue morphology without fine-tuning MLLM weights. On representative HER2 and CAMELYON16 regions, SLMP improves tile-level tumor description accuracy in settings spanning general-purpose and pathology-specialized backbones, with gains of +3.3 to +19.6 percentage points. Random-neighbor ablations confirm that these gains stem from spatial context rather than additional text alone, and inspecting the optimized policies reveals interpretable, tissue-specific decision rules. Besides, without any weight updates or fine-tuning the backbone MLLM, SLMP substantially improves general-purpose MLLMs and narrows its gap to pathology-specialized counterparts, offering a transparent and flexible mechanism for incorporating spatial reasoning into MLLM-based pathology analysis.
[CV-14] Conditional Neural Optimal Transport for Predicting Cellular Phenotypes from Molecular Structure ECCV2026
链接: https://arxiv.org/abs/2608.14293
作者: Gauthier Avité,Maxime Sanchez-Renauld,Nicolas Bourriez,Auguste Genovesio
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at the BioImage Computing (BIC) Workshop, ECCV 2026
Abstract:High-content microscopy enables systematic profiling of cellular responses to chemical perturbations, but the scale of the chemical space makes exhaustive phenotypic characterization experimentally infeasible. This motivates computational models that can predict image-derived phenotypes without acquiring the corresponding treated cells. We formulate molecule-induced phenotype prediction as an inductive conditional transport problem in image representation space. Given a negative-control phenotype and the structure of a molecule, we aim to predict the phenotype induced by the corresponding molecule. We first evaluate classical optimal transport baselines and show that static couplings do not yield useful predictions on large-scale phenotypic image datasets. We then introduce a molecule-conditioned Neural Optimal Transport (NOT) model with a Monge-Gap regularization training objective that learns to transport negative-control unperturbed phenotypes toward perturbed phenotypes using molecular structure as conditioning information. NOT recovers molecule-specific phenotypic effects while reducing microscopy-associated technical variation, thereby facilitating comparisons across experimental batches. On unseen active molecules, the model outperforms baseline approaches, demonstrating that chemically conditioned transport can generalize beyond the molecules observed during training. We identified the molecular encoder as the main limitation to this generalization, while transport in a compressed representation space improves performance and scalability. These results establish NOT as a promising framework for predicting cellular phenotypes from molecular structure and negative-control phenotypes, while highlighting the development of more informative molecular representations as a key direction for improving out-of-distribution performance.
[CV-15] Acoustic UAV Detection in Battlefield Scenarios: Handling Noise Domain Shift and Weak Labels
链接: https://arxiv.org/abs/2608.14287
作者: Vadym Vilhurin,Volodymyr Sydorskyi,Andrii Shevtsov
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Passive acoustic sensing offers a critical, cost-efficient, and, crucially, passive alternative for detecting small unmanned aerial vehicles. However, the practical deployment of acoustic systems is discouraged by extreme environmental noise and sensor-induced domain shift caused by heterogeneous hardware. This paper addresses these challenges by introducing a robust framework optimized for real-world battlefield conditions. We propose the integration of Per-Channel Energy Normalization (PCEN) and attention-based pooling to enhance feature extraction under low signal-to-noise ratio scenarios. We further propose a domain-aware training strategy that leverages auxiliary classes and multi-microphone data to mitigate cross-domain performance degradation. Evaluated on a unique dataset of combat-zone recordings from the Ukrainian frontlines, our approach significantly outperforms existing baselines, increasing the F1 score from 55.4% to 78.6%. This paper was originally presented at the International Conference on Military Communication and Information Systems (ICMCIS), organized by the Information Systems Technology (IST) Scientific and Technical Committee, IST-224-RSY - the ICMCIS, held in Bath, United Kingdom, 12-13 May 2026.
[CV-16] PRM-as-a-Judge 1.5: A Toolkit for Robot Process Assessment
链接: https://arxiv.org/abs/2608.14284
作者: Yuyang Liu,Yanqing Shen,Ruike Chen,Jifan Zhao,Yuxuan Tian,Yichi Zhang,Tianfeng Long,Zixuan Yin,Yipu Wang,Ziheng Qin,Wenxing Tan,Yang Shi,Mingyu Cao,Runze Xiao,Ziqi Wang,Zhixin Yin,Shiwei Chu,Yi-Fan Zhang,Yao Mu,Yuheng Ji,Yihao Wang,Jun Yan,Zhongyuan Wang,Pengwei Wang,Xiaolong Zheng
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Fine-grained robotic evaluation matters for understanding embodied models, going beyond binary success rates and rule-based process scores. We present PRM-as-a-Judge 1.5, a toolkit for robot process assessment that turns rollout videos into dense progress curves and derives multiple fine metrics. PRM-as-a-Judge 1.5 introduces three metrics, building on version 1.0, that characterize failure-side progress, post-drawdown recovery, and success-side execution quality, helping users understand embodied model capability. Based on the rollout videos from benchmarks, we perform a comprehensive assessment of the embodied models, providing some fine-grained metric results and key findings. We further introduce RoboPulse++ to evaluate the reliability of process reward models (PRM), providing evaluators with a more accurate testing platform. Moreover, we release a user-friendly assessment suite, including the benchmark, metric implementation, and visualization tools, to support reproducible manipulation process evaluation. We call on the community to rethink how robots are evaluated and establish transparent, procedural, and reproducible assessment as a foundation for the next generation of embodied intelligence.
[CV-17] MAGneT-3D: Monocular and Domain-Generalizable Temporal 3D Detection ECCV
链接: https://arxiv.org/abs/2608.14282
作者: Mohamed Kotb,Johannes Meier,Christoph Reich,Oussema Dhaouadi,Luis Denninger,Daniel Cremers
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: To appear at ECCVW 2026 (DriveX workshop; Oral paper). Johannes Meier and Mohamed Kotb - both authors contributed equally. Project page: this https URL
Abstract:Monocular temporal 3D detection aims to detect objects in 3D, given a monocular video. Query-based 3D detectors unify detection and cross-view association, but their learnable queries fit the spatial distribution of the training data (e.g., field-of-view). We show that this issue is especially severe when these models are applied to monocular video, hindering generalization to unseen datasets and environments. To address this limitation, we introduce MAGneT-3D, the first method for domain-generalized monocular temporal 3D object detection. Instead of relying on static learnable queries, we propose a Domain-Robust Anchor Generator (DRAG) approach that adaptively derives 3D proposals during inference. To further enable domain generalization, we propose a Temporal Refinement and Identity Merging (TRIM) strategy, reducing dependence on specific 3D proposals. To enable comprehensive domain-generalization evaluation, we establish a cross-dataset benchmark spanning nuScenes, Waymo, Lyft, and ONCE. Under zero-shot domain shifts, MAGneT-3D outperforms all baselines, improving NDS from 12.1% to 18.6% while also increasing in-domain accuracy.
[CV-18] Learning to Forecast Crop Growth from Earth Observation Data
链接: https://arxiv.org/abs/2608.14281
作者: Dominik Senti,Mehmet Ozgur Turkoglu,Michele Volpi,Helge Aasen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Forecasting crop growth across agricultural landscapes is important for improving the productivity, resilience, and operational management of farming systems. In this work, we investigate whether Earth observation time series and meteorological drivers can be used to predict future canopy development at country scale. We focus on winter wheat and formulate crop growth prediction as forecasting future leaf area index (LAI) trajectories beyond the last available Sentinel-2 observation. We evaluate this task on a multi-year dataset which spans the entire country of Switzerland, containing over 20 million pixel-level Sentinel-2-derived LAI time series paired with meteorological variables. Because cloud cover and revisit gaps leave LAI supervision sparse, models fit the few valid (cloud-free) LAI observations yet oscillate implausibly between them, producing trajectories no real canopy could follow. We introduce a lightweight unimodal shape regulariser which improves trajectory plausibility with negligible loss in accuracy. We compare deep learning sequence-to-sequence (Seq2Seq) models with classic machine learning baselines and show that Seq2Seq models generalise well across years, achieving \mathrmR^2 above 0.8 and consistently outperforming conventional approaches. Together, these results demonstrate that remote sensing and weather-driven sequence modelling can learn crop growth dynamics at landscape scale. S
[CV-19] Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing
链接: https://arxiv.org/abs/2608.14266
作者: Yixi Cai,Rundong Li,Yuhan Xie,Qingwen Zhang,Patric Jensfelt,Fu Zhang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE International Conference on Automation Science and Engineering (CASE), 2026
Abstract:LiDAR bundle adjustment is widely utilized in mapping to construct globally consistent point cloud maps. In this paper, we propose the first fully parallel computing framework to accelerate LiDAR bundle adjustment for large-scale mapping, incorporating three key techniques. First, we design an adaptive, asynchronous data loading strategy to efficiently process large-scale point cloud datasets on memory-constrained GPUs. Secondly, we present a novel bottom-up voxelization method for extracting planar features, enabling fully parallelized pre-processing. Thirdly, we build upon a majorization-minimization formulation to accelerate compute-intensive tasks in the optimization via parallel computation, including the computation of residuals, Jacobian and Hessian matrices, and a parallel increment solver. To support our design, we provide both theoretical and experimental analysis of the time complexity of our approach. Extensive benchmarking on large-scale public datasets across various computational platforms validates the robustness and adaptability of our approach, achieving up to a tenfold improvement in computational efficiency while preserving mapping accuracy comparable to state-of-the-art methods. To benefit future research, the implementation code is available on GitHub.
[CV-20] On the Robustness of Temporal Vision-Language Models for Surgical Endoscopy Videos MICCAI2026
链接: https://arxiv.org/abs/2608.14262
作者: Darakshan Rashid,Raza Imam,Ufaq Khan,Muhammad Bilal,Shazad Ashraf,Dwarikanath Mahapatra,Mohammad Yaqub,Muhammad Haris Khan,Imran Razzak,Brejesh Lall,Lena Maier-Hein,Yutong Xie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to MICCAI 2026
Abstract:Temporal vision-language models (TVLMs) offer a reusable, prompt-based interface for surgical video understanding, yet, their robustness under clinically realistic acquisition artifacts in endoscopy remains insufficiently characterized. In practice, degradations such as defocus, haze, motion blur, noise, cautery smoke, and packet loss introduce structured distribution shifts which may compromise video-text alignment. We study the robustness of temporal VLMs under such shifts caused by corruptions in clip frames. We introduce Endo-C6, a compact corruption benchmark of six endoscopy-realistic perturbations evaluated at a fixed high severity, and apply it to public Gastrointestinal (GI) endoscopy and laparoscopic cholecystectomy videos. Under a standardized prompt protocol, we benchmark 3 recent surgical TVLM baselines and analyze robustness in both mean and worst-case settings, spanning 294 dataset-level evaluations. Finally, we present RobustEndoCLIP, obtained by few-shot parameter-efficient tuning with VeRA, outperforming existing TVLM baselines. Our findings show that off-the-shelf TVLMs can exhibit severe worst-case collapse under endoscopy-specific corruptions, whereas lightweight few-shot adaptation can substantially improve corrupted performance and robustness without changing the prompt-based interface. We expect Endo-C6 to support standardized robustness reporting and promote more reliable clinical vision-language systems.
[CV-21] Zero-Shot Skeleton-Based Action Anticipation
链接: https://arxiv.org/abs/2608.14243
作者: Hongsong Wang,Pengbo Yan,Yang Zhang,Qiuxia Lai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Action anticipation (AA) aims to recognize ongoing human or humanoids actions from partial observations, enabling robots to predict intentions before the actions are completed. Although skeleton-based AA offers efficiency advantages, existing approaches assume that all action classes are seen during training, which limits their deployment in real-world scenarios where novel actions inevitably arise. To address this gap, we study the new task of Zero-Shot Skeleton-Based Action Anticipation (ZS-SkAA). This task requires recognizing unseen action classes using only limited early-stage skeleton sequences, combining the challenges of partial observations, temporal dynamics, and zero-shot generalization. To establish foundational research for ZS-SkAA, we introduce:(1) A baseline model comprising a spatio-temporal feature extractor and a mutual information estimation and maximization module. This baseline model explicitly aligns partial visual features with semantic class embeddings across modalities by estimating and maximizing their mutual information, enhancing generalization to unseen classes.(2) A benchmark protocol using the NTU RGB+D dataset, which is adapted for rigorous ZS-SkAA evaluation. Experiments demonstrate the effectiveness of our model as a strong baseline for ZS-SkAA, achieving high zero-shot accuracy on NTU RGB+D. This work establishes ZS-SkAA as a vital research direction for real-world systems requiring generalization to novel actions.
[CV-22] AppleScab-LT: A Longitudinal Real-Field Apple Scab Dataset for Temporal Disease Progression Analysis
链接: https://arxiv.org/abs/2608.14235
作者: Aamir Hilal,Shabir Ahmad Sofi,Neeraj Goel
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The development of reliable plant disease monitoring systems is constrained by limited longitudinal datasets capturing disease progression under natural field conditions. Although existing plant disease datasets have advanced image-based recognition, most consist of static images acquired at a single time point, limiting analysis of temporal disease evolution and severity progression. To address this gap, this study presents AppleScab-LT, a longitudinal real-field dataset developed to monitor apple scab progression through repeated observations of individually tracked infected leaves. Guided by a research-question-driven framework, the dataset was systematically developed, validated, and characterized for reliable longitudinal disease analysis. AppleScab-LT was constructed through systematic orchard monitoring under natural environmental conditions, incorporating longitudinal leaf tracking, expert-guided disease verification, polygon-based annotation, leaf isolation, disease severity quantification, and temporal sequence construction. A comprehensive quality assurance framework, including standardized annotation protocols, expert validation, automated integrity checks, sequence-level verification, and temporal consistency analysis, was applied throughout curation. The dataset contains 21 longitudinal leaf sequences, 2,101 high-resolution images, and 264 progressive temporal samples from repeated monitoring of same infected leaves. It captures variability in severity accumulation, progression rates, monitoring duration, and inter-leaf progression. Quantitative disease descriptors based on pixel severity, color-intensity severity, and normalized relative severity provide standardized measurements for temporal disease analysis. AppleScab-LT provides a reliable resource for temporal disease intelligence, disease progression modelling, precision agriculture, and future crop health monitoring
[CV-23] RankT2I: A Submodular Framework for Discovering Interpretable and Diverse Semantics in Text-to-Image Models ECCV2026
链接: https://arxiv.org/abs/2608.14226
作者: Ritika Allada,Pinar Yanardag
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Recent advances in text-to-image (T2I) models have revolutionized the field of image generation and editing. However, identifying semantics that a T2I model can successfully edit in an image continues to be a challenging task. Most existing approaches require users to manually specify semantics to modify a particular image, a time-consuming process that often involves extensive trial and error. In this paper, we present RankT2I, a novel, training-free, and model-agnostic framework that automates the discovery of editable semantics in diffusion and FLUX-based models. Given a visual domain, we first utilize a multimodal vision-language model to gather a broad set of candidate semantics. We then frame semantic discovery as a set selection problem and use a submodular objective to identify semantics that are relevant, editable, and diverse. Our method helps users efficiently identify a wide range of semantics for text-to-image editing models across several domains while outperforming existing methods.
[CV-24] MMUSV-Sim: A Perception-Oriented Simulation and Data-Generation Platform for Multi-USV Cooperative Perception
链接: https://arxiv.org/abs/2608.14207
作者: Ziao Li,Jianxiong Ye,Biao Tang,Leping Zhang,Kun Zuo,Siyu Huang,Chenqiang Gao
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cooperative perception among multiple unmanned surface vehicles (USVs) combines complementary observations to extend maritime target sensing beyond the view range and field of a single platform. Developing such systems at scale calls for a unified workflow for configurable multi-USV scenarios, multimodal acquisition, and shared annotations. We present MMUSV-Sim, a perception-oriented maritime simulation and data-generation platform built on Unreal Engine 5 and Project AirSim. It provides island, open-sea, and port environments; configurable weather, time of day, and wave conditions; a diverse vessel asset library; and spline-based multi-vessel motion. MMUSV-Sim acquires RGB, depth, semantic, LiDAR, and radar observations across multiple USVs and captures a common world state for per-agent annotation export. Experiments verify that the configured wave settings produce the intended changes in vessel heave, roll, and pitch, and evaluate the geometric consistency between projected annotations and semantic renderings. In LiDAR-based cooperative BEV vessel detection experiments on the generated multi-USV dataset, Early Fusion achieves an AP@0.5 of 72.74, compared with 45.54 using a single USV.
[CV-25] LightTeaNet: A Weakly Supervised Lightweight CNN for Multi-Label Tea Leaf Disease Detection and Localization
链接: https://arxiv.org/abs/2608.14178
作者: Naif Haider Chowdhury,Md Rahim,Syed Farhan Hasan,Murad Hasan,Prithwiraj Bhattacharjee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 11 figures, 5 tables
Abstract:Tea is known as an important crop in many parts of South and Southeast Asia, yet the production of tea is still hampered by the multiple diseases that decrease the quantity and quality. Traditional methods of inspection, which are manual, are not consistent, labor-intensive, and depend on extensive monitoring. This paper introduces a lightweight convolutional neural network (CNN) designed for weakly supervised multi-label classification and disease localization in tea leaves called LightTeaNet. LightTeaNet learns directly from image-level labels and employs Class Activation Mapping (CAM) to localize disease-affected regions automatically, unlike conventional object detection models such as YOLO, which require extensive bounding box annotations. For Parameter efficiency, the network integrates Depthwise Separable Convolutions, and for enhanced feature discrimination, it integrates Channel Attention. LightTeaNet has achieved a Precision of 0.9615, a Recall of 0.8772, and an F1-score of 0.9179, while it shows mAP@0.50=0.1810 without any manual annotations, which delivers a competitive localization performance in the experimental results. These results validate the model as an interpretable as well as a resource-efficient framework for intelligent disease monitoring in agriculture.
[CV-26] Concept Guidance: Precise Training-Free Latent Control for Text-to-Image Generation
链接: https://arxiv.org/abs/2608.14172
作者: Nikolai Röhrich,Isabell Hans,Felix Krause,Björn Ommer
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at GCPR 2026 (Oral). 28 pages, includes supplementary material
Abstract:Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specific guidance (e.g., for precisely controlling how aesthetically pleasing an image looks), and (2) they lack reliability for tasks requiring high local coherence (e.g., generating text or human hands). To tackle these issues, we introduce a novel notion of concept-wise mutual information and find large, concept-dependent differences between individual layers, demonstrating that the generation of specific structures is localized in distinct parts of the network. We exploit this insight by reinforcing the impact of concept-relevant layers in Concept Guidance (CoG), a precise, target-specific guidance method that works for models out-of-the-box without additional training, external models, gradients, or prompt engineering. CoG first quantifies each layer’s concept-specific impact and then guides denoising using a weighted combination of predictions generated with concept-relevant layers skipped. We demonstrate performance increases across various targets and popular models like PixArt-alpha, SD3, SD3.5, and FLUX.1-dev. Code is available at this https URL
[CV-27] SCVIB: Editable State-Conditioned Visual Instance Binding forMulti-Turn Personalized Localization
链接: https://arxiv.org/abs/2608.14148
作者: Xiongtai Yang,Ziyan He,Tao Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce editable state-conditioned visual instance binding, a multi-turn localization setting in which several support-defined instances are introduced across turns and protocol-defined state events determine the final target. We instantiate this setting as SCVIB, comprising 1,050 manually verified support–query base pairs and 1,500 episodes spanning five visual domains, three difficulty levels, and four target-state dependency groups. Direct Seq-free inference reaches only 60.13% Joint@0.5, indicating that resolving the final reference does not ensure effective use of the corresponding visual evidence for query-side localization. We address this gap with TT-VG (Transition-Tree Visual Grounding), which combines a Target-State Transition Tree (TSTT) with Visual Evidence Grounding Adaptation (VEGA). TSTT compiles the visible interaction into protocol-defined events, executes them over versioned target states, and resolves the final-query reference to the corresponding support evidence. Adapted on trajectory-derived same-instance pairs, VEGA performs support-conditioned grounding of the resolved instance using a Visual Evidence Package. TT-VG reaches 70.27% Joint@0.5; under matched target resolution, VEGA exceeds the strongest comparison method by 16.20 points. Gains over direct inference are largest on Counter-Recency and Rollback, which require routing to non-latest or restored support evidence. Together, these results establish SCVIB as a controlled testbed and highlight the effective use of resolved support evidence for query-side same-instance localization as a central challenge in multi-turn personalized localization.
[CV-28] CSG-Mamba: A Convolutional Scoring Gating Vision State Space Network for Endoscopic Polyp Segmentation ICONIP2026
链接: https://arxiv.org/abs/2608.14146
作者: Yuliang Wang,Jiaqi Wu,Jiaye Song,Shuxia Ren
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 6 figures, and 5 tables. Accepted by ICONIP 2026
Abstract:Accurate polyp segmentation is critical for computer-aided colonoscopy, yet endoscopic images often contain low-contrast boundaries, mucosal texture interference, specular highlights, and device-dependent appearance shifts. Vision State Space Models (SSMs) provide efficient long-range modeling with linear complexity, but existing Vision Mamba segmentation models typically convert 2D features into 1D scanning sequences, which may weaken local geometric continuity and over-smooth irregular contours. We propose CSG-Mamba, a convolutional scoring gating Vision State Space network for endoscopic polyp segmentation. Built on a VM-UNet-style asymmetric U-shaped encoder-decoder, CSG-Mamba inserts a Convolutional Scoring Gating (CSG) module at the semantically rich bottleneck. CSG generates a local spatial score map through pointwise and large-kernel depthwise convolutions and recalibrates state-space features by multiplicative gating. Experiments with three random seeds show that CSG-Mamba achieves 0.9220 Dice and 15.87 HD95 on Kvasir-SEG, and 0.7418 Dice and 0.6570 mIoU on CVC-ColonDB, outperforming the baselines on most overlap and recall metrics while maintaining competitive boundary accuracy.
[CV-29] Self-Supervised Visual On-Policy Distillation
链接: https://arxiv.org/abs/2608.14144
作者: Yijiang Li,Yijun Liang,Yunjie Tian,Bingyang Wang,Ke Zhang,Zhenfei Yin,Di Fu,Philip Torr,Nuno Vasconcelos
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Visual on-policy distillation relies heavily on an informative teacher-student asymmetry, through either a larger, stronger teacher or privileged supervision, such as reference answers or ground-truth regions of interest. This raises a fundamental question: where can informative asymmetry come from when nothing privileged is available? We answer this by inverting where the asymmetry comes from. Rather than adding privileged information to the teacher, we subtract information from the student. This asymmetry creates the same effective learning signal for free as a teacher with access to information unavailable to the student, without ground-truth annotations, rewards, or a separate stronger teacher model. Building on this principle, we introduce Self-Supervised Visual On-Policy Distillation (S ^2 VOPD), a simple yet effective method that constructs on-policy learning signals from asymmetric augmented views. S ^2 VOPD distills the teacher’s distribution conditioned on the original image on-policy into the student distribution conditioned on a strongly augmented view of the same image. We systematically explore a broad design space of visual augmentations and uncover that (1) asymmetry matters: all four augmentation families improve performance, while symmetric self-distillation degrades it; (2) strength matters: performance peaks at a moderate strength; and (3) the gap must remain task-consistent: augmentations that completely remove the question-relevant evidence can induce large but uninformative discrepancies. Across six fine-grained perception benchmarks, S ^2 VOPD improves Qwen3.5-4B from 70.7% to 77.4%, above all open-source models compared, up to Qwen3-VL at 235B, and surpasses GPT-5.4. While holding training data the same, it recovers 96% of the improvement achieved by methods with privileged information. Website is at this https URL
[CV-30] PISA: A Pseudo-Individual Source-Domain Feature Adaptation Framework for Test-Time Open-Vocabulary Object Detection
链接: https://arxiv.org/abs/2608.14142
作者: Ziyan He,Xiongtai Yang,Tao Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Open-vocabulary object detection test-time adaptation (OVOD-TTA) aims to address the performance degradation that pre-trained base models suffer when encountering image-domain shifts. Existing source-free OVOD-TTA methods rely either on refined test-time information for re-scoring or on pseudo-labels for self-training, leading to significant accuracy degradation when initial predictions are poor. Meanwhile, most conventional source-domain estimation methods recover abstract, sparse representations suitable for the classification task, but fail to capture the dense, concrete features required for detection. To address these issues, we propose PISA, a novel source-free OVOD-TTA method that can be seamlessly integrated into open-vocabulary visual backbones. The core components of our method are the Corruption-Invariant Feature Extractor (CIFE), the Feature Alignment Module (FAM), and a multi-scale alignment framework (BAA). To capture detection-suitable features, we develop CIFE to exploit the invariance of CLIP’s visual features across corrupted images, ensuring robustness against various corruptions. We further develop FAM and BAA for the pre-training and adaptation to transform the corruption-invariant features into pseudo-individual source-domain features that are close to the original source-domain features. In this way, dense and concrete pseudo-individual source-domain features are used for supervision instead of unreliable pseudo-label signals. Experiments on the corrupted VOC-C, COCO-C, and LVIS-C benchmarks across three base models demonstrate that PISA substantially improves both the localization precision and the category recognition accuracy of the original models. Notably, PISA achieves state-of-the-art performance without requiring access to source-domain data, surpassing existing methods by 3.92% in AP@50% on COCO-C.
[CV-31] SPARGen: Unifying Spatial Perception and Reasoning through Native Multimodal Generation
链接: https://arxiv.org/abs/2608.14138
作者: Jinsheng Quan,Jianhua Li,Siyi Xie,Xuanke Shi,Kewang Deng,Zukai Chen,Feifei Shao,Lei Yang,Quan Wang,Yawei Luo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Spatial perception and reasoning from visual observations require recovering geometric structure, establishing correspondences, and understanding spatial relations. Existing approaches typically address these capabilities separately using task-specific architectures or external geometric modules, limiting knowledge transfer among complementary representations of the same physical scene. We introduce SPARGen, a unified multimodal framework that casts 3D reconstruction, dense correspondence, and spatial reasoning as instruction-conditioned generation tasks. SPARGen serializes compact structured and linguistic outputs as token sequences while generating dense geometric fields in image-aligned forms, enabling spatial supervision to jointly shape shared representations within a native multimodal generative model. Experiments across benchmarks for 3D reconstruction, correspondence, and spatial reasoning show that SPARGen achieves competitive performance across heterogeneous spatial tasks within a single native multimodal generative framework.
[CV-32] HiCo-GS: Hierarchical Context Aggregation and Geometric Consistency for Octree Gaussian Splatting
链接: https://arxiv.org/abs/2608.14136
作者: Wei Zhang,Shengkai Yu,Shiqiang Gong,Qi Zhang,Qiang Li,Qi Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 21 pages, including supplementary material. To appear in the Proceedings of the 34th ACM International Conference on Multimedia (MM '26)
Abstract:Octree-based anchor Gaussian Splatting has emerged as a scalable representation for city-scale novel view synthesis, where multi-level anchors adaptively capture scene content from coarse building structures to fine architectural details. However, we identify a fundamental limitation in existing methods: cross-level feature isolation, where each level’s anchor features are optimized independently with no inter-level communication, causing color drift on building facades and over-smoothing in textured regions. We present HiCo-GS, a high-fidelity reconstruction framework with two complementary modules. Cross-Level Context Aggregation (CLCA) enables bidirectional hierarchical prior injection by leveraging the octree’s spatial containment structure to aggregate per-level context vectors into parent-self-child triplets, fused via a lightweight MLP with residual connection. Coarse-level structural priors flow down to inform fine-level anchors, while fine-level detail statistics feed back to prevent over-smoothing, at negligible computational overhead. Depth-Normal Geometric Consistency (DNGC) regularization enforces agreement between rendered normals and depth-derived normals through an alpha-weighted consistency loss, complemented by edge-aware smoothness losses with progressive warmup that exploit the strong planar priors ubiquitous in urban geometry to suppress floating artifacts. We further introduce the China-Pagoda dataset comprising 8 ancient Chinese pagodas with over 1,200 images each, featuring dense ornamental carvings, curved multi-layer eaves, and repetitive fine-grained textures. Extensive experiments on Mill19, UrbanScene3D, MatrixCity, and China-Pagoda demonstrate that HiCo-GS achieves state-of-the-art rendering quality and substantially cleaner geometry across real-world and synthetic urban this http URL: this https URL.
[CV-33] Fixed-Budget Gaussian Volume Encoding with Structure-Aware Allocation
链接: https://arxiv.org/abs/2608.14112
作者: Michael R. Martin,Joseph Insley,Victor A. Mateevitsi,Silvio Rizzi,Kwan-Liu Ma
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Graphics (cs.GR); Machine Learning (cs.LG)
备注: 10 pages, 6 figures, 6 tables
Abstract:Scientific simulations often produce scalar volumes faster than they can be stored, transferred, and loaded, while in situ reduction must use only a limited share of simulation resources. This work encodes scalar fields as anisotropic Gaussian primitives under a fixed budget. The complete primitive set is allocated analytically from local field structure, including position, orientation, and shape, then refined directly against the scalar field without densification, pruning, or count changes. The selected budget determines encoded storage before refinement and, together with the iteration schedule, provides a controllable refinement-time budget. In a controlled benchmark, truncation-aware field evaluation reduces encoding time by up to 51x; 1.4 million Gaussians encode a billion-voxel volume in at most four minutes on one desktop GPU, with reduced-iteration refinement completing in under one minute. Across five datasets spanning 2.1 million to 1.1 billion evaluated voxels, compression-useful configurations achieve 15.0-38.7 dB PSNR at compression ratios from 2.2x to over 40,000x. Pre-encoding structure statistics characterize fields for which one-shot allocation yields limited gains from additional capacity. Because primitives retain scalar attributes rather than baked appearance, a single compact model serves every subsequent visualization state - supporting post-hoc transfer-function, colormap, lighting, and viewpoint changes without re-encoding.
[CV-34] CoDS: Robust Collaborative Perception via Expert-driven Detection and BEV Segmentation
链接: https://arxiv.org/abs/2608.14085
作者: Jinlong Wang,Yuang Jia,Junhong Lin,Nannan Li,Wei Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 6 figures
Abstract:Collaborative perception breaks through single-view limitations via multi-agent information exchange. However, multi-source noise such as pose errors and communication delays degrades fusion feature quality, constraining perception performance. Joint training of detection and BEV segmentation provides a natural remedy, where segmented road regions help constrain target distributions and detection bounding boxes help recover ambiguous segmentation boundaries. To this end, we propose a robust Collaborative perception framework with expert-driven Detection and bev Segmentation (CoDS). To address spatial inconsistency in fusion quality, we first introduce the Collaborative Reliability Map (CoRM) to explicitly quantify feature quality distribution. Based on CoRM, we design the Semantic Mixture-of-Experts (S-MoE) module to extract differentiated features for inconsistent feature demands. Finally, to further mitigate feature noise degradation, the Bidirectional Task Complementary Interaction (BTCI) refines task-aware features through bidirectional injection. Extensive experiments on OPV2V and V2V4Real datasets show that our CoDS surpasses existing baselines on both tasks and maintains stable robustness under multi-source noise. Code: this https URL and this https URL.
[CV-35] Owner3D: Ownership-Guided Style Writing for Training-Free Localized 3D Stylization
链接: https://arxiv.org/abs/2608.14078
作者: Suchang Tao,Kaifeng Shi,Zhiyan Liu,Zhuoyuan Jiang,Yuqi Ouyang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Localized 3D stylization aims to modify the appearance of a specified object part while preserving the remaining surfaces. In large reconstruction models (LRMs), this task is challenging because style is injected into intermediate appearance representations before rendering, while compact triplane features are shared across target and non-target surfaces, causing style leakage and boundary ambiguity. We propose Owner3D, a training-free framework for localized 3D stylization that integrates localized appearance control directly into the LRM reconstruction process. Specifically, Owner3D introduces ownership-guided style writing to restrict reference-style injection to target regions, producing a single localized stylized triplane without additional training while avoiding separate global style and appearance representations. To resolve appearance ambiguity near semantic boundaries, we further introduce boundary dual slots that maintain separate local feature sources for target and non-target regions. Finally, a surface-first texture readout hierarchically combines surface, 3D, and triplane ownership evidence to robustly recover appearance under incomplete visibility. Experiments on a benchmark constructed from Google Scanned Objects and PartNet demonstrate that Owner3D consistently outperforms existing 3D stylization methods in target-region style fidelity and non-target appearance preservation, reducing appearance leakage by 86.4% and 89.9% compared with StyleSplat and LAENeRF, respectively.
[CV-36] A Pathway to General-Purpose Scientific AI: Multimodal Comprehension of Scientific Images
链接: https://arxiv.org/abs/2608.14075
作者: Jennifer D’Souza,Fahad Ahmed,Cecilia Andrea Bustamante Andrade,Lina Frolova,Poorani Gnanasambandan,Dilshad Hussain,Muhammad Uzair Khan,Nkembeng Kevin Nkengfoa,Paul Praveen J.,Fabio Priante,Sjoerd Franciscus van der Werf,Thomas Frederik Jan van Roeden
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Digital Libraries (cs.DL)
备注: 15 pages, 1 figure
Abstract:Scientific figures and tables encode essential experimental evidence, yet remain difficult for digital libraries and multimodal AI systems to retrieve and interpret. The ALD/E-ImageMiner benchmark and ICDAR 2026 Competition on Information Extraction from Atomic Layer Deposition/Etching Scientific Figures provide 1,951 figures from 205 publications, expert-annotated for classification, data table extraction, summarization, and visual question answering. In these companion proceedings, we present a forward-looking perspective on how the benchmark can guide future scientific-image challenges. We examine how its tasks probe capabilities from visual and quantitative reading to domain-grounded reasoning and evidential justification, and how Bloom-informed question design can support deeper scientific understanding. We propose “scientific conceptual understanding from images” as a long-term benchmark objective, with future directions including broader domains and figure types, contextual and cross-document synthesis, hypothesis evaluation, provenance, uncertainty, counterfactual grounding, and open-ended multimodal research. This perspective connects the ICDAR 2026 challenge to a broader agenda for machine-actionable scientific visual knowledge and verifiable multimodal scientific AI.
[CV-37] InstructVVT: Instruction-Driven Video Virtual Try-On without Auxiliary Spatial Priors
链接: https://arxiv.org/abs/2608.14070
作者: Dingbao Shao,Song Wu,Xinyu Chen,Qian Wang,Jiahang Li,Kuai Jiang,Jiang Lin,Yuhang Liu,Ziyu Chen,Duo Li,Jiaxin Hu,Shengrong Gu,Ziheng Tang,Rongrong Liu,Yanlun Peng,Liang Li,Junlan Feng,Lujia Jin,Ting Zhang,Jian Yang,Zili Yi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 10 figures. Dingbao Shao and Song Wu contributed equally. Zili Yi is the corresponding author
Abstract:Video virtual try-on is a highly constrained editing task requiring the precise replacement of a target person’s clothing while strictly preserving the original video’s spatial structure and temporal dynamics. Existing methods heavily rely on auxiliary handcrafted spatial priors (e.g., masks, poses) for editing control. However, these priors are prone to failure in unconstrained real-world videos and often compress rich visual context into incomplete structural signals. Furthermore, standard reconstruction objectives fail to fully capture try-on-specific human preferences. To address these challenges, we propose InstructVVT, an instruction-driven and reference-guided video virtual try-on framework based on a Diffusion Transformer (DiT) that operates without inference-time spatial priors. Our core insight is to recover fine-grained control directly from the input triplet (source video, reference garment, and instruction) via a dual-level reference conditioning scheme. Specifically, an MLLM infers semantic edit tokens for target disambiguation and structural preservation, while a lightweight conditioning pathway explicitly injects fine-grained visual garment details. Finally, we design a try-on-specific reward and utilize the DiffusionNFT algorithm to align the model with human preferences. Extensive experiments on ViViD-S and TripVVT-Bench demonstrate that InstructVVT outperforms state-of-the-art open-source methods in garment fidelity, structural preservation, and temporal consistency, despite requiring fewer inference-time controls.
[CV-38] Voxel-based 3D Facies Segmentation from Seismic Data: A Comparative Study
链接: https://arxiv.org/abs/2608.14058
作者: Duc-Thanh Pham,Minh-Tan Pham,Anh Nguyen,Van Nguyen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 5 pages
Abstract:Seismic facies segmentation has emerged as a significant challenge in geophysics, requiring robust methods and systems to effectively identify geologically analogous facies with limited labeled data. Although existing studies have shown promising results in 2D facies segmentation, they often preprocess the original 3D seismic volumes into sets of 2D slices, typically the inline and crossline directions, and treat this problem as a purely 2D segmentation task. This simplification introduces discontinuities across slices and fails to preserve the spatial and structural continuity in 3D seismic data, thus limiting the model’s ability to learn coherent geological patterns. In this work, we present a comparative and reproducible benchmark for voxel-based 3D seismic facies segmentation, built upon publicly available seismic volumes including the Netherlands F3 and the Parihaka datasets, with standardized data splits and evaluation metrics. By evaluating the three representative families of modern 3D segmentation architectures, we establish strong baseline results that highlight the potential and remaining challenges for future research in this domain.
[CV-39] Discovery and Spatial Characterisation of Multiple Shortcut Groups for Auditing Vision Model Bias
链接: https://arxiv.org/abs/2608.14051
作者: Akshit Achara,Vishnunarayan Manickam,Thomas Day,Esther Puyol Anton,Alexander Hammers,Andrew P. King
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deep learning models trained on datasets with spurious correlations can achieve high average accuracy whilst relying on shortcut features that do not generalise out of distribution. Whilst out-of-distribution testing highlights subgroup performance disparities arising from shortcut learning, it does not localise the regions within images that are associated with it. Existing research mostly uses attribution maps from interpretability methods to understand the spatial nature of spurious correlations. For example, conditional alignment methods separate task-relevant evidence from evidence tied to spurious correlations by comparing attribution maps from a task model, a sensitive attribute model, and a bias-reduced reference model. This yields shortcut-aligned and task-aligned contribution maps for each image. However, existing methods aggregate these maps across the dataset, potentially masking recurring spatial shortcut patterns that occur only in subsets of images. We address this limitation by grouping per-image shortcut and task contribution maps into recurring spatial patterns using K-means and non-negative matrix factorisation, and visualising the resulting shortcut groups through contribution maps and representative examples. Across CelebA, CheXpert, Waterbirds, Camelyon17, and ISIC2019, and across ResNet and ViT models, the discovered shortcut groups reveal both shared and distinct spatial patterns of shortcut and task contribution, with varying subgroup composition and error rates, enabling targeted inspection of image subsets with higher error rates. We perform input occlusion and internal test-time interventions to show that masking or suppressing task contribution regions substantially degrades the model classification performance and propose a combined shortcut suppression and task amplification feature intervention approach which generally reduces performance disparities.
[CV-40] Evolve Vision-Language-Action Model into an Agent with On-the-fly Tool-use CVPR
链接: https://arxiv.org/abs/2608.14047
作者: Yi Ding,Yanzhao Yu,Xili Dai,Xianbiao Qi,Peiwen Sun,Xueqian Wang,Xiangyu Yue,Jianan Wang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 4 figures, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern (CVPR) Findings
Abstract:This paper integrates end-to-end Visual-Language-Action (VLA) models with agentic tool-use to propose Agentic Robot with Tool-use (ART). ART is a tool-injection framework that tunes any VLA model to leverage off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement. Compared to vanilla VLA models with a whole continuous action solution space, ART reduces the complexity of the action solution space through tool-use, which not only improves generalizability across different tasks but also reduces data dependency. To demonstrate the advantages (high generalizability and low data dependency) of this framework, we first built a dataset of 30K tool-use trajectories and action demonstrations, which is much smaller than those used by baseline methods. We then designed a training regimen for long-trajectory tool-use reasoning in challenging environments. Experiments show that ART achieves a 20% higher success rate than mainstream baselines on simulation and real-world tasks, such as pick-and-place in the dark at novel viewpoints. Empirical results highlight the benefits of an agent-based approach: modular tool utilization enables more efficient training, lightweight deployment, and scalable integration of new tools. This design fosters robustness, adaptability, and extensibility, paving the way for the practical deployment of VLA systems in complex real-world scenarios.
[CV-41] Source-Agnostic Image Translation Based on Latent Aware Adaptive Masking ECCV
链接: https://arxiv.org/abs/2608.14046
作者: Tomislav Dobrički,Byung-Woo Hong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been accepted for publication at the European Conference on Computer Vision (ECCV), 2026
Abstract:In this work, we propose a source-agnostic framework that dynamically refines a binary mask throughout the reverse diffusion process by computing the discrepancies of a pretrained diffusion model’s prediction for each latent time step. Rather than relying on a fixed threshold, our method introduces a time-dependent statistical thresholding scheme derived from the empirical mean and standard deviation of prediction discrepancies across the latent noisy images from the target distribution. This allows the mask to adapt to the model’s varying predictive confidence at different noise levels, effectively isolating domain-specific regions while preserving global structural coherence. Experimental results on the AFHQ and Celeba-HQ datasets demonstrate that our approach outperforms state-of-the-art unsupervised Image-to-Image methods in both realism (FID, KID) and faithfulness (SSIM, LPIPS). By requiring only a pretrained model of the target domain, our approach enables precise, automated localization and seamless translation across diverse source distributions without any specialized training. The project source code is available at: this https URL
[CV-42] Beyond Text Conditioning: A Systematic Study of MLLM -DiT Fusion for Video Generation
链接: https://arxiv.org/abs/2608.14043
作者: Yanbo Ding,Yijia Fan,Caihua Shan,Yifan Yang,Yifei Shen,Weijie Wang,Xirui Hu,Dongsheng Li,Lili Qiu,Yuqing Yang,Yali Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion Transformers (DiTs) have become the dominant paradigm for high-fidelity video generation, yet their ability to perform high-level semantic planning remains limited. While hybrid architectures integrating MLLMs with diffusion backbones have shown strong advantages in image synthesis, such designs remain underexplored in video generation, where existing approaches often treat MLLMs primarily as frozen feature encoders rather than semantic generators. To fill this gap, we systematically study how an MLLM should be integrated with a DiT for video generation by answering three questions: what intermediate representation should bridge the MLLM and DiT, how the MLLM should generate it, and how the DiT should incorporate it during diffusion rendering. Our analysis reveals three key findings: (1) discrete semantic visual tokens produced by an EMA-based tokenizer provide a stable and expressive interface, (2) autoregressive causal modeling is effective for generating these tokens, and (3) explicit visual-token conditioning is more effective than prompt refinement or latent bridging. Based on these findings, we propose BiVidGen, a hybrid framework where an MLLM first generates semantic visual tokens and a DiT renders videos conditioned on both text and these tokens via multi-layer cross-attention. Extensive experiments show that BiVidGen improves semantic alignment and temporal coherence over a fine-tuned DiT baseline, achieving stronger performance on VBench-Long. These results demonstrate that explicit MLLM-based visual planning provides an effective intermediate interface for text-to-video generation beyond text-only conditioning.
[CV-43] E-S2Feat:Semantic-Guided Spiking Local Feature Detection and Description for Event Cameras
链接: https://arxiv.org/abs/2608.14027
作者: Yang Yi,Juntao Hua,Jinpu Zhang,Liangwei Fan,Hui Shen,Dewen Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Benefiting from high temporal resolution and dynamic range, event-based local feature methods have attracted increasing attention. However, event sparsity, noise, and limited texture still hinder robust local feature learning. Deploying such methods on resource-constrained platforms such as unmanned aerial vehicles also requires balancing accuracy and energy efficiency. To address these challenges, this paper proposes \textbfE-S2Feat, a spiking neural network framework for event-based local feature detection and description. The framework jointly optimizes local feature learning from the perspectives of feature representation and selection. First, a module-specific spiking activation mechanism preserves fine-grained structural cues and discriminative information under low-bit, energy-efficient inference, thereby improving overall representation fidelity. Furthermore, a semantic-guided feature modulation mechanism leverages semantic priors to refine keypoint response distributions and enhance local descriptor discriminability, thereby guiding the model to extract local features with greater geometric stability and stronger discriminative capability. Experiments on the ECD and EDS datasets show that the proposed method significantly outperforms baseline methods such as SuperEvent in pose estimation accuracy. It also achieves accuracy comparable to its artificial neural network counterpart while delivering an approximately 4.8-fold improvement in theoretical computational energy efficiency. Visual-inertial odometry experiments on the TUM-VIE dataset further verify the effectiveness and practical application potential of the proposed method in complete SLAM systems.
[CV-44] SSP: An Event-Matched Syn2Sim2Phy Cross-Domain Evaluation Framework for Autonomous Driving VLA Models
链接: https://arxiv.org/abs/2608.14024
作者: Haojie Feng,Peizhi Zhang,Xinrui Zhang,Zhuoren Li,Junpeng Huang,Xiurong Wang,Dongxiao Yin,Yuxiang Zhang,Junfan Zhu,Lu Xiong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language-action (VLA) models for autonomous driving jointly produce scene interpretation, language-based reasoning, and driving trajectories. Existing evaluations often use independently selected synthetic, simulated, and physical data, so measured performance gaps can be confounded by changes in scenario content rather than genuine domain sensitivity. We propose SSP (Synthetic-Simulation-Physical), an event-matched Syn2Sim2Phy evaluation framework that anchors cross-domain comparison to the same safety-critical interaction. Starting from a synthetic long-tail video, SSP builds a validated event specification that preserves road topology, participant roles, relative motion, conflict evolution, passing order, response constraints, and event phases. Platform-specific realizations are then constructed in CARLA and on a closed proving ground and are evaluated only after transfer audits confirm preservation of mandatory event properties. SSP maps heterogeneous outputs from OpenEMMA, LLaViDA, and Alpamayo-R1 into common semantic slots and a 1 s trajectory window to assess output validity, semantic accuracy, critical-interaction recognition, trajectory quality, and risk response. Across Cut-in and vulnerable-road-user crossing cases, the macro-averaged Integrated VLA Capability Scores are 0.259, 0.291, and 0.325 in the Synthetic, Simulation, and Physical domains, respectively, while the best domain varies by scenario. Alpamayo-R1, OpenEMMA, and LLaViDA obtain scores of 0.405, 0.338, and 0.131. SSP provides a reproducible scene-transfer chain and an evidence-qualified evaluation of VLA behavior without assuming that the Physical domain is universally superior.
[CV-45] ForgeWM: Progressive Causal Training for Few-Step Action-Conditioned Video World Models
链接: https://arxiv.org/abs/2608.14022
作者: Xinye Li,Lingshuai Lin,Lei Wang,Liuzhou Zhang,Jialin Cui,Qingshan Li,Guanchu Wang,Qingbin Liu,Xi Chen,Jiang Bian,Wai Lam
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Action-conditioned video world models require low-latency causal generation and reliable responses to game-native controls. Although causal distillation enables one- or few-step video synthesis, extending it to interactive world models remains challenging, as discrete keyboard states and continuous mouse motion must remain aligned with temporally compressed latent chunks during causal training and autoregressive rollout. We introduce ForgeWM, a progressive framework that transforms a bidirectional action-conditioned video generator into efficient few-step world models through domain adaptation, teacher-forced causal training, causal consistency distillation, and on-policy distribution matching with a bidirectional teacher. The resulting budget-specialized students operate at steady-state denoising budgets of 1, 2, and 4 steps. ForgeWM further supports a dual-path deployment protocol combining latency-critical interaction with optional replay-time refinement, where the one-step student re-noises and refines its saved draft. On paired Minecraft trajectories, ForgeWM leads the evaluated systems in Imaging Quality, reference-aligned motion-profile agreement, action-sign accuracy, and mouse-control accuracy, while achieving the lowest reference LPIPS; the same four-stage recipe transfers to gamepad-controlled FPS gameplay. Replay-time refinement matches four-step reference quality while remaining roughly three times closer to the experienced trajectory than regeneration from noise. These results demonstrate ForgeWM’s effectiveness for controllable few-step video generation.
[CV-46] Content Based Video Narration of Gameplay with Vision Language Models
链接: https://arxiv.org/abs/2608.14016
作者: Mathew Varghese
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注: this https URL
Abstract:Live game commentary is scarce: it exists for professional esports broadcasts and almost nowhere else. We present a content-based video narration system that produces spoken, esports-style commentary for arbitrary gameplay recordings using a general-purpose vision-language model (VLM) and a text-to-speech back end, with no game-specific instrumentation, no engine telemetry, and no task-specific training. Three mechanisms carry the system. Temporal mosaic packing arranges nine uniformly sampled frames into a single 3x3 image, letting an image-native VLM reason about motion while consuming one image payload per segment instead of nine. Context-conditioned prompting replays the K most recent narrations as assistant-role history, suppressing the repetition that dominates per-segment captioning of static scenes. Duration-conditioned generation and elastic alignment constrain narration length in the prompt, then time-scale or symmetrically pad the synthesized audio so each utterance fills its segment slot exactly, giving frame-accurate muxing without a forced aligner. The implementation supports either cloud TTS or a 6-bit quantized 4B-parameter on-device TTS model on Apple silicon, making the speech stage fully local. We report a qualitative case study on real-time strategy footage, a cost model showing the mosaic reduces per-minute image payloads by 9x, and a candid account of observed failure modes - hallucinated game state, resolution loss from mosaicking, and prosody artifacts from time-scaling. We release the system as a reproducible baseline, with an evaluation protocol for the quantitative study a full version will report.
[CV-47] MedClaw: Heuristic Agent Harness for Long-Horizon Surgical Video Reasoning
链接: https://arxiv.org/abs/2608.14015
作者: Yingying Fan,Penghui Du,Leyan Zhu,Runze He,Zimeng Wu,Yuxuan Zhang,Liang Chen,Jiahao Xie,Jiangtang Wang,Shuai Shao,Anchao Yang,Yutong Bai,Yan Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Understanding tens-of-minutes surgical videos requires long-horizon temporal reasoning, answering what happens before, after, or across stages of a procedure by grounding the question in visual evidence spread across time. Existing approaches handle this poorly: a one-shot vision-language model (VLM) compresses the whole procedure to fit its context window and loses the detail a “before” or “after” question depends on, while video agents that train the model where to look are data-hungry and transfer poorly to out-of-domain surgery. We build an agent harness that separates reasoning from perception and improves by evolving context rather than optimizing weights. A text-only orchestrator plans which evidence to gather and issues an auditable sequence of tool calls, while frozen vision-language sub-agents execute each call over the pixels, viewing, cropping, inspecting frames, and retrieving external knowledge. We further propose a gradient-free, reward-gated Heuristic Skill Distillation loop that mines the agent’s own low-scoring traces and keeps a candidate skill only when it raises a validation reward, yielding reusable retrieval skills, notably directed re-look. Growing an external skill library rather than tuning weights, the loop adapts from only about 100 labeled examples, far fewer than supervised or reinforcement fine-tuning requires. To evaluate this agent, we introduce MedClawBench, a de-leaked, doctor-grounded benchmark of 1,123 questions over self-built long neurosurgery recordings and a held-out public lecture-video test split. Across both datasets and all four evaluation dimensions, our agent consistently outperforms one-shot VLMs and general video-agent frameworks, with the largest gains on the long, out-of-domain neurosurgery videos. Project page: this https URL.
[CV-48] FIRM: Fine-Grained Intra-Token Representation of Masks for Remote Sensing Reasoning Segmentation
链接: https://arxiv.org/abs/2608.13980
作者: Weidong Tang,Kaiyu Li,Yikai Wang,Yanan Wu,Haotian Gan,Shihong Wang,Xiangyong Cao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reasoning segmentation requires multimodal large language models (MLLMs) to translate implicit instructions into precise pixel-level masks. MLLMs encode an image as visual tokens, each of which merges a group of image patches. In remote sensing images, small targets, thin structures, and adjacent instances can occupy different parts of the same visual token. Assigning a single binary mask label to such a token loses its internal spatial structure, causing nearby targets to merge and object boundaries to become coarse. To bridge this representational gap, we introduce FIRM, a Fine-grained Intra-token Representation of Masks. For each visual token, FIRM predicts a mask code that specifies an r\times r binary sub-cell pattern rather than a single foreground/background label. Given a target identified by the MLLM, the complete grid of mask codes is predicted in one mask pass. Fixed lookup converts the predicted codes into a discrete sub-cell mask, while marginalizing the code distribution yields a soft structural field. To further recover fine-grained boundaries within each sub-cell, we introduce a lightweight continuous renderer that refines this field using pre-merge visual features and image details. Across five reasoning and referring segmentation benchmarks on satellite and UAV images, FIRM achieves leading results, including 70.5/80.5 gIoU/cIoU on LaSeRS and a 3.0 -point average gain on EarthReason. These results demonstrate the value of explicitly representing intra-token mask patterns for fine-grained MLLM segmentation.
[CV-49] ProFocus: Interpreting Affective Experience in Artistic Images with Progressive Visual Focusing
链接: https://arxiv.org/abs/2608.13974
作者: Zhiyan Zhang,Zicheng Yan,Jianqi Chen,Peipei Song,Shanshan Wang,Xun Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Interpreting the emotional responses triggered by images is central to achieving emotional intelligence. Compared with natural images, visual art is intentionally created to elicit emotional responses from its viewers through abstract concepts and visual metaphors, making affective interpretation particularly challenging. However, most existing methods rely on general-purpose visual embeddings (e.g., CLIP), failing to capture the nuanced cues underlying artistic emotion. To address this gap, we propose \textbfProFocus, a novel framework that models affective experience in artistic images via progressive visual focusing. The key idea is to model visual representation learning inspired by a hierarchical cognitive theory of human aesthetic appreciation. Technically, ProFocus contains two core components: a Hierarchical Art Critic (HAC) and a Progressive Hint Fusion (PHF) module. HAC leverages multimodal large language models to generate structured linguistic priors at three cognitive levels–atmospheric style, narrative subjects, and concrete details–thereby translating artistic perception into coherent semantic guidance. Building upon these priors, PHF departs from conventional cross-modal fusion by sequentially injecting the hierarchical hints into visual features, enabling a progressive focusing process that mirrors human perception. This design allows the model to capture subtle affective cues and produce more faithful explanations. Extensive experiments on the ArtEmis v1.0 and v2.0 datasets demonstrate that ProFocus consistently outperforms state-of-the-art methods in both emotion recognition and affective explanation. Project page: this https URL.
[CV-50] Rethinking Auxiliary Modalities in Multimodal Zero-shot Anomaly Detection: From Semantic Fusion to Conditional Modulation
链接: https://arxiv.org/abs/2608.13973
作者: Peng Wu,Xin Ge,Yujia Sun,Guansong Pang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent foundation model-based methods have endowed RGB images with strong zero-shot anomaly detection (ZSAD) through vision-language pretraining. However, RGB observations alone remain limited in perceiving anomalies dominated by geometric deformation, depth variation, or subtle surface changes. Auxiliary modalities can provide complementary structural information, but existing multimodal methods typically fuse them directly into a shared semantic space, which may disturb the text-aligned anomaly semantics established by RGB foundation models and often requires modality-specific architectures. To address this issue, we propose a plug-and-play auxiliary-conditioned enhancement framework for zero-shot anomaly detection. Instead of reconstructing a joint multimodal anomaly semantic space, our framework preserves the original RGB image-text anomaly matching pathway and uses auxiliary observations as conditional signals for RGB feature refinement, allowing auxiliary modalities to seamlessly enhance existing RGB-based zero-shot anomaly detectors. Specifically, a lightweight meta-learning module takes global RGB and auxiliary representations as input and generates sample-adaptive low-rank residual updates to determine how RGB features should be refined. We further construct uncertainty-aware spatial modulation from the initial RGB anomaly response and auxiliary reliability, which determines where local residual updates are strengthened or suppressed. This global-to-local conditional modulation enables selective multimodal enhancement while preserving the original RGB anomaly semantics. Extensive experiments on MVTec 3D-AD and Eyecandies demonstrate that our framework consistently improves multiple popular RGB-based zero-shot anomaly detectors, achieving state-of-the-art performance for multimodal zero-shot anomaly detection.
[CV-51] PPOM: Marginalizing Patch-Grid Phase for CLIP-Based Generalizable Vision-Language Prompt Tuning
链接: https://arxiv.org/abs/2608.13969
作者: Liang Wang,Haoyang Li,Chao Wang,Guodong Long,Jing Jiang,Yan Peng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Prompt tuning adapts CLIP-based vision-language models with few trainable parameters, yet its predictions remain sensitive to the spatial sampling imposed by a frozen vision transformer. In particular, non-overlapping patch tokenization makes predictions depend on the alignment (phase) between image and the patch lattice. To reduce prediction sensitivity to patch-grid alignment, we introduce Patch-Phase Orbit Marginalization (PPOM), a training-free inference operator that treats phase shift as a nuisance variable. Given a patch stride, PPOM evaluates the identity view and reflection-padded translations, pairs opposite shifts into horizontal, vertical, and diagonal antithetic families, and assigns equal mass to these families and the identity prediction to avoid view-count bias during phase integration. In summary, PPOM provides a deterministic interface between prompt adaptation and patch-grid sensitivity. Across multiple prompt-learning hosts, PPOM improves host performance without re-training.
[CV-52] SAFE: Scene-Aware Feature Modulation for Color Constancy with Learned Color Space in Pure-Color Scenes
链接: https://arxiv.org/abs/2608.13967
作者: Yuan-Kang Lee,Kuan-Lin Chen,Chih-Heng Chang,Jian-Jiun Ding
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Color constancy on pure-color scenes is challenging: when most pixels share a narrow band of hues, every chromaticity-based cue collapses to a single point and standard estimators become ambiguous. We propose a compact framework that couples two innovations: (i) SAFE, a Scene-Aware FeaturE modulation network that organizes illumination cues into a structured four-token representation, which is then selectively reweighted based on scene complexity features; (ii) the Learned Color Space (LCS), a scene-dependent chromaticity normalization that directly addresses the chromaticity collapse problem for pure-color scenes. Experiment results show that SAFE consistently improves performance in pure-color scenes. Compared to the best-performing baseline in each metric, it reduces the mean angular error by 10%, the best-25% error by 20%, and the worst-25% error by 5.8%.
[CV-53] Fast Implicit Neural Light Field Representation via Geometric Decomposition and Multi-Resolution Low-Rank Features
链接: https://arxiv.org/abs/2608.13949
作者: Yao Guo,Ligen Shi,Shuchen Sun,Jun Qiu,Chang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 2 figures, submitted to IEEE CYBER 2026
Abstract:Implicit neural representations provide a compact and continuous way to reconstruct dense light fields from sampled ray coordinates. However, fast light field reconstruction remains challenging because a light field is a high-dimensional signal with strong spatial-angular redundancy and structured disparity variations. Directly fitting 4D ray coordinates with a neural network often requires considerable optimization time to recover both view appearance and cross-view consistency. To address this issue, this paper proposes a fast implicit light field representation based on geometric decomposition and multi-resolution low-rank features. The proposed method decomposes a 4D light field into a horizontal disparity plane, a spatial texture plane, and a vertical disparity plane. Each plane is represented by a low-rank structure that combines a low-resolution 2D grid with the element-wise product of two high-resolution 1D line features at multiple resolution levels. The fused features are decoded by a lightweight multilayer perceptron to predict RGB values. Experiments on public light field datasets show that the proposed method achieves competitive reconstruction quality while providing a better trade-off among model parameters, training time, and inference efficiency.
[CV-54] CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification
链接: https://arxiv.org/abs/2608.13939
作者: Bingxin Yu,Xueli Wang,Jerry Zhou,Wenyan Wang,Li Wen,Lan Huang,Xin Feng,Fengfeng Zhou,Kewei Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framework standardizes diagnosis through five ultrasound feature categories that are aggregated into five risk levels (TR1-TR5). Although widely adopted in clinical practice, most deep learning approaches focus on binary malignancy classification, while multi-class prediction and explicit utilization of feature-level supervision remain underexplored, largely due to limited annotated data. In this study, we introduce the STN dataset of 600 thyroid nodules with paired transverse and longitudinal ultrasound images, bounding box annotations, and complete labels for all five TI-RADS feature categories. Following the clinical decision process, we investigate how structured feature information can guide representation learning during training while requiring only images at inference. We demonstrate that text embeddings derived from standardized feature descriptions form a stable surrogate representation for TI-RADS risk levels. Based on this observation, we propose CMCNet, which aligns image embeddings to fixed textual embeddings via a Center-Margin Contrastive Loss that simultaneously promotes intra-class compactness and inter-class separation. Experimental results show that this embedding alignment strategy is more data-efficient and robust than direct multitask learning, and consistently outperforms InfoNCE, center loss, a strong multitask baseline, and a VQA-style multimodal model, particularly in imbalanced settings. The dataset is freely available at doi: https://doi.org/10.5281/zenodo.19125693 and the source code is available at: this https URL.
[CV-55] CoANeRV: Coordinate-Aware Token-Space Neural Video Representation
链接: https://arxiv.org/abs/2608.13938
作者: Jialong Guo,Ke Liu,Mengxuan Li,Jiajun Bu,Haishuai Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 27 pages, 13 figures. Code is available at this https URL
Abstract:Neural representations for videos (NeRV) have shown strong reconstruction fidelity by storing video-specific information in network weights. However, existing formulations typically require either costly per-video optimization or video-specific weight generation, making it difficult to scale to efficient amortized video representation. We propose CoANeRV, a coordinate-aware token-space framework that adapts the broader token-conditioned neural-field paradigm to amortized video representation. CoANeRV forms compact video tokens in one feed-forward pass and uses a shared coordinate-conditioned decoder to reconstruct continuous spatio-temporal queries, avoiding per-video decoder optimization or generation while retaining coordinate-level reconstruction flexibility. To make token-space reconstruction effective, CoANeRV introduces a coordinate-aware decoding architecture that aligns spatio-temporal queries with video tokens through axis-adaptive positional encoding and temperature-modulated cross-attention. Block-wise coordinate querying further reduces peak attention memory, making high-resolution reconstruction practical. Experiments on diverse video datasets show that CoANeRV consistently improves reconstruction quality over prior feed-forward NeRV and INR baselines, reduces peak memory compared with attention-based coordinate decoders, and provides efficient amortized encoding without per-video optimization. These results support the proposed video-specific combination of feed-forward token formation, spatio-temporal coordinate retrieval, and memory-bounded dense querying. The code is available at this https URL.
[CV-56] RGBX-Next: Towards Realistic Generative Rendering from G-Buffers
链接: https://arxiv.org/abs/2608.13929
作者: Zheng Zeng,Marco Salvi,Lifan Wu,Jan Novák,Daqi Lin,Saeed Hadadan,Yichen Sheng,Robert Pottorff,Shiqiu Liu,Ravi Ramamoorthi,Ling-Qi Yan,Miloš Hašan
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Diffusion models have achieved impressive results in image, video, and streaming generation. However, compared to traditional 3D rendering, they still lack precise control over the generated output. We believe a viable path forward is to use generative models as learned renderers conditioned on traditionally rendered G-buffers. We introduce RGBX-Next, a unified generative framework for forward and inverse rendering, which allows estimating G-buffers from images, videos, and streams, and rendering realistic images, videos, and streams from G-buffers. Our key contribution is a general recipe for finetuning diffusion transformer (DiT) models into generative forward and inverse renderers. We show that the resulting models achieve high quality in both realistic generative rendering and intrinsic decomposition. We will make all our models publicly available. We believe that the design principles presented in this paper will benefit future research on controllable generative forward and inverse rendering.
[CV-57] OpenBelief-Nav: Evidence-Preserving Object Memory for Open-Vocabulary Language-Guided Navigation
链接: https://arxiv.org/abs/2608.13923
作者: Dinh Tuan Nguyen,Anh Dao,Phuong Nam Dang,Quan-Dung Pham,Tuyen P. Le,Truong Nguyen,Quan Nguyen
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Open-vocabulary 3D scene graphs provide compact semantic memory for language-guided navigation, but mapped objects are often exposed through a single fused feature or committed semantic label. Such commitment can remove minority yet task-relevant hypotheses from the task-time interface. We present OpenBelief-Nav, an evidence-preserving object memory that retains observation-level phrases, reliability cues, and frame-mask provenance while maintaining separate aggregate geometric and visual representations. Semantically related phrases are consolidated into a vocabulary-independent object belief from which task-specific readouts perform fixed-vocabulary projection or free-form retrieval. On five ScanNet200 and eight Replica scenes, full-belief projection achieves mIoU scores of 0.2742 and 0.2912, compared with 0.2393 and 0.2701 for a matched early-commit readout. Across 78 HM3D-YCB navigation trials, consensus and early-commit retrieval each achieve 60/78 successes, compared with 58/78 for belief-weighted retrieval and 55/78 for DualMap. Across 20 Unitree G1 runs organized as 10 matched evaluation cases, a correction policy permitting at most two verified candidate attempts improves target-confirmation success from 6/10 to 8/10 relative to top-1-only execution. Code will be released upon acceptance at this https URL.
[CV-58] Beyond Control Points: Arcsecond Relative-Motion Estimation of Vision Measurement Platforms With Incomplete or Absent Control Fields
链接: https://arxiv.org/abs/2608.13918
作者: Meng Lian,Jian Wang,Shuixin Pan,Haibo Liu,Yueqiang Zhang,Yulan Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 14 figures. Submitted to IEEE Transactions on Image Processing
Abstract:Long-range vision-based deformation monitoring is highly sensitive to motion of the camera platform. Absolute-pose differencing typically relies on dedicated control data and propagates two independent pose errors into the relative-motion estimate. We develop a control-adaptive differential framework that estimates inter-frame platform motion directly from image displacements and known 3D points. With no dedicated control point, the framework recovers platform rotation from measurement-point observations. One surveyed control point enables prior-constrained translation recovery, while two nonparallel control rays recover full 3D translation. The framework requires neither nonlinear optimization nor an initial pose estimate. Excluding control data from the rotation stage makes the rotation estimate exactly immune to contamination confined to the control field. The inherited differential formulation also cancels translational extrinsic errors exactly. We derive the rotation observability condition, a leakage bound for unmodeled translation and nonrigid point motion, and the single-point axial-prior bias law. Under 0.5-pixel image noise, attitude changes of up to 30~arcmin, and 3D point perturbations of up to 2~mm, the multi-camera estimator achieves a rotation RMSE of 2.97~arcsec and an average runtime of 0.46~ms. With one surveyed control point, its prior-constrained translation RMSE is 1.19~mm. In a bridge experiment without a stable control field, the median coordinate-wise displacement RMSE relative to total-station measurements is 0.85~mm. The estimator also maintains zero divergence under the tested 3D coordinate perturbations on public RGB-D and stereo sequences. These results establish state-of-the-art accuracy, calibration robustness, and computational efficiency among the evaluated methods.
[CV-59] Consensus-gated Multi-Agent Neural Architecture Search for Seismic Fault Segmentation
链接: https://arxiv.org/abs/2608.13889
作者: Shehram Baig,Ahmad Mustafa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Neural networks for seismic fault segmentation are often borrowed from computer vision and medical imaging domains where they train under relatively much larger labeled data resources. Optimizing their architecture under tight labeled data budgets as are common in geophysical applications is not a trivial problem. Manually designing data-optimal architectures is time-consuming while classical neural architecture search (NAS) is restricted to hand-crafted search spaces and large compute budgets. We present an agentic NAS system in which a panel of three large language models (Claude, GPT-5.1, and Gemini~2.5~Pro) debates each candidate architecture to unanimous consensus, authors the complete PyTorch implementation, cross-reviews it, and submits it to an automated validate-train-score loop with a hard 450K parameter budget, keep-or-revert lineage, and a memory of failed mechanisms. Operating on source code rather than a predefined operation menu, the search ran on a single consumer GPU and trained only eight candidates. It discovered \ours: a 425K-parameter encoder-decoder with a strip-pooling bottleneck, squeeze-and-excitation gating, an asymmetric one-conv decoder, and a feature-pyramid fusion neck. Trained under a protocol identical to all baselines on sections derived from the Thebe fault dataset, it attains the highest F1 (0.578) and IoU of all models tested while being the smallest, outperforming a published-capacity U-Net (31M parameters, F1 0.484), DeepLabV3-ResNet50 (39.6M, 0.516), an Attention U-Net(1.83M, 0.502). The search cost 101 LLM calls ( \sim 1.15M input / 0.39M output tokens) and roughly one GPU-day, making consensus-gated LLM panels a practical, low-cost route to domain-specific architecture discovery.
[CV-60] Attention Capture Is Not Detection: A Two-Stage Account of How Humans Miss Localized AI Image Edits
链接: https://arxiv.org/abs/2608.13865
作者: Chiao-Chieh Deng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 4 figures
Abstract:As AI-generated image edits proliferate, the platforms meant to curb the resulting disinformation treat detectability as a single, undifferentiated property: an edit either gets a warning or it does not. We show this is the wrong model. Across a controlled eye-tracking study ( N=59 , Latin-square design, four conditions crossing edit area and semantic plausibility), a mixed-effects analysis reveals that whether an edit is noticed and whether it is correctly judged as fake are dissociable stages, governed by different factors: edit area drives attention capture ( p0.001 ) while semantic plausibility drives judgment accuracy and look-but-fail-to-see (LBFS) error rates ( p0.001 ). This dissociation survives correction for multiple comparisons; a secondary interaction between the two factors does not. This two-stage account extends a long-standing distinction in visual attention research (between pre-attentive capture and effortful recognition) into the new domain of AI-edit detectability. We then test whether a generative eye-movement model can computationally operationalize the attention-capture stage: a Transformer trained to generate scanpaths tracks per-image attention with strong discriminative power (Pearson r=0.77 – 0.82 across held-out stimuli) and, on the harder task of predicting LBFS incidence, modestly outperforms a two-parameter linear baseline even without access to the plausibility label ( r=0.52 vs. r=0.48 ). We report this comparison, our ablations, and our method’s limitations (a single fixed train/validation split, not leave-one-subject-out) without inflation, consistent with responsibly communicating what a machine learning system can and cannot do to help curb AI-driven disinformation.
[CV-61] XSA-MAD: Cross-modal Semantic Alignment for Morphing Attack Detection ICIP2026
链接: https://arxiv.org/abs/2608.13861
作者: Jie Jin,Mahiro Tokumasu,Yu Makino,Masakatsu Nishigaki,Tetsushi Ohki
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted to ICIP2026
Abstract:Morphing attacks pose a serious threat to face recognition systems. However, existing image-based morphing attack detection (MAD) methods often generalize poorly to unseen generation techniques because they rely solely on visual cues. We propose XSA-MAD, a CLIP-based multimodal framework that explicitly models semantic inconsistencies between bona-fide and morphed faces. Morphing concepts are decomposed into four interpretable attributes, including identity, facial geometry, texture, and consistency, and are encoded as structured and attribute-aware textual representations. The image encoder is progressively aligned with this discriminative textual space, resulting in a unified semantic representation that captures generation-invariant and concept-level discrepancies between bona-fide and morph images. Experiments on MAD22 and MorDIFF, following training on SMDD, demonstrate strong generalization across diverse morphing principles. In particular, XSA-MAD achieves an equal error rate of 2.92% on GAN-based morphs and consistently outperforms existing methods under high-fidelity generative attacks.
[CV-62] Face Re-morphing: Differential Morphing Attack Detection via Feature-Space Similarity Changes
链接: https://arxiv.org/abs/2608.13858
作者: Jie Jin,Masakatsu Nishigaki,Tetsushi Ohki
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted to IJCB2026
Abstract:Face morphing attacks pose a serious threat to face recognition systems because a single morphed document image can be matched to multiple contributors. Differential morphing attack detection (D-MAD) addresses this threat by comparing a document image with a trusted live image, but existing methods often rely on static feature differences, constituent-face reconstruction, or multi-cue fusion. This paper proposes Face Re-morphing, a D-MAD method that uses the feature-space response to an additional morphing operation as a detection cue. Given a document image and a trusted live image, the proposed method generates a re-morphed image and uses the change between the document–live and live–re-morphed cosine similarities as the detection score. Experiments on FRLL-Morphs and FEI Morph show that the proposed cue is effective across different morphing conditions, re-morphing methods, and face recognition models. Comparisons with existing methods show favorable results on AMSL and indicate that the proposed method performs well under the Criminal condition on FEI Morph Version~1, particularly when using MorDIFF. These results indicate that re-morphing-induced similarity change provides a complementary cue for D-MAD.
[CV-63] Doomed to Re-Annotate Forever: The ImageNet Story
链接: https://arxiv.org/abs/2608.13783
作者: Illia Volkov,Nikita Kisel,Tetiana Mishkina,Klara Janouskova,Jiri Matas
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 25 pages, 16 figures, 8 tables. Project page: this https URL
Abstract:Top-1 accuracy on ImageNet-1k remains the most commonly reported metric in visual recognition. Quality issues with the dataset have been repeatedly reported, yet the original 2012 noisy labels are still predominantly used. The paper presents a comprehensive effort, which goes well beyond prior correction attempts, towards obtaining accurate and complete ImageNet-1k validation set annotations. The result, ReImageNet, includes multilabel correction, object localization, revised class definitions, and semantic attributes (text-recognition, rendition, reflection, crowd, dominant). The reannotation reveals that approximately 12% of the original ImageNet-1k labels are incorrect, 33.3% of images are multilabel and 3.8% contain no object from an ImageNet-1k class. With the new labels, top-1 accuracy increases by up to 1.2% for supervised models and by 5-6% for MLLMs. We argue that annotation at ImageNet scale cannot realistically be completed in one pass, as errors and definitional issues are discovered only through annotating, and we build our pipeline around repeated refinement and error checking. We observed that human and LLM collaboration with appropriate tooling represents the current quality ceiling for annotation at this scale. ImageNet-1k issues propagate into its derivative test sets, indicating that the problem is structural rather than specific to any single benchmark. All annotations, class definitions, guidelines, and analysis code have been publicly released. Project page: this https URL Annotations: this https URL Code: this https URL
[CV-64] Kolmogorov-Arnold Networks for Spatially Independent Multispectral Land Classification
链接: https://arxiv.org/abs/2608.13769
作者: Katherine L. Bauer,Teemu Harkonen,Simo Sarkka,Arturo Sanchez-Azofeifa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 10 figures
Abstract:Land classification from satellite imagery is important for land management, environmental monitoring, and urban planning. Machine learning methods such as random forests and multilayer perceptrons have shown strong performance on multispectral data, while the Kolmogorov-Arnold network has emerged as an alternative architecture with compact model structures. This study evaluates the Kolmogorov-Arnold network for land classification using Landsat 8 imagery and compares it with random forest and multilayer perceptron models. The models were trained and tested on data from Edmonton, Alberta and evaluated on an independent dataset from Calgary, Alberta across five land classes: agriculture, urban, water, forest, and bare ground. For the Calgary dataset, the Kolmogorov-Arnold network matched the accuracy of the random forest and outperformed the multilayer perceptron, while requiring substantially fewer trainable parameters and providing greater interpretability.
[CV-65] ChartProbe: A Diagnostic Study on Visual Reasoning through Perception Grounding and Simple Reasoning
链接: https://arxiv.org/abs/2608.13766
作者: Mahsa Khoshnoodi,Sarah Adel Bargal
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models (VLMs) remain unreliable on chart questions that require reasoning over visual quantities, and this weakness is usually attributed to a reasoning deficit and addressed with more reasoning supervision. We ask whether the difficulty lies in reasoning itself, or in the simpler skills that reasoning operates on: reading the plotted elements (\emphperception), locating them and binding them to their labels (\emphgrounding), and performing single-step computations such as ranking, totals, and differences (\emphsimple reasoning). We introduce \textbfChartProbe, a diagnostic framework whose probes are generated directly from the code that renders each chart, so every gold answer is exact by construction, needs no human annotation, and attributes each failure to a single skill. ChartProbe enables an intervention prior work does not attempt: instead of synthesizing complex-reasoning data, we withhold complex questions and reasoning traces entirely, fine-tune on one simple skill at a time, and measure transfer to held-out complex-reasoning questions. Across three open-weight VLMs, supervising the simpler skills alone produces large gains on complex-reasoning questions the model never trained on: where these skills are weak and the model can be taught to read the image, training them recovers much of complex reasoning at no reasoning-data cost. The gains hold across three out-of-distribution settings: an unseen chart type (pie charts), a human-written benchmark disjoint from our images and templates (ChartQA), and a non-chart visual domain (CLEVR). Complex visual reasoning can therefore improve without complex-reasoning supervision.
[CV-66] CAST: Closed-form Analytic Semantic Transfer for Zero-Shot Classifier Extension
链接: https://arxiv.org/abs/2608.13751
作者: William Heyden,Habib Ullah,Muhammad Salman Siddiqui,Fadi Al Machot
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large pre-trained models have become foundational components of modern machine learning systems. Yet adapting these models to novel categories typically requires examples from the target distribution. In many domains, however, such data are unavailable. Zero-shot learning (ZSL) permits recognition under these limitations through relying on auxiliary semantic information such as textual descriptions. We introduce CAST (Closed-form Analytic Semantic Transfer), a training-free, image-free framework for extending a pre-trained classifier to previously unseen classes through weight injection. We provide a theoretical foundation for CAST and derive a finite-sample error decomposition that identifies the \emphsemantic extrapolation residual \rho_u . The residual is a computable, model-agnostic measure and provides a principled criterion for dataset curation and benchmark design. Experiments on standard zero-shot learning benchmarks demonstrate that CAST matches or exceeds existing image-free approaches and approaches the performance of few-shot adaptation methods, while requiring neither iterative optimization nor examples from the target distribution.
[CV-67] Limitations of Synthetic Data Generation in Specialized Data-Scarce Domains
链接: https://arxiv.org/abs/2608.13729
作者: Edward Zhang,Marcel Hussing,Tanay Tandon,Shenbagaraj Kannapiran,Jason Hughes,Youkang Wang,Joshua Caswell,Agelos Kratimenos,Yi Fan Li,Milan Manoj,Ethan Sanchez,Sumukh Shrote,Camillo Jose Taylor,Daniel A. Hashimoto,Eric Eaton
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Advances in diffusion-based generative models have motivated the use of synthetic image generation to alleviate data scarcity in vision tasks. While this strategy has shown promise in natural image benchmarks such as ImageNet, its effectiveness in sparse, high-variance real-world domains remains unclear. In this work, we focus on domains where images differ substantially from common image datasets and additional data are expensive to obtain. Against non-generative data augmentation baselines, we evaluate the downstream classifier performance improvements yielded by two schools of generative sparse data extension: distribution modeling and sample perturbation. Across five trauma classification tasks using subject-wise train–validation splits, no generative approach consistently outperforms a strong non-generative baseline. Feature-space analysis reveals recurring failure modes: memorization or collapse, distributional drift, and generation of visually plausible but simplified canonical instances that are easier to classify than real data.
[CV-68] SAGE: Surrogate-gradient Adaptation via Attention-Guided Entropy for Spiking Transformers
链接: https://arxiv.org/abs/2608.13702
作者: Kiran Nair,Rodrigue Rizk,KC Santosh
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
备注: In-Review at a Conference
Abstract:Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep neural networks by exploiting sparse event-driven computation, but their training remains challenging because the non-differentiable spike function requires surrogate gradients whose fixed shape may be suboptimal across layers and training stages. In this work, we introduce SAGE, an uncertainty-modulated surrogate-gradient mechanism for Transformer-based SNNs. SAGE estimates block-level uncertainty from normalized self-attention entropy and uses this signal to adapt the surrogate-gradient slope during training while leaving the inference model unchanged. By modulating only the training-time surrogate parameter, the proposed method preserves the original architecture and deployment cost while improving optimization flexibility. Experiments on CIFAR-10/100 demonstrate that SAGE achieves improved accuracy over fixed-surrogate baselines, with results up to 1-2% consistent gains across multiple simulation time steps. These results highlight the potential of attention-derived uncertainty as a lightweight training signal for adaptive surrogate-gradient learning in transformer-based SNNs.
[CV-69] MedPlex: Deep Vision-Language Co-Adaptation for Clinically Grounded Medical Segmentation BMVC-2026
链接: https://arxiv.org/abs/2608.13690
作者: Rafi Ibn Sultan,Hui Zhu,Chengyin Li,Dongxiao Zhu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted By BMVC-2026
Abstract:Medical image segmentation is still largely treated as a vision-only problem, although clinical interpretation often relies on textual knowledge of anatomy, location, appearance, and surrounding context. Existing text-guided segmentation methods within the Vision-Language Model (VLM) paradigm often use language only as a late conditioning signal, limiting its influence on visual representation learning. We introduce MedPlex (Medical Plexus of Vision and Language), an end-to-end VLM framework that makes text guidance a continuous, clinically grounded component of segmentation learning. Through Bi-Fusion (Bidirectional Fusion), visual and textual representations evolve jointly across the encoding hierarchy. MedPlex further introduces class-level and region-level concept alignment to organize the shared representation at complementary granularities. Class-level alignment anchors each anatomical target to an aggregated clinical concept profile, while region-level alignment preserves individual concepts, such as shape, location, appearance, and texture, through class-specific visual evidence. In this way, language provides structured supervision throughout the encoder rather than serving only as a late-stage cue. MedPlex achieves state-of-the-art performance across CT and MR benchmarks for multi-organ, cardiac substructure, and tumor segmentation, including settings with real free-text clinical supervision. Code: this https URL.
[CV-70] PROVE: Training-Free Prompt Recovery using Verifiable Evidence
链接: https://arxiv.org/abs/2608.13671
作者: Rupayan Mallick,Mahsa Khoshnoodi,Sarah Adel Bargal
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Modern text-to-image models can generate highly realistic images from natural-language prompts, while recent advances in prompt inversion have made it increasingly feasible to recover those prompts from generated outputs, raising new concerns for copyright protection and content ownership. As prompt marketplaces emerge, recovered prompts can enable both the unauthorized reproduction and redistribution of copyrighted creative works, and the exposure of the prompts that encode an artist’s creative recipe in AI-generated content. Existing prompt inversion methods rely on gradient-based optimization, autoregressive captioning, or reinforcement learning. However, optimization-based methods often produce unreadable prompts, captioning methods hallucinate unverified details, and RL-based approaches frequently overfit to specific generators while introducing evaluation circularity. We introduce PROVE (Prompt Recovery with Verified Evidence), a training-free, black-box prompt inversion attack that reconstructs prompts by composing verifiable scene descriptions rather than optimizing token sequences, targeting both original copyrighted works and AI-generated content. The resulting prompts are fully auditable, with every recovered claim grounded in explicit image evidence, and are formalized through a precision-constrained recall maximization objective. Across MS-COCO, Flickr30K, and Lexica, using state-of-the-art text-to-image generators, PROVE consistently outperforms optimization, captioning, and RL-based baselines on image similarity (DINO, LPIPS) and text-image alignment (CLIP), without any training, generator access, or fine-tuning, demonstrating a stronger and more practical prompt inversion attack.
[CV-71] Multiphase-Diff: Diffusion-Based Generative Modeling for High-Contrast Multiphase Physical Systems with Sharp Interfaces
链接: https://arxiv.org/abs/2608.13669
作者: Yining Huang,Zhenyu Liang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Physics-constrained diffusion for high-contrast, sharp-interface multiphase fields faces three coupled difficulties. At coefficient jumps, expanded pointwise strong-form PDE residuals contain singular gradient terms that can penalize physical interfaces. Under extreme contrast, low-magnitude phases may fall below the diffusion noise floor and be erased, misscaled, or generated with negative coefficients, while a global likelihood scale allows high-magnitude phases to dominate supervision. We therefore propose Multiphase-Diff, which makes three corresponding contributions: (i) a conservative flux residual that avoids differentiating discontinuous coefficients and enforces discrete conservation; (ii) an analytic bijective representation that maps low-amplitude signals to order-one latent scales and guarantees coefficient positivity through exponential decoding; and (iii) a Jacobi-preconditioned likelihood that normalizes local residual scales for balanced supervision. Experiments on three complementary multiphase benchmarks demonstrate the superiority of Multiphase-Diff over seven baselines in both physical and distributional fidelity and its robustness across phase contrasts and compositions, establishing its effectiveness for scientific sample generation in this challenging regime.
[CV-72] What to Preserve Where to Adapt: A Depth-Wise Analysis of Forgetting in Continual Gynecological Image Segmentation
链接: https://arxiv.org/abs/2608.13660
作者: Amal Saqib,Tausifa Jan Saleem,Numan Saeed,Mohammad Yaqub
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Medical image segmentation models are typically trained under the assumption that all data are available simultaneously. However, in clinical practice, datasets often arrive sequentially, requiring models to adapt continuously to evolving data distributions. We study this problem in gynecological image segmentation, where substantial heterogeneity across imaging modalities, anatomical structures, and annotation protocols creates a particularly challenging continual learning setting. Under these large distribution shifts, existing continual learning methods struggle to preserve previously learned knowledge, leading to catastrophic forgetting. To better understand forgetting in this setting, we investigate how different encoder–decoder regions influence segmentation performance and forgetting during continual gynecological segmentation. Through block-wise ablation analysis, we observe that ablating early encoder and late decoder regions results in the largest performance degradation, indicating that segmentation performance depends unevenly across the network hierarchy. Using controlled adaptation experiments, we further show that forgetting remains limited when updates are restricted to bottleneck-adjacent regions, but increases sharply once shallower encoders and decoders become trainable, even when only a small subset of parameters is updated. These findings suggest that forgetting in the encoder-decoder architecture is strongly influenced by where updates occur across network depth during continual learning. Full code and analysis pipelines will be made publicly available upon acceptance.
[CV-73] Omni-LiveAvatar: Minute-Level Real-Time Streaming Joint Audio-Visual Avatar Generation
链接: https://arxiv.org/abs/2608.13602
作者: Lunjie Zhu,Xingtong Ge,Fangyu Lin,Yi Zhang,Zhening Liu,Mengfei Li,Yumeng Zhang,Guanglu Song,Yu Liu,Jun Zhang
类目: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注:
Abstract:Joint audio-video generative models serve as foundation for immersive and interactive digital-human generation. Nevertheless, most existing models rely on bidirectional attention and multi-step denoising and can generate only short clips, making them unsuitable for real-time interaction over extended durations. We present Omni-LiveAvatar, the first framework for minute-level, real-time streaming joint audio-video avatar generation. Specifically, we propose (1) a progressive autoregressive distillation pipeline that transfers a large bidirectional joint audio-video diffusion model into a few-step autoregressive generator without auxiliary stabilization mechanisms; (2) a synchronized audio-video long-short-term memory that preserves global consistency under a bounded memory budget; and (3) a hierarchical rolling prompt planning strategy that enables coherent semantic evolution and seamless prompt transitions. Extensive experiments show that Omni-LiveAvatar generates high-quality, synchronized minute-level avatars in real time. In terms of speed, it achieves a 33 \times generation speedup over its teacher, LTX-2, on a single NVIDIA H200 GPU; in terms of generation quality, it outperforms accelerated baselines across visual quality, audio quality, cross-modal synchronization, and human fidelity. Our code is available at this https URL.
[CV-74] UMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for Accelerated MRI
链接: https://arxiv.org/abs/2608.14422
作者: Mahdi Saberi,Toygan Kiliç,Mehmet Akçakaya
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP); Medical Physics (physics.med-ph)
备注: IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Abstract:MRI reconstruction from undersampled k-space measurements is an ill-posed inverse problem. Physics-driven deep learning (PD-DL) methods have shown strong performance for this task by combining the MRI forward model with learned image regularization within algorithm-unrolling frameworks. However, most existing PD-DL methods reconstruct complex-valued images directly, thereby implicitly coupling magnitude and phase within a single learned representation. This coupled regularization may be suboptimal in reconstruction settings where accurate phase modeling plays an important role, such as partial Fourier (PF) imaging, where recovery of the omitted asymmetric k-space measurements depends on the underlying image phase. In such scenarios, explicit modeling of magnitude and phase as separate components may reduce the reliance on externally estimated or predefined phase information. To this end, we propose UMPIRE-Net (Unrolled Magnitude-Phase In REgularization Network), a PD-DL method that introduces separate learned regularizers for magnitude and phase components, together with a novel data-fidelity formulation that enforces measurements consistency. We evaluate UMPIRE-Net for accelerated MRI with PF across different datasets and acceleration factors. Experimental results demonstrate that our proposed method improves reconstruction quality compared with a conventional complex-valued PD-DL baseline, yielding sharper images and reduced artifacts. Code available at: this https URL
[CV-75] A Subjective Study on a New Sharpness Informed Class of Metrics
链接: https://arxiv.org/abs/2608.13989
作者: Uditangshu Aurangabadkar,Vibhoothi Vibhoothi,Darren Ramsook,Anil Kokaram
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IEEE MMSP 2026, 6 pages
Abstract:Perceptual loss functions in Deep Neural Network (DNN) deblurring architectures improve the overall quality of restored images. However, few focus on explicitly targeting sharpness in the restorations. We conduct a subjective study of models trained with and without losses which explicitly target sharpness using a four-protocol approach, exploring preferred sharpness levels and effects on image quality. We introduce a novel dataset of images with uniform sharpness increments along with Difference Mean Opinion Scores (DMOS). Additionally, we propose a novel class of Sharpness Informed (SI) Image Quality Assessment (IQA) metrics which properly penalize over-sharpening. Our new SI-PSNR metric outperforms all other PSNR variants in terms of correlation statistics on IQA benchmarking datasets. We show that, on average, images restored using a sharpness-aware composite loss are preferred in 67% of binarized comparisons, as opposed to losses that do not explicitly target sharpness.
[CV-76] Practical Lossless Volumetric Medical Image Compression via Tri-plane Context Tree Learning
链接: https://arxiv.org/abs/2608.13897
作者: Yuanchao Bai,Yifan Zhao,Kai Wang,Yuanbo Du,Jie Cheng,Teng Fang,Xianming Liu,Wen Gao
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Lossless compression of volumetric medical images is of paramount importance for clinical and research applications where data fidelity is essential. Traditional compression methods are often limited in efficiency due to rigid, handcrafted models. Conversely, deep neural network (DNN)-based compression methods, while effective, demand substantial computational resources, hindering deployment in resource-constrained settings. To address these challenges, we propose a novel tri-plane context tree (TCT)-based method for lossless volumetric medical image compression that delivers high performance without relying on DNNs or external training data. To exploit intra-slice and inter-slice redundancies, we introduce a compact tri-plane context representation that decomposes complex 3D context modeling into efficient 2D modeling on three orthogonal planes. By integrating this representation with a context tree framework, we develop an input-specific TCT model employing an adaptive binary tree structure. At each tree node, the model dynamically selects from a suite of tri-plane based predictors and contextual feature extractors, enabling data-adaptive context modeling tailored to local structural characteristics. Instead of offline training, we sample a subset of the input volume to learn the TCT model by optimizing the minimum description length (MDL) through iterative construction and pruning. With the learned TCT model, each pixel retrieves its corresponding context, computes the prediction residual using the predictor dictated by the context, and performs entropy encoding based on the associated histograms. Experimental results demonstrate that the proposed method achieves compression performance on par with recent DNN-based methods on multiple datasets, while maintaining low computational cost and fast coding speeds, making it highly applicable in practice.
[CV-77] From crown candidates to neighborhood screening: integrating optical GeoAI and spatial modeling for urban-canopy assessment in Davis California
链接: https://arxiv.org/abs/2608.13856
作者: Mohammadreza Narimani,Shreyan Mitra,Parastoo Farajpoor
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Applications (stat.AP)
备注: 17 pages, 10 figures + 5 supplementary figures, 7 tables. Preprint submitted to Taylor Francis. Code: this https URL Data: this https URL
Abstract:Timely urban-canopy information is essential for linking remote sensing with heat, mobility, and neighborhood planning. We developed an optical GeoAI workflow for Davis, California, using 2022 National Agriculture Imagery Program imagery (0.6 m RGB+NIR). DeepForest generated crown candidates; an NDVI threshold, non-maximum suppression, and box-prompted Segment Anything Model (ViT-B) produced a crown-anchored canopy surface. Analyses used the 25.92 km2 Census TIGER municipal boundary and a 100 m grid. The workflow retained 11,741 candidate crowns and mapped 2.43 km2 of canopy (9.37% of the city). On the identical extent, 87.8% of mapped canopy pixels and 97.4% of candidate centers agreed with the 2022 USDA/CAL FIRE LiDAR-assisted canopy product; the optical surface represented 34.2% of the reference canopy area (IoU 0.288; Dice 0.448). Approximately 49% of candidates occurred within 15 m of a road. Canopy was inversely associated with Landsat land-surface temperature (Spearman rho = -0.293; partial rho = -0.370 controlling for built probability), and spatial-lag modeling confirmed clear neighborhood structure. Two transparent attention surfaces combined canopy need with thermal and contextual indicators. The framework provides a reproducible, updateable screening layer that complements structural canopy products and municipal inventories while retaining assumptions, data provenance, and spatial diagnostics for planning interpretation.
[CV-78] Label-Free Deep-Tissue Peripheral Nerve Detection with a Handheld Multimodal OCT Probe and NerveDetNet
链接: https://arxiv.org/abs/2608.13807
作者: Yihan Wang,Ruilin You,Shaobai Li,Jiabin Chen,Bofan Song,Anh D. Le,Rongguang Liang
类目: Optics (physics.optics); Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
备注:
Abstract:Peripheral nerves buried beneath intact tissue are difficult to visualize during surgery and remain inaccessible to white light wide-field imaging and other surface optical imaging methods. Existing OCT nerve studies have largely relied on exposed nerves or polarization contrast with limited depth penetration, restricting their value for subsurface intraoperative guidance. Here, we introduce, to our knowledge, the first label-free framework for detecting peripheral nerves beneath unopened tissue and resolving their depth using intensity-based OCT structural signatures alone. The framework combines a handheld multimodal probe, integrating swept-source OCT with co-registered white light and autofluorescence imaging, with a ``confirm-then-capture’’ workflow designed for practical surgical use. To enable efficient analysis of sparsely sampled OCT volumes, we develop NerveDetNet, a lightweight 2.5D segmentation network that recovers weak and spatially displaced nerve signals by incorporating spatial context, frame-order information, and shift-tolerant correlations across frames through a dedicated nerve feature correlation module. In ex vivo tissue experiments, NerveDetNet consistently outperformed six representative 2D baselines across all frame spacings, achieving a Dice score of 0.725 under the sparsest sampling condition while using approximately half the model parameters. End-to-end validation demonstrated localization of nerves invisible at the surface and depth-resolved detection up to 1.3–1.4~mm below the tissue surface, with OCT derived depth maps overlaid directly onto the surgical view. Together, these results establish a practical label-free approach for subsurface nerve visualization that supports intraoperative compatibility, enables efficient sparse-volume analysis, and provides depth-resolved guidance without tissue opening, contrast agents, or nerve exposure.
[CV-79] VLM- and LLM -Driven Multi-Agent System for PET Image Denoising
链接: https://arxiv.org/abs/2608.13791
作者: Boxiao Yu,Savas Ozdemir,Yang Xing,Fumio Hashimoto,Jiong Wu,Yizhou Chen,Axel Rominger,Ruogu Fang,Kuangyu Shi,Tinsu Pan,Kuang Gong
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Positron emission tomography (PET) imaging suffers from limited spatial resolution and low signal-to-noise ratio, which can compromise quantitative accuracy and lesion detectability. Deep learning-based denoising methods have demonstrated strong potential for improving PET image quality. However, their practical deployment in real-world settings remains challenging, often requiring multiple specialized models and expert interventions, such as identifying motion-induced misregistration artifacts, estimating noise levels to select an appropriate denoiser, and performing lesion-focused quantitative assessment after denoising. Recent advances in vision-language models (VLMs) for image quality understanding and large language models (LLMs) for contextual reasoning provide new opportunities for automated, decision-driven workflows. Inspired by expert workflows for PET image quality enhancement, we propose an VLM- and LLM-driven multi-agent PET denoising framework that dynamically assesses image quality and lesion status, autonomously selects optimal denoising models and parameters, and enables closed-loop feedback with rollback mechanisms. Experiments were conducted on Siemens Biograph Vision Quadra PET/CT data with 1/20 and 1/50 low-dose settings. Individual module evaluations demonstrated the reliability of the agentic components, while the complete framework achieved higher PSNR and SSIM than UNet, GAN, and DDPM baselines at both dose levels. These preliminary results demonstrate the feasibility of using a closed-loop multi-agent framework to adapt PET denoising strategies to different image conditions.
[CV-80] An Interactive Automated 4D-STEM data acquisition and analysis routine for Scanning Electron Nanobeam Diffraction and Ptychography experiments
链接: https://arxiv.org/abs/2608.13752
作者: Mohsen Danaie,Max England,Yiming Xu,Ruomu Zhang,Ed Darnbrough,Josh Willem De Boer,Frederick Allars,Zaeem Najeeb,Aakash Varambhia,Jinseok Ryu,Benjamin Bradnick,Damien McGrouther,Manfred E. Schuster,Christopher S. Allen
类目: Instrumentation and Detectors (physics.ins-det); Computer Vision and Pattern Recognition (cs.CV)
备注: 41 pages, 7 figures, 11 SI figures
Abstract:Modern transmission electron microscopes are versatile instruments which have become indispensable tools for understanding structure and chemical composition at the nano- and atomic scale. In the physical sciences these instruments are still largely manually controlled, requiring significant operator expertise, limiting throughput, and precluding statistical analysis of large datasets. Recent technical advances in both hardware and in control software now allow for the interaction with almost every functionality of the microscope through a programming interface. This enables better experimental design and data collection automation while also reducing operator collection bias and required expertise. In this study, we present an automated data collection routine with machine-driven decision-making to enable the collection of hundreds of 4D-STEM nanobeam diffraction and ptychography data from a large distribution of size-selectively deposited Pt nanoparticles. We present a semi-automated data analysis workflow to extract pertinent information from the large volumes of collected data. For the nanobeam diffraction data, reducing each dataset to its azimuthal variance profile and combining automated crystal orientation mapping with per-particle morphology descriptors reveals the orientation, shape and phase distributions across the ensemble, including a weak 110 texture. For the ptychographic data, an automated screening pipeline identifies on-zone-axis particles and enables atomic-resolution phase imaging and lattice-strain mapping of individual grains. Together these demonstrate how automation turns instrument throughput into statistically meaningful, atomic-scale microstructural information.
[CV-81] RUE-Colon: Exposing a Consistent Transfer Asymmetry in Real-Time Polyp Detection MICCAI2026
链接: https://arxiv.org/abs/2608.13711
作者: Sebastian Doerrich,Andreas Franz Schwab,Francesco Di Salvo,Shyam Nandan Rai,Hanh Huyen My Nguyen,Christian Ledig
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to EndoLINA @ MICCAI 2026 (The International Workshop on Endoluminal Intervention Navigation and Autonomy)
Abstract:Computer-aided detection (CADe) systems for colonoscopy promise to reduce clinical miss rates, yet reliable real-world deployment remains elusive. This translational gap stems in part from a structural flaw in model development: the reliance on curated datasets that under-represent the long negative stretches and procedure-related artifacts characteristic of routine examinations. Training and evaluating architectures strictly on these lesion-centric benchmarks creates an illusion of success, since such benchmarks cannot capture clinically crucial metrics. To expose this gap, we establish TRUE-Colon, a standardized benchmarking protocol that measures key deployment characteristics alongside localization accuracy, and evaluate four real-time architectures (Faster R-CNN, YOLOv8, YOLOv11, RT-DETR) across curated benchmarks (SUN, PICCOLO) and 60 unedited, full-length procedures (REAL-Colon). We observe a consistent transfer asymmetry: models trained strictly on curated clips suffer a severe performance collapse when evaluated on full procedures, whereas procedure-trained models substantially improve rejection of non-polyp content on REAL-Colon, and largely retain their accuracy on curated benchmarks. Beyond transferability, we find that the Transformer detector attains the strongest sensitivity and the earliest, most persistent detections, while the convolutional detectors stay competitive at a higher throughput. Together, these results indicate that both training and benchmarking for deployable CADe should shift from curated, lesion-centric clips toward full-procedure data and deployment-relevant operating points. Source code is available at this https URL.
[CV-82] Secret-Stego Dissimilarity as a Design Axis: Invertible Coverless Image Steganography with Diffusion Models
链接: https://arxiv.org/abs/2608.13597
作者: Hongxin Xu,Jianping Mei,Can Wang,Defang Chen
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Coverless image steganography (CIS) synthesizes a stego image rather than modifying an existing cover image, enabling authorized recipients to reconstruct the original secret image from the stego. Existing diffusion-based CIS methods can generate natural-looking stego images but preserve substantial visual similarity to the secret image. This resemblance risks exposing structural and semantic cues, giving rise to security vulnerabilities that cannot be evaluated solely via recovery fidelity. Achieving substantial visual dissimilarity between the secret and stego images without compromising stego quality and recovery fidelity remains challenging. To address this issue, we propose InvCISD, an invertible diffusion framework that couples the latent representations of the secret and an irrelevant reference image with an invertible network called LIMNet. We first train LIMNet in diffusion latent space, followed by end-to-end fine-tuning of the entire network, i.e., LIMNet integrated diffusion inversion and generation modules. Experiments demonstrate that the proposed method substantially reduces secret-stego visual similarity, improves stego quality, and retains satisfactory secret reconstruction quality. Our further investigation shows that all evaluated methods are highly detectable by the CIS-oriented steganalysis model, indicating that resistance against targeted steganalysis constitutes a critical direction for future CIS research.
人工智能
[AI-0] Handover of In-Context Learning State Across Session Boundaries
链接: https://arxiv.org/abs/2608.14528
作者: Masahiro Kato,Taka Kato
类目: Artificial Intelligence (cs.AI); Econometrics (econ.EM); Statistics Theory (math.ST); Methodology (stat.ME); Machine Learning (stat.ML)
备注:
Abstract:This study investigates the methodological and theoretical properties of session handover in applications that use large language models. A task may continue in a new session when the context reaches the model’s input limit, when the application restarts, or when another agent is asked to finish the task. The application must then decide which information from the earlier session to pass on. We formulate handover as the transfer of a task-relative in-context learning (ICL) state and distinguish exact recovery of earlier material from preservation of the target distribution. Under an exogeneity condition, predictive equivalence characterizes the coarsest deterministic sufficient handover and gives a fixed-length bit requirement. The analysis isolates the effects of the memory constraint, the writer, and the continuation procedure, and quantifies the cost of writing before the realized downstream query is known. We propose a three-part record that stores decisions and constraints exactly, uses task-justified statistics for repeated evidence, and retains original observations whose effect is not preserved by those statistics. Gaussian linear regression gives an exact finite-dimensional handover and finite-bit perturbation bounds, while nonparametric regression gives upper and lower bounds that relate memory to squared prediction error. These results provide a theory and method for deciding what a handover must retain and how its memory requirement depends on the continuation task.
[AI-1] Participatory Moral AI Is Not Neutral: The Invisible Hand of Developers
链接: https://arxiv.org/abs/2608.14522
作者: Taenyun Kim,Edyta Bogucka,Daniele Quercia
类目: Artificial Intelligence (cs.AI)
备注: 35 pages, 11 Figures
Abstract:As AI systems make more morally loaded decisions across society, one response has been moral preference elicitation. In this approach, researchers poll participants on hypothetical dilemmas and use the aggregated votes to train a policy that an AI model then applies at scale. Before any vote is cast, developers make three key choices in the moral AI elicitation pipeline: feature scoping, voter sampling, and question framing. In other words, they decide which features go to a vote, which voters to include, and how to present the question. These choices are often opaque, undocumented, and treated as technical details rather than normative ones. We examine each of these choices within a common empirical study and show that each can shape the preferences produced by moral AI elicitation. Across two phases (N = 809) in three deployment contexts (i.e., AI kidney allocation, AI agents simulating absent workers, and generative AI depictions of the deceased), we examine the three main stages of the moral AI elicitation pipeline. First, morally relevant features shift across contexts. This suggests that feature schemas should not be assumed to transfer across deployment domains. Second, preferences differ by political ideology for roughly one-third of features, with some differences reversing direction. The ideological composition of the voter pool can therefore affect the resulting aggregated preference profile. Third, the wording of the elicitation question can narrow or widen ideological gaps by up to a full scale point. The framing conditions also change how moral foundations are associated with participants’ judgments. Taken together, these findings suggest that voting-based alignment cannot deliver fair or transparent AI by aggregation alone; at minimum, each stage of the moral AI elicitation pipeline should be audited and disclosed.
[AI-2] Learning-to-Transition for Large-scale and High-Order MIMO Detection
链接: https://arxiv.org/abs/2608.14511
作者: Yubo Zhang,Yiyao Liu,Xiaodong Wang
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI)
备注:
Abstract:High-order multiple-input multiple-output (MIMO) detection requires efficient search over a large discrete symbol space while producing reliable soft information for channel decoding. This paper develops a learning-to-transition (L2T) framework that formulates MIMO detection as a stochastic sequence of complete-vector transitions. At each transition, a channel-coupled Transformer updates both the instance embedding and the sampling policy, while a blockwise autoregressive factorization captures inter-stream dependence with moderate sequential complexity. For hard-output detection, a transition network is applied recursively and trained through a residual-to-BER curriculum, which first learns the MIMO search geometry from the exact residual metric and then aligns the policy with transmitted-bit accuracy. For soft-output reception, the well-trained hard policy is cloned at the parameter level into every layer of an untied soft-input soft-output iterative detection and decoding (IDD) receiver. This tied-to-untied transfer preserves the learned zero-prior search dynamics while enabling layer- and round-specific specialization under decoder feedback. Within each IDD round, decoder priors tilt candidate generation according to Bayes’ rule, and likelihood-weighted terminal hypotheses produce posterior and extrinsic log-likelihood ratios for LDPC decoding. A multi-stage training strategy further stabilizes the hard-to-soft transfer by progressively exposing the receiver to synthetic and in-loop decoder-generated priors.
[AI-3] RecipeNet: A Hierarchical Transformer for Recipe Data CIKM2026
链接: https://arxiv.org/abs/2608.14505
作者: Pin-Yen Huang,Sachin Chhabra,Prasanth Sai Gouripeddi,Abhinav Kumar,Baoxin Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at CIKM 2026
Abstract:Recipe data arises in domains such as materials synthesis, pharmaceutical formulation, and industrial manufacturing, where procedures are represented as ordered sequences of steps containing heterogeneous structured fields. Existing tabular learning methods typically flatten this structure into fixed-schema representations, limiting their ability to capture hierarchical field interactions and procedural dependencies. We propose RecipeNet, a hierarchical Transformer architecture that encodes field-level interactions within each step and sequential dependencies across steps through stacked Transformer encoders. Experiments on multiple recipe datasets and tasks demonstrate that RecipeNet consistently outperforms existing tabular models, highlighting the value of hierarchical and sequential modeling for recipe representation learning.
[AI-4] Generating Benchmark Health Data Using a Tabular Diffusion Transformer
链接: https://arxiv.org/abs/2608.14496
作者: Hao Yan,Lisa Pilgram,Dan Liu,Linglong Kong,Fida Dankar,Khaled El Emam
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets. However, existing synthetic tabular data generation methods are largely restricted to single-input-table scenarios and struggle to effectively handle multiple heterogeneous tables with diverse feature sets. To address this limitation, we propose a two-stage framework for cross-tabular data generation. In the first stage, each heterogeneous raw table is transformed into a standardized statistical table with the same set of columns across all tables. Each statistical table captures the marginal distributions of the original columns and the pairwise correlations among them. In the second stage, a diffusion transformer model is trained to capture structural patterns across these homogeneous statistical tables and to generate synthetic statistical tables. Synthetic raw tables are subsequently reconstructed from the generated statistical tables via multivariate Gaussian sampling followed by an inverse probability integral transform. This two-stage CTDG framework enables the learning of a unified generative model from multiple heterogeneous tables and supports the generation of an unlimited number of realistic synthetic heterogeneous tables. Experimental results demonstrate high fidelity in the learned statistical representations and a favorable fidelity-diversity trade-off in the generated synthetic data, validating the effectiveness of the proposed approach.
[AI-5] Optimal Scheduling of Road Maintenance Jobs Considering Impact on Traffic Flows
链接: https://arxiv.org/abs/2608.14491
作者: Charitha Nandepu,Lohitha Kalepu,Gabriele Ciavarella,SangWoo Park
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注: Case Study paper presented in IISE Annual Conference and Expo 2026
Abstract:Network-level maintenance planning requires repeated evaluations of equilibrium traffic flows under road capacity reductions. While equilibrium traffic assignment models are well established, their repeated solution quickly becomes computationally prohibitive and challenging to embed within maintenance scheduling problems. This paper investigates data-driven surrogate models that approximate equilibrium arc flows directly from origin-destination demand, using optimization-based equilibrium solutions as ground truth. A real-world case study based on traffic data from the Newark, New Jersey area demonstrates the effectiveness of the proposed approach as a scalable building block for future maintenance scheduling frameworks.
[AI-6] win: Playing an Unknown Game with a Test-Time Digital Twin
链接: https://arxiv.org/abs/2608.14490
作者: Alexy Skoutnev,Kirill Acharya,Gaston Longhitano,Madeleine Udell,Kevin Ellis,Iddo Drori
类目: Artificial Intelligence (cs.AI)
备注: Project website with action-by-action replays of all 25 runs: this https URL Code: this https URL
Abstract:We present a Test-time World-model Inference (Twin) system, in which a frontier coding agent writes an executable world model for completing continual learning tasks, such as ARC-AGI-3 games. Traditional approaches hand-engineer such models, one custom design per task. Each game hides its rules and goal, and our system constructs them from simulation and interaction alone. Its inductive prior over grid games is strong enough to recover the true transitions of the game and the goal on nearly all levels. Replay validation happens in a twin world model. The harness enforces that an action is not made until the program reproduces every previous observed game transition. Each mismatch between a world model prediction and the actual action result becomes a counterexample that is used to repair the world model. Twin clears 179 out of 183 levels (97.8%), and does so more efficiently than humans in 158 out of 179 levels (88.3%). The system infers the goal before any reward on 156 of the levels it clears (87.2%), and in the remaining levels automatically discovers the goal by search. The benchmark scores completion and action efficiency, between 0 and 100, against humans playing each game for the first time. Played directly, the base model scores only 7.8%; an off-the-shelf harness increases it to 61.1%, whereas our twin world model increases the same base model to 93.3%, clearing 23 out of 25 games. Building a usable world model is simpler than anticipated, whereas the harder problem is inferring the right goal.
[AI-7] Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes WWW
链接: https://arxiv.org/abs/2608.14481
作者: Alexei Odinokov,Rostislav Yavorskiy
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: The 2026 International Conference on Control, Robotics Engineering and Technology (CRET 2026), this https URL
Abstract:As heterogeneous robotic systems deploy across diverse urban zones, maintaining safety amid complex human-robot interactions remains a critical challenge. We present a unified framework that bridges systematic hazard analysis and runtime enforcement using hazard-informed safety envelopes. Rather than treating safety as a static constraint isolated within individual software modules, we introduce a cross-layer safety transformation process spanning symbolic, spatial, and dynamic world models. We show how this representation naturally interfaces with physical AI runtime harnesses to guarantee safe urban mobility.
[AI-8] LP-NAS: Linear Programming-based Neural Architecture Search
链接: https://arxiv.org/abs/2608.14472
作者: Abhishek Shukla,Ankur Sinha,Faiz Hamid
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 20 pages, 5 figures
Abstract:Neural Architecture Search (NAS) aims to automate neural network architecture design, reducing reliance on human expertise. Among the various NAS methods, differentiable NAS has gained prominence due to its efficiency and accuracy compared to conventional NAS approaches. Since differentiable NAS relaxes the architecture search space into a continuous domain, it is possible to apply principles from continuous optimization to NAS. In this paper, we propose Linear Programming-based NAS (LP-NAS), a mathematical programming-based framework for differentiable NAS that is applicable to a wide range of continuous search spaces. LP-NAS formulates a linear program (LP) using the validation-loss gradient and the training-loss Hessian to compute an architecture update direction that improves generalization while preserving the optimality of the model parameters. By following this LP-derived descent direction, LP-NAS efficiently navigates the architecture search space, leading to faster and more effective architecture optimization. We introduce two computationally efficient variants of LP-NAS, namely S-LP-NAS and R-LP-NAS. Applying LP-NAS to the Differentiable Architecture Search (DARTS) search space results in two algorithmic variants, S-LP-DARTS and R-LP-DARTS. Both variants achieve faster convergence and significantly higher validation performance during the early search iterations than the standard DARTS algorithm. Extensive experiments on CIFAR-10 and CIFAR-100 show that LP-DARTS outperforms standard DARTS in both the architecture search and evaluation phases. Additionally, we compare our approach with several DARTS variants (P-DARTS, PC-DARTS, and STO-DARTS) on the CIFAR-10 dataset and demonstrate its effectiveness. Furthermore, we validate the transferability of the discovered architectures through experiments on the ImageNet dataset.
[AI-9] Shift Aware Transfer Learning with Adaptive Dual-Encoder Fusion for PM Forecasting in Data-Limited Environments
链接: https://arxiv.org/abs/2608.14456
作者: Shahab Band,Hamed Mohammadi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Short-horizon forecasting of fine particulate matter (PM2.5) remains difficult when observations from the target domain are limited and the statistical properties of the source and target domains differ. In these settings, models trained only on local data may not capture complex temporal dynamics, while direct transfer learning can result in negative transfer. This study develops a shift-aware dual-encoder transfer framework that combines source-domain knowledge with target-specific representation learning. The source encoder was pretrained using hourly observations from 10 U.S. monitoring locations. The framework was then adapted and evaluated using two years of hourly observations from 77 stations in Taiwan under a chronological train-validation-test protocol. Among the four principal baselines, the frozen-source dual-encoder model achieved the best performance, with MSE = 21.8960, MAE = 3.1597, and R^2 = 0.8725. This corresponds to an MSE reduction of approximately 7.1% relative to TL-v1 and 4.1% relative to TL-v2. The ablation analysis showed that removing the Taiwan-specific branch caused the largest decline in performance. Allowing the source encoder to adapt produced the best overall result, with MSE = 21.6575, MAE = 3.1383, and R^2 = 0.8739. SHAP analysis indicated that predictions were driven mainly by recent PM2.5 observations and meteorological variables related to pollutant transport and dispersion. These results suggest that source-domain knowledge is most effective when target-specific information is preserved and the transferred representation is allowed to adapt under target supervision.
[AI-10] SheetCompass: Hierarchical Relation Graphs for Agent ic Spreadsheet Reasoning
链接: https://arxiv.org/abs/2608.14452
作者: Panjing He,Mingyue Cheng,Yucong Luo,Li Li,Xiaohan Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Spreadsheets are widely used to organize, analyze, and manipulate semi-structured data, yet automated spreadsheet reasoning remains challenging for large language models (LLMs). Real-world workbooks often contain implicit cross-table associations, fine-grained column dependencies, and complex spatial layouts. Existing methods typically flatten these multidimensional structures into sequential strings, losing important intra-sheet boundaries and inter-sheet semantics. Consequently, LLMs cannot exploit the global spatial context that human experts naturally use when inspecting spreadsheets. We propose SheetCompass, a graph-guided and memory-driven agentic framework for spreadsheet reasoning and automation. SheetCompass explicitly models structural relationships within and across worksheets while maintaining task-relevant information in memory, enabling agents to reason more effectively over complex workbooks.
[AI-11] Wyvern: An Agent ic Framework for Generating Grounded Multimodal Reports
链接: https://arxiv.org/abs/2608.14446
作者: Beatrice Alessandra Motetti,Emilien Guandalino,Daniele Jahier Pagliari,Alessio Burrello,Lorenz K. Müller,Konstantin Berestizshevsky,Lukas Cavigelli
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In the current artificial intelligence-driven innovation era, the pace of knowledge growth is accelerating, and is hard to keep up with. While generative models are increasingly used to synthesize content, they often lack in information grounding. To address these peculiarities of our time, we propose Wyvern, a multi-agent framework for the automated generation of grounded, multimodal technical reports. Wyvern allows for the generation of multimodal outputs, integrating images, tables, and text with supporting references in a unified report. Additionally, a particular focus is placed on the grounding of the content, with the implementation of a claims auto-revision stage. We conduct a human evaluation study to assess the quality of our proposed framework. The results show that the figures’ informativeness is perceived as superior to that of a recent baseline in 87% of cases. Furthermore, Wyvern’s reports are rated as more useful than those produced by three alternative methods in 63% to 100% of instances. We also carry out automatic evaluations showing that Wyvern gains up to 2.3 \times in citation recall and 1.6 \times in citation precision with respect to the baselines.
[AI-12] Designing Compact Neural Architectures via Neuron Gating and Mixed Activation
链接: https://arxiv.org/abs/2608.14443
作者: Abhishek Shukla,Ankur Sinha,Faiz Hamid
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 33 pages, 17 figures
Abstract:Neural Architecture Search (NAS) is naturally formulated as a bilevel optimization problem, where the upper-level optimizes the architecture using validation performance and the lower-level trains network parameters using training loss. However, NAS is computationally expensive due to discrete architectural decisions, exponentially growing search spaces, and the high cost of training candidate architectures. This work develops a general bilevel optimization framework for NAS across diverse architectures, including MLPs, CNNs, RNNs, and Transformers, to identify compact architectures with strong predictive performance. We propose three scalable formulations that replace discrete neuron- and activation-level decisions with continuous relaxations, enabling differentiable optimization over otherwise combinatorial architecture spaces. These formulations give rise to three NAS methods: NAS based on Neuron Gating (NAS-NG), NAS based on Mixed Activation (NAS-MA), and NAS based on Neuron Gating and Mixed Activation (NAS-NGMA). Experiments on MLPs and CNNs using MNIST and CIFAR-10 show that the proposed methods consistently identify compact architectures with competitive or improved predictive performance. On MNIST, NAS-NGMA achieves 98.68% test accuracy with 7.69M MLP parameters, while NAS-NG achieves 99.63% accuracy with only 0.26M CNN parameters. On CIFAR-10, the proposed methods consistently outperform vanilla DARTS. Further experiments demonstrate that NAS-NG can optimize substantially over-parameterized and literature-optimal architectures, improving accuracy while reducing parameters. These results establish relaxed bilevel optimization as a scalable alternative to discrete NAS and provide a general framework for efficient neuron- and activation-level architecture optimization.
[AI-13] PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments
链接: https://arxiv.org/abs/2608.14441
作者: Yuhao Zhan,Bingxiang He,Zecong Tang,Chaojun Xiao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Self-evolving agents improve future behavior from interaction experience, yet existing evaluations typically optimize under fixed execution conditions and do not test recovery after those conditions change. To address this gap, we introduce PACE-Bench (Physics Adaptation via Code Evolution), a simulator-grounded benchmark of 144 source-to-target adaptation pairs across six physics domains. Each pair links a source environment to a mutated target environment with the same goal and interface. A code-driven design that succeeds in the source fails in the target, where agents must iteratively adapt it into a working target design using diagnostic sandbox feedback within a limited attempt budget. We compare ten self-evolving methods from four paradigms. The benchmark remains far from saturated: Reflexion + Qwen3-14B succeeds on only 35.9% of full-benchmark pairs, while GPT-5.5 solves 66.7% of the Statics subset under the full budget. Together, these results show that simulator-grounded reflection is more reliable than unverified self-revision, while memory anchors agents to early designs and broad tree search explores without converging. Even revealing exact physical changes does not raise the performance ceiling, pointing to mechanism redesign rather than parameter inference as the central bottleneck. Data and code are available at this https URL.
[AI-14] he Dynamics of Intelligence Explosions
链接: https://arxiv.org/abs/2608.14426
作者: Toby Ord
类目: Artificial Intelligence (cs.AI); Theoretical Economics (econ.TH)
备注: 33 pages, 3 figures
Abstract:AI is increasingly being used to help with AI RD. Under certain conditions this feedback loop might be able to produce an intelligence explosion, with rapidly escalating AI capabilities. I explore the mathematics of the most explosive possibilities, with an eye to understanding what drives the dynamics. I show that singular growth (towards a vertical asymptote) is harder to achieve than would be expected from recent economics-inspired modelling, and that there is an important but neglected class of growth rates that are faster than exponential but don’t lead to a vertical asymptote. I draw out the generation time (the time to go around the feedback loop) as a neglected parameter that plays a pivotal role in determining the behaviour of any intelligence explosion — one cannot have singular growth unless the generation time rapidly approaches zero.
[AI-15] Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations
链接: https://arxiv.org/abs/2608.14425
作者: Toby D. Pilditch
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM evaluations often use fixed sampling budgets, testing every item the same number of times even after estimates are precise. We introduce optstop, a precision-based adaptive stopping framework that treats evaluation as a sequential measurement problem: keep sampling where uncertainty remains high, and stop where estimates are precise or stable enough. The framework builds on hierarchical Bayesian inference, supports binary, ordinal, and continuous outcomes, and keeps every benchmark item eligible for sampling, without requiring a calibrated item bank. It runs live or retrospectively, and includes a safeguard that samples more cautiously as measured performance approaches zero, where rare successes matter most. In an illustrative 200-item, 10-epoch evaluation, it removes 57%-97% of planned trials across nine validation settings, with overall conclusions equivalent to the full run. These results show that LLM evaluation compute can be allocated by uncertainty rather than by fixed repetition counts, with the magnitude of savings depending on evaluation design.
[AI-16] he Past and Future of AI Scientists
链接: https://arxiv.org/abs/2608.14407
作者: Ross D. King
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present a survey of the past and future of AI Scientists: machines capable of automating science. AI Scientists can originate hypotheses, deduce their consequences, design and execute experiments, interpret their results, and revise their beliefs. Such systems are integrated scientific agents, connected to the literature, formal knowledge, mathematical models, simulations, data-analysis systems and physical laboratories. Adam was the first machine to make novel scientific discoveries through cycles of hypothesis formation and physical experimentation. Eve established the architecture of the modern self-driving laboratory. Foundation models, autonomous agents and laboratory robotics now make it possible to build systems far more general than either Adam or Eve. The central problem is no longer whether individual components of science can be automated. They can. The problem is integration. AI Scientists must combine neural learning with logic, probability, mathematics, causal reasoning, simulation, experimental design, robotics and formal scientific records. AI Scientists have the potential to transform science: to make science faster, cheaper, more systematic and more reproducible. AI Scientists could investigate systems too complicated for unaided human science, and enable thousands of AI scientists to work together on single problems. The Nobel Turing Challenge sets the goal of developing by 2050 AI systems capable of automating Nobel-quality discoveries. Progress is ahead of schedule. When we succeed it will create a new form of science and transform the world. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14407 [cs.AI] (or arXiv:2608.14407v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14407 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ross King [view email] [v1] Fri, 14 Aug 2026 15:50:35 UTC (1,493 KB) Full-text links: Access Paper: View a PDF of the paper titled The Past and Future of AI Scientists, by Ross D. KingView PDF view license Current browse context: cs.AI prev | next new | recent | 2026-08 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[AI-17] ripwire: Triggering Aligned Refusal via Statistically Certified Safety Neurons
链接: https://arxiv.org/abs/2608.14392
作者: Wei Zhao,Zhe Li,Peixin Zhang,Jun Sun
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Neuron- and path-level interventions offer the finest-grained route to defending large language models (LLMs) against jailbreak attacks, yet existing methods fall short of this promise, i.e., they often compromise model utility significantly. Specifically, one line of work suppresses toxic neurons to erase harmful semantics, but since such semantics are distributed across the network, blocking every pathway forces a large intervention footprint. An alternative line of research focus on identify safety neurons using external classifiers. While promising, the existing approaches suffer from compromising neurons that are important for the model utility as well. Moreover, both approaches remain always on and thus perturb every benign request even when no attack is present. To address these limitations, we present \ours, a training-free defense that first identifies safety-specific neurons through per-neuron hypothesis tests under false-discovery-rate control together with a utility-specificity filter. Based on this identification, a trigger-style clamp holds the selected neurons at their harmful-conditional mean activations, injecting an internal harmful-input signal that triggers the refusal behavior learned during alignment. The clamp is then realized by two provably equivalent deployment modes, namely a detector-gated inference-time intervention and an offline bias-patch weight edit. Extensive experiments across four safety-aligned LLMs and four representative attacks demonstrate that \ours reduces the average attack success rate to at most 2.0% while incurring a utility drop of only 0.5% to 5.3% on MT-Bench, the smallest among all defenses. Code is available at this https URL.
[AI-18] DeaMoE: Efficient MoE Structure for Fast Small-Batch Decoding
链接: https://arxiv.org/abs/2608.14385
作者: Zewen Jin,Shen Fu,Zeping Duan,Shannon Wang,Weihao Wu,Chengjie Tang,Congkun Ai,Ping Gong,Zijian Dai,Youhui Bai,Cheng Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Mixture-of-Experts (MoE) models have been widely adopted in real-time interactive applications such as coding assistants, real-time audio-video interaction systems. To meet the extremely low response latency requirements of these scenarios, practitioners commonly employ small-batch decoding, under which MoE inference becomes memory-bound and is severely bottlenecked by expert weight loading. However, this bottleneck has received limited attention, and existing solutions such as post-training weight compression or fine-grained expert design during pre-training either degrade model accuracy or introduce additional computation and communication overhead. To tackle this issue, we propose DeaMoE, a decoding-efficient MoE architecture, in which the experts are grouped into several departments, and the experts belonging to the same department share most parameters since they come from the same professional field, and additionally each expert contains a few private parameters to reflect its uniqueness. Moreover, we design customized two-stage routing strategy for DeaMoE to avoid redundant loading, under which DeaMoE greatly improves the efficiency during LLM decoding. Compared with vanilla MoE, DeaMoE reduces per-step loaded weights by up to 50.9% and achieves up to 1.33 end-to-end TPOT speedup for the pre-trained 7B model on A40, and up to 2.00x and 1.97x peak speedup for DeepSeek-V3 on A40 and H100 in microbenchmarks.
[AI-19] Agent Rewind: Recoverable Execution for Long-Horizon LLM Agents
链接: https://arxiv.org/abs/2608.14380
作者: Yu Zhuang,Kefei Chen,Yitong Duan,Shuxin Zheng,Jian Li,Xu-Yao Zhang
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, 5 figures
Abstract:Many real-world tasks require LLM agents to interact with their environments over long execution horizons. Errors that occur early in execution may propagate through both the agent context and environment state, and their effects may be difficult to reverse through subsequent actions. Existing methods mainly seek to reduce such errors through plan refinement and safety checks but provide little support after errors occur. To enable recovery during long-horizon execution, we present AgentRewind, a runtime recovery framework that records aligned checkpoints of the agent context and controlled environment, allowing agents to return to an earlier state and resume execution with information from previous attempts. We also construct MettleBench, a benchmark for evaluating task completion and partial progress on long-horizon engineering assignments containing a series of related requirements. Experiments across tasks, multiple models, execution strategies, and agent harnesses show that AgentRewind improves task success rate and average checklist progress over the compared baselines.
[AI-20] Reflex: Enabling Fast and Predictive Vision-Language-Action Models for Reaction-Critical Manipulation
链接: https://arxiv.org/abs/2608.14379
作者: Yuxuan Chen,Wanruo Zhang,Xiao Li
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages, 6 pages
Abstract:Vision-Language-Action (VLA) models have recently achieved promising performance in robotic manipulation. However, existing benchmarks mainly evaluate generalization on static manipulation tasks and largely overlook dynamic interaction scenarios. To address this gap, we present ReflexBench, a benchmark for reaction-critical manipulation. ReflexBench contains six dynamic tasks and introduces an evaluation framework that decouples simulator stepping from robot control while supporting configurable latency under synchronous and asynchronous inference. Building upon ReflexBench, we propose ReflexVLA, an efficient VLA model designed for reaction-critical manipulation without large-scale robot-data pretraining. ReflexVLA enhances temporal reasoning through latent future prediction and multi-frame temporal fusion within the vision backbone, while reducing deployment latency through batched visual encoding and CUDA Graph replay. Experiments show that ReflexVLA consistently improves dynamic manipulation performance while maintaining competitive accuracy on standard static manipulation benchmarks, and real-world experiments further demonstrate its effectiveness under practical deployment conditions. Project website: this https URL
[AI-21] A Hybrid LLM -Based Framework for Automated Security Annotation Generation in Business Process Models
链接: https://arxiv.org/abs/2608.14370
作者: Md Kamrul Islam,Tiphaine Henry,Mattia Salnitri,Julius Köpke,Sami Souihi
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:The modelling and analysis of secure business processes require the incorporation of security annotations into process models. Although BPMN extensions, including SecBPMN2, exist for this purpose, the derivation of accurate and complete security annotations from natural-language specifications remains a manual, expert-intensive, and error-prone task. This paper presents a hybrid framework that takes a BPMN process model and a security requirements document as input and automatically generates security annotations adhering to the SecBPMN2 specification. The approach combines Large Language Model (LLM)–based semantic extraction with schema-constrained mapping, rule-based normalization, and deterministic validation. The framework is evaluated comprehensively on a curated dataset of 27 process models from various domains. The results indicate that it consistently produces structurally valid SecBPMN2 annotations with high schema completeness. Compared to human security analysts, the system achieves substantially higher precision (0.58 vs. 0.29) while maintaining comparable recall (0.52 vs. 0.50) and reduces erroneous or misplaced annotations by nearly 50%. In addition, annotation generation is significantly faster than manual annotation. These findings demonstrate that hybrid LLM- and rule-based automation can reduce modeling effort while improving consistency and reliability, thereby providing a scalable foundation for security-by-design BPM.
[AI-22] Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes
链接: https://arxiv.org/abs/2608.14367
作者: Keyvan Amiri Elyasi,Lukas Kirchdorfer,Heiner Stuckenschmidt
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Business Process Management Conference
Abstract:The early detection of delayed cases in business processes is a critical capability for organizations. Predictive process monitoring (PPM) supports this task by using historical event logs to predict the remaining time of ongoing cases, enabling timely interventions to avoid missed deadlines and service level violations. Although remaining time prediction has advanced considerably through sophisticated deep learning architectures, little is known about the intrinsic difficulty of delay detection itself. Since performance is typically assessed using aggregate metrics, prior work provides limited insight into how models perform across the target distribution, especially on the operationally most critical cases with large delays. In this paper, we address this gap by analyzing the difficulty of delay detection. Across 14 event logs, we show that remaining times are typically strongly right-skewed, with only a small fraction of cases exhibiting large delays. Existing models capture the mode of this distribution well but perform poorly on high-delay cases. We further uncover pronounced heteroscedasticity, showing that predictive uncertainty increases with delay magnitude. Based on these findings, we evaluate approaches to mitigate the imbalance problem, but find only limited benefits, suggesting that the key underlying problem may not be imbalance but higher uncertainty associated with delayed cases. We show that this correlation can be exploited to substantially improve the identification of delayed cases. Overall, our work provides new insights into the sources of difficulty in delay detection and identifies uncertainty-aware modeling as a promising direction for future PPM research.
[AI-23] Designing Sustainable Federated Learning as a Service using Neural Architecture Search
链接: https://arxiv.org/abs/2608.14359
作者: Keya Patel,Sajib Mistry,Sheik Fattah,Deepak Kanneganti,Aneesh Krishna,Mufti Mahmud,Monowar Bhuyan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The sustainability constraints of FLaaS consumers pose significant challenges to maintaining carbon-feasible federated training in FLaaS environments. These constraints often lead to infeasible consumer participation and unstable federated training under hard carbon constraints. We propose a Sustainable Federated Learning as a Service (SFLaaS), a carbon- constrained Neural Architecture Search (NAS) framework for heteroge- neous sustainable constraints. We introduce a requirement-driven search space that transforms consumer sustainability profiles into a feasible architecture region before federated execution. We develop a consumer-level carbon feasibility estimation mechanism to evaluate candidate architectures under dynamic carbon conditions. We propose a sustainable con- sumer scheduling strategy that adaptively selects feasible consumers and allocates local workloads to preserve consumer participation and statistical data coverage. An evolutionary search strategy jointly optimised for predictive performance, consumer feasibility, and participation coverage under hard carbon constraints. Experiments on real-world datasets and a simulated environment demonstrate the effectiveness of the proposed approach.
[AI-24] Disentangled Shared Representations Improve Morpho-Transcriptomic Integration
链接: https://arxiv.org/abs/2608.14355
作者: Julian Ostermaier,Swann Ruyter,Reuben Dorent,Daniel Racoceanu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Spatial transcriptomics (ST) enables the simultaneous profiling of gene expression and tissue morphology, creating an opportunity to learn multimodal representations capturing shared morpho-transcriptomic structure. However, standard multimodal models often compress modalities into a common latent space without explicitly separating shared and modality-specific sources of variation, which may limit downstream utility. We investigate whether explicit disentanglement of shared and private latent components improves multimodal representation learning for paired Hematoxylin \ Eosin (H\E) and ST data. We compare VAE-based and contrastive approaches, each in standard and disentangled variants, across two cancer cohorts under matched experimental conditions. Representations are evaluated using cross-modal reconstruction, downstream probing and cross-modal probe transfer. The experiments suggest two main trends. First, contrastive objectives yield higher downstream probing performance than VAE-based models. Second, disentangled variants improve the selected reconstruction and probing metrics, although the gains depend on the model family, task, direction, and disentanglement strength. Overall, our results suggest that explicitly factorizing shared and modality-specific information can improve multimodal representation learning for spatial transcriptomics and provides a useful evaluation framework for future foundation models.
[AI-25] ScienceFlow: A long-horizon agent for ML research scientific discovery and beyond
链接: https://arxiv.org/abs/2608.14354
作者: Mingming Zhao,Jiqian Dong,Kangping Xu,Zadid Hasan,Chengrui Fan,Shan Jiang,Shuai Mao,Ting Lingya,Linyi Zou,Tailin Zhou,Yun Hin Chan,Wenkai Zhang,Zhanhong Zhou,Guowei Huang,Hongliang Li,Wenjing Cun,Zhitang Chen,Mingxuan Yuan,Yanhui Geng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Enabling LLM agents to sustain productive, stable, and goal-aligned research over extended horizons is a central challenge for autonomous machine learning and scientific discovery, as progress hinges on continuously managing evolving state, exploration decisions, and computational resources. Pioneering autoresearch agents, despite great success, still lack mechanisms for continuity, recovery from dead ends, and value-driven compute allocation, which inherently undermines overall search efficiency, wastes computational resources, and lowers the chance of ultimate success. To bridge this gap, we introduce ScienceFlow, an end-to-end autoresearch agent framework that organizes long-horizon research work into research segments grounded in executable workspaces. It represents research progress as recoverable executable states, enabling efficient exploration, revision, and execution. Transitions between research segments are governed by Executable-State Transition through Re-Anchoring (ESTRA), which selects either the live state or an archived state as the next anchor and determines whether to continue or redirect the research trajectory. An evidence-aware execution controller allocates resources to physical jobs based on resource availability, remaining budget, and validated progress. We evaluate ScienceFlow on tasks spanning machine learning, scientific modeling, and mathematical optimization. Results on diverse long-horizon benchmarks demonstrate its ability to sustain effective research processes, highlighted by a SOTA 70.22 percent Any-Medal score on the full MLE-bench within a 24-hour budget, outperforming prior reported results by 4.92 percentage points. The efficacy of ScienceFlow further demonstrates that efficient state management, adaptive exploration, and objective-aligned execution are critical for scaling autonomous research beyond short-horizon interactions.
[AI-26] Clearing the Fog: Towards Installing and Refining Proactive Exploration Capabilities in LLM Agents
链接: https://arxiv.org/abs/2608.14339
作者: Zhizhao Guan,Chen Huang,Ziming Liu,Hongru Liang,Wenqiang Lei,See-Kiong Ng,Tat-Seng Chua,Anthony G Cohn
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We study proactive exploration in LLM agents, i.e., the ability to explore an environment to acquire information that improves future decision-making. In this regard, we first identify two fundamental bottlenecks that hinder this capability and then propose \ours, a novel method designed to instill and refine proactive exploration. Specifically, \ours\ consists of two components: (1) Exploratory Data Construction, which synthesizes exploration-rich trajectories to mitigate the hindsight bias of standard demonstrations; and (2) RL Optimization with Contrastive Signal Guidance, which leverages contrastive trajectory pairs to distinguish productive exploration from redundant wandering. Extensive experiments demonstrate the effectiveness of \ours\ and provide insights into the characteristics of proactive exploration. Our code is available at: this https URL.
[AI-27] Program-space Diffusion for Morphology-to-Transcriptomics Prediction
链接: https://arxiv.org/abs/2608.14330
作者: Ruyter Swann,Dorent Reuben,Racoceanu Daniel
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Spatial transcriptomics (ST) enables genome-wide gene expression profiling while preserving tissue architecture, but its cost and limited scalability remain major bottlenecks. This has motivated models that predict spatial expression directly from routine histology. Despite promising results, most existing approaches operate at the gene level without leveraging established transcriptomic modeling practices and rely on heterogeneous gene selection strategies, which complicates fair comparison across methods. We propose to reformulate morphology-to-transcriptomics prediction as conditional generation in transcriptional program space, thereby exploiting coordinated transcriptional variation instead of predicting genes independently. Using consensus non-negative matrix factorization (cNMF), we extract a low-dimensional set of transcriptional programs capturing coordinated expression variation in the training data, and train a conditional diffusion model to generate program activations from histology. This formulation exploits coordinated transcriptional variation and substantially lowers the dimensionality of the conditional generative task. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14330 [cs.AI] (or arXiv:2608.14330v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14330 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-28] Sensor-Driven Mission Synthesis for UAV/UGV Swarms: A TB-CSPN Coordination Architecture with Hardware-Enforced Safety
链接: https://arxiv.org/abs/2608.14306
作者: Uwe M. Borghoff,Paolo Bottoni,Remo Pareschi
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: 15 pages, 4 figures
Abstract:This paper presents a coordination architecture for heterogeneous UAV/UGV swarms that synthesises mission actions from uncertain, multi-modal sensor evidence while preserving hardware-enforced safety at the actuation boundary. The approach combines radar, RF, acoustic, and visual observations with Topic-Based Communication Space Petri Net (TB-CSPN) orchestration to support incremental mission formation under partial and evolving information. Consultant agents transform sensor outputs into temporally bounded semantic tokens, while supervisor agents provide authorisation and policy-governed release of mission transitions. This separation between interpretation, coordination, and execution yields auditable decision paths, constrains non-determinism within the coordination layer through guards and synchronisation, and enables bounded-time integration of heterogeneous evidence. To improve resilience in contested environments, including cyber compromise, spoofing, jamming, and communication loss, the digital coordination layer is complemented by independent analogue safety envelopes that clamp or veto unsafe actuator commands issued to individual vehicles. A coastal-surveillance case study illustrates how the proposed architecture enables dependable, governed, and physically safe swarm coordination under operational uncertainty.
[AI-29] Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning
链接: https://arxiv.org/abs/2608.14290
作者: Kai Chen,Jifeng Ding,Ning Ding,Jiaye Ge,Lixin Gu,Yicheng Gu,Qipeng Guo,Ermo Hua,Haian Huang,Haozheng Hou,Jie Hou,Xiangyu Hong,Che Jiang,Minxi Jin,Cheng Liang,Dahua Lin,Dawei Liu,Kuikun Liu,Chengqi Lv,Haijun Lv,Han Lv,Ningsheng Ma,Biqing Qi,Jianmin Qian,Shiya Su,Youbang Sun,Huanze Tang,Zhongbo Tian,Hanjing Wang,Rui Wang,Ting Wang,Yi Wang,Baiting Wu,Jun Xu,Bowen Yang,Hui Wang,Weida Wang,Haochen Ye,Jiashuo Yu,Shan Yu,Xiaoyi Yu,Qirui Zeng,Qi Zhang,Ming Zhang,Wenwei Zhang,Bowen Zhou,Xinyu Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve compositional reasoning. Using hidden states as cache and carrier, reasoners repeatedly query memory for required knowledge-vectors, while the knowledge is transmitted back to reasoning operators. Through this knowledge-reasoning-separation architecture, Mobius achieves better knowledge compression and reasoning efficiency. Built upon Mobius-v0 architecture: 1) Our 7B model trained-from-scratch achieves similar downstream score as a 7B Transformer baseline with 62.6% of baseline’s training data. 2) Our Intern-S2-Mobius, continually-pretrained from Qwen3.5-35B, achieves similar downstream score while delivering nearly 4x end-to-end inference speedup.
[AI-30] meSage-EV: A Live Benchmark for Agent ic Time Series Analysis in Evolving Environments
链接: https://arxiv.org/abs/2608.14270
作者: Qingren Yao,Yaxuan Kong,Yuqi Nie,Yichen Li,Stefan Zohren,Anna Vettoruzzo,Qingsong Wen,Ming Jin,Joaquin Vanschoren
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Time series analysis in high-stakes domains relies on recurring data releases, where new observations can alter the evidence base and the validity of later conclusions. Existing time series QA benchmarks mostly rely on fixed snapshots, leaving temporal validity and cutoff-aware evidence use unevaluated. We introduce TimeSage-EV, a live benchmark for agentic time series analysis in evolving environments. It tracks 60 real institutional scenarios across 6 domains, comprising 1,485 scenario-period QA pairs from Feb 2023 to May 2026 and spanning monthly, weekly, daily, and irregular release cadences. At each period, large language model (LLM) agents receive time series data and source reports, while the withheld target release provides ground truth. TimeSage-EV evaluates state identification, data summarization, and outlook reasoning. Experiments with frontier LLM agents and TimeSage-1.0, a novel self-evolving agent with a reusable analytical skill library, reveal significant performance gaps across model tiers and recurring failures in temporal validity, exogenous context use, and adaptation. We release TimeSage-EV as a research resource with monthly updates, code, a leaderboard, and failure-mode analyses.
[AI-31] Multi-Objective Bayesian Optimization for Model Merging
链接: https://arxiv.org/abs/2608.14264
作者: Utkarsh Agarwal,Vamshi Bonagiri,Raul Astudillo,Monojit Choudhury
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Model merging combines trained models directly in weight space, offering a compute-efficient alternative to additional fine-tuning. Selecting merge parameters is nevertheless difficult because downstream evaluations are expensive, gradients are unavailable, and source capabilities can conflict. We formulate merge-parameter selection as a black-box multi-objective optimization problem and introduce MOBO-Merge, a merge-operator agnostic framework that uses multi-objective Bayesian optimization to approximate the Pareto front under a limited evaluation budget. We evaluate Qwen3-4B and Llama-3.1-8B in two-model instruction-math and three-model instruction-math-code settings using Linear, SLERP, TIES, and block-wise merge operators. On held-out benchmark partitions, MOBO-Merge obtains higher mean hypervolume than random search in 11 of 12 reported comparisons. The gain is small for one-dimensional Linear interpolation but substantially larger for several TIES, block-wise, and three-objective searches. No merge operator is uniformly best: TIES leads in three of four family-setting combinations, whereas Block-Linear 4x is strongest for the Llama three-model merge. These results show that multi-objective Bayesian optimization is valuable as a search layer for expressive merge parameterizations.
[AI-32] Meteorology-driven Causal Nowcasting of Fugitive Landfill Emissions Enables Proactive Public Health Response
链接: https://arxiv.org/abs/2608.14254
作者: Timothy C. Pearce,David J. T. Smith,Alec Dobney,Alessia Freddo
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph); Geophysics (physics.geo-ph)
备注:
Abstract:Fugitive emissions from waste sites increasingly expose communities to toxic and odorous gases, yet public-health responses remain largely retrospective, with episodes investigated only after residents have been exposed. Here we show that the meteorological drivers of elevated hydrogen sulphide (HS) at a long-monitored European landfill, and the timescales over which they act, can be identified directly from routine monitoring data. We introduce CAIRN (Causal-Anchored Inference for Receptor Nowcasting), a machine-learning framework whose internal memory is matched to these measured timescales: a fast component tracking hour-scale wind-borne transport and a slow component tracking multi-hour weather changes. Trained to predict gas measurements, CAIRN operates using only routine weather variables and the calendar, without hand-engineered features. Its behaviour is consistent with the identified transport mechanisms, and the framework transfers unchanged to a second monitoring station and to co-emitted methane. Combining four such nowcasters produces a site-level, tiered alert aligned with WHO odour guidance that closely reproduces the alert generated by a direct sensor network and tracks an independent record of community odour complaints. Weather-driven nowcasting can therefore estimate community impact as an emission episode unfolds, providing public-health authorities with a validated, graded trigger for intervention and enabling exposure to be reduced during events rather than after them.
[AI-33] Polaris : Multi Agent ic System for Conversational Enterprise Analytics
链接: https://arxiv.org/abs/2608.14246
作者: Varuni H K,Soham Sarkar,Jay Kumar,Goutham Krishnan,Tanvi Johari,Avinash Bharadwaj,Santosh Hegde
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In today’s fast-paced environment, the ability to swiftly access, understand, and act on data is no longer optional; it is essential. Yet most organizations remain data-rich but insight-poor, constrained by the complexity of querying, interpreting, and explaining enterprise-scale information. We present Polaris, a supervisor-led multi-agent framework for conversational enterprise analytics that bridges this gap. Polaris introduces Dynamic Task Coordination (DTC), a decision-theoretic orchestration layer that models agent-task assignment as adaptive bipartite matching, enabling real-time coordination, recovery, and optimization across specialized agents for querying, visualization, and reasoning. By coupling DTC with reason-first, ReAct-style agents, Polaris transforms natural-language queries into coherent analytical workflows that not only retrieve and visualize data but also explain the underlying “why.” Evaluation on structured enterprise datasets demonstrates high semantic fidelity and answer relevancy, underscoring the potential of multi-agent orchestration to deliver trustworthy, end-to-end business intelligence at scale.
[AI-34] Attributing Preprocessing Invariance in Spectral Foundation Models
链接: https://arxiv.org/abs/2608.14227
作者: Dongjun Wei,Hongyi Wu,Yinuo Zou
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
备注:
Abstract:Preprocessing invariance is an appealing goal for spectral foundation models: a frozen model should remain useful when laboratories preprocess spectra differently. It is usually measured by training a classifier under one preprocessing pipeline and testing it under another, with preserved accuracy read as evidence of learning. We revisit that reading, using a Raman foundation model as a case study. Such models normalize their inputs before any learned parameter is applied. If that normalization maps two differently preprocessed spectra to the same vector, the encoder receives identical inputs, so the invariance cannot be attributed to learning. For a normalization that uses each spectrum’s own statistics, this happens exactly when one spectrum is a positive multiple of the other plus a constant. Several standard preprocessing operations take that form. The encoder should therefore be measured against the normalization alone, which has no learned parameters. On six Raman evaluation datasets, the model does not measurably outperform its own normalization. It improves on raw spectra, but so does the normalization alone. Training does improve the encoder over random initialization, and a controlled experiment shows that it learns to ignore a transformation only when that transformation reaches it. A numerical test settles which transformations a given normalization removes. Across released systems in five modalities, most normalizations already remove transformations of that form, and several of those systems claim that invariance as learned. Replicating the comparison on two of them shows no gain either.
[AI-35] A Generalized Parallelogram Rule for Proportional Analogies on Riemannian Manifolds
链接: https://arxiv.org/abs/2608.14220
作者: Pierre-Alexandre Murena,Marcelo Hartmann
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”, usually denoted a : b :: c : d. This notion is formalized in particular with the notion of proportional analogy, which imposes some constraints on the valid analogies. Whereas proportional analogies have been studied mostly in symbolic domains and in vector spaces, their use is limited in non-Euclidean spaces. In this paper, we introduce a proportional analogy relation in Riemannian domains, extending the parallelogram rule used for arithmetic analogies in Euclidean spaces. We illustrate the introduced analogy on various manifolds, such as the sphere, shape spaces and manifolds of probability distributions.
[AI-36] APTER: Adaptive Post-Training with Expert-Grounded Rubrics
链接: https://arxiv.org/abs/2608.14212
作者: Xukai Wang,Liangqi Li,Zhiyue Xu,Jingang Zhou,Xiaoyu Shi,Jiansheng Cai,Bo Zhang,Zhe Li,Xu-Yao Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As large language models enter professional domains, they must satisfy domain constraints, include critical evidence, and provide complete reasoning rather than merely produce fluent responses. Existing post-training methods often rely on holistic preferences or outcome-level verification, while recent rubric-based methods usually generate rubrics independently for each query. In specialized domains, such unconstrained rubrics may omit critical requirements and vary across samples, hindering the diagnosis and targeted repair of persistent capability deficiencies. We propose APTER (Adaptive Post-Training with Expert-Grounded Rubrics), a framework that integrates structured domain knowledge into fine-grained evaluation, optimization, and diagnosis for specialized complex reasoning. First, expert-grounded rubric construction starts from an expert criteria framework built by domain experts, where each criterion represents a stable professional capability. For each query, APTER selects relevant criteria and instantiates them into query-level rubrics linked to their source criteria, turning reusable expert criteria into executable query-level supervision without reference answers. Second, adaptive post-training uses rubric verdicts as both optimization and criterion-level diagnostic signals. Aggregating low-scoring verdicts by criterion ID reveals persistent deficiencies and triggers targeted supervised fine-tuning updates during reinforcement learning. Experiments on mathematical reasoning and medical question answering show consistent gains across both domains. Across three model generations, APTER improves the mathematics and medical averages over the corresponding base models by up to 15.86 and 8.04 points, respectively. Code and rubric datasets are available at this https URL.
[AI-37] raining Fair Tabular Foundation Models ICML2026
链接: https://arxiv.org/abs/2608.14211
作者: Patrik Kenfack,Jesse C. Cresswell,Anthony L. Caterini,Samira Ebrahimi Kahou,Ulrich Aïvodji
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Spotlight paper at the ICML 2026 Workshop on Foundation Models for Structured Data
Abstract:Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training. Despite the increased use of TFMs in high-stakes decision-making, their fairness properties remain largely unexplored. In this work, we incorporate fairness constraints directly into TFM training, enabling fair predictions in a single forward pass. Our approach addresses two key challenges: limited access to sensitive attributes in training data, and the incompatibility of existing fairness techniques with the in-context learning paradigm. We propose FairTFM, a scalable training strategy based on synthetic fairness tasks and a fairness-aware architecture using a gradient reversal layer, which encourages the model to learn representations invariant to sensitive attributes. Experiments on 132 fairness tasks show consistent improvements in fairness while maintaining competitive accuracy.
[AI-38] FreeBalance: Pre-Routing Online Moe Load Balancing via Residual Workload Prediction
链接: https://arxiv.org/abs/2608.14205
作者: Pengfei Chen,Yize Wu,Shouxu Kuang,Ke Gao,Ling Li
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 8 pages, 4 figures
Abstract:Load imbalance poses a major bottleneck to the efficiency of expert parallelism in distributed inference of Mixture-of-Experts (MoE) models. The most heavily loaded rank stalls global execution due to skewed routing distributions, directly increasing latency. While offline expert placement can alleviate persistent imbalance, practical multi-task serving workloads exhibit layer- and batch-dependent routing dynamics, making online load balancing indispensable. Existing approaches rely on routing statistics collected after each MoE router, requiring expert weight load or migration to begin only after routing decisions are available, consequently placing migration overhead on the inference critical path. In this work, we observe that online balancing can instead be largely overlapped with computation before target routing (e.g., attention), if routing distributions can be predicted accurately in advance. Therefore, we propose FreeBalance, a lossless online load-balancing framework that overlaps expert migration with preceding computation stages via residual workload prediction. FreeBalance leverages cross-layer similarities in hidden representations within the residual network to build a lightweight workload predictor. This enables proactive expert migration planning before routing decisions are available, creating substantial overlap between weight transfer and computation-heavy pre-routing stages. Furthermore, a cost model constrains the number of swaps to fully hide the synchronization overhead within the available window. Experiments across models and datasets show that FreeBalance reduces the max-to-mean rank load ratio by 32.8% and end-to-end prefill latency by 13.1%. Specifically, our method hides balancing overhead of an average of 5.1 experts per layer, which would otherwise account for about 8.5% of the critical-path latency.
[AI-39] Can Language Models Understand mmWave Data? Benchmarking Large Language Models for mmWave Radar-Based Human Understanding CVPR2026
链接: https://arxiv.org/abs/2608.14179
作者: Jeongwan Shin,Jaehyeon Kim,Donguk Ko,Jaeho Choi
类目: Artificial Intelligence (cs.AI)
备注: Accepted to CVPR 2026 Findings
Abstract:Large language models (LLMs) have shown remarkable reasoning and generative capabilities, motivating their use as universal reasoning engines for perception. While modern approaches such as vision-language models (VLMs) have attempted to incorporate reasoning capabilities into visual sensing, the integration of LLMs with the millimeter-wave (mmWave) modality-despite its unique advantages under low light and occlusion-remains largely unexplored. The principal bottlenecks stem from the scarcity of radar language pairs, severe cross-dataset heterogeneity, and the absence of a foundational mmWave encoder. We address this gap through a minimal textualization interface that serializes each mmWave point cloud into concise natural language, allowing off-the-shelf LLMs to operate in a question answering (QA) setting. Building on this, we present mmWave-QA, the first benchmark for language-conditioned mmWave human perception. mmWave-QA aggregates heterogeneous public mmWave datasets and harmonizes them via calibration-aware preprocessing and global taxonomy alignment, while providing natural language QA. Spanning six scenarios and five QA tasks, the benchmark enables standardized evaluation across diverse mmWave hardware and experimental conditions, establishing a foundation for scalable research on mmWave-LLM integration. We further evaluate and analyze LLMs on our mmWave-QA, highlighting their zero-shot reasoning potential for radar perception, as well as their robustness under visual degradation.
[AI-40] Structure-Guided Spatiotemporal Attention Graph Neural Network for Traffic Flow Prediction
链接: https://arxiv.org/abs/2608.14177
作者: Xuanmian He,Can Li,Wanjing Ma
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Deep spatiotemporal models integrating graph convolutions and attention mechanisms have demonstrated excellent performance in network-level traffic flow prediction, owing to their exceptional ability to capture complex spatiotemporal dependencies. Despite their predictive success, deployment of such models in safety-critical urban systems remains constrained by their inherent lack of transparency. Existing post-hoc diagnostic methods often struggle with spurious correlations and fail to unveil the intrinsic decision-making mechanisms governing traffic dynamics, resulting in suboptimal interpretability and limited operational trustworthiness. To address these challenges, this paper proposes the Structure-Guided Spatiotemporal Attention Graph Neural Network (SGSAN). Departing from traditional architectures that rely on unconstrained adaptive graphs, SGSAN explicitly learns a static Directed Dependency Graph (DDG) to identify the invariant macroscopic propagation paths of traffic states. We further introduce an InfoNCE-based soft-coupling mechanism that anchors the model’s dynamic spatiotemporal attention to this structural prior, offering a mechanistic account of the model’s decision-making process while ensuring robust forecasting by aligning attention-based reasoning with identified macroscopic dependencies and preventing over-reliance on ephemeral local noise. Furthermore, a decoupled two-stage optimization framework is developed to resolve the fundamental conflict between structural discovery and predictive error minimization. Extensive experiments on multiple real-world datasets demonstrate that SGSAN achieves state-of-the-art predictive accuracy while providing built-in interpretability that organically aligns with the physical logic of traffic networks.
[AI-41] BiasTrace: Linking Reasoning Behaviours to Biased Outputs in LLM s
链接: https://arxiv.org/abs/2608.14161
作者: Varsha Ramineni,Hossein A. Rahmani,Jerome Ramos,Karin Sevegnani,Emine Yilmaz
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLMs exhibit social biases that can produce inaccurate and discriminatory inferences, posing risks in high-stakes applications. While prior work has made progress in measuring and mitigating bias, it largely focuses on final outputs of models, with limited understanding of the mechanisms that produce biased outcomes. Recent advances in LLM reasoning offers a new lens for investigating bias, yet the link between reasoning and bias remains poorly understood. Existing approaches focus primarily on final answer correctness or explicitly biased language, overlooking different behaviours in reasoning that can drive biased outcomes. We introduce BiasTrace, an annotation scheme for labelling reasoning behaviours in model-generated traces and linking them to biased outcomes. BiasTrace captures bias-specific behaviours (e.g., unsupported demographic assumptions) as well as general reasoning patterns that may implicitly contribute to bias (e.g. overthinking). We apply BiasTrace to reasoning traces in bias-sensitive contexts, scaled using validated LLM-as-a-judge methods, producing a large annotated dataset. Our analysis shows that biased outputs often stem from subtle reasoning behaviours rather than explicitly biased language, and that reasoning-level annotations improve bias detection. We further show that BiasTrace behaviours can be exploited for inference-time mitigation. These findings underscore the importance of examining a broader range of reasoning patterns to better understand bias in LLMs.
[AI-42] Removing Temporal Note Redundancy Improves Multimodal Reinforcement Learning for Medicine
链接: https://arxiv.org/abs/2608.14157
作者: Chenran Weng,Joo Seung Lee,Malini Mahendra,Anil Aswani
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Mechanical ventilation is a critical life-support intervention, requiring dynamic adjustments to ventilator settings as a patient’s condition evolves. While reinforcement learning (RL) offers a promising framework for optimizing these sequential decisions, standard approaches rely primarily on structured electronic health record (EHR) data, missing crucial clinical context recorded in free-text notes. Integrating longitudinal clinical notes into RL state spaces is challenging because notes are heavily inflated by temporal redundancy, such as copy-forward text, templating, and repetitive documentation, which dilutes time-local updates and degrades state representation quality. To address this, we propose a redundancy-aware multimodal state representation framework that explicitly removes duplicated note text over time before policy learning. We evaluate two computationally efficient temporal decomposition strategies for removing duplicated note text: (1) an embedding-space decomposition using singular value decomposition on local history subspaces, and (2) an interpretable sentence-level diff operation that filters out previously documented sentences before text encoding. Using real-world ICU data, we demonstrate that state representations constructed by stripping temporal note redundancy significantly outperform both structured-only and raw-note baselines across multiple off-policy evaluation methods (Model-Based Rollouts, Fitted Q-Evaluation, Weighted Importance Sampling, and Weighted Doubly Robust Evaluation). Our findings show that explicitly isolating new clinical information from repeated note text yields higher-quality state representations and directly improves RL performance for clinical decision support.
[AI-43] owards Efficient Multimodal and Multilingual Opinion Extraction for STI: A QLoRA-Based Fine-Tuning Approach
链接: https://arxiv.org/abs/2608.14152
作者: Sheng Hong,Xuanqi Wang,Jiacheng Wang,Yuwei Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in large language models (LLMs) have reshaped semantic analysis. Opinion Extraction (OE) for Science and Technology Intelligence (STI) requires concise core opinions from large information streams. Off-the-shelf models struggle to filter noise from these streams and show limited structured-output reliability in zero-shot multilingual and multi-modal settings. To address information overload and extraction defocus, this study proposes a multimodal core-opinion extraction framework in which visual evidence serves as a contextual anchor for textual judgment. Using VideoLLaMA2 (VL2) and VideoLLaMA2.1 (VL2.1) as the base models, we apply Quantized Low-Rank Adaptation (QLoRA) fine-tuning on a curated dataset of 2,194 multilingual and multimodal samples. Under the selected Image-Augmented setting, fine-tuned VL2.1 generates structured JSON core-opinion outputs, achieving 64.98% Precision, 42.15% Recall, 51.14% F1-score, and 74.00% sample-level accuracy. Relative to the zero-shot VL2.1 setting, it raises the F1-scores of Spanish and Russian from 4.83% and 0.45% to 46.05% and 51.93%, respectively. The framework further incorporates a Fuzzy Cumulative Prospect Theory-based post-extraction triage module for case-level value assessment, providing a case-level value signal for downstream STI screening.
[AI-44] QuaSAR: Quantization Compensation via Stable Activation-Aware Rank Truncation
链接: https://arxiv.org/abs/2608.14149
作者: Lin-Fa Lee,Yi-Yu Chang,Kuo-Hei Yeh
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 8 pages, 6 figures
Abstract:Recent training-free post-training quantization methods restore model accuracy through closed-form residual compensation. To constrain additional model storage overhead, several existing methods gate layer selection by goodness-of-fit, retaining only those layers whose compensation yields a positive residual fit score and discarding the rest. In this paper, we show that, under the low-bit W4A4 setting, this gating mechanism fails to distinguish poorly predictable quantization error from numerical solver failure. Rank-deficient input activations yield severely ill-conditioned or numerically singular Gram matrices, causing the closed-form solver to become unstable and produce spuriously negative fit scores. Consequently, existing goodness-of-fit gates misclassify affected layers as uncompensable and discard them. Many of these discarded layers can nevertheless provide substantial error recovery when their compensation is computed using a numerically stable solver. To address this problem, we propose a parameter-free truncated pseudoinverse solver which removes collapsed directions prior to inversion. On ViT-B with the W4A4 setting, our training-free method achieves 81.42% top-1 accuracy, outperforming prior post-training methods and fine-tuning-based baselines. Combined with joint low-rank and quantization compression, the proposed method reaches a deployable operating point of 80.26% accuracy at 54.7 MB, providing a well-balanced trade-off between model size and accuracy.
[AI-45] BGA: A noise-immune neural distillation framework for malicious signature extraction in high-entropy encrypted flows
链接: https://arxiv.org/abs/2608.14126
作者: Sheng Hong,Yixuan Huang,Weiwei Jiang,Junyuan Zhang,Jiacheng Wang,Ruijian Jiao
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:To mitigate attention dilution in high-entropy TLS 1.3 flows, we propose BGA, a noise-immune neural distillation framework for encrypted threat this http URL methodology first employs Analysis of Variance (ANOVA) to decouple high-discriminatory control-plane features - specifically industrial setpoints - from stochastic cryptographic noise. To resolve the extreme class imbalance within a corpus of 86,878 flow records, a Wasserstein GAN with Gradient Penalty (WGAN-GP) module, enforcing the 1-Lipschitz constraint, is integrated to synthesize high-fidelity minority samples, elevating the detection recall of rare Malicious State Command Injections(MSCI) attacks by 43.2%. At its core, the BGA architecture integrates Bidirectional Long Short-Term Memory (BiLSTM) for temporal dependency extraction and an Adaptive Gated Multi-Head Attention mechanism. This gated unit functions as a neural filter to dynamically suppress encryption artifacts while amplifying malicious signatures. Extensive evaluations on CIC-IDS-2018 and Edge-IIoT benchmarks demonstrate a performance ceiling exceeding 95.2% across all key metrics. Furthermore, noise-injection stress tests confirm BGAs superior structural resilience with a 8.57% performance margin over vanilla Transformers, while its ultra-low inference latency of 0.2820 ms (estimated 1.6920 ms via theoretical scaling for ARM) indicates a high potential for real-time feasibility on heterogeneous industrial edge gateways, providing a promising architectural baseline for future hardware implementation.
[AI-46] raj-LeWM: Path-Aware World-Model Planning via Latent Trajectory Cost
链接: https://arxiv.org/abs/2608.14125
作者: Xiaodi Huang,Ziyi Ding,Jingtian Wan,Yuchen Liu,Yuan Zhang,Xiao-Ping Zhang,Jiayu Chen,Zhang Zhang,Tao Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LeWM is a lightweight visual world model that learns latent dynamics end-to-end from pixels and ranks candidate action sequences by the distance between their predicted endpoints and the goal. However, LeWM has two limitations. First, during training, it learns local next-step transitions without evaluating complete trajectories relative to the task goal. Second, during planning, it ranks candidates solely by predicted endpoint distance. Because model predictions may differ from actual execution outcomes, the candidate whose predicted endpoint is closest to the goal may not perform best when executed in the environment. The evolution of the complete predicted trajectory can therefore provide complementary information beyond endpoint distance. To address these limitations, we propose Traj-LeWM, which retains LeWM’s local-dynamics objective and endpoint score while introducing a goal-conditioned latent trajectory cost (LTC) that aggregates trajectory-level information as a complementary signal. During training, LTC-based trajectory-preference supervision complements next-step prediction in shaping the shared representation. During planning, LTC is combined with endpoint distance to incorporate intermediate-path information into candidate ranking. With joint endpoint-plus-LTC scoring, Traj-LeWM outperforms LeWM on Push-T, OGBench-Cube, Reacher, and Two-Room by 3 , 14 , 7 , and 7 percentage points, respectively. Controlled experiments and ablations further verify the complementary roles of trajectory-level representation shaping and path-aware candidate ranking.
[AI-47] Reinforcement Learning-Based Production Scheduling in an Industry-Based Coating Scenario Using the Digital Model Playground
链接: https://arxiv.org/abs/2608.14122
作者: Arne Kröger,Ralf Buschermöhle,Wilhelm Hasselbring,Henrik Wilbers
类目: Artificial Intelligence (cs.AI)
备注: Copyright 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Abstract:Production scheduling in complex manufacturing environments is challenging when sequence-dependent setup times, stochastic disturbances, and due-date constraints must be addressed simultaneously. While reinforcement learning (RL) methods have shown promising results in research, most studies rely on simplified benchmark processes, limiting their industrial relevance. This paper demonstrates the applicability of RL-based scheduling in an industry-inspired coating process that reflects practical complexities such as sequence-dependent setup times, machine breakdowns, and variable utilization. The open-source Digital Model Playground (DMPG), a discrete event simulation framework, is used to model the scenario and to train RL agents. Two standard algorithms, Deep Q-Networks and Proximal Policy Optimization, are benchmarked against conventional dispatching rules to illustrate feasibility and to provide a transparent testbed for further research. Results indicate that RL-based scheduling achieves balanced improvements across key performance indicators, with PPO delivering the most robust performance. The main contribution of this work is to bridge the gap between academic research and industrial practice by validating RL-based scheduling in a realistic, shareable scenario and by providing a reusable open-source framework for future studies.
[AI-48] Overcoming Shortcut Learning in Graph Neural Networks through Active Explanation Guidance
链接: https://arxiv.org/abs/2608.14121
作者: Taraneh Younesian,Steve Azzolin,Antonio Longa,Francesco Ferrini,Vincenzo Marco De Luca,Stefano Teso
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Graph Neural Networks (GNNs) can solve prediction tasks by unintentionally exploiting shortcuts—that is, edges, nodes, and features that correlate with but are not causal for the prediction—which compromise their reliability in out-of-distribution tasks. We introduce XIGL, an architecture-agnostic human-in-the-loop strategy for removing such shortcuts from GNNs. Our key insight is twofold. On the one hand, reliance on shortcuts can be detected by inspecting GNN explanations. On the other hand, once made aware of such shortcuts, sufficiently expert users can provide tailored corrective feedback, which helps deconfound the model. XIGL supports any query strategy; however, since corrective feedback can be expensive to acquire, we develop an active learning strategy for prioritizing explanations that are more likely to display shortcut behavior, lowering annotation and cognitive costs. We showcase the effectiveness of XIGL, including both existing and proposed explanation-based strategies, on several GNN architectures. Our implementation is available online.
[AI-49] From Fixed Grids to Moving Particles:A Transferable Latent Operator for Fluid Dynamics
链接: https://arxiv.org/abs/2608.14120
作者: Meng Li,Chuqi Chen,Zhengqing Gao,Xi Zhou,Xiao Sun,Yang Xiang,Huaxi Huang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注: 8 pages, 5 this http URL
Abstract:Lagrangian modeling is vital to fluid dynamics, as it characterizes particle transport and complements the Eulerian this http URL, Lagrangian trajectories are less commonly available than Eulerian fields, while most neural operators are trained and evaluated primarily in the Eulerian representation. This mismatch motivates a new learning problem: can a model trained solely on Eulerian observations generalize zero-shot from Eulerian field prediction to Lagrangian particle rollout, without Lagrangian supervision or task-specific adaptation? To address this problem, we propose the Transferable Latent Operator (TLO), which learns a unified flow representation shared by Eulerian field prediction and Lagrangian particle rollout. TLO decouples latent flow evolution from coordinate-dependent decoding: querying the evolving latent representation at fixed spatial coordinates yields Eulerian fields, whereas querying velocities at particle positions and recursively updating these positions enables Lagrangian rollout. Across five fluid-dynamics benchmarks, TLO consistently outperforms existing neural operators in both Eulerian field prediction and zero-shot Lagrangian rollout, with further gains from limited Lagrangian fine-tuning.
[AI-50] Retrieval Grounding Latent Reasoning for Dense Retrieval
链接: https://arxiv.org/abs/2608.14107
作者: Gang Zhou,Xiongxi Yu,Hu Tian,Yang Wei,Lu Pan,Ke Zeng,Shibiao Xu,Xiaolong Zheng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reasoning-intensive retrieval requires text representations to capture not only semantic similarity, but also the reasoning needed to determine relevance under a given retrieval instruction. Existing reasoning-enhanced embedding models improve retrieval by incorporating reasoning information into dense representations, yet their supervision is typically dominated by the final retrieval objective. As a result, latent reasoning trajectories may learn shortcut reasoning patterns that preserve retrieval performance without producing meaningful incremental retrieval gains. We propose Retrieval Grounding Latent Reasoning (RGLT), a latent reasoning framework for dense retrieval that explicitly connects intermediate latent transitions with retrieval improvements. RGLT performs non-autoregressive reasoning in hidden space through an instruction-conditioned latent reasoning trajectory constructed from silent tokens. It combines process-supervised explicit-to-implicit distillation with retrieval-grounded supervision, using stage-wise CoT reconstruction to shape intermediate latent states and retrieval-effect credit to optimize incremental retrieval gains across the latent reasoning trajectories. Experiments on reasoning-intensive retrieval benchmarks show that RGLT consistently outperforms strong baselines while preserving efficient embedding inference.
[AI-51] Forecast Collapse in Time-Series Foundation Models
链接: https://arxiv.org/abs/2608.14106
作者: Shu Wan,Miles Ma,Hank Zhu,Guangqi Liu,Stephen Wang,Qingsong Wen,Huan Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Applications (stat.AP); Machine Learning (stat.ML)
备注: 27 pages, 3 figures, 5 tables. Dataset: this https URL
Abstract:When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-sectional correlation. We call this forecast collapse. Surprisingly, the phenomenon largely disappears when forecasting trading volume under the same setting. We investigate forecast collapse across time-series foundation models (TSFMs), twelve deep-learning forecasting models, and 97 public benchmark configurations, and find that it is closely tied to target predictability. We identify two distinct reasons behind it: low predictability limits the amplitude of calibrated point forecasts, while per-series objectives leave cross-series structure unidentified. These findings reveal a calibration-ranking tradeoff: optimizing squared error leads to flat predictions, whereas directly optimizing cross-sectional correlation improves ranking but can inflate forecast amplitude by more than an order of magnitude. To address this tradeoff, we introduce CalibRank, a simple objective that balances calibration and ranking. On Finance1K, CalibRank nearly triples cross-sectional correlation while keeping amplitude close to the target, and improves correlation on all tested models. Our results reveal a blind spot in conventional time-series evaluation: per-series metrics can hide failures in cross-series structure needed by downstream decisions.
[AI-52] Rewrite Once Validate Anywhere: Producing OWL-Aware SHACL Constraints (Extended Version) ISWC’26
链接: https://arxiv.org/abs/2608.14104
作者: Anouk Oudshoorn,Piotr Gorczyca,Dörthe Arndt
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI)
备注: Technical report of our ISWC’26 paper
Abstract:The Shapes Constraint Language (SHACL) is a W3C recommendation to express syntactic constraints, called shapes, on RDF graphs. SHACL validators are used to test whether a given graph adheres to such a shape. However, RDF graphs often come with OWL ontologies, whose implicit knowledge needs to be taken into account. This is classically handled by first applying reasoning and then performing the constraint checking on the results, often using different technologies which makes the process inefficient and vulnerable for mistakes. To overcome this, we propose to internalise the OWL axioms in the SHACL constraints; we construct a rewriting which takes as input both shapes and an OWL EL ^- ontology – a fragment of OWL EL restricting the usage of existential restrictions – and produces SHACL constraints. This output can then be evaluated by any validator supporting SHACL core regardless of its reasoning support, while yielding the same results as the traditional approach. The implementation of our translation is evaluated both against applying state-of-the-art reasoners and validators consecutively, as against validators with built-in reasoning support. For our benchmark, we show that our approach is in general more efficient in finding violations compared to the sequential approach, thus providing a powerful tool which simplifies combining reasoning with validation. Comments: Technical report of our ISWC’26 paper Subjects: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14104 [cs.LO] (or arXiv:2608.14104v1 [cs.LO] for this version) https://doi.org/10.48550/arXiv.2608.14104 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-53] P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems
链接: https://arxiv.org/abs/2608.14094
作者: Myunghoon Ryu,Geunpyo Park,Sungjoon Lee,XinYu Piao,Jong-Kook Kim
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Cloud-local LLM inference systems have the potential to use the reasoning capability of large cloud models while protecting sensitive user data on personal devices. Cloud-bound requests must exclude personally identifiable information (PII) to prevent external data leakage. Existing privacy-preserving methods rely on prompt perturbation, entity masking, or model fine-tuning, but these approaches may distort contextual semantics or require additional training. This paper proposes P2Skill, a prompt-based skill distillation method in which a local small language model (SLM) autonomously performs decomposition, PII-aware routing, paraphrasing, and reconstruction by following the skill prompts. Skills are iteratively refined from execution failures by a cloud LLM, enabling the local SLM to generalize beyond memorized PII patterns, and therefore P2Skill requires no privacy-specific fine-tuning or learned auxiliary detectors. Evaluation on a four-domain benchmark shows that P2Skill achieves 1.69\times and 3.66\times higher privacy-preserved inference quality than previous baselines.
[AI-54] Mandato: Protocol-Level Enforcement of Digitally Signed Mandates on AI Agent Actions with Cryptographically Chained Audit Trails
链接: https://arxiv.org/abs/2608.14074
作者: Giovanni Racioppi
类目: Artificial Intelligence (cs.AI)
备注: 5 pages, 1 table. comments welcome
Abstract:AI agents increasingly act on external systems through standardized tool-calling protocols such as the Model Context Protocol (MCP), yet no infrastructure layer constrains their actions to what a principal has verifiably authorized: authorization logic lives in application code, is neither signed nor independently auditable, and the resulting logs lack evidentiary value. We present Mandato, a governance proxy that enforces digitally signed mandates on agent actions at the protocol level. A mandate is a machine-readable, cryptographically signed authorization artifact specifying which tools an agent may invoke, under which parameter constraints and contextual conditions, for how long, and on whose behalf; the proxy evaluates every tool call against the applicable mandate chain, blocks non-conforming calls in line, and records every decision – permit, deny, and the evidence for each – in an append-only, hash-chained audit log designed for evidentiary use and periodically anchored via qualified timestamps. The mandate is deliberately modeled on the civil-law institution of delegation of authority, making the artifact legible to lawyers and auditors, not only to engineers. We give the mandate model and its decision semantics, the reference architecture as an MCP-transparent proxy with separated decision and enforcement points, and a mapping of the mechanism onto EU AI Act Articles 12 and 14, GDPR accountability, NIS2, and eIDAS 2, including a roadmap to qualified attestation through Qualified Trust Service Providers (QTSPs). We describe the implementation status of the reference system and a quantitative evaluation plan covering enforcement overhead, audit completeness, and tamper-evidence verification cost.
[AI-55] Scaling Domain Data Repetition in LLM Pretraining
链接: https://arxiv.org/abs/2608.14071
作者: Jingwei Li,Xinran Gu,Rui Dai,Xintong Hao,Chengyin Xu,Yan Wu,Shuran Zheng,Jingzhao Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As large language models scale, their training-token budgets must also increase to maintain an appropriate tokens-per-parameter ratio ((\mathrmTPP)). However, high-quality domain data is much harder to scale than general web data. As model size and the training-token budget increase, its fraction in the training mixture tends to decrease. Repeating the available high-quality data provides an effective way to counteract this dilution, but excessive repetition may lead to overfitting. We study this trade-off under practical LLM scaling, where the training-token budget grows proportionally with model size. For a fixed domain, we first find that, surprisingly at a fixed (\mathrmTPP), the optimal repetition count mildly increases with model size. Across different domains, we find that the optimal repetition count is strongly negatively correlated with the final validation loss of a domain: domains with lower loss can generally benefit from more repetitions. In contrast, the amount of unique domain data is only weakly related to the optimal repetition count. These findings suggest that repetition counts tuned on smaller proxy models with the same (\mathrmTPP) can provide a practical estimate for larger models.
[AI-56] Rethinking Automated Program Repair: The Impact of Bug Complexity Fault Localization and LLM Cost-efficiency
链接: https://arxiv.org/abs/2608.14065
作者: Junchi Liu,Ali Bigdeli,Roya Daneshi,Atu Ambala,Sudipto Ghosh,Fabio Santos
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 20 pages, 6 figures, 10 tables. Accepted at ESEM 2026
Abstract:Background: Software bugs remain a critical challenge in development, necessitating effective Automated Program Repair (APR) techniques. While Large Language Model (LLM)-based APR systems have shown promise, prior studies primarily focus on overall repair effectiveness. The effects of bug complexity, fault localization, reasoning settings, and repair cost-effectiveness remain insufficiently explored. Aims: This study presents a comprehensive empirical analysis of LLM-based APR, focusing on how repair performance is shaped by bug complexity, fault localization, reasoning settings, and costs. Method: We evaluate two APR techniques (ChatRepair and CodeCorrector) using three LLMs (DeepSeek, GPT, and Llama), and examine their performance across diverse levels of bug complexity and localization strategies through a multi-dimensional empirical framework and statistical analysis. Results: Although structurally complex bugs and imprecise fault localization make repair more challenging, LLM-based APR techniques still achieve competitive repair effectiveness. Imprecise fault localization can substantially enlarge the performance gap between APR techniques. Furthermore, higher-cost LLMs and stronger reasoning settings do not consistently yield better cost-efficiency, revealing a nontrivial trade-off between repair effectiveness and computational cost. Conclusions: Over 50% of moderately complex bugs can be repaired by low-cost LLM-based APR techniques. The repair effectiveness gap between APR techniques becomes larger as fault localization becomes less precise. GPT-5 repairs 7 and 39 more complex bugs than DeepSeek-V4-pro and DeepSeek-V3.2, respectively; whereas the total repair cost of DeepSeek-V3.2 shows the best cost-efficiency performance. Comments: 20 pages, 6 figures, 10 tables. Accepted at ESEM 2026 Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14065 [cs.SE] (or arXiv:2608.14065v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.14065 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Junchi Liu [view email] [v1] Fri, 14 Aug 2026 08:23:25 UTC (1,900 KB) Full-text links: Access Paper: View a PDF of the paper titled Rethinking Automated Program Repair: The Impact of Bug Complexity, Fault Localization, and LLM Cost-efficiency, by Junchi Liu and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.SE prev | next new | recent | 2026-08 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[AI-57] Benchmarking data-driven material models on the classic Treloar dataset
链接: https://arxiv.org/abs/2608.14063
作者: Hagen Holthusen,Moritz Flaschel,Denisa Martonová,Ellen Kuhl
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: 27 pages, 8 figures, 7 tables
Abstract:Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges long-established modeling paradigms. But with a growing number of machine-learning-based approaches available, how do they compare in practice? In this paper, we use the classic experimental data of Treloar to benchmark popular frameworks for hyperelasticity: (Generalized-Invariant) Constitutive Artificial Neural Networks, Physics-Augmented Neural Networks, (Adaptive) Material Fingerprinting, and Efficient Unsupervised Constitutive Law Identification Discovery. We compare their fitting performance, computational cost, hyperparameter sensitivity, and ease of implementation. Furthermore, we discuss the trade-offs between predictive accuracy and model complexity. The latter is assessed by quantifying both the number of material parameters in the discovered models and the computational time required to evaluate the constitutive model and its derivatives. The results show that all methods can reproduce the benchmark data remarkably well. Rather than identifying a single winner, we highlight the strengths and limitations of each approach and provide practical guidance for their use. The source code for all six methods, including the training and comparison scripts, as well as all results and data used in this study, is publicly available via this https URL.
[AI-58] Demystifying Agent Skills: Why They Work-Until They Dont
链接: https://arxiv.org/abs/2608.14036
作者: Zhiyuan Jiang,Fangrui Huang,Hanwen Xing,Xander Wu,Yipeng Gao,Rui Cao,Mengdi Wang,Shilong Liu,Yijiang Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge. However, existing evaluations largely measure whether skills improve aggregated task success, leaving a more fundamental question underexplored: \emph\textbfWhen do skills help, why do they work, and where do they fail? Through controlled experiments across various benchmarks, agent harnesses and LLMs, we isolate the effects of representation, outcome annotation, retrieval difficulty, and cross-framework robustness of skills. To further answer this question, we design a contrastive study that combines controlled quantitative experiments with paired trajectory analysis. We normalize 8,135 trial records from controlled experiments and retain 238 valid unique labels from 240 open-coded records. We consolidate these observations into a taxonomy of three high-level categories and twelve skill-use modes: skills work when noisy trajectories become procedural anchors that stabilize execution. Skills improve over Workflow Memory by 6.06 points in matched comparisons. Procedural anchoring accounts for 65.7% of skill cases, versus 4.5% for explicit knowledge injection, showing that skills stabilize action rather than inject missing facts. Retrieval is a separate bottleneck: as pools grow from 5 to 100, actual-use precision falls from 29.6% to 3.3%. Confusable distractors impair offline identification, yet downstream success remains stable; exact ground-truth invocation is neither sufficient nor necessary. Skills fail under brittle assumptions, incompatible contexts, or insufficient adaptation. These findings move evaluation beyond aggregate success rates and guide reliable self-evolving agents.
[AI-59] Agent -Orchestration in Autonomous Chip Design
链接: https://arxiv.org/abs/2608.14035
作者: Linyang Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent developments in large language models (LLMs) and tool-using agents encourage people to explore the potential of using agents in chip design. The core question is what kind of AI we really need in such a sophisticated industry. To this end, we bring the idea of modeling a chip-design superintelligence as an enormous \textitAI-organization. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14035 [cs.AI] (or arXiv:2608.14035v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14035 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-60] AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning
链接: https://arxiv.org/abs/2608.14028
作者: Zhiyue Zhao,Jingyi Wu,Hairuo Liu,Mingyu Liu,Liyang Li,Hengdi Zhang,Tong He,Zhengxue Cheng
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Dexterous manipulation is a fundamental capability for embodied intelligence, but scaling it remains difficult because robot demonstrations are expensive to collect and action spaces vary across embodiments. Policies trained on heterogeneous data can also entangle task-relevant visual cues with embodiment-specific appearance, limiting cross-embodiment generalization. We present AdvDex, a unified Vision-Language-Action framework for learning dexterous manipulation from human and robot demonstrations. First, we introduce OmniShare, a large-scale multimodal dataset of human manipulation demonstrations that provides high-quality kinematic supervision and tactile measurements while reducing reliance on robot teleoperation. Second, we propose the Joint-Aligned Action Space (JAAS), a canonical action representation comprising an \mathrmSE(3) wrist pose and 15 finger joints, thereby functionally aligning human hands, dexterous robot hands, and parallel grippers. Finally, we use domain-adversarial learning to reduce embodiment-specific information in the learned visual representation. Experiments on hand-action prediction and real-world dexterous manipulation show consistent improvements over baselines, effective zero-shot human-to-robot skill transfer, generalization to unseen objects and environments, and data-efficient few-shot adaptation.
[AI-61] Buy the Rumor Sell the News: When Is News Priced In?
链接: https://arxiv.org/abs/2608.14014
作者: Alireza Kargarzadeh,Nariman Khaledian,Navid Parvini,Sid Ghatak,Arman Khaledian
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Statistical Finance (q-fin.ST)
备注:
Abstract:Two old market sayings hold that news is already priced in by the time it is published, and that the rumor is bought while the news is sold. Both place the price move associated with a piece of news before and at publication rather than after it. Whether the claims hold, for which kinds of news, and by how much are basic questions about how fast markets absorb public information. We test them on 4.57 million financial news articles covering roughly 3,000 US stocks (2023-2026). A large language model teacher, distilled into a compact classifier through active learning, assigns each article one of 17 event tags and five attributes; articles are clustered into stories to separate first reports from follow-up coverage; and beta-adjusted abnormal returns are measured around the resulting 1.68 million stock-day events, with 364,405 neutral-sentiment events as a placebo group. Three results follow. First, the price move associated with news concentrates before and at publication: pooled across all signed events, the cumulative move in the news direction by the close of publication day is 2.8 times its value 20 days later, and for rumor-flagged events the rumor day captures the entire move while the subsequent confirmation contributes nothing. Second, measured against the placebo of comparable stocks, markets underreact to numbers and overreact to stories: quantified fundamental news (earnings, dividends, guidance, analyst actions) keeps drifting in the direction of the news for weeks, while soft story-driven news (launches, macro commentary, leadership) gives back its move. Third, news carries width as well as direction: publicity raises volatility before the publication day, and volatility declines once the news is out, because publication resolves uncertainty. The study also produces a table of measured drift for each event tag, usable as a prior in news-conditioned forecasting models.
[AI-62] Simulation-Driven Vehicular Traffic Data Augmentation: Extending Sensor Coverag e Through Virtual Sensing
链接: https://arxiv.org/abs/2608.13993
作者: Davide Andrea Guastella,Eladio Montero Porras,Evangelos Pournaras,Gianluca Bontempi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations. Machine learning models trained on such sparse data cannot generalize to unmonitored locations and must be retrained whenever the sensor infrastructure changes. We propose a simulation-based methodology that addresses this problem by generating augmented traffic count datasets in which each physical sensor is replaced by a virtual sensor placed at a surrogate location in the road network. Virtual sensors are selected by a graph-search heuristic that jointly maximises vehicle-flow continuity and traffic-metric similarity between the original and surrogate locations, while enforcing a minimum spatial displacement to ensure diversity of observed traffic conditions. We validate the method on two Belgian cities: Brussels, using a calibrated model, and Namur, using synthetic models. The augmented datasets preserve the bimodal daily demand profile and the dynamics of traffic at the observed locations.
[AI-63] Nanbeige4.2-3B on Apple Silicon: Fixing Deployment Bugs and Decreasing Looped Transformer Memory Overhead
链接: https://arxiv.org/abs/2608.13987
作者: John T. Halloran
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 8 pages, 3 Tables
Abstract:Nanbeige4.2-3B is a 3B-parameter agentic model built around a Looped Transformer (LT) that reuses one stack of layers for a second forward pass, adding effective depth without additional parameters. Evaluated on Apple Silicon (MPS), we identify five independent bugs which prevent the released checkpoint from running via Hugging Face transformers out of the box (including a silently-zeroed RoPE buffer and calls to removed transformers cache APIs). Furthermore, we show that fixing these bugs is still not sufficient for agentic tasks, due to the LT’s layer-reuse strategy (which effectively doubles peak attention memory) used to achieve parameter efficiency. We thus introduce a chunked-prefill strategy which alleviates the incurred memory-capacity penalty, extending allowable context width by 2.7 \times on 32~GiB shared memory. However, even with the reduced memory overhead, we show that patches are required to render Nanbeige4.2-3B usable; resolving both system prompt and MPS-native memory bugs finally allows reliable evaluation on standard MCP and tool-calling benchmarks. On a subset of MCPMark, the debugged model completes up to 30% of real agentic tasks (up from the original’s 0%), while, on BFCL, it is near-perfect at single tool calls (yet fails the majority of multi-tool tests). We release the patched checkpoint, system prompt optimizer, and evaluation harnesses at this https URL.
[AI-64] Implementing Computational Law in Wolfram Language for the Governance of Artificial Intelligence
链接: https://arxiv.org/abs/2608.13958
作者: James K. Wiles
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Logic in Computer Science (cs.LO)
备注: 25 pages, 1 figure, 2 tables, 12 code listings. Wolfram Language implementation and verification script (31 assertions) available at this https URL
Abstract:How do we govern AI systems whose reasoning we cannot fully inspect? Governance does not require understanding a system’s reasoning. It requires stating what the system is obliged, permitted, and forbidden to do, and checking whether it complied. I present an implementation of Reified Input/Output Logic, the formalism behind the DAPRECO knowledge base, in Wolfram Language: the core I/O axioms, obligations, permissions, constitutive norms, reified eventualities, and temporal operators. I then test whether GPT-4 can translate English legal statements into the formalism, and report the failures: hallucinated functions, omitted temporal scope, deviation from the formalism, and (in the worst cases) code that runs, reads plausibly, but silently encodes the wrong norm. A case study, an AI guard dog operating under a computational contract, shows how formalized rules can extend from a contract directly into the operational code of an embodied agent, producing symbolic, auditable justifications for its behaviour. I argue that computational law can be used as a governance tool and that a desirable goal would be to formalize the law that can and ought to be programmatically executable.
[AI-65] HELIX: Model-Harness Co-evolution for Recursive Self-Improvement
链接: https://arxiv.org/abs/2608.13951
作者: Tianyu Fan,Chao Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Scaling agent capability has largely focused on improving the model, yet an interactive agent acts through a runtime harness that mediates context, tools, control flow, and stopping. The harness shapes both what a model can accomplish and the trajectories from which it learns. This coupling motivates model-harness co-evolution for recursive self-improvement: build harnesses for a fixed model, update the model from verified sibling trajectories, and rebuild the harnesses as model capabilities change. Realizing this loop requires a controlled way to evolve harnesses while preserving intervention identity and effect. We present HELIX, a source-traceable substrate for harness evolution. HELIX decomposes agent systems into typed ports, reusable atoms, recipes, product shells, and runtime policies. It makes interventions explicit and auditable while retaining trajectories, test outcomes, and provenance. Harness evolution thus serves two linked roles: improving fixed-model execution and producing matched successes, regressions, near misses, and alternative solutions as data for subsequent model improvement. We evaluate HELIX in one evolution round on code repair. A 65-candidate portfolio discovers a fixed harness that improves task coverage by 4.0% over Pi, while the full portfolio exposes up to 58.0% more verified coverage through complementary sibling behavior. Selected candidates are assessed with repeated runs and the SWE-bench evaluator. A 200-slot sibling slice yields 438 verified SFT, critic, filter, and preference records. These results show how harness, model, and data form a feedback system: harness evolution expands current capability and creates learning signal for the next model; model updates motivate the next round of harness evolution. HELIX provides an auditable interface for studying this recursive process. Code is available at this https URL.
[AI-66] AI Research Preference Models
链接: https://arxiv.org/abs/2608.13940
作者: Thomas Simon Foster,Bassel Al Omari,Tingchen Fu,Thomas Mann,Carl Domond,Lucia Cipolina-Kun,Bhavul Gauri,Muna Aghamelu,Alexander D. Goldie,Eryk Helenowski,Jean-Christophe Gagnon-Audet,Alberto Pepe,Saba Nazir,Daniel Izcovich,Noam Levi,Rishi Hazra,Karen Hambardzumyan,Nicolas Baldwin,Xian Li,Martin Josifoski,Paris Giampouras,Masoud Jalili Sabet,Anya Sims,Hela Momand,Tatiana Shavrina,Despoina Magka,Jason Weston,Yulin Wang,Anirudh Goyal,João Henriques,Yoram Bachrach,Emily McMilin,Jakob Nicolaus Foerster
类目: Artificial Intelligence (cs.AI)
备注: 34 pages, 17 figures, 6 tables
Abstract:AI research agents (AIRA) can now propose, implement, and evaluate their own machine learning experiments, but progress on frontier tasks is throttled by cost: a candidate solution can be written in minutes, whereas evaluating it can take hours to days of GPU time. An agent can therefore propose far more candidates than it can afford to run, and its progress depends on its research preference: how it allocates a fixed execution budget across many candidates. We introduce AI Research Preference Models (RPMs) that predict which of multiple candidate solutions are most worth executing, without paying the cost of executing them all. We build RPMs from frozen pretrained language models (with no task-specific training), in two forms: an inference-only model that reasons over candidate plans, code, and prior executed solutions, and an agentic model that additionally runs small-scale pilot experiments before deciding. We integrate both into the AIRA-dojo search agent and evaluate on AIRS-Bench, a recent benchmark of machine learning research tasks for AI research agents. The two variants raise the average normalized score from 0.684 to 0.711 and 0.729 respectively, and reach the unguided agent’s 24-hour performance in roughly 15 hours, using less than two-thirds of its execution budget. Our best RPMs also yield new state-of-the-art results on two AIRS-Bench tasks.
[AI-67] When Personal Memory Has No Single Answer: Evaluating LLM Agents under Irreducible Conflict
链接: https://arxiv.org/abs/2608.13921
作者: Lu Yang,Shusheng Xu,Zhuoran Li,Tongkai Yang,Longbo Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM agents increasingly maintain personal memory across sessions, but it can conflict. Preferences depend on context, behavior evolves, and sources can conflict. When a query lacks context, time, or source authority to interpret conflict, treating one memory as definitive converts unresolved conflict into an unjustified, overconfident action. Existing benchmarks recover one answer from conflicting evidence, overlooking whether agents recognize underdetermination, preserve alternatives, seek missing information, and choose appropriate actions. We introduce \underlineTesting \underlineAgents’ \underlineNavigation of \underlineGenuine, \underlineLatent, and \underlineEntangled Memory Conflicts (\textscTANGLE), a benchmark for genuinely unresolvable memory conflicts. It comprises 541 instances across 40 personas and three types: Context-Partitioned Conflict (CPC), Behavior-Oscillation Conflict (BOC), and Source-Contradiction Conflict (SCC). We evaluate two tracks—an oracle track with curated memory and a pipeline track that extracts memory from multi-session dialogues—on five dimensions: conflict perception, causal reasoning, confidence calibration, clarification seeking, and memory faithfulness. Experiments reveal pipeline challenges. With curated memory, models recognize conflicts more reliably than they calibrate actions or seek targeted clarification. With end-to-end pipeline memory, extraction fails to preserve conflict-bearing relations needed for downstream reasoning. Policy comparisons show fixed rules are insufficient when actions must reflect conflict. These findings motivate Conflict-Aware Action Policy (CAAP), which adapts actions to each conflict using available evidence. \textscTANGLE frames conflict handling as recognizing underdetermination, retaining conflicting evidence, and acting without forcing a definitive answer.
[AI-68] Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning
链接: https://arxiv.org/abs/2608.13914
作者: Chun-Hua Lin,Samuel Yen-Chi Chen,Yu-Chao Hsu,Kuo-Chung Peng,Jiun-Cheng Jiang,Chi-Sheng Chen,Tai-Yue Li,Nan-Yow Chen,En-Jui Kuo,Hsi-Sheng Goan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET); Quantum Physics (quant-ph)
备注: 7 pages, 4 figures
Abstract:Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training. Federated learning addresses this practical privacy constraint by enabling collaborative model training while keeping raw biosignal data at their respective sources. However, federated ECG classification remains challenging due to limited client-side samples, imbalanced arrhythmia labels, and non-independent and identically distributed (non-IID) data across clients. These constraints require classifiers that are both communication-efficient and robust to cross-client distribution shifts. In this work, we evaluate a hybrid quantum-inspired Kolmogorov-Arnold network (HQKAN) against a multilayer perceptron (MLP) for five-class arrhythmia classification on the MIT-BIH dataset and three-class classification on the INCART dataset under federated averaging (FedAvg). Across multiple client configurations, HQKAN improves most aggregate and minority-class metrics while using 37.35% fewer trainable parameters and reducing communication cost by 24.89% on MIT-BIH; on INCART, it achieves corresponding reductions of 44.81% and 36.41%. These results indicate that HQKAN offers a compact, communication-efficient and robust alternative to the MLP baseline for privacy-aware federated learning on biosignal data.
[AI-69] CipherSight: Robust Website Fingerprinting via Record-Resource Semantic Supervision under Distribution Shifts
链接: https://arxiv.org/abs/2608.13905
作者: Runhan Song,Qiqi Liu,Chuanzhou Pan,Zhenquan Ding,Youquan Xian,Chongru Fan,Lei Cui,Wei Wang,Zhiyu Hao
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注:
Abstract:HTTPS website fingerprinting (WF) aims to identify visited websites from metadata observable in encrypted traffic. However, real-world deployments introduce a significant out-of-distribution (OOD) problem caused by temporal and geographic changes, while previously unseen websites are common in open-world scenarios. Existing methods primarily learn from raw TCP packet sequences and struggle to capture stable and generalizable website representations, resulting in performance degradation under practical conditions. We propose CipherSight, a TLS-record-based hierarchical framework for robust HTTPS WF. Unlike existing approaches that rely on TCP packet sequences and are sensitive to transport-layer artifacts, CipherSight learns website representations from TLS records by jointly encoding multiple record-level attributes. It introduces a hierarchical architecture that captures both intra-flow dependencies among TLS records and inter-flow interactions across concurrent flows, enabling the model to exploit structural patterns in HTTPS traffic. Besides, to learn robust representations, CipherSight employs a masked record modeling (MRM) task to capture contextual traffic semantics and leverages fine-grained record-resource annotations as privileged supervision through structure-aware objectives and semantic distillation. Experiments show that CipherSight achieves 95.41% accuracy across more than 2,000 website classes in the closed-world setting and maintains over 90% accuracy under both temporal and geographic drift, consistently outperforming all evaluated baselines. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2608.13905 [cs.CR] (or arXiv:2608.13905v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.13905 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-70] MemoryLake on MemoryArena: A Matched Study of Agent Memory Backends
链接: https://arxiv.org/abs/2608.13883
作者: Chaoqun Zhan,Qiang Zhou,Guannan Li,Zhenqiang Huang,Qianjin Wang
类目: Artificial Intelligence (cs.AI)
备注: 16 pages, 7 tables
Abstract:Most agent-memory benchmarks test post-hoc recall, whereas MemoryArena evaluates whether memory supports interdependent, multi-session task completion. We compare MemoryLake, a structured multi-track memory backend, with Mem0, text-embedding-3-small vector RAG, and a long-context control across all five MemoryArena domains. The systems share the same agent framework, requested gpt-5-mini model alias, task samples, and scoring code; the memory integration is the intentionally changed component. Because each backend bundles write, retrieval, consolidation, budgeting, and prompt-assembly choices, the study is a matched system-level comparison, not a representation-only ablation or a cost-matched experiment. On the shared evaluation sets, MemoryLake has the highest observed success rate (SR) in mathematics (9/40), physics (12/20), and progressive retrieval (4/20). Every system has zero SR in travel planning, and web shopping yields a single bundle-level success (long context, 1/150); MemoryLake ranks third on both the travel soft process score and shopping step match. Following MemoryArena’s suite-level convention, a post-hoc equal-weight average over the five SRs is 20.5% for MemoryLake versus 13.6% for the best comparator. These are point estimates: sample sizes are modest, confidence intervals overlap, and we do not report paired significance tests. A separate MemoryLake-only run over all 221 progressive queries yields a failure-counted SR of 26.7% (59/221) and is not a baseline comparison. The results support a workload-dependent view of memory backends and an observed lead among the four evaluated systems on the shared sets; they do not establish benchmark-wide state of the art or a causal advantage of representation structure.
[AI-71] Engineering Reliable Coding Agents : Evaluating and Operating the System Around the Model
链接: https://arxiv.org/abs/2608.13867
作者: Stephanie Jarmak
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Technical review and engineering monograph, 314 pages, 30 figures. Includes an evidence audit, a companion research artifact with 206 reliability records, and runnable protocols for evaluating and operating AI coding agents. August 2026. Source, companion, and reusable protocols: this https URL
Abstract:AI coding agents are commonly evaluated as models but deployed as systems. Their reliability depends not only on model capability, but on the harness, execution state, retrieval, memory and state management, permissions, review interfaces, and resource allocation. This monograph examines those boundaries and develops a framework for evaluating and operating coding agents reliably. It synthesizes 164 scholarly works, 100 practitioner records, 29 benchmark records, and 17 author-system case records through a structured multivocal review, targeted update audits, software-engineering coverage analysis, and distributed-systems evidence synthesis. Across this evidence, many apparent model failures originate elsewhere in the system, while improvements at one layer often fail to propagate to end-to-end outcomes. Evaluation and operation are treated as a dependency chain in which weaknesses in task construction, execution environments, retrieval, state management, verification, or observability can invalidate downstream conclusions. The monograph contributes a versioned catalog of 206 reliability records: 193 gated practices, including 56 developed in depth, plus 13 research leads; an evidence ledger; a framework for dependency and repair asymmetry across the agent lifecycle; measurements and failure cases from operated agent systems; runnable evaluation and reliability protocols; and five reusable agent skills with evidence maps. Together, these provide a system-level methodology for distinguishing model capability from infrastructure effects, designing defensible evaluations, and building systems that recover safely when components fail. The review is structured rather than exhaustive, evidence strength varies by topic, and results depend on workload and configuration. The methods record which search lanes were executed, which remain unexecuted, and limits on evidence-grading claims.
[AI-72] Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inference
链接: https://arxiv.org/abs/2608.13863
作者: Yunchu Han,Zhaojun Nan,Sheng Zhou,Zhisheng Niu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Deep neural network (DNN) inference on mobile devices often incurs high latency and energy consumption due to limited computing and memory resources. To enable energy-efficient DNN inference, most existing studies focus on dynamic voltage and frequency scaling (DVFS) for adjusting the computing frequency, while the impact of memory frequency on the inference performance has been greatly overlooked. In this paper, we consider the impact of memory frequency and computing frequency on DNN inference time, and jointly optimize these two frequencies together with communication resources for energy-efficient DNN inference. Based on a realistic inference time model, we formulate an optimization problem to minimize the energy consumption of all mobile devices under the deadline constraint. For local inference, we derive a near-optimal closed-form solution via convex optimization, while an optimal closed-form solution for transmission power is obtained for edge inference with the given bandwidth. Furthermore, we propose a low-complexity heuristic algorithm to effectively solve the overall problem with polynomial time complexity. Simulation results based on measured data show that the proposed near-optimal solution for local inference can achieve optimal performance under strict deadline constraints, with a performance gap of up to 2.5% compared with the optimal solution. Meanwhile, our proposed algorithm significantly reduces the energy consumption of devices by up to 10.4% compared to other methods.
[AI-73] Federated Prompt Learning: A Unified Framework Empirical Analysis and Future Directions
链接: https://arxiv.org/abs/2608.13844
作者: Qinglin Yang,Chen Qiu,Hongyuan Zhang,Pengdeng Li,Yuan Liu,Zhihong Tian
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Large language models (LLMs) have become core components of cloud-based intelligent services in academia and industry, yet their training and deployment are hindered by high computational costs, data centralization, and privacy concerns. Federated learning (FL) offers a decentralized training paradigm that enables clients to collaboratively train a learning model without sharing raw data, making it a promising solution for privacy-preserving LLM training and reasoning. This paper presents a comprehensive survey of federated prompt learning (FPL) to review recent advances in integrating the federated learning paradigm and large language models, answering the following research questions: RQ1: The fundamental motivations, characteristics, and enabling technologies of FPL, and how it differs from conventional FL and full-model federated fine-tuning; RQ2: The trade-offs FPL approaches exhibit in performance, communication efficiency, computational overhead, scalability, personalization, and heterogeneity handling; RQ3: The remaining security, privacy, robustness, and system challenges, along with key future research directions. To this end, we systematically examine existing FPL methods across the full model lifecycle: pre-training, fine-tuning, and practical applications, while discussing security, privacy, and robustness issues and summarizing existing defense mechanisms. Finally, we highlight open challenges and future directions, aiming to help readers understand how the insights drive research in FPL.
[AI-74] SDO: Subspace Deconflicting Operator for Multi-Adapter Composition ACM-MM2026
链接: https://arxiv.org/abs/2608.13820
作者: Zhongsheng Wang,Zhedong Lin,Qian Liu,Xinyu Zhang,Jiamou Liu
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 6 figures. Accepted by ACM MM 2026 Main Track
Abstract:Composing independently trained adapters within a shared diffusion backbone provides a modular approach to multi-character generation, but naive joint deployment often causes identity mixing, cross-character attribute leakage, and unstable scene composition. We study this interference from a parameter-space perspective and hypothesize that it arises partly from conflicts between overlapping dominant subspaces in shared layers. To address this issue, we propose \textbfSDO, a \textbfSubspace \textbfDeconflicting \textbfOperator for multi-adapter composition. SDO reconstructs layer-wise low-rank updates from the selected adapters, extracts compact subspace signatures, measures pairwise conflict through output-subspace overlap, and applies a permutation-equivariant transformation that suppresses harmful shared directions while retaining identity-specific characteristics. The resulting representations are mapped back to standard adapter updates and can be directly incorporated into existing diffusion inference pipelines. Experiments demonstrate that SDO consistently improves identity fidelity and compositional stability, with particularly clear gains as the number of jointly composed adapters increases.
[AI-75] Optimal Power Allocation and AI Receiver Design for Superimposed DMRS and Data Transmission
链接: https://arxiv.org/abs/2608.13809
作者: Sha Hu,Zhongwang Fu
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI)
备注: 29pages, 12 figures, 4 tables
Abstract:In this paper, we consider transmissions with superimposed (SI) demodulation-reference-symbol (DMRS) and data in orthogonal frequency-division multiplexing (OFDM) based multiple-input multiple-output (MIMO) systems. First, we derive an analytical framework to characterize the iterative behavior between the mean-square errors (MSEs) of channel estimation (CE) and MIMO detection (MD) within an iterative CE and detection (ICED) process. This framework is subsequently utilized to optimize power allocation and pilot patterns between the DMRS and data symbols for SI-DMRS transmission. Second, we design an artificial intelligence (AI) based receiver built upon Transformer encoders for SI-DMRS transmissions, which incorporates an iterative CE and detection (ICED) structure. Simulation results demonstrate that the proposed AI-ICED receiver, combined with SI-DMRS, effectively increases spectral efficiency (SE) compared to conventional systems using non-overlapped DMRS and data symbols.
[AI-76] PPAPlace: Differentiable Cross-Stage Objectives for Chip Placement Optimization
链接: https://arxiv.org/abs/2608.13790
作者: Ruogu Chen,Jie Han
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
备注: 9 pages, 4 figures, 6 tables, accepted at ICCAD 2026
Abstract:Macro placement significantly affects a chip’s post-route performance, power, and area (PPA). Most placement methods optimize half-perimeter wirelength (HPWL) as the primary objective. However, recent benchmarking shows a near-zero correlation between HPWL and post-route timing metrics such as the worst negative slack (WNS) and total negative slack (TNS). As a result, all six evaluated artificial intelligence (AI) placers degraded PPA relative to the hierarchical baseline. Recent efforts have tried to train cross-stage predictors to close this gap. However, existing methods focus on macro-only representations and use pre-route metrics as training labels. A label fidelity study of ten circuits at four design flow stages reveals that HPWL and pre-route timing poorly reflect final post-route timing rankings. In contrast, post-global-routing achieves the best balance between final timing fidelity and label generation cost-effectiveness. Based on this finding, PPAPlace is a timing-driven differentiable surrogate predicting post-route PPA from macro and standard-cell placements. The surrogate is a dual-stream predictor that combines graph attention over the chip netlist with spatial convolution over the placement grid. It is trained on post-global-routing labels. The predicted WNS and TNS gradients flow end-to-end back to cell coordinates. PPAPlace exploits these gradients in two ways: as a co-objective injected into an analytical placer’s optimization loop (PPAPlace-CoOpt), and as a post-placement refinement step that adjusts macro positions via projected gradient descent (PPAPlace-Refine). On five ChiPBench test circuits excluded from training, PPAPlace improves average WNS and TNS by 22% and 51% over the hierarchical baseline while preserving power and routability, using the same predictor without test-circuit retraining. Code is available at this https URL.
[AI-77] FLARE MCMC: Fidelity-based Layer-Adaptive REcursive proposals for MCMC
链接: https://arxiv.org/abs/2608.13774
作者: Harini Venkatesan,Christian Shelton,Ming-Feng Ho,Simeon Bird,Mengxuan Wu
类目: Artificial Intelligence (cs.AI)
备注: This is the author’s accepted manuscript of an article published in SIAM Journal on Uncertainty Quantification. The final version is available at \url{ this https URL
Abstract:Markov chain Monte Carlo (MCMC) requires only the ability to evaluate the likelihood, making it a common technique for inference in complex models. However, it can have a slow mixing rate, requiring the generation of many samples to obtain good estimates and an overall high computational cost. FLARE MCMC is a multi-fidelity layered MCMC method that exploits lower-fidelity approximations of the true likelihood calculation to improve mixing and leads to overall faster performance. Such lower-fidelity likelihoods are commonly available in scientific and engineering applications where the model involves a simulation whose resolution or accuracy can be tuned. Our technique uses recursive, layered chains with simple layer tuning; it does not require the likelihood to take any form or have any particular internal mathematical structure. We demonstrate experimentally that FLARE MCMC achieves larger effective sample sizes for the same computational time across different scientific domains including hydrology and cosmology.
[AI-78] CutClean: Neural Network Pruning for Privacy-Preserving Inference
链接: https://arxiv.org/abs/2608.13773
作者: Leonardo Magliolo,Vito Paolo Pastore,Giuseppe Valenzise,Enzo Tartaglione
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural networks are increasingly deployed in high-stakes applications with growing privacy leakage concerns. We show that this privacy leakage can occur even in the absence of representation imbalances that lead to traditional dataset biases. This poses significant privacy risks when deploying models that process sensitive attributes. In this context, we propose CutClean, a privacy-aware pruning method that allows to reduce privacy information flow through the network, while increasing its sparsity. Our approach employs auxiliary linear privacy heads placed at each network’s block to quantify information leakage, and further applies increasing levels of sparsity to remove the private attribute leakage, measured in terms of the accuracy of the privacy head attached to the last block. Experiments on synthetic and real-world datasets demonstrate that our approach effectively minimizes private information flow while achieving high sparsity rates and preserving classification target accuracy.
[AI-79] Simulation-Aware In-Context Policy Improvement for LLM -Aided Analog Layout Refinement
链接: https://arxiv.org/abs/2608.13767
作者: Bingyang Liu,Ziming Wei,Xiaohan Gao,David Z. Pan
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 7 pages, 3 figures. To appear in the Proceedings of the 2026 International Conference on LLM-Aided Design (ICLAD 2026)
Abstract:Analog IC layout design remains a labor-intensive iterative process dominated by simulation-driven refinement. Although end-to-end layout generators accelerate initial placement and routing, they still require experts to manually tune layout optimization parameters with repeated post-layout simulations for stringent design specifications. While Bayesian Optimization (BO) is widely adopted for parameter tuning in analog IC design, at the layout level it typically requires hundreds to thousands of evaluations, each involving costly parasitic extraction and post-layout simulation, which makes it impractical. Recently, Large Language Models (LLMs) have demonstrated potential in improving the sample efficiency of such simulation-driven tuning. However, their restricted access to geometric layout context and design-specific heuristics limits their ability to manipulate the layout optimization process. In this paper, we propose a simulation-aware LLM multi-agent framework that performs in-context policy improvement (ICPI) by iteratively updating layout optimization parameters exposed by an analog layout generator through an act-observe-reflect loop on compact structured layout representations. Experiments on real-world analog circuits show that, with only tens of post-layout simulations, our approach improves post-layout performance over the generator’s built-in heuristics and BO-based tuning method.
[AI-80] Explanation Multiplicity: Circuit-Level Interpretability Evidence Does Not Survive Defensible Analytic Variation
链接: https://arxiv.org/abs/2608.13754
作者: Ajay Pravin Mahale(Hochschule Trier)
类目: Artificial Intelligence (cs.AI)
备注: 12 pages, 1 figure, 7 tables. Pre-registered analysis plan; code and results available
Abstract:The EU AI Act requires providers of high-risk systems to file technical documentation describing how the system reaches its decisions. Mechanistic interpretability is the obvious source of such evidence, and circuit discovery is its most developed instrument. We ask whether that evidence survives the condition under which it would be relied upon: two competent analysts, the same system, the same tool, different defensible settings. We pre-registered a crossed grid of seven analytic axes, every level taken from a published implementation, and mapped each discovered circuit through a deterministic claim map to a structured Annex IV statement. Across 15,840 pre-registered specifications on GPT-2 small and the indirect object identification task, of which 7,561 produced a claim, the derived statement flips across 73.2% of specification pairs (95% CI 0.725 to 0.738) and the modal claim commands 41.1% of the space. The evidence fails a filability criterion at every tolerance a conformity assessment body would plausibly accept. Standardising the single most influential choice, the evaluation metric, leaves the flip rate at 59.4%. Removing circuit size from the claim entirely and holding it fixed leaves 27.1% (95% CI 0.255 to 0.286), still above the pre-registered threshold. The circuits underlying these claims are structurally near-disjoint, median pairwise Jaccard overlap 4%, and functionally uncorrelated at Cohen’s kappa 0.015, so the instability is not one mechanism described in different words. We give the filability criterion as a standalone protocol, and we report that one of the seven documented discovery objectives does not execute at all on the library’s own canonical task. The study covers one model and one task, and whether the conclusion holds at scale is untested. Comments: 12 pages, 1 figure, 7 tables. Pre-registered analysis plan; code and results available Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.13754 [cs.AI] (or arXiv:2608.13754v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.13754 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ajay Pravin Mahale [view email] [v1] Thu, 13 Aug 2026 20:27:56 UTC (459 KB)
[AI-81] Does ISO-Grounded NFR Specification Improve LLM Code Generation? A Comparison of Rich and Structured Interventions against a Natural-Language Baseline
链接: https://arxiv.org/abs/2608.13742
作者: Joào Pedro Monteiro Pereira,Vinicius Cardoso Garcia
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 11 pages, 2 figures, Accepted for publication at the 20th Brazilian Symposium on Software Components, Architectures, and Reuse (SBCARS 2026)
Abstract:In LLM-based code generation, Non-Functional Requirements (NFRs) are often specified as terse one-line phrases. We ask whether grounding those specifications in ISO/IEC 25010 Quality Model, either as rich natural-language prose (NL-rich) or as structured JSON (Structured), improves code generated on HumanEval/HumanEval-ET compared to a RobuNFR-style one-line baseline (NL-simple). We evaluate four NFRs (performance, error handling, code smell, readability) with ten prompt variations per condition under a fixed model snapshot and paired non-parametric analysis. Primary finding: ISO-grounded enrichment improves static quality proxies (unreadability density falls across all four NFRs (e.g., Performance 0.88 - 0.69 for NL-rich)) and reduces sensitivity to prompt wording, but does not reliably improve functional correctness; for error handling, extended-test pass rate decreases, suggesting tension between defensive coding patterns and exact-output benchmarks. Secondary finding: when ISO content is held constant, NL-rich and Structured differ negligibly in correctness (|delta| = 0.023), indicating that semantic content matters more than JSON-vs-prose format. Practitioners should invest in standard-grounded NFR content rather than serialization form. A fully traceable replication package is provided.
[AI-82] Building AI-Intensive Software with AI: Early Results and a Cautionary Tale on Measuring Development Cost
链接: https://arxiv.org/abs/2608.13730
作者: Victor Barros de Miranda Neves,Kiev Santos da Gama,Vinicius Cardoso Garcia
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 4 pages, 2 figures, Accepted for publication at the International Workshop on Intelligent Software Engineering (ISE 2026) @ CBSoft 2026
Abstract:Empirical reports on the true cost of AI-intensive software development remain scarce, and the few that exist are easy to get wrong in ways that never surface in the final number. We report early results from an ongoing case study: a six-person student team built a full conversational onboarding assistant – RAG-based code chat, guided tours, dependency graphs, technical-debt analysis – over one academic term using pervasive AI assistance. We instrumented development with a three-layer cost model (real AI spend, self-reported human effort, human counterfactual) and initially reported a 19.4x cost ratio. A follow-up pass revealed two independent errors – inferring per-token cost under a flat-rate subscription, and pricing the counterfactual with the wrong regional labor rates – that together had inflated the ratio by roughly 2x; the corrected figure is ~9.9x. We present this correction as an early, generalizable finding in its own right: both errors are easy to make, invisible in the final number, and plausibly common in similar reports. We outline next steps toward a more robust, replicable costing methodology for AI-intensive development.
[AI-83] Coverag e Aware Active Evaluation for Failure Discovery with Paired Systems
链接: https://arxiv.org/abs/2608.13719
作者: Anjali Parashar,Rachel Luo,Apoorva Sharma,Sushant Veer,Edward Schmerling,Carson Sobolewski,Mingxin Yu,Chuchu Fan,Marco Pavone
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 9 main pages followed by Appendix, total 21 pages, 12 figures
Abstract:Autonomous systems can fail in rare and heterogeneous ways, making real-world failure discovery difficult under limited testing budgets. Although cheaper proxies such as simulators, lower-fidelity systems, or related policies can be sampled extensively to find failures, proxy failures often do not transfer to the real world due to sim-to-real and system-to-system gaps. The key challenge is therefore to effectively leverage proxy system information for accurate prediction of severe target system failures. We propose an adaptive failure discovery method that combines proxy evaluations with limited target system results to guide scenario selection for target system testing. Our method learns a local predictor of target risk by correcting proxy failure signals using control-variate-inspired residual modeling. To find failures that are both likely and diverse, we combine this predictor with a support-aware mutual-information objective that favors realistic, well-supported regions while expanding coverage across failure modes. Across autonomous driving, manipulation, and quadruped velocity-tracking tasks, our method discovers up to 2 \times as many failures as random sampling and active-learning baselines, including severe and diverse failures missed by competing methods.
[AI-84] Learning to Assemble Novel Structures with Unfamiliar Parts under Semantic Constraints
链接: https://arxiv.org/abs/2608.13684
作者: Jonghyuk Park,Alex Lascarides,Subramanian Ramamoorthy
类目: Artificial Intelligence (cs.AI)
备注: Accepted to, and to appear in the 20th Conference on Neurosymbolic Learning and Reasoning (NeSy 2026)
Abstract:This paper describes a neurosymbolic architecture for learning to assemble novel structures using evidence from embodied conversations and task demonstrations. We focus on scenarios where an agent encounters, after deployment, semantic constraints on structures–in other words, constraints as to which part types and features make valid structures–that were not available during training, and where it is initially unaware of the relevant structure and component part concepts. The agent must acquire and exploit such knowledge through user interactions while attempting assembly. We study this setting in a simulated toy truck assembly domain, learning from symbolic evidence encoded in natural language and from dense visual observations. Our experiments show that communicating semantic constraints through natural language (e.g., “dump trucks have a dumper”) yields more data-efficient online adaptation than relying only on task demonstrations and/or only naming the parts through natural language.
[AI-85] Fine-Tuning Qwen 3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining Debugging-Aware SFT and Task-Specific SFT
链接: https://arxiv.org/abs/2608.13681
作者: Pu Zhao,Changdi Yang,Yixiao Chen,Yi Gao,Yifan Cao,Haochen Zeng,Yanzhi Wang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注:
Abstract:Translating C code into safe, idiomatic Rust is a longstanding software-engineering goal because it can eliminate entire classes of memory-safety vulnerabilities while preserving the functional behavior of legacy systems. Large language models (LLMs) have shown promise for this task but typically underperform when applied off-the-shelf, since general-purpose pretraining rarely emphasizes idiomatic Rust generation, cross-language semantic equivalence, or the ability to reason about and repair compiler/runtime feedback. In this report we describe a three-stage fine-tuning curriculum applied to Qwen3-27B that is designed to progressively specialize the model for the C-to-Rust (C2Rust) translation task: (1) continued pretraining on Rust-centric corpora to strengthen the model’s prior over idiomatic Rust syntax and standard-library usage; (2) supervised fine-tuning (SFT) on the microsoft/Verus_Training_Data dataset to instill debugging and self-repair behavior over Rust code; and (3) task-specific SFT on paired C/Rust solutions derived from LeetCode problems to teach direct semantic translation. We evaluate the resulting model using the agentic, static-analysis-guided verification framework of SACTOR, which performs structure-aware, two-phase (unidiomatic to idiomatic) translation with foreign-function-interface (FFI)-based end-to-end (E2E) testing. We report success rate, idiomaticity (Clippy lint counts, unsafe-code fraction), and failure-mode analyses, and compare our fine-tuned model against baseline Qwen3-27B and other LLMs evaluated under the same framework.
[AI-86] From BERT to Frontier Agents : Eight Years of Language-Model Progress the Collapse of the Capability-Cost Curve and the Rise of Task-Targeted Models
链接: https://arxiv.org/abs/2608.13675
作者: Pranav Kumar Kaliaperumal
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Between October 2018 and July 2026 AI models progressed from simple systems like BERT to massive agents that solve complex math and write software. The ability to resolve real coding issues improved by nearly six times per year since late 2024. During this time costs dropped sharply with OpenAIs budget model GPT 5 point 6 Luna matching flagship capabilities for just one to six dollars per million tokens beating older versions at a fraction of the price. Top performance is now split across specialized models as Claude Opus 5 leads in frontend coding Claude Fable 5 excels at repository level coding and GPT 5 point 6 Sol dominates terminal tasks. In a grade school math test using the Qwen 2 point 5 model basic methods solved 58 of 100 problems while advanced sampling solved up to 79. A confidence ranking tool correctly identified 47 right answers in its top 50 choices proving highly useful for sorting tasks with all research materials made fully public.
[AI-87] Second Thought: Reasoning in Parallel as LLM Agents Act and Observe
链接: https://arxiv.org/abs/2608.13667
作者: Zhensu Sun,Chengran Yang,Yunbo Lyu,Jieke Shi,David Lo
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:LLM agents in the ReAct paradigm alternate between reasoning, acting, and observing, but deliberate reasoning is confined to the Thought phase: while the agent serializes an action and waits for the environment, its reasoning is frozen. We identify this recurring interval for Action and Observation as a reasoning idle window and ask whether it can host additional reasoning in parallel that serves future turns. Therefore, we propose Second Thought, a training-free inference framework that forks four auxiliary branches the instant each Thought phase concludes, decodes them concurrently with the main loop, and merges the generated thoughts back when the environment observation arrives. In this way, Second Thought relocates the added reasoning off the main thread’s sequential decoding path. Across three agentic benchmarks and three reasoning LLMs, Second Thought lowers the average turn count in all nine (model,benchmark) pairs and reduces main thread decoding in six of them by up to 43% (roughly 20% on average among those settings), while leaving it essentially unchanged in a seventh; Pass@1 shows no significant change in seven of nine pairs and the two significant differences are +12.4 and +10.2 points. Against a compute-matched control that forces an equivalent budget onto the main thread’s own reasoning, it attains strictly higher Pass@1 with 1.3 to 3.2 less sequential decoding in all four settings where the control applies.
[AI-88] Ontology-Grounded Project Memory for Coding Agents
链接: https://arxiv.org/abs/2608.13662
作者: James Adam
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 5 pages. Accepted at NeSy 2026 (Industry Track); to appear in Proceedings of Machine Learning Research vol. 284. Benchmark artifact: this https URL
Abstract:Coding agents have become the primary means of generating new code in many software projects, and the resulting velocity of changes makes keeping track of the reasons behind those changes challenging. This paper introduces MOOSEDev, a system designed to give coding agents structured, ontology-grounded project memory. The system captures architectural decisions, lessons, constraints, and rationales in a knowledge graph exposed to agents via a Model Context Protocol (MCP) interface. Records carry lifecycle status, provenance, and supersession links, queryable via MOOSE, a proprietary neurosymbolic engine that treats the symbolic layer as the primary reasoning substrate. We compared MOOSEDev against a production vector-memory tool on a neutral public corpus of 835 typed records. MOOSEDev returned the expected answer set essentially in full (0.98-1.00) on supersession, set-completeness, and negation questions, whereas the baseline’s top-k retrieval surfaced between 6% and 27%. Conversely, relevance recall and token cost were largely equivalent between the two systems. We also describe a temporal commit-history bootstrap of our own codebase, a pre-registered live trial, and lessons learned.
[AI-89] Exploring ESC Winners with Nested Diagrams
链接: https://arxiv.org/abs/2608.13630
作者: Anurag Sharma,Marcel Nöhre,Gerd Stumme
类目: Artificial Intelligence (cs.AI)
备注: Accepted for CONCEPTS 2026 Data Analysis Showcase (CDAS)
Abstract:We present ConceptFlow, a scikit-learn-compatible Python library for Formal Concept Analysis that constructs and renders nested line diagrams from many-valued formal contexts. Given a many-valued context and a partition of its attributes into conceptual scales, ConceptFlow performs conceptual scaling, computes the factor lattices, identifies filled nodes of the corresponding subdirect product, and produces an interactive visualization. We apply ConceptFlow to the winners of the Eurovision Song Contest from 1975 to 2025, exploring relationships between voting patterns and musical characteristics. Voting support is captured by an outer scale spanning regional, cultural, historical, and political dimensions, while an inner scale captures musical characteristics via tempo and key. The resulting nested line diagram reveals implications across both scales, exposing dependencies between how winning entries were voted for and the musical properties they share. Comments: Accepted for CONCEPTS 2026 Data Analysis Showcase (CDAS) Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.13630 [cs.AI] (or arXiv:2608.13630v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.13630 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-90] Reward Machines for Signal Temporal Logic
链接: https://arxiv.org/abs/2608.13625
作者: Alper Kamil Bozkurt,Shangtong Zhang,Yuichi Motai
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注:
Abstract:Signal temporal logic (STL) provides a formal language for specifying real-time properties of real-valued observations, along with a quantitative robustness score for monitoring satisfaction. Control synthesis from STL specifications is of interest since manual controller design becomes infeasible as real-world systems grow in complexity. Moreover, many modern autonomous and AI-enabled systems lack accurate and complete system models, which makes optimization-based synthesis approaches unsuitable and motivates learning-based control. Prior work uses STL robustness scores as rewards in reinforcement learning (RL) to obtain control policies satisfying given specifications; however, robustness depends on execution history, leading to intractable state space expansion for general long-horizon specifications with arbitrarily nested temporal operators. This work introduces a novel automata-based approach that provides an efficient memory mechanism and associated Markovian rewards suitable for RL frameworks. Our approach constructs a timed alternating automaton from the given STL specifications, augments the state space with automaton locations and clock valuations, and derives rewards from the automaton acceptance condition. We empirically demonstrate that our approach learns policies that achieve higher robustness scores and satisfaction rates than those learned by existing approaches using robustness-based rewards.
[AI-91] Your Probabilistic JEPA Is Secretly a Hidden Markov Model: A State-Space Interpretation of Joint-Embedding Predictive Learning
链接: https://arxiv.org/abs/2608.13621
作者: Yongchao Huang
类目: Artificial Intelligence (cs.AI)
备注: 69 pages
Abstract:A hidden Markov model (HMM) combines three roles: inference of a hidden-state belief from observations, propagation through a Markov transition, and emission back to observation space. We show that full, time-indexed Predictive Information Bottleneck VJEPA (PIB-VJEPA) exposes the same computational structure: a stochastic context encoder plays the role of an amortized filtering distribution, a probabilistic predictor defines latent-state dynamics, and a decoder, inverse target encoder, or induced implicit conditional supplies the emission direction. We distinguish 4 progressively stronger levels of correspondence and give sufficient conditions for exact sequence-level HMM equivalence. To make the connection concrete, we introduce Markov-Chain JEPA (MCJEPA), which replaces the latent predictor by a learned transition matrix; in the finite time-homogeneous case, matrix powers guarantee exact multi-horizon Chapman–Kolmogorov consistency. Conditioned discrete-state transitions, continuous-state Markov kernels, and continuous-time dynamics extend this construction, while deterministic temporal JEPA appears as a degenerate Dirac-kernel special case. We further interpret predictive information-bottleneck learning as seeking a compact predictive state: compression promotes minimality, while residual predictability tests sufficiency. Controlled experiments support transition composition, the filtering interpretation, predictive Markovization in a known synthetic process, and the distinction between JEPA latent prediction and HMM-style sequence learning. Together, these results give temporal JEPA a principled state-space interpretation.
[AI-92] Algorithm Design and Physician Liability
链接: https://arxiv.org/abs/2608.13618
作者: Shujie Luan,Shubhranshu Singh,Tinglong Dai
类目: Artificial Intelligence (cs.AI); Theoretical Economics (econ.TH)
备注: 65 pages, 8 figures, 5 tables; includes online appendix (pp. 51-65)
Abstract:A single clinical algorithm can deliver unequal accuracy across patient groups, and concern about such disparity has grown as artificial intelligence (AI) spreads through clinical decision-making. In response, a liability rule introduced in the United States holds healthcare providers responsible when their reliance on disparate algorithms contributes to erroneous clinical decisions. We examine how such liability considerations reshape (i) an AI firm’s algorithm design decisions that drive group-specific accuracy and (ii) a physician’s decisions to use AI in healthcare delivery. The AI firm designs an algorithm for two patient groups, and improving accuracy for the disadvantaged group is more costly. The physician (who remains the accountable decision-maker) then decides whether to consult AI, weighing the reduction in clinical uncertainty against expected liability exposure when AI errors disproportionately affect the disadvantaged group. We find the liability rule can induce disparate use of AI: the physician may reduce AI use overall and, over an intermediate range of liability, rely on AI less for disadvantaged patients. The effect is non-monotone. As liability increases, the physician’s use of AI for disadvantaged patients first declines, then rises as the firm reallocates investment toward reducing disparity or switches to an equal-accuracy design. Mandating equal algorithmic accuracy across patient groups can then inadvertently harm both groups, because a uniform accuracy requirement distorts the firm’s investment incentives and the physician’s equilibrium AI-use decisions.
[AI-93] How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insights
链接: https://arxiv.org/abs/2608.13617
作者: Himanshu Tripathi,Kaushik Roy,Subash Neupane,Shahram Rahimi
类目: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
备注: This has been submitted and accepted in NeSy 2026 ( this https URL )
Abstract:Verifying whether clinical care follows evidence-based protocols is a natural neuro-symbolic problem, yet the safety-critical setting defeats either paradigm alone. We present an expert-guided pipeline that constrains a large language model strictly to semantic normalization, mapping messy drug and microbiology strings onto a fixed clinical vocabulary, while a Sugeno fuzzy inference system reasons over the normalized events. The fuzzy layer encodes eight Surviving Sepsis Campaign bundle rules and replaces binary judgments with graded scores in [0,1]. Applied to 2,438 MIMIC-IV v3.1 sepsis episodes, it surfaces antibiotic timing as the most critical breakdown (mean 0.24, 13% within one hour), Hour-1 underperformance (mean 36.7%), a 51% elevated-lactate drop-off, and descriptive differences in ICU stay across compliance groups (3.8 versus 5.1 days).
[AI-94] SemPlan: Benchmarking Structured Semantic Planning for LLM -Based Queries over Enterprise Data
链接: https://arxiv.org/abs/2608.13612
作者: Bruno Santos Teixeira
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 11 pages, 3 figures, 9 tables. Submitted to Transactions on Machine Learning Research (TMLR)
Abstract:Natural-language interfaces to enterprise data must translate underspecified requests into governed, executable behavior while controlling invalid queries, policy failures, cost, and nondeterminism. SemPlan Benchmark evaluates this architectural design space with a deterministic synthetic bilingual benchmark containing 1,800 cases in English and Brazilian Portuguese; 1,200 cases form the frozen scientific evaluation subset. Four architectures are compared under the same model configuration: direct SQL generation (A1), a bounded tool-agent baseline (A2), structured semantic-request generation followed by deterministic planning and execution (A3), and a clarification/stateful semantic-plan variant (A4). Across 4,800 primary records, answer correctness was low in absolute terms: 22.25% for A1, 22.58% for A2, 25.67% for A3, and 24.25% for A4. A3 had the highest observed correctness and significantly exceeded A1, A2, and A4 in the pre-specified paired correctness analysis, while A1 retained the highest policy-correct rate and the lowest unsafe-or-invalid rate. A4 had the lowest mean API cost and lowest false-refusal rate. On a preselected 150-case stability subset, answer-correct repeatability ranged from 92.00% to 98.67%. The results support a trade-off interpretation rather than a universal ranking: additional structural constraints changed failure modes and efficiency, but did not monotonically improve correctness or solve ambiguity and multi-turn state consistency.
[AI-95] Evaluating Agent ic Learning Harness Capabilities Without Labels via the Scaling Hypothesis
链接: https://arxiv.org/abs/2608.13608
作者: Aryan Luthra,Kshitij Jain,Siddharth Arya,Bobby Filar,Anna Bertiger
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 18 pages, 6 figures. Accepted at CAMLIS 2025 (Conference on Applied Machine Learning for Information Security)
Abstract:Agentic “Continual Learning Harnesses”, systems that pair an LLM with retrieval or memory to improve from feedback without retraining, have shown growing value in cybersecurity. But their value is conventionally measured by gains against labeled benchmarks, an approach that often fails in operational security settings. Benchmark labels are scarce, stale, and unrepresentative, so a practitioner often cannot tell whether a given harness helps at all or which of two is better for their task. Traditional LLM-as-a-judge offers little signal because it is no stronger than the agent it evaluates, and distillation is unreliable on scarce, sporadic, and biased labels. We propose a framework for evaluating learning harnesses end-to-end without a labeled benchmark, grounded in the scaling hypothesis. A stronger teacher model provides sparsely sampled corrections to a smaller student with a continual learning harness. We score a harness by how much its student converges toward the teacher over time. Across security tasks, model families, and harness designs, we show that improvement relative to the teacher correlates with improvement relative to a held-out gold standard, validating teacher-relative lift as a proxy for true harness uplift when labels are absent. We further show that LLM-as-a-judge between similarly powered models yields no usable signal. These results suggest that a teacher-sized model can be improved through the same harness when humans provide the same kind of sparse, high-precision corrections. Comments: 18 pages, 6 figures. Accepted at CAMLIS 2025 (Conference on Applied Machine Learning for Information Security) Subjects: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2608.13608 [cs.AI] (or arXiv:2608.13608v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.13608 Focus to learn more arXiv-issued DOI via DataCite
[AI-96] Active Perception for Embodied Disambiguation
链接: https://arxiv.org/abs/2608.13605
作者: Yiwei Liu,Luwei Yang
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Natural language provides robots with a flexible task interface, but target ambiguity in embodied environments arises not only from user intent; it can also result from missing taskrelevant physical evidence in the current observation. Existing interactive disambiguation methods primarily obtain additional information by asking the user, whereas occlusion, restricted viewpoints, unreadable text, and unobserved targets require the robot to actively change its observation. We propose an active-perception framework for embodied target disambiguation that uses active observation as the backbone for information acquisition and uses a vision-language model to decide, on the basis of accumulated visual evidence and interaction information, whether to continue observing, request clarification, or complete target selection. Active observation can both directly recover missing discriminative evidence and reveal object names, labels, and semantic attributes, thereby improving user clarification when it remains necessary. Real-robot experiments show that the framework combines physical information acquisition and userintent clarification within a unified embodied disambiguation process.
[AI-97] Measuring Cross-Task Behavioral Consistency in Language Model Agents
链接: https://arxiv.org/abs/2608.13598
作者: Amritesh Banerjee,Pranil Raichura
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agent evaluation relies almost entirely on outcome metrics such as success rate, which capture whether an agent succeeds but not how consistently it behaves. We argue that behavioral consistency across tasks is a distinct and measurable property, and we introduce the Behavioral Consistency Metric (BCM) to quantify it. BCM trains a model to predict task success from behavioral features of agent execution traces, derives a per-trajectory feature-attribution vector, and measures the mean pairwise similarity of these vectors within an agent system. Across roughly 9,000 trajectories from six language model agents on software engineering tasks, our central finding is that cross-task and within-task consistency are distinct axes that can diverge: some systems are locally reproducible, behaving similarly on repeated attempts at one task, yet globally fragmented, with no stable strategy across different tasks, while others are consistent at both scales. Prior work measures only same-task reproducibility and so cannot observe this separation. We further find that consistency is not reducible to success rate, since systems with comparable success can differ sharply in consistency, and that the frontier-versus-open-source consistency gap persists under a within-task control that holds task difficulty constant. We position BCM as a process-level reliability signal that complements outcome metrics, and we are explicit about the conditions under which it is meaningful.
[AI-98] raining-Free Knowledge Transfer Across Model Scales through Activation-Guided Pruning
链接: https://arxiv.org/abs/2608.13596
作者: Jiahe Fan,Si Chen,Yinghao Hou,Aiyuan Zhang,Hong Xie
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 9 pages, 3 figures, and 7 tables
Abstract:Heterogeneous model fusion seeks to combine models that differ in tasks, initializations, architectures, or scales. We study an underexplored cross-scale setting: improving a small recipient language model with a stronger donor despite substantial architectural mismatch. We ask whether useful capabilities can be transferred without explicit neuron-wise semantic alignment. Building on the observation that truncating a large model to a smaller architecture and injecting it with a tiny mixing weight can already improve the recipient, we propose Activation-Prune-Merge (APM), an activation-guided framework for cross-scale fusion. APM constructs task-conditioned activation maps on the donor, selects salient layers, hidden dimensions, attention heads, and MLP neurons to prune it to the recipient architecture, and injects the resulting donor slice into the original recipient using a micro interpolation coefficient. This formulation treats the donor as a source of concentrated functional components rather than requiring precise structural transplantation. Across 16 benchmarks spanning reasoning, mathematics, code generation, instruction following, and classification, APM improves the overall average accuracy from 55.5% to 60.6% over the original 3B recipient. RTE accuracy increases from 64.3% to 82.3%, QNLI from 52.3% to 65.7%, and BoolQ from 70.8% to 79.2%. Analyses of injection ratios and sequential multi-stage fusion further suggest that activation-guided extraction improves the quality of the transferable donor slice while preserving the small-ratio fusion regime. These results provide evidence that cross-scale heterogeneous fusion can succeed without explicit semantic alignment when the donor contribution is sufficiently concentrated and carefully selected.
[AI-99] AI Evaluation Should Work With Humans ICML2026
链接: https://arxiv.org/abs/2608.13577
作者: Jan Kulveit,Gavin Leech,Tomáš Gavenčiak,Raymond Douglas
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to ICML 2026 Position Paper Track
Abstract:This position paper argues that the dominant paradigm of AI evaluation (which focuses on superhuman autonomous performance and so implicitly targets the goal of replacing humans) is guiding AI development in the wrong direction. Instead, the AI community should pivot to evaluating the performance of human–AI teams. We argue that this collaborative shift will foster AI systems that act as true complements to human capabilities and therefore lead to far better societal outcomes than will the current process.
[AI-100] A Year in LLM Serving: Workload Evolution Caching and Load-Balancing
链接: https://arxiv.org/abs/2608.13573
作者: William Nixon,Jon Durbin,Florian Standhartinger,Haryadi S. Gunawi,Juncheng Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Model (LLM) serving has become a critical cloud workload, and realistic traces are essential for motivating and benchmarking serving systems. However, existing LLM serving workload studies remain limited in scale and scope. They often observe short time periods and provide limited visibility into how users interact with models in production. As a result, they do not fully capture how LLM serving workloads evolve over time or how user-model interactions shape production traffic. In this work, we further the understanding of real-world LLM serving workloads through both a global characterization and a longitudinal study of a one-year production trace from Chutes. Unlike prior studies, our trace captures full production behavior across many models and users, including both popular and long-tail models. We analyze the workload from aggregate, temporal, model-level, and user-level perspectives, revealing workload evolution and user-model structure that are typically hidden behind aggregate views. To support future research, we will release the full one-year trace with the paper, enabling downstream studies of production behavior without relying on sampled or synthetically generated workloads. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.13573 [cs.AI] (or arXiv:2608.13573v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.13573 Focus to learn more arXiv-issued DOI via DataCite
[AI-101] Dont Claim Benchmark-Oriented Optimization Improves General Coding Capability – Diverse Evaluation Is Required ICLR2026
链接: https://arxiv.org/abs/2608.13566
作者: Egor Shibaev,Vera Kudrevskaia,Timur Galimzyanov,Mikhail Evtikhiev,Ana Terna,Rastislav Rabatin,Timur Kudashev,Timofey Bryksin,Arina Puchkova,Patrik Bartak,Egor Bogomolov,Sergey Titov
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Accepted to the DL4Code workshop @ ICLR2026
Abstract:Post-training papers, model cards, and blog posts often treat scores on a small set of coding benchmarks (e.g., SWE-bench and LiveCodeBench) as evidence of broad coding capability, both for research artifacts and user-facing systems. We argue that optimization for these benchmarks leads to measuring task-specific performance, creating a meaning gap between measured scores and claims of general coding ability. We examine this gap with a Django-based case study benchmark suite we create. Evaluating foundation models and checkpoints post-trained on SWE-bench trajectories, we find that benchmark rankings frequently fail to generalize. Post-trained checkpoints show little cross-task transfer, and SWE-bench optimization yields limited or no gains on our tasks or on LiveCodeBench. Similarly, fine-tuning on individual Django modalities fails to transfer. We conclude that a small number of benchmarks is insufficient for evaluating diverse models under benchmark optimization pressure. We encourage the community to use differentiated evaluation - holistic assessment for frontier models, multi-task suites for research, and human-in-the-loop studies for narrow task applications. Finally, we argue for creating a capability taxonomy and sustained benchmark maintenance, rather than one-off benchmark releases. Without reliable evaluation standards, engineers and researchers using LLMs and agents have to rely on insufficient evidence to make research, development, and deployment decisions. Comments: Accepted to the DL4Code workshop @ ICLR2026 Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2608.13566 [cs.LG] (or arXiv:2608.13566v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.13566 Focus to learn more arXiv-issued DOI via DataCite
[AI-102] Depth-Aware Sensitivity Analysis of Mixture-of-Experts Models via Magnitude-Based Expert Masking
链接: https://arxiv.org/abs/2608.13565
作者: Pradeep Kumar Sharma,Shantanu Godbole,Hritvik Shrivastava
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Mixture-of-Experts (MoE) architectures scale large language models (LLMs) while preserving computational efficiency through sparse activation. Despite their widespread adoption, the relative importance of individual MoE layers remains insufficiently characterized, particularly for model compression. This paper presents a systematic layer-wise sensitivity analysis of the Qwen3.6-35B-A3B model (40 MoE layers, 256 experts per layer, top-8 routing) using magnitude-based expert masking on the XLCoST cross-lingual code translation benchmark. We conduct a multi-phase study spanning 100, 300, and 500 prompt evaluation scales across three H100 GPU servers. Our central finding is that layer sensitivity is strongly depth-dependent: early layers (0-9) and middle layers (10-29) are highly fragile to expert masking, while late layers (30-39), and especially very-late layers (35-39), tolerate aggressive masking of low-magnitude experts. Flat all-layer masking at 30% retains only 150/300 Good+Similar outputs at 300-prompt scale, whereas late-focused policies retain 249-255/300 while masking 640-1,145 experts. On a later 500-prompt held-out validation slice, the narrow very-late policy (layers 35-39 @ 50%) achieves the strongest quality/masked-expert tradeoff among tested candidates, retaining 419/500 Good+Similar outputs while masking only 640 of 10,240 total experts. We additionally characterize top-k routing width reduction from 8 to 6 active experts per token, which shows a large observed wall-clock reduction on a 100-prompt probe with no Good+Similar loss, though it does not yet compose cleanly with aggressive expert masking. These findings provide an empirical foundation for depth-aware MoE expert masking and establish a practical path toward physical weight surgery, activation-based expert scoring, and training-based recovery.
[AI-103] Inducing Reward-Free Judging Rubrics that Reduce Over-Crediting in Agent Evaluation
链接: https://arxiv.org/abs/2608.13564
作者: Darragh Quinn,David Dylan,Roisin Healy,Fionn Carroll,Maeve Donnelly,Cormac Sheehan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Evaluating language-model agents at scale increasingly relies on a second language model as an automatic judge, because the gold signal, an executable environment reward, is expensive, slow, or unavailable at deployment time. Such a judge is a reward-free proxy whose value depends on whether it can be trusted, yet existing judges either hand-write the scoring rubric, as in G-Eval, or fine-tune the judge’s weights, and both tend to credit fluent but unsuccessful trajectories as successes. We instead induce the text of an agent-judging rubric from a small set of ground-truth-labeled trajectories, grounding it in true outcomes. We present RubricForge, which evolves a judge rubric by reflective evolution against labeled trajectories to maximize agreement with the environment reward, freezes it, and applies it to held-out trajectories in one model call with no environment access. The optimized artifact is human-readable text, so every verdict is attributable to named criteria. Using one frozen 7B model as both agent and judge, on tau-bench (173 labeled trajectories drawn from 220 rollouts) and WebShop (160), the principal gain is faithfulness rather than raw agreement. The edge over a generic G-Eval judge is not statistically significant (McNemar p = 0.248), and absolute-score calibration marginally favors the generic judge (|err| difference -0.048, p = 2x10^-4). Yet RubricForge over-credits failed trajectories roughly half as often (0.115 vs. 0.173 false-pass rate on tau-bench, with three over-credit catches and zero reversals) and ranks graded WebShop outcomes more faithfully (Spearman 0.410 vs. 0.370). For a reward-free evaluator the false-pass rate, not aggregate agreement, is the deployment-relevant quantity, since a false pass ships a broken agent whereas a false fail merely costs a retry.
[AI-104] Universal Thermodynamic Interatomic Potentials for Crystalline Materials
链接: https://arxiv.org/abs/2608.14502
作者: Juno Nam,Bowen Deng,Xiaochen Du,Luis Barroso-Luque,Benjamin Kurt Miller,Rafael Gómez-Bombarelli
类目: Materials Science (cond-mat.mtrl-sci); Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
备注:
Abstract:Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free energy calculations require ensemble averages. We introduce the thermodynamic interatomic potential (TIP), which extends an interatomic potential from its static energy to a thermodynamically consistent Gibbs free energy model, with thermodynamic responses following from temperature and pressure by automatic differentiation. We implement TIP[UMA] using the universal potential UMA, train it on free energies from quasi-harmonic to molecular dynamics fidelity, and calibrate it to higher-resolution calculations or experiment. From a single evaluation, it returns the equation of state of a crystal and locates phase transitions among competing branches, including dynamically stabilized phases. Fine-tuning extends the model to alloy solubility limits and miscibility gaps. TIP makes the free energy as accessible as the potential energy, opening finite-temperature phase stability to high-throughput discovery.
[AI-105] AI-Assisted Discovery and Construction of a Counterexample to the Convergence of Three-Block ADMM with the Identity Matrix as its Third Constraint Block
链接: https://arxiv.org/abs/2608.14396
作者: Kenan Xu,Xiangfeng Wang
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI)
备注: 34 pages
Abstract:The alternating direction method of multipliers (ADMM), as a landmark algorithm, has attracted tremendous research attention and extensive practical applications over the past two decades. It is well known that, although the two-block ADMM enjoys well-established theoretical convergence guarantees, its direct extension to the three-block case may fail to converge, as demonstrated by existing counterexamples [5]. However, to the best of our knowledge, the case in which the third constraint block is the identity remains unresolved: the existing literature gives neither a general convergence proof nor a counterexample for this subclass. In this paper, we give a negative answer: direct three-block ADMM may fail even when the first two blocks are strongly convex quadratics. Using Codex with GPT-5.6 Sol, we construct an explicit rational counterexample candidate and verify it along a piecewise-affine reduction path; exact checks show that direct three-block ADMM on this instance produces a bounded nonconvergent orbit of period 66. Within the same Codex workflow, we further guide a study of multiplier relaxation and clarify when convergence can be restored at the fixed-instance and class levels: a problem-dependent small dual step can restore convergence, whereas no positive relative step works uniformly over the whole class. Furthermore, we also test the recent Kimi Code with Kimi K3 model without the Codex candidate or project-specific route guidance; along a different path it produces an exact locally attracting period-23 certificate, convertible to an equivalent all-identity instance. The comparison suggests that different research-harness configurations can shape the mathematical objects explored and the certificates pursued.
[AI-106] Reaction-Transformation-Aware Flow Matching for Generalizable Transition State Generation
链接: https://arxiv.org/abs/2608.14076
作者: Kaipeng Zeng,Wenxi Zhai,Shengrui Xu,Jie Zhao,Bowen Li,Shiyue Wang,Junchi Yan,Tong Zhu
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Transition-state (TS) structures define the energetic barriers and mechanistic pathways of elementary chemical reactions, yet their identification remains computationally demanding because conventional saddle-point searches require expensive quantum-mechanical calculations. Recent machine-learning approaches have accelerated TS generation by predicting structures from reaction endpoint information, but they primarily learn geometric correspondence between endpoints and TSs, leaving the structural transformations underlying elementary reactions implicitly represented. To address this limitation, we introduce TransTS, a reaction-transformation-aware framework for generalizable TS generation from atom-mapped reactant-product pairs. TransTS explicitly learns atom-level structural transformations between reaction endpoints and integrates them with a unified atom-aligned geometric representation of reactants, TSs and products, enabling reaction-aware equivariant generation of TS geometries. TransTS is designed to provide reliable TS initial guesses for subsequent quantum-chemical refinement, where generated structures are evaluated not only by geometric similarity but also by their ability to converge to validated saddle points and recover the intended reaction pathways. Across IID and zero-shot OOD benchmarks, TransTS demonstrates improved TS initialization quality, with particularly strong generalization to unseen reaction distributions. On the challenging GDB-10-rxn and GDB-17-rxn OOD benchmarks, TransTS generates TS candidates that more frequently converge to validated saddle points and recover the intended elementary reactions after refinement than existing approaches under the same training regime. Scaling reaction coverage and model capacity further improves both geometric fidelity and refinement outcomes.
[AI-107] Data-driven techniques for translational neuroscience and personalized neuro-health
链接: https://arxiv.org/abs/2608.13749
作者: Vishal Subedi,Shashipraba N. K. Rajakaruna,Pratyusha Sarkar,Subhankar Chattoraj,Anjali Khasa,Siddhartha Nandy,Hamza Farooq,Animikh Biswas,Sanjay Chaudhuri,Asim K. Dey,Karuna Joshi,Christophe Lenglet,Ansu Chatterjee
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML)
备注:
Abstract:Neurodegenexrative diseases such as Alzheimer’s disease and Parkinson’s disease are diagnosed most reliably only after substantial, often irreversible, neuronal loss has already occurred, creating an urgent need for quantitative tools that can detect subtle, early, and individual-specific brain changes from neuroimaging data. This review surveys a broad and rapidly evolving toolkit of data-driven techniques for translational neuroscience and personalized neuro-health, organized around four complementary methodological pillars. Throughout, we emphasize how these methodologically diverse approaches converge on a common translational goal: personalized, mechanistically grounded, and clinically actionable models of individual brain health, and we close by discussing the principal open statistical, computational, and clinical challenges that remain.
机器学习
[LG-0] Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training
链接: https://arxiv.org/abs/2608.14498
作者: Hanfeng Lu,Tianyu Feng,Suyi Li,Yuheng Zhao,Wei Gao,Shaopan Xiong,Ju Huang,Siran Yang,Jiamang Wang,Lin Qu,Wei Wang
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 16 pages, 10 figures
Abstract:Vision-language models (VLMs) enable embodied agents to reason and act from visual observations and language instructions. Reinforcement learning (RL) post-training enhances these capabilities using task feedback, but current on-policy RL runtimes execute rollout, reference scoring, and actor training in strict serial phases. While effective for text-only RL, this phase-granular execution is wasteful for VLMs, where processing dense video inputs and prompt prefixes occupies a large fraction of each phase. Because prefix processing is independent of the generated response, it can be run alongside rollout decoding, which leaves GPU compute capacity underutilized, without breaking synchronous on-policy semantics. We present Rollplex, a runtime that decomposes the reference and training phase and moves the prefix computation into the rollout decode window. Realizing this schedule requires more than concurrent kernel launches: naive colocation of Qwen2.5-VL-32,B requires roughly 165,GiB per GPU, while rollout and training prefer different tensor-parallel (TP) degrees and weight layouts. Rollplex addresses these constraints with two mechanisms. Phase-aware memory management controls HBM residency according to producer–consumer lifetimes. Parallelism-aware weight sharing uses the same physical storage for layout-compatible tensors across distinct TP degrees and reconstructs only incompatible tensors, avoiding a complete second actor copy. On 32 H800 GPUs, Rollplex achieves 1.23\times – 1.30\times speedup over serial colocation and 1.57\times – 2.24\times over disaggregation under the same GPU budget, while preserving the synchronous RL update. Comments: 16 pages, 10 figures Subjects: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2608.14498 [cs.LG] (or arXiv:2608.14498v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14498 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-1] Approximate Muon with low-rank adapters
链接: https://arxiv.org/abs/2608.14492
作者: Ben Anson,Conor Houghton,Edward Milsom
类目: Machine Learning (cs.LG)
*备注:
Abstract:The Muon optimizer shows clear benefits versus alternatives when pretraining neural networks. However, it is used less frequently for parameter-efficient fine-tuning (PEFT). One potential reason is that the most common PEFT method, LoRA, does not naturally combine with Muon since it is not mathematically possible to orthogonalize the weight update given by a low-rank parameterization. In this paper, we address this issue by approximating the solution to a relaxed Muon objective in the low-rank setting via linearization and then least-squares. We provide an efficient implementation that uses matmul operations only, as opposed to more complex linear algebra decomposition routines. Our method, sMuon (small Muon), performs favourably across SFT and a ReLoRA pretraining experiment. While results are model- and eval-dependent, we find overall that using Muon for low-rank fine-tuning provides moderate performance improvements.
[LG-2] Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration
链接: https://arxiv.org/abs/2608.14466
作者: Ajith Anil Meera,Pablo Lanillos,Wouter Kouw
类目: Robotics (cs.RO); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: accepted for IWAI 2026
Abstract:An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes. Classical information-seeking and reward-seeking criteria address only one of these objectives at a time. Here, we propose Expected Free Energy (EFE), the principled action-selection objective from active inference, as a unifying criterion for budgeted robotic informative path planning. Maintaining a Gaussian-process belief over the information field, our agent plans continuous trajectories that minimize expected free energy under hard path-length constraints. The results from multiple realizations show that EFE-based planning yields accurate posterior maps and locates the highest-value regions simultaneously, outperforming information-theoretic baselines under the same settings. In robotic exploration, these unified, easy-to-tune principled information-gathering strategies facilitate autonomous deployment while enforcing efficiency and resource constraints.
[LG-3] More Correct Mass Worse Answers: Why Power Sampling Can Fail and How to Fix It
链接: https://arxiv.org/abs/2608.14420
作者: Haohui Yang,Jiaxing Sun,Xiujun Ma
类目: Machine Learning (cs.LG)
*备注: 16 pages, 8 figures, 1 table. Haohui Yang and Jiaxing Sun contributed equally. Xiujun Ma is the corresponding author
Abstract:Power Sampling sharpens a language model’s distribution over complete generation trajectories, offering a verifier-free way to improve reasoning at inference time. It also has the potential to serve as a general-purpose front end for a broad range of downstream sampling methods. However, we uncover a striking paradox: Power Sampling can drive more probability mass toward correct trajectories while degrading the downstream inference it is intended to enhance. Using self-consistency as a representative case, we observe accuracy drops of up to 18.5 percentage points across models and reasoning benchmarks. We trace this paradox to two mismatches. Dose mismatch arises because a fixed exponent induces drastically different amounts of distributional change across problems. Coverage mismatch arises because global sharpening concentrates mass on a narrow set of dominant paths: high pass@k, often interpreted as evidence of preserved diversity, can therefore coexist with the loss of broad reasoning-path support required for downstream aggregation, search, and selection. Guided by this diagnosis, we replace uniform trajectory exponentiation with a deformation-controlled, support-preserving Power target that calibrates sharpening across problems while limiting the suppression of moderate-probability paths. In a same-budget instantiation with weighted self-consistency, the repaired sampler reverses the losses caused by global Power and outperforms standard multi-sample inference across reasoning benchmarks.
[LG-4] CytoBERT: A Foundation Model for Cytometry Data
链接: https://arxiv.org/abs/2608.14414
作者: Syed Abdul Haseeb Qadri,Bjarne C. Hiller,Felix Blanke,Vanja Sophie Cangalovic,Kutalmış Coşkun,Amin Mirzaei,Tom Siegl,Sebastian Bader,Thomas Kirste,Martin Becker
类目: Machine Learning (cs.LG)
*备注:
Abstract:Cytometry measures the complex characteristics of single cells (e.g., counts and protein expression of immune cells) and is widely used across immunological research and clinical settings. However, cytometry data is highly heterogeneous and unstandardized due to experimental protocols and the choice of measured features. While machine learning methods hold the potential to gain deeper insights into cell biology, these challenges make them difficult to apply and transfer across studies. Recent advances in foundation models can alleviate these issues, but corresponding approaches are still scarce in this field. To address this, we provide CytoBERT, a publicly available, open-source, open-weight foundation model for single-cell cytometry data with variable marker panels. CytoBERT is pretrained in a self-supervised manner on a large-scale cytometry corpus (15 human datasets with heterogeneous marker panels and more than 50 million cells) curated through marker standardization, enabling it to learn transferable inter-marker relationships within cells. Fine-tuning CytoBERT for sample-level classification demonstrates that transfer learning across heterogeneous cytometry datasets is feasible, providing a starting point for scalable, generalizable cytometry analysis. Code is available at GitHub.
[LG-5] Boosting Data Augmentation with Stochastic Weight Averag ing
链接: https://arxiv.org/abs/2608.14373
作者: Longde Huang,Axel Flinth,Jan E. Gerken
类目: Machine Learning (cs.LG)
*备注:
Abstract:The symmetries of a learning task have become an important factor in designing modern deep learning solutions. Data augmentation is a straightforward and effective way of incorporating symmetries into a generic neural network. Recent results show that infinitely large deep ensembles show perfect symmetry when trained on augmented data. However, since training ensembles requires repeating the training process many times, this method is costly. In this work, we study stochastic weight averaging (SWA) as an alternative ensembling technique that does not require repeated training runs. We analyze SWA by approximating the stochastic training trajectory at the end of training with an Ornstein–Uhlenbeck process. We show that in the infinite-width limit, SWA on augmented data provides an equiviariance boost that goes beyond what could be expected from the performance increase due to SWA alone. We verify our results with extensive numerical experiments on numerous models spanning computer vision and graph classification with both discrete and continuous symmetries.
[LG-6] Catching the Imposter: Self-Supervised Learning of Physical Coherence with Cross-Entity Feature Permutations
链接: https://arxiv.org/abs/2608.14372
作者: Aleksei Rozanov,Arvind Renganathan,Vipin Kumar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Scientific data often describe entities whose features are jointly governed by the laws of physics, yet existing self-supervised learning (SSL) objectives largely ignore this physical coherence. We introduce imposter, a discriminative pretext task that replaces subsets of an entity’s features with real observations donated by another entity and trains the encoder to identify the swapped features. Because every donated value is individually plausible, the task can only be solved by learning cross-feature physical dependencies. We evaluate the proposed objectives on global ERA5-Land reanalysis data using 21 environmental variables and assess the learned representations on seven downstream tasks spanning climate classification, carbon flux estimation, and streamflow prediction. Our study includes, to our knowledge, the first systematic comparison of self-supervised objectives for land-surface modeling under a shared architecture and pre-training budget. We find that the most effective pretext task depends on the downstream task family rather than any single objective’s superiority, and that imposter provides complementary information when combined with existing SSL objectives. These results suggest that physical coherence is a valuable new source of self-supervision for scientific foundation models.
[LG-7] ATLAS: Discovering Agent Strategies through LLM -Guided Abstraction and Automata Learning
链接: https://arxiv.org/abs/2608.14352
作者: Ignacio D. Lopez-Miguel,Andreas Happe,Jürgen Cito,Ezio Bartocci,Bettina Könighofer,Martin Tappler
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注: 7 pages, accepted for publication at ACM/IEEE MODELS 2026
Abstract:Large Language Model (LLM)-based agents are increasingly used for complex tasks such as software testing and cybersecurity assessment. While these agents demonstrate impressive capabilities, their behavior is difficult to understand, explain, and analyze. Existing evaluations focus mainly on task success and execution traces, offering limited insight into the strategies employed by the agent. We present ATLAS (Automata Learning for Agent Trajectory Analysis and Strategy Discovery), an approach for recovering interpretable behavioral models from agent trajectories. ATLAS combines trace abstraction with automata learning to infer finite-state models that capture observed agent-environment interaction strategies. These models provide human-interpretable insights and support automated analyses of recurring behaviors, decision points, successful task-completion paths, and failure loops. As a proof of concept, we apply ATLAS to trajectories generated by an LLM-based penetration-testing agent. The resulting models expose high-level behavioral strategies for exploiting vulnerable machines that are difficult to identify from raw execution traces alone. We discuss how learned behavioral models can support explainability, model-guided exploration, auditing, and analysis of agentic systems. We further demonstrate symbolic model-based knowledge transfer from powerful frontier models to compact language models. In addition, we show how model transformations can derive concise explanations of agent behavior in a penetration-testing case study comprising 12 vulnerable machines. ATLAS highlights a new opportunity for model-driven engineering: transforming agent trajectories into explicit behavioral models that enable systematic understanding and analysis of otherwise opaque AI agents.
[LG-8] Non-Parametric Spatiotemporal Trajectory Prediction via State-Conditioned Transition Sampling
链接: https://arxiv.org/abs/2608.14349
作者: Michael Fore,Akshay Jain,Justin Downes,Rohan Pradhan,Duncan Botti
类目: Machine Learning (cs.LG)
*备注:
Abstract:We present a training-free method for multi-modal trajectory prediction that achieves comparable accuracy to a 57M-parameter transformer while requiring no GPU and zero learned parameters. The method builds a transition table of historical state-to-next-position pairs and retrieves neighbors using a product kernel over spatial proximity, bearing, speed, and temporal context. Two inference modes operate over this shared representation: diversity-penalized sampling produces trajectories covering distinct plausible routes, while beam search finds the highest-likelihood path. On the TrAISformer benchmark (Danish Maritime AIS), our method achieves competitive accuracy at full data availability and dramatically outperforms the transformer in data-scarce regimes—remaining stable down to 10% of training data where TrAISformer degrades catastrophically. This enables deployment in new geographic regions from an order of magnitude less historical data, and with no GPU training.
[LG-9] CORAL: Curriculum-Optimized Reward Adaptation for LiDAR-Based Goal-Directed Urban Driving
链接: https://arxiv.org/abs/2608.14332
作者: Anisa Saleem,Duksu Kim
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 13 pages, 6 figures
Abstract:Reinforcement learning is promising for autonomous urban driving, but long-horizon goal-directed navigation asks a policy to acquire several competing behaviors at once–reaching a distant goal, tracking a route, avoiding obstacles, obeying signals–and a fixed objective gives no order in which to learn them. This paper presents CORAL, which advances two schedules together: a five-stage curriculum that progressively lengthens routes and tightens behavioral constraints, and a stage-aware reward whose component weights shift emphasis from mission progress toward route following, safety, smoothness, and rule compliance as the task hardens. The policy is a multi-stream actor-critic network trained with Proximal Policy Optimization (PPO) in CARLA on a compact 99-dimensional state pairing a polar LiDAR histogram with vehicle telemetry, ego-frame route geometry, and traffic-rule indicators–no point-cloud encoder, no bird’s-eye-view rasterization. Against two PPO baselines under an identical protocol, CORAL reaches the goal in all twenty evaluation episodes on the longest routes under the full set of behavioral constraints, where the baselines reach 5% and 10%; a factorial ablation shows that neither schedule alone matches their combination: removing either lowers both success and route completion, and disabling both drops success to 55%. Trained in one town, the policy transfers zero-shot to seven unseen towns, succeeding in 68-98% of episodes on routes of the same 100-150 m length, with mean lateral deviation below 0.35 m.
[LG-10] Quantum Multi-Armed Bandits and Linear Bandits: Lower Bounds and Algorithms
链接: https://arxiv.org/abs/2608.14319
作者: Maoli Liu,Zhuohua Li,John C.S. Lui
类目: Machine Learning (cs.LG); Quantum Physics (quant-ph)
*备注: 29 pages
Abstract:We study quantum multi-armed bandits (QMAB) and quantum linear bandits (QLB) in the model of Wan et al. [2023], where the learner queries each arm or action through a quantum reward oracle or its inverse. Prior work gives algorithms over horizon T with regret O(K\log T) for QMAB with K arms and O(d^2\operatornamepolylog T) for d -dimensional QLB. This leaves open whether the K\log T scale is unavoidable and whether the d^2 dependence can be improved. We prove the first minimax lower bounds of \Omega(K\log(T/K)) for QMAB and \Omega(d\log(T/d)) for finite-action QLB, resolving the question raised by Wan et al. [2023] of whether regret independent of T is achievable. At the heart of our argument is a high-confidence single-arm quantum testing lower bound for distinguishing a fixed reward mean from an interval of alternatives, proved by the polynomial method and a Remez-type inequality for trigonometric polynomials. A bandit-to-testing reduction then lifts it to the QMAB lower bound, while a linear embedding gives the finite-action QLB lower bound. Complementing the lower bounds, we give a design-based elimination algorithm for finite-action QLB. When the action set has size \operatornamepoly(d) , its regret is linear in d , improving the prior d^2 dependence and matching our lower bound up to polylogarithmic factors. The algorithm couples a low-bias low-variance quantum mean estimator with a small-support G -optimal design through a query allocation matched to the design weights. The design-based elimination reduces the dimension dependence from d^2 to d^3/2 when using Quantum Monte Carlo estimates. The low-variance estimator then makes reconstruction error aggregate through variance rather than worst-case absolute error, removing the remaining \sqrt d factor.
[LG-11] Detecting Contaminated Code-Generation Prompt Batches via Influence Functions
链接: https://arxiv.org/abs/2608.14303
作者: Francesco Quinzan,Noor Munir,Yishun Lu,Stephen Roberts
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) are increasingly used for code generation, yet they remain vulnerable to prompts that elicit insecure implementations. Existing defenses typically rely on predefined threat models or known vulnerability patterns, limiting their effectiveness against novel attacks. We propose CodeSIFT, a threat-model-agnostic detection method that leverages influence functions to identify batches of prompts that induce anomalous model behavior. Rather than detecting specific vulnerabilities, CodeSIFT measures the parameter-space influence of generated code and uses a statistical test to determine whether a candidate prompt set deviates from a benign reference distribution. To evaluate our approach, we introduce two benchmark datasets covering a variety of vulnerabilities. We evaluate CodeSIFT on three open-weight code LLMs ranging from 3B to 7B parameters, achieving AUROC scores of up to 0.98 at moderate-to-high injection rates, while maintaining well-calibrated false positive rates and substantially outperforming static analysis baselines. These results suggest that influence-function-based detection is a promising direction for identifying malicious code-generation prompts without requiring prior knowledge of the underlying attack class.
[LG-12] Convex losses and their applications to SVM SVR and Shallow Neural Networks
链接: https://arxiv.org/abs/2608.14288
作者: Filippo Portera
类目: Machine Learning (cs.LG)
*备注:
Abstract:We propose multiple new convex losses for SVM and Neural Networks, applied to binary classification tasks. While there are practical limitations in exploiting them with the dual SVM models, we are able to use them with SVM primal formulation and Neural Networks. In detail, the primal SVM problem with the modified losses has been solved with the Particle Swarm Optimization algorithm. We prove that the proposed losses are a generalization of the standard loss, and we experiment them with several small data-sets. This preliminary study shows that using pattern correlations inside the loss function could in theory enhance the generalization performances on some data-sets. To evaluate the performance of each loss, we adopt a Nested Cross-Validation procedure. Results show that generalization measures are the same with or without the new losses. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.14288 [cs.LG] (or arXiv:2608.14288v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14288 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-13] AutoSchema: Live Schema Grounding for Agent ic Text-to-Sparql over Heterogeneous Knowledge Graphs
链接: https://arxiv.org/abs/2608.14228
作者: Yiming Zhang,Koji Tsuda
类目: Machine Learning (cs.LG)
*备注:
Abstract:Life science knowledge graphs make large collections of structured data available through SPARQL, but each resource uses its own schema, identifiers, and links. TogoMCP helps language model agents query these resources by providing curated Metadata Interoperability Exchange files. Creating and maintaining these files still requires language model assisted drafting, validation, and manual review. We study \emphlive schema grounding, where an agent obtains the schema evidence needed for a question directly from the current endpoints. We present \textscautoschema, a general framework for live schema grounding that requires no training. It inspects live schemas, maps entity names in a question to graph identifiers, explores relation paths, and finds possible connections between resources during iterative query construction. We use TogoMCP as our main comparison framework. We evaluate \textscautoschema on Resource Focused Biomedical KGQA, Multi Resource Biomedical KGQA, Longitudinal Biomedical Semantic QA over BioASQ Task B, and Chemistry Knowledge Graph Transfer to a previously undocumented RDF graph. \textscautoschema improves mean factoid accuracy over TogoMCP in the biomedical KGQA tasks and gives consistent gains in the longitudinal BioASQ evaluation. It also reduces iteration budget exhaustion and uses fewer tool calls on average in the core evaluation. The transfer study gives preliminary evidence that live schema grounding can support irregular and previously unseen graphs without first creating a curated schema file.
[LG-14] Connected Subspace Clustering: Hardness a Scalable Heuristic and an Application to Sea Level Geodesy
链接: https://arxiv.org/abs/2608.14215
作者: Johanna Hillebrand,Jan Höckendorff,Jürgen Kusche,Kelin Luo,Heiko Röglin,Melanie Schmidt,Christian Sohler,Bernd Uebbing
类目: Machine Learning (cs.LG)
*备注:
Abstract:Constrained optimization extends classical optimization by integrating side information, making it widely applicable across scientific and engineering domains. Consider a setting where we measure variables at different physical locations. When grouping these measurements, we often want clusters that are both internally similar and physically coherent. Thus, we have a constrained clustering problem where the constraint models coherence. Motivated by an application in geodesy, where contiguous regions of the sea surface must be identified for principal component analysis, we introduce the Connected Subspace Clustering problem: given high-dimensional points and a connectivity graph, partition them into k connected clusters, minimizing their total squared distance to the clusters’ best-fit m’ -dimensional affine subspaces. We prove that, even for m’ = 0 and a grid graph with holes, the problem is NP-hard to approximate within \Omega(n^1/2-\varepsilon) for every \varepsilon0 , where n is the number of measurements. We then introduce an efficient Lloyd-style heuristic that alternates subspace fitting with an iterative merging procedure to enforce connectivity. Our method returns exactly k connected regions by construction, whereas unconstrained methods leave up to 1,966 disconnected fragments at higher cost. In a study of 160 configurations on global sea level time series, our merging-based repair is the strongest of four strategies in 73.75% of cases, and consistently outperforms competitors such as (connected) Ward’s method across all tested cluster counts. The resulting regions isolate signals aligning with climate indices such as the El Nino-Southern Oscillation and Indian Ocean Dipole. Although developed for geodesy, the approach applies to other spatially embedded multivariate time series, such as climate fields, remote sensing, neuroimaging, and sensor networks.
[LG-15] Adaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification PPSN2026
链接: https://arxiv.org/abs/2608.14209
作者: Hengzhe Zhang,Qi Chen,Bing Xue,Lean Yu,Wolfgang Banzhaf,Mengjie Zhang
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: Accepted to PPSN 2026
Abstract:Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering informative transformations of input features that enhance a simple base learner. However, existing approaches often lack explicit mechanisms to preserve important constructed features discovered during evolution, and valuable genetic material can be lost when genetic operators disrupt effective features. This paper introduces an adaptive protection mechanism that leverages feature importance metrics to selectively preserve constructed features during evolution. The mechanism provides stronger protection for more important constructed features while still allowing less important features to be modified and to incorporate useful building blocks from more important features. We evaluate the approach using multiple feature importance calculation methods and demonstrate its robustness across different base learners. Experimental results on 98 regression benchmark datasets show that the proposed mechanism consistently improves solution quality over baseline approaches, and experiments on two credit classification datasets demonstrate that the method also extends effectively to improve search effectiveness beyond symbolic regression.
[LG-16] Revisiting Energy-based Tabular Anomaly Detection: Energy and Reconstruction are Complementary ICONIP2026
链接: https://arxiv.org/abs/2608.14186
作者: Junichiro Niimi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: This is the author’s version of a paper accepted at the 33rd International Conference on Neural Information Processing (ICONIP 2026)
Abstract:Tabular anomaly detection is dominated by classical density-proxy methods (Isolation Forest, OCSVM, LOF), reconstruction-based detectors (Autoencoders, VAEs), and modern non-parametric scorers (COPOD, ECOD, Deep SVDD), all of which approximate the inlier distribution only indirectly; explicit energy-based models are largely absent. Motivated by the recent revival of EBMs in deep learning (e.g., Energy-Based Transformers, JEPA), we revisit the classical Deep Boltzmann Machine (DBM) for this task and hypothesize that its mean-field energy combines more effectively with a reconstruction-based score than same-lineage pairs do. We evaluate a two-hidden-layer DBM on two tabular benchmarks spanning distinct domains (UCI Bank Marketing and NSL-KDD) against eight classical and modern baselines across twenty random seeds. The DBM mean-field energy matches the strongest baseline (the Autoencoder) on Bank Marketing and statistically beats it on NSL-KDD, while significantly outperforming the remaining seven on both datasets. When fused with the Autoencoder via rank fusion, the DBM energy yields a statistically significant improvement on both datasets (AUROC=+0.014, p0.01 on Bank Marketing; +0.002, p0.001 on NSL-KDD); every non-DBM-derived base model instead fails to improve or significantly degrades the AE-paired ensemble. Our position is that classical EBMs, exemplified by the DBM, deserve a place in the tabular anomaly detection toolbox as a non-redundant complementary view to the reconstruction-based scores that dominate current practice.
[LG-17] Deep Reinforcement Learning solution for pickup and delivery routing problems with time window and capacity constraints
链接: https://arxiv.org/abs/2608.14156
作者: Andrew Soroka,Alex Meshcheryakov,Sergey Gerasimov
类目: Machine Learning (cs.LG)
*备注:
Abstract:The task of constructing vehicles optimal routes for pickup and delivery of goods is one of most promising tasks in the context of global urban population growth. Although this kind of problems with small size can be solved by various classical approaches, a fast (or realtime) route optimizer under the constraints of the real world (such as capacity and time windows constraints) for medium-large size problems still remains a highly challenging task. In this work we, for the first time, successfully applied a deep Reinforcing Learning approach (modified JAMPR model) to solve Pickup and Delivery problem with Capacity and Time Window constraints (CPDPTW). We obtained a robust model that gives a fast optimal solution for problems of small and medium size, and gives fast suboptimal solution for problems of larger ( 200) size.
[LG-18] Smart routes: a system for development and comparison of algorithms for solving vehicle routing problems with realistic constraints
链接: https://arxiv.org/abs/2608.14140
作者: Andrew Soroka,German Mikhelson,Alexander Mescheryakov,Sergey Gerasimov
类目: Machine Learning (cs.LG)
*备注:
Abstract:The problem of route optimization with realistic constraints is becoming extremely relevant in the face of global urban population growth. While we are aware of approaches that theoretically provide an exact optimal solution, their application becomes challenging as the problem size increases because of exponential complexity. We investigate the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) and compare solutions obtaining by exact solver SCIP with heuristic algorithms such as LKH, 2-OPT, 3-OPT, the ORTools framework, and the deep learning model JAMPR. We demonstrate that for problem of size 50 deep learning and classical heuristic solutions became close to SCIP exact solution but requires less time. Additionally for problems with size 100, SCIP exact methods around 13 times slower that neural and classical heuristics with the same route cost and on around 50% worse for the first feasible solution on the same time. To conduct experiments, we developed the Smart Routes platform for solving route optimization problems, which includes exact, heuristic, and deep learning models, and facilitates convenient integration of custom algorithms and datasets.
[LG-19] AgilePE: Autonomous UAV Pursuit-Evasion via Self-Play Reinforcement Learning
链接: https://arxiv.org/abs/2608.14135
作者: Wenhao Tang,Tianyang Chen,Zhejun Cui,Boyuan An,Jiayu Chen,Ruize Zhang,Huidong Liu,Tianyue Wu,Qingmin Liao,Fei Gao,Yu Wang,Chao Yu
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 8 pages, 7 figures. Under review
Abstract:Autonomous pursuit-evasion is a fundamental challenge for Unmanned Aerial Vehicles (UAVs), requiring rapid decision-making under tightly coupled dynamics and continuously changing opponent behaviors. Traditional rule-based or differential-game approaches often struggle with high-dimensional aerial interactions and agile maneuvering. We present AgilePE, a complete system for autonomous UAV pursuit-evasion via self-play reinforcement learning. AgilePE integrates agile low-level control, competitive policy optimization, and sim-to-real deployment in a unified framework. The policy directly maps onboard state observations to Collective Thrust and Body Rates (CTBR) commands, enabling end-to-end agile maneuvering without intermediate trajectory planners or waypoint controllers. For training, we use competitive self-play with Prioritized Fictitious Self-Play (PFSP) and a diversified opponent pool, enabling agents to improve against historical policies while stabilizing optimization and reducing policy oscillation. This process leads to the emergence of sophisticated pursuit and evasion strategies. For real-world deployment, we develop a hardware-aligned simulation pipeline that models actuator-response dynamics, communication latency, and domain randomization. The learned policies transfer zero-shot to real quadrotors without task-specific tuning. Real-world experiments reproduce pursuit-evasion tactics observed in simulation, including rapid dodging and flanking, and demonstrate interactive two-agent zero-shot deployment.
[LG-20] Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control
链接: https://arxiv.org/abs/2608.14114
作者: Lukas Zetto,Benjamin Schäfer,Qiong Huang
类目: Machine Learning (cs.LG)
*备注: 10 pages, 9 figures. Accepted for publication in ACM SIGENERGY Energy Informatics Review, Volume 6, Issue 3, September 2026
Abstract:As the integration of volatile renewable energy sources increases the strain on modern power grids, the use of Reinforcement Learning (RL) for autonomous topological reconfiguration has emerged as a promising research field to keep strained grids stable and operational. Compared to traditional redispatching measures, topological actions offer a cheaper and more cost-effective way to manage grid congestion. However, their implementation is hindered by a vast combinatorial action space and strict operational constraints. This paper investigates the effectiveness of model-based AlphaZero-inspired approaches that utilize Monte Carlo Tree Search (MCTS) for proactive grid management. We systematically evaluate how reward functions, observation density, and search guidance influence an agent’s survivability. Our results demonstrate that the optimized AlphaZero approach achieves a peak survivability of 98.43%, significantly outperforming the proximal policy optimization (PPO) variant. We find that conducting the MCTS without guidance from a prior learned policy or value function can enhance training efficiency, and that a straightforward binary survival reward provides more effective search guidance than complex, multi-objective functions. Our findings demonstrate that while AlphaZero is a powerful framework for topological control, pure reinforcement learning is not sufficient; rather, an effective and reliable system requires a ‘minimalist’ integration of domain-specific heuristics, binary rewards, and a restricted observation space of line loads.
[LG-21] Sequence prediction under a lying oracle
链接: https://arxiv.org/abs/2608.14102
作者: Puspabeethi Samanta,Nikhil Karamchandani,Jayakrishnan Nair
类目: Machine Learning (cs.LG); Information Theory (cs.IT)
*备注:
Abstract:We consider the problem of sequential prediction of an m -ary sequence, where at each epoch, (i) the environment selects an outcome from an m -ary alphabet, (ii) the learner selects a probability distribution over the same alphabet (unaware of the outcome generated by the environment), and finally, (iii) the learner incurs a cost that depends on the probability assigned to the outcome. The cost function we consider captures the complexity of predicting the outcome generated by the environment, in a scenario where the aforementioned prediction is performed via comparative queries to a lying oracle. We consider both stochastic and adversarial environments, propose algorithms for both settings, and establish logarithmic upper bounds on their regret.
[LG-22] Resource-Adaptive Primal-Dual Learning for One-Warehouse Multi-Store Systems with Censored Demand
链接: https://arxiv.org/abs/2608.14096
作者: Jiameng Lyu
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:The one-warehouse multi-store (OWMS) system is a fundamental inventory network in which a nonreplenishable warehouse allocates shared stock across multiple stores over time. Existing OWMS learning policies are built around a fixed target calibrated to the initial average resource rate, but such a fixed-target architecture cannot re-center after realized sales change the remaining resource available per future period. We develop Resource-Adaptive Primal-Dual Learning, a new learning framework that tracks the primal-dual resolving path with censored demand as the remaining-resource state evolves. In each period, the current resource rate indexes the target store allocations and dual variable, while censored sales provide gradient estimates for updating both. The analysis combines expected-sales geometry with a moving-target argument to yield logarithmic expected regret, improving on the state-of-the-art square-root-order guarantees of existing OWMS learning policies. The underlying design and analytical ideas may inform other online learning problems with depleting shared resources. Numerical experiments further demonstrate good finite-horizon performance of a practical variant across different horizon lengths and inventory regimes.
[LG-23] When Denoising Hurts: Rethinking the Terminal Step of Diffusion Time Series Forecasters – Extended Version
链接: https://arxiv.org/abs/2608.14067
作者: Dat Nguyen-Cong,Luong Tran,Tung Kieu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Diffusion models offer a natural way to model uncertainty in time series forecasting, yet their iterative sampling process is often treated as a uniformly beneficial refinement procedure. Our study challenges this view by examining how forecast quality evolves throughout reverse diffusion. We find that general temporal structure is often recovered at relatively high noise levels, whereas continued low-noise refinement can introduce statistical drift and degrade the final forecast. Our analysis further suggests that this behavior explains why prior methods often favor relatively narrow diffusion architecture and schedule design. Building on this observation, we propose a label-free global stopping criterion that detects the optimal termination point, eventually speeding up inference and improving predictive accuracy. Additionally, since early stopping terminates inference in high-noise regions, we propose a Bernoulli timestep sampler that concentrates training on this region while preserving coverage of the full diffusion process. Extensive experiments conducted across eight real-world datasets demonstrate the superior performance of our method compared to existing approaches.
[LG-24] Model-agnostic Retrieval-Augmented Extended Forecasting for time series
链接: https://arxiv.org/abs/2608.14054
作者: Juan Pablo Villa Serna,Rohan Asthana,Vasileios Belagiannis
类目: Machine Learning (cs.LG)
*备注: 6 pages, 1 figure
Abstract:Time series forecasting with pretrained foundation models has demonstrated strong zero-shot capabilities. However, achieving optimal performance on time series with short or negligible historical data in domain-specific applications typically requires adaptation via either fine-tuning or RAG. While fine-tuning is effective, it incurs substantial computational costs. This work explores RAG within univariate time series (Retrieval Augmented Generation) as a more efficient alternative, in particular RAF (Retrieval Augmented Forecasting), and introduces RAEF (Retrieval-Augmented Extended Forecasting), a model-agnostic method built upon RAF. RAEF incorporates key refinements to the retrieval and aggregation mechanisms: (1) direct retrieval in input-space rather than embedding-space, reducing inference overhead, and (2) concatenation-based aggregation that preserves temporal structure instead of averaging. Empirical evaluation across multiple benchmark datasets demonstrates that RAEF outperforms RAF in both accuracy and inference overhead. Furthermore, comprehensive comparisons with zero-shot and fine-tuned foundation models show that RAEF achieves competitive or superior performance to fine-tuning while avoiding its computational burden, establishing it as a practical and scalable approach for domain adaptation in time series forecasting.
[LG-25] Adversarial Learning of Classifier-Free Guidance Schedules
链接: https://arxiv.org/abs/2608.14038
作者: Ashwini Pokle,Alexandre Galashov,Arnaud Doucet,Mauricio Delbracio,Valentin De Bortoli
类目: Machine Learning (cs.LG)
*备注:
Abstract:Modern text-to-image diffusion models rely on classifier-free guidance (CFG) to achieve high image fidelity and text alignment. However, CFG typically applies a static, global scale across all timesteps, samples, and conditions – a choice that is generally suboptimal and can introduce artifacts, as different states may benefit from different levels of guidance. While time-varying schedules are known to improve quality, designing them by hand is non-trivial and application-dependent. In this paper, we learn the guidance schedule as a function of diffusion time, conditioning and the current noisy sample, in order to better align sampled images with the text prompt. We frame this as a density ratio estimation problem: a discriminator is trained to estimate the time-dependent log-density ratio between the true and guided marginal distributions, while a lightweight generator network predicts the optimal, state-dependent guidance scale. Empirically, our approach outperforms both heuristic CFG schedules and prior methods for learning dynamic guidance on text-to-image generation benchmarks.
[LG-26] When Does More Correct Data Hurt? Insertion-Stability and the Limits of Dimension-Based Theory
链接: https://arxiv.org/abs/2608.14020
作者: Joseph Sankoorikal Johny
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 13 pages, 1 figure
Abstract:Adding data known to be correct ought to be safe. Not always. Larsen, Pabbaraju and Shetty model the failure with a monotone adversary, which reads an i.i.d. training sample and may append as many further examples as it likes, provided the target hypothesis labels them all. Mehrotra has since settled the cost, showing that for classes of VC dimension d = 2 no learner can guarantee expected error better than Theta((d/n)log(en/d)), a logarithmic factor above the clean PAC rate. Because that rate is a worst case over all classes, it says nothing about which classes actually suffer the penalty, and the answer turns on the learner. We call a learner insertion-stable if feeding it more correctly labeled examples can only shrink the region where it errs. Such learners are immune to the adversary, since on any given sample the risk after insertions never exceeds the risk on the clean part alone, however much is added and however cleverly it is chosen. High- probability guarantees carry over unchanged, and because Closure is insertion-stable every intersection-closed class keeps its clean rate of E[Err] = (21d+34)/n. Immunity is not something the classical dimensions can predict. Two classes can agree on VCdim = Ldim = 2 and still split, one at Theta(1/n) and the other at Theta(log(en)/n), while intervals have unbounded Littlestone dimension and are immune anyway. On Mehrotra’s hard class we prove more than the failure of a single algorithm, showing that no monotone permutation-invariant compression scheme of any finite size attains the clean rate. The question is therefore not whether a class is hard, nor whether a learner is good, but whether the two suit each other. Given an insertion-stable learner that is optimal on clean data, correct additions are free, and without one the cost belongs to the class, so changing the learner will not avoid it. Comments: 13 pages, 1 figure Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2608.14020 [cs.LG] (or arXiv:2608.14020v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14020 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Joseph Sankoorikal Johny [view email] [v1] Fri, 14 Aug 2026 07:07:55 UTC (159 KB)
[LG-27] Emergent Models: Intelligence from Tiny Substrates
链接: https://arxiv.org/abs/2608.14019
作者: Giacomo Bocchese,Nicola Giacobbo,Etienne Guichard,James Wiles,Akshaj Devireddy
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注:
Abstract:Emergent Models (EMs) are a machine learning paradigm based on simple yet open-ended substrates, such as cellular automata, in which modeling is treated not as the learning of a closed-form input-output map but as the emergence, within simple dynamical systems, of computational behaviors that solve external tasks. Such substrates typically iterate a fixed local rule over a latent space for an adaptive number of steps, with an interface linking the latent state to external input/output signals. Training proceeds by evolutionary search. We hypothesize that some instances of this framework are biased toward global generalization: capturing the rule generating the data over its full domain, and therefore extrapolating beyond the training range. Theoretically, we prove that some EMs are latent-universal: with the update rule and interface held fixed, they can realize any partial computable function by varying only the initial condition of the latent state. Empirically, we study a zoo of minimal EM instantiations across discrete and continuous substrates, showing that local-recursive computation at a tiny scale (tens to hundreds of parameters) can extrapolate exactly on simple arithmetic functions, can support control behaviour and online adaptation, while still exposing several limitations. This work is foundational: it does not propose a competitive architecture, but a framework meant to widen the design space of machine learning beyond differentiable feed-forward maps.
[LG-28] Identifiability and Order-Dimension Limits of In-Context Learning on Partial Orders
链接: https://arxiv.org/abs/2608.14004
作者: Faizanuddin Ansari,Debanjan Dutta,Swagatam Das
类目: Machine Learning (cs.LG)
*备注:
Abstract:In-context learning is commonly formalized as inference from examples of a function. Partial orders instead combine transitivity, antisymmetry, and incomparability, so a finite prompt may not determine a queried comparison. We develop a theory of in-context learning on partial orders that separates logical identifiability, prompt teaching cost, structural complexity, and the exact capacity of a formal coordinate-decoder class. A version-space semantics makes background knowledge and open- versus closed-world assumptions explicit. For finite open-world prompts with positive and negative comparisons, we prove an exact completion trichotomy: after taking the reflexive transitive closure of the positive demonstrations, a query is forced true, forced false because every true completion creates a cycle or violates a negative demonstration, or remains genuinely ambiguous. For a known n -element universe, we characterize the open-world teaching number as the number of covers plus a blocker-set hitting number, prove that its maximum over all n -element posets is n(n-1) and is uniquely attained by the antichain, and identify the blocker term as the exact cost of open-world rather than complete-Hasse semantics. We formalize prompt-dependent s -coordinate decoders and use the classical coordinate-order equivalence to obtain an exact representation boundary: dimension at most s is necessary and sufficient, while width at most s is a convenient sufficient condition.
[LG-29] Polar Code Based Federated Learning: Convergence Analysis and Resource Allocation
链接: https://arxiv.org/abs/2608.13961
作者: Han Xiao,Wei Kang,Nan Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data; however, it faces significant communication bottlenecks and channel impairments in practice. Conventional network layer treatments either idealize the channel as error free or apply equal error protection (EEP) to transmitted model updates, failing to account for the inherently unequal importance of quantization bits within a single local model. To address this limitation, we propose a cross layer polar code based FL scheme that leverages the unequal error protection (UEP) property of polar codes under finite block lengths. Specifically, the proposed design selectively protects more significant quantization bits, thereby mitigating the detrimental effects of channel noise. We further provide a rigorous convergence analysis of the proposed scheme, deriving an upper bound on the convergence gap, which we then jointly optimize over the number of quantization bits and the polar code block length across all training iterations. Experimental results demonstrate that both constant and variable block length configurations of our polar code based scheme consistently achieve substantial performance gains over uncoded and LDPC-based EEP benchmarks, with the advantage becoming increasingly pronounced as the channel quality deteriorating. These findings confirm the efficacy of our cross-layer design in enhancing FL robustness and efficiency under realistic channel conditions.
[LG-30] Probabilistic indirect models for undrained shear strength: addressing significant data missing and variability with advanced imputation and machine learning techniques
链接: https://arxiv.org/abs/2608.13934
作者: Haibin Xiong,Shaoheng Dai,Peng Lan,Xuzhen He,Chenxi Tong,Sheng Zhang,Daichao Sheng
类目: Machine Learning (cs.LG); Databases (cs.DB)
*备注:
Abstract:Accurate prediction of undrained shear strength (su) is crucial for geotechnical design, but is often hampered by substantial uncertainty in traditional empirical methods. This study uses the CLAY/10/7490 global database to develop probabilistic indirect models to predict su based on Atterberg limits and piezocone cone penetration (CPTU) measurements. Firstly, the dataset has a high missing data rate and variability. We test three imputation methods - multivariate normal (MN), multiple imputation by chained equations (MICE), and miss forest (MF) - to fill the missing values. To validate their effectiveness, a Probabilistic Extreme Gradient Boosting (PXGB) model is developed, and the imputation methods are evaluated by comparing the PXGB’s performance when trained on the imputed datasets against that on the original incomplete data. Secondly, the indirect model is built by integrating a multi-head attention (MHA) mechanism into an artificial neural network (ANN) to enhance information extraction from limited data, which leads to the MHA-based probabilistic neural networks (MHA-PNN) model. The models’ performance, alongside a conventional MN-based prediction model, was evaluated using root mean square error (RMSE), coefficient of determination (R2), mean absolute percentage error (MAPE), conditional interval width (wCI), and coverage rate (CR). Results demonstrate that the proposed MN-enhanced MHA-PNN model substantially outperforms other models in both prediction accuracy and uncertainty quantification. These findings highlight the potential of this integrated strategy for building robust probabilistic indirect models in geotechnical applications, particularly when confronted with sparse and incomplete datasets.
[LG-31] Post-training Quantization for Hybrid Iterative Generative Models
链接: https://arxiv.org/abs/2608.13932
作者: Jing Gao,Junyi Wu,Wei Wang,Yan Yan,Yao Zhao
类目: Machine Learning (cs.LG)
*备注:
Abstract:Iterative Generative Models (IGMs) span autoregressive and diffusion paradigms, and hybrid variants that couple them can achieve remarkable image-generation fidelity. However, their iterative inference incurs substantial computational overhead, making Post-training Quantization (PTQ) appealing for acceleration, while directly applying vanilla PTQ to hybrid IGMs can trigger model collapse. By analyzing these failures, we identify two critical challenges: Excessive Outliers (EOs) in the activations create an irreconcilable trade-off between preserving normal precision and covering EOs, resulting in severe degradation in generation quality; Amplified Anomalies (AAs) arising unpredictably from minor quantization errors, create a mismatch between calibration and inference, thus iteratively triggering model collapse. To address these challenges, we introduce HyGenQ, a PTQ framework for hybrid IGMs. HyGenQ comprises Hierarchical Cluster Decoupling (HCD) and Scaling Recalibration (SR). HCD identifies and decouples outlier channels via a multi-stage clustering process, effectively isolating EOs while maintaining normal value precision, thereby alleviating performance degradation. SR scales AAs beyond Gaussian Bound, thereby avoiding model collapse caused by aggressive truncation. Extensive experiments demonstrate that HyGenQ successfully quantizes representative hybrid IGMs to 8-bit precision (W8A8), significantly outperforming existing baselines and validating its robustness across different model families.
[LG-32] High-dimensional nonparametric changepoint detection via low-rank degree-two density projection
链接: https://arxiv.org/abs/2608.13922
作者: Guoqing Zhang,Zhaixin Chen
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 20 pages
Abstract:Detecting distributional changes in high dimension is difficult when neither the pre-change nor post-change density is parametrically specified. We introduce a representation-based approach that retains all degree-at-most-two density information while replacing density estimation by matrix mean estimation. For observations in [-1,1]^d , a symmetric feature matrix H_2(X)\in\R^(d+1)\times(d+1) is constructed so that M(f)=\E_f H_2(X) is an isometric encoding of the degree-two orthogonal projection of the density. We scan matrix CUSUMs after rank- r truncation, exploiting the low rank of the projected jump rather than sparsity of individual coordinates. The resulting \LRD estimator has a tent-shaped population objective and a nonasymptotic operator-norm analysis whose leading stochastic term scales as \sqrtrd\log(nd) . For multiple changes, we give a seeded narrowest-over-threshold procedure and prove exact recovery by an induction that preserves an isolating interval for every undetected change. A cross-fitted scalar refinement learns the changing low-rank direction on one fold and localizes on the other, attaining \widetilde O_\Pp(\kappa^-2) error; a matching Le Cam lower bound shows optimality up to logarithms. A geometrically \beta -mixing extension follows from a dependent matrix Bernstein inequality. Experiments with ambient dimension up to 200 , a three-change d=100 sequence, and a 128 -feature human-activity benchmark show that the method remains computationally practical and accurately detects pure dependence changes that are invisible to mean CUSUMs.
[LG-33] MedMix: Specialization-Consistent Federated Sparse MoEs under Modality Heterogeneity
链接: https://arxiv.org/abs/2608.13911
作者: Adiba Orzikulova,Dong Min Kim,Jaehong Yoon,Sung-Ju Lee
类目: Machine Learning (cs.LG)
*备注:
Abstract:Federated multimodal medical AI faces modality heterogeneity at both the client and sample levels: clients may systematically lack access to specific modality types, while individual records within the same client may contain different partial modality subsets. Sparse Mixture-of-Experts (MoE) architectures are a promising remedy for modality-adaptive computation, but their use in federated learning is fragile under cross-client modality heterogeneity, where locally learned routing policies can diverge across clients and drive experts toward incompatible specializations. Different clients may assign the same observed modality configuration to different experts, or train similarly indexed experts on different missing-modality configurations, causing standard aggregation to misalign or overwrite the expert specialization that sparse MoEs are intended to learn. To address this challenge, we propose MedMix, a semantic-alignment framework for federated multimodal sparse MoEs that coordinates cross-client routing and expert specialization using modality context. At the client side, MedMix uses modality-context-aware routing to guide expert selection using each token’s modality identity, position, and incompleteness context. Across clients, it uses consensus-guided routing alignment to construct server-side consensus anchors for shared modality patterns and align local routing distributions across clients. Complementing these routing mechanisms, client-adaptive expert aggregation leverages client-specific modality-pattern prototypes to match and aggregate functionally similar experts across clients. Experiments on real-world multimodal medical datasets show that MedMix achieves the best average F1 across diverse modality heterogeneity and modality incompleteness settings, with especially clear gains under severe heterogeneity.
[LG-34] Fashion Outfit Generation via Unified Sequential Composition Models
链接: https://arxiv.org/abs/2608.13888
作者: Kaicheng Pang,Xingxing Zou,Ruohan Xu,Waikeung Wong
类目: Machine Learning (cs.LG)
*备注:
Abstract:The task of synthesizing stylistically coherent fashion outfits from massive item libraries, known as fashion outfit generation, remains a non-trivial challenge, primarily due to the non-monotonic and implicit nature of aesthetic compatibility, coupled with the exponentially large combinatorial search space. In this paper, we formalize this task as Constrained Ensemble Generation (CEG) and model it as a finite-horizon deterministic Markov Decision Process. To address CEG in fashion, we propose the Unified Sequential Composition Model (USCM), which jointly models set-level compatibility and latent composition intents. Guided by USCM’s learned priors, a Latent Expansion Monte Carlo Tree Search (LE-MCTS) mechanism is proposed to handle item retrieval during composition, balancing local aesthetic synergy with global structural balance. Extensive experiments on the Polyvore Outfits dataset, along with zero-shot evaluations on the iFashion and PolyvoreU datasets, demonstrate that our framework achieves state-of-the-art performance across independent human preference evaluations, automated aesthetic proxies, and structural validity metrics for constrained fashion outfit generation.
[LG-35] Variation Brownian Kernel Ladders
链接: https://arxiv.org/abs/2608.13882
作者: Mahdi Mohammadigohari
类目: Machine Learning (cs.LG)
*备注:
Abstract:Claims about the benefit of depth depend on the complexity assigned to a representation. We introduce the \emphVariation Brownian Kernel Ladder (VBKL), a path-atomic function-space framework that separates nonlinear recursive dictionary construction from linear variation superposition. Starting from linear projections, each atom recursively composes unit-ball profiles from the Brownian reproducing kernel Hilbert space; the full VBKL space is then the signed-measure variation hull of the completed dictionary. We identify each recursive dictionary as a union of Brownian pullback RKHS balls and establish variation-controlled Hölder regularity, compactness and attainment, and strict growth with depth under a local non-degeneracy condition whose trace lies in the support of the input measure. For associated finite lower-support architectures, we derive Rademacher and generalization bounds through Brownian quadratic chaos, signed threshold traces, and VC entropy. We also construct two-stage approximants by discretizing the outer measure and the selected outer Brownian profiles, obtaining an M^-1/2+m^-1/2 error bound, a sharp interpolation constant \sqrtA/2 , and at most 2M active outer-profile basis contributions per evaluation. Controlled experiments illustrate the approximation mechanisms and indicate a favorable limited-data accuracy–complexity trade-off.
[LG-36] HI-MeshGraphNets: Efficient and Accurate Mesh-based Physics Learning with Hierarchical Multi-scale Graph Neural Networks
链接: https://arxiv.org/abs/2608.13827
作者: SiHun Lee,Dong-Hyuk Park,Taesoo Bang,Seung-Hoon Kang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine-learned physical surrogate models have become promising alternatives to mesh-based numerical solvers. Among them, graph neural networks (GNNs) are well suited for representing simulation meshes and learning nodal state evolution through message passing. However, conventional flat message passing becomes inefficient on large, high-fidelity meshes because information propagates only one hop per layer, requiring deep processors for long-range interactions and increasing computational cost, memory usage, and the risk of over-smoothing. To address this limitation, we propose Hierarchical Interpolating MeshGraphNets (HI-MGN), a multiscale extension of MeshGraphNets for efficient long-range communication on unstructured meshes. HI-MGN replaces the flat processor with a hierarchical multiscale processor that coarsens graphs using farthest-point sampling and Voronoi partitioning while preserving the original mesh topology. Message passing on coarse graphs enables information to travel over larger geometric distances with fewer layers, and a learned graph interpolation network reconstructs fine-resolution features. Across three structural and fluid benchmarks, HI-MGN achieves improved accuracy compared with MeshGraphNets and the Bi-Stride Multi-Scale GNN while reducing training time and peak memory usage. The results show that topology-aware hierarchical message passing and learned coarse-to-fine interpolation provide an effective and practical framework for scalable mesh-based physics surrogate modeling. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.13827 [cs.LG] (or arXiv:2608.13827v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.13827 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-37] What preferences can - and cannot - predict in multi-agent online learning ICML2026
链接: https://arxiv.org/abs/2608.13810
作者: Omar Abbadi,Rida Laraki,Panayotis Mertikopoulos
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注: 56 pages, 15 figures; oral presentation at ICML 2026
Abstract:We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavior of game dynamics, asking in particular to what extent the combinatorial data of a game – its preference graph – determine the outcomes of no-regret learning dynamics – such as follow-the-regularized-leader (FTRL). In one direction, we show that the skeleton of every dynamically stable set (i.e. the set of pure profiles it contains) must also be preferentially stable, that is, it must be closed under profitable deviations. We then ask the converse question: when do preferences determine the long-run behavior of the players’ learning dynamics? We begin by showing that preferences characterize asymptotic stability in the case of subgames – i.e. subsets of pure profiles obtained by restricting players’ action sets. Beyond this case however, the equivalence between dynamic and preferential stability collapses: concretely, we construct a three-player game with a preferentially stable set whose span is dynamically unstable, showing in this way that preferences do not suffice as a criterion of dynamic stability. We then bridge this gap via the notion of resilience under aggregate deviations, an easy-to-check payoff-based condition that guarantees asymptotic stability of arbitrary spans of pure strategies.
[LG-38] Stochastic Control Policies for Robust Molecular Transition Path Sampling
链接: https://arxiv.org/abs/2608.13800
作者: Jingqian Liu,Yu-Hsiang Wang,Yanru Qu,Ge Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Transition path sampling (TPS) aims to efficiently generate rare molecular transition trajectories between metastable states and is essential for understanding biomolecular mechanisms. Beyond traditional molecular dynamics (MD)-based sampling, machine learning has become central to state-of-the-art TPS. One major class of methods learns control forces during explicit MD rollouts. By preserving the underlying molecular dynamics, these methods tend to produce more physically plausible trajectories than endpoint-conditioned generators that construct paths directly. However, rollout-based control methods have been reported to exhibit unstable and strongly seed-dependent performance. We recast rollout-based control as learning a path-space proposal distribution and investigate stochasticity placement as a design choice for improving exploration and optimization robustness. We develop two stochastic policies: FS-TPS, which directly parameterizes a state-dependent Gaussian distribution over the control policy output, and LaS-TPS, which samples a compact latent control variable and decodes it into structured, cross-atom-correlated force variation. We conduct extensive multi-seed experiments on three biomolecular systems of increasing size: alanine dipeptide, chignolin, and BBL, a fast-folding protein. Stochastic policies consistently improve transition success and path quality over deterministic-policy baselines while substantially reducing sensitivity to random initialization.
[LG-39] Dynamic Multi-Depot Vehicle Routing with Online Requests: Event-Driven Transformer–DRL and Rolling-Horizon Benchmarking
链接: https://arxiv.org/abs/2608.13799
作者: Faezeh Ardali,Gerald M. Knapp
类目: Machine Learning (cs.LG)
*备注: 7 Pages
Abstract:This paper presents an event-driven learning and benchmarking framework for the Dynamic Multi-Depot Vehicle Routing Problem with progressively revealed requests and evolving vehicle states. Masked MLP and Transformer policies are trained through behavior cloning and proximal policy optimization. Deterministic feasibility masking prevents invalid vehicle–request assignments, while fixed-prefix/flexible-suffix route commitments protect completed, active, and near-term decisions and separately measure vehicle reassignment and resequencing. The learned policies are compared with dynamic insertion heuristics and time-limited rolling-horizon optimization. In a 20-scenario policy benchmark, all methods completed every request without invalid actions, but nearest feasible achieved the lowest mean objective and outperformed the learned policies in routing quality, waiting time, stability, makespan, and runtime. Across five independent training runs, PPO had little average effect on the MLP and improved the Transformer on average, although with greater seed variability. Under the common protocol, nearest feasible achieved the lowest combined objective and route disruption, whereas rolling horizon achieved the lowest waiting times and makespan at substantially higher computational cost. The learned policies retained millisecond-level decisions and transferred to instances with up to 80 requests without retraining, but did not outperform the strongest heuristic. No single method was best across routing efficiency, service responsiveness, stability, and online computation.
[LG-40] Recent Advances in Deep Learning-Based Drug-Target Binding Affinity Prediction
链接: https://arxiv.org/abs/2608.13797
作者: Jafin Khan,Md Hossain Shuvo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Computational approaches to drug discovery involve multiple sub-problems, and among them, drug-target binding affinity prediction plays an important role. Despite recent advances, accurately predicting binding affinity remains an open research area. The major objective of our paper is to perform a comprehensive review and comparative analysis of recent machine learning methods for drug-target binding affinity prediction, with a focus on identifying strengths, limitations, and research gaps. We review representative recent deep learning approaches that use common benchmark datasets and evaluation metrics, covering a range of neural network architectures and representation strategies. In addition, we analyze seven widely used benchmark datasets and commonly adopted evaluation metrics for drug-target binding affinity prediction. Our analysis indicates that although many methods report strong performance on standard benchmarks, their effectiveness is often influenced by dataset bias and limited evaluation settings. Furthermore, most methods exhibit reduced performance in cold-start scenarios, highlighting challenges in generalization. We identify several limitations of current approaches, including dataset imbalance, the lack of standardized evaluation, limited real-world applicability, and challenges in cold-start scenarios. We also discuss future research directions, including better dataset design, more robust evaluation methods, improved handling of cold-start problems, and the integration of multimodal representations.
[LG-41] he Integer Alibi: Localizing Cross-Kernel Divergence in INT8-Quantized LLM Inference
链接: https://arxiv.org/abs/2608.13756
作者: Teng-Ruei Chen
类目: Machine Learning (cs.LG)
*备注: 10 pages (IEEEtran two-column), 4 figures, 4 tables. Pre-registered protocol with append-only amendments; measurement artifacts and per-run manifests with kernel-selection evidence are described in the artifact statement. Companion to arXiv:2608.11693
Abstract:Two GPU kernels implementing the same scaled INT8 GEMM interface are usually treated as interchangeable. We test that assumption: holding the checkpoint, prompts, hardware, inference engine, decoding, and quantization configuration fixed, we swap only the INT8 linear kernel (CUTLASS versus Triton) inside vLLM. At 1.7B each arm reproduces itself bit-for-bit across cold restarts, yet the arms agree on no sequence in any end-to-end comparison we ran (0/8, 0/16, and 0/64). What makes this more than a benchmark discrepancy is an integer alibi: for shared INT8 operands under a verified no-overflow bound, the INT32 dot product is exact and order-independent, so the accumulator cannot be the source of any difference. Feeding both kernels identical operands from every linear layer of Qwen3-1.7B and 8B (196 and 252 layers), we find bit-identical outputs under power-of-two scales, confirming a pinned prediction list 196/196 and 252/252 (pre-registered at 1.7B, pinned but not blind at 8B), and observed differences of at most one bfloat16 spacing under the checkpoints’ real scales. This localizes the divergence to scale application and output rounding after the exact accumulator. Applied as a probe checkpoint, the same intervention restores end-to-end bitwise agreement (8/8 and 16/16 sequences). Cross-implementation FP8 GEMM shows a different signature: both the prevalence and the magnitude of differences grow with reduction depth, while the INT8 fraction stays at parts per million and within one spacing over a 64x range of K. Teacher-forced replay ties layers to tokens: flips concentrate at small logit margins, which predict flip risk with ROC-AUC 0.94 on 16,384 positions. We will release the pre-registration, per-layer predictions, manifests with kernel-selection evidence, and a conformance procedure that turns these controls into a concrete check for kernel interchangeability.
[LG-42] Language-Specific Gaps in AI Safety Training Datasets
链接: https://arxiv.org/abs/2608.13695
作者: Chialuka Prisca-Mary Onuoha,Bright Etornam Sunu,Rashidat Sikiru
类目: Computers and Society (cs.CY); Machine Learning (cs.LG)
*备注:
Abstract:Large language model providers routinely cite multilingual safety benchmarks spanning a dozen or more languages as evidence that their models are safe for non-English-speaking users. We show that these collection-level coverage claims frequently do not survive inspection at the level of an individual language. Auditing 21 resources across 25 language slices, of which 20 count as datasets under our counting rules, spanning three languages chosen to represent low- (Hausa), mid- (Swahili), and high-resource (French) tiers, we find that gaps in provenance, annotation reliability, access, harm-taxonomy coverage, and data reuse recur in patterns that partially, but not fully, track resource level. Using a controlled within-pipeline comparison, we show a Hausa-language slice falling below its own paper’s translation-quality acceptance threshold while the same pipeline’s Swahili output clears the same bar comfortably; this is evidence that these gaps are measurable and addressable, not inherent. We further show that self-harm and sexual-content categories have no native-language coverage in either African-language tier we studied, a total rather than gradated gap that a purely resource-level account does not predict. We connect these findings to a documented, persistent asymmetry in multilingual jailbreak robustness (single-turn attacks largely mitigated, multi-turn attacks still effective), arguing that this asymmetry is structurally consistent with where our audit finds training and evaluation data thinnest. We contribute a reusable slice-level audit methodology, a cross-tier empirical comparison, and concrete recommendations for dataset creators, model providers, and venues aiming to make ``multilingual coverage’’ claims verifiable rather than merely stated. Dataset: this https URL
[LG-43] hint2: Hierarchical World Models for Inference-Time Temporal Logic Guidance
链接: https://arxiv.org/abs/2608.13678
作者: Moritz Zoellner,Anastasios Manganaris,Ahmed H. Qureshi,Rohan Paleja
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: Videos available on our project page: this https URL
Abstract:A central goal of robot learning is to enable robots to execute rich instructions specified at runtime. Large-scale language-conditioned policies have made substantial progress toward this goal, yet still struggle with temporal structure and safety constraints. Linear Temporal Logic (LTL) provides a powerful language to express complex, non-Markovian instructions. However, guiding learned manipulation policies toward LTL satisfaction remains challenging because modern policies generate short-horizon action chunks and replan in closed loop, while almost all LTL specifications are evaluated over long-horizon trajectories. In this paper, we introduce hint ^2 , a method for guiding short-horizon policies toward satisfying complex LTL specifications at inference time using hierarchical world models. Our key idea is to derive two separate guidance objectives using each world model’s abstraction level. A high-level model predicts future action-induced transitions in task-relevant atomic propositions to guide progress through the LTL automaton, while a low-level dynamics model predicts immediate state evolution for accurate local safety guidance. Our results show that hint ^2 overcomes the limitations of current LTL-guided diffusion methods, outperforms existing inference-time steering methods in CALVIN, and successfully completes instructions with complex liveness and safety constraints more elegantly than language-conditioned alternatives. Finally, we demonstrate that hint ^2 can handle complex instructions on a real UR5e manipulator.
[LG-44] EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models
链接: https://arxiv.org/abs/2608.13676
作者: Deeksha M Shama,Punnisa Amornsirikul,Archana Venkataraman
类目: Machine Learning (cs.LG)
*备注:
Abstract:Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG. Thus, there is a critical need for a universal method that can extend the interpretability of any foundation model to alternative and physiologically relevant domains without modifying or retraining the underlying model. Methods: EEG-PRISM leverages linear transformations and established backpropagation rules to map time-channel attribution scores into alternative domains. We derive mappings to the frequency domain via an invertible DFT and to the source domain via an approximately invertible EEG generative model. We evaluate EEG-PRISM in simulated and real data, assessing recovery of ground-truth phenomena across domains with five foundation models and four AI explainers. Results: In simulation, EEG-PRISM achieves near-perfect spectral recovery and 69.2% spatial accuracy. In epilepsy, EEG-PRISM correctly determines that delta-theta activity is most salient and correctly localizes the seizure onset region with 50% accuracy. In autism, EEG-PRISM localizes the predictive delta-alpha biomarkers to frontal and temporal regions, consistent with prior work. Conclusion: EEG-PRISM is a theoretically-grounded post-hoc attribution method with accurate mapping into the spectral and spatial domains. It supports window-level analysis of transient events (e.g., seizures) and group-level identification of clinically relevant biomarkers (e.g., autism), thus advancing interpretable EEG foundation models. Significance: This work enables physiologically-grounded interpretation of EEG foundation models and supports clinically relevant insights such as event localization and biomarker identification.
[LG-45] he Query Knows What to Forget: A Second Erase Direction for Linear Attention
链接: https://arxiv.org/abs/2608.13668
作者: Dhruman Gupta,Aritra Das,Debayan Gupta
类目: Machine Learning (cs.LG)
*备注:
Abstract:Linear attention keeps a state of fixed size. At long context, many stored items share this state, and interference between them degrades retrieval. Gated DeltaNet-2 (GDN-2), like every delta-rule model before it, derives its erase vector from the key of the current token. However, the interference in its reads is measured through the query, and the erase step cannot reach it. We introduce the Query-derived Erase Direction (QED). QED adds a second erase direction derived from the query and orthogonal to the key. In the fast-weight view, a key-directed delta edit cannot change the key-orthogonal part of a read. It uses the editable part to cancel old-state content measured along the query. It also improves retrieval at every length past the training window, and it about doubles the usable context length on S-NIAH-1.
[LG-46] Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments
链接: https://arxiv.org/abs/2608.13652
作者: Haoyi Jia,Sagar Addepalli,Julia Gonski
类目: Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); High Energy Physics - Phenomenology (hep-ph)
*备注:
Abstract:Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and object multiplicity. We present Organized Representation via Contrastive learning for Anomaly detection (ORCA), a two-stage framework that first learns an embedding space via supervised contrastive learning across a diverse set of physics processes, then runs a standard autoencoder in that space to generate event-level anomaly scores. On a simulated dataset consistent with conditions at the High-Luminosity Large Hadron Collider, ORCA delivers significant gains in both breadth and depth of sensitivity to new physics signals with respect to a baseline autoencoder architecture. Beyond improved sensitivity, the contrastive embedding makes the anomalous sample interpretable: because known processes occupy distinct regions of the space, a maximum-likelihood template fit to the embedding distributions can attribute events in an anomalous sample to template physics processes with quantified uncertainties. We demonstrate that the fit accurately recovers injected signal yields, including for signals excluded from the training of the embedding, and characterizes signals absent from the template library through the known processes they most resemble. These results establish ORCA as a route to interpretable anomaly detection-based searches at colliders, where the embedding geometry carries higher dimensional physics information compared to standard one-dimensional output fits, enhancing downstream statistical analysis.
[LG-47] Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertainty
链接: https://arxiv.org/abs/2608.13651
作者: Dimitar Ho
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 37 pages
Abstract:We solve exactly a fundamental problem of adaptive control against adversarial disturbances: regulate the scalar system x_t+1 = ax_t + u_t + w_t , x_0=0 , |w|\infty \le 1 , where the constant pole a \in [-\Delta, \Delta] is unknown in sign and magnitude and \Delta is arbitrarily large. Elementary as the system looks, the least worst-case peak |x|\infty that a causal controller can guarantee against an adversarial pair (a, w) (the value of this game) has, to our knowledge, never been determined for any adaptive control problem with parametric uncertainty of arbitrary size under this criterion; existing theory supplies stability certificates, gain bounds, and regret rates, not the value. That value is \gamma^\star(\Delta) = 1 + \Delta for every \Delta0 . The summand 1 is the irreducible price of the disturbance, and \Delta the exact price of a single, unavoidable identification spike. The optimal policy is certainty-equivalent deadbeat control at the midpoint of the set-membership consistent interval, an instance of the robust oracle \times consistent model chasing architecture. The architecture is forced, not merely sufficient: writing \theta_t := -u_t/x_t exhibits every causal controller as an oracle-selector composition, and optimality pins the selector to the midpoint at the critical histories. The standard tools, classical and modern, each fail quantifiably: probing is punished before it pays, commitment is fatal at sub-disturbance excitation once adaptation is necessary, optimism degenerates to tie-breaking or pays asymptotically at least twice the optimum, and regret certificates are blind to the worst-case peak in both directions. The optimal law contains no exploration mechanism, its learning purely passive. These results give the first exact optimality certificate for consistent model chasing as a design principle for adversarial adaptive control.
[LG-48] Robust Dual-Model Collaborative Random Vector Functional Link Network
链接: https://arxiv.org/abs/2608.13628
作者: A. Quadir,A. Rahaman,Mushir Akhtar,M. Tanveer
类目: Machine Learning (cs.LG)
*备注:
Abstract:Random vector functional link (RVFL) networks are lightweight and fast neural models that offer efficient training and strong generalization through randomized hidden-layer weights and direct input-output connections. However, conventional RVFL models are sensitive to noisy labels, outliers, and imbalanced data, which limits their performance in real-world applications. To address these challenges, we propose the kernel risk-sensitive mean p-power based RVFL (KRPRVFL) model, which integrates the computational efficiency of RVFL with the robustness of the kernel risk-sensitive mean p-power (KRP) criterion. By replacing the standard least-squares objective with a KRP-based loss, KRPRVFL adaptively reduces the influence of corrupted or unreliable samples during training, resulting in improved stability and generalization. Additionally, a collaborative learning mechanism is introduced to enable adaptive interaction among model components, further enhancing robustness in complex and noisy environments. The proposed framework also leverages kernel-induced feature mapping to capture nonlinear relationships without requiring explicit hidden-layer selection, maintaining both efficiency and scalability. Extensive experiments on UCI and KEEL benchmark datasets demonstrate that KRPRVFL consistently outperforms baseline models in terms of accuracy, robustness, and statistical significance, highlighting its effectiveness as a fast, scalable, and reliable solution for challenging classification tasks.
[LG-49] Hard Cases Bad Labels: Testing Error Exposure and Error Location in Uncertainty Sampling Under Bounded Label Noise
链接: https://arxiv.org/abs/2608.13601
作者: John Myron Uy
类目: Machine Learning (cs.LG)
*备注: 14 pages, 5 figures, 4 tables. Code and reproducibility materials: this https URL
Abstract:Active learning can reduce labeling cost by selecting informative examples, but the most uncertain examples may also be the hardest to label correctly. This study tests whether uncertainty sampling fails because it acquires more corrupted labels or because errors concentrated in difficult regions are especially harmful. Margin-based uncertainty sampling is compared with random sampling under clean labels, random classification noise (RCN), and bounded difficulty-dependent noise on three public binary tabular datasets. The design uses 100 paired seeds, nine expected noise rates from 0 to 0.30, annotation budgets from 20 to 120, and logistic regression with regularization re-selected by cross-validation at every budget. An exposure-matched RCN control aligns mean final acquired corruption, while a clean-label extension reaches budget 400. Under clean labels, uncertainty sampling improved normalized balanced-accuracy area under the learning curve by 1.09 to 1.77 percentage points on all datasets. Difficulty-dependent noise reduced this advantage more than RCN at six of eight rates on Breast Cancer Wisconsin, but at no tested rate on Banknote Authentication or MAGIC Gamma Telescope. Exposure-matched analyses found no corrected evidence for a universal additional penalty from structured error location. On clean MAGIC data, uncertainty sampling improved balanced accuracy while reducing average precision and true-positive rate at fixed false-positive rates. Thus, uncertainty sampling was label-efficient, but its apparent robustness depended on dataset, budget, noise structure, and evaluation metric.
[LG-50] Continual Evolution Strategies in Control Tasks GECCO2026
链接: https://arxiv.org/abs/2608.13600
作者: Nicola Pitzalis,Eleni Nisioti,Antonio Carta,Davide Bacciu,Andrea Cossu
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注: Accepted for publication in the GECCO 2026 Companion Proceedings
Abstract:We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without forgetting previous ones. On sequential MuJoCo locomotion tasks, naive ES suffers from severe catastrophic forgetting. Replay substantially improves retention and can induce positive transfer, while larger replay budgets reduce plasticity. Overall, these results show that ES can support continual adaptation in control and that replay is an effective mechanism for mitigating forgetting.
[LG-51] Robust XGBoosting for Regression
链接: https://arxiv.org/abs/2608.13590
作者: Iris Aragón Mladosich,Christophe Croux
类目: Machine Learning (cs.LG); Computation (stat.CO); Machine Learning (stat.ML)
*备注: 30 pages (+15 supplementary), 3 figures
Abstract:XGBoost is a very popular and powerful method for prediction. It iteratively fits simple decision trees to the residuals of the previous step. An efficient and scalable implementation is available. The standard loss function for XGBoost is the quadratic loss, but a Huber loss can also be used. In this paper, we study the robustness of XGBoost and show that its performance can be affected by vertical outliers and leverage points. To address this, we explore alternative loss functions, based on M-, S-, and \tau -estimators from robust regression. Our results indicate that a two-step procedure, referred to as MM-XGBoost, provides the best trade-off between robustness and prediction accuracy.
[LG-52] L-FNO: Lorentzian Fourier Neural Operator for Stochastic Event Dynamics
链接: https://arxiv.org/abs/2608.13562
作者: Songhee Kang,Jihoon Kang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 21 pages, 5 figures (9 pages for the main text)
Abstract:Modern operational systems face uncertainty even in routine conditions, where rare, bursty, and self-exciting events emerge from both exogenous covariates and endogenous event dynamics. Standard neural operators are typically trained as regression-style function-to-function models rather than conditional-intensity estimators, limiting their suitability for sparse event regimes. We introduce the Lorentzian Fourier Neural Operator (L-FNO), a stochastic neural operator that combines an FNO-style covariate path, Lorentzian spectral kernels for history-dependent excitation, and a likelihood-based training objective. We evaluate L-FNO on eight synthetic point-process benchmarks and three real-world datasets covering disease outbreak prediction and semiconductor fault or defect detection. L-FNO improves event likelihood, calibration diagnostics, and rare-event detection over regression- and likelihood-based neural operator baselines. These results show that structured spectral memory and likelihood-based learning provide effective inductive biases for neural operator models of stochastic event dynamics.
[LG-53] Online Inference in Distributional Temporal-Difference Learning
链接: https://arxiv.org/abs/2608.14408
作者: Yang Peng,Liangyu Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 61 pages
Abstract:We study online statistical inference for functionals of the return distribution under a fixed policy. The return distribution is estimated by nonparametric distributional temporal-difference learning from a single Markov trajectory. For the Polyak–Ruppert averaged estimator, we prove that its root- T error converges weakly to a centered Gaussian random element in Cramér space. We also prove that, conditionally on the observed trajectory, the root- T difference between the bootstrap and original averages converges weakly to the same Gaussian limit. These results justify bootstrap inference for smooth statistical functionals, including variance, CVaR, expected shortfall, and expectiles. For nonsmooth statistical functionals, we develop a local asymptotic theory for the estimated return CDF over T^-1/2 -neighborhoods of finitely many thresholds, together with its bootstrap analogue. This theory allows us to conduct inference for nonsmooth statistical functionals characterized by CDF equations, including return quantiles.
[LG-54] Offline Deep Q* Estimation with Diffusion Models
链接: https://arxiv.org/abs/2608.14401
作者: Xiaohong Chen,Yuling Jiao,Lican Kang,Jerry Zhijian Yang,Chen Zhong
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:In offline RL, estimating the optimal action-value function Q^* can be formulated as solving the optimal Bellman equation based solely on offline observations. A fundamental challenge is that the reward function and transition kernel are unknown, so the optimal Bellman operator is not directly observable from data. To address this issue, we propose a novel framework that decouples operator estimation from value function learning. In this approach, we first formulate conditional diffusion models to estimate the reward law and transition kernel, which induces a data-driven approximation of the optimal Bellman operator. We then plug these estimators into the Bellman equation and obtain a deep estimator of Q^* by minimizing the empirical Bellman residual over a neural network function class. Theoretically, we first establish sharp nonasymptotic convergence rates for learning the optimal Bellman operator through an end-to-end analysis of conditional diffusion estimation in total variation distance. We then establish the oracle value-stage rate \widetilde\mathcal O\bigl(n^-\frac2\betad_x+d_a+2\beta\bigr) for the excess Bellman residual risk. Finally, under a concentrability condition, we translate this residual bound into an L^2 convergence rate of \widetilde\mathcal O\bigl(n^-\frac\betad_x+d_a+2\beta\bigr) for the resulting deep estimator of Q^* , where d_x and d_a denote the dimensions of the state and action spaces, respectively, and \beta denotes the Hölder smoothness index of Q^* . Importantly, our theoretical analysis does not rely on completeness assumptions commonly used in deep RL theory. Extensive numerical experiments demonstrate the effectiveness of the proposed method and its strong empirical performance.
[LG-55] Non-Shattering at and Above the Dynamical Temperature in the Spherical Pure p-Spin Model
链接: https://arxiv.org/abs/2608.14369
作者: Taegyun Kim
类目: Probability (math.PR); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG); Mathematical Physics (math-ph)
*备注: 25pages, Comments Welcome!
Abstract:We consider the notion of shattering introduced by Ben Arous and Jagannath for spherical pure p -spin glasses with overlap q . For every p\geq 3 and 0\beta\leq\beta_\mathrmsh§ , we rule out shattering whenever q\leq2^-1/2 or q\sqrt(p-2)/(p-1) . The proof combines a deterministic N+1 bound for disjoint bands in the first range with a general- p sign law showing that their total marked weight has subdominant free energy in the second. A spherical-code bound and Hölder’s inequality give an additional q -dependent obstruction; in particular, they rule out every fixed overlap for 0\beta\leq\sqrt\log2 . For p=3 , the first two ranges already exhaust every fixed q\in(0,1) , so the landscape is not shattered at any T\geq T_\mathrmsh . For p\geq4 , the cases not covered by our criteria are confined to 2^-1/2q\leq\sqrt(p-2)/(p-1) and \sqrt\log2\beta\leq\beta_\mathrmsh§ . In particular, this paper partially resolves Conjecture 1 of the paper above and also suggests new methods to show non-shattering.
[LG-56] Pairton: Iterative Reconstruction of Short-Lived Particles
链接: https://arxiv.org/abs/2608.14278
作者: Andreas Hermansen,Chris Scheulen,Tobias Golling
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)
*备注: 11 pages, 5 figures, to be submitted to Phys. Rev. D
Abstract:We present Pairton, an iterative framework for reconstructing short-lived particles in high-energy collision events. By formulating particle reconstruction as a masked prediction process over graph structures, Pairton learns conditional distributions consistent with a factorised decomposition of decay products and iteratively predicts edges in the adjacency matrix representing particle decay relationships. Leveraging a pairformer-based architecture with dynamically updated pairwise representations, our method incorporates global event consistency. We demonstrate state-of-the-art performance on fully hadronic t\bart decays. Pairton provides a general, flexible paradigm for particle reconstruction and can be readily extended to other topologies, bridging ideas from modern generative modelling and high-energy physics.
[LG-57] Body size predicts how long ant workers live - but not how they age or how they die from heat
链接: https://arxiv.org/abs/2608.14245
作者: Alana Moscardi,Rafael da Silva,Gleycon Silva
类目: Populations and Evolution (q-bio.PE); Machine Learning (cs.LG)
*备注: 6 figures, 10 main-text tables, and 5 supplementary tables
Abstract:In social insects, mortality risk comprises distinct components that may not share the same predictors: lifespan duration, senescence trajectory, and thermal vulnerability. We tested these three axes in 18 Australian ant species using paired field-laboratory survival assays (2,363 cohort-day observations; 1,148 workers). Body size predicted duration (Cox HR = 0.67, p = 0.002), while colony size (p = 0.60) and the size x temperature interaction (p = 0.72) showed no detectable moderating effect. A weak but significant size x foraging-rate interaction was detected (LRT p = 0.014), suggesting that intrinsic physiology remains the most parsimonious explanation for the main size-longevity pattern, although ecological context may contribute. Senescence trajectory was associated with circadian niche rather than size: it was steepest in matinal species (Kruskal-Wallis p = 0.009; matinal vs. crepuscular p = 0.002) and was uncorrelated with body mass (Spearman p = 0.32). Thermal hazard plateaued above 20 degrees C (Delta AIC = -38; p 0.001), with elevated thermal sensitivity in Rhytidoponera (Ectatomminae) above the plateau (5% per degree C, p = 0.015). Circadian regime and lineage identity, not body size, therefore emerge as the most climate-relevant axes, although they are strongly collinear (Cramer’s V = 0.85). These results show that body size captures only one dimension of mortality risk and that size-based vulnerability indices may misrank taxa when senescence and thermal sensitivity are decoupled from body size.
[LG-58] Classical Limits of Spectral Filtering in Quantum Generative Models
链接: https://arxiv.org/abs/2608.14169
作者: Marco Roth
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 12 (main text) + 8 pages (appendix and references), 3 figures
Abstract:Spectral filtering has been proposed as a route to regularization in quantum generative models: the quantum Fourier transform exposes the amplitude spectrum of a quantum circuit Born machine, and a diagonal filter suppresses the high frequencies associated with finite-sample noise, an operation whose classical counterpart seemingly requires manipulating an exponentially long amplitude vector. We examine whether this coherent operation produces anything that classical post-processing of samples from the unfiltered model cannot match. Measuring the filter against convolution with a symmetric probability kernel at matched sampling cost, which accounts for the post-selection overhead of attenuation, we derive necessary and sufficient conditions for the gap between the two to vanish. Magnitude (attenuating) filters obey a dichotomy: at a fixed affordability threshold, the filtered output is either a constant-size Fourier object with an efficient classical sampler, or the passband must widen until no fixed frequency is attenuated and the filter no longer smooths. In neither case does the filter create a quantum-classical separation. Whatever separation survives is inherited from the spectral phase of the input state. Numerical experiments on trained circuit Born machines confirm the classification and show that the deciding phases are invisible to the Born-rule training loss and set by the initialization. Within the diagonal family, pure phase filters remain the only spectral operations exempt from these constraints.
[LG-59] Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis
链接: https://arxiv.org/abs/2608.13937
作者: Yicheng Kang,Yuling Jiao,Xin Geng,Mahesh Nagarajan
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Smart manufacturing processes are often installed with a large number of sensors, imaging devices and computers, which not only enable instant communication across various modules of a production system but also aid in intelligent manufacturing management. In this paper, we introduce MODERN, a deep learning framework for quality monitoring and fault isolation, which integrates these enhanced capabilities into the practice of industrial quality control. Using the architecture of an inception residual neural network, we develop a control chart that monitors the likelihood of a product containing defects. We also propose a faulty region estimator that identifies the defective area using transfer learning. To extend our framework to cases where there are not sufficient training data, we suggest a transfer monitoring technique that requires only a small sample size and a hypothesis testing approach for quantitatively assessing the applicability of our method. Theoretically, we establish the minimax optimal convergence rate for both our defect likelihood estimation and fault diagnosis. Our results lead to a seemingly counter-intuitive managerial implication - it may not always be in a manufacturer’s best interests to keep upgrading its monitoring equipment regardless of the cost. Empirically, we demonstrate the superior performance of our method in comparison with a state-of-the-art approach using both simulated experiments and real data.
[LG-60] SPEAR: Structure Property Explainability with Attention Regularization
链接: https://arxiv.org/abs/2608.13826
作者: Aditya Raghavan,Utkarsh Pratiush,Dalton A. Pearl,Jade Holliman Jr,Katharine Page,Philip D Rack,Sergei V Kalinin
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注:
Abstract:Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions. Although attention mechanisms are frequently treated as inherently explainable, unregularized attention can yield unstable, fragmented, or intensity driven attribution patterns that obscure the physical origin of these relationships. Here we introduce SPEAR (Structure Property Explainability with Attention Regularization), a framework that constrains attention distributions during training to improve their stability, selectivity, and physical interpretability. SPEAR augments attention based regression with a learnable temperature that controls attention concentration and a smoothness penalty that enforces coherence across neighboring spectral positions, treating attention as a learnable explanatory object rather than a post hoc visualization. Using synthetic spectral benchmarks with known generative structure, we show that attention regularization produces smooth, contiguous attribution profiles aligned with causal features while preserving predictive accuracy. Applied to experimental X ray diffraction data from a combinatorial rare earth zirconate thin film library, the regularized model selectively emphasizes physically relevant diffraction features and decouples feature importance from raw peak intensity. The reflection it identified prompted a reassessment of our earlier structural analysis, revealing a correlation between the 220 peak position, the tetragonal distortion that accommodates cation size disorder, and the local thermal conductivity. Attention regularization therefore provides a principled training constraint for explainable structure property regression, yielding mechanistically meaningful explanations without sacrificing predictive performance.
[LG-61] rajectory Dynamics in Self-Supervised Learning Latent Space for Audio Deepfake Detection
链接: https://arxiv.org/abs/2608.13817
作者: Tomás Andrade Weber
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
*备注: 5 pages, 1 figure
Abstract:Human speech production is constrained by physiology, giving rise to characteristic temporal structure on acoustic signals. We hypothesise that these constraints manifest as structured trajectory dynamics in the latent space of Self-Supervised Learning (SSL) models, and that synthetic speech violates them detectably. To test this hypothesis, we train a causal Long Short-Term Memory (LSTM) next-frame predictor on bonafide speech only (Stage 1), using the deepfake-specialised SSL backbone Wav2Vec2-Large-AntiDeepfake, and compare against a static global-average-pooling baseline using identical features, thus isolating the contribution of temporal modelling. A supervised Stage 2, which trains a Multi-Layer Perceptron on the frozen LSTM internal states using labelled data, is included to characterise the role of spoof supervision. Our system achieves competitive or state-of-the-art performance across six benchmarks: ASVspoof 2019/2021, Codecfake, In-the-Wild, MLAAD-EN, and Deepfake-Eval-2024, including best published EER on ASVspoof 2021 (0.75%) and, notably, Stage 1 trained on bonafide speech only surpasses the published supervised baseline from the same backbone on DE2024 (30.35%). On near-domain benchmarks, static and dynamic approaches perform comparably. On harder cross-corpus benchmarks with diverse synthesis methods, trajectory dynamics provide substantial gains, confirming that temporal physiological constraints carry detection signal beyond utterance-level statistics.
[LG-62] On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization
链接: https://arxiv.org/abs/2608.13793
作者: Tyler R. Johnson,Kian Ben-Jacob,Christopher P. Muller,Ramin Bostanabad
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注: 26 pages, 10 figures, 2 tables
Abstract:Machine learning (ML) has become an indispensable part of modern engineering design workflows. A crucial step in training an ML model is the selection of the loss function which can be systematically formulated via various techniques such as maximum likelihood estimation (MLE) and cross-validation . While MLE is one of the most popular, effective, and intuitive mechanisms for training ML models, it is brittle: if the assumptions underpinning it are not met, the trained ML model may generalize poorly. This brittleness affects even Gaussian processes (GPs) which are widely used in engineering design and are often (incorrectly) presumed to be very robust to overfitting. In this paper, we fundamentally evaluate the brittleness of MLE in the context of training GPs for probabilistic regression or classification tasks. We compare theoretically grounded metrics against MLE and propose practical solutions. Our extensive studies demonstrate the effectiveness of our solutions in downstream design tasks such as Bayesian optimization and provide a blueprint for practitioners to build accurate and robust GPs that can even outperform tabular foundation models in terms of prediction accuracy, uncertainty quantification, and inference cost. Our contributions are publicly available via GitHub at this https URL.
[LG-63] Unknown Unknowns: Model Misspecification in Machine Learning for Physics
链接: https://arxiv.org/abs/2608.13633
作者: Juan Cruz-Martinez,Carolina Cuesta-Lazaro,Alexander Held,Michael Kagan
类目: Data Analysis, Statistics and Probability (physics.data-an); Cosmology and Nongalactic Astrophysics (astro-ph.CO); Astrophysics of Galaxies (astro-ph.GA); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); High Energy Physics - Phenomenology (hep-ph)
*备注: 25 pages, 2 figures, part of the VERaiPHY initiative
Abstract:Machine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed on real data, raising the question not just of whether they fit, but of whether they are wrong in ways we did not anticipate: the unknown unknowns. This challenge of model misspecification is not unique to machine learning. In physics, misspecification is sometimes exactly what we want to find: new discoveries appear as failures of existing models. At other times, we want such effects absorbed into the analysis without biasing the measurement. A robust analysis is one that absorbs the misspecifications we are not interested in, while preserving sensitivity to the ones we are. Machine learning can both amplify misspecification and provide new tools to address it. We discuss the challenges of model misspecification, diagnostics for detecting it, and strategies for mitigation. No single diagnostic can confirm that a model is correctly specified: detection and mitigation are two halves of an iterative loop, in which a battery of complementary diagnostics is applied, the model is updated, and the process repeated. Robustness against unknown unknowns is ultimately less about any single technique than about a disposition: a willingness to suspect one’s own model, and to design analyses that can survive being wrong in ways one did not anticipate.
[LG-64] Learning Unsteady Aneurysm Hemodynamics with Physics-Informed DeepONets
链接: https://arxiv.org/abs/2608.13629
作者: Oscar L. Cruz-Gonzalez,Valérie Deplano,Badih Ghattas
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注:
Abstract:Clinically actionable, patient-specific hemodynamic assessment, specifically wall shear stress, vortex structure and pressure distributions, is critical for determining risky or unfavorable evolution in Abdominal Aortic Aneurysms (AAA). While Physics-Informed Deep Operator Networks (PI-DeepONets) show promising results in complementing established 5 tools such as Computational Fluid Dynamics (CFD), a persistent architectural challenge remains for complex 3D flows. In this direction, we propose a Modified Multi-Input Multi-Output PI-DeepONets (M3PI-DeepONet) designed for predicting unsteady flows in an idealized AAA geometry. Central to our model is the Aggregated Injection strategy, where latent representations from multiple input branches are fused prior to trunk injection, allowing the coordinate basis to adapt to multiple physical constraints. To the best of our knowledge, this is the first architecture to combine the layer-wise gating mechanism with a multi-branch operator-network topology, yielding an input-adaptive trunk basis. Additionally, we integrate the 3D Navier-Stokes equations as governing physical laws, so the model is trained based on physics-informed residuals, initial and boundary conditions, and only 0.3% of the labeled internal data together with the selected branch-conditioning signals. The M3PI-DeepONet simultaneously predicts unsteady 3D flow velocity and pressure fields with an average relative L2 velocity error below 4% and pressure error around 5% while achieving a conservative retained-cycle inference speedup of approximately 36x compared to reference CFD simulations once the branch inputs used for conditioning are available. This work advances the application of deep learning in cardiovascular disease modeling, marking step toward real-time, non-invasive clinical diagnostics.
[LG-65] VoiceDesigner: Text-to-Voice Generation and Editing via Unified Diffusion Modeling and Data Augmentation
链接: https://arxiv.org/abs/2608.13613
作者: Jiarui Hai,Karan Thakkar,Ke Chen,Yunyun Wang,Jiaqi Su,Rithesh Kumar,Mounya Elhilali,Zeyu Jin
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)
*备注:
Abstract:Recent breakthroughs in generative models have made text-to-voice generation (TTV) possible, enabling the synthesis of speech directly from textual voice descriptions. However, existing systems face two key challenges. First, they struggle to generate a diverse range of voices, spanning real-world human speakers and fictional characters. Second, they lack robust and flexible voice editing capabilities, such as voice cloning and the ability to modify attributes like emotion and tone. In this paper, we propose VoiceDesigner, a unified framework for text-to-voice generation and editing that supports diverse and controllable voice design. To tackle the above challenges, we propose solutions from two perspectives. First, we develop a hybrid data pipeline that leverages digital signal processing techniques and speech generation models to construct a diverse voice dataset covering both real-world and fictional voices. Second, we introduce a diffusion transformer with architectural improvements to better handle complex conditioning and enhance multi-task performance, enabling unified voice generation and editing. Through subjective and objective evaluations, VoiceDesigner achieves superior prompt alignment with both voice descriptions and editing instructions, while maintaining competitive perceptual quality and voice usability compared to state-of-the-art TTV models.
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