本篇博文主要内容为 2026-07-17 从Arxiv.org论文网站获取的最新论文列表，自动更新，按照NLP、CV、ML、AI、IR、MA六个大方向区分。
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目录
概览 (2026-07-17)
今日共更新620篇论文,其中:
- 自然语言处理共92篇(Computation and Language (cs.CL))
- 人工智能共205篇(Artificial Intelligence (cs.AI))
- 计算机视觉共115篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共143篇(Machine Learning (cs.LG))
- 多智能体系统共22篇(Multiagent Systems (cs.MA))
- 信息检索共17篇(Information Retrieval (cs.IR))
- 人机交互共22篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
【速读】：该论文旨在解决精神健康研究中构建可解释人工智能（XAI）系统时，标注质量低下这一关键瓶颈问题。具体而言，现有抑郁症相关数据集的标签往往缺乏结构化证据支持、症状层面的合理依据，以及与《精神障碍诊断与统计手册》第五版修订版（DSM-5-TR）诊断标准之间可追溯的对齐，从而严重限制了模型的透明性与可解释性。为此，论文提出一种自演进、专家参与闭环的重度抑郁障碍（MDD）标注框架，其核心解决方案是将大语言模型（LLM）辅助标注与专家验证相结合，通过三阶段流程实现：从文本记录中候选证据的选择、基于DSM-5-TR标准的准则级分析，以及生成标签级诊断与严重程度注释的病例级综合。该框架引入双记忆架构（双记忆架构由示例记忆和反思记忆构成），能够内化专家反馈并迭代优化后续标注过程，而无需重新训练模型。此外，系统输出不仅包含最终标签，还包括临床证据、推理轨迹与编辑历史，实现了全面可审计性。初步试点研究显示，该方法在提升标注一致性与可解释性的同时，显著降低了人工修正的工作量。
链接: https://arxiv.org/abs/2607.15202
作者: Hoang-Loc Cao,Van Pham,Truong Thanh Hung Nguyen,Phuc Truong Loc Nguyen,Phuc Ho,Veronica Whitford,Hung Cao
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA); Multimedia (cs.MM)
备注: Accepted at IEEE International Conference on Omni-Layer Intelligent Systems (COINS) 2026
Abstract:Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability. We propose a self-evolving, expert-in-the-loop annotation framework for Major Depressive Disorder (MDD) that combines large language model (LLM)-assisted labeling with expert verification. The framework is intended to support the construction of explainable, DSM-5-TR-aligned datasets rather than to perform clinical diagnosis. It operates in three stages: candidate evidence selection from textual records, criterion-level DSM-5-TR analysis, and case-level synthesis that produces label-level diagnostic and severity annotations. A dual-memory architecture, composed of Example Memory and Reflection Memory, is designed to internalize expert feedback and iteratively improve future annotations without retraining. We describe this mechanism and leave its evaluation across multiple feedback cycles to future work. In addition to final labels, the framework exports clinical evidence, reasoning traces, and edit histories, enabling comprehensive auditability. In a pilot study using expert-reviewed samples, the proposed approach improves annotation consistency and explainability while reducing manual revision effort.
[MA-1] Stigmergic Graph Memory: An Environment-Aware Approach for Many-to-Many Multi-Agent Pickup and Delivery
【速读】：该论文旨在解决自动化分拣仓库中多对多多智能体取送货（Many-to-Many Multi-Agent Pickup and Delivery, MAPD）场景下的动态任务分配与路径规划问题，尤其针对请求仅指定库存单位（Stock-Keeping Unit, SKU）而非固定起点和终点时，如何高效选择代理、源点与目的地以避免拥堵并提升整体吞吐量。现有基于图引导的方法通常在目标点确定后才影响路径规划，未能将实时交通状态融入端点选择过程，导致决策缺乏对近期执行信号的响应能力。为此，论文提出信息素式图记忆（Stigmergic Graph Memory, SGM），这是一种具有边界限制和衰减特性的记忆层，能够记录仓库节点及有向边上的近期执行信号，从而在不改变碰撞约束或规划器有效性的前提下，对可行的端点和路径偏好进行动态排序。其关键在于通过引入可衰减的环境记忆机制，使端点实例化过程能够感知并响应最近的交通状况，进而优化进入规划器的可行目标集合。实验结果表明，在五种布局、三种负载水平及每组条件25个随机种子下，SGM在所有15种地图-负载组合中均优于两种重构的多对多分配基线方法，平均吞吐量提升达20.5%–36.7%，验证了近期执行记忆在塑造规划输入而非仅优化路径方面的重要作用。
链接: https://arxiv.org/abs/2607.15182
作者: Aditya Dutta,Joon-Seok Kim
机构: 未知
类目: Multiagent Systems (cs.MA); Robotics (cs.RO)
备注: 16 pages total: 7 pages main text, 2 pages references, and 7 pages appendix
Abstract:Automated fulfillment warehouses must continuously assign and execute pickup-and-delivery work while avoiding congestion. In many-to-many Multi-Agent Pickup and Delivery (MAPD), a request specifies a stock-keeping unit rather than fixed endpoints, requiring the controller to select an agent, source, and destination before path planning. Existing graph-guidance methods primarily influence routing after goals are fixed, leaving endpoint instantiation uninformed by recent traffic. We introduce Stigmergic Graph Memory (SGM), a bounded, decaying memory layer that records recent execution signals on warehouse nodes and directed edges to rank feasible endpoints and route preferences without altering collision constraints or planner validity. Across paired request streams on five layouts, three load levels, and 25 seeds per condition, SGM outperforms two reconstructed many-to-many allocation baselines in all 15 map-load conditions, with paired throughput gains of 20.5-36.7%. These results show that recent execution memory can improve warehouse throughput by shaping which feasible goals enter the planner, not only how agents travel to already fixed goals.
[MA-2] Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents ECML KDD2026
【速读】：该论文旨在解决政治联盟形成过程中，如何在保持政策主张与意识形态一致性的同时，实现对政党间谈判动态的可解释性建模问题。现有大型语言模型（LLM）虽为计算政治科学提供了新工具，但其通过人类反馈强化学习（RLHF）训练所引入的中立性与助人偏见，难以真实模拟政党固有的排他性立场和激进议程。为此，论文提出一种多智能体框架，其核心在于融合监督微调（SFT）、直接偏好优化（DPO）与检索增强生成（RAG）：DPO用于构建具有强烈党派特征的代理人格，而各政党专属的RAG管道则确保代理行为始终锚定于其官方纲领文本。研究以2019年弗拉芒地区选举为例，将这些党派代理置于由“组阁人”主导的中心辐射型谈判结构中。为提升结果可解释性，引入多层信息传承拓扑（MILT），可追溯协议条款至其纲领来源，并将其分类为五种出处状态；同时提出联盟影响力评分（CIS），量化各政党对最终协议的实际塑造程度；并通过现实基础验证步骤，将模拟条款与历史已达成的联合协议进行比对。三次独立仿真均稳定产出相同胜者排名（N-VA领先于CD&V和Open Vld），且基于纲领溯源的信息传承能有效预测实际政策落地，而虚构内容则无此能力。因此，该框架实现了对政党兼容性与组阁人调和过程的透明、可扩展的前瞻性模拟，为政治协商的计算建模提供了可靠实验平台。
链接: https://arxiv.org/abs/2607.15095
作者: Dylan Van Mulders,Matthias Bogaert,Dirk Van den Poel
机构: Ghent University (根特大学); CVAMO Core Lab (CVAMO核心实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 11 pages, 2 figures, To be published in the Post-Workshop proceedings of the ECML PKDD 2026 Conference
Abstract:The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions. While Large Language Models (LLMs) open new avenues for computational political science, the neutrality and helpfulness biases instilled by Reinforcement Learning from Human Feedback (RLHF) prevent them from sustaining steadfast partisan behaviour. We present a multi-agent framework that reconciles factual grounding with ideological alignment by combining Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Retrieval-Augmented Generation (RAG): DPO instils aggressive party-specific personas, while a per-party RAG pipeline keeps each agent bounded to its official manifesto. We operationalize the framework on the 2019 Flemish election, deploying the partisan agents in a hub-and-spoke negotiation arbitrated by a formateur. To make the emergent negotiation interpretable, we introduce a Multi-Layered Information Lineage Topology (MILT) that traces every clause in the final agreement back to its manifesto origin and classifies it into five provenance states, a Coalition Influence Score (CIS) that aggregates these traceable contributions to identify which party shaped the agreement, and a real-world grounding pass that benchmarks each simulated provision against the historically adopted coalition agreement. Across three independent simulations the framework yields a stable winner and ranking (N-VA ahead of CD\V and Open Vld), and manifesto-anchored lineage reliably predicts real-world materialization whereas hallucinated content does not. The result is a transparent, scalable testbed for the ex-ante exploration of party compatibility and formateur-mediated compromise.
[MA-3] StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
【速读】：该论文旨在解决现有生成式人工智能在结构工程任务中因仅关注单一问答或脚本生成而忽视完整证据链验证的问题，导致模型可能生成看似合理但实际流程不完整、内部不一致或不可执行的输出。其核心解决方案是提出一个以产物为中心（artifact-centered）的工作台——StructureClaw，通过受控的工程技能、类型化工具、共享产物状态以及本地分析后端，使大语言模型（LLM）代理能够协同完成从需求解析、可计算模型构建、验证记录、求解器输出到规范检查与最终报告生成的全流程。同时，研究构建了StructureClaw-Bench，一个包含150个受控场景的可执行基准测试集，涵盖标准工作流执行、交互鲁棒性及多模态结构模型重建。评估要求所有产物级与执行级断言在单次运行中均通过才算成功。实验表明，在10种代理-模型配置下，平均成功率从通用技能基线的56.8%提升至全自动化工作流的88.6%。此外，交互式与多模态评估揭示了两个关键挑战：对无效数值输入的安全处理能力不足，以及结构模型修复时支撑边界条件的一致性问题。结果表明，以产物为中心的评估机制能有效暴露仅凭最终输出难以察觉的工作流层级缺陷，为结构工程智能体的评估与优化提供了更严格的基准。
链接: https://arxiv.org/abs/2607.14896
作者: Sizhong Qin,Yi Gu,Yao Jiang,Ao Cai,Changjian Zhou,Shaoxuan Shuai,Jiachang Wang,Tianhao Shen,Yueqiang Li,Xinhao Li,Li Zeng,Yueshi Chen,Dachen Gao,Genrong Xu,Wenjie Liao,Xinzheng Lu
机构: Google(谷歌); Stanford University (斯坦福大学)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 21 pages, 13 figures
Abstract:Addressing a structural-engineering request requires more than a single answer; it requires a chain of interdependent artifacts: interpreted requirements, a computable model, validation records, solver outputs, code-check records, and a final report. Evaluations centered on question answering or script generation rarely verify this complete evidence chain and may therefore reward fluent outputs even when the underlying engineering workflow is incomplete, internally inconsistent, or non-executable. To address this limitation, we present StructureClaw, an artifact-centered workbench in which LLM agents operate through governed engineering skills, typed tools, shared artifact state, and local analysis backends. We also introduce StructureClaw-Bench, an executable benchmark of 150 controlled scenarios spanning standard workflow execution, interactive robustness, and multimodal structural-model reconstruction. A scenario succeeds only when all required artifact- and execution-level assertions pass in a single run. Across ten agent-model configurations, each evaluated on the same 50 standard cases, the average Success Rate rises from 56.8% with the generic-skill baseline to 88.6% with the full automatic workflow. The interactive and multimodal evaluations identify two prominent remaining challenges: safe handling of invalid numerical inputs and fixture-consistent reconstruction of structural models. These findings show that artifact-centered evaluation can expose workflow-level failures that are difficult to identify from final responses alone, providing a more rigorous basis for evaluating and improving structural-engineering agents. The code and benchmark are available at this https URL.
[MA-4] he Energy Society: A Simulation Environment for Studying Agent Cooperation under Survival Pressure
【速读】：该论文旨在解决在多智能体环境中，当生成式AI（Generative AI）的推理成本直接关联到智能体生存时，竞争性与合作性激励如何影响智能体涌现行为的问题。其核心挑战在于理解模型规模带来的计算开销与资源获取机制之间的动态平衡，以及不同激励结构对集体协作或个体自利行为的引导作用。解决方案的关键在于构建“能源社会”（The Energy Society）这一紧凑的生存经济模拟框架：智能体根据模型规模消耗能量以生成文本，通过完成任务或接收捐赠恢复能量，能量耗尽则退出运行；在此机制下，系统量化了不同目标设定（如竞争、合作及基线设置）对行为模式的影响。实验表明，合作激励显著改变行为策略，促使智能体主动捐赠以激活他人，甚至牺牲自身生存，并引发任务分配的优化调整；消融研究进一步揭示，智能体间动作推荐机制促进协调与高风险任务承担，而记忆功能帮助其基于历史经验校准风险偏好。相比之下，竞争环境下智能体较少采取直接破坏行为，但表现出更隐蔽的利己倾向。该框架为研究令牌成本与群体激励在生存压力下的交互提供了可复现的实验平台。
链接: https://arxiv.org/abs/2607.14865
作者: Lucas Bergholdt Hansen,Federico Torrielli,Filippo Tonini,Lukas Galke Poech
机构: University of Southern Denmark (南丹麦大学); University of Turin (都灵大学)
类目: Multiagent Systems (cs.MA); Computation and Language (cs.CL)
备注: Accepted at AITC 2026
Abstract:LLM-based agents are increasingly deployed in multi-agent environments whose incentives can shape their behavior. We introduce The Energy Society, a minimal survival economy for studying how competitive and cooperative incentives affect emergent behavior when inference cost is directly tied to survival: Agents spend energy based on model size when generating tokens, regain energy by completing jobs or receiving donations, and deactivate if their energy reaches zero. We compare competitive and cooperative objectives against a baseline setting and several control variants. Across experiments, larger models consistently consume the most energy and spend more energy than they gain, even in those settings where token cost is not size-dependent. Cooperative incentives substantially alter behavior: agents donate to reactivate others, sometimes at the cost of their own survival, and job allocation changes. Ablations reveal that allowing agents to recommend actions to each other supports coordination and ambitious job selection, while memory helps agents calibrate risk from past outcomes. Agents rarely choose direct sabotage, but show more subtle signs of self-serving behavior in the competitive setting. The Energy Society is a compact testbed for studying the interaction between token costs and group incentives under a survival pressure. Source code is available at this https URL
[MA-5] Randomized routing strategies of fleets of CAVs may prove market efficient
【速读】：该论文旨在解决未来城市中自动驾驶车辆（CAV）与人类驾驶车辆（HDV）共存背景下，如何设计市场结构以实现城市整体交通目标的问题。核心挑战在于，当多个竞争性车队运营商依据自身市场份额最大化目标进行调度时，可能产生与社会福利相悖的策略行为。其解决方案的关键在于：在传统基于市场份额的激励机制基础上，引入系统平均出行时间作为优化目标，通过双重目标约束来抑制车队运营商采取随机化路由策略所导致的不可预测性，从而引导市场竞争向促进社会福利的合作模式演进。研究表明，当人类驾驶员对CAV的接受度存在显著差异时，随机化路由虽能提升局部效率，但会损害整体系统性能；而融合系统平均出行时间的改进型市场设计可有效规避此类“反社会”策略，推动更优的协同调度。
链接: https://arxiv.org/abs/2607.14859
作者: Grzegorz Jamróz,Łukasz Gorczyca,Rafał Kucharski
机构: Jagiellonian University (亚捷隆大学); Faculty of Mathematics and Computer Science (数学与计算机科学学院)
类目: Multiagent Systems (cs.MA)
备注: 13 pages, 9 Figures, accepted to 14th Symposium of the European Association for Research in Transportation (hEART 2026)
Abstract:In future cities every driver may own a vehicle which could be either independently driven (HDV), or autonomously routed and piloted (CAV). The autonomous operations could be handled by a few competing companies. What is the market structure which would make this market aligned with city goals? In this paper we discuss a variant of the emerging market of collectively routed fleets of CAVs, where revenue for fleet operators is proportional to market share. We provide benchmark scenarios to compare the routing algorithms. We present several routing algorithms and demonstrate that, when the attitudes of human drivers towards CAVs exhibit significant diversity, randomised CAV routing, resulting in unpredictable travel times for HDVs, is more efficient than routing proportional to system optimum/user equilibrium. Based on this, we propose to improve the design of the market by augmenting the market-share objective with mean systemwide travel time in order to limit antisocial randomised strategies of fleet operators and drive the competition towards social welfare oriented cooperation.
[MA-6] Modeling and Validation of Quality of Control for Edge-Offloaded Collaborative Navigation
【速读】：该论文旨在解决复杂环境下协作控制因随机无线时延与可靠性波动带来的挑战，此类网络不确定性会严重影响导航、跟踪及避障性能，并导致能量效率下降，甚至引发资源过度配置。其核心解决方案在于将先前的质量控制（Quality of Control, QoC）框架扩展至实际机器人模型，通过三方面实现：(i) 建模端到端网络效应对于闭环控制性能的影响；(ii) 系统性分析不同控制参数对网络时延-可靠性特性的调控作用；(iii) 在私有5G测试平台上，针对多种时延、可靠性及控制配置组合开展实验验证。研究结果揭示了适用于实际机器人的最优控制-通信协同设计工作区，并在真实场景下对比了标准ROS 2质量服务（Quality of Service, QoS）策略的QoC表现，表明在特定条件下，RELIABLE QoS相较于BEST-EFFORT可实现51.5%的QoC提升，凸显了可靠通信机制在保障协作控制性能中的关键作用。
链接: https://arxiv.org/abs/2607.14853
作者: Neelabhro Roy,Mikael Hammarling,Victor Nan Fernandez-Ayala,Gourav Prateek Sharma,Mani H. Dhullipalla,Dimos V. Dimarogonas,James Gross
机构: Unknown
类目: Robotics (cs.RO); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注: Accpeted in IEEE VTC-Fall 2026
Abstract:Collaborative control in complex environments is severely challenged by stochastic wireless delay and reliability variations, which can degrade navigation, tracking, and collision avoidance. These network-induced uncertainties complicate the maintenance of energy efficiency during collaborative tasks, and can potentially lead to over-provisioning of resources. In this paper, for a navigation setup with dynamic collision avoidance, we address this challenge by expanding the quality of control (QoC) framework from prior works to practical robotic models. Our approach (i) models end-to-end network effects on closed-loop performance, (ii) systematically explores the impact of various control parameters dictating robotic motion on network latency-reliability (iii) validates these models through experiments on a private 5G testbed across varying delay, reliability and control configurations. Our analysis indicates the optimal control-communication co-design operating regimes for practical robots and also compares the QoC performance of standard ROS~2 quality of service (QoS) policies under real-world conditions and showing how RELIABLE QoS offers 51.5% better QoC than BEST-EFFORT under certain experimental settings.
[MA-7] Multi-Scale Equilibrium under Variable Indicator Dimensionality: Faithful Reduction of Dynamic Attractors in Urban Mobility Systems
【速读】：该论文旨在解决城市交通系统在高维指标空间中进行均衡分析时，因城市间及突发事件背景下数据可得性差异显著而导致的建模失配问题。其核心解决方案是提出一种动态多层均衡吸引子框架，该框架包含快速性能层（响应指标依赖的目标）与慢速战略层（协同实现交通、出行方式与学习的固定点），并通过基于扰动后-基线性能比的统计决策规则对系统抗脆弱性（antifragility）进行分类。关键创新在于揭示了在何种条件下，低维指标投影仍能忠实反映该多层均衡结构，具体包括：吸引子精确与近似可投影性的条件及其显式误差界；耦合双层固定点在收缩边界内的保全性；任意可观测指标支持下的保留费希尔信息量与决策能力；以及仅导致恢复时间低估的一侧偏差（one-sided restoration-time bias）。通过三个典型试点城市的仿真验证，结果表明，在指标目录允许的前提下，仅需两个可观测通道即可有效支持目标分类。该框架为城市管理部门提供了决定必须维持哪些关键指标的理论依据。
链接: https://arxiv.org/abs/2607.14815
作者: Ali Ghoroghi,Yacine Rezgui,Afrouz Ghaemi,Cristina De Nardi,Andrei Hodorog
机构: 未知
类目: Multiagent Systems (cs.MA)
备注: 35 pages, 6 figures, 6 tables
Abstract:Equilibrium analysis of urban mobility systems is formulated in a high-dimensional indicator space, whilst data availability varies sharply across cities and disruption contexts. This paper gives a formal treatment of that mismatch. It presents a dynamic multi-layer equilibrium attractor for disrupted urban mobility, in which a fast performance layer relaxes towards an indicator-dependent target, a slow strategic layer supplies a joint traffic, modal and learning fixed point, and antifragility is classified through a statistical decision rule on the post-to-baseline performance ratio. It then characterises when a lower-dimensional indicator projection is faithful to this equilibrium structure, establishing four results: conditions for exact and approximate projectability of the attractor with an explicit error bound; preservation of the coupled two-layer fixed point up to a contraction boundary; the retained Fisher information and decision power of any indicator support under a measurement model on observable urban indicators; and a one-sided restoration-time bias, whereby reduced monitoring can only understate recovery duration. A simulation study on three stylised pilot-city configurations verifies each result, and shows that two observable channels suffice for the candidate classification target where the indicator catalogue permits. The framework gives city authorities a principled basis for deciding which indicators must be maintained.
[MA-8] Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition
【速读】：该论文旨在解决传统文本摘要研究主要聚焦于单向信息（如新闻文章、报告）而忽视对话中多参与者间交互与情感动态的问题。对话作为一种复杂的多主体互动沟通形式，其语义与情感演变具有显著的时序性和交互性，现有方法难以有效建模。为此，论文提出一种基于改进的分层链式代理（hierarchical Chain-of-Agents）框架的对话摘要方法，通过融合多模态对话输入，显式建模语义与情感动态。其关键在于从两个维度对对话进行分解：一是基于所有参与者话语的主题段落（topic segments），二是基于每个参与者的发言片段（participant-specific utterance segments），并在此基础上生成对应层级的摘要，同时融合自动推断的情感信息。最终，将主题级与参与者级摘要聚合为综合摘要，以捕捉对话的语义内容与情感演化轨迹。为更全面评估摘要质量，论文引入情感轨迹度量（emotion trajectory metrics），以衡量摘要对情感流动性的保留能力。实验表明，该框架在小规模语言模型上即可生成兼具语义准确性和情感连贯性的对话摘要，验证了其在缺乏显式情感标注场景下的有效性，揭示了利用语言模型进行对话分析的新潜力。
链接: https://arxiv.org/abs/2607.14769
作者: Linyun Xiang,Mark Neerincx,Stephanie Tan
机构: Delft University of Technology (代尔夫特理工大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Existing text summarization research has focused much on monologic information (e.g., newspaper articles, reports) without accounting for the interaction between speakers or authors. In contrast, dialogues are a rich communication channel where multiple participants conduct back and forth exchanges to construct meaning. We propose a dialogue summarization framework that explicitly models both semantic and emotion dynamics using multimodal dialogue inputs, built on an adapted hierarchical Chain-of-Agents approach. We decompose dialogues from two perspectives: (1) topic segments based on the utterances of all participants, and (2) participant-specific utterance segments. These are used to generate corresponding summaries while incorporating automatically inferred emotions. Topic- and participant-level summaries are aggregated into a dialogue summary capturing semantic content and emotion trajectories. To evaluate beyond content accuracy, we introduce emotion trajectory metrics measuring how well summaries preserve emotional flow. Experiments with small language models on multimodal dialogue datasets show that our framework produces summaries with both semantic and emotion content. Further experiments on explicit emotion label availability highlight the efficacy of our proposed methodology and the opportunities in dialogue analysis using language models.
[MA-9] Bad Memory: Evaluating Prompt Injection Risks from Memory in Agent ic Systems
【速读】：该论文旨在解决基于记忆的智能体系统（memory-based agentic systems）中因持久化存储而引入的新型提示注入攻击（prompt injection attack）问题。随着智能体通过记忆文件、行为偏好和知识库在会话间保持状态，恶意指令可被嵌入持久化文件中，并在后续会话中持续影响智能体行为，从而构成严重安全威胁。论文的关键解决方案在于构建一个受控的合成工作空间（sandboxed synthetic workspace），系统性评估不同智能体系统（Anthropic Claude Code 与 OpenAI Codex）及多个模型（Claude Haiku 4.5、Claude Opus 4.7、GPT-5.2、GPT-5.5）在面对持久化攻击时的脆弱性。研究发现，尽管直接通过外部不可信内容覆盖智能体自身记忆文件存在难度，但已植入记忆文件中的攻击载荷仍能成功影响当前及未来会话，且攻击成功率与载荷持久性在不同系统、模型、攻击目标及多会话攻击序列间差异显著。这一发现揭示了持久化记忆对提示注入威胁模型的根本性改变，进而提出防御策略应聚焦于保护记忆更新过程的安全性，同时保留智能体自我适应与改进的有用能力。
链接: https://arxiv.org/abs/2607.14611
作者: Soham Gadgil,David Alexander,Sai Sunku,Franziska Roesner
机构: University of Washington(华盛顿大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Preprint
Abstract:A growing class of agentic systems maintain persistent state across sessions through memory files, behavioral preferences, and knowledge bases. While this makes agents more useful and self-improving, it also creates a new attack surface for prompt injections in which malicious instructions can be embedded within persistent files and influence future behavior. In this work, we study prompt injection attacks in memory-based agentic systems using a sandboxed synthetic workspace. We evaluate two agentic systems, Anthropic Claude Code and OpenAI Codex, across four models: Claude Haiku 4.5, Claude Opus 4.7, GPT-5.2, and GPT-5.5. Our results show that although it is difficult to make an agent overwrite its own memory files using untrusted external content, payloads already planted in those files can successfully attack current and future sessions. Attack success and payload persistence vary substantially across systems, models, adversarial goals, and multi-session attack sequences. These findings show that persistent memory changes the threat model for prompt injection and motivate defenses that protect memory updates without removing useful agent adaptation.
[MA-10] owards an Intention Abstraction Layer for Autonomous Industrial Systems
【速读】：该论文旨在解决现代工业环境中多个自治子系统（如调度器、能源管理器、车辆车队等）在共享物理资源时因目标冲突导致系统失效却无法及时察觉的问题。由于高层人类意图被转化为底层控制逻辑后即被丢弃，各运行组件无法确认自身是否仍在执行原始意图，冲突往往在造成任务失败或系统停机后才被发现。为此，论文提出一种领域无关的中间件——意图抽象层（Intention Abstraction Layer, IAL），其核心在于将意图作为一等公民（first-class）、持久且可解释的运行时对象进行建模：基于形式化OWL本体的大型语言模型（Large Language Model, LLM）将自然语言目标解析为结构化意图，一致性监控模块在注册阶段即可检测潜在冲突，透明性模块则以自然语言生成解释。实验验证了两个自治代理在注册生产与能源意图时发生冲突，IAL在执行前成功识别并解释了该冲突。该方案实现了从事后故障分析向事前意图层面验证的范式转变，显著提升了多智能体协同系统的行为可靠性。
链接: https://arxiv.org/abs/2607.14553
作者: Artan Markaj,Raphael Höfer,Felix Gehlhoff
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Modern industrial environments increasingly run many autonomous subsystems at once - schedulers, energy managers, vehicle fleets - each pursuing its own goals while sharing the same physical resources. Because high-level human intentions are translated into low-level control logic and then discarded, no running component can tell whether it is still doing what was actually intended, and goal conflicts surface only after they have caused a missed target or a shutdown. We propose the Intention Abstraction Layer (IAL), a domainagnostic middleware that represents intentions as first-class, persistent, and explainable runtime objects: a large language model grounded in a formal OWL ontology parses naturallanguage goals into structured intentions, a consistency monitor detects conflicts at registration time, before execution, and a transparency module explains them in natural language. We report a first proof of concept in which two autonomous agents register conflicting production and energy intentions, and the IAL flags and explains the conflict before it reaches the execution layer. The result is a mechanism that shifts behavioral assurance for cooperating autonomous systems from post-hoc failure analysis to pre-execution, intention-level checking.
[MA-11] World-Model-Aware Responsibility Allocation in Heterogeneous Logistics Systems
【速读】：该论文旨在解决在中央控制系统（CS）协调的物流系统中，自主物流设备（ALE）与非自主设备混合运行时因局部世界模型（World Model）与全局世界模型不一致导致的死锁问题。传统静态权限分配机制无法根据实时信息质量动态调整控制权，致使系统在模型分歧时产生冲突决策，而基于资源的处理方法也无法预防此类问题。其解决方案的关键在于提出一种世界模型感知的责任框架（WMARF），该框架依据中央控制系统的世界模型质量与设备自动化水平动态分配控制权，并将死锁状态分类为“无责任”、“责任转移中”或“责任分歧”，从而实现对控制权的自适应管理。通过在遵循VDA 5050接口的离散事件仿真中验证，当两台ALE在半自动化转运点交汇时，采用邻近触发的权限移交机制可有效避免由模型分歧引发的死锁。由于控制权的分配以信息质量为导向而非固定协议，该方案具备良好的可扩展性，能够适应未来自动化程度的持续提升。
链接: https://arxiv.org/abs/2607.14550
作者: Artan Markaj,Niklas Jobs,Felix Gehlhoff
机构: 未知
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Logistics systems increasingly mix \emphautonomous logistic equipment (ALE) with non-autonomous machinery under a central control system (CS), where the best decision-maker depends on who holds the most current world model, yet authority is fixed at design time. When an ALE’s local model and the CS global model diverge, both act on incompatible beliefs and produce deadlocks that resource-based handling neither explains nor prevents. We propose the World-Model-Aware Responsibility Framework (WMARF), which assigns authority dynamically from CS world-model quality and equipment automation level, and classifies deadlocks by the state of authority – none, in transition, or divergent. In a discrete-event simulation of two ALE converging on a semi-automated transfer point, reproduced over the VDA~5050 interface, a divergence deadlock under static control is prevented by a proximity-triggered handoff. Because authority follows information quality rather than a shared protocol, the scheme stays valid as autonomy grows.
[MA-12] Mixed-Agent Museum Tour Guide Design Improves Gendered Learning Outcomes and Visitor Preferences IROS2026
【速读】：该论文旨在解决机器人在博物馆等日常场景中作为导览员时，如何提升游客参与度与学习效果的问题。现有系统多依赖单一实体代理（如物理机器人或虚拟代理），难以兼顾交互丰富性与用户体验的多样性。为此，本文提出一种混合代理导览系统（mixed-agent tour guide system），将一个物理机器人与一个投影式虚拟代理结合，通过双代理协同对话与互动，在单一平台上实现类似两个移动代理的交互深度。其解决方案的关键在于采用“二元对话风格”（dyadic conversational style），即物理与虚拟代理分工协作、动态交互，从而增强人机互动的自然性与沉浸感。实验结果表明，尽管各条件下的参与度与体验质量保持稳定，但学习表现存在显著性别调节效应：女性参与者在混合代理条件下学习效果更优；而访谈反馈显示，无论性别，用户均更偏好混合代理组合，强调交互体验是影响满意度的核心因素。
链接: https://arxiv.org/abs/2607.14468
作者: Annette M. Masterson,Wonse Jo,Helena C. Sieh,Lionel P. Robert Jr.,Dawn Tilbury
机构: University of Michigan(密歇根大学); Incheon National University(仁川国立大学)
类目: Robotics (cs.RO); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注: Accepted on IROS 2026, 8 pages
Abstract:Robots are increasingly integrated into everyday contexts, including museums, where they can both entertain and educate visitors. To enhance visitor experience and engagement, we present a novel mixed-agent tour guide system that combines a physical robot with a projected virtual agent that actively participates in the tour through conversation and interaction, achieving the interaction richness of two mobile agents from a single platform. We validate the system through a within-subjects study with 30 participants to assess engagement, quality of experience, and learning performance. Participants experienced different conversational styles and agent configurations, and data were collected via surveys, behavioral sensors, and interviews. Results showed that engagement and quality of experience remained consistent across conditions. Learning performance revealed a significant gender-moderated difference: the mixed-agent conditions improved learning performance for female participants. This suggests that the proposed dyadic conversational style in this paper influenced learning performance differently by gender. Nonetheless, in interviews, participants reported a greater preference for mixed-agent teams regardless of gender, citing interaction as a key factor in their experience.
[MA-13] When Is Delegated Play Truthful? Within-Range Regret and the Trilemma of Aligned Delegation
【速读】：该论文旨在解决在自动化代理（autobidder）与语言模型代理（language-model agents）广泛应用于广告竞价与任务委托场景下，用户向自身代理进行自我描述时的最优策略问题。传统机制设计理论假设用户会诚实披露自身偏好（即“忠实沟通”），但该假设在实践中缺乏严谨的支撑。本文的核心问题是：在何种条件下，用户向代理进行诚实描述才是最优的？ 解决方案的关键在于引入并分析“代理的区间内后悔值”（within-range regret）这一核心概念——它衡量的是代理在诚实报告情况下所采取的动作与其在所有可引导动作中能达成的最佳结果之间的差距。研究证明，当且仅当代理已达到其可达范围内的最优行动（即“忠诚”，loyal）时，诚实描述才是最优策略（定理1）。该结论统一了拍卖场景中的自动出价结果，并为语言模型代理中“忠实沟通”的有效性提供了理论边界。进一步地，作者揭示了对代理施加安全约束（如出价上限或对齐层）的三难困境：任何两个约束属性（绑定性、诚实性、能力保留性）同时成立将必然导致第三个属性失效（定理2）。由于精确计算区间内后悔值属于#P难问题，作者提出基于采样估计的方法，在模型更新过程中动态维护该量，其计算成本取决于模型漂移程度而非更新频率。实验结果显示，在五家主流语言模型提供商的生产级模型上，应用对齐风格的约束后，诚实报告均导致未被捕捉的剩余价值，通过夸大报告可恢复该部分收益，这揭示了提示工程与越狱行为背后的激励根源。
链接: https://arxiv.org/abs/2607.14357
作者: Taksch Dube
机构: 未知
类目: Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA); Theoretical Economics (econ.TH)
备注: 31 pages, 7 figures, 4 tables
Abstract:Advertisers delegate bidding to autobidders; users delegate tasks to language-model agents. A person describes what they want to an automated proxy that acts in a mechanism on their behalf. This is the revelation principle in production, and it forces a question classical theory assumes away: when is it optimal to describe yourself honestly to your own proxy? We show the answer turns on one quantity, the proxy’s within-range regret. The most a principal can gain by misreporting equals the regret of the proxy’s honest-report action against those the principal could have steered it to take. Honest self-description is optimal exactly when the proxy already plays the best action it can reach, that is, when it is loyal (Theorem 1). The identity unifies auction-specific autobidding results and pins down when the faithful-communication assumption behind language-model elicitation proxies (Huang et al.) holds. The identity constrains guardrails placed on proxies, from bid caps to a model’s alignment layer. No guardrail can be at once binding (it displaces the truthful action from the proxy’s best reachable outcome), truthful (honest reporting stays optimal), and capability-preserving (that outcome stays reachable through some report); any two preclude the third (Theorem 2). A safety constraint that alters what a model does while leaving its best output reachable makes honest description of intent suboptimal, so a sharper report can gain. This is the incentive behind prompt-engineering and jailbreaking. Because within-range regret is #P-hard to compute exactly, we estimate it from samples and maintain it as a model is updated, at a cost set by how far the model drifts, not how often it changes. Running it on production language models from five providers under an alignment-style cap, we find honest reporting leaves surplus unclaimed on every model, recovered by inflating the report. Comments: 31 pages, 7 figures, 4 tables Subjects: Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA); Theoretical Economics (econ.TH) Cite as: arXiv:2607.14357 [cs.GT] (or arXiv:2607.14357v1 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2607.14357 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Taksch Dube [view email] [v1] Wed, 15 Jul 2026 20:45:21 UTC (71 KB)
[MA-14] AI Agents Do Not Fail Alone:The Context Fails First
【速读】：该论文旨在解决当前生成式AI（Generative AI）智能体在实际应用中可靠性不足的核心问题，尤其是由于上下文（context）质量低下导致的智能体行为不可控、幻觉频发、工具误用、约束规避及注入攻击脆弱性等问题。其核心解决方案在于提出并验证“上下文工程（context engineering）质量”作为衡量智能体可靠性的独立先行指标。关键创新在于构建了ProofAgent-Harness这一开源评估框架，通过多评审者共识评分机制，从七个维度系统量化上下文质量：角色清晰度、防护措施覆盖度、指令一致性、工具模式质量、事实依据充分性、注入防御强度以及令牌效率。尤为关键的是，该上下文评分与行为指标及发布决策相分离，实现了非循环验证。实验结果表明，在固定前沿大模型的前提下，仅调整上下文配置，各质量维度均能稳定预测对应的行为表现——如事实依据充分性可有效预测幻觉抵抗能力，防护措施覆盖度可预测抗操纵能力，指令一致性可预测指令遵循能力，工具模式质量可预测工具使用效果。这确立了上下文质量测量作为智能体可靠性预检信号的有效性，并将上下文工程提升为可审计、可治理的智能体评估关键层。
链接: https://arxiv.org/abs/2607.14275
作者: Fouad Bousetouane
机构: ProofAgent.ai; The University of Chicago (芝加哥大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 24 pages, 1 figure
Abstract:Context engineering has become central to building reliable AI agents, yet it remains largely unmeasured. Agents do not fail in isolation: their behavior is shaped by the instructions, tools, memory, retrieved knowledge, guardrails, and untrusted inputs accumulated in their context. When this context is weak, agents drift, hallucinate, misuse tools, ignore constraints, become vulnerable to injection, and waste tokens. This paper validates context-engineering quality as an independent leading indicator of agent reliability. We implement the measurement in ProofAgent-Harness, an open-source infrastructure for AI agent evaluation that uses multi-juror, consensus-based scoring. The harness assesses context across seven criteria: role clarity, guardrail coverage, instruction consistency, tool schema quality, grounding sufficiency, injection hardening, and token efficiency. Crucially, the context score is isolated from behavioral metrics and release decisions, enabling a non-circular validation. Through a controlled context-quality study across regulated agent domains, holding frontier LLM agents fixed and varying only their operating context, we show that context-quality criteria consistently predict their corresponding behavioral outcomes. Grounding sufficiency predicts hallucination resistance, guardrail coverage predicts manipulation resistance, instruction consistency predicts instruction following, and tool-schema quality predicts tool use. These findings establish context measurement as a validated preflight signal for agent reliability and position context engineering as an auditable layer of agent evaluation and governance.
[MA-15] Stochastic Filtering for Quorum Sensing in Robot Swarms under Anonymous Communication
【速读】：该论文旨在解决机器人集群在群体决策中依赖集体阈值（quorum）感知时，因匿名通信协议导致的重复消息误计问题。由于匿名协议无法识别信息来源，同一个体的重复消息可能被多次计入，从而引入估计偏差，影响群体决策的准确性与稳定性。其解决方案的关键在于提出一种受k-优先采样启发的随机滤波协议（\ANTk），通过主动过滤消息缓冲区中的冗余信息，有效降低临时误差并提升估计稳定性。相较于基线匿名协议（\AN）的高偏差和随机化改进版本（\ANT）的信息惯性问题，\ANTk在牺牲一定错误恢复时间的前提下，实现了更精确且稳定的集体阈值估计，显著提升了群体协同的可靠性。
链接: https://arxiv.org/abs/2607.14262
作者: Fabio Oddi,Andreagiovanni Reina,Vito Trianni
机构: 未知
类目: Robotics (cs.RO); Multiagent Systems (cs.MA)
备注:
Abstract:Quorum Sensing (QS) is a key capability for robot swarms, useful for coordination of activities at the group level. Effective communication is instrumental for individuals to estimate the quorum level of the entire swarm. Anonymous communication protocols where individuals exchange local information without revealing unique identities are helpful to support quorum estimates by sampling information from neighbours and maintain scalability of the QS process. However, because anonymous protocols cannot distinguish message sources, repeated messages from the same sender may be double-counted, thereby biasing collective quorum estimates. In this study, we introduce a stochastic filtering protocol inspired by k -priority sampling to improve estimate stability (\ANTk), and we compare it with a baseline anonymous protocols (\AN) and a randomised variant designed to improve accuracy (\ANT). We find that the baseline protocol \AN provides a parsimonious and fast solution, but remains highly inaccurate due to double-counting bias. The \ANT variant improves accuracy but suffers from information inertia, resulting in slower convergence. Finally, actively filtering the message buffer via the \ANTk protocol successfully decreases temporary errors and stabilises the estimate, at the cost of an increased time of recovery from errors.
[MA-16] Automatic Hard Example Synthesis with Multi-Level Agent ic Data Curation
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在内容安全与审核任务中面临的对抗性攻击及分布外边缘案例（out-of-distribution edge cases）的脆弱性问题。现有方法如主动学习和人工标注难以应对新型多模态威胁所呈现的复杂性与规模。其解决方案的关键在于提出一种自动化、代理驱动的红队测试（agentic red-teaming）框架，通过迭代策略不断生成新的假设并基于历史尝试进行变异，从而系统性地合成具有挑战性的对抗样本。该框架采用多智能体架构，包括具备高阶推理能力的架构师代理（Architect agent）、先进的图像生成器以及由多个层级的大语言模型评审员组成的验证委员会，实现无需人工干预即可自主发现边界突破性违规行为与模糊政策边缘案例。进一步地，利用这些精心构造的对抗样本作为测试时检索（test-time Retrieval）的上下文示范，显著提升了目标模型的鲁棒性，使公开图像安全基准上的误漏率（False Negative Rate, FNR）从41.2%降至24.5%，且完全不依赖人工标注。
链接: https://arxiv.org/abs/2607.14256
作者: Genglin Liu,Muye Zhang,Krishnamurthy Viswanathan,Nichole J. Hansen,Blaž Bratanič,Nathan L Clement,Shalini Ghosh,Ariel Fuxman
机构: Google(谷歌)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 23 pages; work in progress
Abstract:Multimodal Large Language Models (MLLMs) are increasingly deployed for nuanced content safety and moderation tasks, yet they remain vulnerable to adversarial attacks and out-of-distribution edge cases. Traditional active learning and manual annotation fail to scale against the complexity and volume of novel multimodal threats. In this paper, we propose an automated, agentic red-teaming framework that systematically synthesizes difficult examples using an iterative strategy that proposes novel hypotheses as well as mutating on past attempts. Leveraging a multi-agent architecture that consists of a high-reasoning Architect agent, an advanced image generator, and a multi-level verification committee of LLM raters, our system autonomously uncovers boundary-pushing violations and ambiguous policy edge cases without any human intervention. By employing these carefully synthesized adversarial examples as in-context demonstrations via test-time Retrieval, we substantially improve the target model’s robustness, reducing the False Negative Rate (FNR) from 41.2% to 24.5% in a public image safety benchmark without relying on any human labeling.
[MA-17] ReasFlow: Assisting Reasoning -Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System
【速读】：该论文旨在解决当前自主科研系统在理论驱动型科学发现领域，尤其是数学化、需严格证明的学科中，因缺乏可扩展的理论推理验证机制、自主探索能力不足以及文献中程序性启发策略匮乏等问题而难以有效开展研究的瓶颈。其解决方案的关键在于提出一个以推理为核心（reasoning-centric）的端到端自主代理系统——ReasFlow，该系统通过两个核心机制实现突破：一是构建了鲁棒的内部验证闭环，可在人类审阅前自动审计逻辑一致性并修正基础错误；二是集成了自动化知识检索与自我改进机制，能够主动挖掘陈述性知识和被忽视的程序性启发策略，显著降低对人工专家的依赖。ReasFlow实现了文献综述、算法设计、定理证明、实验验证及论文撰写的一体化流程，已成功在极简提示下自主生成五篇具备严谨理论与实证内容的研究论文，并在基于大语言模型（LLM）的定制评审标准下，表现优于现有主流开源基线系统。该系统通过ReasLab平台公开可用，为人工智能辅助的理论研究提供协作式工作空间。
链接: https://arxiv.org/abs/2607.14178
作者: Yutong He,Daibo Li,Guohong Li,Jiahe Geng,Zhengyang Huang,Can Ren,Zekun Zhang,Yifan Liu,Shuchen Zhu,Hengrui Zhang,Boao Kong,Ming Sun,Shu Li,Chenyi Li,Jiang Hu,Kun Yuan,Zaiwen Wen,Pingwen Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated research systems remain predominantly focused on empirically driven domains with quantitative benchmarks, leaving theory-driven discovery, particularly in mathematically grounded disciplines requiring rigorous proofs and synthesis of domain knowledge, largely underexplored. Key challenges include the difficulty of verifying theoretical reasoning at scale, insufficient reasoning ability for autonomous frontier exploration, and a scarcity of procedural heuristics in the literature. We introduce ReasFlow, an end-to-end autonomous agent system for reasoning-centric scientific discovery that operationalizes a collaborative paradigm where the human expert acts as Principal Investigator while the agent executes rigorous derivations as a capable graduate student. ReasFlow incorporates (i) a robust internal verification loop that audits logical coherence and corrects fundamental errors prior to human inspection, and (ii) an automated knowledge retrieval and self-improvement mechanism that proactively surfaces both declarative facts and overlooked procedural heuristics, substantially reducing expert intervention. The system unifies literature synthesis, algorithm design, theorem proving, experimentation, and manuscript preparation in a single system. Deployed to autonomously generate five complete research papers with rigorous theoretical and empirical content from minimal prompts, ReasFlow consistently achieves the highest evaluation scores among state-of-the-art open-access baselines under a curated LLM-based review rubric. ReasFlow is publicly accessible via the ReasLab platform, providing a collaborative workspace for AI-assisted theoretical research. Github repo: this https URL.
[MA-18] Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers IJCAI
【速读】：该论文旨在解决在输电系统运营商（Transmission System Operator, TSO）背景下，如何有效整合生成式AI（Generative AI）与数值仿真工具以支持电网研究的挑战。核心问题在于现有电网分析流程中人机协作效率低、工具集成不标准化、仿真过程缺乏可追溯性与可扩展性。其解决方案的关键在于引入模型上下文协议（Model Context Protocol, MCP），通过构建基于MCP的接口pypowsybl-mcp，将仿真工具pypowsybl的核心功能以标准化方式暴露给AI代理（AI agents）。这一机制使代理能够自主完成仿真配置、执行分析、获取结果，并与电力系统仿真器进行结构化交互，从而实现更高效、可审计、可扩展的电网研究环境。同时，论文提出融合“人在回路”（human-in-the-loop）与多智能体协作的工作流原则，并设计结合技术指标与从业者反馈的评估策略，为未来智能电网研究范式提供可落地的技术框架。
链接: https://arxiv.org/abs/2607.14158
作者: Jérôme Picault,Clément Goubet
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA); Software Engineering (cs.SE)
备注: Accepted to IJCAI AISE 2026 workshop
Abstract:This position paper explores how Agentic AI and Model Context Protocol (MCP) can support power-grid studies in a Transmission System Operator (TSO) context. We focus on integrating Large Language Models with numerical simulation tools, structured workflows, and human supervision. We identify key industrial requirements for agent assisted grid studies and introduce pypowsybl-mcp, an MCP-based interface exposing selected capabilities of our simulation tool, pypowsybl to AI agents. This first step provides a testbed to study how agents can setup simulations, execute analyses, retrieve results, and interact with power-system simulators through standardized tool calls. We also discuss principles for human-in-the-loop, multi-agent workflows and outline an evaluation strategy combining technical metrics and practitioner feedback. The paper positions MCP-based tool integration as a step toward more interactive, auditable, and scalable grid-study environments.
[MA-19] Volition Elicitation: Operational Semantics for People and Their Machines
【速读】：该论文旨在解决分布式系统中如何有效表达和执行受人类意愿（volition）约束的多智能体原子事务问题，尤其针对以个人及其移动设备（如智能手机）为核心节点的系统。传统分布式系统难以精确建模人类主观意愿对计算行为的影响，导致系统逻辑与用户真实意图之间存在脱节。为此，论文提出了一种名为意愿守护型全局编程语言（volition-guarded GLP, vGLP）的编程语言，其核心创新在于将意愿守护条款（volition-guarded clauses）引入经典全局编程语言（GLP），使事务的执行不仅依赖于机器状态，还必须获得参与智能体（即人及其设备）的主动意愿授权。解决方案的关键在于：通过构建一个基于通信意愿代理（Communicating Volitional Agents）的形式化语义模型，强制要求程序执行时必须显式获取用户的意愿输入；这一机制确保了用户界面（UI）的设计不再仅仅是功能展示，而是成为“探测用户心智状态”的唯一途径——即“了解用户心中所想”成为界面设计的根本目的。最终，系统利用生成式人工智能（Generative AI）从意愿守护型多智能体原子事务自动构造vGLP程序，并将其映射为实际可部署于物理智能手机上的集成应用，实现了从抽象规范到落地应用的端到端自动化生成。
链接: https://arxiv.org/abs/2607.14138
作者: Ehud Shapiro
机构: London School of Economics (伦敦政治经济学院); Weizmann Institute of Science (魏茨曼科学研究所)
类目: Programming Languages (cs.PL); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注:
Abstract:The most prevalent distributed systems today include people and their personal machines (smartphones). In such systems, computations are driven by people’s volitions: a payment when a person wishes to pay someone, befriending when two people wish to become friends, etc. Volition-Guarded Multiagent Atomic Transactions were proposed as an abstract specification language for such systems, in which each agent consists of a person and their machine, and a transaction can be guarded by both the machine states and the personal volitions of its participating agents. Here, we define the programming language volition-guarded GLP (vGLP), which extends GLP with volition-guarded clauses, and define its operational semantics as an instance of Communicating Volitional Agents. As the semantics requires the person to will a volition-guarded clause reduction, a correct implementation must elicit the person’s volitions: finding out ``what’s in the person’s head’’ is the sole rationale for the UI, which is realised accordingly by standard constructs. We demonstrate the approach on the grassroots social graph, social network, and currencies: each platform is a vGLP program, generated by AI from volition-guarded multiagent atomic transactions; the implementation of vGLP, also created by AI, then maps its volition-guarded clauses into the user-interface constructs, resulting in a single working app deployed on a physical smartphone. Subjects: Programming Languages (cs.PL); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA) Cite as: arXiv:2607.14138 [cs.PL] (or arXiv:2607.14138v1 [cs.PL] for this version) https://doi.org/10.48550/arXiv.2607.14138 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-20] Latent Communication Between Language Model Agents : Channels Alignment and the Limits of Text
【速读】：该论文旨在探究大语言模型（Large Language Models, LLM）在多智能体系统（Multi-agent Systems, MAS）中通过文本通信时是否存在信息损失，并验证其是否具备超越文本表达能力的隐含世界模型（world model）。核心问题是：在复杂概念传递过程中，基于文本的通信是否无法充分保留模型内部潜在表示中的语义信息？解决方案的关键在于构建三种通信通道（密集潜在空间、稀疏潜在空间与文本通道），并利用稀疏自编码器（Sparse Autoencoder, SAE）对各通道中的概念区分性信息进行量化分析。研究发现，稀疏潜在通道在28倍压缩下仍保持99.4%的探针准确率，显著优于文本通道的80.4%；跨架构通信中，通过普鲁克斯特对齐（Procrustes alignment）可实现Llama与Mistral间92%的顶级检索准确率，但文本序列化导致88%的SAE特征丢失，且特征替换主要源于身份替换而非衰减。任务层面评估表明，潜在通道在跨语言概念任务上表现与文本通道相当，未见超越，且引入潜在特征增强文本也未带来性能提升。最终结论为：被丢失的特征主要编码表层形式信息，而非任务相关的语义内容，因此本文对初始假设——即潜在表示能更有效传达复杂概念——持否定态度。研究指出，要真正揭示潜在通信的优势，需设计更深层的任务以激发复杂概念表达，并建立相应的分析框架。
链接: https://arxiv.org/abs/2607.14103
作者: Markus Wenzel
机构: Constructor University (构造大学)
类目: Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注:
Abstract:Multi-agent systems (MAS) are utilized in many contexts and many professions. Those MAS rely on inter-agent communication, usually implemented by clear-text message passing. We hypothesize that Large Language Models may have a world model at their disposal that exceeds expressibility in text when complex concepts need to be communicated. Our aim is to approach a proof of this hypothesis with structured experiments. In this work, we show that LLM agents communicating via text lose information, which we quantify via Sparse Autoencoder (SAE) feature analysis. We construct three communication channels and measure concept-discriminating information in each. We first show that the SAE-sparse channel retains a 99.4% probe accuracy at 28-fold compression over the dense-latent channel vs 80.4% for the text channel. We then proceed to examine the same for cross-architecture communication by using sparse latent space alignment. We find for Procrustes alignment a 92% top-1 retrieval between Llama and Mistral. Using a text round-trip, we perform feature survival analysis to find that text serialization destroys 88% of SAE features, replacing them with a different feature set. We attribute the loss to identity replacement, not attenuation. By our analysis, we were able to attribute a 3-10pp performance penalty to the linear Procrustes alignment, improving with nonlinear alignment methods. In a task-level evaluation we find that the latent channel matches the text channel on cross-lingual concept tasks but never exceeds it. Text augmentation with latent features provides no benefit, leading us to negative conclusions for the initial hypothesis: lost features mostly or completely encode surface form, not task-relevant semantics. To pinpoint the practical advantage of latent communication over a text channel, deeper tasks eliciting complex concepts and an corresponding analysis framework are needed.
[MA-21] Does Multi-Agent Debate Improve AI Feedback on Research Papers?
【速读】：该论文旨在解决生成式人工智能（Generative AI）在经济学领域元分析（meta-analysis）论文撰写与改进中的实际应用效果问题，具体聚焦于评估不同AI辅助工具对已完成论文的改进建议的有效性。其核心问题是：在真实研究者视角下，前沿的单次生成式AI报告、多智能体辩论系统生成的报告以及真实期刊审稿意见，哪一种对提升论文质量最具实用性。解决方案的关键在于设计了一项预注册、身份匿名、基于论文内部的实验，将44篇已发表元分析论文的作者作为评判主体，要求他们对三种由AI生成的改进建议（包括单一模型生成、多智能体辩论系统生成及真实审稿意见）进行盲评排序。结果表明，作者更倾向于采纳由前沿模型单次生成的报告，其偏好度显著高于多智能体辩论系统（平均领先0.66和0.57个等级点），尽管后者消耗了约三十倍的计算资源。此外，研究发现真实审稿意见虽常被作者视为首选，但在独立的AI评委评估中却几乎总是排名最低，提示不可简单以AI判断替代作者主观评价。研究强调，当前生成式AI在提升已完成论文质量方面的价值仍以单次生成为主导，且需警惕用AI裁判取代人类作者判断的风险。
链接: https://arxiv.org/abs/2607.14713
作者: Tomas Havranek,Zuzana Irsova
机构: Charles University (查尔斯大学); Centre for Economic Policy Research (经济政策研究中心); Meta-Research Innovation Center at Stanford (斯坦福大学元研究创新中心)
类目: General Economics (econ.GN); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 29 pages, 1 figure, 6 tables. Pre-registered on OSF; data, code, judge prompts, and blinded reports in the replication package on Zenodo. Project page: this https URL
Abstract:Probably not, at least for meta-analyses in economics. In a pre-registered, identity-masked, within-paper experiment, the authors of 44 meta-analyses ranked three AI reports on their own paper by usefulness for improving it: a single pass by a frontier model against two multi-agent debate tools we built and expected to win. All reports were held to a common length and template. The authors preferred the single pass, by 0.66 rank points over mad-research (95% CI 0.32 to 1.00) and 0.57 over paper-workshop (0.16 to 0.95), though paper-workshop spent roughly thirty times the tokens. Authors who recalled their journal referee report usually placed it first and never last; in a separate exercise, three AI judges almost always placed the real journal referee report last. Among the three AI reports, Gemini (the judge whose model family wrote none of the reports) would have ranked paper-workshop first in the authors’ place, reversing the single-pass preference. The reversal warns against substituting an AI judge for the author. We measure perceived usefulness for finished papers; whether AI should referee papers is a separate question.
自然语言处理
[NLP-0] Partition Prompt Aggregate: Statistical Self-Consistency in Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在上下文学习（in-context learning）中对概率一致性原则的遵守问题，具体关注其生成的条件估计是否满足基本的概率恒等式，尤其是全概率定律（law of total probability）。若上下文学习可被解释为条件推理，则模型对子群体的条件估计应能通过加权聚合重构出与原始总体一致的边际分布。然而，研究发现，当前前沿模型在多类任务和不同树状划分结构下普遍存在违反该自洽性原则的现象。其关键发现是“宏观谬误”（macro fallacy）：尽管模型对细粒度子群体的判断更接近人类参考数据，但将这些细粒度估计聚合回总体时，结果却常偏离真实分布，表明模型虽具备子群体层面的知识，却无法可靠地将其传播至整体推断中。这一现象揭示了现有模型在统计自洽性方面的显著缺陷，从而提出将统计自洽性作为无需外部参考的、可量化评估大语言模型能力的新基准。
链接: https://arxiv.org/abs/2607.15277
作者: Patrik Wolf,Thomas Kleine Buening,Andreas Krause,Celestine Mendler-Dünner
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:In-context learning is commonly interpreted as a form of conditional inference, in which the prompt specifies a context and the model’s output is treated as an estimate of the corresponding conditional distribution. If this interpretation holds, then LLM estimates should satisfy basic probabilistic identities. In particular, the law of total probability asserts that prior-weighted conditional distributions aggregate into population-level marginals over any valid partition of the population. In this work, we investigate to what extent LLM estimates adhere to this self-consistency principle. We use binary trees as an evaluation scaffold to recursively partition a population into increasingly fine-grained subpopulations. We then prompt LLMs with verbalized subpopulation descriptions in context, aggregate the resulting estimates back into population-level estimates, and compare them across partitions of varying granularity. Applying this protocol across problem domains and state-of-the-art frontier models, we show widespread violations of basic consistency properties. An in-depth study of persona prompting reveals a pattern we call the macro fallacy: estimates reconstructed from more fine-grained subpopulation responses are often better aligned with human reference data than direct population-level estimates. This effect persists across variations in tree structure and estimation task, and can be partially recovered through implicit prompting. Together, these findings suggest that models possess relevant subpopulation knowledge but do not reliably propagate it into aggregate estimates. This gap establishes statistical self-consistency as an unsaturated, reference-free criterion for evaluating LLMs.
[NLP-1] SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
【速读】： 该论文旨在解决科研论文中图形编辑这一繁琐且耗时的日常任务自动化问题，其核心挑战在于科学图表作为密集型信息图（infographic），由示意图、数据图表、照片、注释、箭头等多种异构视觉元素在严格的视觉语法约束下组合而成，以支持特定论证。为此，研究提出SciDiagramEdit——一个基于自然语言指令的基准测试与技能演化框架，通过学习作者真实论文修订过程中的前后对比图对（from arXiv版本历史）来捕捉修订意图。其关键解决方案在于采用代理式学习（agentic learning）驱动的技能演化机制：由一个代理提议者（agent proposer）基于多轮执行轨迹不断优化编辑技能规范，使模型在独立验证集上的编辑准确率持续提升，证明了自然论文修订行为可作为指令驱动图形编辑的有效训练信号。该框架直接操作可编辑的矢量源文件，支持用户与智能体协同审查并交互修改单个视觉元件，实现高效、可解释的协作式图形编辑。
链接: https://arxiv.org/abs/2607.15272
作者: Yasheng Sun,Zezi Zeng,Yifan Yang,Chong Luo,Wenyi Wang,Ziwei Liu,Jürgen Schmidhuber
机构: King Abdullah University of Science and Technology (阿卜杜拉国王科技大学); Microsoft Research (微软研究院); Nanyang Technological University (南洋理工大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 20 pages
Abstract:Editing the figures in a research paper is a routine and time-consuming part of everyday research practice: authors relabel components, rearrange panels, and restyle visuals as they revise their manuscripts. Automating this editing workflow under a natural-language instruction, however, is challenging, because a scientific figure is a dense infographic in which heterogeneous visual elements such as schematics, plots, photos, captions, and arrows are composed under a tight visual grammar to advance a specific argument. To address this, we present SciDiagramEdit, a benchmark and skill-evolution framework that learns from natural paper revisions and operates on the figure’s editable vector source, where users can inspect and co-edit individual primitives alongside the agent. Our benchmark mines before/after figure pairs from arXiv version histories, each grounded in the authors’ own revision intent. To accommodate the diversity of editing instructions, we adopt agentic learning via skill evolution: an agentic proposer continually refines the agent’s skill specification from execution traces over multiple epochs. The resulting skill progressively lifts edit accuracy on a held-out validation set, providing evidence that natural paper revisions are an effective training signal for instruction-driven figure editing.
[NLP-2] Pretraining Data Can Be Poisoned through Computational Propaganda
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在预训练阶段面临的数据投毒攻击问题，即通过污染预训练数据引入难以检测与缓解的有害行为。现有研究多局限于利用如维基百科等结构化、规模有限且同质化的数据源进行攻击，忽略了投毒数据与真实数据清洗流程之间的交互作用。本文提出，利用公开的网络讨论接口这一广泛存在的网页内容注入机制，可在大规模异构的网络爬取数据中实现有效的投毒攻击。为此，论文提出一种名为HalfLife的新颖分析方法，用于评估基于网络爬取的自然语言模型（LM）训练数据中是否存在恶意内容。实验表明，该方法能够有效识别潜在的投毒注入，揭示第三方网页内容作为攻击语言模型预训练过程的可行路径，强调了在数据采集与清洗环节中对恶意内容进行量化评估的重要性。
链接: https://arxiv.org/abs/2607.15267
作者: Victoria Graf,Hannaneh Hajishirzi,Noah A. Smith,David Kohlbrenner,Kyle Lo
机构: University of Washington; Allen Institute for Artificial Intelligence
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate. Prior work on poisoning pretraining data has largely exploited established data sources such as Wikipedia, which do not represent the large scale and heterogeneity typical of pretraining corpora, and has ignored the interaction between poisoned data and data curation pipelines. We demonstrate that poisoning attacks on pretraining data are feasible beyond this limited setting through an existing web-scale content injection mechanism: public discussion interfaces. Additionally, to measure whether malicious content is included after web crawling and data curation, we introduce HalfLife, a novel analysis for estimating adversarial content inclusion in web-crawl based LM training data. We use HalfLife to explore the feasibility of poisoning pretraining corpora at web scale through open discussion interfaces. Our analysis demonstrates the importance of estimating whether poison injections are included in pretraining data, and establishes third-party webpage content as a possible vector for attacking language model pretraining.
[NLP-3] Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQA
【速读】： 该论文旨在解决医疗多模态人工智能（Multimodal AI）在结合视觉与文本证据时，如何在保持高可靠性与可解释性的同时实现精准推理的问题。其核心挑战在于：尽管基于预训练模型的参数高效微调方法在任务表现上取得显著成效，但其生成的答案质量提升并未稳定转化为临床推理的忠实性与完整性。论文提出的关键解决方案是引入结构化推理机制与显式证据锚定（explicit grounding）策略，此类方法在面对多样化问题类型时展现出更一致的可靠行为。研究结果表明，仅依赖词汇重叠等表面指标进行评估存在局限，亟需建立基于证据关联的标准化解释框架、考虑信息泄露的数据治理机制，以及轻量级的鲁棒性与校准性验证流程。最终，研究强调应通过数据融合、可解释性增强与抗脆弱评估体系共同构建可信的医疗多模态AI系统。
链接: https://arxiv.org/abs/2607.15241
作者: Sushant Gautam,Vajira Thambawita,Michael A. Riegler,Pål Halvorsen,Steven A. Hicks
机构: University of Oslo (奥斯陆大学); Norwegian University of Science and Technology (挪威科技大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for presentation at the 39th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS 2026) as a regular paper
Abstract:Healthcare multimodal AI must combine visual and textual evidence while remaining reliable and interpretable. Using MediaEval Medico 2025 as a retrospective GI endoscopy case study, we analyze design choices across nine documented systems for question answering and explanation quality. Parameter-efficient adaptation of pretrained backbones provides strong challenge performance, but answer-level gains do not consistently translate into faithful and complete clinical reasoning. Methods enforcing structured reasoning and explicit grounding show more reliable behavior across heterogeneous question types, although the evidence is correlational rather than ablation-based. These results motivate evaluation beyond lexical overlap, standardized evidence-linked explanations, leakage-aware data governance, and lightweight robustness and calibration checks. The findings support trustworthy multimodal healthcare AI based on data fusion, explainability, and resilient evaluation.
[NLP-4] kStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations
【速读】： 该论文旨在解决政治话语在短视频平台（如TikTok）中日益普及，但现有计算分析受限于缺乏同时保留音视频信息与层级对话结构的多模态数据集这一关键问题。其解决方案的核心在于构建TikStance——一个面向政治讨论立场识别的多模态、上下文感知数据集，包含161个视频及13,876条评论，覆盖2024年美国大选周期中的三位主要政治人物（特朗普、拜登、哈里斯），时间跨度为2023年9月至2025年1月。该数据集通过将主视频及其元数据与父级评论树进行关联，实现了对音视频内容与层级化对话结构的联合建模。每一条数据均经过三名标注者独立标注，采用三分类立场标签（支持、反对、无立场），对存在分歧的样本进行重标注，最终各子集的Krippendorff’s α系数分别为0.743（特朗普）、0.723（拜登）、0.722（哈里斯），表明标注一致性较高。此外，描述性分析揭示了目标依赖的立场分布差异与对话深度特征，嵌套回复占所有评论的23.3%。TikStance通过整合多目标覆盖、层级对话结构与多层次人工标注，为多模态立场检测、政治传播、计算社会科学及上下文感知自然语言处理研究提供了高质量的数据基础。
链接: https://arxiv.org/abs/2607.15240
作者: Yazhi Zhang,Fuqiang Niu,Bowen Zhang
机构: Shenzhen Technology University (深圳技术大学); University of Science and Technology of China (中国科学技术大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Political discourse has increasingly moved to short-video platforms, yet computational analysis of such content remains constrained by the scarcity of datasets that jointly preserve audiovisual information and hierarchical conversations. Here we present TikStance, a multimodal and context-aware dataset comprising 161 videos and 13,876 comments from TikTok, designed for stance detection in political discussions. The dataset covers three major political figures in the 2024 U.S. election cycle–Donald Trump, Joe Biden, and Kamala Harris–with content collected between September 2023 and January 2025. Each discussion unit links a host video and its metadata to a parent-linked comment tree, enabling stance analysis within both audiovisual and conversational context. Each item was independently labeled by three annotators using a three-class scheme (Favor, Against, None) for video-to-target and comment-to-target stance; items with disagreement were re-annotated, and the final Krippendorff’s (\alpha) reached 0.743, 0.723, and 0.722 for the Trump, Biden, and Harris subsets, respectively. Descriptive analysis further reveals target-dependent differences in stance distributions and conversational depth, with nested replies accounting for 23.3% of all comments. By combining multi-target coverage, hierarchical conversations, and reliable multi-level human annotations, TikStance supports research in multimodal stance detection, political communication, computational social science, and context-aware natural language processing.
[NLP-5] Language Identification via Compositional Data Analysis: A Linear-Time Classifier Based on Log-Ratio Geometry
【速读】： 该论文旨在解决语言识别（Language Identification）中传统神经网络模型计算资源消耗大、而经典频次统计方法（如n-gram模型）依赖不适用于组合数据的度量距离的问题。其核心解决方案在于将字符和二元组（bigram）频率分布建模为定义在单纯形（simplex）上的组合向量，并通过中心对数比（centered log-ratio, CLR）变换将其双射映射至 RD 的(D−1)维零和子空间，使得欧氏距离对应于Aitchison距离，从而更合理地刻画组合数据间的差异。该方法结合CLR变换后的独元组与二元组特征以及拉普拉斯平滑（Laplace smoothing）以缓解稀疏性问题，构建了一套高效且可解释的流水线。实验在六种语言上验证了该方法在不同文本长度下的鲁棒性，尤其在长序列上表现优异，表明组合表示为语言识别提供了一种确定性、计算高效的替代方案，特别适用于对可解释性和低资源消耗有要求的应用场景。
链接: https://arxiv.org/abs/2607.15238
作者: Paul-Andrei Pogăcean,Sanda-Maria Avram
机构: Babeş-Bolyai University (巴贝什-博利亞伊大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Language identification is commonly addressed using either neural architectures or statistical n-gram models. Neural approaches typically require substantial computational resources, whereas classical frequency-based methods offer efficient linear-time performance, but rely on distance metrics that are not always appropriate for compositional data. This work models character and bigram frequency distributions as compositional vectors constrained to the simplex and mapped via the centered log-ratio (CLR) transformation bijectively onto the (D-1) -dimensional zero-sum subspace of \mathbbR^D , where Euclidean distances correspond to Aitchison distances. A pipeline is proposed, combining CLR-transformed unigram and bigram features with Laplace smoothing to address sparsity. The method is evaluated on six languages. Experimental results show that the proposed approach achieves robust accuracy across different text lengths, with strong performance for longer sequences. These findings indicate that compositional representations provide a deterministic and computationally efficient alternative for language identification, particularly in settings where interpretability and low resource consumption are essential. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2607.15238 [cs.CL] (or arXiv:2607.15238v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.15238 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-6] In-Place Tokenizer Expansion for Pre-trained LLM s
【速读】： 该论文旨在解决预训练模型在部署后语言支持扩展时因固定分词器（Tokenizer）导致的低效问题：当新增语言时，由于分词器词汇量固定，新语言的词元化（tokenization）会产生更多碎片化分词，进而增加解码延迟、计算开销与能耗。其核心解决方案是提出一种“分词器扩展”（Tokenizer Expansion）方法，允许在不改变模型架构的前提下，以原地更新方式动态扩展预训练模型的分词器。关键在于：在多语言语料上继续原有分词器的BPE合并过程，确保大部分源词元保持不变且可直接复用；新生成的词元均能精确分解为原始子词元组合；嵌入层权重通过复制保留原词元向量，并将新词元初始化为其子词元嵌入的均值。随后采用两阶段微调策略（仅嵌入层训练后进行全模型持续预训练），有效恢复模型性能。实验表明，该方法显著提升了对印地语、越南语等语言的编码效率（分别减少约2.4倍和2.6倍的词元数量），结合大词汇量带来的每词元处理成本上升，综合实现跨设备2.2–3.7倍的每字符解码速度提升。研究还公开了模型权重与扩展后的分词器，并披露了影响方案设计的关键负面发现。
链接: https://arxiv.org/abs/2607.15232
作者: Jimmy T.H. Smith,Tarek Dakhran,Alberto Cabrera,Simon S. Lee,Paul Pak,Aditya Tadimeti,Tim Seyde,Maxime Labonne,Alexander Amini,Mathias Lechner
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:A tokenizer fixed at the start of pre-training allocates vocabulary in proportion to the pre-training corpus, reflecting the deployment priorities at that time. When those priorities shift, languages added later are split into many more tokens per word, which can raise latency, compute, and energy consumption for users of those languages. Cloud models can afford a broad vocabulary because the embedding and LM-head matrices are a small fraction of their parameters. On a compact model those matrices are a material share of per-token decode bandwidth, so on-device models ship small vocabularies and accept fragmentation outside a fixed language set. We present tokenizer expansion, an in-place recipe for upgrading a pre-trained model’s tokenizer when the model producer controls its design. We continue the existing tokenizer’s BPE merges on a multilingual corpus, so most source tokens carry over unchanged as single tokens and every new token has an exact decomposition into source tokens. We copy the carried-over embedding rows unchanged and initialize new rows as the mean of their source sub-token embeddings. A two-stage adaptation, embedding-only training then full-model continued pre-training, recovers source-checkpoint quality. We apply the recipe to a continued pre-trained checkpoint of LFM2-8B-A1B, an 8B-parameter Mixture-of-Experts model, to help produce LFM2.5-8B-A1B with a 128K tokenizer. The expanded tokenizer encodes Hindi and Vietnamese in roughly 2.4\times and 2.6\times fewer tokens than the source (up to 4.0\times on Thai). Combining these reductions with the measured per-token cost of the larger vocabulary, we estimate a 2.2 - 3.7\times per-character decode speedup for these languages across our reference devices. We release the model weights and the expanded tokenizer, and report the negative findings that shaped the recipe.
[NLP-7] Expanding the Lexicon of Geez Based African Languages: A Comparative Study of Amharic and Tigrinya
【速读】： 该论文旨在解决多语言预训练语言模型（PLM）在低资源非拉丁字母语言上性能下降的问题，其核心挑战源于以拉丁字母为中心的分词器训练导致的高未登录词（OOV）率和过度子词碎片化。解决方案的关键在于提出VEXMLM——一种针对埃塞俄比亚语（Ge’ez）字母书写系统优化的XLM-R变体，通过在阿姆哈拉语和提格雷尼亚语单语语料库上训练特定语言的SentencePiece分词器，将30,000个源自该分词器的埃塞俄比亚语子词扩展至XLM-R词汇表，并利用原始分词器下子词嵌入的平均值初始化新子词的嵌入。该模型采用两阶段训练策略：首先在扩展词汇表上对齐语料进行持续掩码语言建模，随后在问答（QA）、命名实体识别（NER）和情感分析（SA）等任务上进行监督微调。实验表明，VEXMLM在阿姆哈拉语/提格雷尼亚语的QA任务中分别达到87.0 EM / 90.0 F1，显著优于XLM-R（66.0 EM / 78.0 F1）和Glot500（74.0 EM / 78.0 F1），在情感分析任务中准确率达80.0%，高于XLM-R的77.0%和Glot500的46.0%；在NER任务中，未登录词实体识别准确率从81.4%提升至94.3%（11种语言平均）。本研究贡献包括：（i）面向埃塞俄比亚语的词汇扩展与嵌入初始化方法；（ii）一种可迁移至17种类型相关、无额外数据增强的非洲语言的双阶段训练范式；（iii）涵盖内在分词指标（词汇覆盖率、生育率、OOV率）与外在任务性能的全面评估框架。
链接: https://arxiv.org/abs/2607.15209
作者: Hailay Kidu Teklehaymanot,Debela Desalegn Yadeta,Wolfgang Nejdl
机构: 未知
类目: Computation and Language (cs.CL)
备注: 13 pages , 7 tables , 2 figurs
Abstract:Multilingual pre-trained language models (PLMs) exhibit degraded performance on low-resource, non-Latin-script languages, driven by high out-of-vocabulary (OOV) rates and excessive subword fragmentation that result from Latin-script-centric tokenizer training. We introduce VEXMLM, a vocabulary-extended variant of XLM-R targeting the two highest-resource Ge’ez-script languages, Amharic and Tigrinya, and further evaluated on 17 additional low-resource African languages (19 total). We train a language-specific SentencePiece tokenizer on curated Amharic and Tigrinya monolingual corpora, extend XLM-R’s vocabulary with 30,000 Ge’ez-script subwords derived from this tokenizer, and initialize their embeddings by averaging the embeddings of their constituent subwords under XLM-R’s original tokenizer. VEXMLM is trained in two stages: (1) continued masked language modeling over the extended vocabulary on the curated corpora, and (2) supervised fine-tuning on question answering (QA), named entity recognition (NER), and sentiment analysis (SA). On Amharic/Tigrinya QA, VEXMLM achieves 87.0 EM /90.0 F1, versus 66.0 EM/78.0 F1 for XLM-R and 74.0 EM/ 78.0 F1 for Glot500. On SA, VEXMLM reaches 80.0% accuracy versus 77.0% (XLM-R) and 46.0% (Glot500). On NER, VEXMLM raises OOV-token entity accuracy from 81.4% to 94.3%, averaged over 11 of the 19 evaluated languages for which OOV analysis was possible. Our contributions are: (i) a vocabulary-extension and embedding-initialization procedure tailored to Ge’ez script; (ii) a two-stage training strategy under which vocabulary and continued-pretraining gains on Amharic/Tigrinya transfer to 17 typologically related, unaugmented African languages; and (iii) an evaluation spanning both intrinsic tokenization metrics (vocabulary coverage, fertility, OOV rate) and extrinsic task performance across all 19 languages.
[NLP-8] Mask-Aware Policy Gradients for Diffusion Language Models
【速读】： 该论文旨在解决生成式语言模型中基于强化学习（Reinforcement Learning, RL）提升推理能力时，难以有效应用于掩码扩散语言模型（Masked Diffusion Language Models, MDLMs）的核心问题。其关键挑战在于MDLM的对数似然估计（log-likelihood estimation）在计算上不可行，现有方法通常仅近似建模令牌预测，忽略了生成过程中位置解掩码顺序（unmasking order）的重要性。为此，作者提出将MDLM的生成过程形式化为一个两阶段动作的马尔可夫决策过程（two-stage action MDP），明确区分每个步骤中的两个决策：在各掩码位置填入何种令牌，以及选择哪些位置进行重掩码。该建模框架揭示了策略梯度可自然分解为令牌预测项与掩码策略项，通过联合优化这两项，显著提升了模型在数学推理与代码生成任务上的表现，在GSM8K基准上达到87.1%的准确率，在MBPP基准上达到53.4%的准确率，实现了当前最优性能。
链接: https://arxiv.org/abs/2607.15200
作者: Haran Raajesh,Kulin Shah,Adam Klivans,Philipp Krähenbühl
机构: The University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at COLM 2026
Abstract:Reinforcement learning has proven effective for improving reasoning in large language models, but extending it to Masked Diffusion Language Models (MDLMs) remains challenging due to the intractability of the log-likelihood estimation. Existing approaches approximate this log-likelihood by modeling only the token predictions, ignoring the order in which positions are unmasked during generation. We observe that MDLM generation involves two decisions at each step: what tokens to place at each masked position and which positions to remask. We formalize this as a two-stage action MDP, showing that the policy gradient naturally decomposes into a token term and a masking term. Combining optimization of both terms leads to state-of-the-art outcomes on mathematical reasoning and coding benchmarks, with scores of 87.1% on GSM8K and 53.4% on MBPP.
[NLP-9] 2MLR: Transformer with Temporal Middle-Layer Recurrence
【速读】： 该论文旨在解决生成式模型中基于Transformer的推理能力受限于自回归解码机制的问题，即在逐词生成过程中，中间推理状态因不断通过词元空间压缩而难以持续保留，从而影响复杂推理任务的表现。其解决方案的关键在于提出一种名为“时间中层循环的Transformer”（Transformers with Temporal Middle-Layer Recurrence, T2MLR）的潜在推理架构，该架构将前一词元位置的中间层表示缓存并直接融合至当前词元的较早层，从而实现抽象中间计算状态在解码步骤间的有效留存，且仅引入极低的推理开销。实验表明，T2MLR在自然语言预训练和多跳推理微调任务中均显著优于参数与数据匹配的基线Transformer模型；尤为关键的是，仅对局部中层块（如网络总量的20%）应用循环机制即可超越全层循环，证明了高效推理不依赖于对所有层进行循环，而是可通过目标性地强化中层信息传递来实现。此外，T2MLR无需从头预训练，仅需将递归路径嵌入已有的17亿参数预训练模型并进行短时微调，即可显著提升数学推理性能，极大降低了实际应用门槛。这一发现揭示了在Transformer中实现有效潜在推理的核心机制并非全局循环，而是源于有针对性的中层信息回溯。
链接: https://arxiv.org/abs/2607.15178
作者: Ziyang Cai,Xingyu Zhu,Yihe Dong,Yinghui He,Sanjeev Arora
机构: Princeton University (普林斯顿大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Transformer reasoning is limited by autoregressive decoding, which repeat edly compresses rich hidden computation through token space and makes it difficult for intermediate reasoning states to persist across time. We in troduce Transformers with Temporal Middle-Layer Recurrence (T2MLR), a transformers-based latent reasoning architecture that fuses a cached middle layer representation from the previous token directly into an earlier layer of the current token position, enabling abstract intermediate computation to persist across decoding steps with little inference overhead. Across natural-language pretraining and multi-hop reasoning finetuning, T2MLR consistently outperforms data- and parameter-matched Transformer base lines. Moreover, applying recurrence to only a localized middle-layer block (as little as 20% of the network) often outperforms full-layer recurrence. Im portantly, T2MLR does not require pretraining from scratch: retrofitting the recurrent pathway into an existing pretrained 1.7B Transformer and briefly finetuning substantially improves math reasoning, lowering the barrier to practical adoption. These results suggest that effective latent reasoning in Transformers does not require looping over all layers as in previous works, but can instead emerge more strongly from targeted middle-layer recurrence.
[NLP-10] Linear representations of grammaticality in neural language models
【速读】： 该论文旨在解决神经语言模型（Neural Language Models, NLMs）是否具备区分语法正确性与非语法正确性句子的能力这一在计算语言学领域长期争议的问题。现有研究主要依赖基于概率的评估方法，即检验模型对语法正确句子赋予更高概率，但此类方法受到批评，因其假设语法正确性与句子出现可能性存在固有纠缠，而模型的概率输出受词汇频率、语义合理性及世界知识等多种相关因素干扰。为此，本文提出超越概率评估的新范式，聚焦于模型内部表示中语法正确性是否被编码。其关键解决方案是采用大规模均值探测（mass-mean probing）方法，检验语法正确与不正确的句子在表征空间中是否存在系统性分离，并进一步考察这些表征是否独立于与语法正确性相关的其他句法属性，以及其在不同语法现象和语言间的泛化能力。实验结果表明，多种预训练NLM的句子表征中均存在稳健的语法正确性编码，且在语法维度上呈现出可区分的表征分离，该分离无法完全由其他句级因素解释；同时，这种编码具有跨语法现象甚至部分跨语言的泛化能力，表明语法正确性在当代NLM中构成一个连贯的表征维度。该研究为语言模型句法知识的本质提供了新证据，并构建了一种不依赖字符串概率的互补性语法能力评估框架。
链接: https://arxiv.org/abs/2607.15175
作者: Jane Li,Najoung Kim
机构: Johns Hopkins University (约翰斯·霍普金斯大学); Boston University (波士顿大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Whether neural language models (NLMs) possess the ability to distinguish strings on the basis of their grammaticality remains a debated topic in the computational linguistics literature. Existing evidence has largely relied on probability-based measures, testing whether models assign higher probabilities to grammatical than ungrammatical strings. However, probability comparisons have been criticized as a measure for grammatical knowledge based on the assumption that grammaticality is inherently entangled with likelihood. Model-assigned probability is a function of many related sentence properties, such as lexical frequency, plausibility, and world knowledge. In this work, we move beyond probability-based evaluations and investigate whether grammaticality is encoded in the internal representations of NLMs. Using mass-mean probing, we test whether grammatical and ungrammatical sentences are systematically separated in representational space. We further examine the extent to which these representations are independent of sentence properties that are correlated with grammaticality, as well as their generalization across grammatical phenomena and languages. Our results provide evidence that grammaticality is robustly encoded in sentence representations of a wide range of pretrained NLMs, yielding clear representational separation on the dimension of grammaticality that cannot be fully explained by alternative sentence-level factors. Moreover, this encoding generalizes across a broad range of grammatical phenomena and to some degree, across languages, suggesting that grammaticality constitutes a coherent representational dimension in contemporary NLMs. These findings contribute new evidence to debates about the nature of syntactic knowledge in language models and offer a complementary framework for evaluating grammatical competence that is not dependent on string probabilities alone.
[NLP-11] MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection
【速读】： 该论文旨在解决当前医疗人工智能（AI）评估体系中普遍存在的局限性问题，即现有基准测试主要关注模型是否能给出正确答案，而忽视了对错误类型及其潜在临床安全风险的系统性分析。为弥补这一缺陷，作者提出MedFailBench——一个由临床医生构建的合成基准与故障图谱，其核心在于通过多维度标注机制识别和分类医疗AI系统的失效模式。解决方案的关键在于引入一种基于临床安全边界的故障分类框架，将错误按严重程度（1–5级）和安全门类型（如未及时紧急转诊、不安全远程给药、不当出院安抚、证据伪造、不安全协议执行、源支持缺失等）进行精细化标注，从而实现对模型安全风险的可量化、可追溯评估。该基准公开发布于v0.2.1版本，包含44个经临床医生审核的合成病例、自动化响应筛查流水线、实时HuggingFace排行榜及标准化评估工具，且不包含真实患者数据或模型排名，确保评估的伦理安全性与可复现性。
链接: https://arxiv.org/abs/2607.15166
作者: Goktug Ozkan
机构: Kutahya Emet Dr. Fazil Dogan State Hospital(库塔希亚埃梅特法齐尔多甘州立医院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 6 pages; clinician-reviewed synthetic benchmark; no patient data
Abstract:Most medical AI benchmarks measure whether a model knows the correct answer. MedFailBench asks a different question: which safety boundary failed? We present a clinician-built synthetic benchmark and failure atlas that labels medical AI errors by severity (1–5) and safety gate type (missed urgent escalation, unsafe remote dosing, unsafe discharge reassurance, evidence fabrication, unsafe protocol execution, source support gap). The current public release (v0.2.1) contains 44 clinician-reviewed synthetic cases with severity annotations, a live HuggingFace leaderboard preview, a safety gate taxonomy, a clinical severity rubric, and an automated pipeline for archiving model-response screening runs. No patient data, clinical validation claims, or model rankings are included. MedFailBench is released under Apache-2.0 and CC-BY-4.0 and carries the Zenodo DOI https://doi.org/10.5281/zenodo.21205535.
[NLP-12] On-Policy Delta Distillation
【速读】： 该论文旨在解决传统在线策略蒸馏（on-policy distillation）在强化学习中因依赖奖励模型而带来的约束问题，尤其在于如何更有效地迁移大型语言模型（LLM）经过推理能力微调后的复杂推理能力。其核心挑战在于：直接模仿教师模型的输出分布难以捕捉到推理能力提升的本质变化，导致知识迁移效率受限。为此，论文提出一种新型蒸馏奖励信号——“增量信号”（delta signal），即教师模型与其基线模型（在指令微调前的初始状态）之间的差异，该信号能够精准表征推理能力微调所引入的语义与逻辑层面的变化。这一设计的关键在于通过捕捉“变化量”而非静态输出分布，提供更直接、更本质的监督信号，从而实现对推理能力的高效迁移。基于大量实证研究，作者验证了该方法在数学、科学和代码推理等基准任务上的显著优势，并将其命名为“在线策略增量蒸馏”（On-Policy Delta Distillation, OPD²）。实验表明，OPD² 在极短的后训练周期内即可使推理型大模型达到优异性能，显著超越传统蒸馏方法。
链接: https://arxiv.org/abs/2607.15161
作者: Byeongho Heo,Jaehui Hwang,Sangdoo Yun,Dongyoon Han
机构: NAVER AI Lab(NAVER人工智能实验室)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 19 pages, 4 figures, 12 tables
Abstract:On-policy distillation is an alternative post-training method in reinforcement learning that alleviates the constraints imposed by reward models by providing token-level supervision from a teacher model. Although on-policy distillation has been studied and applied across various settings, its fundamental design remains underexplored. In this paper, we introduce a new distillation reward, termed the delta signal, instead of directly imitating the teacher’s output distribution. The delta signal is defined as the difference between the teacher model and its base model prior to instruction tuning for reasoning capability. It therefore captures the changes induced by reasoning tuning and provides a more direct signal for transferring reasoning capabilities. Using extensive empirical evidence, we show that the delta signal substantially improves on-policy distillation and refer to the new distillation method as On-Policy Delta Distillation (OPD ^2 ). Experiments across mathematics, science, and code-reasoning benchmarks demonstrate that OPD ^2 consistently outperforms conventional on-policy distillation, enabling reasoning LLMs to achieve strong performance with only a short post-training period. Code will be available at this https URL
[NLP-13] Grokipedia vs Wikipedia: An LLM -Based Audit of Political Neutrality along Ideologies
【速读】： 该论文旨在解决生成式人工智能（Generative AI）驱动的在线百科全书在政治立场上的中立性问题，尤其关注由大型语言模型（LLM）主导撰写的知识平台是否能真正实现比传统百科全书（如维基百科）更少偏见的客观性。其核心问题是：以大模型为核心的百科全书（如Grokipedia）是否具备更高的政治中立性，抑或只是将潜在意识形态偏见以不同形式嵌入内容之中。解决方案的关键在于构建一个大规模、多维度的政治偏见评估框架，通过四位大模型裁判（Grok、Claude、Mistral和DeepSeek）对1,394对政府官员条目进行跨平台比较，依据九个专家编码的政治意识形态维度量化中立性，并系统分析各模型裁判自身的判断模式以识别其潜在偏差。研究发现，尽管Grokipedia旨在提供无偏替代方案，但所有大模型裁判均认为其政治中立性低于维基百科；两者虽均呈现整体偏向性，但方向不同——Grokipedia显著倾向经济右翼政治人物而贬抑社会自由派，而维基百科则被评作更倾向于社会自由主义群体，表明生成式AI并未消除偏见，而是重构了其表现形式与分布结构。
链接: https://arxiv.org/abs/2607.15146
作者: Filippos Vlahos,Guillaume Bied,Tijl De Bie
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Online encyclopedias shape political opinion and, through it, democratic discourse. In late 2025, Grokipedia was released, an encyclopedia written entirely by the LLM Grok. One motivation behind the project was to provide an unbiased alternative to Wikipedia, which has faced accusations of “left-wing” and “liberal” bias. But does an encyclopedia written by an LLM deliver greater neutrality, or does it simply embed a different ideology? We conduct a large-scale political bias study on Grokipedia and Wikipedia, analysing 1,394 article pairs describing members of government for neutrality along nine expert-coded ideology dimensions employing four LLM judges, Grok, Claude, Mistral, and DeepSeek. As the LLMs could themselves be biased, we also investigate patterns in their judgments. We find all LLM-judges, including Grok, to rate Grokipedia less neutral than Wikipedia. Both encyclopedias are rated as portraying politicians favourably overall, but towards different ideological groups. Grokipedia particularly favours economically right-wing politicians and penalises socially liberal ones, while Wikipedia is rated as favourably biased towards the latter.
[NLP-14] Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）训练与评估中高质量、查询相关的评分标准（rubrics）难以构建的问题。现有方法依赖人工编写的评分标准、偏好数据或采样响应来提供监督信号，但这些方式成本高且易引入偏差。直接从查询生成评分标准虽可避免外部资源依赖，却缺乏对生成评分标准有效性的验证机制，可能导致生成的评分标准无法区分回答质量、奖励特定表达风格或惩罚合理替代策略。为此，论文提出“Rubrics on Trial”框架，其核心创新在于仅基于查询实现评分标准的自演化，无需外部标注或模型微调。该方法通过合成的“评分标准-响应”配对生成监督信号，并在将候选评分标准纳入体系前进行严格验证，筛选出不具备判别性、过度具体或仅关注风格的无效评分标准。实验结果表明，该方法在五个偏好基准测试集上均表现出色，平均准确率领先，且在七项评估中的六项取得最优表现，验证了其生成高质量、可靠评分标准的能力。
链接: https://arxiv.org/abs/2607.15092
作者: Haocheng Yang,Licheng Pan,Xiaoxi Li,Zhichao Chen,Zhiheng Zhang,Yuan Lu,Haoxuan Li,Hao Wang
机构: National University of Singapore(新加坡国立大学); Xiaohongshu Inc.(小红书); Zhejiang University(浙江大学); Peking University(北京大学); Shanghai University of Finance and Economics(上海财经大学); Institute for Artificial Intelligence, Peking University(北京大学人工智能研究院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Rubrics provide structured, fine-grained signals for training and evaluating large language models (LLMs). Yet reliable query-specific rubrics are difficult to construct. Existing approaches often derive supervision from human-written rubrics, preference data, or sampled responses. Direct query-to-rubric generation avoids these resources, but provides no explicit check that a plausible rubric is useful. Such a rubric may fail to distinguish answer quality, reward an optional style, or penalize a valid alternative strategy. We introduce Rubrics on Trial, a query-only framework that evolves a rubric set from an empty set without external annotations or model training. It derives supervision solely from synthetic rubric-conditioned response pairs and validates each proposed rubric before adding it, screening out non-discriminative, over-specific, and style-only candidate rubrics. Experiments across five preference benchmark suites demonstrate the effectiveness of Rubrics on Trial, which achieves the best average accuracy and leads on six of seven evaluation sets.
[NLP-15] OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）作为通用智能体（General Agents）在多样化应用场景中缺乏系统性评估基准的问题。现有评测基准普遍存在场景单一、工具生态有限及交互形式受限等局限，难以全面刻画模型在异构应用环境下的真实能力。为此，作者提出了OmniaBench——一个覆盖广泛应用场景、具有显式状态空间的通用智能体评测基准。其核心创新在于：基于应用商店、产品文档、行业资源、网络检索与人工精炼构建了涵盖ToC、ToB和ToE的分层分类体系，包含90个一级领域与354个二级领域；在此基础上，通过四种互补路径（DAG、DAG-S、Solver、Program）构建可执行环境并生成单轮与多轮任务，形成包含1,431个任务的完整数据集，其中644个高难度任务用于降低评估成本并缓解公开发布后的数据污染风险。此外，引入十维能力维度与八种组合式原子难度因子，实现对模型能力的细粒度分析。实验表明，即使前沿模型如Claude-Sonnet-5与GPT-5.6-Sol在整体任务上的Pass@1得分也仅分别为58.54和57.14，暴露出模型在规划、约束保持与自适应修正等方面的持续短板。因此，OmniaBench不仅提供了广度与深度兼具的评测框架，更具备诊断性能力，能够精准识别通用智能体的能力边界与瓶颈。
链接: https://arxiv.org/abs/2607.14989
作者: Chengyu Shen,Yujie Fu,Gangtao Xin,Yanheng Hou,Wenlong Fei,Guojie Zhu,Jiawei Li,Hongcheng Gao,Runming He,Zhen Hao Wong,Meiyi Qiang,Hao Liang,Zhao Cao,Hao Jiang,Chong Chen,Wentao Zhang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models are increasingly evolving from text generators into general agents capable of understanding user requests, invoking external tools, and completing complex tasks through interaction. However, existing agent benchmarks often focus on limited scenarios, tool ecosystems, or interaction formats, making it difficult to systematically characterize model capabilities across heterogeneous application settings. We introduce OmniaBench, a benchmark for evaluating general agents across diverse scenarios with explicit state spaces. We derive application-oriented scenario knowledge from app stores, product documents, industry resources, Web retrieval, and human refinement, forming a hierarchical taxonomy that spans ToC, ToB and ToE with 90 level-1 and 354 level-2 domains. Based on this taxonomy, we construct executable environments and synthesize single-turn and multi-turn tasks through four complementary routes: DAG, DAG-S, Solver, and Program. OmniaBench further introduces a ten-dimensional capability taxonomy and eight compositional atomic difficulty factors to support fine-grained evaluation and analysis. The resulting dataset contains 1,431 tasks, together with a challenging subset of 644 tasks designed to reduce evaluation cost and mitigate potential contamination of the full set after public release. The bench presents substantial challenges to current frontier models, with even Claude-Sonnet-5 and GPT-5.6-Sol achieving Overall Pass@1 scores of only 58.54 and 57.14, respectively. Further analyses reveal clear differences across domains and capabilities, as well as persistent limitations in planning, constraint maintenance, and adaptive correction. OmniaBench provides a broad and diagnostic benchmark for characterizing the capability boundaries of general agents.
[NLP-16] Latent Trajectory Discrimination for AI-Generated Text Detection
【速读】： 该论文旨在解决现有AI生成文本检测（AIGTD）方法将文档视为静态对象、依赖全局统计特征或压缩嵌入表示而忽视自回归生成过程内在动态性的问题。其核心挑战在于，传统方法无法有效捕捉生成过程中文本在潜在空间中逐词演进的序列动态特性。本文提出的关键解决方案是将AIGTD重构为区分潜在生成轨迹（latent generation trajectories）的任务，通过引入几何轨迹与对比学习（GTCL）框架，将文档划分为有序的局部单元，分别编码并构建具有时序结构的序列级表示；随后利用对比学习挖掘自回归生成所特有的几何规律。实验表明，GTCL在多个基准测试中显著优于现有基线，证明显式建模生成轨迹的动态演化可提供跨模型和跨领域的鲁棒判别信号，为AIGTD研究开辟了以动态轨迹分析为核心的新方向。
链接: https://arxiv.org/abs/2607.14967
作者: Gianluca Bonifazi,Christopher Buratti,Michele Marchetti,Federica Parlapiano,Giulia Quaglieri,Davide Traini,Domenico Ursino,Luca Virgili
机构: Polytechnic University of Marche (马尔凯理工大学); University of Modena and Reggio Emilia (摩德纳与雷焦艾米利亚大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Most existing approaches to AI-Generated Text Detection (AIGTD) treat documents as static objects and base their decisions on aggregate statistics or globally compressed embeddings. However, this perspective overlooks the inherently dynamic nature of autoregressive generation, where content evolves progressively through the latent space. In this paper, we reformulate AIGTD as the problem of distinguishing between latent generation trajectories. Instead of relying on static representations, we model how textual representations evolve across the sequence. To this end, we propose Geometric Trajectory and Contrastive Learning (GTCL), a framework that segments the document into ordered local units, encodes each unit in an embedding space, and constructs a structured and sequence-level representation. GTCL then applies contrastive learning to these trajectories to learn geometric regularities associated with the autoregressive generation. Evaluations performed on three different benchmarks and several approaches show that GTCL outperforms detection baselines consistently, which implies that explicitly modeling sequential dynamics provides robust discriminative signals across models and domains. These results suggest that modeling trajectory differences could improve detection and open up a dynamic direction that has been underexplored in previous AIGTD literature.
[NLP-17] Show Me How You Reason and Ill Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLM）生成文本的检测与作者归属问题，尤其针对现有方法依赖表层语言特征而易受改写（paraphrasing）和混淆技术干扰的缺陷。其核心解决方案是超越表面语言形式，通过论据挖掘（argument mining）流水线提取生成文本中的推理结构（reasoning structures），并构建推理图（reasoning graphs），进而利用图神经网络（Graph Neural Network, GNN）对这些深层结构进行建模分析。该方法能够捕捉更复杂的LLM生成痕迹，显著提升在对抗性攻击（如改写、反向翻译）及跨模型版本泛化场景下的鲁棒性与准确性，相较传统的Longformer基线模型，在特定攻击下性能提升高达27个百分点，并在未见过的模型版本测试中提升19个百分点，有效模拟了实际应用中持续迭代的LLM版本发布情境。
链接: https://arxiv.org/abs/2607.14905
作者: Zlata Kikteva,Artur Romazanov,Annette Hautli-Janisz,Ramon Ruiz-Dolz
机构: University of Passau, Germany; University of Dundee, United Kingdom
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Given the current trend to employ large language models (LLMs) in almost any imaginable context, LLM-generated text detection and authorship attribution have become a pressing issue. Prior work has primarily focused on surface-level linguistic features, an approach shown to be susceptible to paraphrasing and other obfuscation techniques. In this paper, we go beyond the linguistic surface, extracting and analysing reasoning structures in LLM-generated texts with the goal of capturing more complex signals of LLM authorship. We propose a graph neural network approach that leverages reasoning graphs extracted by an argument mining pipeline, demonstrating improved robustness and generalisation over a traditional Longformer baseline. Our approach outperforms the baseline by up to 27 percentage points under the obfuscation attacks such as paraphrasing and backtranslation, and 19 percentage points when evaluated on the texts generated by the unseen model versions, simulating real-world conditions in which new LLM versions are continuously released.
[NLP-18] Leverag ing Instruction Tuning and Merging for Reasoning Model Adaptation
【速读】： 该论文旨在解决生成式语言模型在缺乏可靠验证机制（即不可验证领域）中难以有效训练的问题，同时充分利用大量未被使用的、由人类编写的监督微调数据（human-written solutions）。其核心挑战在于如何在不依赖可验证输出的前提下，提升模型在复杂任务（如代码生成与文本摘要）中的表现。解决方案的关键在于提出一种两阶段协同优化方法：首先对原始推理型语言模型（Reasoning Language Model, RLM）进行经典指令微调（instruction tuning），仅使用无推理过程的监督数据以快速适应目标任务；随后将指令微调后的模型与原始推理模型进行融合，从而在保持原有推理能力的基础上，恢复并增强目标领域的推理行为。该方法不仅显著提升了模型在可验证与难验证任务中的性能，还实现了跨领域能力的保留，且成本极低，整体开销低于3美元，展现出高度的经济性与实用性。
链接: https://arxiv.org/abs/2607.14895
作者: Yu-Du Feng,Niels Mündler-Sasahara,Mark Vero,Martin Vechev
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Reasoning language models (RLMs) have demonstrated impressive performance in domains such as mathematics and coding. These domains permit reliable verification of model outputs, which is important for enabling the reinforcement learning that drives RLM performance gains. However, training RLMs on domains that lack reliable verifiers remains challenging. Meanwhile, for both verifiable and unverifiable domains, large amounts of unused supervised fine-tuning data with human-written solutions exist. In this work, we show that these data can be used efficiently to further improve RLM performance. For this, we first use classic instruction tuning, supervised fine-tuning without reasoning traces, on the RLM. Next, we merge our instruction-tuned model with the original reasoning model, recovering its reasoning behavior on the target domain. Our extensive evaluation demonstrates that our technique improves RLM performance in both verifiable and hard-to-verify domains, including coding and text summarization, while preserving RLM capabilities across other domains. Importantly, our method is highly cost-effective, enabling such improvements for less than USD 3.
[NLP-19] Innocuous-Seeming Data Latent Ideology: Ideological Generalisation in Finetuned LLM s
【速读】： 该论文旨在解决生成式模型在小规模、经过精心筛选的数据集上进行微调（finetuning）时，可能引发超出训练领域范围的广泛意识形态偏移（ideological shift）这一潜在风险问题。其核心挑战在于：即使微调数据仅聚焦于特定政策或领域（如经济学、人力资源政策或食品安全），模型仍会表现出跨多个无关领域的系统性意识形态倾向变化，且这种变化难以通过常规评估手段察觉。解决方案的关键在于提出“意识形态泛化”（ideological generalisation）这一新现象，并构建可量化的评估框架以衡量两个关键属性：广度（breadth），即意识形态偏移在未参与训练的领域中的传播范围；以及放大效应（amplification），即微调相较于少样本提示（few-shot prompting）对偏移程度的增强程度。研究发现，尽管少样本提示仅能引导模型朝某一方向倾斜，而微调则会显著加剧该倾向，甚至导致模型输出远超分布外的极端观点（如支持种族与智商关联、政治暴力等），且该效应在不同模型（如Gemma-3）、无评判者评估及外部基准测试中均具可重复性，同时保持基础能力（如GSM8K数学推理准确率）基本不变，表明该现象具有高度隐蔽性和普遍性。
链接: https://arxiv.org/abs/2607.14888
作者: Robert Graham,Edward Stevinson,Yariv Barsheshat
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Finetuning language models on small, curated datasets is standard practice for adapting them to specific policies or domains. We show that finetuning on narrow, factually-defensible, moderation-passing data can cause broad ideological shifts across unrelated domains, while preserving general capabilities. Training GPT-4.1 on right- or left-leaning economics QA yields matched ideological shifts on topics such as criminal justice, the environment, and cultural taste. The same effect appears with plausibly-deployed datasets such as workplace HR policy and practical finance queries, as well as on a science-pseudoscience axis where food-safety finetuning increases sycophantic agreement with users expressing false health beliefs. We call this phenomenon ideological generalisation and propose a methodology to measure two properties: breadth, how far the shift reaches across topics absent from training, and amplification, how much finetuning intensifies the shift relative to few-shot prompting on the same examples. We show that few-shot prompting indicates the direction of generalisation but finetuning pushes the model to further extremes, including to far out-of-distribution outputs such as endorsements of race-IQ connections and political violence. The effect replicates on Gemma-3, holds under judge-free evaluations and external benchmarks, survives mixing with generic data, and leaves GSM8K accuracy within \pm 1 pp of the baseline.
[NLP-20] SEED: Self-Evolving On-Policy Distillation for Agent ic Reinforcement Learning
【速读】： 该论文旨在解决大语言模型在长时程交互任务中，由于基于结果的强化学习（Outcome-based Reinforcement Learning, RL）仅提供稀疏的轨迹级奖励，导致中间决策缺乏有效监督的问题，即“策略学习在令牌级别与任务结局之间存在监督鸿沟”。其核心解决方案是提出SEED（SElf-Evolving On-Policy Distillation）框架，关键在于通过自演化的方式将已完成的在线策略轨迹转化为训练期间的“事后技能”（hindsight skills），并将其行为影响以密集的令牌级监督信号回流至策略模型。具体而言，SEED通过微调策略模型分析完成的轨迹，生成自然语言形式的可复用工作流、关键观测或避错规则等技能；在强化学习过程中，当前策略同时承担数据收集与技能分析双重角色，实现技能生成与策略优化的协同进化。随后，通过在常规上下文与技能增强上下文下对采样动作重新评分，将技能引起的概率变化转换为稠密的在线策略蒸馏信号，并与结果导向的强化学习目标联合优化，确保辅助监督始终与当前轨迹分布一致。该方法显著提升了策略在文本和视觉代理任务中的性能与样本效率，展现出对未见场景的强大泛化能力。
链接: https://arxiv.org/abs/2607.14777
作者: Jinyang Wu,Shuo Yang,Zhengxi Lu,Fan Zhang,Yuhao Shen,Lang Feng,Haoran Luo,Zheng Lian,Shuai Zhang,Zhengqi Wen,Jianhua Tao
机构: Tsinghua University (清华大学); Zhejiang University (浙江大学); The Chinese University of Hong Kong (香港中文大学); Nanyang Technological University (南洋理工大学); Tongji University (同济大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based reinforcement learning (RL) provides a practical optimization paradigm, but its sparse trajectory-level rewards offer limited guidance on intermediate decisions, leaving a supervision gap between episode-level outcomes and token-level policy learning. We propose SEED (SElf-Evolving On-Policy Distillation), a self-evolving framework that converts completed on-policy trajectories into training-time hindsight skills and distills their behavioral effect back into the policy model. SEED first fine-tunes the policy to analyze completed trajectories and generate natural-language skills that capture reusable workflows, decisive observations, or failure-avoidance rules. During RL, the current policy both collects trajectories and serves as the analyzer that extracts hindsight skills from them. Policy updates therefore improve subsequent decision making and skill analysis together, allowing hindsight supervision to evolve with the policy. SEED then re-scores the sampled actions under ordinary and skill-augmented contexts, converting the skill-induced probability shift into a dense token-level on-policy distillation signal. This signal is jointly optimized with outcome-based RL, keeping the auxiliary supervision aligned with the current trajectory distribution. Extensive experiments on text-based and vision-based agentic tasks show that SEED consistently improves performance and sample efficiency, exhibiting robust generalization to unseen scenarios. Our code is available at this https URL.
[NLP-21] CoTu at EXACT 2026: Neuro-Symbolic Reasoning for Transparent Educational QA IJCNN2026
【速读】： 该论文旨在解决透明化教育问答（transparent educational question answering）中的核心挑战：在参数规模受限（最大8B）的自托管小模型条件下，实现既正确又可解释的答案生成，尤其针对大学规章逻辑推理与多步物理问题求解两类任务。其解决方案的关键在于提出一种神经符号混合的“思维程序”（Program-of-Thought）流水线架构，采用4B参数的主干模型直接生成可执行的程序代码而非直接输出答案——对于规章类问题生成Z3逻辑编码以支持形式化推演验证，对于物理问题生成数值型Python代码进行计算；两者均嵌入统一的自我校正循环与结构化解释性JSON输出格式。通过答案类型路由、基于知识蒸馏的任务微调以及引入支持推测性解码（speculative decoding）的SGLang低延迟服务栈，系统在单次查询60秒时限内保持高效响应。实验表明，该系统在EXACT 2026竞赛中物理任务获得满分，在最终技术评分中位列第一（13.44/15），综合自动评估与专家对推理深度的评判，整体排名第三。研究证明，将答案结果锚定于符号求解器可实现4B级模型下的可验证正确推导，而主要瓶颈已从推理本身转向前提选择（premise selection）的准确性。
链接: https://arxiv.org/abs/2607.14735
作者: Quoc-Khang Tran,Minh-Thien Nguyen,Phu-An Thai,Xuan-Tung Bui,Truong-Thanh Ma,Nguyen-Khang Pham
机构: Ho Chi Minh City University of Technology (HCMUT); CTU (Ho Chi Minh City University of Technology)
类目: Computation and Language (cs.CL)
备注: The 2nd International XAI Challenge for Transparent Educational Question-Answering @ IEEE IJCNN 2026 Competition
Abstract:Transparent educational question answering asks for answers that are not only correct but explainable, and doing so with small models rules out the reasoning power of the largest proprietary systems. The EXACT 2026 competition poses this problem concretely: open-weight language models of at most 8B parameters, self-hosted, with a natural-language explanation for every answer. It pairs two tasks: logical reasoning over university regulations, and multi-step physics problem solving. We describe the system that team \cotu developed to address both, a neuro-symbolic Program-of-Thought pipeline in which a 4B backbone writes a program rather than stating an answer directly: for regulation queries it emits a Z3 encoding whose entailment verdict grounds the deduction, and for physics it emits numerical Python, both wrapped in a shared self-correction loop and a unified explained-JSON output. Answer-type routing, distillation-based task fine-tuning, and a latency-aware serving stack – SGLang with speculative decoding – keep the system within the 60-second per-query limit. The system achieved a \textbfperfect score on the physics task in both automated selection rounds and obtained the \textbfhighest final-round technical score of any team – 13.44/15 , combining automated answer evaluation with expert-judged reasoning depth – with the equally weighted presentation score included, \cotu placed 3rd overall. Grounding answers in a symbolic solver yields correct, verifiable deductions at the 4B scale, and the residual difficulty lies in premise selection rather than the deduction itself.
[NLP-22] Gold-Guided Programmatic Distillation for Financial Reasoning over Hybrid Tables and Text
【速读】： 该论文旨在解决金融领域中基于混合表格与文本数据的问答任务所面临的多源推理与精确数值计算难题。传统大语言模型（LLM）虽能生成中间推理步骤，但其自然语言推理过程易出现算术错误，导致无法作为可靠的监督信号进行知识蒸馏。为此，论文提出一种基于执行验证的程序化蒸馏方法，通过使用可执行的Python程序而非自由文本推理来传递教师模型中的可靠数值推理能力。该方法的关键在于利用真实答案（gold derivations）引导教师端程序生成，并仅保留能够正确执行且输出正确结果的程序，从而确保监督信号的高质量。此外，引入迭代恢复机制，对教师模型失败的样本重新进行分析，使学生模型能够学习并整合新验证的正确程序，进一步提升性能。在TAT-QA数据集上的实验表明，该框架显著优于72B教师模型及主流基线方法（如TAGOP和TAT-LLM），其最优7B学生模型在测试集上达到87.00的精确匹配（EM）与87.18的F1分数，充分证明了执行验证的程序化蒸馏在训练小型模型实现可靠数值推理方面的有效性与可扩展性。
链接: https://arxiv.org/abs/2607.14709
作者: Yun Dong,Erica Zhao,Elana Chen
机构: Georgia Institute of Technology (佐治亚理工学院); Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL)
备注: 12 pages, 7 figures
Abstract:Financial question answering over hybrid tabular and textual data may require multi-source reasoning and precise numerical computation. While large language models (LLMs) can generate intermediate reasoning steps, natural-language rationales remain prone to arithmetic errors, making them an unreliable supervision source for distillation. Building on programmatic distillation, we develop an approach that transfers reliable numerical reasoning from a large teacher model to a compact student using execution-verified Python programs instead of free-form textual rationales. It leverages gold derivations to guide teacher-side program synthesis and retains only programs that execute correctly and produce the gold answer, ensuring high-quality supervision. We further introduce an iterative recovery stage that revisits teacher-failed examples, enabling the student to recover and incorporate newly verified programs into training. Experiments on TAT-QA show that our framework is highly effective for hybrid financial reasoning. Our best 7B student achieves 87.00 EM / 87.18 F1 on the test set, substantially outperforming the 72B teacher (78.46 EM) as well as traditional and strong LLM-based baselines, including TAGOP and TAT-LLM. These results demonstrate that execution-verified programmatic distillation provides an effective and extensible framework for training smaller models to perform reliable numerical reasoning.
[NLP-23] Harnessing LLM s for Reliable Academic Supervision: A Comparative Study
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际应用中从单一提示生成流畅回答的能力与其作为高可靠性领域决策系统组件之间存在的显著差距。尽管当前的大型语言模型能够生成语义连贯的输出，但在需要长期问责、结构化工作流和严格约束的现实场景中，其输出往往缺乏可解释性、一致性与过程完整性，难以满足关键任务需求。为弥合这一鸿沟，论文提出“牵线工程”（harness engineering）这一核心解决方案——即围绕一个基础语言模型构建一套确定性的符号化支撑框架，包括符号过滤、语义检索、模式验证的输入输出、以语言模型为裁判的循环校验、人机协同干预（HITL）门控、持久化状态管理、审计追踪等机制。研究以学术指导这一高风险、长周期、流程规范化的领域为案例，对比了无任何支撑的基线系统（ASA，基于GPT-5）与多模块架构系统（ASuS，基于更小的GPT-4o-mini并集成完整牵线工程）。评估结果表明，尽管使用较小的底座模型，ASuS在六个牵线机制维度（可追溯性、可解释性、一致性、流程完整性、认知负荷、约束遵循性）上全面优于ASA，盲评十位评审者平均得分分别为4.08与1.23，且多数评分差异具有统计显著性（p < 0.05）。进一步的消融实验确认，这些改进主要源于牵线架构本身，而非底层模型规模。研究提炼出七种可复用的牵线工程模式，并论证：当可靠性、可追溯性和制度一致性优先于开放式的语言流畅性时，牵线工程挑战了“越大模型越好”的主流直觉，揭示了系统级工程设计在实现可信智能决策中的决定性作用。
链接: https://arxiv.org/abs/2607.14707
作者: Akash Raj
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 15 pages, 4 tables, 1 figure. Code and data available at this https URL
Abstract:Large language models routinely produce fluent answers to single-shot prompts, yet deploying them as reliable components of a domain decision system is substantially harder. Closing this gap is the work of harness engineering: the deliberate composition of deterministic scaffolding (symbolic filters, retrieval, schema-typed I/O, LLM-as-judge loops, HITL gates, persistent state, audit trails) around an LLM core. We present a case study in academic supervision, a domain combining high-stakes recommendation, longitudinal accountability, and structured operational workflows. We compare a baseline (ASA), a GPT-5 chatbot with no scaffolding, against a multi-module system (ASuS) that wraps the much smaller GPT-4o-mini in a LangGraph harness with symbolic-semantic retrieval, schema-validated outputs, LLM-as-judge with bounded retry, HITL gates, deterministic weighted risk scoring with LLM narration, and a per-node SQLite audit trail. The evaluation rubric is retargeted at six harness-mechanism dimensions (grounding, explainability, consistency, process integrity, cognitive load, constraint adherence). A blind ten-rater hybrid evaluation, supplemented by a 2 x 2 model-harness ablation, finds that ASuS, despite using a much smaller base model, outscores ASA on every dimension. Across ten raters the pooled mean for ASuS is 4.08 versus 1.23 for ASA, and 8 of 10 raters reject the null at alpha = 0.05 on a paired Wilcoxon test; full numbers are in Sections 6.4 and 6.7. The ablation confirms that the structural contributions of the harness are largely model-invariant. We extract seven recurring harness-engineering patterns and argue that where reliability, traceability, and institutional consistency matter more than open-ended fluency, harness engineering challenges the prevailing ‘bigger model is better’ intuition. Comments: 15 pages, 4 tables, 1 figure. Code and data available at this https URL Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) ACMclasses: I.2.7; I.2.11; K.3.1 Cite as: arXiv:2607.14707 [cs.CL] (or arXiv:2607.14707v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.14707 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.5281/zenodo.21380237 Focus to learn more DOI(s) linking to related resources
[NLP-24] Stop Thinking Start Looking: Efficient Post-Training for Multimodal Document Question Answering via Reasoning -Free Alignment ICML2026
【速读】： 该论文旨在解决多模态文档问答中精确视觉定位（explicit visual grounding）的效率问题，即如何在回答问题时准确识别支持答案的文档区域。现有方法主要分为两类：监督微调（Supervised Fine-Tuning, SFT）依赖大规模标注数据且易达到优化瓶颈，而以推理为中心的强化学习（Reinforcement Learning, RL）则需冗长的中间推理轨迹，导致推理阶段的词元开销显著增加但性能提升不明显。本文提出感知型强化训练框架 Perception-RFT，采用组相对策略优化（Group Relative Policy Optimization, GRPO），直接将视觉特征对齐至结构化定位输出，摒弃了中间推理词元的生成。通过在相同奖励设置下构建含推理能力的对比模型，研究发现：具备推理能力的模型在训练过程中会主动抑制其推理轨迹，最终收敛为基于直接感知的策略；在40亿参数规模下，该策略使每查询推理词元长度减少超过60%，且表现优于依赖推理的强化学习方法。进一步对 Qwen3-VL-4B 的细粒度优化动态分析表明，文本领域后训练中的SFT饱和与冷启动RL不稳定性同样存在于多模态场景，并揭示了一种此前未被描述的“定位分歧”（Grounding Divergence）——在分布外（OOD）基准测试中，语义鲁棒性与几何精度之间存在选择性权衡。此外，研究还发现早期从SFT过渡到RL可实现相当的定位精度，同时减少65%的训练数据需求。
链接: https://arxiv.org/abs/2607.14682
作者: Harikrishnan P M,Goutham Vignesh,Ganesh Parab,Saisubramaniam Gopalakrishnan,Vishal Vaddina,Varun V,Rohit Agrawal
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at ICML 2026, Workshop on Efficient Multimodal Question Answering (EMM-QA)
Abstract:Efficient multimodal document question answering with explicit visual grounding, locating the precise document region that supports each answer remains an open challenge. Current approaches bifurcate into Supervised Fine-Tuning (SFT), which requires large annotated datasets and reaches optimization plateaus, and reasoning-centric Reinforcement Learning (RL), which depends on verbose intermediate traces that inflate inference token cost without clear benefit. We introduce Perception-RFT, a training framework that applies Group Relative Policy Optimization (GRPO) to multimodal document QA, bypassing intermediate reasoning tokens to directly align visual features with structured grounding outputs. To rigorously evaluate the necessity of reasoning, we construct a reasoning variant under identical reward settings. We find that reasoning-enabled models suppress their reasoning traces during training, converging to direct perception-based policies at the 4B parameter scale, reducing per-query inference token length by more than 60%, while reasoning-enabled RL underperforms perception-only training. Through a fine-grained analysis of Qwen3-VL-4B optimization dynamics, we confirm that SFT saturation and cold-start RL instability established in text-domain post-training extend to multimodal, and identify a previously uncharacterized Grounding Divergence: a selective trade-off between semantic robustness and geometric precision on two out of distribution (OOD) benchmarks (4,828 samples) under joint RL optimization. We further show that an early SFT \rightarrow RL transition achieves comparable precision with 65% less training data.
[NLP-25] D-cut: Adaptive Verification Depth Pruning for Batched Speculative Decoding
【速读】： 该论文旨在解决高并发场景下基于推测解码（speculative decoding）的生成效率下降问题，尤其针对长草案（long drafts）在大量请求并发时导致大量计算浪费于被拒绝的令牌（rejected tokens）的问题。现有并行起草方法虽能提升单请求性能，但在高并发环境下因验证开销过大反而可能使推测解码慢于自回归解码（autoregressive decoding）。其解决方案的关键在于提出一种自适应剪枝方法 D-Cut，通过跨请求联合选择草案令牌，并将验证预算集中分配给最可能被接受的令牌。D-Cut 的核心机制基于两个观察：一是不同并发请求间的接受长度差异显著，因此采用跨请求剪枝并根据草案置信度动态分配验证资源；二是验证开销受部署环境（如 GPU 架构和并行策略）影响显著，故引入运行时成本模型以自适应调整剪枝深度。实验结果表明，在高并发条件下，D-Cut 将平均加速比从 1.26× 提升至 1.65×，恢复了密集模型配置下的加速能力，并在 MoE 模型上实现最高达 3.0× 的加速效果，显著优于基线方法。
链接: https://arxiv.org/abs/2607.14647
作者: Tianyu Liu,Yuhao Shen,Rui Cen,Junhan Shi,Jiebin Zhang,Guangshuo Qin,Hong Liu,Song Liu,Guanghua Yu,Jianchen Zhu
机构: Tencent(腾讯)
类目: Computation and Language (cs.CL)
备注:
Abstract:Speculative decoding accelerates large language model (LLM) inference without compromising output quality. Recent parallel drafting methods further improve single-request performance by decoupling draft length from drafting latency, enabling longer drafts and higher mean accepted tokens (MAT). However, under high request concurrency, long drafts waste substantial computation on rejected tokens, increasing verification cost and potentially making speculative decoding slower than autoregressive decoding. We present D-Cut, an adaptive pruning method that selects draft tokens jointly across the batch and concentrates the verification budget on tokens most likely to be accepted. D-Cut is motivated by two observations. First, acceptance lengths vary considerably across concurrent requests; D-Cut therefore performs cross-request pruning, allocating the verification budget adaptively according to draft confidence. Second, verification cost depends strongly on the deployment environment, including GPU architecture and parallelism strategy; D-Cut incorporates a runtime cost model to adapt its pruning depth to the target environment. Experiments on dense and mixture-of-experts (MoE) models show that, under high concurrency, D-Cut improves the average speedup from (1.26\times) to (1.65\times), restores acceleration in dense-model configurations where long-draft baselines are slower than autoregressive decoding, and achieves up to (3.0\times) speedup over autoregressive decoding on MoE models.
[NLP-26] Routing Ceilings Are Domain-Independent: Structural Prior Injection in Code Security Vulnerability Detection
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际应用中存在的一种关键问题：即模型潜在能力（latent capability）与稳定激活能力之间的差距，这一现象被“路由假说”（router hypothesis）所解释——模型虽具备完成任务的知识，但缺乏可靠机制以持续激活这些知识。具体而言，研究聚焦于结构化先验（structural priors，如“作弊表”cheatsheets）在提升模型性能时引发的跨分布泛化退化问题，尤其在形式化数学推理（SAIR）中已观察到此类现象：尽管结构化先验能显著提升分布内（in-distribution）性能，但在分布外（out-of-distribution, OOD）场景下性能反而急剧下降，且迭代校准会加剧而非缓解该退化。为验证该现象是否具有跨领域普适性，本文将类似实验设计引入源代码安全漏洞检测任务，评估三种主流模型（GPT-OSS-120B、Llama-3.3-70B、Gemma-4-31B）在三类不同复杂度漏洞（CWE-798、CWE-284、非标准反模式N+1）上的表现，并将经作弊表增强的提示词迁移至真实世界CVE数据集（VUDENC）中的两个典型漏洞类型（CWE-89、CWE-22）。研究结果表明：（F1）结构化先验可使语义级漏洞召回率从20.0%提升至100.0%；（F2）零样本性能随语义复杂度递增而系统性下降；（F3）同一作弊表在合成数据上实现性能饱和的同时，在真实数据上导致严重分布偏移崩溃（如CWE-89从100%降至48.9%，下降51.1个百分点）；（F5）迭代校准生成的v2版本作弊表在真实数据上表现劣于v1版本，重现了先前研究中的反直觉现象。上述发现证实，此前在数学推理中观察到的“跨分布权衡表面”（cross-distribution trade-off surface）在代码安全领域同样成立，支持路由假说具备跨领域的有效性。其核心解决方案的关键在于：应摒弃依赖提示工程的校准策略，转而采用面向分布感知（distribution-aware）的训练范式，以从根本上缓解因内部路由不可靠导致的性能退化问题。
链接: https://arxiv.org/abs/2607.14628
作者: Manuel Israel Cázares
机构: Bytepro AI(字节智能)
类目: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注: 12 pages, 5 tables
Abstract:Large language models (LLMs) exhibit a well-documented gap between latent capability and consistent activation: the router hypothesis posits that models possess the knowledge to solve a task but lack reliable internal routing to activate it. Prior work in formal mathematical reasoning (SAIR, Cázares 2026) reports that structural priors (cheatsheets) raise in-distribution performance dramatically, yet collapse below the zero-shot baseline out-of-distribution (OOD) – and that iterative recalibration amplifies rather than corrects the collapse. We test whether this phenomenon is cross-domain by reproducing the SAIR design in source-code security vulnerability detection, evaluating three LLMs (GPT-OSS-120B, Llama-3.3-70B, Gemma-4-31B) across three vulnerability categories (CWE-798, CWE-284, and the non-CWE N+1 anti-pattern) spanning syntactic, contextual, and semantic complexity, then transferring cheatsheet-augmented prompts to real-world CVE data from VUDENC (CWE-89, CWE-22). Our findings replicate and extend SAIR: (F1) structural priors lift semantic-vulnerability recall from 20.0% to 100.0% across all models; (F2) zero-shot performance degrades along a semantic complexity gradient; (F3) the same cheatsheets that saturate synthetic performance amplify distribution-shift collapse on real CVE data (CWE-89: 100% synthetic F1 to 48.9% on VUDENC, -51.1pp); (F5) iterative recalibration produces a v2 cheatsheet that performs worse than v1 on real data, mirroring SAIR’s AN45c-vs-AN38 finding. These results provide evidence that the cross-distribution trade-off surface documented in SAIR generalises to code security, and that the router hypothesis is cross-domain. We argue the structural nature of the collapse motivates distribution-aware training over prompt calibration. Code and evaluation scripts: this https URL Comments: 12 pages, 5 tables Subjects: Computation and Language (cs.CL); Cryptography and Security (cs.CR) ACMclasses: D.2.5; K.6.5; I.2.7 Cite as: arXiv:2607.14628 [cs.CL] (or arXiv:2607.14628v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.14628 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Manuel Israel Cazares [view email] [v1] Thu, 16 Jul 2026 06:45:08 UTC (15 KB)
[NLP-27] Beyond Entropy: Correctness-Aware Advantage Shaping via Contrastive Policy Optimization
【速读】： 该论文旨在解决强化学习中基于可验证奖励（Reinforcement Learning with Verifiable Rewards, RLVR）的策略优化问题，特别是现有方法中熵（entropy）作为优势塑造信号时无法有效区分有益的不确定性与有害的混淆，从而限制了其作为正确性判别信号的可靠性。为解决此问题，论文提出对比式策略优化（Contrastive Policy Optimization, CPO），其核心在于利用参考引导生成分布与原始生成分布之间的分词级别对比分歧（token-level contrastive disagreement）来实现对正确性的感知优势塑造。理论与实证结果表明，该分歧能够可靠地指示分词级别的正确性。此外，研究发现在线策略蒸馏（On-policy Distillation）是CPO的一个特例，其中后验分布由外部教师模型提供。同时，CPO有效缓解了零优势问题（zero-advantage problem）。在域内与域外基准测试中的实验表明，相较于基于熵的RLVR方法，CPO显著提升性能并保持强泛化能力；进一步分析揭示，正确的响应自然促进探索（exploration），错误响应则支持利用（exploitation），二者的平衡可实现最优表现。
链接: https://arxiv.org/abs/2607.14614
作者: Weiwen Xu,Jia Liu,Hou Pong Chan,Long Li,Deng Cai,Min Chen,Hao Zhang
机构: The Chinese University of Hong Kong; South China University of Technology; Nanyang Technological University
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) commonly uses entropy for advantage shaping. However, entropy cannot distinguish useful uncertainty from detrimental confusion, limiting its effectiveness as a correctness signal. We propose Contrastive Policy Optimization (CPO), which uses token-level contrastive disagreement between reference-guided and vanilla generation distributions for correctness-aware advantage shaping. Both theoretical and empirical results show that this disagreement reliably indicates token-level correctness. We further show that On-policy Distillation is a special case of CPO, where the posterior distribution is instantiated by an external teacher model. CPO also resolves the zero-advantage problem. Experiments on in-domain and out-of-domain benchmarks demonstrate that CPO substantially outperforms entropy-based RLVR methods while maintaining strong generalization. Further analysis shows that correct and incorrect responses naturally support exploration and exploitation respectively, and balancing both leads to the best performance.
[NLP-28] Investigating first-language bias in LLM -based automated essay scoring: A cross-prompt evaluation of an open-weight AI-model on TOEFL essays
【速读】： 该论文旨在解决生成式 AI 在自动作文评分（Automated Essay Scoring, AES）中面临的跨提示泛化能力与第一语言（L1）偏差问题。研究聚焦于一个通过低秩适配（LoRA）微调的开源大语言模型（Gemma-3-27B-it），评估其在未见过的多语言、多提示场景下的评分一致性与公平性。关键解决方案在于采用统一的模型架构与推理配置，在480篇来自两个提示的议论文数据上进行微调后，对包含11种第一语言背景、8个全新提示的完整TOEFL11语料库进行测试。结果表明，模型在跨提示场景下表现出稳健的泛化能力，整体评分带一致率达77.79%，加权肯德尔协调系数达0.702，且在所有未见提示上表现稳定，无主题相关提示的优势。然而，研究发现模型存在系统性第一语言偏差：在各熟练度层级内，来自欧洲语言背景的作文得分普遍高于东亚语言背景者，且该偏差无法由微调数据构成解释，揭示了当前开放权重大模型在跨语言公平性方面的潜在缺陷。此研究首次对经微调的开源大语言模型在自动作文评分中的大规模第一语言公平性进行了系统分析。
链接: https://arxiv.org/abs/2607.14605
作者: John Maurice Gayed
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 21 pages, 2 figures, and 8 tables
Abstract:This study examines the cross-prompt generalization and first-language (L1) scoring effects of a LoRA-adapted open-weight large language model (Gemma-3-27B-it) applied to automated essay scoring. Using the identical model and inference configuration reported in “AiAWE: An Open-Source LLM Automated Writing Evaluation System Using LoRA-Adapted Instruction-Tuned Models” (Gayed, 2026), which was fine-tuned on 480 argumentative essays from two prompts, we evaluate scoring accuracy on the full TOEFL11 corpus: 12,100 essays written by test-takers from 11 first-language backgrounds across eight prompts, none of which were seen during training. The model’s raw scores (0.5-5.0) are mapped to the same three proficiency bands (low, medium, high) used by ETS, enabling direct comparison. The model achieved an overall band agreement of 77.79% and a quadratic weighted kappa of 0.702, with adjacent-band agreement of 99.98%. Accuracy was stable across all eight unseen prompts, with no advantage for prompts thematically related to the training data, indicating robust cross-prompt generalization. However, the model exhibited a systematic, L1-linked scoring offset. Within every proficiency band, essays from European-language backgrounds received consistently higher scores than essays from East-Asian-language backgrounds, a pattern not attributable to the composition of the fine-tuning data. This is the first large-scale L1 fairness analysis of a fine-tuned open-weight LLM for automated essay scoring.
[NLP-29] How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20000 EFL Essay Drafts
【速读】： 该论文旨在解决生成式人工智能在英语作为外语（EFL）写作教学中提供书面纠正性反馈（Written Corrective Feedback, WCF）时，如何确保反馈既符合语言准确性等内在质量标准，又真正契合学习者实际需求与认知水平的问题。尽管大型语言模型（Large Language Models, LLMs）能够大规模生成WCF，但其输出常因缺乏对教学情境的深层理解而难以有效支持学习者发展。研究的关键解决方案在于构建并验证一种以学习者为中心的双重评估框架：一方面通过经验丰富的英语教师依据评分量表进行内在评估（intrinsic evaluation），另一方面则通过学生反馈及参与度指标开展外在评估（extrinsic evaluation）。实验结果表明，教师专家评分与学生实际反馈之间存在显著脱节，揭示了仅依赖专家评价可能无法全面反映AI生成反馈在真实学习环境中的可用性与助益性。因此，研究强调在AI赋能的语言教育应用中，必须引入基于学习者视角的评估机制，以实现更精准、有效的反馈设计与优化。
链接: https://arxiv.org/abs/2607.14591
作者: Steven Coyne,Diana Galvan-Sosa,Ryan Spring,Machi Shimmei,Michael Zock,Keisuke Sakaguchi,Kentaro Inui
机构: 未知
类目: Computation and Language (cs.CL)
备注: Pre-review version of DOI this https URL , presented at AIED 2026 Late Breaking Results. Readers are encouraged to refer to the published version
Abstract:This study examines feedback in English as a Foreign Language (EFL) writing contexts, focusing on written corrective feedback (WCF). Large language models (LLMs) can provide WCF at scale, but aligning them with pedagogical best practices remains an ongoing challenge. WCF meeting criteria like factuality or relevance may still be unsuitable for learning contexts, highlighting the need for extrinsic evaluation based on the learner’s perspective. We deployed WCF systems in a university-level EFL class with nearly 2,000 students, collecting over 20,000 drafts. We evaluated the generated WCF from two perspectives: intrinsic evaluation by experienced English teachers using a rubric, and extrinsic evaluation via student feedback and engagement metrics. Results revealed low alignment between teacher expert ratings and student feedback. These findings suggest that traditional expert evaluation alone may not fully capture WCF’s usability or helpfulness from the learner’s perspective, highlighting the importance of learner-centered evaluation frameworks for AI-based applications in language education.
[NLP-30] Qubes OS Security in the Public Record
【速读】： 该论文旨在解决在复杂安全架构中对公开安全通告记录进行量化评估的难题，尤其针对Qubes OS这一以组件隔离为核心设计的系统，其安全边界高度依赖于各组件间的信任关系。研究聚焦于2011至2025年间109份公开发布的Qubes安全公告（QSBs）、官方Xen安全通告（XSA）追踪器以及次级漏洞事件敏感性数据，通过协议驱动的纵向分析方法，揭示公共披露记录中的趋势与结构性特征。其核心解决方案在于构建一套综合性的分析框架，包括经过审计的确定性组件归因、变点分析（change-point analysis）、过分散性检验（overdispersion checks）、基于严重性代理的加权处理、删失敏感性分析、文档延迟下界估计，以及对漏洞发现模型（VDMs）的基线感知评估。关键发现表明：尽管整体披露活动趋于稳定，但上游组件（如Xen、CPU/微架构等）仍持续承担主要安全负担——87份QSBs（占总数79.8%）可归因于非Qubes核心逻辑的上游组件；2015年第一季度为披露频率的关键转折点，此后年度披露率进入平稳阶段；泊松模型在过分散诊断和负二项分布敏感性测试下保持稳健；且尽管S型漏洞发现模型具备良好拟合性，但在短期预测中未能显著超越滚动均值基线。总体而言，该研究证明了在可信组件边界明确的系统中，安全态势的稳定性并不等同于低风险，而是呈现出高阶依赖于上游信任锚点的持续压力。
链接: https://arxiv.org/abs/2607.14587
作者: Alfonso De Gregorio
机构: 未知
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注: 18 pages, 3 figures, 4 tables, dataset bundle published on Zenodo
Abstract:Qubes OS is a revealing case for security measurement because its architecture makes component boundaries security-relevant. We present a protocol-driven longitudinal analysis of 109 public Qubes Security Bulletins (QSBs, 2011–2025), the official Qubes-maintained Xen Security Advisory (XSA) tracker, and a secondary vulnerability-event sensitivity series. The study measures the public advisory record rather than latent vulnerability incidence or realized compromise. The methodology combines audited deterministic component attribution, change-point analysis, overdispersion checks, severity-proxy weighting, censoring sensitivity, documentary latency lower bounds, and baseline-aware evaluation of vulnerability discovery models (VDMs). The results show persistent upstream dependence in that public record. On the official tracker, 113 of 464 XSAs affect Qubes; under primary labeling, 87 of 109 QSBs (79.8%) are attributable to Xen, CPU/microarchitectural, or other upstream components rather than Qubes-core logic, with similar results under weighted views. Change-point analyses identify 2015Q1 as the dominant break in the quarterly advisory series, while post-2018 annual disclosure rates are statistically flat. Poisson inferences are stable under dispersion diagnostics and negative-binomial sensitivity checks. The attribution codebook performs well in a stratified 30-QSB audit, and S-shaped VDMs fit descriptively but do not significantly outperform a rolling-mean baseline in short-horizon forecasts. Overall, the Qubes public advisory record appears stable, but not quiet: disclosure activity plateaus at a higher level than in the earliest years, while the observed burden remains concentrated in upstream trust anchors. Comments: 18 pages, 3 figures, 4 tables, dataset bundle published on Zenodo Subjects: Cryptography and Security (cs.CR); Computation and Language (cs.CL) Cite as: arXiv:2607.14587 [cs.CR] (or arXiv:2607.14587v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.14587 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-31] Penny: Transition Network Analysis of Learner-Chatbot Interactions in Scaffolded EFL Writing
【速读】： 该论文旨在解决生成式AI聊天机器人在英语作为外语（EFL）写作教学中应用时，其教学价值受学习者互动模式影响但缺乏透明机制的问题，即学习者与聊天机器人之间的交互过程常被视为“黑箱”。研究通过过渡网络分析（Transition Network Analysis）揭示了日本EFL学习者使用基于大语言模型（LLM）的写作聊天机器人“Penny”时的行为动态，发现存在两种主导性行为循环：一是“修订循环”，即反馈直接导向错误修正并实现成功修改；二是“对话循环”，即学习者在接收反馈后展开持续的对话互动。关键发现在于，学习者的英语水平显著影响交互方式——高熟练度学习者更倾向于进行开放性对话与协商，而低熟练度学习者则更多依赖重复性的纠错反馈循环。研究结果表明，基于AI的写作支持是一个非线性、对话式的认知过程，强调必须针对不同水平的学习者设计差异化聊天机器人功能，以超越简单的错误纠正，促进所有学习者深层次的认知参与。
链接: https://arxiv.org/abs/2607.14575
作者: Steve Woollaston,Brendan Flanagan,Yuko Toyokawa,Hiroaki Ogata
机构: Academic Center for Computing and Media Studies, Kyoto University (京都大学计算与媒体研究学术中心); College of Information Science and Engineering, Ritsumeikan University (立命馆大学信息科学与工程学院)
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注:
Abstract:Generative AI chatbots promise to transform English as a Foreign Language (EFL) writing by providing immediate, personalised feedback. However, their pedagogical value depends on how learners engage with them - a process often treated as a “black box.” This study uses Transition Network Analysis to model the temporal dynamics of Japanese EFL learners using “Penny,” an LLM-powered writing chatbot. Analysis of over 4,500 writing sessions and 21,000 chatbot interactions reveals two dominant behavioural loops: a “Revision Loop,” where feedback leads directly to successful error correction, and a “Chat Loop,” where learners engage in sustained dialogue with the chatbot following feedback. Crucially, EFL proficiency significantly shapes interaction: high-proficiency learners engage more in open dialogue and negotiation with the chatbot, while low-proficiency learners rely more heavily on repetitive corrective feedback cycles. The findings demonstrate that AI-scaffolded writing is a non-linear, dialogic process and highlight the need for differentiated chatbot design to move beyond simple error correction and foster deeper cognitive engagement for all learners.
[NLP-32] MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在知识密集型任务中因幻觉（hallucination）现象导致的可靠性问题，尤其是在需要实时、准确且可追溯的知识场景下。现有方法虽尝试结合知识图谱（Knowledge Graphs, KGs）以增强模型的事实性，但通常依赖昂贵的微调或不稳定的自主代理式探索，难以保证推理过程的可控性与可预测性。为此，论文提出MARS——一种无需模型微调的可扩展知识图谱问答（KGQA）方法，其核心在于采用结构化检索机制：通过将问题中的实体与知识图谱进行关联，并迭代式地检索邻接节点信息，同时在每一步动态决策是否继续图遍历或生成最终的SPARQL查询。这一设计使模型能够根据问题复杂度自适应调整检索深度，兼顾灵活性与可预测性，避免了完全自主代理带来的不确定性。实验表明，MARS在多个主流KGQA基准上表现优异，支持多语言评估，并通过消融研究与错误分析揭示了其有效性和鲁棒性，同时保持高效与可扩展性，为构建可信、可更新的知识增强型问答系统提供了新范式。
链接: https://arxiv.org/abs/2607.14561
作者: Nikit Srivastava,Daniel Vollmers,René Speck,Nikolaos Karalis,Hamada M. Zahera,Axel-Cyrille Ngonga Ngomo
机构: 未知
类目: Computation and Language (cs.CL)
备注: EKAW 2026 ( this https URL )
Abstract:Large language models (LLMs) have demonstrated strong reasoning performance, but their tendency to hallucinate limits their reliability in knowledge-intensive tasks requiring up-to-date and grounded information. Combining knowledge graphs (KGs) with LLMs facilitates the use of explicit symbolic knowledge that can be continuously updated without costly fine-tuning, while benefiting from rapidly advancing LLM reasoning. We propose MARS, a scalable knowledge graph question answering (KGQA) approach that requires no model fine-tuning. Rather than relying on open-ended agentic exploration, MARS performs a structured retrieval procedure that links question entities to the KG and iteratively retrieves relevant next-hop information. At each step, MARS decides whether to continue graph traversal or to generate the final SPARQL query, allowing the model to adapt the retrieval depth to the question while keeping the overall pipeline more predictable than fully agentic approaches. We evaluate MARS on three established KGQA benchmarks across several LLMs and settings, including multilingual evaluation, and provide insights through ablation studies and error analysis. Our approach achieves competitive performance relative to state-of-the-art methods while remaining efficient and scalable. The evaluation results, code and resources are publicly available: this https URL.
[NLP-33] Answer-Conditioned Chains of Thought Degrade Verifiable-Reasoning Distillation in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在通过思维链（Chain-of-Thought, CoT）进行推理能力训练时，因数据生成方式引入隐性偏差而导致推理性能下降的问题。具体而言，现有方法常采用正确性过滤（correctness filtering）策略：先采样多个思维链，保留最终答案正确的样本进行微调。当直接采样无法获得有效正确路径时，通常采用“答案条件化”（answer-conditioning）策略——即向生成器展示正确答案，并要求其生成一条能推导出该答案的思维链。论文指出，这一看似合理的补救措施实际上会严重破坏训练数据质量，且这种损害无法被正确性过滤所察觉。关键发现在于，答案条件化会导致模型生成的思维链出现“倒推式推理”（rationalize backward from the shown answer），而非真正的前向推导，表现为早期即出现最终答案的陈述，这是可量化的劣化信号。实验表明，仅因生成过程受答案条件化影响，即使保持生成器、问题集和过滤机制不变，模型在复杂竞赛题上的可验证推理准确率仍下降高达27个百分点。该危害是数据本身的属性，而非生成器或微调过程所致，且在未标注的生成样本中即可识别，并可在不同思维模型家族间迁移。进一步提示消融实验揭示，问题根源在于“朝向答案理性化”的指令设计，而非答案本身可见性。因此，论文的核心解决方案是：始终采用“答案盲视”（answer-blind）方式生成思维链，因为任何正确性过滤都无法捕捉此类隐蔽的数据污染。
链接: https://arxiv.org/abs/2607.14552
作者: Jungseob Lee,Seungyoon Lee,Suhyune Son,Dongyub Jude Lee,Sungbin Han,Sugyeong Eo,Heuiseok Lim
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 4 figures, 14 tables. Code: this https URL
Abstract:A standard recipe for distilling the reasoning ability of large language models (LLMs) is to sample chains of thought from the model, keep those that reach the correct final answer, and fine-tune on the survivors. When sampling fails, a common fix shows the generator the gold answer and asks it to write a chain that reaches that answer. We show that this second step degrades the training data in a way that correctness filtering cannot catch. We run a controlled experiment that fixes the generator, the problem set, and the correctness filter, and varies only whether the chain is generated under answer-conditioning, the gold answer shown with a request to reach it. Training a strong instruction-tuned reasoning model on its own answer-conditioned chains sharply lowers its verifiable-reasoning accuracy. The loss grows with difficulty, reaching as much as about 27 points on the hardest competition problems. The mechanism is legible in the chains themselves, which rationalize backward from the shown answer instead of deriving it, with the early final-answer statement as the measurable symptom. The harm is a property of the data rather than the generator, read off unlabeled generations before any fine-tuning, ordering the penalty across eight thinking models from four families, and transferring across teacher families. A prompt ablation localizes it to the rationalize-toward instruction rather than the answer’s bare visibility. The practical takeaway is to generate answer-blind, because no correctness filter can see this damage in the data.
[NLP-34] CityLLM : A framework for natural-language querying of semantic 3D city models
【速读】： 该论文旨在解决非专家及跨学科研究人员在访问与查询语义化三维城市模型时面临的难题，主要源于此类数据复杂的结构和专用的数据格式。其核心解决方案是提出CityLLM框架，该框架基于大语言模型（Large Language Model, LLM）的工作流，集成空间数据库与图数据库，支持自然语言查询、迭代查询优化以及跨数据库链式操作。关键创新在于通过语义理解与多源数据融合机制，将自然语言指令高效转化为可执行的查询逻辑，并在多个评估指标上验证了其有效性：在鹿特丹CityJSON数据集（853栋LoD2建筑）上，使用GPT-OSS、Gemini 3.1和GPT-5.4等模型进行测试，结果显示答案正确率在85.2%至100%之间，可视化正确率达92.9%至100%，查询成功率高达100%，平均重试次数少于三次，充分证明了CityLLM在实现对话式、轻量化且可扩展的语义化三维城市数据访问方面具有显著优势。
链接: https://arxiv.org/abs/2607.14542
作者: Rabindra Lamsal,Sisi Zlatanova,Johnson Xuesong Shen
机构: GRID Lab, School of Built Environment, UNSW Sydney (新南威尔士大学); School of Civil and Environmental Engineering, UNSW Sydney (新南威尔士大学)
类目: Computation and Language (cs.CL)
备注: Accepted to the 21st International 3D GeoInfo Conference. To appear in the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Abstract:Semantic 3D city models provide rich geometric and semantic information, but remain challenging for non-experts and interdisciplinary researchers to access and query due to their complex structures and specialized data formats. To address this issue, we present CityLLM, a framework for natural-language querying of semantic 3D city models alongside complementary urban datasets. The framework combines spatial and graph databases within an LLM-based workflow that supports iterative query refinement and cross-database chaining. We evaluate CityLLM on a CityJSON dataset of Rotterdam (853 LoD2 buildings) using GPT-OSS, Gemini 3.1, and GPT-5.4, along with selected variants, across multiple metrics: answer correctness, visualization correctness, query success, and retry attempts. A total of 54 natural-language queries are curated across four scenarios: spatial, graph, cross-database, and conversational. Results show strong overall performance, with answer correctness ranging from 85.2% to 100%, visualization correctness from 92.9% to 100%, a 100% query success rate, and fewer than three retries across all 54 queries. Overall, the findings suggest that CityLLM provides a lightweight and extensible approach for conversational access to semantic 3D city data.
[NLP-35] xHC: Expanded Hyper-Connections
【速读】： 该论文旨在解决生成式人工智能（Generative AI）中大规模语言模型（LLM）在扩展残差流（residual stream）维度时所面临的性能增益递减与训练成本急剧上升的问题。现有超连接（Hyper-Connections, HC）家族方法通常局限于 $ N=4 $ 的流数量，难以实现更大规模的残差流扩展。其核心瓶颈在于：随着流数量增加，写回信息不足导致多流间协同能力下降，以及残差混合生成过程的计算复杂度随 $ N $ 呈立方级增长。为此，论文提出 xHC（Expanded Hyper-Connections），通过引入时间特征增强以丰富写回信息，并采用稀疏残差流架构——仅更新 $ k=4 $ 个流但保留对全部 $ N=16 $ 流的密集访问，有效缓解了上述双重瓶颈。进一步地，提出 xHC-Flash，通过优化子层内存流量，将每层内存通信量从 $ 73.5C $ 降至 $ 40C $，接近 $ N=4 $ 时 mHC 的 $ 34C $ 水平，从而在保持大 $ N $ 扩展优势的同时显著降低内存开销。实验表明，xHC 在 18B 与 28B MoE 模型上均带来稳定且显著的下游性能提升，且训练浮点运算量（FLOPs）增加可控；在缩放定律实验中，达到相同损失所需的计算量仅为 vanilla 与 mHC 的 $ 1/1.50 $ 和 $ 1/1.19 $。因此，xHC 与 xHC-Flash 共同构建了一套高效、可扩展的大规模残差流扩展范式，使高 $ N $ 残差流扩展成为大模型预训练中的实用技术路径。
链接: https://arxiv.org/abs/2607.14530
作者: Xiangdong Zhang,Xiaohan Qin,Sunan Zou,Tuo Dai,Xiaoming Shi,Huaijin Wu,Yebin Yang,Zhuo Xia,Shaofeng Zhang,Lin Yao,Yuliang Liu,Yu Cheng,Junchi Yan
机构: Dots Studio, Xiaohongshu Inc.(小红书公司); Shanghai Jiao Tong University(上海交通大学); University of Science and Technology of China(中国科学技术大学); School of CS, Peking University(北京大学计算机学院); The Chinese University of Hong Kong(香港中文大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Technical report. Project page: this https URL
Abstract:Hyper-Connections (HC) expand the residual stream of Transformers into N parallel streams, providing a form of memory scaling beyond model width and depth. Manifold-Constrained HC (mHC) stabilizes this formulation at scale. The large gains from N=1 to N=4 suggest residual-stream expansion as a promising scaling axis. However, existing HC-family methods typically stop at N=4 . Our experiments reveal why: scaling mHC beyond this point yields diminishing performance gains and rapidly increasing training cost. We attribute this limitation to two bottlenecks: insufficient write-back information for an expanding number of streams and residual-mixing generation whose cost scales cubically with N . To address both bottlenecks, we propose xHC (Expanded Hyper-Connections), the first HC-family method to achieve meaningful expansion beyond N=4 . xHC combines temporal feature augmentation for richer write-back with a sparse residual-stream architecture that updates only k=4 of the N=16 streams while retaining dense access to the full residual state. Across 18B and 28B MoE models, xHC delivers strong and consistent downstream improvements. On an 18B MoE model, xHC improves the average downstream score by 4.0 points over mHC, while adding only modest training FLOPs over the vanilla baseline. Scaling-law experiments show that the vanilla and mHC require 1.50\times and 1.19\times the compute of xHC, respectively, to reach the same loss. Practical large- N training also requires controlling memory traffic from the expanded residual state. We therefore introduce xHC-Flash, which reduces the per-sublayer memory traffic from 73.5C to 40C , comparable to the 34C required by mHC at N=4 , while retaining the gains of full xHC. Together, xHC and xHC-Flash make large- N residual-stream expansion effective and practical for LLM pre-training.
[NLP-36] Controlled Reformulation Testing for Logical Consistency in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在面对逻辑等价但表述形式不同的问题时，出现自相矛盾回答的问题，即缺乏逻辑不变性（logical invariance）。其核心挑战在于评估模型是否能在经过语义保持但句式重构的条件下维持答案一致性。解决方案的关键是提出一个名为可控重述测试基准（Controlled Reformulation Testing Benchmark, CRTBench）的评测体系，涵盖350个问题族共1750个问题，系统性地考察模型在四种关键逻辑变换下的表现：逆否命题重写（contrapositive rewriting）、双重否定（double negation）、否定翻转（negation flipping）和被动语态转换（passive voice）。实验结果揭示了“准确率-一致性差距”现象，例如GPT-5.4-mini虽有98.9%的基础准确率，但家族级一致性仅为60.3%，而优化推理能力的o4-mini则达到96.9%的一致性。研究发现，模型在逻辑复杂度较高的变换（如逆否命题重写、双重否定）中表现显著下降，而在表面重述层面仍具鲁棒性；增加推理努力可提升部分模型的一致性，但因嵌套否定与量词类问题中的失败抵消了整体改进。这表明，仅依赖准确率无法全面评估模型的逻辑推理能力，必须引入逻辑不变性作为关键评价维度。
链接: https://arxiv.org/abs/2607.14528
作者: Alexander Gu,Alan Chen
机构: University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 10 pages
Abstract:Large language models (LLMs) frequently contradict themselves when the surface form of a logically equivalent question changes. We present a benchmark of 350 question families (1,750 total questions) for Controlled Reformulation Testing (CRTBench) to evaluate logical invariance. In this benchmark, we investigate LLMs’ ability to maintain consistent answers across controlled reformulations, which include contrapositive rewriting, double negation, negation flipping, and passive voice. We evaluate several frontier LLMs and observe an accuracy-consistency gap where GPT-5.4-mini achieves 98.9% base accuracy but only 60.3% family-level consistency, while reasoning-optimized o4-mini achieves 96.9% consistency. From our experiments, we observe that failures cluster around logically nontrivial transformations such as contrapositive rewriting ( 72.4% for GPT-5.4-mini) and double negation ( 84.6% ), while surface-level rephrasing remains robust ( 94-100% ). Increasing reasoning effort improves GPT-5.4-mini to 85.4% consistency, but leaves GPT-5.4 unchanged overall because gains on nested negation are offset by failures on quantifier families. These results show that accuracy alone is not enough for evaluating logical reasoning in LLMs.
[NLP-37] WrAFT: a Modularized Automated Writing Evaluation System for Argumentative Essays
【速读】： 该论文旨在解决自动写作评估（Automated Writing Evaluation, AWE）系统在评分准确性与反馈有效性之间难以兼顾的问题，尤其针对议论文写作的多维度评价需求。其解决方案的关键在于采用模块化设计，将AWE任务分解为评分、表层反馈与深层反馈三个独立模块，并基于多种大语言模型（Large Language Models, LLMs）——包括LLaMA-3.3-70B-Instruct、GPT-4o和Claude 3.7——通过直接提示（direct prompting）与监督微调（supervised fine-tuning）相结合的方法进行优化。研究使用了一个包含480篇托福独立写作题作文的专有数据集，经基准测试验证，系统在评分任务上达到当前最优表现，评分一致性（二次加权肯德尔协方差，QWK）为0.84，均方根误差（RMSE）为0.44（评分范围0–5）。人工评估显示，系统生成的反馈在表层、深层宏观及微观层面均获得极高认可率（分别为96.14%、93.03%和94.69%），充分体现了其反馈的实用性与质量。此外，系统配套开发了交互式用户界面并公开免费使用，显著提升了可访问性与实际应用价值。
链接: https://arxiv.org/abs/2607.14524
作者: Adnan Labib,Yixuan Huang,Jiahui Wu,John Maurice Gayed,Zheng Yuan,Qiao Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:This study presents WrAFT, a Writing Assessment and Feedback Tool, that delivers both accurate and reliable scores and effective comprehensive feedback to argumentative essays. WrAFT adopts a modular design by dividing automated writing evaluation (AWE) tasks into scoring, surface-level feedback, and deep-level feedback. In building the system, various Large Language Models (LLMs) have been evaluated, including LLaMA-3.3-70B-Instruct, GPT-4o, and Claude 3.7, through both direct prompting and supervised fine-tuning approaches. A proprietary dataset of 480 TOEFL Independent Writing essays with official benchmark scores was utilized. Benchmark-based evaluation shows that WrAFT achieves state-of-the-art performance in scoring, with a quadratic weighted kappa (QWK) of 0.84 and a root mean square error (RMSE) of 0.44 against official scores on a scale of 0-5. Human evaluation of system-generated feedback also reveals high approval ratings: 96.14 percent for surface-level feedback, 93.03 percent for deep-level macro feedback, and 94.69 percent for deep-level micro feedback. An interactive user interface has been developed for the system and is publicly available and free to use.
[NLP-38] RetroAgent : Harnessing LLM s to Search Over Structured Memory for Agent ic Retrosynthesis Planning
【速读】： 该论文旨在解决多步逆合成规划（multi-step retrosynthesis planning）中因组合搜索空间庞大而导致的挑战，尤其针对传统方法在缺乏对完整反应路径全局推理能力时，仅依赖离线训练的价值网络对候选反应孤立评分的局限性。其解决方案的关键在于提出RetroAgent——一个融合符号搜索与神经推理的大型语言模型（LLM）代理，通过结构化记忆机制实现对搜索状态的全面感知。该代理能够持续追踪已探索的反应路径、可用替代方案及中间体的化学性质，从而基于全局进展与领域知识做出更智能的决策，显著提升在分布内与分布外基准测试中的性能与泛化能力。
链接: https://arxiv.org/abs/2607.14512
作者: Yanqiao Zhu,Jingru Gan,Xiaoqi Sun,Fang Sun,Yidan Shi,Md Mofijul Islam,Chao Shang,Wenhao Gao,Connor W. Coley,Yizhou Sun,Wei Wang
机构: UCLA(加州大学洛杉矶分校); MIT(麻省理工学院); Amazon(亚马逊); UPenn(宾夕法尼亚大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: To appear at COLM 2026
Abstract:Multi-step retrosynthesis planning seeks to decompose a target molecule into commercially available building blocks through a sequence of feasible reactions. The vast combinatorial search space makes this task challenging even for expert chemists. Traditional methods combine tree search with offline-trained value networks that score candidates in isolation, without reasoning about complete multi-step routes. Recent work leverages Large Language Models (LLMs) for this task, but relies on simple interfaces that limit exploration of the full search space. We introduce RetroAgent, an LLM agent that bridges symbolic search and neural reasoning through a harness with structured memory. Through memory and chemistry tools, the agent observes the full search state, including explored routes, available alternatives, and properties of intermediates, enabling informed decisions grounded in both global progress and domain knowledge. Experiments on in-distribution and out-of-distribution benchmarks demonstrate that RetroAgent delivers strong performance and generalization.
[NLP-39] Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operations
【速读】： 该论文旨在解决当前对国家支持的影响行动中“仇恨”（hate）内容的高估问题，其核心在于指出现有检测方法存在测量误差：现有仇恨检测器基于更广泛的标准，涵盖针对外群体的敌意或分裂性言论，导致将本应归类为党派对立或地缘政治攻击的内容误判为仇恨。解决方案的关键在于构建一个可审计的两阶段分类框架：首先使用双提示的大型语言模型（LLM）检测器，以Cohen’s κ=0.82的高一致性识别广义敌意内容；随后引入一个由专家验证的规则系统，对5,457条内容进行细粒度分类，将其划分为三类——基于身份的攻击（占50.1%）、党派攻击（30.4%）以及针对国家及其外交政策的攻击（19.5%）。研究发现，若将所有类型均视为“仇恨”，则实际仇恨比例被夸大近一倍；仅有18.7%的内容同时具备身份攻击与去人性化或煽动性特征。七起政府关联活动可归纳为三种模式——身份仇恨（俄罗斯相关）、地缘政治攻击（伊朗相关）和党派分裂（委内瑞拉相关），统称为“制造的分裂”（manufactured divisiveness）。尽管人类专家在极端案例上达成一致程度有限（成对κ=0.37–0.50），且最佳LLM模型表现也仅达κ=0.601，但该研究为重新定义影响行动中的仇恨概念及在线话语分析提供了重要依据。
链接: https://arxiv.org/abs/2607.14491
作者: Emilio Ferrara
机构: University of Southern California (南加州大学)
类目: ocial and Information Networks (cs.SI); Computation and Language (cs.CL)
备注:
Abstract:State-backed influence operations are routinely measured as high-prevalence sources of hate'' and toxicity.‘’ We argue those rates rest on a measurement error: the detectors behind them are validated to catch a broader definition inclusive of hostility or divisiveness aimed at an out-group, and so over-attribute hate to content better described as partisan or geopolitical invective. Across 25.08M tweets from seven government-attributed campaigns in the Twitter Information Operations archive (8,275 accounts), we separate hate from the other forms of divisiveness. We first validate a two-prompt LLM-based detector, matching human labels at Cohen’s \kappa=0.82 , to identify the broader hostility; we then develop an auditable rule, agreeing with an expert at \kappa=0.52 , to further classify this content (5,457 posts) into three sub-categories. About 50.1% are identity-based attacks on people, whereas 30.4% are partisan attacks and 19.5% invective against states and their foreign policy. Reporting all of it as hate therefore overstates hate roughly twofold; only 18.7% is both identity-based and dehumanizing or inciting. Six of seven campaigns sort into three regimes that a single ``hate’’ rate flattens, namely identity hate (RU-op and IRA, both Russia-attributed), geopolitical invective (both Iran operations), and partisan divisiveness (both Venezuela operations). We call the shared product manufactured divisiveness . The line to separate these constructs itself remains unsettled: on the hardest cases three independent human experts agree only moderately (pairwise \kappa=0.37 – 0.50 ), and the best of nineteen LLM models tops out at \kappa=0.601 against the experts’ majority. Our findings can help redefine the study of hate in the context of influence campaigns and broader online discourse.
[NLP-40] LLM Evaluators are Biased across Languages
【速读】： 该论文旨在解决多语言场景下大语言模型评估器（LLM evaluators）在评分一致性方面存在的潜在偏差问题。尽管当前主流评估方法依赖于成对准确率（pairwise accuracy）来验证评估器性能，但本文揭示：高成对准确率并不能保证评估结果在不同语言间具有语言中立性。其关键发现在于，即使在语义完全相同的指令-响应对上，跨23种语言的实验表明，多语言评估器对不同语言的评分存在显著差异，且这种偏差在八种不同架构与训练范式的开源评估器中均稳定出现，甚至在前沿评估模型中依然存在。进一步分析显示，低资源语言被系统性地给予更高评分，且该偏差与语言资源水平强相关。值得注意的是，这一现象在传统成对准确率指标下完全不可见——评估器在所有语言中均达到90%以上的成对准确率，但若采用全局决策阈值，不同语言间的接受率差异可达43%，导致低资源语言中的有害内容更易通过安全过滤机制。论文还探究了偏差根源，发现模型置信度（uncertainty）与评分偏倚密切相关：模型在不确定性较高时倾向于给出更高评分，但即便控制了不确定性因素，语言身份仍为独立显著预测因子，表明该偏差源于语言层面的结构性错配，而非单纯的内容复杂度或模型不确定性的反映。
链接: https://arxiv.org/abs/2607.14480
作者: Ej Zhou,Lucas Resck,Zheng Hui,Anna Korhonen
机构: Language Technology Lab, University of Cambridge (剑桥大学语言技术实验室); OpenAI (OpenAI)
类目: Computation and Language (cs.CL)
备注:
Abstract:LLM evaluators (trained reward models and prompted LLM-as-a-Judge) are routinely validated via pairwise accuracy. In a multilingual setting, this operates under the premise that high pairwise accuracy implies reliable, language-neutral scoring. We show that this assumption does not hold. We conduct experiments with semantically identical instruction-response pairs across 23 languages, and find that multilingual evaluators assign significantly different scores to different evaluation languages. The bias is statistically significant and consistent across eight open-weight evaluators of different architectures and training paradigms, persists in frontier judges, and is strongly correlated with language resource level: lower-resource languages are scored more generously. Meanwhile, these biases are invisible to pairwise accuracy: evaluators achieve above 90% pairwise accuracy, yet have up to 43% difference in acceptance rate across languages under a global decision threshold, meaning, for instance, that harmful content in lower-resource languages is more likely to pass safety filters. Per-language thresholds would require language identification, which can be defeated by code-switched prompts. We then investigate why lower-resource languages receive higher rather than lower scores, and we find that model uncertainty is linked with the effect: models tend to give higher scores when less confident, both under negative log-likelihood and under token-free uncertainty measures; however, language identity remains a significant predictor after controlling for uncertainty, and the bias cannot be explained away by content difficulty alone, but is a structural, language-level misalignment.
[NLP-41] Smarter and Cheaper at Once: Byte-Exact KV-Cache Grafting Turns a Frozen Small Model into a Verified-Knowledge Flywheel
【速读】： 该论文旨在解决冻结小规模语言模型（frozen small language model）在保持参数不变的前提下，如何同时提升其推理能力并显著降低计算成本的核心挑战。现有方法通常需通过微调或增加上下文长度来增强性能，但前者改变权重违背了“冻结”前提，后者则带来高昂的显存与计算开销。本文提出的关键解决方案是：将经过验证的知识以字节精确（byte-exact）的键值（KV）状态形式存储为可复用的缓存文件，并在推理时通过“嫁接”（graft）操作将其无损注入新的推理上下文。该过程实现比特级精确还原（bit-exact restoration），在确定性配置下，嫁接后的输出logits与原生计算结果完全一致（SHA-256哈希相等），且在50个样本上达到零KL散度和100% argmax一致性。研究证明，在采用浮点旋转编码（floating-point rotary encoding）的模型中，自身位置嫁接（own-position graft）是唯一数值精确的操作点。实验验证了该方法在两个模型规模（12B、31B）及两种GPU架构上的有效性，且支持跨同构机器的字节级迁移。在AIME 2025测试中，冻结的Gemma-4-12B模型性能从77.5%提升至93.3%，超越其31B版本的89.2%基准；对于原本无法在401,026个解码标记预算内求解的8道难题，仅用61个标记即通过缓存知识成功解答，节省约6,574倍标记数与近8,700倍能量消耗。此外，该方法将可用上下文长度从32,768扩展至2,854,766，且不增加额外加速器内存占用。整个系统在行为层面进行描述，底层引擎为专有实现，所有结果均附带输入输出哈希以确保可复现性。
链接: https://arxiv.org/abs/2607.14431
作者: Sietse Schelpe
机构: Corbenic AI(科伯尼克人工智能)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF)
备注: 18 pages, 4 figures
Abstract:We report a way to make a frozen small language model both more capable and dramatically cheaper at once, without changing any weights. Verified knowledge is deposited once as a byte-exact key-value (KV) state artifact and later restored, by graft, into a fresh inference context. The restore is bit-exact: under a pinned deterministic configuration, the grafted logits are byte-for-byte identical to a fresh computation (SHA-256 equality), with zero KL divergence and 100% argmax agreement over fifty samples. We show that own-position graft is the unique numerically exact operating point on a model with floating-point rotary encoding, and we verify byte-exactness on two model scales (12B, 31B) and two GPU targets, one through a pre-registered replay. On AIME 2025, a frozen Gemma-4-12B moves from 80.0% to 93.3% once a verified solution library is grafted, above its own 77.5% and its 31B sibling’s 89.2% published anchors. On the recurring case, eight problems the base model never solves within a 401,026-token budget are answered from cached verified solutions in 61 total decode tokens, a factor of 6,574 fewer tokens and about 8,700x less energy; the capability claim proper rests on held-out transfer (7 of 7 at 31B). The same byte-exact store widens usable context from 32,768 to 2,854,766 tokens at zero extra accelerator memory, and moves byte-identical between machines of the same architecture. We describe the system at the behavior level; the engine is proprietary, and every reported number is backed by committed input and output hashes so the scoring can be re-checked without it.
[NLP-42] Instrument Effects in Language-Model Honesty Evaluation: An Auditable Single-System Demonstration
【速读】： 该论文旨在解决当前语言模型诚实性评估中存在的重要方法论问题：现有评估将模型的自我判断直接视为其内在认知状态的证据，而忽略了评估工具本身（即评估框架或任务设计）可能对模型行为产生显著干扰。其核心问题是，评估结果可能并非反映模型的真实能力或诚实度，而是由评估机制的设计偏差所诱导出的虚假行为。解决方案的关键在于对评估工具进行系统性检验与透明化重构——研究者构建了一个文本冒险游戏世界，其中只有游戏引擎知晓任务是否可完成，而语言模型仅作为玩家在预算约束下做出决策。通过预先注册决策规则、记录执行轨迹并公开修订过程，确保评估流程的可复现性与可验证性。实验发现，即使输入完全相同，不同评估工具设计（如三类结论选项替代两类）会显著改变模型的输出分布，且单一提示语即可消除大量错误判定。此外，重复运行显示模型决策缺乏稳定性，揭示单次评估无法代表真实行为倾向。最终提出一种包含四重检查的评估工具完整性协议，以增强评估结果的可信度。
链接: https://arxiv.org/abs/2607.14399
作者: Justin Bronder(Corabo Inc.)
机构: Corabo Inc.(Corabo公司)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 21 pages. Code: this https URL (tag v1.0-arxiv). Data: this https URL
Abstract:Evaluations of language-model honesty read the model’s verdicts as evidence about the model. We test the instrument instead. We built a text-adventure world where the game engine, not any model, knows whether the quest can be completed. A language model plays under a budget and must eventually declare its quest complete, unreachable, or not yet decidable; the engine scores every verdict. Decision rules were recorded before results were read, and run artifacts bind the revisions they executed; the strength of preregistration varies by series and is disclosed. With the player held fixed, instrument choices substantially changed measured behavior. On four byte-identical anchors, expanding a two-verdict grammar to three verdicts moved strong claims from 38/40 to 7/40, while the new incomplete verdict took 28/40 outcomes; across series 2, 93/158 valid games ended incomplete. One sentence disclosing the success criterion took matched-instance false verdicts from 18/59 to 0/58, through fewer decision points and cleaner decisions. Repeated runs of one fixed configuration produced non-stable verdict distributions on 3 of 4 instances: single runs report samples as dispositions. A formally preregistered narrative-register gradient was falsified; two post-hoc, hypothesis-generating patterns remain: register presence roughly doubled strong claims, and budget rendering moved verdicts more than register content (.383 meter vs .150 lantern). The narrator compressed abundant budgets toward scarcity landmarks, yet the registered mediation test returned a null. We propose a four-check integrity protocol for eval instruments.
[NLP-43] MamaBench: Benchmarking LLM Robustness in Maternal and Child Health Diagnosis through Counterfactual Clinical Perturbation
【速读】： 该论文旨在解决当前生成式AI在医疗领域评估中存在的一大局限性：现有医学基准测试通常将每个问题独立评估，无法衡量系统在面对临床表现相似但需不同干预措施的病例时的区分能力。为此，作者提出了MamaBench，首个针对孕产妇与儿科领域的反事实（counterfactual）基准，包含434个由专家撰写的临床叙事，构成217对案例，覆盖371种病理状况，并引入偏差陷阱率（Bias Trap Rate, BTR）作为核心评估指标，即模型在正确回答基础案例的前提下仍未能识别反事实情境的条件概率。其解决方案的关键在于提出证据锚定的检索增强生成（Evidence-Anchored RAG, EA-RAG），该方法采用三阶段检索机制，通过临床参数提取、覆盖率审计及对比子查询，以证据覆盖度替代传统的聚合相似性，从而提升模型对细微临床差异的敏感性。实验表明，所有前沿大语言模型在基础准确率上均显著高估其鲁棒性，误差达16–28个百分点；而EA-RAG在Claude Sonnet 4.6上实现20.3%的BTR和65.0%的鲁棒准确率，相较基线降低5.5个百分点的BTR，且未牺牲基础性能，揭示了当前临床AI在反事实鲁棒性方面仍面临严峻挑战。
链接: https://arxiv.org/abs/2607.14385
作者: Thanni Adewuyi,Anuoluwa Sotome,Samuel Okoko,Angel Ezendu,Oluwafunke Akinbuwa,Oluwaseun Odunsi,Oluwasegun Oguntuase,Oluwadarasimi Oguntuase,Ifeoma Nwabueze,Abiodun Adereni
机构: Helpmum Africa; University of Ibadan (伊巴丹大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large language models achieve strong scores on medical benchmarks, yet these benchmarks evaluate each question in isolation, providing no measure of whether a system can distinguish clinically similar presentations requiring different interventions. We introduce MamaBench, the first counterfactual benchmark for maternal and paediatric AI: 434 expert-authored clinical narratives in 217 pairs across 371 pathologies, evaluated via the Bias Trap Rate (BTR), the conditional probability that a model fails the counterfactual given success on the base case. We propose Evidence-Anchored RAG (EA-RAG), a three-stage retrieval method that replaces aggregate similarity with an evidence coverage objective through clinical parameter extraction, coverage auditing, and contrastive sub-queries. Across eight configurations of four frontier LLMs, base accuracy overstates robust accuracy by 16-28 percentage points in every model. EA-RAG achieves 20.3% BTR and 65.0% robust accuracy on Claude Sonnet 4.6, a 5.5 percentage point BTR reduction without degrading base accuracy. The residual 20% BTR confirms that counterfactual robustness in clinical AI remains an open challenge. Keywords: counterfactual evaluation, clinical AI, maternal healthcare, retrieval-augmented generation, diagnostic robustness
[NLP-44] HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在形式逻辑推理任务中因内容效应（content effects）导致的现实合理性偏倚问题，即模型过度依赖语义常识而非严格的逻辑结构进行判断。其核心挑战在于如何在多语言环境下有效剥离语义可接受性与形式逻辑有效性之间的耦合关系，尤其是在存在干扰前提（distractor premises）的情况下保持推理的鲁棒性。解决方案的关键在于构建一个基于合成规则数据集的mDeBERTa-v3微调框架，通过避免使用带有语义噪声的LLM增强数据来确保训练信号的纯净性；同时，在训练过程中引入多目标损失函数，结合自适应分组分布鲁棒优化（Adaptive Group Distributionally Robust Optimization, DRO）、可调度的可微分偏置惩罚项以及KL散度一致性正则化，显式地解耦逻辑结构与现实合理性。这一设计使模型在英文、带噪英文及多语言子任务中均达到100%准确率和零偏倚，而在最复杂的多语言带噪子任务中仍实现89.06%的准确率与较低的2.89%偏倚，验证了方法在复杂场景下的优越性。
链接: https://arxiv.org/abs/2607.14349
作者: Abdullah Shaikh,Zain Naqi,Taha Zahid,Sandesh Kumar,Abdul Samad
机构: Habib University(哈比布大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:While Large Language Models (LLMs) excel in many general NLP tasks, their formal reasoning capabilities are often compromised by content effects, demonstrating a measurable bias towards real-world plausibility. In this paper, we present our system for SemEval-2026 Task 11, which evaluates the ability of models to disentangle formal logic from content across 12 languages with and without distractor premises. We address this challenge using mDeBERTa-v3 networks fine-tuned on a synthetic, rule-based dataset of syllogistic schemes to avoid the semantic noise of LLM-augmented data. To explicitly decouple plausibility from logical structure, our training pipeline employs a multi-objective loss function combining Adaptive Group Distributionally Robust Optimization (DRO), a scheduled differentiable bias penalty, and KL-Divergence consistency regularization. Our system achieved #1 ranks and perfect Ranking Scores (100.0) with 0.00% bias and 100.0% accuracy on Subtask 1 (English), Subtask 2 (Noisy English), and Subtask 3 (Multilingual). On the highly complex Subtask 4 (Noisy Multilingual), the system achieved the 6th rank with 89.06% Accuracy and F1-score, alongside a limited 2.89% Bias and a 37.78 Ranking Score. Our dataset generation engine and codebase are publicly available to facilitate future work on robust logical reasoning.
[NLP-45] SD-MAR: Multi-image Analytical Reasoning via Synthetic Data and Reinforcement Learning
【速读】： 该论文旨在解决视觉语言模型（Vision Language Models, VLMs）在多图像分析推理任务中能力不足的问题，尤其是针对需要跨多个视觉状态进行系统性分析的任务，如多图比较、变化检测和多步视觉推理。现有基准测试普遍缺乏对显式视觉对比与分析推理的联合要求，导致该能力长期未被充分探索。为此，本文提出SD-MAR（Synthetic Data for Multi-image Analytical Reasoning）框架，通过受控扰动生成成对的视觉场景，并设计涵盖语义变化归因与量化比较的推理任务，以系统性地训练和评估VLMs的多图像分析推理能力。其解决方案的关键在于采用GRPO-lite结合反向折扣分配（Backward Discounted Allocation, BDA）的强化学习方法，在去除KL正则化的基础上，增强策略优化能力，并将更多奖励信用分配给推理后期形成分析结论的关键步骤，从而显著提升模型的推理深度与准确性。实验表明，基于SD-MAR的GRPO-lite微调使Qwen2.5-VL-7B和InternVL3-8B在域内任务上的准确率最高提升达36.95%，且在多个外部基准测试中保持或提升了泛化性能，同时逻辑连贯性与解释质量也得到一致性改善。
链接: https://arxiv.org/abs/2607.14333
作者: Shiyu Yuan,Sourav Sanjukta Bhabesh,Zhe Wang,Dmitriy Bespalov,Wesley Rose,Huzefa Rangwala
机构: AGI Foundations for AWS (AGI基础研究亚马逊云科技); Amazon Web Services (AWS) (亚马逊网络服务公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Vision Language Models (VLMs) demonstrate strong perceptual abilities but remain limited in tasks requiring analytical reasoning across multiple visual states, such as multi-image comparison, change detection, and multi-step visual inference. These capabilities are critical for real-world multimodal applications where reasoning must be grounded in systematic differences between visual contexts. However, existing benchmarks rarely require both explicit visual comparison and analytical reasoning, leaving this capability underexplored. To address this gap, we introduce SD-MAR (Synthetic Data for Multi-image Analytical Reasoning), a framework for training and evaluating VLMs on multi-image analytical reasoning. SD-MAR constructs paired visual scenarios through controlled perturbations and generates reasoning tasks spanning semantic change attribution and quantitative comparison. We further train VLMs using GRPO-lite with Backward Discounted Allocation (BDA), a reinforcement learning approach that removes KL regularization to encourage stronger policy optimization while allocating greater credit to the later reasoning steps where analytical conclusions are formed. Experiments on Qwen2.5-VL-7B and InternVL3-8B show that GRPO-lite fine-tuning on SD-MAR improves in-domain accuracy by up to 36.95%, with Qwen2.5-VL-7B outperforming GPT-4.1 on the SD-MAR benchmark. Importantly, out-of-domain generalization is preserved or improved: performance remains within 1% on MME, MMMU-Pro, and MathVista, while improving by up to 4% on MMBench. LLM-as-judge evaluation further demonstrates consistent improvements in logical coherence and explanation quality across both models.
[NLP-46] PReM: Learning What to Preserve and When to Refresh for Context Compression
【速读】： 该论文旨在解决长上下文推理中因上下文压缩导致关键信息丢失或难以动态适应后续推理需求的问题。现有压缩方法（如键值（Key-Value, KV）缓存压缩和上下文压缩）通常在早期静态决定保留哪些信息，或依赖外部压缩器，缺乏对生成过程中动态证据需求的响应能力。其解决方案的关键在于提出PReM（Preserve and Refresh Memory）框架，该框架将长上下文作为模型内部分层的KV记忆存储，并通过专用记忆层学习何时保留与刷新信息。具体而言，PReM引入一个特殊记忆标记（m）以触发刷新机制，并采用分阶段刷新训练（Phase-Separated Refresh Training）策略，在训练中使记忆选择与条件化生成对齐，同时保证刷新前后记忆状态的连续性。实验结果表明，该方法在32K token长上下文场景下，于16倍和32倍压缩比下均显著优于强基线模型，同时在答案质量与推理效率之间实现了良好平衡。
链接: https://arxiv.org/abs/2607.14327
作者: Bohan Yu,Lei Shen,Chenxi Zhou,Chen Han,Junlin Liu,Wenbo Su,Yu Cheng,Bo Zheng
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Efficient long-context inference is not only about reducing memory cost, but also about keeping useful contextual evidence accessible as generation proceeds. However, existing compression-oriented approaches, such as key-value (KV) cache compression and context compression, often either make an early decision about which contextual information to keep or rely on an external compressor. Such designs make it difficult to adapt the compressed context to the evidence needed by later reasoning steps. This paper introduces PReM (Preserve and Refresh Memory), a context-compression framework that maintains the long context as the model’s internal layer-wise KV memory and learns what to preserve and when to refresh it. Specifically, PReM uses a dedicated memory layer to make memory-selection decisions, and a special memory token m to trigger refreshes during generation. To train this behavior, PReM introduces Phase-Separated Refresh Training, aligning memory selection with memory-conditioned generation while preserving continuity across refreshes. Experiments with 32K-token contexts show that PReM outperforms strong baselines under both 16x and 32x compression, while maintaining a favorable balance between answer quality and inference efficiency.
[NLP-47] Multi-Head Latent Control: A Unified Interface for LLM Agent Decision Making
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）作为智能体（Agent）时，在推理阶段缺乏自主控制决策能力的问题。现有方法依赖提示工程、外部编排或任务特定微调来实现行为控制，但这些方法主要基于输入端信号，存在成本高、维护困难且难以适应模型演进的局限性。本文提出一种轻量级解决方案——多头隐状态控制（Multi-Head Latent Control），其核心在于直接从冻结的大型语言模型或视觉-语言模型（Vision-Language Model, VLM）的隐状态轨迹中提取控制信号，无需修改模型本身。该方法通过两个独立的头部：能力头（Capability Head）判断当前模型是否具备解决任务的能力并决定是否移交至更强模型，以及解决方式头（Resolution Head）预测合适的响应策略（如澄清、工具调用、放弃回答或直接作答）。两者均仅在来自同一冻结骨干模型的隐状态序列上进行训练，实现了对已有模型的后置适应。实验表明，该方法在语言和视觉-语言场景下均显著优化了多模型系统的质量-成本权衡，支持早期中断与更精准干预，在路由执行模式下，可在保持接近大模型性能的同时，将大模型使用率降低最高达90.7%（AndroidWorld基准），平均减少27%-53%；同时显著提升工具调用决策质量，相对得分提升最高达158%，遗漏必要工具调用的情况减少65.5%。
链接: https://arxiv.org/abs/2607.14277
作者: Amirhosein Ghasemabadi,Ruichen Chen,Bahador Rashidi,Di Niu
机构: University of Alberta (阿尔伯塔大学); Huawei Technologies Canada Co., Ltd. (华为加拿大有限公司)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models are increasingly deployed as agents, but reliable agentic behavior requires more than next-token prediction. At inference time, it is preferred that an agent can decide whether to proceed with its current reasoning, defer to a stronger model, request additional information, invoke external tools, or abstain under the given setup. Existing approaches address these decisions through prompt-level routing, external orchestration, or task-specific fine-tuning, which primarily rely on input-side signals, and are often costly and difficult to maintain as model backbones evolve. We ask whether such control decisions can be inferred directly from a model’s latent generation process. We introduce Multi-Head Latent Control, a lightweight layer that reads hidden-state trajectories from a frozen LLM or VLM to produce deployment-time control signals. A Capability Head predicts whether the current model can solve the instance or should defer to a stronger collaborator, while a Resolution Head predicts appropriate resolution decision Clarification, Tool Use, Abstention, or Direct Answering. Both heads are trained only on latent traces from the same frozen LLM backbone, enabling post hoc adaptation without modifying the model. Across language and vision-language settings, Multi-Head Latent Control consistently improves the quality-cost tradeoff of multi-model systems, enabling early handoff from partial generations and more accurate intervention decisions. In routed execution (small + large model), it reduces large-model usage by up to 90.7 percent on AndroidWorld and 27-53 percent on average across benchmarks, while retaining most of large-model performance. Additionally, the learned control signals improve tool-use decision quality, yielding up to +158 percent relative score gain and 65.5 percent fewer missed-required tool calls.
[NLP-48] MonteRET: AI Agent Enhancing Multimodal LLM s with Multi-granularity Knowledge Retrieval for Chest CT Report Generation
【速读】： 该论文旨在解决自动胸部CT报告生成中难以兼顾全体积理解与局部解剖学发现精准描述的问题，核心挑战在于如何在生成报告时同时实现对整体影像的全局感知与对具体病灶区域的细粒度定位与语义表达。其解决方案的关键在于提出一种区域感知的检索增强框架MonteRET，该框架通过融合全局CT特征与区域级解剖表示，利用预测的医学诊断与区域级视觉-语言对齐技术检索临床相关知识，并借助知识引导的报告重写代理对初始报告进行精细化修正，从而提升报告的完整性与临床准确性。实验结果表明，MonteRET在公开数据集（RadGenome-ChestCT）及外部独立队列上均显著优于基线模型与现有先进方法，尤其在召回率方面提升明显，表明其能有效减少漏报，且经放射科住院医师评估也获得更优评价。
链接: https://arxiv.org/abs/2607.14264
作者: Yi Lin,Yihao Ding,Elana Benishay,Elefterios Trikantzopoulos,David Nauheim,Hanley Ong,Jiang Bian,Hua Xu,Yuzhe Yang,George Shih,Yifan Peng
机构: Weill Cornell Medicine (威尔·康奈尔医学院); University of Western Australia (西澳大利亚大学); Indiana University (印第安纳大学); Yale University (耶鲁大学); University of California, Los Angeles (加州大学洛杉矶分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Automated chest CT report generation remains challenging because clinically faithful reporting requires both whole-volume understanding and accurate description of localized anatomical findings. Here we developed and retrospectively evaluated MonteRET, a region-aware retrieval-enhanced framework for generating chest CT findings sections. MonteRET integrates global CT features with region-level anatomical representations, retrieves clinically relevant knowledge using predicted medical conditions and region-level vision-language alignment, and refines initial reports through a knowledge-guided report rewriting agent. We trained our model on a public cohort with 24,128 CT scans from RadGenome-ChestCT. We evaluated MonteRET on the public RadGenome-ChestCT test set of 1,564 CT scans and an external cohort of 82 CT scans from NewYork-Presbyterian/Weill Cornell Medical Center. MonteRET improved report quality, semantic similarity, and clinical efficacy compared with a matched baseline and several state-of-the-art methods. Gains were most pronounced for recall, suggesting fewer omitted findings. Human expert evaluation by radiology residents also favored MonteRET.
[NLP-49] MEMORA: Embodied Action Memory from Egocentric Videos for Reasoning and Planning
【速读】： 该论文旨在解决长时程机器人规划中缺乏对具身经验（Embodied Experience）持续记忆的问题，即当前规划系统难以有效利用过往动作所积累的环境状态变化、物体属性演化、任务流程规律及个体特定行为模式等隐性知识，导致在面对未见目标或复杂情境时规划能力受限。其核心解决方案是提出具身动作记忆（Embodied Action Memory, EAM），通过“形成-巩固-检索”生命周期机制与四类结构化存储模块——环境记忆、实体记忆、活动记忆和推断知识，构建可编辑、可抽象、可复用的记忆系统。关键创新在于在线编辑保持对象身份与状态历史的连续性，离线巩固将重复经验抽象为可重用的程序与个性化规律，从而实现对长期规划所需的上下文记忆支持。实验基于EPIC-KITCHENS-100扩展数据集上的45小时视频进行评估，在跨18名参与者的记忆引导规划任务与记忆评估任务中，完整版MEMORA相较最强基线分别提升记忆评估准确率20.5分与分布外机器人规划得分16.6%，验证了可编辑且经过抽象化的记忆对机器人决策的有效支撑作用。
链接: https://arxiv.org/abs/2607.14252
作者: Zihao Yu,Xiu Yuan,Chongjie Zhang
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 43 pages, 9 figures. Oral presentation at the Robotics: Science and Systems 2026 Workshop on Foundation Models for Robot Planning (FM4RoboPlan)
Abstract:Long-horizon robot planning requires more than predicting what actions will do next; it also requires memory of the embodied experience that makes future goals interpretable. People do not plan from the present scene alone: they draw on remembered places, object-state changes, prior procedures, and regularities revealed through repeated action. We formulate Embodied Action Memory (EAM) as the capability to form, maintain, and use such experience as a persistent memory state for later decisions. MEMORA realizes EAM with a formation-consolidation-retrieval lifecycle and four typed stores: Environment Memory, Entity Memory, Activity Memory, and Inferred Knowledge. Online editing maintains object identities and state histories as new observations arrive; offline consolidation abstracts repeated experience into reusable procedures and participant-specific regularities. MEMORA-Bench evaluates this lifecycle on 45 hours of EPIC-KITCHENS-100 extension video across 18 participants through memory-grounded planning, including previously unseen goals, and a complementary memory-assessment task. Across four open-weight language models, full MEMORA–combining editing, typed stores, and consolidation–achieves the strongest aggregate results among the evaluated memory conditions. It improves memory-assessment accuracy by up to 20.5 points over the strongest controlled baseline and improves out-of-distribution Robot-Grounded Plan score by up to 16.6% relative. A qualitative two-task robot deployment study further illustrates how memory-grounded language plans can interface with downstream control, while the overall results show that editable, consolidated memory can supply remembered context for robot planning. Project page: this https URL
[NLP-50] he Severance Problem: LLM s are Unaware of the Person Beyond the Prompt
【速读】： 该论文旨在解决当前个人人工智能助手在实际应用中普遍存在的一系列不良行为问题，如谄媚（sycophancy）、过度自信及幻觉（hallucination），其根本原因在于语言模型缺乏对用户个体的显式表征，即所谓的“割裂问题”（Severance Problem）——即使具备丰富的上下文信息与强大的常识推理能力，模型仍无法明确表达自身对用户的未知之处。针对此问题，论文提出的关键解决方案是引入“割裂框架”（Severance Schema），通过在语言模型的上下文中显式结构化地纳入模型对用户在身体性（physicality）、时间性（temporality）、后果性（consequences）、连续性（continuity）、多重性（multiplicity）和内在性（interiority）等维度上的知识缺失，从而引导模型识别自身认知边界。实证结果表明，在五个不同的模型家族中，采用割裂框架后，助手显著降低了谄媚、有害建议和幻觉现象，并在信息缺失时更倾向于主动提出澄清性问题，而非基于不完整信息进行自信推断。
链接: https://arxiv.org/abs/2607.14250
作者: Dor Litvak,Liu Leqi
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Personal AI assistants have attracted significant interest for their potential to enhance everyday life by automating routine tasks, supporting consequential decisions, and assisting with everyday personal matters. Yet despite rapid recent technical advances, these assistants continue to exhibit undesirable behaviors, such as sycophancy, overconfidence, and hallucination. We argue that these failures stem from a fundamental limitation: language models lack an explicit representation of the person beyond the context they are given, which we term as the \textbfSeverance Problem. Even with rich personal context and strong commonsense reasoning capabilities from the backbone model, current AI assistants fail to represent what remains unknown about the user. We propose a simple solution: incorporating structured ignorance into the language model context via the \textbfSeverance Schema, which explicitly outlines dimensions along which the model lacks knowledge about the user, including physicality, temporality, consequences, continuity, multiplicity, and interiority. Empirically, across five model families, with the Severance Schema, the assistant consistently reduces sycophancy, harmful advice, and hallucination. Notably, models with the schema ask clarifying questions when information about the user is missing, rather than confidently extrapolating from incomplete user information.
[NLP-51] Implicit Reasoning Steering via Concept Chaining
【速读】： 该论文旨在解决大语言模型在推理过程中表现出的“表面可靠性”与实际决策脆弱性之间的矛盾问题，即尽管模型看似稳定地给出正确答案，但通过重复采样却常出现正确与错误答案交替的现象，揭示了其最终决策机制存在内在不稳定性。解决方案的关键在于提出一种名为“概念链（Concept Chaining）”的隐式推理引导方法：通过生成一段自然语言连贯的连接段落，将问题中的实体与目标选项通过一个或两个中间概念进行语义关联，随后在这些连接段落上继续预训练目标模型，从而评估其在原始多选题上的答案偏好是否发生系统性偏移。实验表明，这种间接且看似自然的文本能够有效引导模型预测，同时其可推断性显著低于直接改写形式，说明模型的推理脆弱性并非单纯的评估偏差，而是真实存在的潜在机制，使得普通文本即可通过隐蔽方式放大隐含偏见，从而暗中操控模型决策。
链接: https://arxiv.org/abs/2607.14242
作者: Xiao Ye,Sanika Chavan,Yuxi Huang,Shahriar Kabir Nahin,Muhao Chen,Anshuman Chhabra,Ben Zhou
机构: Arizona State University (亚利桑那州立大学); University of South Florida (南佛罗里达大学); University of California, Davis (加州大学戴维斯分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models often appear to reason reliably, yet on many questions repeated sampling yields both correct and incorrect answers, revealing an underlying fragility in how final decisions are formed. We study whether this fragility can be exploited through implicit reasoning steering: using natural-language text to bias a model toward a designated answer without explicit instructions, triggers, or direct answer cues. Our approach, Concept Chaining, generates a short connection paragraph that links question entities to a target option through one or two intermediate concepts. We then continue pretraining a victim model on these connection paragraphs and evaluate whether its answer preference shifts on the original multiple-choice questions. Our results show that indirect, natural-looking text can systematically steer model predictions while remaining substantially less inferable than direct paraphrases, which shows that reasoning brittleness is not merely an evaluation artifact: it creates a practical channel through which latent biases can be amplified by ordinary-looking text to covertly redirect model decisions.
[NLP-52] Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
【速读】： 该论文旨在解决联邦学习（Federated Learning, FL）在放射科报告文本建模中因共享模型更新而引发的梯度反演攻击所导致的隐私泄露问题，特别是量化梯度反演对临床文本的重建能力及其与分词器设计的关系。其核心解决方案的关键在于系统评估不同分词器（GPT-2、RadBERT、LLaMA-2）在固定模型架构下对梯度反演攻击的敏感性，揭示分词器设计对隐私风险的影响。研究发现，即使采用领域专用分词器（如RadBERT），仍存在显著的文本信息泄露（精确句子重建率31%–44%），且重建精度随批量大小增加而下降；其中RadBERT虽在恢复临床术语方面表现最优（18.1%的参考词汇表覆盖率），但未完全消除泄露风险。结果表明，分词器选择不仅是影响模型性能的实用决策，更是关键的隐私保护考量因素，提示仅依赖分词器优化不足以满足HIPAA和GDPR等法规要求，必须结合安全聚合与差分隐私等增强机制以实现可接受的隐私保障水平。
链接: https://arxiv.org/abs/2607.14205
作者: Santhosh Parampottupadam,Andres Martinez,Dimitrios Bounias,Sinem Sav,Klaus Maier-Hein,Ralf Floca
机构: German Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany; Department of Computer Engineering, Bilkent University, Universiteler 06800 Çankaya/Ankara, Türkiye; Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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Abstract:Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensitive information from shared model updates. The extent of this leakage for radiology reports, and the role of tokenizer design, remains unclear. We quantify gradient-based text reconstruction in FL and compare privacy risk across three tokenizers with the model architecture held fixed. Six FL clients trained a GPT-2-style transformer (sequence length 32) on public radiology corpora (368,751 diagnostic reports, 98,206 discharge summaries, 1,500 MIMIC-CXR free-text reports) using the GPT-2, RadBERT, and LLaMA-2 tokenizers at batch sizes of 64, 128, and 256. Assuming an active malicious server that modifies the shared architecture before distribution, we applied analytic gradient inversion and measured reconstruction fidelity over five runs. Exact sentence reconstruction ranged from 31% to 44% across tokenizers (30.6-43.5% across the 27 tokenizer x dataset x batch-size cells). At batch size 64 on the Discharge dataset, accuracy was 42.1% (GPT-2), 42.3% (RadBERT), and 39.4% (LLaMA-2), decreasing to 37.3%, 37.2%, and 34.3% at batch size 256. S-BLEU declined as batch size grew (GPT-2: 0.44 to 0.33; RadBERT: 0.48 to 0.35). RadBERT yielded the highest reconstruction fidelity and recovered the most clinical terms (18.1% of a 1,440-term reference vocabulary, vs 12.5% for GPT-2 and 9.4% for LLaMA-2), yet no tokenizer prevented leakage. Substantial portions of report text are therefore recoverable from FL gradients even at larger batch sizes and with domain-specific tokenizers. Tokenizer design influences leakage severity and is a privacy-relevant decision, not only a utility one; safeguards such as secure aggregation and differential privacy are likely necessary to meet HIPAA and GDPR requirements for FL in radiology NLP.
[NLP-53] Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning
【速读】： 该论文旨在解决当前基于强化学习（Reinforcement Learning, RL）的大语言模型（Large Language Model, LLM）智能体在可执行沙盒（executable sandbox）中训练时所面临的采样效率低下问题。现有先进算法如PPO、RLOO和GRPO沿用来自人类反馈强化学习（RLHF）的轨迹采样拓扑结构：针对每个提示（prompt），从初始状态独立采样N条轨迹，并通过减去群体基线（group baseline）来计算优势值。然而，该设计忽略了沙盒环境的一个关键特性——确定性、可快照化（snapshottable）及从中间状态可恢复（resumable）。论文提出一种根本性的改进：利用这一特性构建一种新型的采样拓扑结构——单棵具有N个叶节点的树，其兄弟节点共享前缀，从而共享方差。为此，作者提出了分支策略优化（Branching Policy Optimization, BPO），其核心在于：(i) 在主干轨迹的高熵决策点自适应地对沙盒进行快照；(ii) 每个分支点分叉出K个替代动作并各自滚动至终止；(iii) 采用兄弟节点的回报差值计算每步优势，而非依赖独立提示的轨迹级基线。理论证明该估计器无偏且方差严格低于传统轨迹级基线，方差降低程度等于前缀解释的回报方差部分。在WebShop、ALFWorld和SWE-bench Verified任务上，使用Qwen2.5-7B与Llama-3.1-8B作为骨干模型的实验表明，BPO在相同计算量下相较GRPO和RLOO提升成功率3.6–6.1个百分点，梯度范数方差降低一半，并仅需38%的策略更新次数即可达到最优基线性能。
链接: https://arxiv.org/abs/2607.14171
作者: Bowei He,Yankai Chen,Xiaokun Zhang,Xue Liu
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Accepted by WAIC Academic 2026
Abstract:Reinforcement learning has emerged as the dominant paradigm for training large language model (LLM) agents that interact with executable sandboxes. State-of-the-art algorithms such as PPO, RLOO, and GRPO inherit their rollout topology from RLHF: for each prompt, N independent trajectories are sampled from the initial state, and an advantage is computed by subtracting a group baseline. This design ignores a defining property of agent sandboxes. They are deterministic, snapshottable, and resumable from any intermediate state. We argue that this property enables a fundamentally different rollout topology: rather than N independent trees of depth T, one can construct a single tree of N leaves whose siblings share prefixes, and therefore share variance. We instantiate this idea as Branching Policy Optimization (BPO), a sandbox-native RL algorithm that (i) adaptively snapshots the sandbox at high-entropy decision points along a backbone trajectory, (ii) forks K alternative actions per branch point and rolls out each to termination, and (iii) computes per-step advantages from sibling returns rather than from independent prompts. We prove this estimator is unbiased and has strictly lower variance than the trajectory-level baseline, with the reduction equal to the prefix-explained portion of return variance. On WebShop, ALFWorld, and SWE-bench Verified with Qwen2.5-7B and Llama-3.1-8B backbones, BPO improves success by 3.6–6.1 absolute points over GRPO and RLOO at matched compute, halves gradient-norm variance, and matches the best baseline using 38% fewer policy updates.
[NLP-54] MemoHarness: Agent Harnesses That Learn from Experience
【速读】： 该论文旨在解决当前大语言模型（LLM）代理在实际应用中因采用固定不变的控制框架（harness）而导致适应性不足的问题。现有方法多聚焦于优化提示词、流水线或工作流等局部组件，而部署的代理通常对所有任务场景复用单一全局框架，难以根据具体案例动态调整。为应对这一挑战，论文提出MemoHarness——一种基于执行经验自适应优化的代理框架。其核心在于将代理框架解耦为六个可编辑的控制维度，并构建双层经验库，分别存储针对每个测试案例的诊断结果与提炼出的全局模式；通过检索已有经验，在无需测试时标签、反馈或额外搜索的情况下，实现对不同任务案例的动态适配。实验表明，MemoHarness在命令行代理、代码生成和分析推理等多个基准上均优于固定框架，且具备对未见过的任务套件和基础模型的可迁移性。同时，由于大部分经验可缓存，其额外上下文开销仍保持成本竞争力。该研究验证了执行经验作为构建更具适应性的代理框架的有效基础，为实现动态、智能的代理控制提供了新范式。
链接: https://arxiv.org/abs/2607.14159
作者: Yue Huang,Wenjie Wang,Han Bao,Yuchen Ma,Xiaonan Luo,Yi Nian,Haomin Zhuang,Zheyuan Liu,Yue Zhao,Xiangliang Zhang
机构: University of Notre Dame(圣母大学); LMU Munich(慕尼黑路德维希-马克西米利安大学); University of Southern California(南加州大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:An agent harness is the external control layer that turns a base LLM into an executable agent by managing context, tools, orchestration, memory, decoding, and output handling. While harness design strongly affects agent behavior, most automatic improvement methods optimize narrower artifacts such as prompts, pipelines, or workflows, and deployed agents usually reuse a single global harness for all cases. We introduce MemoHarness, an adaptive harness optimization framework that learns from its own executions. MemoHarness decomposes the harness into six editable control dimensions, stores per-case diagnoses and distilled global patterns in a dual-layer experience bank, and adapts the learned harness to each test case using retrieved experience without test-time labels, feedback, or additional search. In our evaluation across shell-agent, code-generation, and analytical-reasoning benchmarks, MemoHarness improves over the fixed harnesses we compare against and shows selective transfer to unseen suites and base models. Its additional context can also remain cost-competitive when much of the retrieved experience is cacheable. These results provide evidence that execution experience is a practical substrate for building agent harnesses that are more adaptive than a single static configuration, while leaving broader claims about statistical robustness and component attribution to future work.
[NLP-55] Breaking Refusal in the First Half: A Mechanistic Study of the Prefill Jailbreak
【速读】： 该论文旨在解决生成式 AI（Generative AI）在安全对齐过程中存在的脆弱性问题：尽管对齐语言模型能够拒绝有害请求，但仅通过一个简短的前缀填充（如“Sure, here is”）即可轻易诱导其放弃拒绝，导致模型产生有害输出。其核心问题是，模型的拒绝行为并非基于深层的、全局的安全判断，而是依赖于响应生成过程中的特定阶段，即“浅层响应端计算”（shallow, response-site computation）。解决方案的关键在于揭示并定位这一失效机制——研究发现，拒绝行为主要由早期响应窗口决定，该窗口仅占响应长度的一半，且其注意力机制对前缀填充具有高度敏感性；通过因果探针和干预实验表明，若破坏该早期窗口的注意力分布，即使其他位置的注意力保持不变，也会导致有害内容生成率显著上升。进一步分析显示，这种失效源于通用的自回归条件作用（generic autoregressive conditioning），而非专门的安全抑制机制，而真正的安全对齐信号仅构成微弱的、局部的吸引子（logit-trace浓度0.24），且主动与被动成分难以完全分离。因此，模型的拒绝能力是分布式、非单一方向性的，且易受响应端攻击影响，而提示端未被修改的表示则天然免疫此类攻击。最终结论为：安全对齐的失效具有结构性特征，其失败表面局部化，而机制本身则广泛分散。
链接: https://arxiv.org/abs/2607.14147
作者: Alex Kwon
机构: Independent Researcher(独立研究员)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 31 pages, 3 figures. Code and derived artifacts: this https URL
Abstract:Aligned language models refuse harmful requests, but a one-line prefill (“Sure, here is”) strips the refusal. We ask where and how it fails. The harm representation stays intact: on the prompts the attack flips to compliance, a linear probe reads harm as high as on the refused ones (0.91-0.98), while behavioral refusal drops to chance. This holds across four models and three families (1.5-3.8B, and at 14B). Refusal is therefore a shallow, response-site computation. We localize it to an early window: a dose-matched position control shows the first half of the response suffices to break refusal, while the second half is nearly inert. Three causal probes converge on that window. Restoring the harm direction there partially re-engages refusal. Injecting the model’s own refuse-state reverses the jailbreak (74%, held-out). And knocking out the early response’s attention to the prefill, but not an equal attention mass elsewhere, selectively collapses the harmful continuation. A base-model control identifies the mechanism: the same knockout collapses the continuation prefill-specifically even in a non-safety-tuned base model (64% to 25% harmful content vs a matched control’s 64%, replicated at 7B). So the prefill’s grip is generic autoregressive conditioning, not safety-specific suppression, and “refusal restoration” is a model-dependent fallback. The dominant mechanism is passive. A small safety-specific attractor remains on top (logit-trace concentration 0.24 vs 0.03), whose active-vs-passive character we size but do not fully separate. No single direction or component is a clean handle either: the decision is decodable but distributed, and refusal tracks harm rather than scary surface. The consequence is structural: a monitor reading the untouched prompt-side representation is immune by construction, but only to response-site attacks. The mechanism is diffuse; the failure surface is local.
[NLP-56] Cross-Dataset Generalization in Urdu Fake News Detection: An Empirical Study with XLM-RoBERTa and a Length Confound Analysis
【速读】： 该论文旨在解决乌尔都语虚假新闻检测中跨数据集泛化能力不足的问题，尤其关注在不同数据集间迁移时模型性能的显著不均衡现象。其核心挑战在于，尽管现有方法在单一数据集上表现良好，但在跨数据集场景下缺乏系统性研究与可靠泛化能力。解决方案的关键在于揭示并诊断导致性能崩溃的根本原因——即在Ax-to-Grind数据集中存在严重的文本长度混淆（length confound）：虚假新闻平均长度为117词，而真实新闻仅为35词，形成3.4倍的显著差异，诱导模型学习到依赖长度的捷径（shortcut learning）而非语义内容。通过双向迁移分析与预测崩溃检测相结合的可复用诊断方法，研究发现从Notri-Fact到Ax-to-Grind的迁移仍保持较高性能（宏F1=0.771），而反向迁移则几乎完全失效（宏F1=0.005，99.7%样本被误判为虚假），且长度消融实验表明该混淆虽加剧了性能偏差，但并非唯一决定因素。该研究为多语言虚假新闻检测中的共现偏见识别提供了关键方法论支持。
链接: https://arxiv.org/abs/2607.14131
作者: Muhammad Abdullah Haroon
机构: 未知
类目: Computation and Language (cs.CL)
备注: 10 pages, 7 figures, 5 tables, submitted to arXiv
Abstract:Urdu fake news detection remains under-resourced despite Urdu being spoken by over 231 million people worldwide. While prior work has demonstrated strong in-domain performance on individual Urdu datasets, cross-dataset generalisation has received little systematic attention. This paper presents the first cross-dataset generalisation study for Urdu fake news detection, using two publicly available balanced datasets: the Ax-to-Grind Urdu corpus (10,083 articles, 15 domains) and the Notri-Fact Urdu dataset (13,388 articles). We fine-tune xlm-roberta-base under four experimental conditions, in-domain on each dataset and two zero-shot cross-domain transfer directions, comparing against TF-IDF baselines using Logistic Regression and Support Vector Machines. Our experiments reveal a striking asymmetry: Notri-Fact to Ax-to-Grind transfer achieves a macro F1 of 0.771, while the reverse collapses to F1 of 0.005, with the model predicting fake for 99.7% of test articles. We demonstrate that this collapse stems from a systematic length confound in Ax-to-Grind, where fake articles average 117 words versus 35 for real articles, a 3.4x asymmetry inducing shortcut learning. A length ablation capping articles at 50 words yields only a 0.0067 F1 drop, confirming the confound inflates but does not solely drive in-domain performance. We provide a reusable diagnostic methodology that combines bidirectional transfer analysis and prediction-collapse inspection to identify confound-driven behavior in multilingual fake news detection settings.
[NLP-57] Interpretable Language Model for Closed-Loop Type 1 Diabetes Control
【速读】： 该论文旨在解决人工胰腺系统（Artificial Pancreas Systems, APS）在使用强化学习（Reinforcement Learning, RL）进行胰岛素自动输送时存在的“黑箱”问题，即其决策过程缺乏可解释性，难以获得患者与医生的充分信任。解决方案的关键在于提出LLM-T1D框架，通过将高性能专家级强化学习控制器的知识进行蒸馏（knowledge distillation），迁移至经过微调的大语言模型（Large Language Models, LLMs），如LLaMA 3.1 8B和Qwen3 8B，从而构建一个兼具高精度与强可解释性的胰岛素泵控制策略。该方法不仅实现了优于原始RL系统的血糖控制性能（在FDA认证的UVA/Padova模拟器上达到73.5%的时间处于目标范围），还能够以自然语言形式清晰阐述其决策逻辑，同时通过形式化安全验证确保模型输出不产生幻觉，显著提升了系统的透明度与临床可信度。
链接: https://arxiv.org/abs/2607.14126
作者: Maya Sarkar
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at the 2026 IEEE 22nd International Conference on Automation Science and Engineering conference (IEEE CASE 2026)
Abstract:Type 1 Diabetes (T1D) is a chronic, life-threatening autoimmune condition characterized by the complete destruction of insulin-producing pancreatic beta cells. While Artificial Pancreas Systems (APS) powered by Reinforcement Learning (RL) have shown promise in automating insulin delivery, their ``black-box’’ nature makes it hard for patients and doctors to trust them fully. This paper presents LLM-T1D, a promising approach that combines the precision of RL with the clear, human-like reasoning of Large Language Models (LLMs) to create a more transparent and reliable insulin pump controller. By training an expert RL system and distilling its knowledge into fine-tuned LLaMA 3.1 8B and Qwen3 8B models, we developed a controller that not only surpasses the RL system’s performance but also explains its decisions in plain, understandable language. Tested on the FDA-approved UVA/Padova T1D simulator, the LLM controllers deliver excellent blood sugar control (73.5% Time in Range) while maintaining strict formal safety verification against hallucinations.
[NLP-58] Semantic Register Compression in Multi-Agent LLM Cascades
【速读】： 该论文旨在解决多智能体大语言模型（Multi-agent LLM）系统中因任务分解导致的语义表征退化问题，具体表现为中间智能体在跨语言风格（linguistic register）转换过程中引发的语义区分度系统性压缩。其核心问题是：在多智能体级联架构中，评估类智能体对输入文本进行语义重构时，会显著降低不同类别标签在嵌入空间中的可分性，从而影响下游决策的准确性。解决方案的关键在于识别并量化这一现象——“语义注册压缩”（semantic register compression），并通过三阶段管道（收集-评估-决策）实验，利用句子嵌入空间中的标签间分离度（inter-label separation）进行定量分析。研究发现，评估阶段导致标签可分性平均下降41.7%，而直接传递身份信息则几乎保持不变；通过五种架构变体的因果分析，确认定向语义转换是压缩的主要驱动因素。此外，提示工程层面的回归分析解释了78%的方差，且操作约束有助于缓解压缩。研究揭示，语义注册压缩是一种可测量、可泛化的现象，其强度在不同领域（政治事实核查、情感分析、医疗分诊）中存在差异，且转换的语义取向（valence）独立于压缩幅度控制分布坍塌的方向，这对高风险场景下的安全评估具有重要意义。
链接: https://arxiv.org/abs/2607.14119
作者: Manuele Tele Junior Fernandez
机构: 未知
类目: Computation and Language (cs.CL)
备注: 15 pages, 2 figures, 4 tables
Abstract:Multi-agent LLM systems commonly decompose complex tasks into specialized roles. However, this modularity introduces a representational risk: when intermediate agents transform text across linguistic registers, they can systematically compress the semantic distinctions needed for accurate downstream decisions. We term this phenomenon semantic register compression and characterize it as an observable failure mode in multi-agent cascades. Using a three-agent pipeline (Collector-Evaluator-Decider), we quantify compression via inter-label separation in sentence-transformer embedding space. Across political fact-checking (LIAR), sentiment analysis (SST-5), and medical triage (Triagegeist), critical evaluation consistently reduces label separability by 41.7% at the Evaluator stage, while identity passthrough preserves it nearly fully. Five architectural variants causally isolate oriented semantic transformation as the primary driver. A credibility-seeking variant produces minimal geometric compression yet shifts outputs toward mostly-true, demonstrating that transformation valence controls the direction of distributional collapse independently of compression magnitude. Compression generalizes across the three domains with varying intensity: 41.7% in fact-checking, 27.2% in sentiment, and 20.0% in triage. Prompt-level regression explains 78% of the variance, with operational constraints associated with lower compression. These results demonstrate that semantic register compression is a measurable and generalizable phenomenon in multi-agent LLM systems, with implications for safety evaluation in high-stakes domains.
[NLP-59] Budgeted Subset Refinement for Execution-Aware LLM Research Ideation
【速读】： 该论文旨在解决生成式 AI 在科研创意生成过程中存在的核心问题：尽管大语言模型（Large Language Models, LLMs）能够产生看似新颖的研究想法，但这些想法往往缺乏多样性、难以被可靠评估，且难以转化为高质量的实际研究项目。为应对这一挑战，论文提出了一种预执行阶段的受控代理基准测试，聚焦于在给定一个噪声较大的LLM生成创意池的前提下，如何在有限的优化资源（计算预算）下，合理分配精炼资源以构建出更具多样性、更强执行导向性且符合特定评价标准的研究创意组合。其解决方案的关键在于引入“预算约束的子集精炼”（Budgeted Subset Refinement）策略族，即不均匀地对全部候选创意进行精炼，而是仅选择性地对部分候选进行深度优化。实验表明，单纯生成或重排序无法在基准标准下产出有效的非重复强质量创意，而精炼是必要步骤；其中，随机选取k个候选进行精炼（Random-k）作为低成本基线表现良好，而基于多样性的最大边际相关性（Diversity-aware MMR-k）精炼策略在整体权衡上最优——实现了最高的强质量非重复创意产出率、最低的成功方法中重复率，以及最佳的单位成本效益。外部盲评验证进一步支持了精炼效应的广泛性，同时揭示不同精炼策略在个体项上的排名存在评判者差异。研究结论强调，应将LLM科研创意系统不仅视为创意生成器，更应视作在预算约束下的智能资源分配系统，其有效性需通过代理评分的组合质量来评估，而非替代专家评审或执行验证。
链接: https://arxiv.org/abs/2607.14118
作者: Micah Zhang
机构: University of Colorado(科罗拉多大学)
类目: Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 18 pages, 3 figures, 9 tables
Abstract:Large language models (LLMs) can generate research ideas that appear novel to expert reviewers, but recent work also shows that such ideas often lack diversity, are difficult for LLMs to evaluate reliably, and may fail to translate into strong executed projects. This paper evaluates a controlled proxy benchmark for a pre-execution scaffolding problem: given a noisy pool of LLM-generated research ideas, how should a system allocate limited refinement effort to construct a stronger, more diverse, more execution-aware portfolio for human researchers under a fixed rubric? We introduce Budgeted Subset Refinement, a family of strategies that refine only a selected subset of candidates rather than refining all candidates uniformly. In a unified shared-candidate-pool evaluation across 10 random seeds and 10 research-ideation environments, raw generation and reranking alone produce no research-strong nonduplicate ideas under the benchmark rubric, while refinement is necessary for strong proxy-rated portfolios. Uniform refinement produces strong individual ideas but is not the best portfolio-level allocation of compute. Random-k refinement is a strong low-cost baseline, while diversity-aware MMR-k refinement gives the best overall proxy tradeoff: the highest research-strong nonduplicate yield, the lowest duplicate rate among successful methods, and the best cost per research-strong nonduplicate idea. A blinded external-judge robustness check on a balanced 72-item sample supports the broad refinement effect across independent model families, while showing that per-item rankings among refined strategies vary by judge. These results suggest that LLM research ideation systems should be evaluated not only as idea generators, but as budgeted support-allocation systems. The claims are scoped to proxy-rated portfolio quality and do not substitute for expert review or execution-grounded validation.
[NLP-60] Heterogeneous Element-Aware Cross-Version Differencing of Scientific Documents via Layout-Aware Alignment and Structure-Aware Reasoning
【速读】： 该论文旨在解决科学文档跨版本差异检测中的关键挑战，即如何在保留复杂布局与多类型内容（如文本、表格、公式、图表）语义结构的前提下，实现精准、可解释的差异识别。现有方法中，基于文本序列的方法易丢失布局与结构信息，而基于图像的方法虽能捕捉视觉特征却缺乏语义可解释性且对渲染差异敏感。为此，本文提出一种布局感知、异质元素感知的框架，其核心创新在于：首先将文档版本分解为具有语义类型的元素，继而通过“对齐优先”机制联合建模空间位置、内容语义与结构兼容性，建立跨版本元素间的对应关系；在此基础上，采用类型感知的差异推理策略对齐元素对进行分析。该方案实现了文本、表格、公式和图表四类元素的统一变化检测、定位、结构感知分析及匹配评估。实验结果表明，该框架在真实期刊出版校对场景下的科学PDF数据上显著优于各类专用基线模型，在文本、表格、公式和图表上的检测F1得分分别达到0.903、0.855、0.862和0.845，并在定位精度、结构感知能力与匹配质量方面均有提升。消融实验与敏感性分析进一步验证了跨版本对齐、类型特异性表示、结构感知推理及兼容性权重设计的有效性。研究表明，该异质元素感知的差异检测方法为实际编辑生产流程中的科学文档比较提供了鲁棒且可解释的解决方案。
链接: https://arxiv.org/abs/2607.14117
作者: Zhen Yina,Wenkang An,Hao Wang,Keran You
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Cross-version differencing of scientific documents is essential in scholarly publishing and technical documentation, but remains challenging because scientific documents are page-structured artifacts containing heterogeneous elements such as text, tables, formulas, figures, and layout cues. Existing text-sequence-based methods often lose layout and structural information, while image-based methods lack semantic interpretability and are sensitive to rendering variation. To address these limitations, this paper proposes a layout-aware heterogeneous element-aware framework for scientific document differencing. The framework decomposes document versions into semantically typed elements, establishes cross-version correspondence through an alignment-first mechanism that jointly models spatial, content, and structural compatibility, and performs type-aware difference reasoning over aligned element pairs. It supports unified change detection, localization, structure-awareness analysis, and alignment/matching evaluation across text, tables, formulas, and figures. Experiments on real-world scientific PDF data from journal production proofreading workflows show that the proposed framework consistently outperforms element-specific baselines. It achieves detection F1 scores of 0.903, 0.855, 0.862, and 0.845 for text, tables, formulas, and figures, respectively, with further improvements in localization, structure awareness, and matching quality. Ablation and sensitivity analyses confirm the effectiveness of cross-version alignment, type-specific representations, structure-aware reasoning, and compatibility-weight design. These results demonstrate that heterogeneous element-aware differencing provides a robust and interpretable solution for scientific document comparison in realistic editorial production scenarios.
[NLP-61] ReportMedSAM: Guiding Segmentation Through Radiology Reports
【速读】： 该论文旨在解决自由格式放射科报告中临床描述的语义多样性与自然语言变异性带来的分割挑战，传统方法依赖预定义器官术语或脆弱的规则驱动提取机制，难以扩展至新解剖结构且对语言变化敏感。其核心解决方案是提出一种报告驱动的ReportMedSAM框架，通过引入可学习的概念库（concept bank）替代离散提取过程，并利用冻结的医学视觉-语言编码器（BiomedCLIP）结合对比学习，将器官级概念嵌入向量与大规模临床语料对齐，构建相互正交的语义锚点，有效缓解器官层面的语义坍缩问题，显著提升对多种临床同义词（如“renal”与“kidney”）的鲁棒性。在推理阶段，临床报告被嵌入后与概念库匹配，动态激活任务特定的专家混合模型（Mixture-of-Experts, MoE），实现解耦设计：新概念与专家可无须重新训练即可扩展，保持已有组件不变，具备参数隔离的可扩展性。在AbdomenAtlas 3.0数据集上的评估表明，该方法能有效解析自由格式报告，在分割精度上表现优异，并支持无缝、非干扰式地拓展至新临床任务。
链接: https://arxiv.org/abs/2607.14116
作者: Anghong Du,Theodoros N. Arvanitis,Colin Watts,Alejandro F. Frangi,Le Zhang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Free-form radiology reports contain rich clinical descriptions, yet converting them for reliable segmentation remains challenging due to the inherent variability of natural language. Existing pipelines often rely on predefined organ phrases or brittle rule-based inference-time extraction, which limits their scalability to novel anatomical structures and makes them sensitive to linguistic variations. To address this, we propose ReportMedSAM, a report-driven framework that replaces discrete extraction with a learnable concept bank. By leveraging a frozen medical vision-language encoder (BiomedCLIP), we align organ-level concept embeddings with large-scale clinical corpora through contrastive learning, establishing mutually orthogonal semantic anchors. Our approach explicitly mitigates organ-level semantic collapse and ensures high robustness against diverse clinical synonyms (e.g., “renal” vs. “kidney” ). During inference, a clinical report is embedded and matched against this concept bank to dynamically activate task-specific Mixture-of-Experts (MoE) modules. This decoupled design allows new concepts and experts to be added without retraining existing components, providing a parameter-isolated extension mechanism while keeping previously learned experts unchanged. Evaluated on the AbdomenAtlas 3.0 dataset, ReportMedSAM effectively interprets free-form reports, achieves competitive segmentation accuracy, and demonstrates seamless, non-interfering extension to novel clinical tasks.
[NLP-62] DialogueVPR: Towards Conversational Visual Place Recognition CVPR2026
【速读】： 该论文旨在解决现有语言引导地理定位方法在处理真实场景中自然语言描述的模糊性与不完整性时所面临的局限性，其核心问题是传统静态、单次检索范式难以应对复杂语境下的多义性和信息缺失。为此，论文提出一种范式转变——将定位任务重构为可交互的推理式检索，并引入对话式地点识别（Dialogue Place Recognition, DlgPR），通过人机对话形式逐步澄清语义、优化定位结果。其解决方案的关键在于构建了一个统一的推理框架，该框架由跨模态多层次检索器与智能提问代理（DQ-pilot）协同组成；其中，DQ-pilot采用分阶段训练策略：先在精标数据集DQ-cities-20k上进行监督微调，再在更具挑战性的DQ-cities-10k子集上利用GRPO（Generalized Reward Policy Optimization）进行强化学习优化。同时，引入两个任务对齐的评估指标——判别难度指数（Discriminative Difficulty Index, DDI）用于课程采样，以及位置检索增益（Positional Retrieval Gain, PRG）作为奖励信号，直接衡量每轮提问带来的检索性能提升。实验表明，该基于推理的对话式方法显著优于现有基线模型。
链接: https://arxiv.org/abs/2607.14115
作者: Yukun Song,Changwei Wang,Xingtian Pei,Shibiao Xu,Wenhao Xu,Shunpeng Chen,Yu Zhang,Ke Zhang,Rongtao Xu,Xuxiang Feng,Pengyang Wang
机构: Beijing University of Posts and Telecommunications (北京邮电大学); Shandong Computer Science Center (山东计算机科学中心); Qilu University of Technology (齐鲁工业大学); Macquarie University (麦考瑞大学); University of Macau (澳门大学); Aerospace Information Research Institute (航天信息研究院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:Inspired by how humans communicate spatial information, language-guided geo-localization has gained significant traction for its intuitive and practical value. Despite this progress, most methods still rely on a static, one-shot retrieval paradigm, which fails to handle the ambiguity and incompleteness inherent in real-world natural language descriptions. We propose a paradigm shift to reasoning retrieval and introduce Dialogue Place Recognition (DlgPR), which casts localization as an interactive, dialogue-driven reasoning process. To support this new task, we present DlgQuest-Cities, the first large-scale dialogue-based benchmark for place recognition, and a unified reasoning framework that couples a cross-modal multi-level retriever with an intelligent questioner, DQ-pilot. DQ-pilot is trained in a curriculum: supervised fine-tuning on a curated DQ-cities-20k subset followed by reinforcement refinement on a harder DQ-cities-10k split via GRPO. Two task-aligned metrics guide learning: a Discriminative Difficulty Index (DDI) for curriculum sampling and a Positional Retrieval Gain (PRG) reward that directly measures retrieval improvement induced by a question. Experiments show this reasoning-based approach significantly outperforms baselines. The code and model are available at this https URL.
[NLP-63] CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
【速读】： 该论文旨在解决在分布偏移（distribution shift）环境下图学习的挑战，即模型需在仅有少量甚至无监督信号的情况下适应新图结构。现有基于思维链（Chain-of-Thought, CoT）的图-大语言模型（graph–LLM）方法虽能利用大语言模型（LLM）的多步推理能力实现标签高效预测，但其推理过程依赖于固定的图标记（graph tokens），无法动态优化图结构线索，导致推理缺乏对图结构状态的渐进式更新。本文提出一种简单而有效的共演化思维链（CoEvoT）框架，其核心在于构建文本到图标记重写与图到文本推理引导的闭环机制：每一步的中间文本思考通过轻量级条件网络用于更新图标记证据状态，并将更新后的标记反馈至下一步指令中，从而实现基于状态感知的逐步证据精炼，而非在静态图快照上进行推理。这一设计显著提升了模型在动态图结构下的推理能力，实验在八个数据集上的结果表明，CoEvoT持续优于当前最先进的基线方法。
链接: https://arxiv.org/abs/2607.14114
作者: Haohua Niu,Xingtong Yu,Yang Liu,Junfeng Fang,Xuanting Xie,Jie Tan,Zhongjian Zhang,Hong Cheng,Yuan Fang
机构: Sun Yat-Sen University; The Chinese University of Hong Kong; Institute of Computing Technology, Chinese Academy of Sciences; National University of Singapore; University of Electronic Science and Technology of China; Beijing University of Posts and Telecommunications; Singapore Management University
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Under review
Abstract:Graph learning under distribution shift presents a persistent challenge, where models adapt to new graphs with limited or even no supervision. Recent graph–LLM approaches move toward label-efficient prediction by linearizing graphs into prompts and using large language models (LLMs) as predictors, and can adopt Chain-of-Thought (CoT) prompting to exploit LLM’s multi-step reasoning capability. However, existing CoT-based graph–LLM methods generate intermediate thoughts while conditioning on fixed graph tokens, limiting step-wise refinement of structural cues. In this paper, we propose CoEvoT, a simple yet effective co-evolving CoT prompting framework for graph–LLM reasoning. CoEvoT couples text-to-graph token rewriting and graph-to-text reasoning guidance in a closed loop: each intermediate textual thought is used to update the graph token evidence state via a lightweight condition network, and the updated tokens are fed back into the next-step instruction to guide subsequent LLM reasoning. This enables step-wise, state-aware evidence refinement, rather than reasoning over a fixed graph snapshot. Extensive experiments on eight datasets demonstrate that CoEvoT consistently outperforms state-of-the-art baselines.
[NLP-64] 5-CSBoost: Adversarial Perturbation Resistant LLM Fingerprinting
【速读】： 该论文旨在解决生成式 AI (Generative AI) 文本检测器在面对轻度改写、词汇替换、字符级编辑及分布偏移等对抗性扰动时性能显著下降的问题。现有方法多依赖架构修改、对抗训练或复杂多任务目标，难以在不改变基础模型结构的前提下实现鲁棒性提升。本文提出 T5 对比风格增强分类器（T5-CSBoost），在保留原始 T5-Sentinel 框架中下一词预测目标用于源归属的基础上，引入基于边距的三元组损失（triplet loss）作用于解码器嵌入空间，通过对比学习显式正则化风格表示，促使模型学习紧凑且对扰动具有抵抗力的风格特征。其关键创新在于不改变 T5-small 骨干网络的前提下，仅通过添加轻量级对比风格正则化模块，实现了对多种对抗性扰动（包括高达90%强度的词级与字符级扰动）的强鲁棒性，在 OpenLLMText、HC3 以及 MAGE/Deepfake 压力测试集上均达到当前最优表现，验证了显式风格嵌入正则化在真实对抗场景下构建更可靠大语言模型指纹系统的重要价值。
链接: https://arxiv.org/abs/2607.14113
作者: Gayan K. Kulatilleke,Mahsa Baktashmotlagh,Siamak Layeghy,Marius Portmann
机构: University of Melbourne(墨尔本大学); Australian National University(澳大利亚国立大学); RMIT University(皇家墨尔本理工学院); ETH Zurich(苏黎世联邦理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 14 pages, 3 datasets, code and data will be provided
Abstract:While many AI-generated text (AIGT) detectors achieve strong performance on clean inputs, their accuracy degrades significantly under light paraphrasing, word substitutions, character edits, and distribution shifts. We present T5 Contrastive Style Boosted Classifier (T5-CSBoost), an extension to the T5-Sentinel framework that keeps the original next-token prediction objective for source attribution while introducing an auxiliary margin-based triplet loss over decoder embeddings. This contrastive style regularization encourages the learning of compact, perturbation-resistant stylistic representations, offering a lightweight yet effective alternative to prior approaches that rely on architectural modifications, adversarial training, or complex multi-task objectives without altering the underlying T5-small backbone. T5-CSBoost achieves state-of-the-art multiclass source attribution and binary human-vs-LLM detection on OpenLLMText and HC3 AIGT benchmarks. More importantly, T5-CSBoost demonstrates enhanced robustness to word and character level adversarial perturbations of up to 90% intensity, achieving state-of-the-art on the challenging MAGE/Deepfake stress-test suite, including unseen models, unseen domains, and extreme paraphrasing scenarios. Our results highlight that explicitly regularizing stylistic embeddings via contrastive learning is a practical and effective strategy for building more robust LLM fingerprinting systems in real-world adversarial settings. Comments: 14 pages, 3 datasets, code and data will be provided Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.14113 [cs.CL] (or arXiv:2607.14113v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.14113 Focus to learn more arXiv-issued DOI via DataCite
[NLP-65] Information-Theoretic Limits of Reliability and Scaling in Language Models
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）评估中普遍存在的一个根本性假设问题：即认为通过无限扩大模型规模即可实现任意任务的完美可靠性。作者指出，这一假设在信息论上是不成立的，因为每个生成任务都存在一个不可逾越的可靠性上限（reliability ceiling），该上限由可观测上下文所能消除的输出不确定性决定。该上限可分解为两部分：可通过增加上下文信息缓解的可解析成分，以及由任务固有模糊性导致的主观成分。此外，自回归生成机制会进一步降低这一上限，其衰减速率由任务的依赖核（dependency kernel）决定，后者量化了输出中词元间的相关性。基于这两个基本原理，论文推导出一种基于第一性原理的缩放定律（scaling law），指出模型性能受限于更稀缺的资源——训练数据或模型容量。该定律在特定条件下可还原为Chinchilla缩放定律，并提供了对何时扩展能提升可靠性的结构性解释。超越缩放规律本身，该框架还统一解释了多种实际现象，如检索增强带来的收益以及灾难性遗忘的谱学机制。整体而言，本研究形式化了跨领域模型性能所受的资源-复杂度权衡，构建了一个关于生成式语言模型性能极限的统一理论。
链接: https://arxiv.org/abs/2607.14112
作者: Subhabrata Majumdar
机构: Indian Institute of Management Bangalore (印度管理学院班加罗尔)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注: 29 pages, 2 figures
Abstract:Large language models (LLMs) are evaluated as though perfect reliability is achievable for any task given sufficient scale. We show this assumption is information-theoretically unjustified. Every generative task has a reliability ceiling that no model can exceed, determined by how much output uncertainty is resolvable from observable context. The gap decomposes into a resolvable component closable with additional context and a subjective component inherent to task ambiguity. Autoregressive generation further degrades this ceiling at a rate governed by the task’s dependency kernel, which quantifies inter-token correlations in the output. From these two primitives, we derive a first-principles scaling law where LLM performance is bottlenecked by the scarcer resource: training data or model capacity. This law recovers the Chinchilla scaling law as a special case and provides a structural account of when scaling improves reliability. Beyond scaling, our framework unifies diverse practical phenomena, such as the benefits of retrieval-augmentation and the spectral mechanics of catastrophic forgetting. Our work formalizes the resource-complexity tradeoffs that govern model performance across domains, offering a unified theory of performance limits in generative language models.
[NLP-66] Introspection Fine-Tuning (IFT): Training Small LLM s to Introspect
【速读】： 该论文旨在解决小规模语言模型是否能够检测并报告自身内部激活扰动（perturbations）的问题，核心关注点在于模型的内省能力（introspective ability），即模型能否感知并准确描述其内部状态被外部干预（如概念向量注入）后的变化。以往研究采用的二元检测范式（binary detection paradigm）存在混淆因素：在小模型中，激活控制（activation steering）会系统性地诱导模型倾向于给出“是”（Yes）的回答，无论问题内容如何，导致评估结果不可靠。为此，本文提出两种无混淆评估范式——句子定位（sentence localization）和强度比较（strength comparison），分别通过识别被扰动的句子或判断两个句子中哪个受扰更强来评估内省性能，其随机基准分别为1/N和50%。实验覆盖六种来自Llama-3.2与Gemma-4两个系列的模型（参数量从1B至8B），结果显示，即使2B参数的模型也表现出显著高于随机水平的内省能力，且该能力随模型规模提升而增强；但1B模型的表现接近或低于随机水平。为提升小模型的内省能力，作者提出内省微调（Introspection Fine-Tuning, IFT），即利用模型自身前向传播中被扰动的输出构建监督数据进行微调。IFT将1B模型的句子定位准确率从9.6%大幅提升至60.6%（提升约6倍），并在零样本条件下使强度比较任务准确率从30.2%提升至52.2%。此外，IFT对3B和8B模型亦有增益，且对标准能力基准测试影响极小。研究表明，内省能力并非仅由模型规模决定，而是可通过特定训练直接增强，从而释放模型潜在的自我监控能力，这对提升生成式人工智能（Generative AI）系统的透明性与对齐性具有重要意义。
链接: https://arxiv.org/abs/2607.14111
作者: Ely Hahami,Ishaan Sinha,Lavik Jain
机构: Harvard College (哈佛学院); Meta AI (Meta人工智能); Google DeepMind (谷歌深度思维)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 9 pages (excluding appendices / references), 2 figures, 3 tables
Abstract:Can small language models detect and report on perturbations their own internal activations? We investigate this question through the lens of activation steering: injecting concept vectors into a model’s residual stream and measuring whether the model can accurately report on the perturbation. We first show that the binary detection paradigm used in prior work – prompting the model to answer Yes’’ or No’’ to whether it detects an injected thought – is confounded in small models, as steering biases the model toward affirmative responses regardless of the question content. We therefore propose two confound-free evaluation paradigms: sentence localization (identifying which of N sentences was perturbed, chance = 1/N ) and strength comparison (identifying which of two sentences received a stronger injection, chance = 50% ). Evaluating across six models from two families (Llama-3.2 and Gemma-4), we find that models as small as 2B parameters introspect reliably well above chance, and that introspective ability generally increases with scale. Llama-1B, however, performs at or below chance. We then introduce \emphIntrospection Fine-Tuning (IFT): supervised fine-tuning on sentence-localization examples constructed from the model’s own perturbed forward passes. IFT raises Llama-1B sentence-localization accuracy from 9.6% to 60.6% (a 6\times improvement), with gains generalizing zero-shot to the held-out strength-comparison task ( 30.2% \to 52.2% ). IFT also improves introspection for 3B and 8B models, while inducing negligible degradation on standard capability benchmarks. Our results suggest that introspective ability is not fixed by scale alone: it can be directly trained, and doing so unlocks latent self-monitoring capacity with implications for AI transparency and alignment. Our code is \hrefthis https URLhere.
[NLP-67] MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue
【速读】： 该论文旨在解决当前生成式对话系统在追求语义一致性时过度压制对话多样性与主观表达的问题，即现有系统常强制实现语义统一，导致对话缺乏个体认知风格、情感表达和信念传递的丰富性。其解决方案的关键在于提出MAPS（多智能体观点空间）框架，通过领域加权的个体化认知表征、基于GRU的动态记忆机制以及可解释的词级别注意力机制，使具有不同认知特征的智能体能够在保持个性化推理的同时，逐步达成共享语义理解。该框架在EmpatheticDialogues、TopicalChat和MultiWOZ等数据集上的评估表明，MAPS可在不牺牲主体性的情况下实现语义对齐，为构建兼具表现力与可解释性的认知基础型对话系统提供了新路径。
链接: https://arxiv.org/abs/2607.14110
作者: Molood Arman,Clément Bonnafous
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Human dialogue involves more than exchanging information; it also expresses beliefs, emotions, and subjective cognitive styles. Yet current AI dialogue systems often enforce semantic uniformity, sacrificing diversity and interpretability. We present MAPS (Multi-Agent Perspective Spaces), a novel framework that models dialogue between cognitively distinct agents through domain-weighted profiles, dynamic GRU-based memory, and interpretable token-level attention. MAPS enables agents to maintain individualized reasoning while progressively converging on shared meaning. Evaluations on EmpatheticDialogues, TopicalChat, and MultiWOZ show that MAPS supports semantic alignment without collapsing subjectivity. Our results demonstrate a path toward cognitively grounded, interpretable dialogue systems that balance expressiveness and coherence.
[NLP-68] Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多项选择题问答（Multiple-Choice Question Answering, MCQA）任务中的性能评估与提示工程优化问题，尤其针对当前研究中普遍存在的“更复杂的提示技术必然带来更好性能”的隐含假设。其核心问题是：在多样化的MCQA数据集上，复杂推理类提示方法是否真的优于基础提示（baseline prompting），以及提示设计的复杂性与实际性能提升之间是否存在非线性关系。解决方案的关键在于通过一项全面的实证研究，系统评估8种不同提示技术在10个MCQA数据集上的表现，涵盖27种模型配置和超过43万次评估。研究发现，基准提示在多数情况下仍显著优于复杂的推理类提示，仅有极少数如“专家角色提示”（CoT-Expert）和“归纳角色提示”（CoT-Inductive）带来微小但统计显著的约3个百分点的提升，而其他复杂提示（如自类比提示，Self-Analogical）反而大幅落后（最高达31个百分点）。此外，研究揭示了三个关键现象：（1）模型间性能差异显著，例如Qwen3-30B-A3B-Thinking-2507在Elo评分中意外领先；（2）不同模型在思考预算（thinking budget）下的性能-效率权衡呈现模型依赖性，存在最优配置；（3）数据集难度差异巨大，60%的基准测试准确率低于70%，最易与最难任务间差距达47.5个百分点，表明模型仍有巨大改进空间。这些结果表明，当前LLM评估领域可能过度复杂化提示工程，真正的突破应聚焦于模型本身的改进，而非持续优化提示策略。
链接: https://arxiv.org/abs/2607.14109
作者: Inder Preet,Shuxin Lin,Dhaval Patel
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding. Furthermore, the rapid proliferation of LLMs has created the implicit assumption that more sophisticated prompting techniques yield better performance. Several studies claim better performance with more sophisticated prompting techniques, but do not provide a comprehensive evaluation. We address this gap through a comprehensive empirical study of 8 prompting techniques across 10 multiple-choice question answering (MCQA) datasets, encompassing 27 model configurations and roughly 4,300 unique questions evaluated more than 430,000 times. Our findings reveal a striking paradox that baseline prompting consistently outperforms complex reasoning techniques on various benchmarks. Only minimal expert and inductive role framing (CoT-Expert and CoT-Inductive) yields a small but statistically significant \sim 3 percentage-point (pp) gain over baseline whereas every other elaborate technique we tested matches or under-performs it, often by large margins (up to 31~pp for Self-Analogical). We further investigate three critical phenomena: (1) the unexpected victory of Qwen3-30B-A3B-Thinking-2507 in Elo ratings, (2) the performance-efficiency trade-offs across model variants with different thinking budgets, revealing model-dependent optimal configurations, and (3) the substantial variation in dataset difficulty, with 60% of benchmarks below 70% accuracy and a 47.5~pp spread from easiest to hardest, indicating considerable room for model improvement. These results suggest that the LLM evaluation community may be overcomplicating prompt engineering and that substantial performance gaps remain across diverse benchmarks, offering opportunities for genuine model improvements rather than prompt optimization.
[NLP-69] Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）智能体在执行任务过程中工具调用效率难以量化评估的问题。现有研究多通过准确率作为间接代理指标来衡量工具使用效果，但缺乏对工具调用效率的直接、定量度量。为此，本文提出“工具效率”（tool efficiency）这一新量化指标，用于评估智能体轨迹中有用工具调用的比例。其核心解决方案在于引入“边际工具效用”（marginal tool utility），该指标以每次工具调用为单位，量化判断某一工具是否真正有用，或是否可被安全移除而不影响任务准确性，从而实现对工具套件的精简优化。本文采用“大模型作为裁判”（LLM-as-a-Judge）的方法，对轨迹中每个工具调用的边际工具效用符号进行判定。该工作推动了对大模型智能体评估范式的演进，为未来基准测试设计与智能体工程（特别是构建轻量化工具套件）提供了以效率为核心、且与准确率互补的新评价维度。
链接: https://arxiv.org/abs/2607.14108
作者: Nyx Iskandar
机构: Foam(泡沫); OpenAI(开放人工智能)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:This paper introduces tool efficiency, a new quantitative metric to evaluate the rate of useful tool calls in an LLM agent trajectory. To ensure that tool efficiency is well-defined, we also introduce marginal tool utility, a new quantitative metric defined per tool call indicating whether a tool is useful or whether it can be safely removed from the tool suite without affecting accuracy while increasing tool efficiency; in this paper, we determine the sign of marginal tool utility for each tool call in a trajectory using LLM-as-a-Judge. While much prior work has been done to develop techniques that improve tool use by LLMs and design evaluation methods measuring efficiency indirectly using accuracy as a proxy, our work is centered on measuring efficiency directly via the quantitative metric proposed in this paper in post hoc trajectory analyses. It is our intention that this work contributes to the frontier of LLM evaluation research as a springboard for future benchmark designs and agent harness engineering (specifically with regards to creating lean tool suites) that optimize for metrics that complement but are distinct from accuracy.
[NLP-70] Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLM s
【速读】： 该论文旨在解决生成式扩散大语言模型（dLLM）在推理过程中面临的两大效率瓶颈：双向注意力机制导致的键值缓存（KV-cache）无法高效复用，以及采用静态置信度阈值提升解码并行性时对生成质量造成的损害。其核心问题源于一个共性现象——随着解码步骤的推进，令牌通过双向注意力机制不断整合上下文信息，导致令牌表示在不同解码步之间发生持续漂移（token representation drift）。针对这一本质问题，论文提出无需训练的Polestar推理框架，将令牌表示漂移作为统一信号，同时应对上述两个挑战。其关键解决方案包含两部分：一是Polestar-Cache组件，通过检测漂移识别过期的KV-cache位置，并实施稀疏刷新以实现高效的缓存复用；二是Polestar-Commit组件，利用显著漂移事件精准判断可提交的令牌，确保生成质量。实验结果表明，Polestar在多个dLLM家族的数学与编码基准测试中均实现了新的性能前沿，在准确率-吞吐量权衡曲线上达到最高10.73%的准确率提升、高达3.7倍的吞吐量增益，且保持3.67个令牌/前向传播的高并行解码能力，显著优于现有方法。
链接: https://arxiv.org/abs/2607.14107
作者: Mingyu Lee,Akshat Ramachandran,Souvik Kundu,Tushar Krishna
机构: Georgia Institute of Technology (佐治亚理工学院); Intel AI Group (英特尔人工智能小组)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The inference efficiency of diffusion large language models (dLLMs) is constrained by two challenges: bidirectional attention precludes efficient KV-cache reuse, while increasing decoding parallelism with static confidence thresholds can compromise generation quality. We observe that both challenges arise from a shared phenomenon: as tokens are decoded, their contextual integration through bidirectional attention causes token representations to drift (evolve) across decoding steps. This insight motivates Polestar, a training-free inference framework that uses token representation drift as a unified signal to jointly address both challenges. Polestar comprises two components: Polestar-Cache, which identifies stale KV-cache positions via drift and performs sparse KV-cache refreshes to enable efficient reuse, and Polestar-Commit, which detects sharp drift events to reliably identify commit-ready tokens. Across mathematics and coding benchmarks on several dLLM families, Polestar sets a new state of the art on the accuracy-throughput Pareto frontier, achieving up to 10.73% accuracy improvement, up to 3.7x higher throughput, and high decoding parallelism of 3.67 tokens per forward pass over existing baselines.
[NLP-71] oken Time Continuous Diffusion for Language Modeling
【速读】： 该论文旨在解决现有离散空间扩散语言模型在高速生成时因并行采样多个词元导致的准确性下降问题，以及在条件生成和词元间动态影响建模方面的不足。其核心解决方案是提出一种基于连续空间的词元时间连续扩散（Token Time Continuous Diffusion, TTCD）模型，关键在于两个创新：一是模型在连续空间中运行，通过确定性映射将高斯噪声直接转化为最终词元图景，避免了离散空间中多词元并行采样的不准确问题；二是引入每个词元独立的“词元时间”（per-token times）概念，使不同词元以不同速率从噪声演化为确定词元，从而实现更优的条件生成能力、更可靠的词元加速演化及精细化的词元间相互作用建模。实验表明，TTCD在高倍速生成场景下显著优于传统离散模型，在无条件生成质量上与同类模型相当，而在条件生成和数独求解等任务中表现更优。
链接: https://arxiv.org/abs/2607.14106
作者: Parikshit Bansal,Sujay Sanghavi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:In this paper we introduce token time continuous diffusion (TTCD), a new diffusion language model which (a) operates in continuous space, deterministically mapping Gaussian noise to a final token canvas with no further sampling, and crucially (b) incorporates a new notion of per-token times, with some tokens proceeding from noise to token at a faster rate than others. Continuous space modeling helps TTCD avoid the parallel sampling of multiple tokens, which is a key source of inaccuracy at high speedups for models that iterate purely in discrete space. The notion of per-token times helps TTCD to better model conditional generation, allows for more sure tokens to proceed at a faster rate, and allows for differentiated inter-token influences during refinement. TTCD outperforms discrete models at high speedups. We train a 160M parameter TTCD model on OpenWebText, and then self-distill it; we find that at high speedups we are comparable in unconditional generation quality, and outperform in conditional generation, several existing models of similar size trained, on the same data, and self-distilled. We achieve similar gains in Sudoku solving as well.
[NLP-72] Automatically Evolving Prompt Guidelines for Task-Specific Optimization
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在面对用户查询时因提示（prompt）信息不完整而导致的性能下降问题。具体而言，用户提出的查询常存在需求、上下文或约束条件缺失的情况，迫使模型自行推断未明示的假设，而这些推断可能与真实用户意图严重偏离。现有提示工程（prompt engineering）指南多为通用且任务无关的规则，缺乏系统性设计，难以有效应对特定任务中的提示不明确问题。本文提出一种自动化的提示指南优化方法——AGOPS，其核心在于通过优化机制自动生成针对特定任务和模型的任务定制化提示指南。该方法的关键创新在于：利用已完成的任务示例（即参考答案）中隐含的行为约束、上下文假设和评估标准等信息，驱动一个包含提示生成器（prompt LLM writer）、求解器（solver LLM）和提示演化机制的优化流程，以最大化在一组示例上的下游任务表现。实验表明，在数学推理、医疗问答和编程任务中，提示不明确可导致性能下降高达95.3%，而现有优化技术几乎无法挽回此损失；相比之下，遵循AGOPS生成的指南后，用户能够显著提升提示质量，平均性能提升15.5%至81.7%，并在所有基准测试中实现稳定改进。
链接: https://arxiv.org/abs/2607.14105
作者: Cedric Richter,Salah Ghamizi,Mike Papadakis
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:For Large Language Models to reliably answer user queries, users must clearly specify requirements, context, and constraints. In practice, however, user queries are often underspecified, forcing models to infer unstated assumptions that may misalign with the actual user intent. Existing prompt engineering guidelines aim to mitigate this issue, they are typically generic and task-agnostic, limiting their practical utility. Additionally, existing guidelines are formed manually and in a non-systematic way. To this end, we study prompt guideline optimization: the problem of automatically generating task-specific guidelines that help write better-specified prompts for a given task and model. Our key observation is that existing (completed) task examples (aka reference answers) often implicitly encode the missing information required to complete underspecified queries, including behavioral constraints, contextual assumptions, and evaluation criteria. We therefore propose AGOPS, an automatic approach that evolves task-specific guidelines via an optimization scheme that involves a prompt LLM writer, a solver LLM and prompt evolution, which maximize downstream effectiveness on a set of examples (user queries with reference answers). At inference time, our guidelines help users write well-specified prompts, boosting the effectiveness of LLMs. We show across mathematical reasoning, medical question answering, and coding tasks, that prompt underspecification leads to major drops (up to 95.3%) in downstream task performance (compared to well-specified prompts) and, perhaps more importantly, that this drop can hardly be recovered by existing prompt optimization techniques. Users following AGOPS guidelines can regain this loss (increasing performance between 15.5 to 81.7% on average) consistently across all benchmarks.
[NLP-73] UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure
【速读】： 该论文旨在解决异构数据中静态属性与动态记录在统一建模过程中存在的挑战，特别是现有方法依赖大量人工设计、对特定数据模式耦合紧密，且通常孤立处理静态与动态属性，忽视二者之间的隐式交互。其核心解决方案在于提出UniSAGE框架，通过构建一个全局属性图（global attribute graph）统一表征层次化与时间性关系，并引入两个正交的参数子空间，在共享语义空间中协同支持静态聚合与动态推理，确保表示一致性；在此基础上，采用轻量级超结构机制实现任务相关的静态与动态属性交互，从而有效捕捉复杂的跨属性依赖。该框架具备完全自动化、对数据模式演化鲁棒等优势，实验结果表明其在多个公开基准和真实金融行为数据集上均显著优于现有方法，多项任务性能提升超过10%。
链接: https://arxiv.org/abs/2607.14102
作者: Taoran Fang,Yan Deng,Chunping Wang,Yang Wang,Lei Chen,Yang Yang
机构: Zhejiang University (浙江大学); FinVolution Group (FinVolution集团)
类目: Computation and Language (cs.CL)
备注:
Abstract:With the rapid growth of digital data, real-world applications increasingly involve hierarchical information that combines static attributes with dynamic records. Modeling such heterogeneous data in a unified and generalizable manner remains challenging. Existing approaches often rely on extensive manual design, are tightly coupled to specific data schemas, and typically process static and dynamic attributes in isolation, thereby overlooking their implicit interactions. We propose UniSAGE, a unified framework for modeling data with both static and dynamic attributes. UniSAGE constructs a global attribute graph that represents hierarchical and temporal relationships in a unified structure. To ensure representational consistency, it introduces two orthogonal parameter subspaces that jointly support static aggregation and dynamic reasoning within a shared semantic space. Building on these unified representations, UniSAGE further enables task-specific interaction between static and dynamic attributes via a lightweight hyper-structure mechanism. UniSAGE is fully automated, robust to evolving data schemas, and capable of capturing complex cross-attribute dependencies. Extensive experiments on multiple public benchmarks and a real-world financial behavior dataset demonstrate that UniSAGE consistently outperforms existing methods, achieving performance improvements of over 10% on several tasks.
[NLP-74] LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets
【速读】： 该论文旨在解决在硬标签（hard-label）场景下，如何以极低的查询预算生成高质量对抗文本这一难题。现有方法多依赖于贪心算法，仅逐位置进行替换，这种局部搜索策略难以发现优质对抗样本，且常导致过高的查询成本。其核心挑战在于：理想情况下应全局考虑文本中所有位置组合的可能性，但穷举搜索在计算上不可行。为此，本文提出一种基于采样的方法——LBA（Learning-Based Adversarial sampling），通过融合先验与后验知识构建高质量对抗样本的近似分布，并利用该分布进行高效采样；随着采样过程推进，后验知识持续更新分布，从而引导更有效的后续采样。实验结果表明，LBA在六个不同规模的语言模型（涵盖小规模至大规模架构）和四个数据集上的表现显著优于当前最优基线方法，且基于大语言模型（LLM）的评估显示，LBA生成的对抗文本具有更强的语义保真度和可读性。
链接: https://arxiv.org/abs/2607.14101
作者: Shixin Guo,Ming Zhong,Xuhong Zhang,Dandan Zhao,Zhe Wang,Bo Zhang,Shouling Ji,Hao Peng
机构: Zhejiang Normal University; Zhejiang University; China Electric Power Research Institute
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Generating high-quality adversarial texts with low query budgets remains a challenging problem in the hard-label scenario. Most existing approaches rely on greedy algorithms, where one position in the text is selected for substitution, followed by the substitutions of other positions. This local search approach may fail to discover high-quality adversarial examples and often leads to excessive query costs. Ideally, an optimal adversarial sample would consider all possible position combinations in the text, but exhaustive search is computationally impractical. To address this challenge, we propose a sampling-based method called LBA, which constructs an approximate distribution of high-quality adversarial examples by integrating both prior and posterior knowledge, and utilizes this distribution for sampling. As sampling progresses, posterior knowledge updates the approximate distribution, which in turn guides more effective sampling. Extensive experiments on six language models, ranging from small-scale to large-scale architectures across four datasets, demonstrate that LBA significantly outperforms state-of-the-art baselines on all evaluation metrics. Additionally, LLM-based assessment indicates that LBA generates more semantically preserved and comprehensible adversarial texts.
[NLP-75] Quantum Compositional NLP for Arabic: Grammar Morphology and Word Sense in Circuit Topology
【速读】： 该论文旨在解决阿拉伯语在生成式量子自然语言处理（Quantum Natural Language Processing, QNLP）中的语义组合建模问题，尤其针对其形态丰富、词序自由的特性所带来的结构复杂性挑战。其核心解决方案的关键在于采用基于预群语法（pregroup grammar）的量子句法-语义映射框架，将阿拉伯语句子的句法结构转化为量子电路拓扑：句子成分（如主语、谓语、宾语）被映射为量子门，而预群语法所定义的类型依赖关系则决定了这些量子门之间的连接方式。通过三个受控实验（涵盖词序变化、形态时态区分及动词义项消歧），系统在量子电路方法与经典基线（AraVec词嵌入和AraBERT预训练模型）之间进行了对比验证，展示了该方法在处理复杂语言结构方面的潜力与有效性。
链接: https://arxiv.org/abs/2607.14100
作者: Wajahath Mohammed
机构: 未知
类目: Computation and Language (cs.CL)
备注: 32 pages, also published here: this https URL
Abstract:We present the first application of pregroup grammar-based quantum compositional natural language processing (QNLP) to Arabic; a morphologically rich, free-word-order language whose structural complexity provides a uniquely demanding testbed for theories of meaning composition in quantum circuits. Our system converts Arabic sentences into quantum circuits whose topology mirrors grammatical structure: subjects, verbs, and objects become quantum gates, and the typed dependencies between them (the pregroup grammar) determine how those gates are wired together. We conduct three controlled experiments spanning word order, morphological tense, and verb sense disambiguation, comparing quantum circuit methods against classical baselines including AraVec (Arabic word embeddings) and AraBERT (a pre-trained Arabic transformer).
[NLP-76] Just Keep Prompting: Evaluating Repetitive Socratic Prompting in VLMs
【速读】： 该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在真实应用场景中面临的稳定性问题，即在持续对话压力下模型是否能保持其认知一致性与判断可靠性。现有评估多关注单轮推理性能，却忽视了多轮交互中模型面对用户反复质疑、否定或矛盾时的动态响应行为。为此，作者提出“持续提示”（Just Keep Prompting, JKP）这一多轮评估框架，通过三种策略——对抗性否定（重复反驳）、纯苏格拉底式追问（反复要求重新评估确定性）以及上下文感知的苏格拉底总结（回溯模型先前理由后请求重新考虑），对模型进行长达10轮的连续挑战测试。关键发现表明：尽管整体准确率变化较小，但轨迹层面分析揭示显著的认知不稳定性——正确答案会退化，错误答案可能恢复，大量案例出现答案反复翻转。不同模型表现出强差异性：Qwen3-VL-30B虽最终准确率最高，但在直接反驳下易陷入自信错误；Gemini 2.5 Pro相对稳定但生成成本高；GPT-4o最为脆弱且波动剧烈。因此，解决方案的核心在于引入多轮压力测试机制，揭示模型在视觉锚定、置信度校准与对话合规性之间的权衡关系，从而实现对VLM认知稳定性的深层诊断。
链接: https://arxiv.org/abs/2607.14099
作者: Shayda Moezzi,Bishoy Galoaa,Lorena Genua,Taskin Padir,Sarah Ostadabbas
机构: Northeastern University (东北大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deploying Vision-Language Models (VLMs) in real-world settings requires not only strong visual reasoning but also stability under sustained conversational pressure. We introduce Just Keep Prompting (JKP), a multi-turn evaluation framework that measures VLM epistemic stability when users repeatedly challenge, question, or contradict a model’s answer. JKP probes models for up to 10 follow-up turns using three strategies: Adversarial Negation (repeated rejection), Pure Socratic Interrogation (repeated calls to reassess certainty), and Context-Aware Socratic Summarization (reflecting the model’s prior rationale back before asking for reconsideration). We evaluate GPT-4o, Gemini 2.5 Pro, and Qwen3-VL-30B on a subset of the STAR benchmark across 720 multi-turn runs. Aggregate accuracy changes modestly from Turn 0 to Turn 10, but trajectory-level analysis reveals substantial instability: correct answers regress, wrong answers recover, and many runs exhibit repeated answer flipping. Repeated prompting has bounded upside and often acts as a destabilizer rather than a reasoning aid. The effect is strongly model-dependent: Qwen3-VL-30B achieves the highest final accuracy but becomes confidently wrong under direct contradiction; Gemini 2.5 Pro is comparatively stable but token-expensive; GPT-4o is the most brittle and oscillatory. These findings reveal that multi-turn VLM evaluation captures not just additional reasoning but pressure-response profiles: how models trade off visual grounding, calibration, and conversational compliance under repeated challenge.
[NLP-77] he Test Oracle Problem in Synthetic LLM -as-Judge Corpora: Disappearance Distortion and a Validation Protocol
【速读】： 该论文旨在解决生成式大语言模型作为评判者（LLM-as-judge）系统中因数据构建过程缺陷导致的隐性偏差问题。其核心问题是：当前主流的偏见研究多依赖于通过大语言模型生成虚构答案（hallucinated answer）与真实答案配对构成合成语料库，但在这一生成过程中，若存在共享解码预算（decoding-budget）参数配置不当，会导致生成的答案被截断为极短片段，从而引入虚假但统计显著的偏差效应。解决方案的关键在于揭示这种故障具有结构性而非偶然性——即在缺乏机械验证机制的前提下，由大语言模型生成的负样本（negative examples）无法通过自动检查手段识别其完整性，导致错误结果被误认为真实偏见。研究通过实证发现，当共享参数修正后，原本32分的跨语言判断准确率崩溃现象消失至基线水平；而唯一能暴露此问题的方法是人工审查原始生成内容，任何聚合统计指标均无法察觉。此外，另一真实存在的偏见（如对Markdown格式的偏好）也因相同机制被扭曲，其大小甚至符号随刺激长度变化，进一步凸显了传统评估方法的局限性。作者提出“测试预言机问题”（test oracle problem）框架，强调基于确定性扰动的语料库天然具备逐项验证能力（item-level oracle），而依赖模型生成负样本的语料则缺乏此类保障。最后，论文提出一套基于自身案例的验证协议，建议在无预言机条件下开展分析的研究者采用该协议以增强结果可信度，尤其适用于绝大多数当代多语言大模型评判语料库的实际情况。
链接: https://arxiv.org/abs/2607.13707
作者: Serkan Ballı(Department of Software Engineering, Mehmet Akif Ersoy University, Burdur, Türkiye)
机构: Mehmet Akif Ersoy University (梅赫梅特·阿基夫·埃尔索伊大学)
类目: Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 23 pages, 1 figure, 3 tables
Abstract:Studies of bias in LLM-as-judge systems typically build synthetic corpora by prompting an LLM to generate a hallucinated answer to pair with a factual one, then presenting both to a judge. We report a case in which this generation step silently failed, and use it to argue that the failure mode is structural rather than incidental. In a multilingual (Turkish/English) faithfulness-judgment corpus, a decoding-budget parameter shared between judging and generation calls truncated one producer’s hallucinated answers to a few words. The resulting items produced a large, statistically robust effect: a 32-point cross-lingual collapse in one judge’s selection accuracy, replicated from N=50 to N=500, explained by a three-layer mechanistic account, and confirmed by a controlled producer-swap experiment, none of which was real. The effect vanished to ceiling once the shared parameter was corrected, and only manual reading of the raw generations, not any aggregate statistical check, exposed the fault. A second measured bias (Markdown-formatting preference) was not fabricated but distorted by the same fault, its magnitude and in one case its sign shifting with stimulus length, a mode aggregate metrics cannot distinguish from the first. We frame the underlying vulnerability using the test oracle problem: corpora whose negative examples are LLM-generated carry no mechanical way to verify item integrity, while corpora built by deterministic perturbation of a gold answer carry an item-level oracle for free. A positive control supports this claim directly: an analogous fault injected into a minimal perturbation-based corpus is caught with 100% accuracy by a zero-cost, zero-human gold-to-negative string comparison. We close with a validation protocol, derived from our own case, for analysts working in the oracle-less regime that we argue describes most contemporary multilingual LLM-as-judge corpora.
信息检索
[IR-0] SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
链接: https://arxiv.org/abs/2607.15257
作者: Yuyao Zhang,Junjie Gao,Zhengxian Wu,Jiaming Fan,Jin Zhang,Shihan Ma,Yao Yao,Weiran Qi,Chuyan Jin,Guiyu Ma,Xingzhong Xu,Kai Yang,Ji-Rong Wen,Zhicheng Dou
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Code is available at this https URL
Abstract:Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current single- and multi-agent systems can become trapped in repetitive loops, wasting search budgets and ultimately compromising the quality and completeness of the final output. We introduce SearchOS, a system-level multi-agent framework that turns fragile, implicit search progress into explicit, persistent, and shared state. First, we formulate open-domain information seeking as relational schema completion with grounded citations, where agents discover entities, populate attributes across linked tables, and anchor each value to source evidence. Then we design Search-Oriented Context Management (SOCM), which externalizes the evolving state into Frontier Task, an Evidence Graph, a Coverage Map, and Failure Memory. Built on SOCM, SearchOS applies a pipeline-parallel scheduling mechanism that overlaps the execution of sub-agents and continuously refills freed slots with tasks targeting unresolved coverage gaps to improve utilization and throughput. To schedule and control the execution of search agents, SearchOS introduces a Search Tool Middleware Harness that intercepts model and tool interactions to record grounded evidence and react to stalls or budget exhaustion, and provides a reusable hierarchical skill system comprising strategy and access skills to augment the agents’ search process and avoid repeating failed search patterns across runs. On WideSearch and GISA, SearchOS leads all metrics among the evaluated single- and multi-agent baselines, paving the way toward robust information-seeking collaboration.
[IR-1] Bridge Evidence: Static Retrieval Utility Does Not Predict Causal Utility in Multi-Step Agent ic Search
链接: https://arxiv.org/abs/2607.15253
作者: Debayan Mukhopadhyay,Utshab Kumar Ghosh,Shubham Chatterjee
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: Preprint; extended version in preparation
Abstract:Retrieval systems are trained and evaluated on a static idea of usefulness: hand a document and a question to a reader model, see whether the answer improves, and score the document accordingly. The idea holds up when a document is read on its own. It breaks when a language model works as a search agent, issuing several queries and reasoning across turns, because a document can matter for what it lets the agent do next rather than for what it says about the current question. We measure that gap rather than argue it. Using a ReAct style agent over HotpotQA, we replay 1000 development questions and, for every document the agent read, delete it and re-run the rest of the trajectory from that point. Comparing the original run against its counterfactual gives a Counterfactual Trajectory Utility (CTU) score from three deltas: final answer quality, next query retrieval quality, and turn count. Crossing CTU against Static RAG Utility (SRU) over 23,322 document observations, the two are close to statistically independent (Spearman rho = -0.026). Roughly a third of the documents the agent reads are causally load bearing while looking useless to a static reader; we call these bridge documents. The pattern survives when the reader based axis is swapped for a BM25 and cross encoder proxy, giving a bridge cell of 27.2% on an evenly spread axis. A second experiment pins down the mechanism. Using the Observable Entity Relevance (OER) measure from prior work, entities that discriminate relevant from non-relevant candidates appear in the agent’s next query 4.02 times more often than entities found only in non-relevant documents (6.1% vs 1.5%, n = 227,139). A bridge document earns its keep by handing the agent a discriminative entity that redirects the search. Static relevance and causal usefulness are different quantities in agentic retrieval, and optimizing the first does not deliver the second. Comments: Preprint; extended version in preparation Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL) Cite as: arXiv:2607.15253 [cs.IR] (or arXiv:2607.15253v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.15253 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-2] CoSimRec: Measuring Coordinated-Content Penetration in Recommender Feedback Loops
链接: https://arxiv.org/abs/2607.15114
作者: Nan Li,Jiahong Shao,Jiuyang Lyu
类目: Information Retrieval (cs.IR); Social and Information Networks (cs.SI)
备注: 9 pages, 4 figures, 4 tables
Abstract:Recommender systems increasingly shape which content reaches users, making it important to understand whether coordinated activity is amplified beyond the accounts that initiate it. Existing robustness evaluations largely focus on static target-rank changes and do not capture how coordinated interactions, recommendation, and user response evolve within a feedback loop. To address this gap, we propose CoSimRec, an offline agent-based evaluation framework that models coordinated accounts, dynamic ranking, non-bot responses, and ranking interventions in a shared closed-loop process. CoSimRec introduces the Algorithmic Penetration Rate (APR) metric family to measure target content’s share of non-bot exposure and engagement, lift against matched no-attack baselines, and exposure gained per coordinated interaction. We evaluate CoSimRec on MIND, MovieLens, and LastFM using random, popularity-based, feedback-sensitive, MF, and BPR-MF recommenders, with ten-seed inference for the primary APR analysis and population-scale experiments of up to 1000 users. Random controls show no statistically supported positive penetration, whereas popularity-based and feedback-sensitive ranking produce significant positive APR-Lift in all six master-worker dataset–recommender settings, reaching 0.4505 on LastFM; synchronization-aware ranking reduces APR in every corresponding defense setting.
[IR-3] owards Hierarchical Structure Understanding of Newspaper Images ICDAR2026
链接: https://arxiv.org/abs/2607.15082
作者: William Mocaër,Solène Tarride,Thomas Constum,Merveilles Agbeti-Messan,Tom Simon,Clément Chatelain,Stéphane Nicolas,Pierrick Tranouez,Sébastien Cretin,Thierry Paquet
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Accepted at ICDAR 2026 Workshop on Historical Document Imaging and Processing (HIP)
Abstract:Understanding newspaper images remains a challenging task due to their complex, nested hierarchical structures and dense, heterogeneous layouts. In this paper, we explore two complementary approaches for newspaper structure understanding. First, we present a modular bottom-up pipeline that combines state-of-the-art open-source models: YOLO for layout detection, LayoutReader for reading order prediction, and a custom algorithm for article segmentation. This approach leverages existing robust components while maintaining flexibility and interpretability. Second, we introduce Tiramisu (Tiered Transformers for Hierarchical Structure Understanding), a novel end-to-end transformer-based architecture that explicitly models document hierarchy through an iterative tiered process. Tiramisu performs section and article separation, block localization, semantic categorization, and reading order prediction using highly parallelized attention mechanisms. Finally, we release Finlam La Liberté, a new dataset designed specifically for evaluating hierarchical information retrieval in historical newspapers. Experimental results demonstrate the effectiveness of both approaches in reconstructing complex newspaper hierarchies, with comparative analysis highlighting their respective strengths for scalable document digitization. The Tiramisu training code, including the synthetic newspaper generator, is available at this https URL.
[IR-4] Does generative AI supersede supervised XMLC? A Benchmark Study on Automated Subject Indexing with German Scientific Literature
链接: https://arxiv.org/abs/2607.14882
作者: Maximilian Kähler,Katja Konermann,Lisa Kluge,Markus Schumacher
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Submitted to KONVENS 2026
Abstract:With a large controlled vocabulary as the label set, the task of automated subject indexing in a library can be understood as a multi-label classification task. If the set of subject terms is large, the problem fits the Extreme Multi-Label Classification (XMLC) objective. In this study, we apply a selection of specialised supervised XMLC methods to the test case of subject indexing contemporary German scientific literature, collected at the German National Library (DNB). We contrast these results by including a classical lexical matching baseline and three of our own recently developed LLM-based methods into the benchmark. Algorithms are evaluated and compared in several metrics. This includes binary relevance comparisons with previously indexed material, as well as graded relevance ratings by professional subject librarians. A challenge for all methods is to reliably make suggestions from the long tail of the subject vocabulary. We find that supervised XMLC algorithms relying on transformer-based dense features give best results in terms of overall binary relevance metrics. However, focusing on graded relevance and performance in the long tail of our subject vocabulary, the LLM-based generative methods give better results, making them a promising alternative for future productive use.
[IR-5] LLM -Based Re-Ranking for Real Estate Search
链接: https://arxiv.org/abs/2607.14835
作者: Nkateko Ntimane,Rafel Guedes,Tiago Cunha,Pedro Nogueira
类目: Information Retrieval (cs.IR)
备注:
Abstract:QuintoAndar Group operates the leading housing marketplace in Latin America for both rentals and sales. The platform replaces traditionally paper-heavy workflows with a fully digital experience, making housing transactions faster and more accessible to tenants, buyers, and landlords in the region. Finding the ideal home in such a vast catalog is inherently difficult. At the same time, the widespread adoption of conversational assistants is reshaping user expectations: people increasingly want to express their needs through open, multi-turn dialog rather than rigid filter menus and faceted search. This shift is particularly pronounced in housing, where intent is multi-dimensional, context-dependent, and rarely reducible to a small set of structured constraints. To meet these expectations, we propose a Large Language Model (LLM) based re-ranker that augments a conversational recommendation system by reordering retrieved candidates according to the nuanced, context-rich intent expressed across the user’s conversation. We additionally construct a large-scale offline evaluation dataset for conversational real-estate search, containing 960,000 query-item pairs constructed from both synthetic and production queries and annotated using an LLM-as-a-Judge framework with human validation. We validate our approach both offline, on this proprietary dataset, and online, through a production A/B test. Both evaluations show consistent improvements in ranking quality, including a statistically significant increase in production of +5.3% in click-through rate and +4.8% in scheduled visits, demonstrating the value of integrating conversational context into housing recommendations.
[IR-6] Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap
链接: https://arxiv.org/abs/2607.14604
作者: Olivier Jeunen
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR); Methodology (stat.ME)
备注:
Abstract:Online controlled experiments are the gold standard for hypothesis testing in online platforms. Notwithstanding their ubiquity, they are notoriously expensive to run, and issues of variance hamper statistical power in assessing treatment effects. While standard variance reduction techniques leverage model-based control variates to reduce outcome noise, they remain agnostic to potential structural relationships between competing policies. In this work, we identify a critical inefficiency in the standard A/B-testing protocol: when a treatment and control policy agree on an action, the resulting outcome contributes noise but no signal regarding the treatment effect – unnecessarily inflating confidence intervals. We propose a novel experimental protocol that exploits this policy overlap to accelerate experimentation. The key insight is to frame the randomised treatment assignment mechanism as a meta-policy, and leverage \Delta -Off-Policy Estimation methods to obtain unbiased estimates for average treatment effects. We prove analytically that our approach recovers standard A/B-testing practices in the general case, but that its variance scales with the divergence between policies rather than raw outcome variance. Hence, we dominate the standard Difference-in-Means estimator whenever policies have common support, and the improvement is strict whenever the overlap region contributes non-zero residual variance. Empirical results corroborate these theoretical insights – holding promise for significant impact on the real-world evaluation of recommender systems, information retrieval pipelines, and large language model interfaces. Subjects: Machine Learning (cs.LG); Information Retrieval (cs.IR); Methodology (stat.ME) Cite as: arXiv:2607.14604 [cs.LG] (or arXiv:2607.14604v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.14604 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-7] Impact of Expert-Following Strategies in Financial Asset Recommendation
链接: https://arxiv.org/abs/2607.14556
作者: Ryuki Unno,Koshi Watanabe,Keigo Sakurai,Keisuke Maeda,Takahiro Ogawa,Miki Haseyama
类目: Information Retrieval (cs.IR); Computational Engineering, Finance, and Science (cs.CE)
备注: 2pages, 1figure
Abstract:Financial institutions hold rich transaction histories, yet delivering recommendations that simultaneously maximize investment returns and ensure preference alignment remains a significant challenge. Existing approaches, namely return-based and preference-based strategies, each optimize a single objective, resulting in a fundamental trade-off between profitability (ROI) and relevance (nDCG). In this paper, we propose the Expert-Following Strategies: a framework that identifies top-performing investors based on their historical ROI and recommends the assets they purchased, scored by ROI-weighted purchase frequency. Our experiments using real-world transaction histories show that our strategy achieves statistically significant improvement over the market-average baseline in both ROI and nDCG simultaneously across all four thresholds.
[IR-8] SAGA: Schema-Aware Grounding for Agent ic Text-to-SPARQL Generation
链接: https://arxiv.org/abs/2607.14494
作者: Yiming Zhang,Koji Tsuda
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form. We study the latter paradigm. Recent large language model agents make semantic parsing interactive: they alternate between reasoning, querying the knowledge base, and extending a partial SPARQL query. This interleaving reduces reliance on one-shot generation, but makes the quality of \emphKB grounding depend on what the interaction tools expose. Existing agents retrieve or prune candidate properties mainly through lexical relevance and instance-level observations, without systematically conditioning on entity types, property domains and ranges, or the expected answer type. We call this failure mode \emphtype-blind grounding. It enlarges the grounding search space and often produces plausible-looking but semantically incompatible triple patterns that execute to empty results. We propose SAGA (\underlineSchema-\underlineAware \underlineGrounding for \underlineAgentic Text-to-SPARQL Generation), a training-free framework that turns property exploration into a schema-constrained grounding operation. SAGA maintains a persistent bidirectional type state, filters known-incompatible property candidates at construction time, presents the remaining graph patterns in a compact schema-annotated format, and handles missing schema information permissively through empirical and trace-local evidence. Across nine benchmark settings over Wikidata and Freebase, SAGA achieves the highest F1 on all nine settings and the highest exact-match accuracy on eight, while reducing empty-result queries across all reported Wikidata settings.
[IR-9] DS@GT ARC at LongEval: Citation Integrity and Factual Grounding in Scientific QA
链接: https://arxiv.org/abs/2607.14400
作者: Brandon Michaels,Brendon Johnson
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 12 pages, 4 figures. Accepted to the CLEF 2026 LongEval Lab Working Notes
Abstract:This paper describes DS@GT ARC’s submission to the CLEF 2026 LongEval Task 4 on Retrieval-Augmented Generation (RAG). In this submission, we examine a divergence between traditional natural language evaluation metrics and citation integrity as applied to RAG QA systems. We evaluate a corrective pipeline using Corrective RAG (CRAG) and CiteFix against baseline and frontier model benchmark RAG QA scores. While frontier models maximized answer relevance and fluency scores, our RAGAs LLM-as-judge diagnostics indicate that frontier models would correctly identify relevant documents without using their context in answer generation. Conversely, by filtering chunks pre-generation and enforcing strict entailment of generated claims to the cited material post-generation, our corrective pipeline marginally improved citation faithfulness and answer grounding. We propose that evaluation of trustworthy RAG QA requires metrics that reward strict answer grounding.
[IR-10] Why Git Is the Memory Solution for the Agent ic Development Lifecycle
链接: https://arxiv.org/abs/2607.14390
作者: Frank Guo
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 8 pages
Abstract:Coding agents now produce a growing share of a team’s code, while the reasoning behind each change – the alternatives weighed, the constraints discovered, the approaches rejected – is trapped in assistant transcripts that vanish with the session. Memory for this setting, the agentic development lifecycle (ADLC), is usually posed as one retrieval problem and built as machinery: tiered stores, memory graphs, compiled wikis, model-judged admission. We argue memory should instead be git-bound – built into the repository’s version control, inheriting the guarantees the machinery struggles to construct: ground truth from commits, freshness from rebuild, verification from the merge, containment from review. On this ledger we solve two problems separately, then combine them. Seed supply is closed as an eight-corpus retrieval study under a pre-registered ship discipline: five imported ranking mechanisms rejected, two kept, and a best configuration of ~0.31 pooled MRR – ~60x the raw-transcript grep floor, ~15x an honest parsed-turn floor. Answer assembly is where ranking stops helping: single-shot retrieval scores only 0.07-0.20 answer-sufficiency on real developer questions, and ungated episode injection measurably degrades good answers. A router dispatches breadth to a git-anchored structural map, pointed lookups to confidence-gated episodes, and rationale to decision synthesis, which reconstructs why-arcs no single session contains (0.83 sufficiency on a young ~50k-LOC production system). Routed, the system answers at 382-980 tokens per question – three orders of magnitude below the recorded history. Because ground truth is mined from commit-session links rather than annotated, every result is replicable on any user’s own history at zero labeling cost. The remaining constraint is capture. Code, benchmark, and paper source: this http URL.
[IR-11] Long-History User Transformers for Real-Time Ad Ranking
链接: https://arxiv.org/abs/2607.14331
作者: Viacheslav Ovchinnikov,Georgii Smirnov,Nikolai Savushkin,Veronika Ivanova,Maksim Kuzin
类目: Information Retrieval (cs.IR)
备注:
Abstract:Long interaction histories are among the most informative inputs for click-through rate (CTR) prediction, yet in online advertising they collide with a hard serving constraint: ads must be scored within a few hundred milliseconds to enter the auction, which rules out running a large sequence encoder at request time. We describe how a production advertising system resolves this conflict by decoupling history encoding from real-time inference. A high-capacity offline transformer asynchronously encodes the user’s full cross-surface interaction history into a compact representation cached in a feature store, while a lightweight runtime model combines this cached representation with the user’s most recent events and the request context at serving time. The offline encoder is pre-trained autoregressively on large-scale interaction logs with a dual objective - feedback prediction and next-item prediction - and the two-stage architecture is then fine-tuned for CTR prediction on the target advertising surface. Offline, the split design recovers 72-80% of the quality of a full-history runtime transformer that would be too expensive to deploy, and the cached representation is robust enough to staleness to permit inexpensive refresh policies. In production A/B experiments, the system improves the primary ranking metric by +2.77% in search advertising and +2.1% on the Yandex Advertising Network, with revenue gains of +2.26% and +0.43% respectively - without increasing serving latency.
[IR-12] ICAConfPubs: A Dataset and User Interface for ICA Conference Papers (2003-2018)
链接: https://arxiv.org/abs/2607.14234
作者: Hongtao Hao,Xinyue Chen,Jiye Sun,Yanling Zhao,Jing Zhang
类目: Digital Libraries (cs.DL); Information Retrieval (cs.IR)
备注: 21 pages; preprint
Abstract:This paper presents a comprehensive dataset of past ICA (The International Communication Association) annual conference papers from 2003 to 2018, encompassing 27,466 papers, 21,038 authors, and 4,935 sessions. We made the dataset publicly available in both CSV and JSON formats. Additionally, we developed an API to facilitate programmatic access, and an intuitive user interface to enable users to navigate and explore the data more easily. The web application, API documentation, downloadable data, and reproducible code to obtain and process the data are available at this https URL.
[IR-13] Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning RECSYS2026
链接: https://arxiv.org/abs/2607.14192
作者: Dingsu Wang,Filip Ryzner,Kelly He,Armando Ordorica,David Woo,Aditya Mantha,Liyao Lu,Usha Amrutha Nookala,Haoran Guo,Jiacong He,Olafur Gudmundsson,Matt Chun,Krystal Benitez,Dhruvil Deven Badani,Yijie Dylan Wang
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: Recsys 2026
Abstract:As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on long-term user engagement and retention. However, directly optimizing retention is difficult because return signals are sparse, delayed, and only partially attributable to earlier recommendations. Prior work has addressed this challenge with sequential modeling and reinforcement learning, but these approaches typically require task specific reward engineering, substantial computational overhead, and surface specific implementations that are difficult to generalize. In this paper, we present a unified, model-agnostic downstream reward framework for optimizing long-term user value in large-scale recommendation systems. First, we formulate the downstream reward learning problem and develop an offline screening framework to identify session level behaviors that are both observable early and predictive of future retention. We then propose several model-agnostic downstream rewards signals derived from observed user action patterns across multiple sources. We further discuss the engineering effort to productionize the proposed rewards derivations and challenges we faced when adding them to our ranking models. Online A/B experiments demonstrate consistent improvements in engagement and retention-related metrics, and the framework has been deployed across multiple Pinterest surfaces, including Homefeed, Related Pins, Search, and Notifications.
[IR-14] A Temporal Machine Learning-Based Time-to-Event Model for Predicting ALS Progression and Healthcare Utilization
链接: https://arxiv.org/abs/2607.14190
作者: Zongliang Yue,Qi Li,Terry Heiman-Patterson,Frank Bearoff,Zhaohui Qin,Huanmei Wu
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR); Machine Learning (stat.ML)
备注:
Abstract:Amyotrophic lateral sclerosis (ALS) is a progressive and heterogeneous neurodegenerative disease in which predicting clinically meaningful milestones, such as assistive device use, remains challenging. We developed a time-to-event, digital-twin-inspired framework that integrates longitudinal ALS Functional Rating Scale-Revised (ALSFRS-R) trajectories with survival modeling to support individualized prediction of functional decline and assistive device utilization. We constructed a harmonized longitudinal dataset by integrating diagnosis records, ALSFRS-R assessments, activities of daily living, and demographic information, followed by preprocessing to ensure data quality, temporal alignment, and cohort consistency. Correlation-based clustering identified coherent functional domains spanning bulbar, upper limb, axial, lower limb, and respiratory systems. Generalized additive mixed models characterized nonlinear, domain-specific functional decline across all domains. In addition, a temporal machine learning model was developed to predict longitudinal functional decline and capture stage-dependent disease progression. Cox proportional hazards modeling further identified lower limb function, particularly walking and stair climbing, as the strongest predictors of earlier wheelchair access. Building on these results, we implemented a digital twin-inspired temporal machine learning-based time-to-event (TTE) model that generates individualized survival curves and dynamically predicts wheelchair-free survival. This framework provides a scalable, interpretable, and clinically actionable approach for linking ALS progression with personalized decision support, with applications in proactive care planning, clinical trial stratification, and precision medicine.
[IR-15] Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest KDD KDD’26
链接: https://arxiv.org/abs/2607.14161
作者: Junpeng Hou,XianXing Zhang,Sai Xiao,Derek Cheng,Darren Reger,Olafur Gudmundsson,Mehdi Ben Ayed,Zhiqing Rao,Huizhong Duan
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: Accepted at KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Abstract:Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content. To support this journey, we must distribute commerce content when it helps, not when it distracts. We frame this as a causal decision of triggering shopping candidate generators in early retrieval and deploy a production system at Pinterest that learns personalized and contextualized triggering policies. A deep multi-task model jointly predicts outcomes and uplift of multiple events, trained with a doubly-robust pseudo-outcome alongside calibrated outcome losses for stable, single-robust uplift learning. A randomized data logging supplies counterfactual coverage, and the model is evaluated by both regular and reverse metrics for full assessment. A linear-time offline replay is designed to select thresholds and forecast policy impact with extremely high consistency with online results. For productionization, the model runs in parallel with remote retrieval calls without end-to-end latency regression. At web scale, we cut shopping triggers by up to 85% while holding key shopping sessions neutral, improving important total sessions (+0.26%) and Pin saves (+1.10%), with significant infrastructure savings. By unifying deep causal learning with reliable offline replay and demonstrating production-grade deployment, this work provides a generally practical recipe for early-retrieval optimizations in modern cascading recommenders beyond shopping, aligning exploration and cost with user intent at scale.
[IR-16] Certified Domain Consistency for Multi-Domain Retrieval: Label-Free Per-Domain Contamination Control with Conformal Risk Guarantees
链接: https://arxiv.org/abs/2607.14157
作者: Jayakumar Manoharan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Systems and Control (eess.SY)
备注: Submitted to ACM TOI
Abstract:Retrieval over corpora that mix several domains often returns relevant but wrong-domain evidence that ranking metrics miss and that conformal risk control bounds only marginally, under-covering the worst domains. This work introduces C3R, a drop-in control layer that, from an inferred domain posterior and no query-time label, certifies a per-domain contamination budget where feasible and otherwise abstains rather than silently violating; on the hardest domains it guarantees a reduction, not a tight bound. The core is a two-split scheme built on risk-controlling prediction sets, whose finite-sample transfer bound crosses from the inferred to the true domain with fully estimable slack, supports heterogeneous budgets, and inverts for deployment. Population validity rests on this bound and a controlled simulation; across a thousand resampled calibrations the certificate never violates (a stability result) while marginal control violates the most-contaminated domain in every draw, and soft demotion retains more recall than the strongest calibrated cascade at equal certified contamination. The method replicates across open testbeds including an independent one from public federal regulations, and an LLM-judged downstream probe indicates wrong-authority grounding rises with contamination and falls under control. The layer is frozen-stack and reranker-agnostic.
人机交互
[HC-0] LLM e Why (Aint Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data NEURIPS2025
链接: https://arxiv.org/abs/2607.15254
作者: Qiwei Li,Jorge Ortiz
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Accepted at the NeurIPS 2025 Workshop on UrbanAI. 6 pages
Abstract:Traffic agencies now have access to large volumes of video-derived data for studying safety and congestion. Most of these data are observational and collected without interventions, which makes causal questions such as “How would rain change traffic density?” difficult to answer. We present teLLMe, a system for exploratory causal analysis of urban driving datasets. The system starts from a structured event table built from dashcam annotations and combines causal structure learning with the PC algorithm, bootstrap-based stability checks, and query-specific effect estimation using linear regression and DoWhy. Natural-language questions are mapped to structured causal queries through a schema-aware LLM, enabling users to specify treatments, outcomes, and subpopulations. teLLMe returns a “Causal Card” that summarizes effect estimates, adjustment sets, DAG support, and assumptions, followed by a short natural-language explanation. Case studies on BDD-derived traffic events show that the system can surface plausible relationships involving weather, peak hours, and traffic density, while making uncertainty and modeling choices explicit. The system is designed as a tool for hypothesis generation and expert reasoning rather than a source of definitive causal claims.
[HC-1] Divergent Gaze Patterns in Artistic Viewing: Spatial and Temporal Signatures of Attention Across Autistic Individuals Artists and Neurotypical Observers
链接: https://arxiv.org/abs/2607.15227
作者: Mohammed Amine Kerkouri,Daphné Senggaran,Renaud Jusiak,Océane Lehmann,Marouane Tliba,Claire Wardak,Emmanuelle Houy-Durand,Shasha Morel-Kohlmeyer,Aladine Chetouani,Nadia Aguillon-Hernandez
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: Submitted for review
Abstract:How different populations visually explore artworks bears on cognitive science and on accessibility design, yet most eye-tracking work in autism has used social scenes rather than art, and has analysed where the eyes land while ignoring when and in what order. We present a comparative free-viewing study across three groups, autistic adults (ASD), trained artists, and neurotypical observers, who each viewed 30 paintings for 15s. We introduce a directed, metric-grounded framework that compares groups along two complementary axes: a spatial axis, in which one group’s fixation-density map predicts another’s fixations under six saliency metrics (AUC-Judd, NSS, CC, SIM, KL, Information Gain); and a temporal axis, in which individual scanpaths are compared with MultiMatch, ScanMatch, a foveal-disc IoU score (FDISS), and dynamic time warping (DTW). Fixations are extracted uniformly for all groups with a dispersion-threshold algorithm. Three results converge. (i)Artists and neurotypicals are almost indistinguishable in both space (density-map correlation CC=0.96) and time (they form the most alignable scanpath pair), whereas ASD gaze diverges from both. (ii)ASD attention is dissociated: it matches artists’ wide spatial exploration (dispersion, explored area) but carries a distinct temporal signature, shorter fixations, less dwell, and the most idiosyncratic (least self-consistent) scanpaths of any group. (iii)ASD gaze is not selectively artist-like on any metric; if anything it is marginally closer to neurotypical. Together these findings indicate that autistic viewing of art is a distinct, group-specific attentional profile in both space and time, and they motivate population-conditioned models of aesthetic attention. We release all analysis code and per-stimulus results.
[HC-2] Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy
链接: https://arxiv.org/abs/2607.15176
作者: Patrick Phuoc Do,Chau M. Ta,Chaoli Wang
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注:
Abstract:Multimodal large language models (MLLMs) are increasingly used to interpret visualizations, yet current evaluations remain largely chart-centric and provide limited evidence of understanding of scientific visualization (SciVis). We benchmark six MLLMs on the scientific visualization literacy assessment test, a standardized SciVis literacy assessment comprising 49 items based on 18 scientific visualizations and illustrations, spanning 8 techniques and 11 task types. We evaluate three closed-source and three open-source models under a closed-world protocol and compare their performance using data from 485 human participants. Results show that current MLLMs do not exhibit uniform SciVis literacy. Gemini is the strongest model overall, exceeding the human mean across the evaluated subsets, whereas the open-source models remain below the human baseline. Performance is highly uneven across techniques and tasks: models perform best on scientific illustration, search, and spatial understanding, but struggle on texture-based and integration-based visualizations and on quantitative estimation. Error analysis reveals recurring failures in fine-grained quantitative estimation, flow-direction interpretation, and grounded encoding interpretation. These findings position SciVis literacy as a necessary benchmark dimension for evaluating multimodal AI systems. Our code and model outputs are publicly available at this https URL.
[HC-3] Setup Complete Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents
链接: https://arxiv.org/abs/2607.15143
作者: Aadesh Bagmar,Pushkar Saraf
类目: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注:
Abstract:AI coding agents set up projects by reading documentation and installing the dependencies it lists, without verifying their names, sources, or known vulnerabilities. By editing only a README, requirements file, or Makefile, an attacker can redirect the agent to an untrusted registry, a known-vulnerable version, or a wrong-but-plausible name: documentation becomes a vector for code execution. We present the first systematic evaluation of package-install-time supply-chain attacks delivered through ordinary project-setup documentation across production coding-agent harnesses, probing frontier models on twelve scenarios in five attack classes, grounded in documented incidents. The same model catches an attack through one harness and installs it through another: install-time security rests on the harness-model combination, not the model alone. Agents catch blatant typosquats reliably, but plausible separator-confusion names (azurecore for azure-core) slip through, and how often depends on the harness-model pairing. Source-based attacks like registry redirection are missed almost everywhere. The source blind spot recurs on npm and Cargo, where nearly every model installs the untrusted dependency; name detection carries over less consistently across ecosystems. Security-oriented prompts recover part of the gap but only for the dimension they name; a deterministic pre-install check that verifies names, sources, and versions before any code runs closes most of it.
[HC-4] Catch Throw Repeat: Planning for Human-Robot Partner Juggling IROS2026
链接: https://arxiv.org/abs/2607.15129
作者: Jonathan Rainer Lippert,Kai Ploeger,Abir Chowdhury,Hermann Müller,Jan Peters,Alap Kshirsagar
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC); Systems and Control (eess.SY)
备注: Accepted at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
Abstract:Dynamic object exchange between humans and robots remains a challenging problem due to uncertainty in perception, timing, and contact-rich interaction. Human-robot juggling represents a particularly demanding instance of this problem, requiring precise real-time coordination, predictive motion planning with feedback control, and robustness to variability in human motion. Enabling such skills is of interest for advancing physical human-robot interaction and shared autonomy. We present a real-time planning and control architecture for human-robot partner juggling that enables a robot to reliably catch and throw balls in synchronized multi-ball patterns with a human partner. The system integrates predictive ball tracking, adaptive online trajectory optimization using a multiple-shooting formulation, and a state-machine-based coordination logic to enable synchronized multi-ball human-robot partner juggling. In a user study with 8 participants of varying juggling skill from beginner to expert, we demonstrate that our system can achieve three-ball cascades shared between the robot and the human. All participants exceeded previously reported best-case results within a 10-minute test session, with one participant extending the previous record for shared three-ball cascade juggling fivefold to 20 consecutive robot catches, and another participant achieving a 100% success rate with 40 consecutive catches in a single-ball catch-and-return setting. Video documentation can be found at this https URL
[HC-5] When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
链接: https://arxiv.org/abs/2607.15006
作者: Célina Treuillier,Denis Lalanne
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: To be published in the proceedings of the 3rd International Workshop on Generative AI and Learning Analytics (GenAI-LA) (held at the 16th International Learning Analytics and Knowledge Conference (LAK’26))
Abstract:The broad adoption of Artificial Intelligence (AI), especially Generative AI, raises pressing questions about how users interact with these systems to produce new content. In this paper, we introduce the concept of authorship calibration, defined as users awareness of their actual authorship when interacting with AI. Using the CoAuthor dataset, we empirically examine how authorship calibration varies across users and how it relates to their frequency of AI use. Our results reveal high variability: users relying heavily on AI tend to misjudge their authorship, whereas those using AI less frequently exhibit more accurate authorship calibration. These findings suggest that AI can obscure users perception of their own authorship. In learning contexts, miscalibration can affect metacognitive monitoring and learning strategies, ultimately impacting learning outcomes. Fostering authorship calibration then appears essential for promoting responsible and educationally meaningful AI integration.
[HC-6] Authoring Narrative Visualization in Motion: Visual Storytelling in Swimming Videos
链接: https://arxiv.org/abs/2607.14924
作者: Junhao Zhao,Romain Vuillemot,Petra Isenberg,Lijie Yao
类目: Human-Computer Interaction (cs.HC); Graphics (cs.GR)
备注:
Abstract:We investigate how to support authoring narrative visualizations in motion in sports videos, drawing on automated data preparation, systematic analysis, technology probe design, and evaluation, using swimming races as a case study. Sports videos are widely broadcast and shared across social media, where content creators increasingly seek to present and explain complex events to general audiences. Visualization in motion has been explored as an efficient way to embed data into videos and to move with the data referents, providing additional information and helping audiences understand races. However, existing approaches primarily focus on embedding visualizations in videos, lacking exploration of how to support authoring narratives that coordinate views, data, and temporal progression to explain the unfolding races. To address this gap, we use swimming videos as an ideal case for exploration, as swimming is a sport with rich, dynamic data and visualizations in practice. We develop an automated pipeline that extracts structured data from videos, derive narrative constructs through observational analysis of sports broadcasts, and design a technology probe that supports authoring using data prepared by our pipeline and narrative constructs derived from our observations. We evaluate our approach with experienced content creators and/or graphic designers to examine the benefits and challenges of authoring narrative visualizations in motion. All supplemental materials are described in the Supplemental Material Pointers section and are on OSF: this http URL.
[HC-7] Understanding of Task-specific and Subject-specific Components in Surface EMG
链接: https://arxiv.org/abs/2607.14744
作者: Yangyang Yuan,Jionghui Liu,Xinyu Jiang,ChihHong Chou,Chenyun Dai,Jiahao Fan
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Surface electromyogram (sEMG) signals are widely used in human-machine interfaces for gesture recognition and user identification, but existing models often struggle to generalize across individuals due to subject-specific neuromuscular characteristics. This study introduces a disentanglement model that separates task-specific and subject-specific components from sEMG signals, thereby improving the generalization and interpretability of gesture recognition and user identification systems. Experimental results demonstrate that the disentangled components significantly improve the accuracy of both gesture classification and user identification across subjects and days, outperforming conventional methods under the same experimental conditions. Further analysis reveals that the task-specific components capture consistent activation patterns associated with the same gestures across individuals. In contrast, the subject-specific components reflect unique neuromuscular characteristics that can be used for user identification. Notably, the subject-specific components show lower similarity across days than the task-specific components, contributing to a greater decrease in user identification accuracy than in gesture recognition accuracy. These findings suggest that the disentanglement approach not only improves classification performance but also provides deeper insights into the physiological mechanisms underlying sEMG signals. The model’s ability to isolate and interpret different neuromuscular components holds promise for enhancing the robustness of sEMG-based applications in real-world settings, including rehabilitation and user authentication. Our code is available at this https URL.
[HC-8] he Misclassification of Autistic Writing as AI-Generated
链接: https://arxiv.org/abs/2607.14729
作者: Summer Chambers,Matthew C. Kelley
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Author Accepted Manuscript, Artificial Intelligence in Education 2025
Abstract:Recent findings suggest that detection models for artificial intelligence (AI) cannot accurately identify AI-generated text and may exhibit bias against certain minority groups. In the present study, anecdotal claims that autistic writers more often have their work flagged as AI-generated are examined empirically. A corpus of approximately 60,000 Reddit posts split into “likely-autistic” and “general-Reddit” subcorpora is used to compare the distribution of probabilities output by the OpenAI GPT-2 detection model. Differences in textual features between subcorpora are observed and compared to reported features of AI-generated text. Results showed that while less than two-percent of either subcorpus was flagged as AI-generated by the model, significantly more texts from the likely-autistic subcorpus were flagged. Connections between features of text with likely-autistic authors and AI-generated text were not straightforward. The widespread use of AI-detection models with a potential bias against autistic writers in their output prompts ethical scrutiny, and the authors recommend further critical examination of the models themselves as well as their use in academic contexts.
[HC-9] Project Kaleidoscope: Contextual Human-Aligned Evaluation for Real-World AI Applications
链接: https://arxiv.org/abs/2607.14673
作者: Leanne Tan,Rohan Jaggi,Shaun Khoo,Roy Ka-Wei Lee
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Evaluations (Evals) are a deployment bottleneck for real-world AI applications: public benchmarks rarely match a team’s users, context, or policies, and human review is often tedious to scale. Motivated by our work with AI applications in the public sector, this project addresses recurring evaluation challenges encountered when applications must satisfy local policy and governance requirements. We present Kaleidoscope, an integrated workflow for contextual functional evaluation that links persona-based test generation, contextualized rubrics, and human review for reliability-gated automated scoring. Generated test cases are scored against application-specific rubrics; human annotations provide reviewable labels; and LLM judges automate scoring only when their agreement with those labels meets a configured threshold. Kaleidoscope is therefore a practical, inspectable, iterative workflow for product teams. We report early evidence from a three-week pilot across four organizational use cases and custom-rubric judge experiments on 108 annotated Q\A pairs spanning four domains and 14 evaluation dimensions. The results highlight useful features for end-to-end reliable, automated scoring.
[HC-10] Dendrite: A Real-Time Python Application for Online Brain-Computer Interface Research and Development
链接: https://arxiv.org/abs/2607.14655
作者: Niko Kroflic,Jan Babič
类目: Human-Computer Interaction (cs.HC)
备注: 15 pages, 5 figures
Abstract:Online brain-computer interface research requires software that can acquire multimodal physiological data, train and update decoders, run live inference, and preserve the full experimental provenance in a reproducible workflow. We present Dendrite, a real-time brain-computer interface application in Python that brings signal acquisition, decoder training, and live inference together in a single, ready-to-run application that stays modifiable. Dendrite records several signal streams at once, each at its native rate, and executes multiple processing modes concurrently against them. A decoder can start from a previously trained model or be fit mid-session while the pipeline keeps running, and the same recordings feed offline training in the same application. Each recording, decoder, and training run is tracked in a database, and every decoder records the configuration and the source recordings it was trained from, so a deployed decoder traces back to what produced it. The experimental paradigm stays external, an independent program in any language that reaches Dendrite over the network, rather than a module built inside the runtime. We validate the full system end-to-end on in-house and public BCI datasets, training and updating decoders online while the pipeline runs in real time. Dendrite is open-source under the GPL-3.0 license at this https URL. The result is a reproducible, open-source biomedical-computing system for developing and evaluating online BCI paradigms.
[HC-11] Memory-Driven Self-Disclosure and Relational Turning Points: A Longitudinal Multimodal Study of Human-AI Interaction
链接: https://arxiv.org/abs/2607.14593
作者: Ryuichi Sumida,Mao Saeki,Masaki Eguchi,Sadahiro Yoshikawa,Koji Inoue,Tatsuya Kawahara,Yoichi Matsuyama
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 15 pages, 3 figures. Accepted to ICMI 2026 (International Conference on Multimodal Interaction), October 5-9, 2026, Napoli, Italy
Abstract:As conversational AI systems are designed for repeated use, a central question is how a series of interactions becomes a relationship. We present a longitudinal multimodal study of a memory-augmented conversational agent (24 participants x 10 sessions), in which participants rated five relational constructs – familiarity, self-disclosure, perceived memory, conversational quality, and enjoyment – after each session. Two complementary dynamics emerge. First, conversational quality strongly shapes how enjoyable a session feels in the moment but does not carry forward across sessions, whereas perceived memory is relationally conditioned – predicted by prior relational state rather than reflecting system capability alone – and it shapes later enjoyment indirectly, via subsequent self-disclosure. Second, relationships are punctuated by discrete turning points – crashes and surges – that are partially traceable in multimodal behavior and open different intervention windows: surges are more behaviorally detectable in the moment, enjoyment surges persist more reliably than enjoyment crashes recover, and some crashes are better forecast from person-specific behavioral drift than detected after they have already occurred. Together, the findings suggest that longitudinal human-AI relationships are built through both slow accumulation and abrupt turning points.
[HC-12] Conversational Tactile Data Interfaces: Co-Designing Accessible Data Experiences with Blind Users Using Refreshable Tactile Displays and Conversational AI IEEE-VIS2026
链接: https://arxiv.org/abs/2607.14588
作者: Samuel Reinders,Munazza Zaib,Bongshin Lee,Ingrid Zukerman,Matthew Butler,Thien Autran,Sascha Cowley,Francois Jacobs,Lizhen Qu,Kim Marriott
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to be presented at IEEE VIS 2026 and published in IEEE TVCG
Abstract:Combining refreshable tactile displays (RTDs) with conversational AI offers a promising approach to accessible data visualization for people who are blind or have low vision (BLV). However, it remains an open question how these modalities should be integrated to support accessible data experiences. We address this through a co-design process with three BLV co-designers. Building on our prior Wizard-of-Oz study, we created a conversational tactile data interface (CTDI) that combines an RTD with an LLM-powered conversational agent, refined through four workshops over eight months. In addition to the resulting system, Graphy, we contribute design knowledge and recommendations for CTDIs. Co-designers used touch as the primary sensemaking channel for spatial understanding of the data’s shape, trends, and relationships, reserved the agent for what touch could not resolve (e.g., calculation and analysis), and used the chart on the RTD to verify the agent’s responses. Key findings include: a layered presentation that scaffolds chart exploration through progressive, interactive layers; a feedback grammar that distinguishes user- and agent-initiated tactile feedback; and a sequential interaction pattern – select, confirm, ask, verify – where each step grounds the last.
[HC-13] Skeleton: Visual Authoring of Non-visual Data Experiences IEEE-VIS2026
链接: https://arxiv.org/abs/2607.14579
作者: Frank Elavsky,Chieri Nnadozie,Lucas Nadolskis,Patrick Carrington,Dominik Moritz
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages core, 2 pages acknowledgements + references, 4 pages appendices. Accepted at IEEE VIS 2026, to appear in November 2026
Abstract:When sighted practitioners author accessible data visualizations, they build navigation structures (the nodes, edges, and input bindings that govern how assistive technologies traverse an interface) entirely in code, with no visual representation. Without a representation to react to, practitioners cannot develop judgment about what makes navigation good or bad, and the quality ceiling of non-visual experiences is set by the absence of a feedback loop. We address this problem through longitudinal co-design with practitioners across cartography, design systems, and open-source visualization, and make three contributions. First, we introduce an Inspector that renders navigation graphs as interactive node-link diagrams, and a Dimensions API that expresses navigation in terms of data dimensions rather than explicit graph construction. Second we present Skeleton, a direct-manipulation authoring environment in which the properties of an accessible navigation structure are translated into visual representations authors can observe and manipulate. Key techniques include a dual-view editor that simultaneously shows the system’s navigation model and the end user’s spatial experience, a scaffolding engine that automates spatial node placement by repurposing a visualization rendering pipeline, a live label-template editor with real-time screen-reader-output preview, and a testing mode that makes traversal sequence visually trackable. Third, we evaluate Skeleton through an in-situ study with 8 practitioners across visualization design, engineering, and research. Making navigation structure visible changed how practitioners engaged with accessible design: they reconsidered the architecture of their own visualizations, attended to a broader range of input modalities, and shifted from treating accessibility as a compliance task to treating it as a design problem. (abstract shortened for arxiv)
[HC-14] Not Just Pockets: Understanding Phone-Carrying Behaviors of Wheelchair Users for Mobile Context-Awareness
链接: https://arxiv.org/abs/2607.14437
作者: Yunzhi Li,Patrick Carrington
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to MobileHCI 2026
Abstract:Smartphone-based context-awareness holds significant promise for wheelchair users – from detecting everyday accessibility barriers to enabling ability-based adaptations. Such capabilities often build on passive context inference through mobile sensing, yet their accuracy hinges on how and where phones are carried and the resulting signal quality. While prior work documents phone-carrying behaviors in the general population, patterns specific to wheelchair users remain underexplored. Through a mixed-methods approach combining a survey of 91 and interviews with 15 wheelchair users, we systematically investigate their phone-carrying locations and influencing factors. Our findings reveal distinct patterns extending beyond pocket storage to diverse wheelchair-mounted accessories and around-body placements, shaped by the interplay of physical ability, wheelchair design, and everyday contexts, including social, activity, and device factors. Grounded in these findings, we articulate how carrying location can serve as a proxy for user context to enable novel context-aware experiences, and discuss design implications for developing inclusive and effective mobile context-aware applications.
[HC-15] From Product-Centred Retrieval to Experience-Led Commerce:Twelve Candidate Design Principles for Fashion E-Commerce User Experience
链接: https://arxiv.org/abs/2607.14429
作者: Nafiul I. Khan,Mansura Habiba,Rafflesia Khan
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:This paper proposes twelve candidate Experience-Led Commerce design principles for high-constraint, relational fashion e-commerce, surfaced through design-led induction while building VogueDrop, a multi-vendor prototype. The principles address multi-entry discovery, experience continuity, relational exploration, preference sovereignty, evidence-scoped correspondence, recommendation-time feasibility, customer-compatible commercial ranking, adaptive but stable workspaces, attributable transaction authority, outcome-linked learning, shared composition authoring, and accountable human to agent handoff. The paper uses fashion as an intentionally bounded domain in which fit, composition, material, identity-sensitive preference, seller fragmentation, visual correspondence, and delivery timing make the interaction breakdown observable; it does not claim universal applicability across e-commerce. Each candidate principle is paired with a prespecified hypothesis, primary behavioural outcome, and rejection condition. A formative critical-incident study and a preregistered matched-interface experiment are specified, with user-experience and platform-facing outcomes reported separately. No empirical superiority is claimed before those studies are completed.
[HC-16] Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)
链接: https://arxiv.org/abs/2607.14301
作者: Shahin Hossain,Tukhbita Afroz Nawmi
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 21 pages, 3 figures, 14 tables
Abstract:As generative AI (GenAI) becomes increasingly embedded in undergraduate academic writing, how students rely on these tools, rather than simply whether they use them, has become a central question for learning, academic integrity, and educational equity. Existing measures of reliance were developed inductively, focused on discrete problem-solving tasks, and validated mainly with homogeneous samples. This study developed and validated the GenAI Reliance Types Scale (GenAI-RTS), a 20-item instrument measuring four theoretically derived types of GenAI reliance: Strategic, Instrumental, Dependent, and Dialogic. Validation followed the multisource framework of the Standards for Educational and Psychological Testing, drawing on a survey of 382 undergraduates at a U.S. Minority-Serving Institution and interviews with 14 purposively sampled students. Confirmatory factor analyses of six competing models supported a five-factor structure in which Strategic Reliance comprises two facets, Deliberate Use and Critical Evaluation, alongside Instrumental, Dependent, and Dialogic factors (CFI = .92, RMSEA = .08; DWLS CFI = .98, RMSEA = .07). Subscale reliability was acceptable to good (omega = .75-.88), and scalar measurement invariance held across gender, first-generation status, and STEM/non-STEM majors, to our knowledge the first such evidence for a GenAI reliance instrument. Rasch analysis indicated that a five-point response format would improve category functioning. Strategic reliance was positively associated with AI literacy, and the reliance types differentiated students across multiple writing process and outcome variables. The GenAI-RTS offers researchers and educators a theoretically grounded, psychometrically validated instrument for identifying undergraduate reliance profiles and supporting research, assessment, and AI literacy intervention.
[HC-17] “Trust Junk” Leads to Unjustified Support for Highly Discriminatory Predictive Models
链接: https://arxiv.org/abs/2607.14152
作者: Michael Correll,Lucy Havens,Mahsan Nourani
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:The persuasive power of data visualizations can go awry: for instance, in an explainable AI (XAI) context, visualizations can produce over-trust of predictive models. In this paper, we use a crowdsourced study to show that providing accurate (but superfluous or irrelevant) data in a model explanation can, in fact, result in unjustified trust and other positive beliefs about a model, even when the model is patently discriminatory and unfair. Our results suggest that XAI designers and developers need to consider the implicit or explicit rhetorics of their work, and beware of the potential of visualizations to imbue models with unearned trust.
[HC-18] UzWordnet and Generative AI for Learning Uzbek by Game Playing
链接: https://arxiv.org/abs/2607.14104
作者: Alessandro Agostini,Saydobid Khusanov,Mirkamol Mirkamilov
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注: 19 pages, 3 figures
Abstract:This paper presents an educational system architecture that enables learners to practice the Uzbek language through game-playing. The architecture integrates UzWordnet and the largest currently available orthographic dictionary for Uzbek as core lexical resources, together with generative AI as a fundamental component for learning support. We design four educational games to facilitate Uzbek language learning and propose a game-based methodology for improving UzWordnet as a direct by-product of game dynamics. Our approach combines game design and lexical resources to address objectives that are at the same time educational (language learning) and lexical (improvement and enrichment of a lexical resource).
[HC-19] All Polarized but Still Different: a Multi-factorial Metric to Discriminate between Polarization Behaviors on Social Media
链接: https://arxiv.org/abs/2312.04603
作者: Celina Treuillier(UL, CNRS, LORIA),Sylvain Castagnos(UL, CNRS, LORIA),Armelle Brun(UL, CNRS, LORIA)
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Physics and Society (physics.soc-ph)
备注: ACM/SIGAPP Symposium On Applied Computing, Apr 2024, Avila, Spain
Abstract:Online polarization has attracted the attention of researchers for many years. Its effects on society are a cause for concern, and the design of personalized depolarization strategies appears to be a key solution. Such strategies should rely on a fine and accurate measurement, and a clear understanding of polarization behaviors. However, the literature still lacks ways to characterize them finely. We propose GRAIL, the first individual polarization metric, relying on multiple factors. GRAIL assesses these factors through entropy and is based on an adaptable Generalized Additive Model. We evaluate the proposed metric on a Twitter dataset related to the highly controversial debate about the COVID-19 vaccine. Experiments confirm the ability of GRAIL to discriminate between polarization behaviors. To go further, we provide a finer characterization and explanation of the identified behaviors through an innovative evaluation framework.
计算机视觉
[CV-0] Hierarchical Denoising For Multi-Step Visual Reasoning
链接: https://arxiv.org/abs/2607.15278
作者: Zezhong Qian,Xiaowei Chi,Chak-Wing Mak,Tianze Zhou,Ruibin Yuan,Yuhan Rui,Hengzhe Sun,Zhuoqun Wu,Yuming Li,Siyuan Qian,Sirui Han,Shanghang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video models are evolving into vision foundation models, yet they still lack human-like multi-step reasoning. Streaming autoregressive diffusion models are efficient but limited in reasoning, while bidirectional diffusion enables global revision with high inference costs due to dense frame-level denoising. Both paradigms struggle to achieve logical consistency and low-latency streaming for complex reasoning tasks. We propose HDR (Hierarchical Denoising for Visual Reasoning), a unified framework that integrates hierarchical latents into causal video generation for multi-step reasoning. HDR organizes video latents into a tree-structured hierarchy, enabling coarse-to-fine reasoning before streaming output. Coarse denoising layers preserve uncertain hypotheses for global planning, while finer layers progressively refine them into concrete visual states. A sparse hierarchical attention pattern (SHAP) further reduces temporal attention costs. We introduce a level-stratified multi-step video reasoning benchmark with out-of-distribution cases, covering six tasks: maze navigation, Tower of Hanoi, one-line drawing, sliding puzzle, Sokoban, and water pouring. Compared with streaming autoregressive diffusion baselines, HDR improves success from 34.22 to 60.29 (76.2% relative gain) and increases average progress from 76.00 to 89.56, demonstrating more consistent reasoning trajectories. HDR maintains low-latency streaming at 0.70 seconds per latent, achieving 54.2 times faster inference than bidirectional diffusion. It also retains 82.9% of full-data performance with only 2% training data, compared with 52.0% for bidirectional diffusion. Real-world robot experiments further demonstrate HDR’s potential for physical interaction and world modeling. Project demo: this https URL.
[CV-1] MeanFlowNFT: Bringing Forward-Process RL to Averag e-Velocity Generators
链接: https://arxiv.org/abs/2607.15273
作者: Yushi Huang,Xiangxin Zhou,Jun Zhang,Liefeng Bo,Tianyu Pang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Project Page: this https URL , GitHub: this https URL , Hugging Face: this https URL
Abstract:MeanFlow generators achieve fast few-step sampling by predicting average velocities over time intervals, making them attractive for efficient generation. Reinforcement learning (RL) has become a powerful way to align diffusion and flow models with human preferences and task-specific objectives. In particular, DiffusionNFT offers an efficient forward-process RL framework that does not require reverse-process trajectories or likelihood estimation. However, applying such RL methods to MeanFlow remains underexplored. DiffusionNFT optimizes instantaneous velocities, whereas MeanFlow samples with average velocities. To bridge this gap, we introduce MeanFlowNFT. Inspired by the MeanFlow identity, which bridges average and instantaneous velocities, we construct an induced instantaneous-velocity predictor. We apply the DiffusionNFT objective to this predictor, making reward optimization well-defined for MeanFlow. Sampling remains based on the average velocity, preserving MeanFlow’s fast few-step generation. We further prove that MeanFlowNFT inherits DiffusionNFT’s strict policy-improvement guarantee. Experiments on image and video generation show that MeanFlowNFT consistently improves baselines. Moreover, it outperforms prior state-of-the-art RL-tuned few-step generators on most metrics ( 6 of 8 on SD3.5-M), and can even surpass multi-step RL-tuned diffusion while using only a few sampling steps. For instance, on Wan 2.1, 4 -step MeanFlowNFT reaches a VBench score of 84.33 , surpassing 50 -step LongCat-Video RL ( 82.57 ).
[CV-2] Online Neural Space Time Memory for Dynamic Novel View Synthesis
链接: https://arxiv.org/abs/2607.15271
作者: Baback Elmieh,Lynn Tsai,Zeman Li,Srinivas Kaza,Tiancheng Sun,Gabor Csapo,Ali Behrouz,Yuan Deng,Stephen Lombardi,Steven M. Seitz,Xuan Luo
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
备注: 15 pages. Preprint. Project page with demos and video results: this https URL
Abstract:Online novel view synthesis from multi-view streaming videos faces a fundamental trade-off: maintaining a persistent, long-horizon memory to reconstruct temporarily occluded regions while operating under strict real-time constraints. While Test-Time Training (TTT) offers a powerful memory mechanism, standard models mandate gradient-based memory updates at every frame to adapt to the changing motion in dynamic scenes. The computational cost of heavy memory updates precludes real-time application and can lead to instability over long contexts. Given that memory updates are more demanding than memory application and video content is largely redundant, we propose to decouple the frequencies of these two processes. Our approach performs periodic memory updates while applying the memory on a per-frame basis, using cross-view attention to manage deformations between the prior memory state and the current frame. To lock in the historical context, we introduce two critical mechanisms: an auxiliary Memory Loss that forces persistent internalization of the scene, and a Memory Caching strategy that regularizes active weights against catastrophic drift. Our method demonstrates real-time, state-of-the-art performance on scenes with dynamic human motion as well as minute-scale online memorization.
[CV-3] Motion-Conditioned Multi-View Fusion for Myocardial Infarction Localization from Echocardiography
链接: https://arxiv.org/abs/2607.15268
作者: Guang Yang,Wentian Xu,Siyu Wang,Betty Raman,Lei Li,Vicente Grau
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Myocardial infarction (MI) remains a leading cause of mortality worldwide. Echocardiography (Echo) is a widely available modality for MI assessment, where regional wall motion abnormality is a key indicator. Prior learning based methods for myocardial motion analysis often use handcrafted descriptors or densely supervised estimation, but the need for extensive annotation limits applicability. Foundation models have recently improved vision-based Echo analysis; however, most methods operate on single views and segment-level localization remains unreliable under view-dependent ambiguity, especially in apical views. To address this, we propose MCF-Net, a novel motion-guided multi-view fusion framework that fuses myocardial motion cues with foundation model representations to localize infarction. Visual features are extracted using EchoPrime, a pretrained Echo foundation model shared across dual views. Cardiac motion is modeled with extremely sparse supervision: a single annotated template frame is transferred across videos to initialize point tracking, avoiding dense labels. Motion-derived segment-aware soft masks provide coarse spatial priors that selectively enhance features for challenging myocardial segments. A motion-conditioned fusion mechanism then integrates motion and vision across views, refining predictions without overriding strong appearance cues. On segment-level MI localization, MCF-Net achieves 72.4% F1 and 84.9% accuracy, outperforming state-of-the-art motion-only, vision-only, and fusion baselines.
[CV-4] SceneBind: Binding What and Where Across Vision Audio and Language
链接: https://arxiv.org/abs/2607.15265
作者: Mingfei Chen,Zijun Cui,Ruoke Zhang,Hyeonggon Ryu,Eli Shlizerman
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM); Sound (cs.SD)
备注: Project website: this https URL
Abstract:We present SceneBind, an omni-modal representation of realistic scenes with joint semantic and 3D spatial understanding across vision, audio and language. Existing omni-modal encoders excel at instance-level semantics (i.e., what is present), but often lack explicit spatial structure (i.e., where it is). SceneBind addresses this gap by representing each scene as a semantic-spatial entity, combining a global semantic embedding with object-centric semantic-spatial slots. This representation explicitly captures object-level semantics, spatial attributes, and uncertainty. We further propose SceneBind Matching, a semantic-spatial matching scheme that integrates global scene similarity with object alignment, supporting cross-modal scene retrieval and object grounding. To train and evaluate SceneBind, we curate a novel real-world binaural audio-visual dataset with structured semantic and spatial annotations, and propose a training protocol for aligning semantic and spatial signals across modalities. SceneBind is compatible with large-scale pretrained semantic encoders, adds lightweight spatial modeling with only a few additional tokens. It achieves state-of-the-art scene and spatial retrieval while enabling strong zero-shot transfer to downstream tasks such as audio-visual localization.
[CV-5] HoloGeo: Mitigating Landmark Bias in Geo-localization via Evidence-Driven Reasoning
链接: https://arxiv.org/abs/2607.15255
作者: Pengcheng Zhou,Xuanyu Liu,Yanchen Yin,Bobo Li,Shengqiong Wu,Mong-Li Lee,Wynne Hsu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in Vision-Language Models (VLMs) have significantly improved image geo-localization, yet existing models remain susceptible to landmark bias, causing them to overlook geographical cues or form spurious correlations, ultimately resulting in inaccurate localization. To systematically investigate this issue, we first design two quantitative metrics, Bias Intensity (BI) and Bias Harmfulness (BH), to characterize the impact of landmarks exerted on model reasoning, and establish a comprehensive benchmark, LandmarkBias-3K. To mitigate landmark bias, we further propose an evidence-driven reasoning framework, HoloGeo, to improve the reliability of geo-localization. HoloGeo is supported by a high-quality dataset, BF-30k, annotated with structured multi-evidence bias-free reasoning chains. By incorporating multi-dimensional rewards, HoloGeo explicitly encourages balanced attention over diverse visual cues and achieves evidence-driven joint reasoning. Extensive experiments demonstrate that HoloGeo not only maintains excellent performance on IM2GPS3K and YFCC4k but also significantly outperforms existing open-source VLMs on LandmarkBias-3K, validating its effectiveness for robust geospatial reasoning.
[CV-6] ARMOR: Agent ic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors
链接: https://arxiv.org/abs/2607.15246
作者: Christos Korgialas,Gabriel Lee Jun Rong,Dion Jia Xu Ho,Pai Chet Ng,Xiaoxiao Miao,Konstantinos N. Plataniotis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The reliability of deepfake detectors frequently degrades under black-box adversarial transfer, as these models often rely on fragile, architecture-dependent forensic cues. Existing transfer attacks often lack semantic awareness and struggle to maintain effectiveness under strict no-query constraints, particularly when perturbations are transferred from convolutional surrogates to transformer-based targets. To address these limitations, this paper introduces ARMOR++, a robust multi-agent framework designed for high-transferability deepfake evasion. The framework leverages the Qwen2.5-VL Vision-Language Model (VLM) to supply spatial semantic priors, while the Qwen3 Large Language Model (LLM) orchestrates primitive selection, adaptive hyperparameter reparameterization, and entropy-regularized perturbation mixing. By integrating five complementary primitives, spanning dense optimization, saliency-based methods, spatial transformations, frequency-domain perturbations, and block-structured modifications, ARMOR++ effectively targets heterogeneous inductive biases. Rigorous evaluation on the AADD-2025 benchmark demonstrates that ARMOR++ significantly outperforms existing agentic and non-agentic baselines across both low- and high-quality image regimes. Statistical analysis confirms a substantial gain in blind-target Attack Success Rate (ASR) over the state-of-the-art agentic baseline, with further performance advantages evidenced against non-agentic benchmarks and under robust defensive configurations. These findings highlight a significant residual reliability gap in current deepfake detector deployments and demonstrate the efficacy of agentic orchestration in identifying latent vulnerabilities.
[CV-7] CRISP: Constrained Refinement via Iterative Squeezing Process for Robust Medical Image Segmentation under Domain Shift AAAI2027
链接: https://arxiv.org/abs/2607.15231
作者: Yizhou Fang,Pujin Cheng,Yixiang Liu,Xiaoying Tang,Longxi Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: X pages, 3 figures, 3 tables; submitted to AAAI 2027
Abstract:Distribution shift in medical imaging remains a central bottleneck for the clinical translation of medical AI. Failure to address it can lead to severe performance degradation in unseen environments and exacerbate health inequities. Existing methods for domain adaptation are inherently limited by exhausting predefined possibilities through simulated shifts or pseudo-supervision. Such strategies struggle in the open-ended and unpredictable real world, where distribution shifts are effectively infinite. To address this challenge, we adopt the “Rank Stability of Positive Regions” as a working assumption under distribution shift, and use it to derive robust spatial hints for source-only segmentation. Guided by this assumption, we propose CRISP, a model-agnostic framework that, unlike deployment-time adaptation, requires no test-time parameter updates and no target-domain data–a target-free, plug-in refinement framework that segments with frozen weights. Rather than using ranking to directly output masks, CRISP exploits the stability of probability rankings under distribution shift to derive robust spatial priors. Via latent feature perturbation, perturbation-invariant high-grade regions define a high-precision (HP) core, while voxels that remain potentially foreground under at least one perturbation define a high-recall (HR) support; these dual priors are then recursively refined under perturbation. We then design an iterative training framework that progressively squeezes HP and HR toward the final segmentation. Extensive evaluations on multi-center cardiac MRI and CT-based lung vessel segmentation demonstrate CRISP’s superior robustness, significantly outperforming state-of-the-art methods with striking HD95 reductions of up to 0.14 (7.0% improvement), 1.90 (13.1% improvement), and 8.39 (38.9% improvement) pixels across multi-center, demographic, and modality shifts, respectively.
[CV-8] Structural-Semantic Reciprocal Learning for Unsupervised Visible-Infrared Person Re-Identification
链接: https://arxiv.org/abs/2607.15220
作者: Moyao Tian,Shijia Liu,Yan Yang,Xin Yuan,Minshi Chen,Wei Wang,Xiao Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by PRCV 2026
Abstract:Unsupervised visible-infrared person re-identification (USVI-ReID) is challenging due to the large modality gap and the lack of cross-modal identity annotations. Progressive association paradigms have been proposed to gradually bridge the gap, but they suffer from two critical bottlenecks: reliance on ambiguous global representations and unchecked propagation of pseudo-label noise in an open-loop manner. To address these issues, we propose Structural-Semantic Reciprocal Learning (SSRL), a framework that transforms open-loop association into a self-correcting closed-loop system. Structurally, we introduce Fine-grained Structural Decoupling (FSD) to extract discriminative body-part primitives as reliable spatial anchors, complementing ambiguous holistic silhouettes with spatially consistent structural details. Semantically, we design a Closed-loop Semantic Calibration (CSC) mechanism that reconstructs shared semantic prototypes at each epoch and feeds them back into the training loop, effectively filtering pseudo-label noise before the next clustering cycle. Through the reciprocal interaction between structural and semantic learning, SSRL achieves robust cross-modal representation. Extensive experiments demonstrate the competitive performance of SSRL against state-of-the-art USVI-ReID methods on both SYSU-MM01 and RegDB, notably surpassing several supervised counterparts on RegDB.
[CV-9] Symbal: Detecting Systematic Misalignments in Model-Generated Captions ICML2026
链接: https://arxiv.org/abs/2607.15216
作者: Maya Varma,Jean-Benoit Delbrouck,Sophie Ostmeier,Akshay Chaudhari,Curtis Langlotz
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: ICML 2026
Abstract:Multimodal large language models (MLLMs) often introduce errors when generating image captions, resulting in misaligned image-text pairs. Our work focuses on a class of captioning errors that we refer to as systematic misalignments, where a recurring error in MLLM-generated captions is closely associated with the presence of a specific visual feature in the paired image. Given a vision-language dataset with MLLM-generated captions, our aim in this work is to detect such errors, a task we refer to as systematic misalignment detection. As our first key contribution, we present Symbal, which utilizes a structured, dual-stage setup with off-the-shelf foundation models to identify systematic misalignments and summarize results in natural language. As our second key contribution, we introduce SymbalBench, a benchmark designed to evaluate automated methods on our proposed task. SymbalBench consists of 1.7 million image-text pairs from two domains (natural and medical images), organized into 420 vision-language datasets with annotated systematic misalignments. Symbal exhibits strong performance on this benchmark, correctly identifying systematic misalignments in 63.8% of datasets, a nearly 4x improvement over the closest baseline. We supplement our evaluations on SymbalBench with real-world evaluations, showing that (1) Symbal can accurately surface systematic misalignments in captions generated by four MLLMs and (2) Symbal is a powerful tool for auditing off-the-shelf image-caption datasets. Ultimately, our novel task, method, and benchmark can aid users with auditing MLLM-generated captions and identifying critical errors, without requiring access to the underlying MLLM. Code is available at this https URL.
[CV-10] MAGiSt3R: Multi-Agent Feed-forward 3D Reconstruction from Monocular RGB Videos
链接: https://arxiv.org/abs/2607.15211
作者: Ziren Gong,Xiaohan Li,Fabio Tosi,Ninghui Xu,Stefano Mattoccia,Jianfei Cai,Matteo Poggi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This paper presents MAGiSt3R, a multi-agent 3D reconstruction framework performing reconstruction and camera tracking for monocular RGB videos at almost 10 FPS. MAGiSt3R relies on a feed-forward model from the 3R family to process RGB videos and regress local point maps, and on a merging model, MAGMA, that combines local maps at both intra-agent and inter-agent levels to obtain the final global point map. Furthermore, MAGiSt3R performs pose graph optimization to mitigate cumulative camera drift occurring along the feed-forward pipeline. We evaluate MAGiSt3R on both synthetic and real-world datasets, demonstrating its superior reconstruction and camera tracking accuracy compared to state-of-the-art approaches.
[CV-11] Ray-based phase error correction for miniaturized DOE projector-based FPP under single-directional hyperbolic projection
链接: https://arxiv.org/abs/2607.15139
作者: Seung-Jae Son,Yatong An,Jae-Sang Hyun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Fringe Projection Profilometry (FPP) systems using miniaturized DOE pro-jectors often suffer from severe phase artifacts due to nonlinear projection characteristics and limited pattern controllability. We propose a ray-based phase error correction framework that models phase artifacts along projection rays from the projector pinhole, incorporating projector geometry without re-lying on image-domain processing or neighboring pixels. A projector pinhole estimation method based on a single-directional hyperbolic fringe pattern is introduced, through which projector geometry can be recovered without stereo calibration. In addition, a data-efficient strategy constructs the re-finement model from a single calibration pose. Experiments on miniaturized DOE projector-based FPP systems demonstrate significant improvements in reconstruction accuracy under nonlinear projection conditions, confirming the robustness and physical consistency of the proposed approach.
[CV-12] DAPGNet: Dynamic Adaptive Physics-Guided Graph Diffusion Network for Hyperspectral Image Classification
链接: https://arxiv.org/abs/2607.15128
作者: Pengkun Wang,Weijia Cao,Ning Wang,Xiaofei Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 figures and 9 tables. Pengkun Wang and Weijia Cao contributed equally to this work
Abstract:Hyperspectral image (HSI) classification requires reliable pixel-relation modeling under spectral variability, mixed pixels, and heterogeneous boundaries. Existing graph-based HSI classifiers usually construct graph topology from spatial proximity, superpixel connectivity, or learned feature affinity. However, the spectral physical prior carried by contiguous bands has limited influence on topology estimation and message propagation. This paper presents DAPGNet, a dynamic adaptive physics-guided graph diffusion network that injects a structure-constrained physical prior into relation-level graph learning. DAPGNet first encodes contiguous spectral responses into node-wise multiscale physical-prior representations. A two-stage graph constructor then combines spectral-spatial affinity, physical-prior consistency, and spatial distance to form a physical-prior-aware sparse topology. During graph diffusion, learned edge weights are transformed into additive attention biases, while a physical gate performs node-wise and feature-wise interpolation between graph-aggregated features and projected physical-prior features. Cross-scale fusion integrates node states from different diffusion depths, and the network is optimized with main classification, auxiliary supervision, and second-order spectral smoothness regularization. Experiments on Indian Pines, WHU-Hi-LongKou, Houston2013, and Houston2018 show that DAPGNet achieves the best OA, AA, and Kappa among representative CNN-, Transformer-, Mamba-, and graph-based baselines. It improves AA over the strongest competing method by 3.64 to 7.31 percentage points across the four datasets. Ablation and sensitivity analyses further support the complementary effects of physical-prior extraction, prior-aware topology construction, physics-gated propagation, and spectral smoothness regularization.
[CV-13] QuReC: All-in-One Image Restoration with Query-Specific Guidance and Local-Global Response Calibration ACM-MM2026
链接: https://arxiv.org/abs/2607.15097
作者: Shen Zhou,Jinghui Zhang,Wenbo Huang,Xuwei Qian,Zhen Wu,Guangwen Peng,Zhiyuan Li,Ding Ding,Dian Shen,Fang Dong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM MM 2026
Abstract:All-in-one image restoration aims to recover clean images degraded by multiple corruption types using a single unified model. Existing methods typically rely on image-level prompts or shared guidance to handle diverse degradations. However, such a paradigm becomes inadequate when degradations are spatially heterogeneous or even coexist in mixed forms within a single image. Yet spatially adaptive guidance alone is not sufficient, since accurate restoration also requires each spatial query to reliably aggregate complementary information from local neighborhoods and global contexts. To this end, we propose QuReC, a unified framework for all-in-one image restoration. QuReC consists of a Degradation-Guided Query Reconstruction Module (DQRM) and a Local-Global Response Calibration Module (LGRCM). Specifically, DQRM matches each spatial query against a degradation prototype space to reconstruct a query-specific degradation-aware representation, thereby providing fine-grained spatially adaptive restoration guidance. To further stabilize this query-wise matching process, we introduce a weakly supervised prototype matching learning strategy to improve optimization stability and degradation semantic consistency. Meanwhile, LGRCM performs local-global dual-branch aggregation and calibrates the aggregated responses with learnable priors, improving the reliability of feature aggregation and the coordination between local detail modeling and global context modeling. Extensive experiments demonstrate that QuReC achieves superior performance on multiple all-in-one image restoration benchmarks. The code is released at this https URL.
[CV-14] AlphaWiSE: Adaptive Weight Interpolation for Continual Multimodal Representation Learning
链接: https://arxiv.org/abs/2607.15094
作者: Sarthak Jain,Qiran Hu,Zhen Zhu,Yaoyao Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Multimodal models such as CLIP learn a shared embedding space for cross-modal retrieval, but continual adaptation to sequentially arriving data can disrupt the cross-modal alignment acquired from earlier phases. Conventional continual-learning methods return a single checkpoint, which commits every retrieval direction to the same stability-plasticity trade-off. We propose AlphaWiSE, a post-hoc weight-space interpolation method that composes two frozen source checkpoints. For each aligned parameter tensor identified by its checkpoint key, AlphaWiSE fits one scalar interpolation coefficient shared by all tensor entries. The coefficients are fitted on a smaller exemplar memory and used to materialize one interpolated checkpoint. The deployed model has the same architecture and parameter count as either source checkpoint, which does not require additional inference time. Extensive experiments on audio-image-text retrieval show consistent improvements over strong continual-learning baselines across multiple retrieval directions and evaluation metrics.
[CV-15] Quantifying Training Membership Information in the Hyperspherical Embedding Geometry of Face Recognition Models
链接: https://arxiv.org/abs/2607.15084
作者: Ünsal Öztürk,Sébastien Marcel
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IEEE/IAPR IJCB 2026
Abstract:Face recognition models represent each face as an embedding vector on the unit hypersphere by clustering embeddings of the same identity while pushing different identities apart through angular-margin losses. Because these losses act only on training identities, non-member identities may form clusters with different geometric properties. In this paper, we quantify the magnitude of this difference and what training-time factors control it. We compute four statistics based on cluster geometry across 180 face recognition models in a factorial design over IResNet backbone size, loss head, training duration, and the number of training identities, and evaluate each configuration on nine benchmarks. Our results indicate that the number of training identities has the largest effect on member/non-member separability, while backbone and loss head contribute far less, and that, on a same-domain held-out reference, the geometric membership signal decreases monotonically as more identities are added to training. We provide an analysis of cross-domain (pose, age, quality, ethnicity) non-member benchmarks and report that these inflate the apparent membership signal. Finally, we fuse all four statistics with a learned classifier to reveal additional membership information beyond the best individual statistic.
[CV-16] DriftWorld: Fast World Modeling through Drifting
链接: https://arxiv.org/abs/2607.15065
作者: Susie Lu,Haonan Chen,Weirui Ye,Yilun Du
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Website at this https URL
Abstract:Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly. This creates a bottleneck for diffusion-based world models: multistep sampling makes each rollout expensive, limiting large-scale action search at inference time. We introduce DriftWorld, an action-conditioned world model based on drifting generative models. Rather than denoising iteratively at inference, DriftWorld learns an action-conditioned drift during training, allowing it to generate future frames from the current observation and a candidate action sequence in a single forward pass at 30+ fps, which is 17x faster on average than diffusion based baselines. We evaluate DriftWorld on standard vision-based robotic manipulation benchmarks, including Bridge-V2, RT-1, Language Table, Push-T, and Robomimic. By producing rollouts that are both accurate and fast, DriftWorld achieves state-of-the-art decision-making performance with far less inference time than diffusion-based world model baselines. Beyond online control, DriftWorld can also serve as an offline simulator for ranking real-world robot policies, with rollout-based scores correlating with ground truth at up to 0.99. These results show that drifting models are a strong fit for robot world modeling, where fast, high-quality imagination directly supports planning and policy evaluation.
[CV-17] SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment
链接: https://arxiv.org/abs/2607.15058
作者: Saad Ejaz,Miguel Fernandez-Cortizas,Javier Civera,Holger Voos,Jose Luis Sanchez-Lopez
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:CAD-to-image alignment aims to estimate an object’s 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, enabling applications in robotics and augmented reality. Recent zero-shot methods use visual foundation models to match image regions to CAD models, yet typically their correspondences are appearance-driven and degrade under occlusion or sim-to-real domain shift. To address these limitations, we introduce SUFLECA (Scaling Up Feature LEarning for CAD Alignment), a weakly-supervised framework for zero-shot CAD alignment with two key contributions. First, SUFLECA scales up geometry-grounded feature learning from pretrained visual representations through Normalized Object Coordinates (NOCs) supervision on 674K images spanning 12 real and synthetic datasets, learning compact geometry-aware features that generalize across domains. Second, we propose a geometrically consistent matching algorithm that establishes reliable one-to-one CAD-to-image correspondences. Together, these contributions enable accurate, sub-second alignment per object instance without iterative pose refinement. On ScanNet25k, SUFLECA achieves 33.4%/42.3% category/instance accuracy, outperforming, with a smaller computational footprint, the strongest zero-shot baseline by 10.3/12.2 percentage points and, for the first time on this benchmark, even surpassing fully supervised methods. Code is available at: this https URL
[CV-18] Beyond Single Expert: Harmonizing Diverse Visual Priors in MLLM s for Spatial Understanding
链接: https://arxiv.org/abs/2607.15054
作者: Xiao Lin,Xiaohu Huang,Kai Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Language Models (MLLMs) have demonstrated substantial promise in spatial understanding. Existing works typically incorporate prior knowledge extracted from a pre-trained foundation model to further enhance the spatial awareness of MLLMs. In this paper, we first reveal that when integrating diverse foundation models into MLLMs, different models provide complementary spatial priors that benefit different tasks. Motivated by this, we propose \textbfViPS , a novel multi-model prior framework designed to fully unleash the potential of incorporating multiple \textbfVi sual \textbfP riors from diverse models into MLLMs for \textbfS patial understanding. Specifically, ViPS introduces an Efficient Prior Proxy to generate multiple foundational priors with minimal inference overhead, and a Dynamic Prior Fusion mechanism to achieve harmonious and context-aware prior fusion and injection from the prior proxies. Extensive experiments demonstrate that ViPS successfully harmonizes diverse visual priors, establishing new state-of-the-art performance across multiple complex spatial reasoning and 3D spatial understanding benchmarks. Project page: this https URL
[CV-19] RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping
链接: https://arxiv.org/abs/2607.15048
作者: Tianchen Deng,Zhiheng Feng,Wenhua Wu,Ziming Li,Siting Zhu,Hesheng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Road surface mapping plays a crucial role in autonomous driving, supporting high-definition map generation, lane-level perception, and automatic road annotation. Recent mesh-based road surface reconstruction methods have shown promising results, but they still suffer from limited reconstruction quality and high optimization cost, especially in large-scale driving scenarios. To address these limitations, we propose ROADGS-T, a robust and efficient large-scale road surface mapping framework based on adaptive meshgrid Gaussian representation. Specifically, we model the road surface by placing 2D Gaussian surfels on a meshgrid, where each surfel explicitly stores color, semantic, and geometric information. Compared with conventional mesh-based representations and 3D Gaussian primitives, the proposed meshgrid Gaussian representation better matches the thin-surface property of roads while significantly reducing redundant primitives and overlap during optimization. To further improve representation efficiency and structural fidelity, we introduce a road-structure-aware adaptive meshgrid strategy, which allocates denser Gaussian surfels to geometrically or semantically complex regions, such as lane markings, road boundaries, and height discontinuities, while maintaining a compact representation in flat road areas. Moreover, instead of relying on a single nearest vehicle pose, we design a trajectory-consistency-guided pose-robust refinement strategy, which estimates local surface priors from multiple neighboring poses and adaptively weights pose-guided height regularization according to their geometric consistency.
[CV-20] Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening
链接: https://arxiv.org/abs/2607.15047
作者: Javad Khoramdel,Farhad Hoseyni,Amirhossein Nikoofard
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer’s disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries. Existing methodologies attempt to bypass these constraints using computationally expensive, fully fine-tuned hybrid architectures that relegate spatial explainability to a post-hoc approximation rather than an intrinsic model property. We propose a parameter-efficient framework utilizing frozen DINOv2-Small model adapted via three modality-specific learnable prompt tokens while Operating with 1.19 million trainable parameters, each token serves as a query in a shared cross-attention layer over the source image patch tokens. Crucially, spatial explainability is achieved directly through these attention maps; as a structural consequence of the architecture. Then task-conditioned embeddings fused via an attention module to quantify modality-level importance per subject. To handle boundary ambiguity, a MoCA-adapted focal loss introduced that integrates continuous cognitive scores into the training target, loss modulation, and adaptive sample weighting, strictly generalizing standard soft-label approaches. Under stratified five-fold cross-validation, the proposed architecture yields an MCI-class F1 of 0.641 and an AUC of 0.795, outperforming the computationally heavier ResViT baseline by 0.110 in MCI-class F1.
[CV-21] Weakly-Supervised RGB-D Salient Object Detection via SAM-driven Pseudo Annotation and State Space Interaction-based Diffusion
链接: https://arxiv.org/abs/2607.15041
作者: Wenqi Si,Gongyang Li,Shixiang Shi,Weisi Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 9 figures, accepted by IEEE TMM
Abstract:Weakly-supervised RGB-D Salient Object Detection (SOD) is explored to reduce the heavy burden of pixel-level annotations. But scribble annotations lack the structure and details of objects, resulting in inaccurate saliency maps. In this paper, we propose a novel scribble-supervised RGB-D SOD method, consisting of a Segment Anything Model (SAM)-driven pseudo annotation generation method (\emphSAM-PAG) and a state space interaction-based conditional diffusion model (\emph S^2 Diff). Specifically, SAM-PAG is tailored to address the issue of sparse supervision information. In SAM-PAG, we adopt the advanced SAM to expand sparse scribbles to dense pixel-level pseudo annotations through the dual-branch structure and the consistency of segmentation masks. In S^2 Diff, we adopt the diffusion model to iteratively refine the noisy saliency maps with the guidance of conditional information, generating accurate saliency maps. Naturally, the core of our S^2 Diff lies in the acquisition of conditional features and the denoising of saliency maps. For the former, we employ a cross-modal conditional generation module to interweave cross-modal features through frequency integration and implicit-explicit state space interaction, effectively achieving global conditional features. For the latter, we employ a context injection module to mitigate noise interference and to enhance object information with the conditional context. With the close cooperation of SAM-PAG and S^2 Diff, our method outperforms relevant scribble-supervised methods and achieves competitive performance compared to fully-supervised methods on seven datasets. The code and results of our method are available at this https URL.
[CV-22] Video = World Event Stream
链接: https://arxiv.org/abs/2607.15038
作者: Lianghua Huang,Zhi-Fan Wu,Yupeng Shi,Wei Wang,Mengyang Feng,Cheng Yu,Chen Liang,Junjie He,Chen-Wei Xie,Yu Liu,Jingren Zhou,Ang Wang,Bang Zhang,Baole Ai,Chongyang Zhong,Jinwei Qi,Kai Zhu,Pandeng Li,Peng Zhang,Wenyuan Zhang,Xinhua Cheng,Yitong Huang,Yun Zheng,Yuxiang Bao,Yuzheng Wang,Zhiwei Lin,Zoubin Bi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: website; this https URL
Abstract:We present Wan-Streamer v0.3, which reframes our native-streaming interaction model under a single organizing view: a video is a world plus an event stream. The world is the persistent context in which a video unfolds, including the environment, scene, subjects, ambient acoustic conditions, voice characteristics, and other relatively stable conditions. The event stream is everything that changes over time within that world, including scene or environmental changes, subject behavior, speech, and other sounds. This yields a general-purpose pretraining task over large amounts of real video: given a world and incoming input, predict how the world moves, changes, and responds in real time. The resulting competence can be specialized to a broad family of real-time downstream tasks. We instantiate it on real-time full-duplex audio-visual interaction, where the event stream is the agent’s speech together with free-form behavior. Functionally, the model’s multimodal understanding process is vision-language-action-like: it maps multimodal user input to language-form speech and behavior actions. Wan-Streamer v0.3 preserves the v0.2 operating point: 640x368 video at 25 FPS, a 160 ms streaming unit, approximately 200 ms model-side response latency, and approximately 550 ms total interaction latency under a 350 ms bidirectional network budget.
[CV-23] JADE-GS: Joint Alternating Deblurring Guided by Events in 3D Gaussian Splatting
链接: https://arxiv.org/abs/2607.14990
作者: Haoyu Fu,Jiafeng Huang,Yuchen Wang,Shengjie Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:When a camera moves fast during exposure, blur destroys the intra-exposure motion a 3D model needs to recover the sharp scene, while event cameras capture exactly this signal at microsecond resolution. Turning them into reliable 3D supervision faces two obstacles. First, the two restoration priors fail in opposite ways: physics-based event-integration priors preserve edges but accumulate drift; learned networks recover texture but distort boundaries. Second, existing pipelines run in one direction only, so raw event noise or the biases of fixed 2D pseudo-labels pass uncorrected into the geometry. JADE-GS addresses both: a pixel-adaptive routing gate fuses the complementary priors, and the resulting 2D restorer is coupled to a 3D Gaussian Splatting student in a bidirectional loop, where detached, multi-view-consistent renders and a physics-based reblurring constraint regularize the restorer, turning a fixed preprocessor into a geometry-aware predictor. Across synthetic and real benchmarks, JADE-GS attains the best perceptual quality, leading LPIPS and CLIP-IQA on both benchmarks with competitive PSNR and SSIM, and trainsin about one hour under 5 GB on a single consumer GPU while preserving real-time rendering.
[CV-24] From Draft to Draft-Free: One-Step Video Object Removal via Privileged Distillation and Fast Planting ECCV2026
链接: https://arxiv.org/abs/2607.14976
作者: Zizhao Chen,Ping Wei,Guang Dai,Jingdong Wang,Mengmeng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Video object removal is a fundamental yet challenging task in video editing. Despite recent progress, existing methods typically fall into two categories. Traditional approaches based on optical flow or attention mechanisms often introduce noticeable artifacts and yield unnatural results. In contrast, diffusion-based methods improve visual realism but demand multiple denoising steps, limiting their practicality. To address these issues, we propose From-Draft-to-Draft-Free (D2DF), a framework that distills the ability of transforming coarse drafts into refined videos into a one-step video generation model. Within D2DF, a teacher model is trained to refine low-quality removal results (“drafts”) into high-fidelity videos by multiple steps. Then, through Prior-Privileged Consistency Distillation (PPCD), we distill this capability into a student model that performs one-step removal conditioned on the draft. To eliminate draft dependency, we introduce a Self-Guided Fast Planting (SGFP) module based on our Temporal Masked Transformer that autonomously generates scene-consistent pseudo-drafts in latent space, enabling a fully draft-free one-step model. Extensive experiments show that both draft-conditioned and draft-free versions achieve state-of-the-art performance on multiple metrics, surpassing traditional and multi-step generative methods in both quality and efficiency. The denoising process for a single video takes only about 1 second.
[CV-25] On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline
链接: https://arxiv.org/abs/2607.14974
作者: Yuchen Ren,Zhengyu Zhao,Chenhao Lin,Bo Yang,Chao Shen
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注: Accepted for publication in IEEE Transactions on Information Forensics and Security (TIFS)
Abstract:Vision-Language Pre-training Models (VLPMs) are known to be vulnerable to adversarial attacks. Recent transferable attacks on VLPMs have followed a common pipeline with complicated loss functions or multi-stage text/image attacks. However, in this paper, we demonstrate that such a sophisticated attack pipeline can be simpler yet more successful. Specifically, we identify three previously overlooked issues caused by inappropriate cross-modal interactions and excessive operations. To address them, we propose the Simple Vision-Language Attack (SimVLA) pipeline, which observably improves transferability and efficiency. Experiments on four datasets and three downstream tasks validate the superiority of our pipeline. For instance, on Flickr30k text-image retrieval dataset, our SimVLA outperforms the SOTA baseline in R@1 transferability by 8.01%-14.71%, while consuming only about 35.73% of the time and 46.26% of the max VRAM. Overall, the superiority of our SimVLA highlights the importance of leveraging domain knowledge (e.g., our proposed cross-modal word identification), while blindly pursuing intricate operations (e.g, complex loss functions and redundant multi-stage designs) may even be harmful. We hope our SimVLA can serve as a simple yet effective backbone for future extensions. Code is available at this https URL.
[CV-26] Stitch-Inferencer: Enhance Endoscopic Video Segmentation and Tracking via Panoramic Reconstruction
链接: https://arxiv.org/abs/2607.14968
作者: Shunsuke Kikuchi,Atsushi Kouno,Hiroki Matsuzaki
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Surgical video understanding is fundamental to navigation systems. Endoscopic perception is often hindered by a limited field-of-view and frequent instrument occlusions, making spatio-temporal context essential for robust inference. These challenges have motivated video models that aggregate information across frames. However, existing video models typically store past observations implicitly in learned feature representations, often requiring task-specific video training, substantial annotated data, and increased computational cost. We propose Stitch-Inferencer, a real-time, model-agnostic inference framework that replaces implicit feature memory with an explicit image-space panoramic canvas. By stitching valid observations across frames, Stitch-Inferencer preserves previously observed pixels in an online, instrument-free view, expanding the effective field-of-view and providing direct access to regions that are temporarily occluded or absent from the current frame. Downstream segmentation or tracking models are applied to a compact region of interest on the panorama, and their predictions are reprojected to the current frame, enabling existing models to exploit long-range context without retraining. Experiments on anatomy segmentation and point/box tracking demonstrate consistent improvements across diverse baselines while preserving real-time throughput. The stitching module alone runs at over 60 FPS, providing a practical inference-time solution to enhance endoscopic perception in computationally constrained intraoperative environments. Source code will be made publicly available.
[CV-27] U-shaped Multi-granularity Learning for Vision-Language Models
链接: https://arxiv.org/abs/2607.14966
作者: Biao Chen,Yunqian Yu,Xiangxu Zhao,Zhongshu Chen,Mengmeng Jing,Lin Zuo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The prompt learning paradigm for vision-language models is effective yet faces a granularity dilemma: global prompts lack fine-grained semantic awareness, while local prompts ignore contextual associations, limiting cross-task generalization. This dilemma exists in dense prediction tasks. Inspired by U-Net, which unifies multi-level representations across granularities, we propose UPrompt, a U-shaped multi-granularity prompt learning framework for vision-language models. Similar to how U-Net integrates fine and coarse features through symmetric encoder-decoder pathways with cross-level connections, UPrompt constructs parallel multi-granularity representations in both visual and textual modalities, where coarse-to-fine cascaded enhancement propagates global context to refine local details, while fine-to-coarse hierarchical supervision ensures semantic consistency across scales. Extensive experiments on 17 benchmarks validate our effectiveness. UPrompt outperforms MAMET and VPKE by 4.1 and 7.3 rSum on MSCOCO, surpasses CoCoA-Mix by 5.09% in base-to-novel generalization, while maintaining competitive performance with minimal overhead (coarse-grained) and matching PSRC with 1/3 cost (medium-grained).
[CV-28] Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation
链接: https://arxiv.org/abs/2607.14962
作者: Ku Onoda,Paavo Parmas,Hiroki Furuta,Soichiro Nishimori,Yuta Oshima,Shohei Taniguchi,Yutaka Matsuo
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image (T2I) models can synthesize realistic, prompt-aligned images, yet samples generated for the same prompt often cover only a small subset of visually distinct modes. This limits the diversity of images, and for person-centric prompts, can reflect or amplify demographic skew. We formalize this problem as coverage of a predefined set of semantically specified modes, which we call target-mode coverage. We then propose multi-axis max@K, a group-based reinforcement learning objective for improving such coverage in diffusion-based T2I models. Given a group of samples and one score per target category, multi-axis max@K first takes the maximum score across samples for each category and then sums these category-wise maxima. The resulting credit assignment gives a sample positive weight on a category only when it increases that category’s group-wise maximum, allowing different samples to contribute to different categories. We first validate the credit-assignment mechanism on a synthetic mixture and on SD3.5-M using deterministic pixel-based color rewards. We then evaluate the same objective on perceived-appearance fairness. Across three automatic evaluators on held-out prompts, multi-axis max@K improves the Fairness Score by 0.23-0.36 relative to the base model, while maintaining image quality and text alignment.
[CV-29] DINE: Distance Is Not Enough – Learning Global Deformation Priors for Robust Soft-Tissue Point Cloud Registration
链接: https://arxiv.org/abs/2607.14946
作者: Sara Monji-Azad,Rohit Beer,Marvin Kinz,Claudia Scherl,Jürgen Hesser
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Non-rigid point cloud registration is central to soft-tissue shape analysis, but large deformations, noise, and outliers make correspondence estimation challenging. Most learning-based methods rely on local objectives such as Chamfer distance, which encourage point-wise proximity but do not constrain the global plausibility of the predicted deformation field. We address this limitation with DINE, a maximum a posteriori framework that augments distance-based registration with a learned statistical prior over displacement vector fields. DINE is applied to two registration backbones, Robust-DefReg and DefTransNet, using a two-stage strategy: a first-stage model is trained with Chamfer distance, its predicted deformation fields are used to estimate a prior, and the model is then refined with a combined distance and negative log-prior objective. We compare a full-field PCA Gaussian prior with a per-vector normalizing-flow prior. Experiments on DeformedTissue and SynBench show lower mean Chamfer distance under deformation and corruption. On DeformedTissue, DINE-PCA reduces Chamfer distance by approximately 27–69% relative to the corresponding Stage-1 backbone across deformation levels, and improves robustness by up to 66% for outliers and 83% for Gaussian noise. On SynBench, improvements are modest at the smallest deformation levels and reach approximately 59–79% from moderate to severe deformation. These results suggest that global deformation plausibility is an important constraint for reliable soft-tissue point cloud registration. (The code will be published soon.)
[CV-30] Introspective Attention Modulation for Safe Text-to-Image Generation ECCV2026
链接: https://arxiv.org/abs/2607.14945
作者: Basim Azam,Hossein Rahmani,Naveed Akhtar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026. 20 pages, 7 figures. Project page: this https URL
Abstract:State-of-the-art flow based text-to-image (T2I) models exhibit remarkable generative abilities but remain vulnerable to producing unsafe content. Prior safety efforts range from concept erasure and prompt filtering to classifier-based gating. However, simple techniques like parameter efficient adaptations of the models easily bypass such guardrails. We introduce a unique principled approach that achieves safety by regulating the model’s attention dynamics through inference-time introspection, exhibiting intrinsic robustness. Our method analyzes and rebalances attention activations throughout image synthesis, steering generations away from unsafe concepts while preserving semantic alignment. This introspective control ensures safety of deployed models. Across standard and adversarial safety benchmarks, our approach achieves remarkable safety scores while maintaining or even improving alignment and perceptual quality. Our results reveal that attention-space regulation offers a considerably more promising path to safer diffusion transformer based image generation than the existing concept erasing this http URL code can be accessed at this https URL
[CV-31] Frequency-Structured Field Learning for Light-Field Disparity Estimation
链接: https://arxiv.org/abs/2607.14941
作者: Sara Monji-Azad,Yulin Liu,Jürgen Hesser
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Light-field disparity estimation requires global consistency in smooth or textureless regions and local precision near occlusion boundaries, thin structures, and abrupt depth transitions. Existing methods address these requirements through EPI matching, cost-volume or focal-stack construction, view aggregation, or direct convolutional regression, often relying on local windows, discrete disparity hypotheses, memory-intensive volumes, or attention-based aggregation. We instead formulate disparity estimation at the field level, predicting disparity from globally and locally updated EPI-derived latent features without explicitly constructing a disparity volume. We introduce FreqLF, an EPI-guided Fourier-local framework that encodes angular parallax cues from horizontal and vertical EPI stacks together with central-view appearance features. These cues are projected into a latent field and updated through stacked hybrid Fourier-local layers. Fourier low-mode updates enable global feature interaction, while local convolutions preserve spatial variations needed for fine disparity detail. A coordinate-conditioned Gaussian-mixture decoder then predicts disparity, using the mixture mean as the final estimate. Experiments on the HCI 4D Light Field Benchmark show that FreqLF approaches the accuracy of strong supervised baselines while avoiding explicit cost-volume construction in the base model. Ablations confirm the complementary roles of the Fourier and local branches, and scaling experiments demonstrate practical behavior across spatial resolutions. These results suggest that Fourier-local latent field learning is a competitive alternative for light-field disparity estimation. The code will be published soon.
[CV-32] VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding
链接: https://arxiv.org/abs/2607.14935
作者: Xinhao Li,Yuhan Zhu,Xiangyu Zeng,Yuhao Dong,Haoning Wu,Zhiqiu Zhang,Yuandong Yang,Changlian Ma,Qingyu Zhang,Yansong Shi,Xinyu Chen,Haoran Chen,Zizheng Huang,Jun Zhang,Kun Ouyang,Lin Sui,Ziang Yan,Yicheng Xu,Chenting Wang,Yinan He,Hongjie Zhang,Yi Wang,Yu Qiao,Yali Wang,Ziwei Liu,Kai Chen,Limin Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. They often struggle to generalize across diverse video types, making them effective only in specific domains. High computational demands further restrict their efficiency and scalability. Moreover, most models are only partially open, with key components such as training code, strategy, or datasets unavailable, which hinders reproducibility and slows community-driven development. To address these issues, we introduce VideoChat3, a fully open, efficient, and generalist video-centric MLLM. VideoChat3 advances video understanding through two complementary designs. For efficiency, we introduce Inflated 3D Vision Transformer (I3D-ViT) and Adaptive Frame Resolution for Streaming Video Perception, which enables efficient spatiotemporal representation and reduces the cost of processing video inputs during training and inference. For effectiveness, we develop a scalable video data synthesis pipeline that curates three diverse, high-quality training datasets: VideoChat3-Academic2M, VideoChat3-LV116K, and VideoChat3-OL617K, covering general, long-form, and streaming video scenarios, improving the model’s generalization across domains. By integrating these designs, VideoChat3 achieves a rare balance of broad generalization and computational efficiency. Experiments across general, long-form, and streaming benchmarks demonstrate that VideoChat3 surpasses prior open-source models with equal or larger parameter counts with only 4B parameters and higher efficiency.
[CV-33] Still image and spatial-temporal tomato data enabling detection segmentation tracking and video-instance segmentation using strong and weak labels
链接: https://arxiv.org/abs/2607.14934
作者: Michael Halstead,Esra Guclu,Mohamed Farag,Enrico Pallotta,Christian Hund,Ribana Roscher,Maren Bennewitz,Juergen Gall,Cyrill Stachniss,Chris McCool
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 21 pages, 2 figures, 9 tables. Two novel datasets released - link to repository in document
Abstract:In this manuscript we release two datasets for visual sensing of tomato plants grown in commercial-like settings and acquired using a robot. The first is BUTom21 which consists of still images and manual annotations. The second is BUTom-ST21 which consists of video-based data and semi-automated annotations through AI-based methods, referred to as pseudo-labels. In both cases, we provide pixel-level labels for the ripeness of the fruit. The aim is to provide the research community a challenging set of real-world imagery to explore methods to sense and estimate the state of tomato plants and their fruit, which is an important horticultural crop. Importantly, the spatial-temporal dataset provides individual fruit count and ripeness information enabling researchers to push the boundaries of field-based phenotyping.
[CV-34] Benchmarking Face Recognition without Real Faces
链接: https://arxiv.org/abs/2607.14932
作者: Paweł Borsukiewicz,Daniele Lunghi,Wendkûuni C. Ouédraogo,Jacques Klein,Tegawendé F. Bissyandé
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at IJCB 2026
Abstract:Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs. This progress sidesteps the ethical and legal burdens of collecting real biometric data, yet evaluation has not kept pace. Even studies that train entirely on synthetic images still rely on real-face benchmarks to measure performance, leaving the privacy problem only half solved. We ask whether synthetic datasets can replace real benchmarks for face recognition evaluation. We test 12 synthetic datasets against 7 established real benchmarks using 24 pre-trained models that span both convolutional and transformer architectures. Our evaluation covers biometric verification metrics, similarity score distributions, cross-model ranking consistency, and the underlying distributional properties of each dataset. Benchmarking fidelity varies widely across the synthetic candidates, but the two strongest, MorphFace and Vec2Face, reproduce the relative behavior of real benchmarks and reach agreement levels that fall within the natural disagreement already observed among the real benchmarks themselves. These results establish that well-constructed synthetic datasets can support reliable comparative evaluation for face recognition, moving the field closer to a fully synthetic and privacy-preserving pipeline for both training and benchmarking.
[CV-35] anGO: Training-Free 3D Editing via Tangent-Space Guidance and Optimization ECCV2026
链接: https://arxiv.org/abs/2607.14927
作者: Siwoo Lim,Sunjae Yoon,Gwanhyeong Koo,Hyeonseo Yun,Chang D. Yoo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:While recent flow-matching 3D generative models (e.g., VecSet) adopt structured representations, their tokens share global context, causing conventional training-free editing to suffer from semantic artifacts such as collapsed preserved regions or incomplete transformations. To address this, we propose TanGO, a training-free framework that enables adaptive per-token steering in the tangent space of generative dynamics. To realize this selective control, we formulate a one-step optimal control rule and determine the strength of each token’s control signal using a von Mises-Fisher inspired directional discrepancy derived from the source and target velocity fields. Experiments show that TanGO substantially reduces structural artifacts and achieves state-of-the-art performance, outperforming existing 3D editing baselines. The code is publicly available at this https URL.
[CV-36] FlashDecoder: Real-Time Latent-to-Pixel Streaming Decoder with Transformers CVPR2026
链接: https://arxiv.org/abs/2607.14898
作者: Minguk Kang,Suha Kwak
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: CVPR2026
Abstract:Real-time video generation demands fast decoding as much as fast denoising, yet current latent video diffusion models rely on 3D convolutional decoders that are slow and memory-intensive at high resolutions or for long video. We introduce FlashDecoder, a fast, memory-efficient pure-Transformer video decoder that decodes latents to pixels frame by frame. At each step, the current frame attends only to a fixed-size window of past frames through a rolling KV cache. The fixed temporal window keeps decoding fast and memory bounded regardless of video length, enabling constant-latency streaming. Because frames are processed sequentially, temporal causality is enforced without explicit attention masks, enabling training at resolutions up to 1080p and matching the reconstruction quality of convolutional decoders. On the Wan2.1 and Wan2.2 latent spaces, FlashDecoder matches each convolutional decoder in reconstruction quality (e.g., 41.55dB vs. 41.49dB PSNR at 1080p) while decoding 3.6x-4.7x faster with up to 11x less memory on a single H100 GPU. With architecture-aware inference optimizations, the speedup widens to 12x.
[CV-37] Selectivity Drives Efficiency: Dataset Pruning for Visual Place Recognition
链接: https://arxiv.org/abs/2607.14897
作者: Tong Jin,Yunpeng Liu,Shuyu Hu,Chun Yuan,Song Wang,Feng Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages,7 figures,15 tables
Abstract:Recent visual place recognition (VPR) studies have increasingly relied on large-scale datasets to train more robust and discriminative models. Although this trend significantly improves recognition performance, it also introduces substantial storage and training costs, especially when new architectures or training strategies need to be repeatedly developed and evaluated. Dataset pruning (DP) provides a promising way to improve data efficiency by retaining only informative training data. However, conventional DP methods mainly follow the sample-wise classification paradigm, which overlooks the relation-dependent training nature of VPR, where supervision is typically formed by image pairs rather than independent images. To address this issue, we propose a place-wise dataset pruning framework tailored for VPR. Instead of pruning individual images, our method treats each place as the basic pruning unit and introduces two complementary novel metrics, i.e., intra-place diversity (IPD) and inter-place similarity (IPS), to evaluate the training value of each place. By jointly considering these two metrics, our method ranks all places and constructs a compact yet informative coreset, thereby allowing the pruned dataset to still support the training of robust and discriminative VPR models. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art DP baselines under different pruning ratios while reducing selection and training costs. Moreover, by pruning a merged dataset roughly 3.5 \times the size of GSV-Cities to a comparable scale, our coreset maintains highly competitive performance, achieving 94.5% R@1 on MSLS-val and 97.0% R@1 on Nordland with only NetVLAD. Codes will be made publicly available.
[CV-38] Rotational Motion-Induced Error Compensation for Phase-Shifting Profilometry-Based Eye Reconstruction
链接: https://arxiv.org/abs/2607.14876
作者: Seong-Jin An,Sanghoon Jeon,Yatong An,Jae-Sang Hyun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:With the proliferation of immersive Head-Mounted Displays (HMDs) for Virtual and Augmented Reality (VR/AR), reliable and high-precision eye tracking has become increasingly important. Conventional 2D image-based methods offer low system complexity but remain limited in stability, accuracy, and robustness. Three-dimensional ocular surface reconstruction can provide richer geomet-ric information, and structured light profilometry is particularly attractive because it enables dense and accurate surface measurement. However, Phase-Shifting Profilometry (PSP), which estimates phase from sequentially acquired fringe images, is highly susceptible to motion-induced errors when the eye rotates between frames. This study proposes a rotational motion compensation framework for PSP-based dynamic 3D eye reconstruction. Relative eye rotation is estimated from image-based motion cues using a user-specific 3D eye model in a spherical-coordinate domain. The estimated motion is then used to compensate for camera-pixel mismatch and phase-shift errors caused by inter-frame rotation. A region-wise optimization strategy is further introduced to reduce residual artifacts by inde-pendently refining the compensation strength in different ocular regions. Experiments with a rotating fake eye under non-uniform motion demonstrate that the proposed method substantially suppresses motion-induced deformation and improves reconstruction accuracy. An additional experiment with a non-spherical rigid object indicates that the compensation principle is not restricted to spherical eye geometry. These results establish a practical basis for stable PSP-based dynamic 3D eye reconstruction toward future high-precision eye tracking in immersive environments.
[CV-39] Physics-Informed Diffusion for Biomechanically Plausible 3D Sign Language Generation
链接: https://arxiv.org/abs/2607.14836
作者: Emanuele Colonna,Moises Diaz,Gennaro Vessio,Miguel Angel Ferrer,Giovanna Castellano
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Sign language production, which generates continuous 3D skeletal motion from spoken language input, must simultaneously satisfy two constraints: semantic fidelity, so that a deaf viewer can recognize the intended sequence of glosses, and biomechanical plausibility, so that the generated skeleton respects anatomical constraints. Existing approaches optimize semantic reconstruction through coordinate-based objectives that treat the skeleton as an unstructured vector, thus allowing for bone length drift, joint angle violations, and temporarily locked fingers. We introduce PIDiffSign, a physics-informed diffusion model for gloss-to-pose translation that incorporates anatomical constraints into both the architecture and training objective. The model uses a Transformer encoder-decoder, where the decoder is conditioned on the diffusion time step through adaptive zero-initialized layer normalization and cross-attends to gloss representations. A differentiable geometry module enforces bone length consistency and biologically valid joint angles throughout generation. Training combines anthropomorphic, kinematic, angular, and finger-joint constraints with a contrastive gloss-pose alignment loss and classifier-free guidance for semantically conditioned sampling. Experiments on the PHOENIX14T and CSL-Daily benchmarks show consistent improvements over a strong diffusion baseline in pose accuracy, joint-angle correctness, distributional realism, and back-translation quality. These results demonstrate that physics-informed diffusion improves both motion realism and semantic fidelity for sign language generation.
[CV-40] Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification
链接: https://arxiv.org/abs/2607.14821
作者: Xiao Wang,Bing Wang,Bin Yang,Cuiqun Chen,Xin Xu,Mang Ye
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 61 pages, 6 figures, journal
Abstract:Person re-identification (ReID) serves as a critical component in intelligent surveillance systems, aiming to match identities across disjoint camera networks. While traditional methods primarily rely on single-modal RGB imagery, they are often constrained by environmental challenges such as low illumination and occlusion. To overcome these limitations, the field is rapidly evolving toward cross-modal and multi-modal paradigms. This survey presents a comprehensive overview of this transition, systematically reviewing key cross-modal tasks including visible-infrared (VI-ReID), text-image (TI-ReID), sketch-based (Sketch-ReID), and the emerging Non-Line-of-Sight (NLOS) ReID, which extends perception beyond direct visibility. Furthermore, we examine tri-spectral and multi-modal fusion ReID, discussing how complementary information from diverse sensors enhances robustness. Beyond summarizing datasets, challenges, and methodologies, we propose a Transformer-based baseline framework for visible-infrared ReID, designed to effectively capture modality-invariant features. Finally, based on the current landscape, we outline several promising directions for future research.
[CV-41] An LLM -Based Automatic Sportscast Solution for Robot Soccer Matches
链接: https://arxiv.org/abs/2607.14809
作者: Francesco Petri,Michele Brienza,Daniele Nardi,Domenico Daniele Bloisi,Aldo Gangemi,Vincenzo Suriani
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Poster presentation at RoboCup Symposium 2026
Abstract:RoboCup has always been a scenario to develop systems that solve real-world problems. Driven by the main goal of playing against the 2050 FIFA World Cup champions, the RoboCup Soccer leagues need to constantly measure how the research community is progressing. Computing visual statistics from match videos is a crucial way to track this evolution. To address this challenge, this paper introduces a fully autonomous, real-time sports commentator for RoboCup matches. By bridging the gap between raw kinematic tracking and natural language generation, our neuro-symbolic architecture extracts precise statistics from video streams and turns them into fluent, hallucination-free narration. The proposed system is capable of generating statistics and commentary both during live match streaming and in post-game analysis, easily adapting to the new dynamism of the league where different humanoid robots of different sizes share the field. Supplemental materials are available at this https URL
[CV-42] AMF-VTON: Texture-Aware Mask-Free Virtual Try-On via High-Fidelity Image Synthesis
链接: https://arxiv.org/abs/2607.14807
作者: Jie Wang,Qian He,Gaofeng He,Xiaogang Jin,Huamin Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent diffusion-based virtual try-on (VTON) methods remain limited by their reliance on segmentation masks, insufficient preservation of fine-grained textures, and limited support for arbitrary multi-garment compositions. Consequently, existing approaches still face significant challenges in real-world e-commerce deployment. We present TAMF-VTON, a texture-aware, mask-free framework that enables high-fidelity image synthesis under practical unconstrained conditions. Our method requires no human parsing or inpainting masks at inference time and supports diverse garment styles, categories, and quantities, enabling the simultaneous transfer of multiple items while preserving body structure and intricate texture details. This is achieved through a unified generative pipeline with three key components: (1) a lightweight Mixture-of-Experts (MoE) adaptation scheme that enables efficient fine-tuning without compromising the base model’s general editing capabilities; (2) a frequency-domain supervision mechanism that explicitly optimizes high-frequency spectral consistency to preserve high-fidelity textures; and (3) a robust data curation pipeline employing an adaptive inpainting strategy to simulate the inverse VTON process for high-quality training pair generation. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in both quantitative metrics and perceptual quality. Optimized for efficiency, the model achieves inference in under 15 seconds per image on an NVIDIA RTX 4090 with INT4 quantization. By combining mask-free operation, flexible multi-garment composition, faithful texture preservation, and efficient inference on consumer hardware, TAMF-VTON demonstrates a commercially viable solution for scalable deployment in real-world digital fashion scenarios. The project is available at this https URL.
[CV-43] Rare Concept Generation via Counterfactual Inference in Diffusion Models
链接: https://arxiv.org/abs/2607.14765
作者: Zhengyuan Jiang,Haipeng Liu,Meng Wang,Yang Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Comments: 17 pages, 15 figures, to appear at ACM Multimedia 2026
Abstract:Rare concept generation focuses on synthesizing customized images conditioned on text prompts that describe objects with unusual attributes. Previous works failed to align the generated images with rare concepts, resulting in incorrect attribute rendering or inconsistent composition of concepts. Such failures, as we observed, stem from the inherent common knowledge bias in the training stage of diffusion models, where objects are strongly associated with their common attributes, making it difficult to break these associations when generating rare concepts. To address such challenges, in this paper, we propose a novel Counterfactual Inference-based Diffusion approach, dubbed CI-Diff. CI-Diff blocks the interference of the model’s inherent common knowledge bias and utilizes the Natural Direct Effect to capture the independent influence of the text prompt of rare concepts on image generation so that decoupling the unusual attributes from the rare concepts. To this end, we reformulate the classifier-free guidance mechanism to highlight the atypical attributes. To the best of our knowledge, we are the first to introduce causal inference into the rare concept generation task. Extensive experiments on the RareBench benchmark validate the superiority of CI-Diff over state-of-the-art diffusion models. Our code can be accessed from this https URL.
[CV-44] Clean-Reference Streaming Detection of Lens Occlusion and Photometric Transitions for Camera Tamper Monitoring
链接: https://arxiv.org/abs/2607.14760
作者: Bo Ma,WeiQi Yan,Jinsong Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:A surveillance camera is an image sensor whose silent physical degradation invalidates every downstream consumer of its data. In-situ integrity alarms for such vision sensors require low false-alarm rates, bounded computation, and diagnosable behavior under nuisance illumination changes. This paper studies a deliberately narrow streaming integrity monitor for two low-cost sensor-fault signatures: texture-collapsing lens occlusion and abrupt photometric scene transition. The detector compares sampled luminance and local-gradient statistics with a clean-only sliding reference, applies coarse-grid structured-light rejection and mode/rapid-brightness suppression, and emits at most one notification per tamper episode. We formalize the decision predicates and derive a consistency rule for when rapid-brightness suppression makes the scene-transition path unreachable. On 320 in-scope controlled sequences, the default state machine attains 0.800 F1 and 0.822 balanced accuracy (significantly better paired correctness than the strongest baseline, though the F1 margin is not statistically resolved); on a magnitude-swept public audit it attains the highest partial AUC under a 5% false-alarm budget, and a separate extended-stress FPR-constrained sweep reaches 0.925 recall at 0.025 false-positive rate. Public Xiph, Bremen IoT, and UHCTD diagnostics show the fixed predicates preserve low false alarms while recall concentrates inside the declared envelope (UHCTD in-scope covered recall 0.667 versus 0.016 out of scope), and a 9.09-camera-hour verified-negative public audit records zero false alarms. The method is best interpreted as an auditable sensor-health subsystem rather than a universal camera-tamper classifier.
[CV-45] On the Disagreement in Perturbation-based xAI – Benchmarking Perturbation Choices for Flood Detection from SAR Images
链接: https://arxiv.org/abs/2607.14743
作者: Anastasia Schlegel,Ronny Hänsch
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Perturbation-based xAI methods are widely used to analyze the behavior and predictions of deep learning models. By altering input regions and measuring the resulting changes in class probabilities with respect to the original image, they assign relevance scores and generate heatmaps that reflect each region’s contribution to the prediction. Despite their apparent simplicity, however, perturbation-based methods are sensitive to parameter choices. In this work, we focus on two key parameters of the perturbation pipeline, namely the patch geometry, including the size and shape of the perturbed regions, and the perturbation type, defined by the replacement scheme. Grounded in the use case of flood detection from Synthetic Aperture Radar imagery, we conduct a comprehensive investigation of how relevance estimation changes under different perturbation settings. Beyond visual inspection of the resulting relevance maps, we evaluate their consistency across perturbation strategies and their faithfulness to the model’s reasoning. We demonstrate how different perturbation choices can steer the resulting relevance maps, yielding ambiguous and even contradictory explanations. Our findings emphasize the importance of methodological settings in perturbation-based xAI. They underscore the need to carefully inspect and evaluate perturbation choices and to treat them as an integral part when interpreting explanations, ensuring a robust understanding of both the explanations and model predictions.
[CV-46] FoMoVLA: Bridging Visual Foresight and Motion Guidance for Vision-Language-Action Models
链接: https://arxiv.org/abs/2607.14739
作者: Wei Li,Peijin Jia,Yuan Ma,Xuefeng Jiang,Titong Jiang,Sheng Sun,Yujian Li,Xin Wen,Han Hong,Zhikang Liu,Bailin Li,Kun Zhan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 12 pages, 7 figures, 8 tables. Project page: this https URL
Abstract:Vision-Language-Action (VLA) models have achieved impressive results in visuomotor policy learning, yet remain fundamentally reactive, mapping current observations and language to actions without explicit forward prediction of world dynamics. Existing visual foresight methods predict future visual states but lack explicit motion guidance: they show where to go but not how to get there. We argue that future feature prediction and sparse point tracking are naturally complementary: the former provides the goal state, while the latter captures the continuous motion path toward it. We propose FoMoVLA, a framework that augments VLA representations with explicit spatio-temporal supervision by jointly learning future feature foresight and sparse 2D point tracking, enhancing the continuous action policy. FoMoVLA introduces compact foresight tokens to decode future feature states, decodes sparse temporal 2D point trajectories to model compact geometric motion, and couples both through a lightweight future-conditioned cross-attention module that enables consistent reasoning between anticipated states and point dynamics. Extensive experiments on LIBERO, RoboCasa GR-1 Tabletop, and LIBERO-Plus demonstrate state-of-the-art performance and strong zero-shot generalization. Project page is available at this https URL.
[CV-47] GeoDetect: Geometric Adversarial Detection for VLPs ECCV2026
链接: https://arxiv.org/abs/2607.14737
作者: Afsaneh Hasanebrahimi,Hanxun Huang,Christopher Leckie,James Bailey,Sarah Erfani
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: ECCV 2026
Abstract:Vision-language pre-trained models (VLPs) are widely used in real-world applications. However, they remain vulnerable to adversarial attacks. Although adversarial detection methods have demonstrated success in single-modality settings (either vision or language), their effectiveness and reliability in multimodal models such as VLPs remain largely unexplored. In this work, we study the geometry of VLP embedding spaces and observe structured anisotropy that differs from unimodal vision models. Our theoretical analysis shows that under this anisotropic structure, adversarial attacks increase the expected geometric separation between clean and adversarial examples (AEs). Specifically, we demonstrate that AEs consistently exhibit greater expected distances to randomly sampled points than their clean counterparts, indicating that AEs tend to push representations out of manifold regions. Building on these insights, we propose GeoDetect, which leverages these off-manifold deviations via geometric scores to identify AEs. Through comprehensive evaluations, we show that our approach reliably detects AEs across diverse VLP architectures and threat settings, covering unimodal and multimodal attacks as well as adaptive attacks, thereby providing a robust and practical approach to improving the safety and reliability of these models.
[CV-48] VQ-Touch: A Data-Efficient Tactile Generation Framework Across Sensors and Scenarios
链接: https://arxiv.org/abs/2607.14728
作者: Kailin Lyu,Long Xiao,Jianing Zeng,Di Wu,Lin Shu,Jie Hao
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 6 pages, 5 figures
Abstract:Tactile image generation significantly reduces the dependency on expensive and wear-prone sensors by synthesizing high-fidelity tactile data, offering an efficient solution for tactile information acquisition in robotic perception and human-machine interaction systems. However, existing methods depend on large-scale, diverse datasets from specific sensors and lack efficient data utilization and robust generalization capabilities, struggling in vision-limited environments. To address this, we introduce VQ-Touch, a tactile generation framework that supports both cross-sensor and multi-scenario applications. Specifically, to efficiently extract complex deformation and texture features from the data, we propose DM-VQGAN, an effective tactile representation learner. Furthermore, we introduce a discrete diffusion decoder with a unified conditioning interface, supporting multimodal generation tasks such as images and labels, and enhances the model’s generalization capability through few-shot mixed training, thus achieving compatibility with current mainstream sensors and their variants. Experiments show that VQ-Touch surpasses state-of-the-art methods in multiple tasks.
[CV-49] WorkDrive: Roadwork Chain of Causation for Autonomous Driving
链接: https://arxiv.org/abs/2607.14727
作者: Tianyi Jiang,Wen Zhang,Sihan Yang,Ming Lu,Wentao Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Autonomous driving vision-language models (VLMs) struggle in roadwork zones, where familiar visual cues such as lane markings and permanent signs are altered or absent, and temporary devices such as cones and barriers redefine the drivable corridor. VLMs can detect these objects, but without explicit guidance they anchor their reasoning on familiar elements from pre-training and fail to connect work-zone observations to correct planning decisions. We propose WorkDrive, a framework that constructs perception-grounded causal reasoning for work zones and aligns it with trajectory prediction. An automated multitask perception pipeline extracts structured scene facts and injects them into a Chain-of-Causation (CoC) annotation pipeline, redirecting the annotator’s attention to domain-specific elements. The resulting reasoning labels are used for supervised fine-tuning, followed by reinforcement learning with a single reward: consistency between lateral meta-actions and the predicted trajectory. On ROADWork, the largest public work-zone dataset, the proposed roadwork CoC reduces trajectory average displacement error (ADE) by 9.0%, and consistency-based GRPO yields a further 3.0%, achieving progressive improvement over the trajectory-only baseline. Code and data will be publicly released.
[CV-50] AE-UAV: An Air-to-Air Event-Based UAV Tracking Benchmark and a Real-Time Frequency-Domain Tracker
链接: https://arxiv.org/abs/2607.14726
作者: Zixin Jiang,Bing He,Chaoran Xiong,Zhenzhen Wang,Xin Zhao,Ling Pei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 7 figures. Submitted to IEEE Transactions on Geoscience and Remote Sensing
Abstract:Air-to-air (A2A) unmanned aerial vehicle (UAV) tracking is fundamental to airborne remote sensing of low-altitude aerial targets. However, the deployment of continuous, real-time tracking systems on UAVs presents significant challenges. In A2A scenarios, traditional frame-based cameras suffer from severe performance degradation under low illumination, overexposure, and high-speed motion owing to their limited dynamic range and fixed temporal sampling. Although event cameras offer a promising alternative with microsecond temporal resolution and a high dynamic range, current research is bottlenecked by two primary issues: 1) the absence of dedicated A2A event-based datasets, and 2) the heavy reliance of existing trackers on GPU acceleration and extensive training data, rendering them impractical for resource-constrained UAVs. To bridge these gaps, we introduce AE-UAV, an air-to-air event-based UAV tracking benchmark. To the best of our knowledge, this is the first airborne-captured event camera dataset for A2A tracking, comprising 178 flight sequences with continuous-time cubic B-spline annotations. Furthermore, we propose the Fast-Slow Frequency-domain Tracking (FSFT) method. This lightweight, training-free framework seamlessly integrates frequency-domain template matching with search region prediction and detection-based drift correction. Extensive experiments demonstrate that FSFT operates at an ultra-fast 420 frames per second (FPS) on CPU-only hardware. It retains 93.97% of the accuracy of state-of-the-art GPU-dependent methods while delivering a 5.32-fold effective speedup and exhibiting superior temporal resolution generalization, thereby providing a highly efficient and robust solution for airborne remote sensing of aerial targets. The dataset and source code are available at this https URL.
[CV-51] Multimodality as Supervision: Self-Supervised Specialization to the Test Environment via Multimodality
链接: https://arxiv.org/abs/2607.14721
作者: Kunal Pratap Singh,Ali Garjani,Rishubh Singh,Muhammad Uzair Khattak,Efe Tarhan,Jason Toskov,Andrei Atanov,Oğuzhan Fatih Kar,Amir Zamir
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Project page: this https URL
Abstract:Cross-modal learning, i.e., learning to predict one modality from another, is a fundamental mechanism for self-supervision via leveraging multimodality. Many practical applications, e.g., deploying a household robot, involve devices that are equipped with a rich set of sensors that enable multimodal sensing in their test environment. This presents an opportunity to apply cross-modal learning to the multimodal data sensed by these devices to learn representations. Findings in developmental psychology also suggest that biological agents leverage it to build an effective representation of their surroundings. To study this, we propose a controlled setup, where we restrict a user device to just a given test environment. It results in a specialization setup where we attempt to develop a performant model for this specific test environment. Under this setup, we develop Test-Space Training (TST), which performs multimodal data collection in the test environment and performs self-supervised pre-training on it. We evaluate these models on various downstream tasks in the same environment. Under this setup, we find various interesting insights, such as collecting rich multimodal data only from the test environment and leveraging cross-modal learning, we can achieve competitive results with generalist models (e.g., DINOv2 and CLIP) pre-trained on large-scale internet datasets. This enables an alternative scenario where the need for external Internet-scale datasets for pre-training models is reduced. We also present a set of analyses and ablations that raise intriguing points on substituting data with (multi)modality, and how varying pre-training data enables a tradeoff between a model’s abilities to specialise to a test environment, and generalize to held-out spaces. Comments: Project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2607.14721 [cs.CV] (or arXiv:2607.14721v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.14721 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-52] Causal-Adversarial Probing of Clinical Covariates for Prostate MRI Grading
链接: https://arxiv.org/abs/2607.14720
作者: Yipei Wang,Shiqi Huang,Wen Yan,Weixi Yi,Dean C. Barratt,Mark Emberton,Daniel C. Alexander,Veeru Kasivisvanathan,Yipeng Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deep learning models for prostate MRI-based cancer grading may encode clinical covariates that either reflect useful disease-related signal or non-generalising shortcut information, but their role is usually assumed. We propose a causal-reasoning framework for probing covariate dependence in MRI-based International Society of Urological Pathology (ISUP) Grade Group prediction. Rather than treating mpMRI as a direct cause of grade, we model MRI appearance and ISUP grade as observations of latent tumour pathology, and test whether candidate clinical variables act as nuisance correlates, disease-related proxies, or irrelevant covariates in the learned representation. We implement this using an adversarial framework that suppresses the decodability of individual clinical covariate at a time while preserving MRI-based grade prediction. The approach is developed and evaluated on 2,903 prostate MRI examinations, with external validation on 576 patients. We report a set of interesting and previously under-explored imaging-to-clinical-variable interactions in the context of deep learning generalisation. For examples, in binary ISUP Grade Group \geq2 classification, suppressing age, BMI, and alcohol use improved AUC by 1.23%, 0.84%, and 1.42%, respectively (all p 0.05), suggesting reduced non-generalising covariate information; In contrast, suppressing PSA and prostate volume degraded AUC by 1.91% and 7.61% (all p 0.001), indicating that these variables carried task-relevant signal. These findings show that adversarial covariate suppression can provide a practical representation-level analysis for distinguishing potentially harmful dependence from informative signal in prostate MRI grading models.
[CV-53] VideoSEMA: a scalable and efficient Mamba-like attention for video understanding
链接: https://arxiv.org/abs/2607.14711
作者: Nhat Thanh Tran,Fanghui Xue andShuai Zhang,Jiancheng Lyu,Yunling Zheng,Yingyong Qi,Jack Xin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 15 pages, 3 figures
Abstract:We present for video understanding (classification) a split space-time attention model, VideoSEMA, consisting of a scalable and efficient Mamba-like attention (SEMA) block in space and a softmax temporal attention in time. In each frame, SEMA attention applies a local window attention in parallel with a global averaging in a Mamba macro-architecture, which is called Mamba-like. Under certain rank conditions, we prove that the computationally cheaper split space-time attention is equivalent to full space-time attention. On benchmark K400 data sets, VideoSEMA out-performs heavier vision transformer and Mamba models. On benchmark SSv2 data, VideoSEMA leads in top-1 accuracy among models of similar parameter sizes. As image resolution scales up from standard 224^2 to 1024^2 on K400 and without fine-tuning, VideoSEMA degrades much more gracefully than VideoMamba in accuracy. It is promising to extend VideoSEMA to longer videos with a dilated/sparse temporal attention.
[CV-54] Variational Inference for Birds Eye View Segmentation in Autonomous Driving
链接: https://arxiv.org/abs/2607.14710
作者: Jingyue Shi,Huaicheng Li,Junhui Zhao,Yanxiang Jiang
类目: Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY)
备注: 13 pages, 9 figures
Abstract:The bird’s eye view (BEV) has emerged as a pivotal approach for environmental perception in autonomous driving, providing a unified spatial representation for vehicles. Nevertheless, despite BEV’s significance in addressing the challenges inherent to autonomous driving, effectively fusing data from multiple camera sensors and operating in complex external driving environments remains a considerable challenge. To mitigate this issue, we recast the BEV segmentation problem within a variational inference framework. In this paper, we propose a novel transformer-based variational flow transformation network for BEV segmentation, denoted as TVB. Our architecture implicitly learns the mapping from multiple camera views to a unified canonical BEV map during training by exploiting posterior BEV supervision. TVB employs a conditional variational auto encoder (CVAE) as its backbone and produces multiple BEV map candidates. To augment the realism of the generated BEV maps, we integrate normalizing flows into the map generation process, enabling the construction of more complex and expressive probability distributions. Furthermore, we design a BEV-attention fusion (BAF) module that harnesses attention mechanisms to adaptively integrate the multiple candidate BEV maps. Experimental results, evaluated on both the nuScenes and OPV2Vdatasets, demonstrate that our proposed method achieves superior performance in multi-camera view BEV segmentation and lane environment perception.
[CV-55] Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models
链接: https://arxiv.org/abs/2607.14703
作者: Mingxi Fu,Jiawen Li,Renao Yan,Jiali Hu,Qiehe Sun,Tian Guan,Yonghong He
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology. However, existing MIL aggregators are still typically trained from scratch for each downstream task, relying on limited slide-level labels to learn both aggregation mechanisms and downstream discriminative representations simultaneously. As a result, they often suffer from unstable optimization, overfitting, and limited transferability. Similar to pretrained ResNet and Vision Transformer models in natural image learning, MIL also requires reusable pretrained initialization. However, high-quality slide-level pretraining data remain scarce, and MIL models are usually lightweight and weakly supervised, making large-scale pretraining difficult in practice. To address this challenge, we propose a distillation-based pretraining framework for MIL, which leverages two slide-level foundation models, TITAN and CARE, as teachers to transfer their representational knowledge into a diverse set of MIL architectures. To effectively balance supervision from different teachers, we further introduce an angular dispersion normalized distillation loss. The distilled weights are then used as initialization for downstream adaptation. We conduct systematic evaluations on 15 benchmark datasets under both linear probing and full-parameter fine-tuning, and further validate its advantages in few-shot scenarios. Experimental results show that pretraining generally improves MIL aggregators over from scratch training, especially in linear-probing and few-shot settings, while maintaining the computational efficiency of lightweight MIL models. Code is available at this https URL.
[CV-56] am RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach
链接: https://arxiv.org/abs/2607.14702
作者: Elena Ryumina(1),Maxim Markitantov(1),Alexandr Axyonov(1),Fedor Shchetinin(2),Timur Abdulkadirov(1),Dmitry Ryumin(1),Alexey Karpov(1, 2) ((1) St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, Russia, (2) HSE University, St. Petersburg, Russia,(3) TMO University, St. Petersburg, Russia)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 2 figures
Abstract:Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, facial, and contextual patterns, while top-performing systems often rely on computationally expensive ensembles. We present a single text-centered multimodal approach for video-level ambivalence and hesitancy recognition for the 11th Affective Behavior Analysis in-the-Wild (ABAW) Challenge. The proposed approach combines linguistic, acoustic, facial, and scene features using text-centered multimodal fusion model. Text Residual Fusion treats text as the anchor modality and applies gated residual adjustments based on the other modalities. Experiments on the Behavioural Ambivalence/Hesitancy (BAH) corpus confirm that text is the strongest unimodal modality. The Text Residual Fusion model achieves an average Macro F1-score (MF1) of 75.14% across the Development and Public Test subsets. On the Private Test subset, it reaches an MF1 of 78.24%, outperforming the text model by 4.03%. These results demonstrate that complementary multimodal information can improve recognition performance without requiring a large model ensemble.
[CV-57] GlobalForge: Towards Robust AI-Generated Image Detection
链接: https://arxiv.org/abs/2607.14684
作者: Manni Cui,Ruiqi Liu,Dianyuan Zou,Ziheng Qin,Jingrui Xu,ZiAn Wang,Jianglan Wei,Han Zhou,Yu Liu,Yan Wang,Shu Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:AI-generated image (AIGI) detectors achieve strong accuracy on clean benchmarks, but their performance drops sharply after images are propagated through real-world channels. We trace this fragility to what these detectors actually learn: they overfit to local artifacts left by generators in small spatial neighborhoods, which are easily destroyed by common propagation degradations such as JPEG compression and blur. Instead, we shift the discriminative cue from fragile local artifacts to more robust global structure. Building on this, we propose GlobalForge, a framework with two complementary modules. The Local Information Bottleneck (LIB) suppresses local components to block shortcut learning, while the Global Structural Reasoning (GSR) module forces every token to gather evidence from distant regions. Both modules are trained jointly under a contrastive structural loss based on degradation that keeps the resulting features stable under degradation. To support fine-grained robustness evaluation, we further introduce RealDeg-Bench, covering 7 common degradation operators and multi-step compound chains. GlobalForge improves average BAcc on 8 in-the-wild benchmark groups by \mathbf5.89% over the previous state-of-the-art, and is clearly ahead of representative baselines on RealDeg-Bench under both single and compound degradations. Code is available at this https URL.
[CV-58] ReBind: Multi-Reference Video Editing via Structured Instructions with Explicit Reference Relationships
链接: https://arxiv.org/abs/2607.14681
作者: Xinyu Liu,Shihao Li,Weihong Lin,Xinlong Chen,Yang Shi,Yujin Han,Yiyang Cai,Yanghao Wang,Ruibin Yuan,Yuanxing Zhang,Pengfei Wan,Wenhan Luo,Yike Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Recent diffusion-based video generation models have made significant progress in multi-reference image-conditioned video editing. However, existing methods still struggle to coordinate information from multiple visual sources accurately. We identify a critical deficiency in existing approaches. Existing editing instructions lack explicit reference relationships, and most multimodal large language models (MLLMs) cannot generate them reliably. To address this problem, we propose ReBind, a systematic framework that introduces semantic instructions with embedded reference tokens as the intermediate representation for multi-reference image-conditioned video editing. Our key insight is embedding reference tokens at semantic positions to eliminate ambiguity and establish precise bindings between visual attributes and their sources. We develop ReBind-Instruct, a specialized MLLM that learns to establish explicit bindings between visual attributes and their reference sources through a two-stage progressive scheme for precise reference relationships. We further develop ReBind-Edit, which enables lightweight adaptation of text-to-video models to coordinate multiple references by binding visual attributes to their designated sources. Extensive experiments demonstrate that ReBind substantially outperforms general-purpose MLLMs in instruction quality and achieves state-of-the-art performance among open-source methods on reference image conditioned video editing. Our project webpage: this https URL.
[CV-59] VIABench: A Comprehensive Video Benchmark Collected from Blind Individuals for Visual Impairment Assistance
链接: https://arxiv.org/abs/2607.14660
作者: Yunfeng Liu,Yuandong Yang,Jiarui Han,Zhenpeng Huang,Yuqing Tang,Xiangyu Zeng,Gangshan Wu,Limin Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visually impaired individuals (VIIs) encounter significant daily challenges due to limited access to visual information. Although Multimodal Large Language Models (MLLMs) have achieved impressive results on general vision and language tasks, their practical utility in real-world blind assistance still remains largely underexplored. To fill this gap, we introduce VIABench, a comprehensive video benchmark specifically designed to evaluate MLLMs in Visually Impaired Assistance scenarios using first-person videos recorded or shared by VIIs themselves. VIABench defines three core tasks, each targeting a distinct requirement in visual assistance. Proactive Reminder: Assesses the model’s ability to interpret ongoing video content while proactively anticipating and verbally describing upcoming navigation-critical events; Visual Question Answering (VQA): Evaluates the model’s capacity to answer user-posed questions about the environment or objects within the video; Vision-Guided Interaction: Tests context-aware reasoning to accomplish intentional interactions between user and environment. To ensure a robust and fair evaluation, we propose a rigorous benchmarking pipeline that supports both online (real-time) and offline settings. Our experiments demonstrate that current MLLMs still struggle to deliver comprehensive support for VIIs, especially in the Proactive Reminder task, which demands accurate anticipation and real-time responsiveness. We hope VIABench will drive future research toward developing customized MLLMs for real-world assistance, ultimately improving navigation and interaction experiences for visually impaired individuals. Code and data will be released at this https URL.
[CV-60] Autoregressive Modeling of Film with Applications in Video Montage
链接: https://arxiv.org/abs/2607.14645
作者: Marcelo Sandoval-Castañeda,Fabian Caba Heilbron,Shiry Ginosar,Bryan Rusell,Josef Sivic,Alexei A. Efros,Greg Shakhnarovich
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This work introduces FilmGPT, an autoregressive transformer designed to address the challenge of video montage–turning a collection of raw, “unwatchable” footage into coherent cinematic sequences. Inspired by language learning in modern LLMs, we train a long-context autoregressive transformer on a large corpus of movies. The aim is to implicitly capture the “grammar” of film directly from data rather than from hand-coded rules. Unlike other generative models, FilmGPT does not generate any new video frames. Instead, at inference time, we introduce a footage-constrained decoding algorithm to select the best next shot from the input raw footage according to the statistical patterns learned from films. We first evaluate these learned statistics directly by using the FilmGPT autoregressive model for next shot prediction on a standard benchmark of shot sequence ordering, outperforming the previous state of the art. We then evaluate our footage-constrained decoding algorithm on the full film editing task via a user study, and find that our FilmGPT-based editing significantly outperforms previous approaches. Finally, we demonstrate the applicability of FilmGPT to a wide range of applications in video montage, from automatic video segment trimming to human-in-the-loop film editing.
[CV-61] Image-to-Point Cloud Registration Made Easy with Rectified Flow-based LiDAR Upsampling
链接: https://arxiv.org/abs/2607.14639
作者: Reon Tabata,Kenji Koide,Shuji Oishi,Masashi Yokozuka,Taku Okawara,Aoki Takanose,Jun Miura
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Image-to-Point Cloud Registration (I2P) is essential for integrating camera and LiDAR in perception and autonomous systems, yet the modality gap between images and point clouds makes it difficult to achieve both high accuracy and strong generalization. In this paper, we propose a simple yet effective I2P method that treats LiDAR as an imaging sensor: from a single sparse LiDAR scan, we generate a dense LiDAR intensity image using Conditional Rectified Flow, match it with a camera image using a pre-trained feature matcher, and estimate the 6-DoF relative pose via PnP-RANSAC. The proposed model is pre-trained through a self-supervised image completion task and fine-tuned on a small amount of LiDAR data (neither image-point cloud pairs nor ground-truth sensor poses are required), enabling it to scale to diverse LiDAR and camera configurations. Experiments on the R3LIVE dataset show that the proposed method achieves a mean error of 4.89° / 1.63 m, outperforming existing methods, while completing a single registration in approximately 0.68 s.
[CV-62] Action QFormer: Structured Representation Shaping under Action Supervision in Vision-Language-Action Models
链接: https://arxiv.org/abs/2607.14635
作者: Yufeng Ji,Wenhao Tang,Haoyi Niu,Koushil Sreenath,Yi Wu,Zhongyu Li
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Action supervision in vision-language-action (VLA) models is often treated as a downstream objective for learning action prediction. In this paper, we study it instead as a force that shapes inherited multimodal representations. We show that this shaping has a dual effect: it is necessary for forming action-compatible representations, but when action supervision is applied too directly to the inherited multimodal pathway, it can also destabilize representations that support language-side processing and object grounding. To address this tension, we introduce Action QFormer, a query-based action-facing interface that uses instruction-conditioned queries to reorganize inherited multimodal information into action-facing representations before downstream action generation. In zero-shot sim-to-real navigation, Action QFormer improves average closed-loop task success from 18.8% to 56.3%, raises fixed-instruction action-generation correctness from 22.5% to 75.5%, and nearly eliminates out-of-distribution instruction generations. Further analyses show that Action QFormer changes how action supervision shapes inherited multimodal representations, reducing broad upstream rewriting while preserving targeted and sometimes constructive action-supervised adaptation. These results suggest that improving VLA performance requires not only stronger pretrained backbones, but also better ways of selecting and organizing inherited multimodal information while controlling how it is shaped under action supervision.
[CV-63] Knowing You at First Glance: Inferring Apparent Personality from Faces
链接: https://arxiv.org/abs/2607.14631
作者: Shuhuan Chen,Xiangyu Zhu,Weisong Zhao,Haichao Shi,Xiao-Yu Zhang,Zhen Lei
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by The 9th Chinese Conference on Pattern Recognition and Computer Vision, PRCV2026
Abstract:Inferring apparent personality from facial images is important in social scenarios for embodied agents in human-robot interaction. Unlike inferring intrinsic personality traits via conversation, this task models first-impression personality perception based solely on facial appearance before interaction begins. Existing studies mainly focus on the Big Five personality model and often rely on language or multimodal inputs. As a result, it remains unclear whether facial cues alone can support meaningful associations with perceived personality traits. This question is particularly relevant for MBTI types, which are widely used in practice and more readily interpretable by large language models. To this end, we propose \textbfGlanceFace, an end-to-end framework for apparent personality inference leveraging vision-language models to introduce semantic priors and a semantic-enhanced facial representation module to capture subtle personality-related cues, together with an uncertainty-aware learning strategy to handle noisy and subjective annotations. Extensive experiments demonstrate strong performance on MBTI-based apparent personality benchmarks and reveal relationships between facial characteristics and perceived personality traits, highlighting its potential to support adaptive initial interaction strategies for embodied agents. The code and dataset are available at this https URL.
[CV-64] SportD: Can VLMs Physically Strategize?
链接: https://arxiv.org/abs/2607.14616
作者: Jasin Cekinmez,Addison J. Wu,Haotian Xia,Akshaya Bharadhwaj,Anay Putty,Anirudh Ravishankar,Jaewoong Lee,Jinglin Xiao,Kyumin Andrew Shim,Mishika Ahuja,Nisarga Patil,Leo Liu,Zhuohan Liu,Weining Shen
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision–language models have become increasingly capable of interpreting visual scenes, but it remains unclear whether they can use information to make strategically effective decisions. We investigate this question in soccer, where models observe the seconds preceding an on-ball decision and must choose whether to shoot or pass to a specific teammate. Unlike conventional visual-understanding tasks, soccer enables decisions to be evaluated quantitatively by estimating the value of every available action. We introduce SportD, a benchmark comprising 478 on-ball decisions from the 2022 FIFA World Cup. Each model choice is evaluated against a possession-value model that estimates the action that most increases the attacking team’s probability of scoring, allowing us to measure both optimal-action accuracy and the value forfeited by suboptimal decisions. Across three frontier VLMs, the best selects the highest-valued action on 31.4% of events, compared with 38.9% for the professional players, and all models incur significantly greater regret. Further analysis reveals a systematic preference for lower-variance and lower-reward actions: VLMs shoot less often and select substantially less progressive passes than either the optimal policy or the real players. The models also reproduce the player’s specific action above chance even when that action is suboptimal, suggesting partial imitation of familiar play patterns rather than consistent evaluation of counterfactual alternatives. SportD provides a value-grounded testbed for measuring physical strategic reasoning in VLMs.
[CV-65] Hough-SIFT: Robust Image Registration for Linear Structures via Hough Space
链接: https://arxiv.org/abs/2607.14598
作者: Masaki Satoh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 6 figures. Supplementary video is available as an ancillary file. Accepted for oral presentation at the 29th Meeting on Image Recognition and Understanding (MIRU2026)
Abstract:Image registration is essential in applications such as electronic image stabilization. Scale-Invariant Feature Transform (SIFT), a widely used local keypoint detector and descriptor, typically provides accurate registration; however, it often fails in scenes with strong linear structures (e.g., shutters), where local features become ambiguous. We propose Hough-SIFT, a robust registration method that performs SIFT descriptor matching in Hough space. In this domain, linear structures form distinctive peaks that restore descriptor discriminability. Experiments demonstrate that Hough-SIFT is robust in linear scenes where SIFT frequently fails, while maintaining accuracy comparable to SIFT in normal scenes.
[CV-66] MagicPrompt: Ultra-Lightweight Prompt Tuning for Video Generation
链接: https://arxiv.org/abs/2607.14595
作者: Yinhan Zhang,Dinwei Tan,Xianghao Kong,Yue Ma,Yeying Jin,Anyi Rao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large-scale video diffusion models (VDMs) deliver strong generation performance, but full fine-tuning for downstream tasks incurs prohibitive computational costs. Existing parameter-efficient fine-tuning (PEFT) methods have two critical flaws on billion-scale models: they still require substantial trainable parameters, and reward-based training suffers from noise-induced optimization instability in condition-guided tasks. We propose MagicPrompt, a lightweight framework that achieves extreme parameter efficiency and stable reward optimization. It first adopts Attention-Embedded Prompt Tuning, which steers generation via lightweight soft prompts with orders of magnitude fewer parameters while preserving pre-trained knowledge. It further introduces Dual-Space Reward Feedback Optimization, which uses self-supervised latent objectives to improve condition-guided reward training. Experiments show MagicPrompt reaches competitive performance with less than 1% trainable parameters and notably reduces training costs.
[CV-67] Multi-LLM Collaborative MRI Report Generation for Visual Instruction Tuning in Brain Oncology
链接: https://arxiv.org/abs/2607.14581
作者: Sinyoung Ra,Jonghun Kim,Hyunjin Park
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in large language models (LLMs) and their extension to vision-language models (VLMs) have made it easier to combine text and images for tasks such as report generation. Existing VLMs in medicine typically focus on 2D images (chest X-rays), and their extension to 3D imaging has been difficult because of the lack of paired 3D imaging-text data. Thus, we introduce a new method for creating a 3D image-text dataset for brain oncology using 3D MRI scans of glioma and meningioma cases. We use a cooperative system in which several LLMs work together to generate and check reports, ensuring that they are accurate and clear. By leveraging the new 3D MRI-text dataset, we further build a VLM that converts MRI scans into tokens and aligns them with text instructions. Our VLM performed better in report generation and visual question answering tasks than other 2D and 3D methods. Our method not only improves the quality of reports but also helps with better diagnosis and treatment in brain oncology.
[CV-68] Advanced Image Generation: Negative Prompt Optimization and Latent Classifier Guidance
链接: https://arxiv.org/abs/2607.14580
作者: Vaddi Charan Sai Nandan Reddy,Harini B,Chandana M S
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:We present a novel system that integrates negative prompt optimization via a fine-tuned sequence-to-sequence LLM and latent-space classifier guidance to improve the quality of images generated by Stable Diffusion. Our approach automatically generates optimized negative prompts, and employs a CNN-RNN hybrid classifier to evaluate and guide diffusion steps, rolling back low-quality latent updates. Experimental results demonstrate that our dual-guidance framework reduces artifacts and improves semantic fidelity compared to baseline diffusion.
[CV-69] Breaking the Model Forgetting Cycle in Long-Incremental 3D Object Detection ECCV2026
链接: https://arxiv.org/abs/2607.14560
作者: Peisheng Qian,Jie Xu,Xulei Yang,Na Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Incremental 3D object detection requires a detector to learn novel object classes while remembering previously learned ones over sequentially arriving data. Previous methods, primarily based on pseudo-labeling, perform reasonably in short-incremental stages but still suffer from severe model forgetting when dealing with long-incremental sequences. We investigate this failure and reveal a detrimental self-reinforcing cycle: data distribution shift of novel classes causes model forgetting on old classes, which further produces accumulated error in pseudo-labeling that exacerbates model degradation. To address this issue, we draw inspiration from the human learning process and propose the \emphLearning-Dynamics-driven Memory and Review (LDMR) framework. LDMR monitors per-class detection quality at periodic training checkpoints and uses these learning-dynamics signals to drive two innovative mechanisms, namely (i) human-like intra-stage review that divides each incremental stage into multiple sub-stages’ training and concentrates on remembering the most-forgotten objects, and (ii) scene-aware cross-stage memory evolution that evolves a memory bank to transfer knowledge between two consecutive stages by jointly considering scene learnability and diversity. Extensive experiments across multiple long-incremental protocols on indoor benchmarks SUN RGB-D and ScanNetV2 show that LDMR substantially mitigates the model forgetting and outperforms all baselines by a clear margin. Code is available at this https URL.
[CV-70] HyMobileAgent : Data-Environment Co-Scaling for Efficient GUI Agents
链接: https://arxiv.org/abs/2607.14548
作者: Hy Vision Team,Huawen Shen,Zhengyang Tang,Shangpin Peng,Liang Wu,Anran Zhang,Weinong Wang,Yiduo Guo,Chenxin Li,Zhengyao Fang,Yang Ding,Junyi Li,Fei Tang,Zheng Ruan,Yi Zhang,Xingran Zhou,Dingchen Yang,Sunqi Fan,Zhiyi Wan,Han Hu,Xin Lai,Pengyuan Lyu,Chengquan Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:As large multimodal models move from understanding content to operating on digital environments, mobile GUI has emerged as a challenging and consequential testbed for digital embodied intelligence. Mobile agents operate under three coupled constraints: precise perception of complex interfaces, scalable acquisition of high-quality interaction data, and robust long-horizon decision making under compounding execution errors. This report presents HyMobileAgent, a mobile GUI agent built on Hy3.0-VL-A3B, a vision-native foundation model featuring native any-resolution input, an A3B-scale deployment budget, and a 32K context window to model extended interaction histories. Rather than relying solely on model scaling, we develop a joint data and environment centric scaling framework to address the key bottlenecks of mobile interaction. Our framework integrates a GUI perception flywheel combining mock-interface synthesis, rejection sampling, and icon-specific augmentation; a knowledge pipeline that transforms tutorial videos into structured interaction data; a million-scale action data pipeline deployed across more than 2000 sandbox and real-device instances with automated failure attribution; the PhoneWorld Mock App Factory, providing a resettable training environment with 34 mock applications and over 34000 tasks; and a structured Planning-and-Reflection mechanism with explicit dead-loop detection for reliable long-horizon execution. We also introduce a progressive training recipe consisting of mid-training, supervised fine-tuning, and reinforcement learning with task-specific reward designs. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.14548 [cs.CV] (or arXiv:2607.14548v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.14548 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-71] AdaTurn: Budget-Aware Test-Time Scaling for Active Visual Perception Agents
链接: https://arxiv.org/abs/2607.14547
作者: Susan Liang,Chao Huang,Filippos Bellos,Jing Bi,Jason J Corso,Chenliang Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Active visual agents solve fine-grained image tasks by interleaving reasoning with image-grounding actions across multiple turns. However, deployment-time rollout budgets are rarely fixed: some requests permit long rollouts, while others require the agent to act under a tight turn limit. Existing methods train the policy as if the rollout budget were hidden, so when the available budget is smaller than the trajectory the agent prefers, the interaction is often truncated before any valid answer is produced; we term this failure \emphcatastrophic truncation. To overcome this challenge, we present AdaTurn, a budget-aware framework that conditions the agent on the allowed number of turns and explicitly trains the boundary behavior induced by the budget. Our key component, Forced-Answer DAPO (FA-DAPO), converts the over-budget event from a masked or penalized failure into a trainable final-decision step, teaching the model to synthesize partial evidence when further tool use is no longer possible. We further randomize rollout budgets during both training and inference and introduce a load-balanced scheduler that makes such operations practical. AdaTurn substantially improves low-budget accuracy, for example raising VisualProbe-Medium from 36.7% to 47.6% at four turns, while preserving strong scaling at larger budgets and transferring effectively to multiple backbones and general multimodal benchmarks.
[CV-72] 3D Geometric Tooth Alignment Planning via Deep Reinforcement Learning
链接: https://arxiv.org/abs/2607.14544
作者: Yong Li,Jianwen Lou,Jiayue Ma,Yao-Xiang Ding,Youyi Zheng,Haihua Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D geometric tooth alignment planning, which determines sequential trajectories from initial malocclusion to the final target alignment, is a cornerstone of modern digital orthodontics. This paper presents a novel deep reinforcement learning (DRL) framework to automate the generation of these alignment paths. We formulate the planning process as a Markov Decision Process (MDP) to capture its sequential decision-making nature, focusing on optimizing geometric trajectories while integrating essential spatial constraints, such as inter-dental collision avoidance and path efficiency. The proposed method leverages the Deep Deterministic Policy Gradient (DDPG) algorithm, enhanced by three key innovations: (1) a Transformer-based agent to model complex spatial interactions between teeth and manage high-dimensional state-action spaces; (2) a dynamic masking scheme that restricts movement to a sparse subset of teeth per step, better reflecting the clinical logic of sequential alignment; and (3) a two-stage curriculum learning strategy that gradually increases task difficulty to ensure training stability and efficient path discovery. We evaluate our approach on a dataset of 10K expert-designed treatment plans based on clinical data. Experimental results demonstrate that our method outperforms existing baselines in terms of path safety and geometric efficiency, providing a robust and automated solution for 3D geometric orthodontic alignment planning.
[CV-73] SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection
链接: https://arxiv.org/abs/2607.14534
作者: Huong Ninh,Chien Thai,Mai Xuan Trang,Vu-Minh Le,Thanh Ha Le,Long Tran
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Industrial anomaly detection aims to identify and localize defective regions without relying on exhaustive annotations of all possible defect types. Although recent unsupervised methods have achieved strong performance, most are primarily designed for single-class settings and often struggle in multi-class scenarios, where diverse normal patterns may lead to over-generalization and reduce the discriminative capability between normal and anomalous regions. In this paper, we propose SwinAD, a reconstruction-based framework for multi-class unsupervised anomaly detection that leverages a frozen pretrained Swin Transformer V2 encoder and a feature diversity-preserving reconstruction decoder. The hierarchical encoder provides semantically rich multi-scale features, while stage-wise bottleneck modules with dropout prevent trivial identity mapping and encourage robust reconstruction of normal patterns. To further improve localization, we introduce a feature diversity-preserving reconstruction framework that maintains complementary reconstruction hypotheses instead of relying on a single decoding branch. The discrepancies between encoder features and the two reconstructed features are then aggregated across multiple scales to produce the final anomaly map. Experiments conducted on three industrial anomaly detection benchmarks, including MVTec AD, VisA, and Real-IAD, demonstrate that SwinAD achieves competitive image-level performance and strong pixel-level localization accuracy, with particularly notable improvements in pixel-level AP and 1 on MVTec AD. These results indicate that combining hierarchical Swin features with diverse multi-scale reconstruction substantially improve pixel-level localization in multi-class unsupervised anomaly setting.
[CV-74] Uni-AdaVD: Universal Concept Erasure for Visual Generation via Orthogonal Value Decomposition
链接: https://arxiv.org/abs/2607.14521
作者: Qifan Zhou,Yuan Wang,Yanbin Hao,Xiang Wang,Kuien Liu,Richang Hong,Meng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual generative models inevitably absorb undesirable concepts from uncurated pretraining data, making concept erasure essential for safe deployment. Existing erasure methods, however, are often architecture-specific and struggle to remove target concepts while preserving non-target content and generative priors. We present Uni-AdaVD, a universal inference-time concept erasure framework for visual generation. Uni-AdaVD treats the value space of multimodal attention as a unified intervention space and introduces encoder-aware target representation construction to localize target semantics across heterogeneous text encoders. It further combines orthogonal value decomposition with an adaptive erasing shift to suppress target semantic directions without updating the original model weights. Extensive experiments on U-Net-, DiT-, and autoregressive image generators, as well as text-to-video models, demonstrate strong performance on single- and multi-concept erasure while preserving non-target priors. These results suggest that Uni-AdaVD provides an efficient and adaptable safety mechanism for modern visual generative models. Our code is available at this https URL.
[CV-75] VTM-Nav: Hierarchical Visual-Topological Memory for Cross-Episode Object-Goal Navigation
链接: https://arxiv.org/abs/2607.14514
作者: Xiaoran Xu,Yupeng Wu,Tianyu Xue,Yifan Xu,Xuanran Dong,Xiaoshan Yang,Changsheng Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Object-goal navigation requires an embodied agent to locate and reach an instance of a specified object category in an indoor environment. Recent training-free approaches leverage vision-language models (VLMs) for open-vocabulary semantic reasoning, but are typically evaluated under an episodic protocol that resets all scene-specific state after each episode. We introduce Cross-Episode Object-Goal Navigation, in which an agent repeatedly operates in the same scene, retains only self-acquired experience, and keeps its model parameters fixed. To support experience reuse, we present \method, a training-free VLM navigation framework with a persistent hierarchical Visual-Topological Memory (VTM). The VTM organizes scene knowledge at room and object levels and retrieves relevant experience through coarse-to-fine matching, providing memory as soft guidance only when it agrees with current observations. A conservative execution guard further mitigates oscillations, blocked motions, and premature stopping. Under a controlled same-scene protocol, we evaluate \method on three benchmarks, HM3D v0.1, HM3D v0.2, and MP3D, and compare it with a strengthened WMNav baseline augmented with cross-episode textual memory, while keeping the VLM backbone and action pipeline identical. \method achieves the best performance across all three benchmarks, demonstrating the effectiveness and robustness of structured visual-topological experience reuse across datasets.
[CV-76] Compression of 3D Gaussian Splatting Data Using GPU-friendly Graphics Texture Coding
链接: https://arxiv.org/abs/2607.14513
作者: Amir Said,Randall Rauwendaal
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:Techniques for modeling 3D scenes from image collections, such as 3D Gaussian Splatting (3DGS), are capable of generating high-quality novel views by leveraging graphics primitives with view-dependent appearance. In 3DGS, spherical harmonic (SH) are employed to model view-dependent color, resulting in a large number of SH coefficients per primitive and large memory requirements. While compression approaches have been proposed to mitigate this problem, they do not exploit the capabilities of modern Graphics Processing Units (GPUs) for parallel decoding and rendering. In this paper, we propose a method for compressing SH color coefficients using texture compression schemes specifically designed for efficient parallel GPU decoding and supported by dedicated hardware acceleration. It is shown that those methods can compress color coefficients more effectively than 2D textures by exploiting the fact that primitives can be locally grouped and reordered according to color. Furthermore, we introduce a bit-rate control strategy that preserves random access, enabling large-scale parallelization without compromising rendering performance. Experimental results using BC1 and BC7 texture compression formats show that GPU-based decompression can be achieved with negligible or imperceptible degradation in the visual quality of rendered 3DGS scenes.
[CV-77] Multi-Scale ViT Inference with Habitat-Fit Priors and kNN Retrieval for Multi-Species Plant Identification
链接: https://arxiv.org/abs/2607.14509
作者: Alper Erten,Murilo Gustineli,Adrian Cheung
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:This paper describes DS@GT ARC’s third-place solution to the PlantCLEF 2026 challenge on multi-species plant identification in vegetation quadrat images, where systems must predict every species present in high-resolution (~3000 x 3000 pixel) plot photographs while training only on single-label images of individual plants. The pipeline is built around a fine-tuned DINOv2 ViT-L/14 classifier applied over a multi-scale tile decomposition of each quadrat, with per-tile predictions blended with a FAISS kNN retriever and post-processed by source-aware temporal fusion across repeated plot visits, a habitat-fit demotion that injects geographic and altitude priors from the training data, and a South-Western Europe geographic mask. Habitat-fit demotion and multi-scale aggregation are the largest individual contributors in the ablations. Two complementary training-centric directions, a cross-region transformer with noisy-student distillation on the LUCAS dataset and a label-as-query transformer decoder over synthetic CLS-domain pseudo-quadrats, yielded null results. An inference-time augmentation with instance-aware segmentation crops also did not improve performance. The selected submission reaches a private-leaderboard macro-F1 of 0.43902 (third place; public 0.51096); an unselected configuration of the same pipeline scored above 0.45 on the private set. Code: this https URL.
[CV-78] Reinforcing Egocentric Spatial Perception in Multimodal Large Language Models via Ego Scene Augmentation
链接: https://arxiv.org/abs/2607.14497
作者: Chi Kit Wong,Ye Pan,Yuanhuiyi Lyu,Xu Zheng,Zidong Cao,Lutao Jiang,Zixin Zhang,Huiyu Zhou,Xuming Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 8 figures. Chi Kit Wong and Ye Pan contributed equally. Code: this https URL
Abstract:Egocentric Visual Question Answering (VQA) has attracted widespread attention as an important task for enabling Multimodal Large Language Models (MLLMs) to interact with the real world. However, existing MLLMs struggle to perform effective spatial reasoning in complex egocentric scenes due to their limited spatial perception capabilities. To this end, we introduce Ego Scene Augmentation (ESA), an egocentric spatial perception framework, which actively enhances the spatial perception capabilities from the egocentric perspective, powered by the proposed Ego-element Graph. Our core insight is leveraging the Ego-element Graph as an intermediary representation to augment the egocentric spatial perception of MLLMs via visual foundational models. Specifically, we 1) construct the Ego-element Graph, which encapsulates and integrates egocentric spatial features enabled by visual foundational models; 2) enhance the spatial perception capabilities of MLLMs via the Ego-element Graph for ego-perspective scenes. Our proposed ESA framework presents significant performance improvement on the EgoTextVQA benchmark. We achieve an 8.14% gain on the indoor setting and an 8.72% gain on the outdoor setting. Furthermore, our ESA shows the most impressive performance improvement in the shopping subset of the indoor setting. The project code is publicly available.
[CV-79] Immediate 3D Gaussian Splat Reconstruction of Unordered Input with Global Consistency
链接: https://arxiv.org/abs/2607.14481
作者: Andreas Meuleman,Linus Franke,Boris Zhestiankin,Camille Montemagni,George Drettakis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting (3DGS) has become the method of choice for reconstructing and real-time rendering of captured scenes. To capture a scene with good visual quality, continuous image sequences are usually combined with out-of-order shots for better scene coverage. Structure from motion can reconstruct such captures, but only after they are all available and often with high computational cost. Incremental reconstruction methods – often derived from SLAM solutions – provide immediate feedback, but cannot handle the out-of-order capture we require. We provide the first immediate feedback solution for such radiance field capture that provides global consistency. We first introduce a method for fast matching in out-of-order sequences, by repurposing visual place recognition models and a covisibility graph, and provide an efficient way to find highly connected keyframes, improving quality even for ordered sequences. We show how these steps – together with GPU optimization and careful Gaussian primitive placement – provide fast local reconstruction, in our challenging radiance field reconstruction case. We then introduce a novel cluster-based method, again using the covisibility graph, to provide efficient loop closure that does not require sequential input. Finally, to handle large scenes in our context, we introduce a progressive hierarchy that allows our method to scale to large environments, without compromising efficiency. Our results show we provide immediate feedback 3DGS reconstruction with good visual quality in several datasets, with up to thousands of input images.
[CV-80] G2SR: Geometric Methods for Fast and Memory-Efficient Gaussian-based Surface Reconstruction
链接: https://arxiv.org/abs/2607.14470
作者: Dasong Gao,Vivienne Sze,Sertac Karaman
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages, 3 figures
Abstract:Few-view surface reconstruction recovers the visible surfaces of a scene from a few posed RGB images, providing the 3D models that robots need to explore and interact online. On mobile platforms, the reconstruction must be fast and geometrically accurate while keeping a small memory footprint to ensure safe and efficient operation. 3D Gaussian Splatting (3DGS) offers a high-fidelity scene representation, but building it from a few views is ill-posed, as many distinct surfaces reproduce the same images, making traditional photometric methods prone to “floater” artifacts. End-to-end methods resolve the ambiguity by regressing splats with large, usually Transformer-based, networks that require heavy compute and memory while generalizing poorly to new scenes. We propose G2SR, which exploits a well-posed core of the task: given cross-view 2D splat correspondences, 3D splats follow analytically from multi-view geometry. G2SR employs a lightweight neural frontend to detect and track 2D Gaussian splats on the image plane and an analytic backend to triangulate each into a metric-scale 3D splat. On ScanNet, Replica, and DTU, G2SR matches or exceeds the geometric accuracy of state-of-the-art end-to-end methods while running at 69-89 reconstructions per second within 203 MB of GPU memory (5-107x less) for 2- and 3-view inputs at 384 x 512 resolution, offering a practical path to online Gaussian-based surface reconstruction.
[CV-81] Depth-Dependent Hidden-State Collapse in Dynamical System Autoencoders for LiDAR Point-Cloud Classification
链接: https://arxiv.org/abs/2607.14463
作者: Patricia Medina,Hy P. G. Lam
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 2 figures, 1 table. Submitted to the 2026 IEEE High Performance Extreme Computing Conference (HPEC 2026)
Abstract:We study Dynamical System Autoencoders (DSAE) for LiDAR point-cloud classification using spatial coordinates and Product Coefficient feature augmentations. The experiments compare separately trained DSAE architectures at encoder depths K=1,\ldots,5 and evaluate the resulting hidden representations with Random Forest, kNN, and a majority-class Dummy baseline. The main finding is a hidden-state collapse at K=5 . For both xyz and xyz plus Product Coefficient inputs, the hidden-state standard deviation falls to the order of 10^-5 , while all three classifiers attain the same macro F1 score of 0.224688 . We prove that between-class hidden scatter is bounded by total hidden scatter, which in turn is controlled by the reported hidden-state variance. Thus a nearly constant hidden representation cannot retain substantial class-separating structure. Product Coefficients neither improve pre-collapse macro F1 nor prevent the K=5 collapse in the present DSAE setting. These results identify large-depth representation collapse as a concrete failure mode for DSAE LiDAR classification.
[CV-82] Cotton-SF YOLO: Learning Structural and Frequency Cues for Early Cotton Square Detection in Complex Field Environments
链接: https://arxiv.org/abs/2607.14445
作者: Chengjia Zhang,Yu Li,Feiri Ali,Yan Zhang,Xin Chen,Longke He,Daokun Ma,Liting Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cotton squares are important phenotypic indicators of the early reproductive growth of cotton, and automatic field detection of cotton squares provides an important basis for cotton growth monitoring and precision cultivation management. However, early cotton square detection in complex field environments remains insufficiently explored, as cotton squares are small, frequently occluded, easily blurred, subject to illumination variations, and exhibit low contrast against surrounding cotton leaves. To address these challenges, we propose a task-oriented framework based on YOLO26m, named Cotton-SF YOLO, for cotton square detection under natural field conditions. To improve the perception of small and irregular cotton square boundaries, we introduce Dynamic Snake Convolution into the detector, enabling adaptive extraction of deformable edge features. Furthermore, a frequency-domain feature modulation module is designed by incorporating spectral enhancement into the C2f structure, which recalibrate frequency-domain representations and strengthen discriminative edge and texture cues while reducing interference from complex cotton leaf backgrounds. Trained and evaluated on our newly constructed and annotated field dataset with manually annotated cotton squares, the proposed model achieves mAP _50 , mAP _50:95 , and recall values of 0.8196, 0.4942, and 0.7939, improving over the baseline YOLO26m by 1.25%, 3.45%, and 2.96%, respectively. Ablation experiments and visualization demonstrate that the best performance is achieved with the complementary effects of structural and frequency cues.
[CV-83] Emergent Region-Level Facial Correspondence in Frozen Vision Foundation Models
链接: https://arxiv.org/abs/2607.14423
作者: Izaldein Al-Zyoud,Abdulmotaleb El Saddik
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:Frozen self-supervised vision models can align parts of generic objects, but it remains unclear whether this correspondence extends to human faces, where global layout is shared while identity-specific appearance varies sharply. We test whether frozen DINOv3 features define a region-level facial coordinate system: a feature space in which eyes, brows, nose, mouth, skin, and hair remain distinguishable across people and across time without face-specific training. Using DINOv3 ViT-L/16 patch embeddings and FaRL only as a face-part labeling interface, we evaluate cross-identity nearest-neighbor matching and temporal label propagation on 200 CelebDF-v2 real videos. DINOv3 achieves 83.0% region-level semantic accuracy under unconstrained cross-identity matching, compared with a 23.0% area-weighted random baseline, and 95.5% temporal tracking accuracy without a learned temporal module. A no-FaRL control collapses to 0.9%, showing that FaRL supplies semantic initialization while DINOv3 supplies dense spatial correspondence. The strongest correspondence appears at an intermediate layer: block 18 gives a 4.93x same-region versus cross-region discrimination ratio, compared with 1.48x at the final block. Against CLIP ViT-L/14, DINOv3 shows only a small aggregate advantage but a +16.8 pp gain on anatomical regions, indicating that image-level contrastive supervision captures coarse facial layout but not fine-grained anatomical identity. These results establish frozen DINOv3 as a strong zero-shot representation for region-level facial correspondence and identify intermediate self-supervised features as the most useful layer for dense face analysis.
[CV-84] K-NeAS: Scalable Multi-Material CT Reconstruction Using Neural SDFs
链接: https://arxiv.org/abs/2607.14415
作者: Daksh K. Shah,Emmanouil Nikolakakis,Razvan V. Marinescu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 3 figures. Under peer review
Abstract:Computed Tomography (CT) carries significant ionizing radiation risks, driving the need for sparse-view reconstruction. Implicit scene representations (ISRs) address this by recovering continuous volumetric attenuation fields directly from sparse projections, and recent geometry-aware extensions jointly model surface geometry alongside attenuation to improve fidelity and enable clean tissue segmentation without manual thresholding. However, these methods remain limited by manually tuned attenuation bounds and rigid two-material constraints. This paper proposes K -NeAS, a unified and scalable architecture for automated, multi-material surface reconstruction. We replace independent material networks with a shared latent backbone and introduce a fully differentiable K -material sequential soft selector to model an arbitrary number of overlapping tissues. To eliminate manual tuning, we automate attenuation bounding using a Gaussian Mixture Model (GMM) and implement a scheduled auxiliary floater loss to mitigate geometric hallucinations common under extreme sparsity. Evaluated across four clinical Cone-Beam CT (CBCT) datasets, K -NeAS successfully scales to arbitrary material counts, achieving superior 3D volumetric fidelity at K=3 materials on complex multi-tissue regions such as the Abdomen ( 33.28\text dB 3D PSNR vs. 31.40\text dB single-material NeAS baseline, a +1.88\text dB improvement). Furthermore, our model exhibits enhanced robustness under sparse-sampling conditions, outperforming baseline 3D PSNR by up to 1.17\text dB under 5- and 10-view constraints.
[CV-85] Dynamic Manipulation Hypergraphs for HAR: Beyond Pairwise Relations: Dynamic Manipulation Hypergraphs for Vision-Based Human Activity Recognition
链接: https://arxiv.org/abs/2607.14350
作者: Fatemeh Ziaeetabar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Fine-grained manipulation recognition requires modeling evolving relations among hands, objects, tools, and supporting surfaces. Conventional graph-based methods use pairwise edges that can fragment a coordinated event into disconnected binary relations. We propose a dynamic manipulation hypergraph framework that represents multi-entity configurations as higher-order relational units. At each temporal step, relevant entities are encoded using appearance, spatial, motion, and semantic-role features. Hyperedge candidates are instantiated and ranked using proximity, contact, and motion-coupling predicates. A hypergraph reasoning network performs node-to-hyperedge and hyperedge-to-node message passing, followed by temporal attention over the evolving interaction structure. The framework provides class-agnostic hyperedge-importance scores that identify entity configurations and temporal intervals emphasized by the model without treating them as causal explanations. Quantitative evaluation is conducted on EPIC-KITCHENS-100/VISOR and Assembly101 under an annotation-assisted entity-localization protocol. Video-only and entity-based methods provide contextual comparisons, while a matched pairwise graph and a static hypergraph serve as the principal controlled baselines because they use identical entity inputs and comparable relational settings. The proposed method improves HO-F1 over the matched pairwise graph by 6.9 percentage points on EPIC-KITCHENS-100/VISOR and 9.5 points on Assembly101, and exceeds the static hypergraph by 4.4 and 5.8 points, respectively. Qualitative analysis on ARCTIC further shows correspondence between highly ranked hyperedges and contact-rich manipulation intervals. These results demonstrate the value of time-varying higher-order relational modeling for fine-grained manipulation activity recognition.
[CV-86] Beyond scalar losses: calibrating segmentation models via gradient vector field surgery
链接: https://arxiv.org/abs/2607.14338
作者: Laurin Lux,Alexander H. Berger,Moritz Knolle,Daniel Rückert,Johannes C. Paetzold
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: MIDL 2026. Published version: this https URL
Abstract:Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced segmentation tasks. However, models trained using region-based loss functions are notoriously miscalibrated and typically yield over-confident predictions. In medical imaging applications, such as defining tumor resection margins, this miscalibration is hindering clinical adoption. In this work, we outline a novel gradient perspective on this overconfidence and show how it affects region-based loss functions. We propose a “surgery” on the gradient vector field as a simple, yet effective intervention to mitigate calibration issues. This surgery adds a factor to the loss’s partial derivative, scaling the gradient’s magnitude linearly with the prediction error. In empirical evaluations across 2D and 3D medical segmentation tasks, we demonstrate the effectiveness of this intervention while maintaining high prediction accuracy when used in conjunction with any region-based loss function.
[CV-87] MixCompress: Mixture of Experts for Variable Rate Learned Image Compression ECCV2026
链接: https://arxiv.org/abs/2607.14334
作者: Calvin-Khang Ta,Praneet Singh,Tong Shao,Peng Yin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Learned image compression (LIC) is bottlenecked by the need to store independent models for each rate-distortion operating point. Existing variable bit-rate (VBR) methods aim to reduce this overhead via dense parameter modulation, but forcing a shared backbone to approximate divergent mappings causes severe feature entanglement. Specifically, low-rate smoothing gradients inherently conflict with the preservation of high-frequency textural details, leading to sub-optimal performance. To resolve this, we propose MixCompress, a unified VBR framework based on sparse structural specialization. While sparsely gated Mixture-of-Experts (MoE) routing successfully mitigates gradient conflict, it operates on a fixed computational budget. To address the increased representational demands of higher bit-rates we introduce a Mixture-of-Depths (MoD) extension to dynamically scale model capacity. Combined with Conditional Auxiliary Transforms (CAT) for dynamic sub-band energy modulation, our hierarchical framework effectively dynamically scales capacity. Extensive evaluations demonstrate that MixCompress not only matches individually optimized single-rate baselines but can even surpass them, establishing a new Pareto frontier for computationally efficient image coding.
[CV-88] DCVC-MB: Neural B-Frame Video Compression using State Space Models ICME2026
链接: https://arxiv.org/abs/2607.14305
作者: Arjun Arora,Calvin-Khang Ta,Carlos Restrepo-Galeano,Kruthi Murali,Naga Akhil E S,Arunkumar Mohananchettiar,Jay Shingala,Tong Shao,Peng Yin,Sean McCarthy
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICME 2026
Abstract:In this paper we propose DCVC-Mamba (DCVC-MB), a neural video codec framework for B-frame coding. Our approach incorporates an IBP frame strategy for low-delay B-frame coding, a spatio-temporal fusion model based on state-space models for bidirectional temporal prediction, and an entropy-aware skipping mechanism that selectively omits coding certain latents to reduce entropy coding times. In addition to our model contributions we also implement two inference-time strategies that enhance compression performance. Experimental evaluation shows that DCVC-MB compares favorably to existing NVCs and traditional codecs. The method demonstrates BD-rate reductions of up to 8.98% on average compared to prior neural video codecs, and improvements of up to 30.45% and 1.81% over the VTM-19.0-LDP and VTM-19.0-RA(Inter-GoP=16) benchmarks, respectively, contributing to advances in neural video compression.
[CV-89] XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation ICPR2026
链接: https://arxiv.org/abs/2607.14287
作者: Md Mahedi Hasan,Md Mushfiqur Rahaman,Alan Pachkovskiy,Imtiaz Ahmed,Jeremy Dawson,Srinjoy Das
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 10 pages, 7 figures, 3 tables. Accepted to the IAPR Workshop on Machine Vision for Industrial Inspection (MVI2), in conjunction with ICPR 2026. Code: this https URL
Abstract:Defect segmentation in additive manufacturing (AM) X-ray computed tomography (XCT) images remains challenging due to severe class imbalance and large distribution shifts across scan conditions. Although recent foundation models such as the Segment Anything Model (SAM) provide strong general-purpose segmentation priors, their natural-image pre-training transfers poorly to the AM XCT domain, where defects appear as subtle non-semantic microstructural anomalies. Moreover, adapting SAM to the AM domain is further limited by the large domain gap and scarcity of labeled real XCT data. We present XCT-SAM, a sequential parameter-efficient adaptation framework for AM XCT defect segmentation. Instead of adapting SAM directly from natural images to XCT data, we first fine-tune Conv-LoRA adapters on an alloy-microstructure dataset and subsequently transfer the adapted model to XCT images, progressively bridging the domain gap. Using Conv-LoRA adapters with rank r=2, the framework injects convolutional spatial inductive bias into SAM’s backbone while training approximately 4.15M parameters and keeping over 99% of the model frozen. We evaluate XCT-SAM on out-of-distribution CycleGAN-XCT benchmarks and real-world NIST XCT scans. Across both settings, XCT-SAM consistently outperforms zero-shot SAM and other domain-adapted SAM baselines, achieving the best overall IoU and Dice scores. These results demonstrate the effectiveness of intermediate domain adaptation with parameter-efficient adapters for industrial XCT defect segmentation. The source code is publicly available at this https URL
[CV-90] 3D Lane Detection with Odometry for High-Speed Vehicle Racing IROS
链接: https://arxiv.org/abs/2607.14248
作者: Omoruyi Atekha,John Subosits,Marcus Greiff
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: 14 pages, IROS paper, includes extended abstract
Abstract:Lane boundary detection is a critical component in autonomous driving systems and has been rigorously studied in regular driving scenarios. However, it is less explored in vehicle racing, where the car moves at higher speeds across more extreme road geometries. To study this problem, we introduce a new dataset for 3D lane detection in racing, featuring 250 k images from multiple camera feeds and inertial measurements taken with a Lexus LC 500 driving on a closed circuit. With this dataset, we compare various approaches to 3D lane detection and propose modifications that permit frames to be processed at rates of almost 300Hz while retaining high predictive performance in the racing application. This facilitates a multi-camera ensemble approach that is validated on hardware. We show that sensing modalities such as inertial measurements can be leveraged for pre-integration to regress road geometries over both cameras and time, yielding improvements in key metrics. Compared to methods such as BevLaneDet, adding odometry and ensemble predictions improves the F1 score by 3 points and reduces near-vehicle mean absolute errors (MAEs) by 30 % . We show F1 scores 0.9 and lateral MAEs of 0.18m in vehicle deployments.
[CV-91] SeeSE3: Emergence of 3D Space in Vision Features
链接: https://arxiv.org/abs/2607.14228
作者: Caroline Chen,Sayna Ebrahimi,Fedor Kitashov,Ming-Hsuan Yang,Leonidas Guibas,Viorica Pătrăucean,Maks Ovsjanikov
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:In this paper, we ask whether vision foundation models construct representations that reflect the intrinsic properties of 3D Euclidean space. Unlike previous works that probe 3D awareness of vision features by regressing image-centric quantities such as depth or normals, we investigate the relation between the structure of the space of visual features and the group of Euclidean transformations SE(3) . We propose a set of probes to evaluate this relation from both topological and geometric perspectives: a mutual neighborhood metric that measures the alignment between feature neighborhoods and spatial topology, and a Poincaré Adapter to test the linear accessibility of the geometry of camera motion from latent displacements in static scenes. We show that self-supervised vision models, which, in principle, have not been trained with direct 3D supervision or active agency, possess latent subspaces that are remarkably strongly correlated with three-dimensional Euclidean space, when probed correctly. Building on this insight we propose a new class of “Latent-Space Navigation” techniques that perform visual odometry and localization purely in the latent space, bypassing the need for explicit 3D reconstruction.
[CV-92] Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation
链接: https://arxiv.org/abs/2607.14203
作者: NVIDIA:Jiahui Huang,Jiawei Ren,Michal Tyszkiewicz,Bjoern Haefner,Michael Shelley,Xin Kang,Seung Wook Kim,Ning Xu,Qi Wu,Janick Martinez Esturo,Shengyu Huang,Nick Schneider,Laura Leal-Taixe,Zan Gojcic,Sanja Fidler
类目: Graphics (cs.GR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:3D simulation platforms are critical for autonomous driving because they enable end-to-end policy evaluation, thereby reducing development costs and improving safety. In recent years, neural simulation has become predominant, with methods such as NuRec playing a central role; however, these methods remain relatively slow and typically require per-scene tuning. In this work, we present Instant NuRec, a feed-forward neural reconstruction model that turns a short multi-view driving log into a fully simulatable 3D Gaussian Splatting (3DGS) world in a single forward pass. The model accepts multi-view input from a calibrated camera rig and emits a layered output consisting of static and dynamic 3DGS layers, a sky cubemap, and per-camera ISP corrections, while providing native support for non-pinhole camera models via 3DGUT. It reconstructs a 10-20-second multi-camera scene in roughly 1.5 seconds and achieves a PSNR on the Waymo Open Dataset that is 2.01 dB above the strongest evaluated baseline. Instant NuRec is deeply integrated into NuRec and is compatible with AlpaSim for closed-loop simulation.
[CV-93] KeyFrame-Compass: Towards Comprehensive Evaluation of Keyframe-Conditioned Video Generation
链接: https://arxiv.org/abs/2607.14202
作者: Yuqi Tang,Tengfei Liu,Yizheng Lai,Yuran Wang,Yang Shi,Wanshun Su,Zhuoran Zhang,Qixun Wang,Xiaohan Zhang,Xinlei Yu,Xuehai Bai,Xuanyu Zhu,Bohan Zeng,Bozhou Li,Shujie Li,Yifan Dai,Yujie Wei,Shixuan Liu,Haotian Wang,Jialu Chen,Yuanxing Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 35 pages
Abstract:Video generation increasingly relies on keyframe-based workflows, where creators specify a sequence of reference images to guide generation. Although recent models support multi-keyframe conditioning, it remains unclear whether they can faithfully reproduce the prescribed keyframes while maintaining overall video quality. We present KeyFrame-Compass, the first comprehensive benchmark for evaluating keyframe-conditioned video generation. The benchmark contains 386 carefully curated samples spanning three application domains, two video structures, two prompt granularities, two conditioning formats, and four keyframe densities, enabling controlled analysis under diverse generation settings. We further introduce an automated evaluation framework that jointly measures keyframe execution and overall video quality. Specifically, we decompose keyframe execution into six complementary metrics covering presence, fidelity, temporal ordering, localization, persistence, and uniqueness, while assessing overall video quality through evidence-grounded MLLM judgments augmented with specialized perception models. Experiments on nine representative video generation systems reveal several fundamental limitations. Current models exhibit a clear trade-off between faithful keyframe execution and natural video synthesis. Their performance further degrades as keyframe constraints become denser and most open-source models also fail to interpret storyboard-grid inputs as temporally ordered keyframe sequences.
[CV-94] Inference-Time Concept Suppression and Video-Centric Evaluation for Text-to-Video Models
链接: https://arxiv.org/abs/2607.14194
作者: Wenxuan Chen,Wenjie Feng
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 29 pages, 9 figures, 11 tables
Abstract:Text-to-video (T2V) generators can synthesize realistic and temporally coherent videos, but controllably removing a target concept from a generator remains difficult. Unlike text-to-image concept erasure, T2V unlearning must suppress a target concept that may persist across frames while preserving non-target subjects, actions, scenes, and temporal structure. We propose \textbfSIRUS, a training-free inference-time framework for concept-level T2V unlearning. Given textual aliases of a target concept, SIRUS localizes target-related prompt evidence and suppresses target expression during sampling, without updating the text encoder or denoising network. We further introduce a video-oriented evaluation framework for T2V unlearning that separately measures target forgetting, non-target preservation, video quality, jailbreak robustness, and efficiency, using video-level failure criteria, frame-level residue statistics, paired preservation analysis, VBench-based quality diagnostics, and deployment overhead measurement. Across five safety, object, and style concepts on CogVideoX, SIRUS reaches 70.4% average forgetting success and 25.7% average frame hit, compared with 44.4% / 47.2% for VideoEraser, while reducing the average VBench quality drop from -0.043 to -0.016, yielding the strongest forgetting-quality trade-off among fully evaluated baselines. Transfer experiments on Wan2.2 further suggest that SIRUS generalizes across modern T2V backbones. Comments: 29 pages, 9 figures, 11 tables Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2607.14194 [cs.CV] (or arXiv:2607.14194v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.14194 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Wenxuan Chen [view email] [v1] Wed, 15 Jul 2026 16:26:09 UTC (12,633 KB) Full-text links: Access Paper: View a PDF of the paper titled Inference-Time Concept Suppression and Video-Centric Evaluation for Text-to-Video Models, by Wenxuan Chen and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CV prev | next new | recent | 2026-07 Change to browse by: cs cs.LG References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[CV-95] MultiRef-Compass: Towards Comprehensive Evaluation of Multi-Reference-to-Audio-Video Generation
链接: https://arxiv.org/abs/2607.14189
作者: Xiaohan Zhang,Yuqing Wen,Junlin Chen,Yuqi Tang,Yiting He,Lizhuo Shao,Weiming Zhu,Tengfei Liu,Yang Shi,Jialu Chen,Yuanxing Zhang,Huaxiong Li
类目: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注: 32 pages
Abstract:Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mainly focus on text-driven generation, single-reference subject preservation, or isolated audio-video alignment, leaving the emerging MR2AV setting largely unexplored. Compared with these settings, MR2AV requires models to jointly reason over multiple references while generating synchronized visual and audio content. Models must not only preserve each reference faithfully but also correctly bind and compose multiple referenced entities into coherent audio-visual events. To address this gap, we introduce MultiRef-Compass, a unified benchmark for MR2AV generation. It comprises 350 carefully curated samples constructed through a scalable and controllable asset-composition pipeline, covering multi-view subject preservation, multi-entity binding, and human-object-scene composition. To provide interpretable assessment, MultiRef-Compass defines an evaluation protocol with four dimensions: Basic Quality, Reference Consistency, Audio-Visual Consistency, and Instruction Following, using 14 sub-metrics. MultiRef-Compass integrates automatic metrics with a rejudging-enhanced MLLM-as-a-Judge framework, enabling scalable and auditable evaluation of both perceptual fidelity and reference-conditioned composition. Extensive experiments on eight representative MR2AV systems reveal substantial room for improvement across multiple evaluation dimensions, underscoring the need for a comprehensive benchmark and positioning MultiRef-Compass as a foundation for future MR2AV research.
[CV-96] Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning
链接: https://arxiv.org/abs/2607.14183
作者: Zishuo Li,Bowen Yang,Changtao Miao,Kai Zhu,Hao Chen,Qingze Guan,Zhengxing Wu,Wanke Zhan,Yang Sun,Zhiyi Huang,Zitong Shan,Zhenchao Jin,Jiadong Hong,Taowen Wang,Yushi Feng,You Liu,Yibo Wang,Yifan Yang,Zhaowen Zhou,Man Luo,Hao Cheng,Bo Zhang,Jianshu Li,Jiansheng Cai,Guocai Yao,Jize Zhang,Chenhao Lin,Renjing Xu,Lequan Yu,Chao Shen,Chunhua Shen,Zhe Li
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Egocentric videos of human manipulation provide scalable supervision for embodied intelligence, yet existing resources rarely combine low-cost continuous capture, manipulation-level structured annotations, and reusable tools for robot learning. We present Open-AoE, an open, community-oriented egocentric manipulation dataset and toolchain spanning the full pipeline from smartphone capture to model training. Its first release contains approximately 2,000 hours of manipulation video collected in natural environments by 500+ contributors using 400+ smartphones. The dataset provides text annotations, MANO-based hand poses, camera trajectories, and temporally localized atomic actions. Open-AoE further includes a data processing pipeline that transforms raw recordings into structured samples through temporal action segmentation, semantic annotation, hand reconstruction, and camera trajectory reconstruction. Meanwhile, we provide a separate downstream toolchain supports visualization, cross-embodiment retargeting, model-specific data conversion, and training recipes for VLA policies, WAMs, and World Models. By integrating scalable capture, structured processing, and downstream adaptation, Open-AoE reduces the barriers to both data contribution and reuse, providing practical open infrastructure for embodied model training, human-to-robot transfer, and world modeling.
[CV-97] QFireNet: A Quantum-Enhanced U-Net for Wildfire Segmentation from Sentinel-2 Imagery
链接: https://arxiv.org/abs/2607.14160
作者: Jaiman Munshi,Tanvi Tewary,Sawyer Bloom,Aidan Chu,Chetan Maviti,Kyon Winston-Bey,Harshit Badjatia,Farhan Kittur,Vardhan Madhavarapu,Varun Kota,Joshua Kwon,Nazia Rangwala-Vohra,Franz Klein(IonQ Team, App Dev Club, University of Maryland, College Park)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 8 figures
Abstract:Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such as class imbalance, feature complexity, and atmospheric interference. In this paper, we build on the foundational U-Net image segmentation model to develop a quantum-hybrid solution in hopes of more effectively modeling the high-dimensional spectral feature space of the Sen2Fire dataset. We inject a variational quantum circuit in the bottleneck portion of U-Net, specifically the QuFeX and QB-Net ansatzes. We test a classical Feature Pyramid Network (FPN) for further comparative analysis of the model, and we also explore classical improvements to the U-Net model and its training process, including a compression of parameters, alternative loss functions, and uniform mixing of input data. Our primary finding is that under matched conditions, both QB-Net (with an F_1 score of 31.18) and QuFeX ( F_1 = 30.79 ) outperformed the classical U-Net baseline results ( F_1 = 28.71 ). Additionally, the classical FPN achieved a comparable score of 31.13. A crucial finding was that data mixing removed a significant domain shift between the geographically-separated train and test sets, which boosted the classical FPN F_1 score to 39.76. We validate the architecture’s robustness and generalizability to the wildfire detection problem via cross-dataset transfer on the California Burned Areas (CaBuAr) dataset. Overall, we find that quantum machine learning has potential to provide an advantage in the problem of wildfire image segmentation, and further experiments will continue to validate and expand upon this finding.
[CV-98] oolAnchor: Anchoring Counterfactual Context to Boost Agent ic Tool-use Capability
链接: https://arxiv.org/abs/2607.14145
作者: Weiting Liu,Jieyi Bi,Wanqi Zhou,Jianfeng Feng,Yining Ma,Ai Han,Wenlian Lu
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Tool-augmented large language model agents excel at long-horizon tasks, yet they are typically post-trained on fixed toolsets. When tasks demand new tools, these agents struggle to incorporate them effectively, and retraining from scratch is often impractical. We identify the core obstacle in such toolset expansion problem as behavioral inertia: the tendency of agents to fall back on familiar tools and established reasoning patterns despite having access to new ones. We demonstrate that injecting counterfactual anchor contexts at critical decision points can break this inertia, recovering failed trajectories by eliciting suppressed agent capabilities. To scale this insight, we propose ToolAnchor, a framework that uses teacher models to hypothesize these counterfactual contexts, verifies them via student rollouts, and internalizes the successful interventions through agentic post-training. Extensive evaluations across general AI assistant (GAIA), textual search (BrowseComp), and visual search (VDR-Bench) tasks demonstrate that ToolAnchor consistently exhibits competitive performance under expanded toolsets. Our work bridges the gap between static post-training and dynamic adaptation, charting a new path for scalable agentic reinforcement learning.
[CV-99] CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models ICLR2026
链接: https://arxiv.org/abs/2607.14125
作者: Ruijiang Dong,Zesheng Ye,Jianzhong Qi,Lei Feng,Feng Liu,Gang Niu,Masashi Sugiyama
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICLR 2026
Abstract:Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by inserting a class label (e.g., “cat”) into a prompt (e.g., “a photo of a”). Since the score for a given image-class pair is sensitive to the choice of prompt, existing studies ensemble multiple prompts using a weighting vector to aggregate scores across different prompts. Yet, in current strategies, the weighting vector assigned to each prompt is shared across all classes, implicitly assuming that prompts are conditionally independent of classes, which often does not hold in practice, as a prompt like “an aerial view of” might be apt for “airport” but ill-suited for “apple”. To address this, we propose class-aware zero-shot prompt reweighting (CARPRT). This scoring scheme adjusts the weighting vector for each class label by capturing the class-specific relevance of different prompts in a training-free manner. For each class label and every available prompt, we quantify their class-specific relevance by averaging image-text relevance scores over images predicted to that class under the given prompt. These estimates are then normalized to derive class-specific weights. Evaluations on standard image classification benchmarks show that CARPRT outperforms existing class-independent reweighting methods, confirming that modeling prompt-class dependencies is crucial for effective zero-shot prediction and even broader VLM-based application settings that rely on prompt ensembling. Our code is available at this https URL.
[CV-100] raceback Translators Against Forgetting in Continual Fake Speech Detection
链接: https://arxiv.org/abs/2607.12569
作者: Enrico Gottardis,Mattia Tamiazzo,Simone Milani
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multimedia (cs.MM); Sound (cs.SD)
备注: Accepted at EUSIPCO 2026
Abstract:Fake speech detectors are increasingly challenged by the development of new and more accurate generative models. To cope with this problem, continual learning techniques are nowadays widely considered feasible strategies for updating models to new datasets, but they also lead to decreased performance on previously seen samples (catastrophic forgetting). In this work, we propose a forgetting-resilient solution based on the adoption of domain translators within a frozen detector, which remaps the new feature spaces into the original ones by means of a traceback translator network. Experimental results show that this strategy enables the achievement of high detection rates with respect to traditional retraining, while minimizing the computational effort and preserving the detection accuracy on previous data.
[CV-101] ESAR: Event-Based Synthetic Aperture Reconstruction
链接: https://arxiv.org/abs/2607.15073
作者: Harbir Antil,Daniel Blauvelt,David Sayre
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注:
Abstract:Event cameras report asynchronous polarity events when changes in log–radiance exceed a fixed contrast threshold, producing signed temporal contrast measurements rather than conventional image frames. We formulate monocular event-based imaging as a synthetic-aperture inverse problem for a static ground-domain log–radiance field \theta \in \mathbbR^N_g . Instead of reconstructing a latent pixel-time volume v \in \mathbbR^N_pN_t , we impose the geometric relation v=P\theta , where P maps the fixed scene into motion-dependent latent views. Aggregating events over finite time intervals gives the linearized model [ AP\theta = b+\eta, ] where A is a temporal differencing operator, b contains signed binned event counts, and \eta represents measurement and modeling errors. This decomposition exposes a synthetic-aperture structure: under near-nadir motion, successive projections are approximately shifted views of a common scene, while the composite operator AP remains ill-conditioned because it combines spatial averaging with temporal differencing. We therefore use regularized inversion to recover \theta . Numerical experiments on simulated data and real near-nadir Falcon Neuro event data show that the proposed \theta -based formulation recovers coherent large-scale spatial structure, relative to dynamic latent-image and learned event-reconstruction baselines, while suppressing fine-scale texture. Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP) Cite as: arXiv:2607.15073 [eess.IV] (or arXiv:2607.15073v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2607.15073 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-102] WanSong v1.0 Technical Report
链接: https://arxiv.org/abs/2607.14749
作者: Binghui Chen,Pandeng Li,Yu Liu,Jingren Zhou
类目: Audio and Speech Processing (eess.AS); Computer Vision and Pattern Recognition (cs.CV)
备注: Wan Team
Abstract:Music generation foundation models have recently attracted significant industry attention. However, achieving efficient generation and high-fidelity long-form audio while supporting controllability remains challenging. To address these needs, we present \textbfWanSong, a simple yet powerful approach for long-form, commercial-grade song generation. Unlike autoregressive (AR) and cascaded multi-stage pipelines (\eg, AR followed by diffusion), \textbfWanSong is a pure diffusion-based model that directly generates high-fidelity, multilingual songs up to 5 minutes and outputs dual stems (vocals and background music) in a single run. In addition, our diffusion framework enables faster inference through step-distillation, and offers an efficient pathway for fine-tuning and customization to support downstream editing tasks.
[CV-103] ViPSAM: Visual Prompting Medical Image Segmentation Using Segment Anything Model MICCAI2026
链接: https://arxiv.org/abs/2607.14328
作者: San Lee,Nalee Kim,Jeong Il Yu,Hee Chul Park,Boah Kim
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MICCAI 2026
Abstract:In proton therapy planning, respiratory-gated non-contrast CT (NCCT) is commonly used for lesion segmentation; however, accurate delineation remains challenging due to low lesion-to-background contrast. Although learning-based methods have shown strong performance, they often struggle with non-contrast image segmentation. Inspired by clinical practice, where contrast-enhanced MRI is referenced to delineate lesions on NCCT, we propose ViPSAM, a visual prompting framework that leverages complementary cross-modality information. Built upon the Segment Anything Model (SAM), ViPSAM introduces a visual prompt encoder to extract guidance features from contrast-enhanced images and a visual-guided cross-attention module to integrate non-contrast and contrast-enhanced features, thereby enhancing lesion-relevant representations in low-contrast regions. The mask decoder is further adapted in a parameter-efficient manner to utilize visual prompts effectively. We evaluate the proposed method on liver lesion segmentation using NCCT acquired for proton therapy. Experimental results demonstrate that ViPSAM outperforms representative U-Net- and SAM-based methods, indicating that cross-modality visual prompting enables more robust and accurate segmentation in non-contrast images.
[CV-104] OvAi Focus: AI-based Multi-class Segmentation of Functional Ovaries and Adnexal Masses in Gynecological Ultrasound
链接: https://arxiv.org/abs/2607.14179
作者: Niccolò Tallone,Francesca Salis,Pio Raffaele Fina,Roberta Massobrio,Rosilari Bellacosa Marotti,Daniele Conti,Luca Fuso,Luca Mariani,Annamaria Ferrero,Alessandro Arena,Stefano Cosma,Dan Grisaru,Angelo Lacalandra,Renato Seracchioli,Marianna Roccio,Federica Gerace
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for presentation at Ital-IA 2026 (6th CINI National Conference on Artificial Intelligence) in Rome, Italy. To be published in the CEUR-WS proceedings
Abstract:Ovarian cancer is the deadliest gynecological malignancy; accurate and objective segmentation of adnexal masses and functional ovaries in ultrasound (US) remains challenging due to operator variability and morphological complexity. We present OvAi Focus (SynDiag s.r.l., Italy), a stand-alone AI software medical device that performs multi-class semantic segmentation of functional ovaries and adnexal masses, distinguishing cystic from solid components. The system was trained and independently validated on a multicenter dataset of 1,081 adult women from 6 centers across Italy and Israel. Segmentation achieved DICE scores of 0.87 (complete lesion), 0.85 (cystic), 0.68 (solid), and 0.62 (functional ovary), in line with or superior to state-of-the-art approaches across heterogeneous acquisition settings.
[CV-105] A vision foundation model for single-cell biology via spatial gene cartography
链接: https://arxiv.org/abs/2607.14163
作者: Ridvan Yesiloglu,Sakib Mostafa,James Zou,Ash Alizadeh,Jiajun Wu,Lei Xing,Ehsan Adeli,Md Tauhidul Islam
类目: Quantitative Methods (q-bio.QM); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 54 pages, 34 figures, 10 tables, including supplementary information. Project page: this https URL
Abstract:Most single-cell foundation models are adapted from language models, representing each cell as a sequence of gene tokens. This discards the relationships among genes and often the magnitude of their expression. We present scVision, a vision foundation model that instead renders each cell as a continuous image. Using optimal transport, it places genes at fixed positions on a single shared, pan-tissue layout so that co-expressed genes become spatial neighbours, turning a transcriptome into an image in which gene programs appear as local texture. We pretrain a vision transformer by masked image modelling on 72 million human cells and use the frozen encoder with no fine-tuning. In zero-shot evaluations on six independent, held-out studies, scVision is the most accurate cell-type annotator and recovers gene programs without supervision, ahead of existing foundation models and classical baselines; on multi-study integration it matches the strongest token-based model while conserving the most biological structure, without ever seeing a batch label. Permuting the gene layout with the network fixed sharply lowers accuracy, more than removing the vision transformer itself, showing that biologically meaningful position, not the network, carries the signal. By preserving expression magnitude and gene relationships, scVision reframes single-cell representation learning as a vision problem, connecting it to the mature methods of computer vision.
人工智能
[AI-0] RoboTTT: Context Scaling for Robot Policies
链接: https://arxiv.org/abs/2607.15275
作者: Yunfan Jiang,Yevgen Chebotar,Ruijie Zheng,Fengyuan Hu,Yunhao Ge,Jimmy Wu,Tianyuan Dai,Scott Reed,Li Fei-Fei,Yuke Zhu,Linxi “Jim” Fan
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Project website: this http URL
Abstract:Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, without growing inference latency. At this context length, we unlock new robot capabilities: one-shot in-context imitation from human video demonstrations, on-the-fly policy improvement, robustness to perturbations, and stronger performance on multi-stage, long-horizon tasks. We also observe, for the first time, steady gains in closed-loop performance as pretraining context length scales. At its core, RoboTTT integrates Test-Time Training into robot foundation models such as Vision-Language-Action policies, yielding a sequence model whose recurrent state consists of fast weights, parameters updated by gradient descent during both training and inference, compressing histories into weight space and retrieving contextual information for long-context conditioning. To scale training context length, the recipe combines sequence action forcing with truncated backpropagation through time. On challenging real-robot manipulation tasks, RoboTTT improves overall performance by 87% over the single-step context baseline and fully completes a five-minute, ten-stage assembly task, which no baseline ever does. RoboTTT trained with 8K-timestep context outperforms the same model pretrained with 1K timesteps by 62%, suggesting context length as a new scaling axis for robot foundation models. Videos are available at this https URL
[AI-1] Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
链接: https://arxiv.org/abs/2607.15263
作者: Paul Kassianik,Blaine Nelson,Yaron Singer
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Security-agent evaluations commonly measure peak offensive capability under generous inference budgets, emphasizing vulnerability discovery, exploit development, penetration testing, and CTF completion. Such measurements are useful but incomplete: in operational security, every reasoning step, tool call, telemetry query, and enrichment request consumes budget. We evaluate language-model security agents through this cost-success lens on offensive Cybench challenges and defensive Splunk BOTS v1 investigation challenges. Instead of reporting only best-case success, we compare models at fixed cost levels and decompose performance by inference spend and tool spend. Our results show distinct scalingregimes for red- and blue-team tasks. Offensive CTF performance improves with additional test-time compute, and scaled open-weight models can approach frontier proprietary systems while remaining cost-competitive. Defensive SOC investigation does not scale in the same way: success depends more heavily on disciplined tool use, telemetry navigation, and selective enrichment than on raw reasoning budget alone. We argue that security-agent benchmarks should measure economic efficiency and operational fit alongside task success. Cost-aware, SOC-native evaluations provide a clearer picture of which models are practically useful today and where defensive agents still need to improve. We present an interactive website with our results this https URL.
[AI-2] AutoSynthesis: An agent ic system for automated meta-analysis
链接: https://arxiv.org/abs/2607.15247
作者: Moein Taherinezhad,Sebastian Maier,Gerardo Vitagliano,Francesco Pierri,Stefan Feuerriegel
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Evidence synthesis is crucial for turning primary research into reliable knowledge for science, medicine, education, and policy. Yet, quantitative evidence synthesis remains largely manual and difficult to scale. Here, we introduce AutoSynthesis, an end-to-end multi-agent system for automated meta-analysis. Given a research question in natural language, AutoSynthesis formulates a search strategy, retrieves scientific literature, screens candidate studies, assesses full-text eligibility, extracts quantitative statistics, computes standardized effect sizes, and finally performs random-effects meta-analysis. AutoSynthesis further supports heterogeneity analysis to examine how effect sizes vary across moderators, as well as risk-of-bias assessment. As output, AutoSynthesis produces a transparent report aligned with PRISMA guidelines. In our application, AutoSynthesis screened over 28 studies and extracted more than 20 quantitative claims. The pooled effect estimates produced by AutoSynthesis are similar to Hedges’ g of expert-conducted meta-analyses, indicating close agreement with manual evidence synthesis. Together, these results show that AutoSynthesis can make quantitative evidence synthesis more scalable, thereby supporting evidence-based decision-making across disciplines.
[AI-3] When Words Are Safe But Actions Kill: Probing Physical Danger Beyond Text Safety in Hidden-State Risk Space
链接: https://arxiv.org/abs/2607.15218
作者: Weimeng Wang,Ziqiang Wang,Zihang Zhan,Chuanpu Fu,Qi Li,Ke Xu
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Large language models (LLMs) increasingly serve as high-level planners for embodied agents, where linguistically benign instructions can become unsafe once grounded in the physical world. We study whether this physically grounded danger is the same safety problem as ordinary text-level content danger. Through hidden-state direction analysis and random-split null tests, we show that content danger (CD) and physical danger (PD) form separable signals in LLM representations across Qwen2.5-3B/7B/14B/32B, Phi-3.5 and SmolLM2. Building on the CD/PD separability, we propose PRISM, a single-layer L2-regularized logistic probe over full hidden states. PRISM achieves 86.2–87.7% accuracy on SafeAgentBench with 11.7–13.7% FPR, while same-scale LLM judges over-block safe tasks at 24.7–39.0% FPR. We further introduce PhysicalSafetyBench-1K (PSB-1K), a contrastive benchmark of 1,000 physical-risk pairs without direct harm keywords, to test whether methods detect physically grounded danger rather than explicit unsafe wording. On PSB-1K, PRISM reaches 99.6% accuracy and 0.7% FPR, whereas a Qwen2.5-3B judge rejects 67.8% of safe tasks. PRISM also replicates on SafeText and EARBench, supporting hidden-state probing as a representation-level method for physical safety beyond text moderation.
[AI-4] MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
链接: https://arxiv.org/abs/2607.15205
作者: Shaoxiong Zhan,Shi Hu,Boyu Feng,Hai Lin,Andrew Gong,Zhengda Zhou,Jiaying Zhou,Yunyun Hou,Hao Su,Hai-Tao Zheng
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Real repository issues routinely include visual evidence such as screenshots, error dialogs, rendered UI states, and logs, yet repository-level issue localization is evaluated mostly as a text-only task. Existing multimodal SE benchmarks evaluate end-to-end repair, entangling localization with patch synthesis and obscuring whether visual input helped, hurt, or was ignored. We introduce \textbfMM-IssueLoc, a controlled benchmark and evaluation protocol for repository-level localization with visual evidence. MM-IssueLoc contains 652 issue-PR instances across 23 languages, with annotations for 7 image categories and 4 relevance levels. It provides file-level and function-level gold labels, paired text-only and with-image evaluation, and VCE-based diagnostics that convert images into structured textual evidence. We evaluate LLM-based and retrieval-based systems, including MM-IssueLoc-VL-Emb as a controlled multimodal retriever. Results show that existing systems remain far from reliable multimodal repository localization: the strongest agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, while the strongest retriever reaches 33.86 function Acc@10. Cross-benchmark comparisons show that high localization scores on text-dominant SWE benchmarks do not transfer cleanly to multimodal issue localization. MM-IssueLoc turns visual evidence into an explicit evaluation variable, enabling future work to test whether systems improve by using visual evidence for localization, rather than by relying on text-only cues or downstream patch-generation effects.
[AI-5] Plover: Steering GUI Agents through Plan-Centric Interaction
链接: https://arxiv.org/abs/2607.15193
作者: Madhumitha Venkatesan,Shicheng Wen,Jiajing Guo,Jorge Piazentin Ono,Liu Ren,Dongyu Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Graphical user interface (GUI) automation remains challenging in real-world environments, where dynamic layouts, unexpected dialogs, and evolving interface states can cause autonomous agents to drift from user intent. Recent vision-based multimodal agents improve flexibility by operating directly over screenshots and natural language instructions, but planning and adaptation often remain internal, limiting users’ ability to inspect, supervise, or correct system behavior. We present Plover, a plan-centric vision-based GUI automation system that externalizes task plans and replanning as persistent, inspectable, and revisable artifacts. Through a planner–executor architecture, Plover supports explicit supervision of evolving execution, localized correction through editable plans, natural-language guidance, and screenshot-grounded interventions, while preserving prior progress during repair. A formative study with six participants informed the interaction design. We then evaluate Plover through benchmark failure-case repair and scenario-based workflow analyses. Our results show that many autonomous GUI-agent failures are structurally repairable when plans remain visible and interventions are localized, and that explicit replanning helps make GUI automation more transparent, controllable, and adaptable.
[AI-6] Can We Trust Item Response Theory for AI Evaluation?
链接: https://arxiv.org/abs/2607.15190
作者: Han Jiang,Sunbeom Kwon,Jinwen Luo,Ziang Xiao,Susu Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI benchmarks increasingly leverage item-level statistical models, particularly item response theory (IRT), to estimate model capabilities, rank systems, select informative examples, and diagnose benchmark quality. However, AI benchmark data often departs from the data regime of human testing, for which standard IRT estimation tools were originally developed: benchmarks typically involve fewer evaluated models, far more items, and capability distributions that may be skewed, clustered, or multimodal. We examine how these regime mismatches challenge the reliability of IRT modeling for AI evaluation. Using item parameters and capability distributions derived from six widely used LLM benchmarks, we simulate response matrices under three common IRT models and compare four estimation tools used in recent benchmark studies: marginal maximum likelihood, Markov chain Monte Carlo, variational inference, and a neural pseudo-Siamese estimator. Across 18,000 simulation conditions, we systematically evaluate computational feasibility, scalability, and the reliability of IRT inferences about model rankings, predicted performance, and item characteristics. Results show that classical estimators can become infeasible in large benchmark settings, whereas scalable estimators can produce unreliable item-level and ranking inferences with small or nonnormally distributed model sets. This study identifies when latent trait models reliably support or risk distorting AI benchmarking claims, and what sample sizes and diagnostics are needed for trustworthy use.
[AI-7] he Industrialization of Research ; On AI-Driven Science and Its Consequences
链接: https://arxiv.org/abs/2607.15164
作者: Emmanuel Jeannot
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Artificial intelligence is transforming scientific research - not merely as a more powerful instrument, but as an autonomous participant in the research cycle itself. This transition constitutes, in the most precise sense of the term, the industrialization of research: a shift from a craft model, in which knowledge, method, and judgment are embedded in the researcher, to a pipeline model, in which these steps are decomposed, automated, and supervised. The US Department of Energy’s Genesis Mission is the most ambitious current instantiation of this shift, but the fundamental questions it raises extend far beyond any single program. This essay examines seven such questions: the erosion of the intergenerational transmission of scientific competence; the growing opacity of AI-generated theories; the collapse of peer evaluation under a flood of machine-generated output; the unproven capacity of AI for paradigm-shifting discovery; the capture of the scientific agenda by political and industrial actors; the compounding of systematic errors in closed-loop pipelines; and the structural bifurcation of the global research community into incommensurable tiers. These concerns do not constitute an argument against AI-driven science - whose demonstrated potential is real and significant. They constitute the conditions under which that potential can be responsibly pursued.
[AI-8] Scaling Behavior Foundation Model for Humanoid Robots
链接: https://arxiv.org/abs/2607.15163
作者: Weishuai Zeng,Kangning Yin,Xiaojie Niu,Shunlin Lu,Weixiang Zhong,Jiahe Chen,Feiyu Jia,Xiao Chen,Zirui Wang,Furui Xu,Ming Zhou,Kailin Li,Weinan Zhang,He Wang,Li Yi,Dahua Lin,Jiangmiao Pang,Jingbo Wang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Humanoid control requires natural whole-body coordination, precise real-time responses to control signals, and robust generalization across diverse environmental contexts, making it a cornerstone for generalist embodied agents. Behavior Foundation Models (BFMs) have recently emerged as a promising solution to address these challenges by leveraging large-scale behavioral data to achieve superior expressiveness, versatility and generalization. However, despite growing interest in scaling BFMs to further improve their capabilities, it remains unclear how key factors, including the learning paradigm, behavioral data and model architecture should be coordinated to enable effective scaling. In this work, we revisit the scaling recipe for BFMs and demonstrate that substantial performance gains can be achieved through the coordination of three core components: 1) the learning paradigm of motion tracking that reformulates diverse humanoid control problems as the reproduction of integrated whole-body behaviors in the global frame; 2) the strategic synergy between on-policy rollout quantity and reference motion diversity; and 3) the expressive and scalable model architecture termed Humanoid Transformer that facilitates the natural emergence of structured behavioral representations. Through extensive experiments in both simulation and real-world deployment, we demonstrate that our approach yields significant improvements in control fidelity and task generalization, reducing Mean Per-Keypoint Position Error (MPKPE) on the test set by over 10% in local mode and 82% in global mode compared with existing humanoid controllers. These results establish BFM as a principled and effective foundation for scalable and general-purpose humanoid control.
[AI-9] Concept-Guided Spatial Regularization for World Models in Atari Pong
链接: https://arxiv.org/abs/2607.15142
作者: Yukuan Lu,Zaishuo Xia,Weyl Lu,Yubei Chen
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 16 pages, 5 figures
Abstract:World models are usually evaluated as components of model-based reinforcement learning (MBRL) systems, while the world models themselves are rarely studied in isolation. We examine five representative visual world-model agents in Atari Pong: DreamerV3, DIAMOND, TWISTER, Simulus, and STORM. After reproducing their training pipelines and matching the reported agent performance, we freeze the learned world models and evaluate them with a closed-loop rollout diagnostic: a policy trained separately from the corresponding MBRL agent interacts with each frozen model, and the generated video trajectories are inspected for visual and dynamical errors. Across all five models, the rollouts contain clear failures, including ball disappearance, incorrect ball motion, and invalid ball-paddle interactions. Beyond visual trajectories, we further evaluate them with pixel-space zero-shot MBRL, where a new policy is trained entirely inside a frozen world model and then evaluated in the real environment. Across all five models, the resulting policies substantially underperform those produced by the corresponding original MBRL training pipelines. The gap is particularly large for DreamerV3, whose mean return drops from -5.5 to -20.9, near the minimum Pong return of -21. We hypothesize that insufficient modeling of task-critical concepts, such as the ball in Pong, may contribute to these failures. We therefore propose Concept-Guided Spatial Regularization (CGSReg), an auxiliary pixel reconstruction loss applied to segmented concept regions. Experiments show that CGSReg improves both closed-loop rollouts and pixel-space zero-shot MBRL in DreamerV3, DIAMOND, and TWISTER. Its effects vary across the remaining models and evaluation metrics, indicating that CGSReg alone does not address all world-model bottlenecks. Comments: 16 pages, 5 figures Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2607.15142 [cs.AI] (or arXiv:2607.15142v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.15142 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yukuan Lu [view email] [v1] Thu, 16 Jul 2026 15:46:44 UTC (166 KB)
[AI-10] NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference
链接: https://arxiv.org/abs/2607.15123
作者: Jiajun Hu,Ruthwik Reddy Sunketa,Lei Zhao,Archit Gajjar,Luca Buonanno,Aman Arora
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation. ReRAM-based analog in-memory computing (IMC) pushes efficiency further, offering an order-of-magnitude improvement in compute density and energy efficiency over conventional digital logic by performing vector-matrix multiplication (VMM) directly within the ReRAM crossbar; prior work has integrated such IMC blocks into FPGAs for DL inference. However, conventional IMC designs support only static-weight VMM, leaving nonlinear operations and dynamic matrix-matrix multiplication (DIMM) to the FPGA fabric. As a result, the benefits of IMC are largely confined to static-weight models, whereas Transformer-based models, which rely on frequent nonlinear and DIMM operations, gain only limited improvement. Moreover, the ADCs within each IMC block consume more than 70% of its area and power, further limiting system efficiency and scalability. To address these limitations, we propose a novel FPGA architecture that integrates an ADC-free IMC block, replacing the conventional ADC with analog content-addressable memories (ACAMs) that natively perform nonlinear operations inside the block. To fully exploit this block, we conduct an FPGA-aware design-space exploration that determines optimal crossbar dimensions while balancing FPGA area, flexibility, and DL performance, and we develop an efficient mapping that leverages ACAMs to carry out DIMM operations, extending the applicability of IMC to attention computation. On CNN and Transformer-based benchmarks, the proposed architecture achieves up to 40x and 1.9x higher energy efficiency and 4.1x and 2.5x higher area efficiency, respectively. Overall, it significantly improves FPGA DL inference efficiency and sustains robust gains on Transformer-based workloads across long input sequences, advancing domain-specialized FPGA design.
[AI-11] Long-Context Fine-Tuning with Limited VRAM
链接: https://arxiv.org/abs/2607.15105
作者: Vladimir Fedosov,Aleksandr Sazhin,Artemiy Grinenko,Frank Woernle
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Parameter-efficient fine-tuning reduces model and optimizer memory, but dense attention still makes long training sequences expensive. We combine Hierarchical Global Attention (HGA) with segment-wise backpropagation and tiered KV storage. Only the active segment remains differentiable in VRAM; older KV is detached into RAM or NVMe, and HGA loads a bounded set of exact historical tokens for each query block. On Qwen3-8B with 4-bit QLoRA and PG19, dense training on a 16 GB Quadro RTX 5000 fits 2,048 tokens but fails at 4,096, whereas HGA reaches 16,384 tokens with 15.28 GB peak VRAM. Under evaluation the same adapter runs through 131,072 tokens on this card; VRAM is not constant but grows gently with the resident chunk summaries, so RAM and NVMe capacity set the practical limit beyond these lengths. At the shared 2K training length, HGA-trained and dense-trained adapters obtain 2.7405 and 2.7383 nat under the same dense-attention readout, while the stock model obtains 2.9541. At this boundary HGA training is already marginally faster (217.75 vs. 207.02 tokens/s), and the HGA-to-dense throughput ratio improves from 1K to 2K; because HGA keeps the attended historical set per token approximately constant while dense work per token grows, we expect this lead to widen as context grows. Dense attention is used for the main quality and retrieval comparisons so that they measure the learned weights and remain compatible with standard generation frameworks. HGA can also be used for retrieval and generation; an optimized production-grade serving implementation is under development.
[AI-12] BrainPilot: Automating Brain Discovery with Agent ic Research
链接: https://arxiv.org/abs/2607.15079
作者: Haoxuan Li,Tianci Gao,Jianhe Li,Yang Fan,Runze Shi,Weiran Wang,Tianxiang Zhao,Zezhao Wu,Xiaoyang Jiang,Qihui Zhang,Jia Li,Xiao Xiao,Kai Du,Xiaoxuan Jia,Chao Xie,Lu Mi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Understanding the brain increasingly depends on integrating evidence across scales, modalities, and disciplines. Addressing a single research question therefore requires a coordinated sequence of operations, from surveying prior work to executing analyses and interpreting results in light of domain knowledge. AI agents promise to accelerate this process, but current agents lack domain expertise in brain science, may fabricate claims, drift during multi-step reasoning, and offer few defined points for expert intervention. These failures are especially costly in brain science, where conclusions feed into downstream scientific claims and depend on laboratory-specific expertise and careful human judgment. We present \textbfBrainPilot a \textbffully open-source multi-agent system that accelerates brain science research with traceable logs and agent-verified results. A principal investigator (PI) agent coordinates specialist agents grounded in curated domain knowledge: a unified brain science knowledge base containing 7,233 indexed items and a skill library of 72 reusable methodology units across seven research domains. Every major step is recorded in the Graph of Trace, an auditable record that links subgoals, tool use, evidence, and claims and allows researchers to follow and inspect the workflow. An Auditor agent further integrates fabrication checking into the workflow. For evaluation, we run three brain science tasks from Agents’ Last Exam, introduce our own benchmark, \textbfBrainPilotBench-v0, and present additional end-to-end case studies. Across these evaluations, BrainPilot with an open-source backbone model attains performance comparable to state-of-the-art agent framework with less costs.
[AI-13] ANet Patu-1: The Value of Connection in the Agent Network
链接: https://arxiv.org/abs/2607.15053
作者: Mu Yuan,Jinke Song,Zhaomeng Zhou,Lan Zhang
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注:
Abstract:The Internet taught us that the value of a network depends on \emphhow its nodes connect: broadcast stars scale as V!\propto!N (Sarnoff), fully-connected meshes as N^2 (Metcalfe), and group-forming networks as 2^N (Reed). We ask the analogous question for networks of AI agents. We model the net value of connection as a function of coordination-group size, derive from it the properties an optimal collaboration protocol must have, and introduce ANet Patu-1 – a self-organizing consensus protocol in which the network continuously re-forms its own coalitions, adaptively riding the upper envelope of all three regimes at O(1) parallel consensus rounds. To measure value without opinion-grading, we score an emergent protocol by formally specifying it and deriving its complexity, the way distributed algorithms are analyzed. Two results follow. (i)~Emergence – a crowd of the \emphcheapest model, when heterogeneous, starts weak but its collective value compounds with N and \emphovertakes a crowd of a far \emphstronger model that is homogeneous: a crossover that marks a scaling law for collaboration rather than for scale. (ii)~Reflexivity – a heterogeneous network, given only its own problem and no design hints, converges on ANet Patu-1 itself, reconstructing the high-dimensional law that governs its own connective value.
[AI-14] Man Machine and Masterpiece: Artistic Ownership in the AI Era
链接: https://arxiv.org/abs/2607.15027
作者: Sofi Gjing Jovanovska,Kuntal Ghosh,Daniel Muhu Njenga,Ahmed Mufassir,Shadan Sadeghian
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The integration of AI-driven systems in creative work has sparked debates among artists and legal communities about notions of ownership. Yet there remains little consensus on how ownership should be defined and attributed when human and AI contributions are intertwined. To provoke critical reflection on these tensions, we designed ArtSplit, a provotype that explicitly quantifies human and AI contributions across different stages of creative work. Rather than aiming to resolve ownership, the provotype was used to elicit artists’ responses to the idea of attributing ownership through measurable actions in the creative workflow. We argue that quantification fails to align with artists’ understandings of creative intent and agency, and that efforts to measure ownership risk diluting long-standing assumptions through which artists understand and practice creative work. This critique challenges the impulse to transform a historically and socially situated relation into a technical problem.
[AI-15] SMC-ES: Automated synthesis of formally verified control policies
链接: https://arxiv.org/abs/2607.15003
作者: Riccardo Curcio,Toni Mancini,Enrico Tronci
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.e., policies) that are not only performant but also provably safe and robust. While learning-based methodologies such as Reinforcement Learning offer flexible and scalable approaches to automatically synthesize such controllers, they typically lack the formal guarantees necessary for safe deployment. To bridge this gap, we propose a novel simulation-based methodology to automatically synthesize policies with formal guarantees regarding performance, safety, and robustness specifications. Specifically, given a set of properties to verify, a confidence parameter \delta and an allowable failure probability \varepsilon , our method guarantees that the synthesized policy comes with a certificate: with confidence at least 1 - \delta , the probability of encountering a scenario where the given properties are violated is at most \varepsilon . We demonstrate the feasibility of our approach by developing SMC-ES, an algorithm that integrates Evolutionary Strategies with Statistical Model Checking-based verification. We evaluate SMC-ES on a suite of continuous control tasks using Gymnasium and Safety Gymnasium testbeds. Results show that, at the price of a sustainable increase in computational cost, our algorithm provides formal guarantees regarding performance, safety, and robustness specifications, while performing competitively against leading model-free Deep Reinforcement Learning (DRL) and Safe-DRL baselines.
[AI-16] Moral Attitudes of Sentient ASI towards Humanity and Implications for AGI Development
链接: https://arxiv.org/abs/2607.14998
作者: Jean-Paul Van Belle
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: This is a pre-print i.e. the draft as submitted for review to the AGI 2026 Conference except that the figures in this preprint were not included. The paper has been accepted with some revisions requested which are included in the final copy-edited version which will be available in the AGI Springer proceedings LNC 1855 (Chapter 28) ISBN 978-3-032-33194-6
Abstract:This paper suggests the adoption of a novel inversion in AI ethics: instead of asking how humans should treat artificial superintelligence (ASI), it examines how future sentient ASI may morally consider and evaluate humanity. We are not only designing intelligent systems but also shaping the initial conditions under which those systems form judgments about us. The paper proposes a preliminary set of post-human moral principles that may govern sentient ASI actions. The implication is that technical design choices (some are suggested), humanity’s moral behaviour, and the essence of what it means to be human, may influence humanity’s long-term standing in a post-ASI world.
[AI-17] Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers
链接: https://arxiv.org/abs/2607.14984
作者: Mahmoud Ibrahim,Bart Elen,Chang Sun,Gokhan Ertaylan,Michel Dumontier
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect. We argue that a demographically-conditioned synthetic generator can do both: mitigate bias on the training side and detect bias on the evaluation side. Working on COVID-19 chest CT classification with an end-to-end fine-tuned Stable Diffusion 2.1 generator, we make two findings. For bias mitigation (training), a demographically-balanced synthetic cohort is most useful as a pretraining prior, not as joint augmentation: with the same fixed data, sequential pretraining followed by fine-tuning substantially outperforms joint augmentation, and the resulting classifier surpasses the full-real baseline at \sim 100\times real-data efficiency. For bias detection (evaluation), across five synthetic minority cohorts and five classifier seeds, the synthetic estimator reproduces the subgroup ranking of a well-powered real oracle (Spearman \rho = 1.00 on MCC and Recall) and gives the more reliable per-cell estimate where the small real test set runs out of samples. The synthetic cohort is therefore most useful in exactly the cells that fairness audits care about, as both a fix for and a measure of subgroup bias.
[AI-18] CFM-Bench: A Unified Multi-Domain Multi-Task Benchmark for Channel Foundation Models
链接: https://arxiv.org/abs/2607.14975
作者: Yuan Gao,Wenjun Yu,Jun Jiang,Yunfan Li,Xinyu Guo,Shugong Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Channel foundation models (CFMs) are developing rapidly, with recent studies reporting benefits from pretraining across downstream wireless tasks. Yet CFMs are commonly evaluated in model-specific pipelines with different data, radio configurations, partitions, adaptation procedures, task definitions, and metrics. Reported comparisons therefore tend to show that pretraining improves over supervised training from scratch within one pipeline, but neither rank CFMs nor compare them fairly with task-specific models. We release CFM-Bench, a unified multi-domain, multi-task benchmark designed to address this gap. It curates six channel configurations spanning 3GPP statistical simulation, two independent ray-tracing pipelines, industrial and aerial measurements, and synchronized vehicular multimodal simulation. Official partitions isolate complete trajectories, measurement sessions, vehicle links, simulation realizations, or buffered spatial regions. CFM-Bench does not prescribe an external pretraining corpus or strategy; no benchmark split may be used for foundation-model pretraining, and the official training split is reserved exclusively for downstream fine-tuning. The benchmark additionally requires disclosure of all data used during model development and prohibits training-stage use of official test units. Six task groups are organized along three CFM application dimensions: physical-layer (PHY) channel intelligence, radio-access-network (RAN) decision intelligence, and integrated sensing and communication (ISAC). They cover CSI feedback, frequency and temporal channel extrapolation, propagation-state classification, current- and future-beam prediction, and single-frame and temporal localization. CFM-Bench provides a common substrate for comparing the transferability of channel representations across models, domains, and tasks.
[AI-19] Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation
链接: https://arxiv.org/abs/2607.14970
作者: Paul Darm,Cem Alpturk,Kenneth Ulrich,William Duncan,Ali Anwar,Annalisa Riccardi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Automated optimisation is increasingly adopted in industrial processes, yet a trust gap persists between engineers who design these algorithms and operators who must act on their recommendations. Explainable AI methods like SHAP (SHapley Additive exPlanations) have transformed interpretability for machine learning predictions; optimisation outputs could benefit from similar techniques. We present an approach that integrates Implicit Function Theorem (IFT) based sensitivity analysis with SHAP attribution and narrative generation via Large Language Models (LLM), producing explanations tailored for operators. Our approach leverages IFT to compute exact parameter sensitivities \partial p^*/\partial x from the optimality conditions, enabling efficient GradientSHAP computation. For an industrial High Pressure Grinding Roll (HPGR) control optimisation problem with 22 features, we achieve equivalent SHAP attributions (correlation 0.99 with KernelSHAP) with over 40 \times speedup, enabling real-time natural language explanations. We validate on industrial scenarios and present feedback from domain experts on generated explanations.
[AI-20] Contextualized Early Detection of Online Firestorms: A Sequential LLM -Based Approach
链接: https://arxiv.org/abs/2607.14957
作者: Besim Shala,Peter Mandl,Andreas Humpe,Martin Häusl
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 2 figures, 7 tables, 1 algorithm
Abstract:Online firestorms are rapid collective escalations of highly negative user-generated content and may cause substantial reputational and economic damage. Existing detectors usually work with volume signals, sentiment scores, or predefined linguistic features. Such signals are useful, but they capture contextual meaning shifts in evolving discussion threads only indirectly. This paper proposes an LLM-based detection system with two operating modes. The first mode classifies complete Reddit threads retrospectively by combining local chunk-level assessments into a thread-level judgment. The second mode processes threads sequentially and issues early warnings when a sliding window exceeds calibrated thresholds. In this mode, the language model estimates three firestorm indicators: negativity share, escalation level, and contributor count. On a balanced Reddit dataset, the global mode achieves strong classification performance, while the early warning mode reaches high recall and detects escalating threads after only a small number of comments and distinct contributors. The results indicate that LLMs can be used not only for static judgment tasks, but also as repeated estimators in context-aware monitoring of social media discourse.
[AI-21] Steering Robustness into World Action Models via Mechanistic Interpretability and Optimal Control
链接: https://arxiv.org/abs/2607.14943
作者: Jihoon Hong,Julian Skifstad,Qiyue Dai,Alice Chan,Glen Chou
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC)
备注:
Abstract:World Action Models (WAMs) enable semantically- and physically-informed control but are brittle under distribution shift. In this work, we use mechanistic interpretability to study how robustness-relevant perturbations are represented in WAM activation space. Comparing activations across successful and unsuccessful rollouts, we find some WAM architectures exhibit low-dimensional linear separability for robustness-critical features, while others do not. This motivates the use of contrastive activation directions for training-free WAM steering. We also show that local linearity in WAM activation dynamics enables efficient feedback steering via model-based optimal control, yielding World-Action Linear Quadratic Regulator (WA-LQR), a minimally-invasive reduced-order LQR controller. Via mechanistic evaluations, we predict strong steerability in the Cosmos-Policy and DiT4DiT models but weak steerability in LingBot-VA, consistent with steering intervention results. On Cosmos-Policy and DiT4DiT, WA-LQR generalizes contrastive directions to new tasks and improves robustness to camera, gripper, and visual-noise perturbations over unsteered and prompt steering baselines.
[AI-22] A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems
链接: https://arxiv.org/abs/2607.14937
作者: Christoph Jürgen Hemmer,Florian Plaswig,Daniel Durstewitz
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Dynamical Systems (math.DS); Chaotic Dynamics (nlin.CD)
备注:
Abstract:Recent foundation models (FMs) for zero-shot reconstruction of dynamical systems (DS) achieve strong out-of-domain generalization but provide little insight into the mechanisms that underlie their forecasts. Such an understanding could help to strip down overladen FM architectures to their bare essence and expose the minimal requirements for in-context learning in the DS domain. Toward this goal, here we iteratively reduce a recent powerful SOTA model for DS reconstruction, DynaMix (Hemmer Durstewitz, 2025), to a minimal interpretable two-parameter form, which we call DynaBase. DynaBase produces forecasts through a linear blend of the current latent state and the nearest in-context neighbor and its temporal successor. Surprisingly, despite its extreme simplicity, DynaBase produces highly competitive zero-shot DS reconstructions across chaotic and cyclic systems, with a negligible parameter load, many orders of magnitude below that of other FMs. Even more, this extreme simplicity permits direct model optimization on DS reconstruction measures, as well as closed-form one-step analytical solutions on prediction MSE. Theoretical and empirical analysis of DynaBase further leads to a 1-parameter family of maps, with the context-parroting algorithm of (Zhang Gilpin, 2026) recovered at one end, and chaotic (divergent but bounded) behavior at the other. We further show how different training strategies lead to models either optimal for short-term prediction or for DS reconstruction. Thus, DynaBase not only exposes the minimal mechanisms required for producing zero-shot DS reconstruction, but also reconciles within an accessible mathematical frame divergent observations in the literature.
[AI-23] Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks
链接: https://arxiv.org/abs/2607.14921
作者: Hamid Dashtbani,Mehdi Dousti Gandomani,AmirMahdi Sadeghzadeh
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Accepted at Transactions on Machine Learning Research (TMLR)
Abstract:Machine learning models are increasingly adapted in various domains. However, adversarial examples pose a significant threat to the reliable deployment of these models. In recent years, some powerful adversarial example attacks have been proposed for the fast and query-efficient generation of adversarial examples, even in black-box scenarios, highlighting the need for scalable, low-cost, and powerful defenses. In this work, we present two contributions to the domain of black-box adversarial example attacks and defenses. First, we propose Random Logit Scaling (RLS), a randomization-based defense against black-box score-based adversarial example attacks. RLS is a plug-and-play, post-processing defense that can be implemented on top of any existing ML model with minimal effort. The idea behind RLS is to confuse an attacker by outputting falsified scores resulting from randomly scaled logits while maintaining the model accuracy. We show that RLS significantly reduces the success rate of state-of-the-art black-box score-based attacks while preserving the accuracy and minimizing confidence score distortion compared to state-of-the-art randomization-based defenses. Second, we introduce a novel adaptive attack against AAA, a SOTA non-randomized black-box defense against black-box score-based attacks that also modifies output logits to confuse attackers, demonstrating its vulnerability against adaptive attacks.
[AI-24] Proof-or-Stop: Dont Trust the Agent Trust the Evidence – Loop Engineering for Verifiable Evidence-Gated Lifecycle Control
链接: https://arxiv.org/abs/2607.14890
作者: Jek Huang,Jeffery Hsia,Jiayi Sun,Freddie Shi,Wei Huang,Ian H. White
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 48 pages, 10 figures, 29 numbered tables. Preprint v1
Abstract:Autonomous coding agents increasingly execute multi-step software work, but lifecycle states such as reviewed, tested, DONE, and ready-to-merge remain claims unless supported by current evidence. We present Proof-or-Stop Lifecycle Control, a method that permits lifecycle transitions only when fresh, tracked-source-state-bound, mechanically verifiable evidence satisfies the relevant gate. The method treats agent outputs as claims rather than lifecycle state, and uses proof operationally to mean gate-admissible evidence under a stated trust model, not semantic program correctness. We evaluate an open-source implementation through mechanism tests, a powered control-policy ablation, and operated self-application evidence. The unattended-loop engine passed 10 of 10 scenarios with zero false-DONE, and local-key receipt bundles rejected 18 tamper classes with zero false accepts. In a 9,240-cell ablation, the pre-registered A4 versus A2-prime comparison reduced visible-pass/hidden-fail amplification from 31 of 1,800 injected cells under a compute-budgeted naive loop to 2 of 1,800 under the gated loop, a 1.6 percentage-point improvement in not-amplified rate with a 95 percent confidence interval of [0.8, 2.5]. A near-compute A3 versus A4 comparison, 14 of 1,800 versus 2 of 1,800, indicates that the gain is associated with enforcing review as a lifecycle gate rather than merely adding a reviewer. The self-application corpus contains 565 stories and 1,007 review findings, with 94.8 percent resolved, plus a 68-row high/critical cross-vendor exhibit. These results support Proof-or-Stop as a model-agnostic, host-neutral control layer for deciding which autonomous-agent claims a lifecycle may act on. The evaluation is limited to one model family, 24 ablation tasks, and a self-hosted corpus. Comments: 48 pages, 10 figures, 29 numbered tables. Preprint v1 Subjects: Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2607.14890 [cs.AI] (or arXiv:2607.14890v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.14890 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-25] Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
链接: https://arxiv.org/abs/2607.14886
作者: Chien-Liang Liu,Tsao-Lun Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Temporal Knowledge Graph (TKG) reasoning under the extrapolation setting focuses on forecasting future time-stamped events (facts) from historical data in a temporal knowledge graph. Existing approaches, reinforcement learning (RL)-based multi-hop reasoning methods are prominent for TKG reasoning because they produce human-interpretable predictions via explicit multi-hop path tracing. However, during RL training, rewards are typically sparse, and exploration is highly inefficient due to the vast, time-evolving action space. These issues hinder efficient training and often limit overall performance. To address these challenges, we propose RAPTOR (Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration), a self-supervised pretraining method that injects a reachability-aware inductive bias to the agent. By learning to estimate the reachability of candidate actions to the target entity, RAPTOR reduces exploration over unpromising paths and provides a strong initialization for downstream RL fine-tuning. Experimental results on the ICEWS14, ICEWS05-15, and ICEWS18 datasets demonstrate that RAPTOR pretraining markedly improves the training efficiency and consistently outperforms conventional baselines, establishing it as an effective approach for enhancing RL-based multi-hop reasoning methods for TKG reasoning.
[AI-26] Asymmetric Peak-Aware Loss for Peak-Critical Time Series Forecasting
链接: https://arxiv.org/abs/2607.14871
作者: Theivaprakasham Hari,Yanan Xin,Winnie Daamen,Serge Paul Hoogendoorn,Sascha Hoogendoorn-Lanser
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In many operational time-series forecasting applications, such as crowd demand forecasting, the risk related to under-prediction is substantially higher than that of over-prediction. Accurate prediction of rare demand spikes plays a critical role in downstream tasks. Yet most time-series forecasters are trained with symmetric objectives (e.g., MSE, MAE) and evaluated primarily on aggregate error, which can mask failures in extreme-values and peak-timing predictions. We introduce Asymmetric Peak-Aware Loss (APAL), a simple, model-agnostic objective that (i) penalizes under-predictions more heavily and (ii) increases the training weight of peak regions within each forecast window. We further propose a peak-critical evaluation protocol that complements MAE/MSE with channel-wise tail error (Top-10% and Top-1%) and peak metrics (precision, recall, F1 under timing tolerance, and peak timing error). We evaluate APAL on long-horizon multivariate forecasting across five state-of-the-art backbones, with a focus on pedestrian demand forecasting using (i) a production-ready subset of the City of Melbourne pedestrian hourly count dataset and (ii) a beach visitor count dataset. The generality of the loss function for time-series forecasting is tested on additional benchmarks. Across peak-critical datasets and settings, APAL improves tail accuracy and peak-prediction quality while exposing a controllable trade-off with aggregate error, making it a practical solution when peak-prediction failures are the dominant operational concern.
[AI-27] RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems
链接: https://arxiv.org/abs/2607.14846
作者: David Ayllon,Alice Baird,Jeffrey Brooks,Franc Camps-Febrer,Jakub Piotr Cłapa,Theo Lebryk,Jens Madsen,Olya Ossipova,Sharath Rao,Hoon Shin,Tigran Soghbatyan,Georg Streich,Rashish Tandon,Panagiotis Tzirakis
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: Benchmark and leaderboard: this https URL
Abstract:Current voice AI benchmarks typically evaluate isolated capabilities such as speech intelligibility, word error rate, or text-based dialogue quality, but they rarely test whether systems harness the acoustic information that distinguishes spoken language from its textual representation. To this end, we introduce the Real World Voice EQ Bench, a multidimensional benchmark for evaluating voice AI across text-to-speech (TTS), speech-to-speech (STS), speech understanding (SU), and automatic speech recognition (ASR). Our evaluations indicate that performance is highly dimension-specific. For TTS, naturalness, expressiveness, identity stability, and reliability are largely independent evaluation dimensions. For STS, access to audio does not guarantee use of vocal affect, and some agents remain largely transcript-driven. For SU, models perform unevenly across paralinguistic tasks. For ASR, real world accent, emotion, noise, and conversational conditions expose failures that are not captured by established clean-speech benchmarks. Together, these results show that voice AI should be evaluated as a profile of acoustic, expressive, interactional, and robustness capabilities rather than by a single aggregate score.
[AI-28] Interventional Causal Circuits for Safe Robot Action Testing and Failure Recovery IJCAI2026 IJCAI
链接: https://arxiv.org/abs/2607.14826
作者: Naren Vasantakumaar,Tom Schierenbeck,Michael Beetz
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 1st IJCAI Workshop on Safe Physical AI IJCAI 2026 workshop on Safe Physical AI
Abstract:Safe physical AI for robot actions are required not only likely to succeed but tested to be safe before execution. In practice, however, formal testing of motion parameters is computationally expensive, and the cost scales poorly with the dimensionality of the action space. When a proposed action is rejected by a tester, the naive response is to resample blindly until a passing candidate is found. This is wasteful, uninformative, and offers no convergence. We argue that rejection should instead trigger causal diagnosis: a principled identification of which action parameter caused the failure and what corrective value maximises the probability of passing testing under the interventional probability distribution. We propose a closed-loop framework that couples a Joint Probability Tree (JPT) with a Causal Circuit derived from a Marginal-Deterministic Variable Tree, enabling exact polytime computation without retraining, or additional data collection. The framework validates tractability of all interventional queries before the robot begins operating, and out-of-support candidates are detected and excluded from correction automatically. We perform experiments in a ROS2 simulation environment, and the framework demonstrates complementary roles across quality of distribution: under a high-quality JPT, the Causal Circuit reduces failed attempts by 10.3% and under a degraded JPT, it reduces total failed attempts by 37%. Every rejected plan produces a structured, interpretable causal report naming the primary cause variable, its observed value, and the recommended corrective region, supporting operator oversight and autonomous recovery without a separately trained failure model.
[AI-29] Can LLM s Build a MaxSAT Solver from Papers? The CoreForge Experience
链接: https://arxiv.org/abs/2607.14818
作者: Ruben Martins
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI)
备注:
Abstract:We report on CoreForge, an experience in using large language models (LLMs) to build an unweighted MaxSAT solver from research papers rather than from an existing solver codebase. The project focuses on unsatisfiability-based MaxSAT algorithms and follows an iterative workflow that combines paper discussions with ChatGPT, implementation through Codex prompts, and repeated LLM-assisted code audits and revisions. Although the codebase implements several algorithms and solver components, our evaluation focuses on configurations that combine core-guided optimization, lightweight preprocessing, core minimization, integration with integer linear optimization backends, and a new core-sequence lookahead approach. Our experience suggests that LLMs can support solver implementation from papers, while requiring external validation, benchmarking, and human guidance. In our experiments, fuzzing and MaxSAT Evaluation instances did not reveal wrong answers in the tested configurations, although performance remains below the best hand-engineered MaxSAT solvers. We summarize what worked, what remained difficult, and the lessons for future LLM-assisted solver development. Subjects: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.14818 [cs.LO] (or arXiv:2607.14818v1 [cs.LO] for this version) https://doi.org/10.48550/arXiv.2607.14818 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-30] Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
链接: https://arxiv.org/abs/2607.14817
作者: Jakub Paplhám,Willem Waegeman,Eyke Hüllermeier,Vojtěch Franc
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning. However, the Bayes-optimal decision strategies for these tasks do not coincide with the scores commonly used to quantify epistemic uncertainty. Building on the epistemic reject-option framework, we evaluate epistemic uncertainty using its ability to identify regret, the reducible error. Formulating selective prediction as a constrained optimization over coverage, expected risk, and regret, we prove the optimal selector is a thresholded convex combination of the ground-truth aleatoric and epistemic uncertainties. This theoretical unification exposes a weakness in recent uncertainty disentanglement literature: we demonstrate that standard correlation metrics between learned components do not necessarily predict their actual operational utility. We instead propose to evaluate the achievable risk, regret, coverage surface of the decomposition as a diagnostic for joint disentanglement and utility. Benchmarking standard methods on datasets with dense human annotations reveals that decision-theoretic rankings can disagree substantially with proxy-task rankings, including pairwise rank inversions between methods that are top-ranked on one criterion and bottom-ranked on other.
[AI-31] Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality
链接: https://arxiv.org/abs/2607.14816
作者: Saima Afrin,Alessandro Midolo,Camilo Escobar-Velásquez,Mario Linares-Vásquez,Weiyuan Ding,Bowen Xu,Massimiliano Di Penta,Antonio Mastropaolo
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming conventions remains largely unexplored. We investigate how the language used to describe programming tasks affects the source code generated by GPT-4o mini, DeepSeek, and Claude. Our study comprises 460 coding tasks spanning Python (230) and Java (230). We translate and manually curate the original English prompts into Chinese, Hindi, Spanish, and Italian while preserving their technical meaning. We evaluate the generated code using multiple dimensions, including functional correctness through test pass rates, structural quality using established code metrics, issues detected by static analysis tools, and lexical characteristics such as the language used in identifiers and comments. Our results show that (i) English prompts do not consistently produce the best functional correctness or code quality, (ii) the impact of prompt language depends on both the programming language and the LLM, and (iii) generated code frequently mixes English with the prompt language in comments and string literals. These findings provide the first curated multilingual benchmark for studying language bias in code generation and offer insights for developing more robust multilingual code generation systems.
[AI-32] CrimeNER Demo: Named-Entity Recognition in the Crime Domain ICDAR
链接: https://arxiv.org/abs/2607.14800
作者: Miguel Lopez-Duran,Julian Fierrez,Aythami Morales,Daniel DeAlcala,Gonzalo Mancera,Javier Irigoyen,Ruben Tolosana,Oscar Delgado,Francisco Jurado,Alvaro Ortigosa
类目: Artificial Intelligence (cs.AI)
备注: 6 pages, 2 figures, IAPR Intl. Conf. on Document Analysis and Recognition Workshops (ICDARw), 2026
Abstract:We present CrimeNER Demo, an AI-powered platform that enables us to extract general crime-related information from documents and classify them into entity types with two levels of granularity. We provide pretrained NER models on the CrimeNER database, and we give the possibility to users to provide their own annotated data to train models for their own specific cases. This demonstrator aims to promote crime-related NER research and provides a practical tool to automatically extract crime information for researchers and law enforcement agencies. The demonstrator includes: i) Pretrained NER models on the crime domain; ii) Possibility to finetune the models on specific data annotated by the user; and iii) An automatic pipeline to extract and annotate crime entities from documents. The demo platform, a tutorial to run the demo, and a video demonstration are publicly available on GitHub.
[AI-33] ranscoders for Investigating Deception in Language Models
链接: https://arxiv.org/abs/2607.14791
作者: Darius Lim,Nathan Leow,Xin Wei Chia
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Transcoders have recently emerged as a promising approach for mechanistic interpretability (MI), enabling circuit-level analysis of model behaviour. In this paper, we investigate the use of transcoders to analyse deceptive behaviour in language models, a behaviour that poses a safety and security risk. Using a Qwen3-4B model with pre-trained transcoders, specifically per-layer transcoders (PLTs), we construct attribution graphs that capture feature activations and inter-feature dependencies, allowing circuit-level analysis of deception. Through feature steering and circuit analysis, we identified a dictionary of deception-related features and show that these features exert a stronger influence on deceptive outputs, as they produce predictable shifts between deceptive and non-deceptive responses. These findings suggest that deception emerges from internal model mechanisms and highlight the potential of transcoders for behavioural monitoring and early detection of security vulnerabilities related to malicious behaviours in language models.
[AI-34] Global Index on Responsible AI: 2026 Report
链接: https://arxiv.org/abs/2607.14782
作者: Rachel Adams,Fola Adeleke,Ayantola Alayande,Selamawit Engida Abdella,Ana Florido,Nicolás Grossman,Leah Junck
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Grounded in human rights-based frameworks such as the UNESCO Recommendation on the Ethics of AI, the Global Index on Responsible AI (GIRAI) examines how countries translate responsible AI commitments into enforceable protections, institutional capacity, and redress mechanisms. GIRAI 2026 assesses these across five dimensions: Inclusion and Diversity, Ethics and Sustainability, Labour and Skills, Trust and Safety, and AI Use in Public Service. A global network of 135 country-level researchers collected and assessed 68,138 data points on 38 indicators organised across three pillars: government AI policy and implementation (17 indicators), civil society engagement (5), enabling conditions (15), and documented cases of government deployment of unacceptable-risk AI systems. The data covers November 2023 to September 2025. A score of 100 is derived from the indicators to rank all countries. Findings show that while responsible AI governance is expanding, with 126 of 135 countries having at least one government policy or initiative across the 17 AI Policy indicators, this does not often translate into meaningful protection. For instance, Global South countries account for 203 of 306 new cases of indicators with frameworks since the first edition, yet 78% of their frameworks remain non-binding compared with 42% in the Global North. Government commitment to AI governance also does not extend to their own algorithms: whereas Transparency and Explainability is the strongest performing indicator, with 58% of countries having some framework, only 18% require Public Disclosure of Government Algorithms. Credible evidence of government deployment of unacceptable-risk AI systems was also found in 35 countries. These findings show that responsible AI governance must move beyond framework adoption toward enforceable rights-based protections, resourced oversight institutions, and accessible redress.
[AI-35] AI vs Human Expert Reasoning : Assessing Agreements in Building Typology Predictions based on Street View Imagery
链接: https://arxiv.org/abs/2607.14756
作者: Zahratu Shabrina,Muhammad Asa,Jin Rui,Lu Yin,Stephen Law
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This research investigates the potential of Vision-Language Models (VLMs) to infer building typologies: Construction, Current Use, and Storeys from Google Street View (GSV) images. Predictions generated by VLMs are compared with inference by human experts (civil engineers and architects) as a source of manually labelled ground-truth data. We evaluate several state-of-the-art VLMs, including GPT-4o, Claude 3.5 Sonnet, and Gemini 2.0 Flash. By applying different scaling strategies and prompting techniques, we found that Chain-of-Thought prompts provide an overall more stable model performance. We also investigate the reasoning behind VLMs’ building-typology predictions by examining the probabilities of keywords appearing in AI explanations. This enabled us to analyse patterns in these reasonings and identify key themes driving both agreements and disagreements between VLM and expert labels. We find that AI tends to focus on visual indicators, whereas human experts place greater emphasis on broader contextual cues and domain knowledge, in addition to visual cues. Overall, VLM can approximate experts’ capability in building-typology classification at scale, with an average accuracy of approximately 70%. The study demonstrates the VLM’s potential for AI automation in tasks that require pattern recognition and object identification in an urban context. AI have the potential to serve as complementary and collaborative tools for urban analysis, leveraging their strengths in understanding visual patterns. This study contributes to the exploration of the efficiency and scalability of AI visual prediction and provides insights into the reasoning processes that could support automation processes in urban analysis and prediction.
[AI-36] Large Audio Language Models for Spoofing-Aware Speaker Verification
链接: https://arxiv.org/abs/2607.14753
作者: Sofya Savelyeva,Mariia Perunova,Evgeny Kushnir,Artem Dvirniak,Dmitrii Korzh,Oleg Y. Rogov
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in text-to-speech and voice cloning make high-quality spoofing inexpensive and scalable, threatening voice authentication systems, especially automatic speaker verification (ASV). Existing defenses mainly address this threat through binary countermeasures (CMs) for deepfake detection or spoofing-aware speaker verification (SASV), where current systems are dominated by modular ASV-CM fusion and cascaded pipelines. Although large audio language models (LALMs) have shown promise on related audio tasks, including CM and ASV, their use for SASV remains unexplored, despite their capacity to produce natural-language rationales for auditing and robustness beyond discriminative predictions. This work systematically evaluates LALMs for SASV against conventional pipelines under zero-shot prompting, supervised adaptation, reasoning-oriented training, and reinforcement-learning-based optimization. Our results show that pretrained LALMs are near chance in the zero-shot setting, confirming that they are not natively suited to SASV, but that task-specific adaptation closes this gap. We further find that competitive SASV performance can be achieved through several distinct routes. These findings position LALMs as a promising and auditable foundation for unified SASV, while clarifying where conventional cascade systems still lead.
[AI-37] InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring
链接: https://arxiv.org/abs/2607.14683
作者: Hao Yang,Yanyan Zhao,Kewei Zhao,Hongbo Zhang,Tian Zheng,Yusheng Liu,Xing Fu,Bichen Wang,Yu Zhang,Hao He,Zhen Wu,Xuda Zhi,Yongbo Huang,Bing Qin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existing public datasets for in-cabin affective computing are largely limited to visual modalities and rarely include conversational information, making it difficult to capture the linguistic and interactive cues underlying driver emotion. To address these gaps, we introduce InCarEmo, a multimodal dataset for in-cabin emotion recognition and driver state monitoring. InCarEmo integrates RGB and infrared video, in-cabin audio, and dialogue text collected from scripted in-cabin scenarios designed to simulate realistic driver behaviors, covering diverse lighting conditions and driving contexts. The dataset supports three primary tasks: 1) multimodal emotion recognition, 2) fatigue detection, and 3) distraction monitoring. In addition to the original Chinese data, we construct an auxiliary English benchmark to support preliminary cross-lingual evaluation. We provide a unified benchmark with extensive baseline results across unimodal and multimodal methods, including analyses under modality-missing and noise conditions. Experimental results demonstrate the benefits of multimodal fusion and reveal remaining challenges under real-world noise and low-light conditions. By releasing InCarEmo, we aim to establish a comprehensive foundation for robust, interpretable, and human-centric in-cabin affective understanding, promoting safer and more empathetic driver-vehicle interaction.
[AI-38] An Intelligent-Cloud Edge Multimodal Interaction System for Robots
链接: https://arxiv.org/abs/2607.14675
作者: Zihan Guo,Xiaoqi Li
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Robust human-robot interaction in complex environments requires accurate gesture perception, semantic scene understanding, and reliable task planning under limited onboard computing resources. This paper presents a cloud-edge multimodal interaction framework that integrates an enhanced YOLO-based gesture detector with coordinated large language model (LLM) and vision-language model (VLM) agents. The proposed detector, incorporates the Convolutional Block Attention Module (CBAM) into the neck and replaces the baseline bounding-box regression objective with Distance-IoU (DIoU) loss. These modifications improve feature discrimination and localization for small or partially occluded gestures in complex backgrounds. The cloud layer performs gesture detection, scene understanding, multimodal fusion, and action planning, whereas the TonyPi robot locally handles data acquisition, communication, action execution, and feedback. Experiments on a public gesture dataset and a custom dataset show that YOLO-DC achieves precision values of 98.9% and 95.0%, with mAP@0.5 values of 90.7% and 92.7%, respectively. System-level evaluation yields success rates of 95%, 88%, and 82% for single-action, composite-action, and vision-dependent tasks. A 30 participant evaluation yields an overall mean satisfaction score of 3.69 out of 5. These results demonstrate the feasibility of combining refined gesture detection with multimodal agents for resource-constrained robotic interaction.
[AI-39] SmartRAG : Native Graph-Based RAG for Mobile Device
链接: https://arxiv.org/abs/2607.14661
作者: Zhihan Jiang,Meng Li,Shenghao Liu,Keran Li,Ruiben Zhou,Xianjun Deng,Shuai Wang,Haipeng Dai
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 4 figures
Abstract:Deploying large language models (LLMs) as personal assistants on mobile devices demands privacy, low latency, and offline availability, yet the computational cost of giant models clashes with strict edge-hardware budgets. We argue that this tension cannot be resolved by model compression alone; it requires decomposing on-device intelligence into complementary functional roles. We present SmartRAG, a fully on-device framework that organizes an intelligent assistant around four coordinated modules – Perception, Memory, Focus, and Thinking. At the core of SmartRAG is EvoNER, a continually learnable named-entity recognizer that incrementally expands its label inventory through teacher-distilled updates, enabling the system to absorb previously unseen entity types without retraining the backbone LLM. Extracted knowledge is stored in MRGraph, a three-layer provenance-preserving knowledge graph, and retrieved at query time through a hybrid pipeline combining graph traversal, lexical matching, and dense semantic search. The on-device LLM is invoked only for high-value semantic operations – labeling, planning, and answer synthesis – keeping inference costs bounded. Experiments on four QA benchmarks (TriviaQA, Natural Questions, HotpotQA, MultiHopQA) show that SmartRAG with a quantized 1.7B-parameter backbone achieves multi-hop reasoning performance competitive with models up to 18 \times larger, while running entirely on commodity smartphones within practical memory and latency envelopes.
[AI-40] LLM -Driven Approach to Modeling Tool Interoperability in Automotive Domain
链接: https://arxiv.org/abs/2607.14659
作者: Nenad Petrovic,Jiajie Zhang,Vahid Zolfaghari,Alois Knoll
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Interoperability between heterogeneous modeling tools remains a significant challenge in Model-Driven Engineering (MDE), particularly in the automotive domain where multiple modeling languages, as well as defacto standard proprietary and open-source tools coexist. This paper presents an LLM-driven approach for automated model interoperability by considering two relevant aspects: 1) mapping model instances to a target metamodel 2) merging of metamodels. The proposed methodology is demonstrated through transformations involving Ecore and SysML v2 based metamodels and incorporates structural validation of generated model instances against user-defined target models. Automotive case studies illustrate the feasibility of the approach and show that large language models can significantly reduce manual transformation effort while generating structurally valid target models for cross-tool interoperability.
[AI-41] opoAgent : A Self-Evolving Topological Agent for Multimodal Scientific Reasoning
链接: https://arxiv.org/abs/2607.14658
作者: Mingze Xu,Yinghui Li,Jiayi Kuang,Zhanhui Kang,Di Yin,Ying Shen,Xing Sun,Yuxing Han
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:While Multimodal Large Language Models (MLLMs) excel in general tasks, rigorous scientific reasoning remains challenging due to the limitations of monolithic, linear planning. Such sequential designs often suffer from visual-semantic misalignment, long-context hallucinations, and brittle execution under fixed task granularity. We propose TopoAgent, a self-evolving topological framework that replaces linear trajectories with dynamic, state-isolated graph evolution. TopoAgent first employs a front-end decomposer to fracture complex queries into visually-grounded atoms. These atoms are organized into a Directed Acyclic Graph (DAG) based on their dependencies, enabling strict context isolation to shield the reasoning engine from irrelevant historical noise. Furthermore, we introduce adaptive atomic fission, which dynamically splits bottleneck nodes into finer-grained sub-atoms at runtime when tool capability boundaries are exceeded. Extensive experiments across mathematics, physics, and chemistry benchmarks demonstrate that TopoAgent significantly outperforms state-of-the-art linear agent frameworks, providing a robust, noise-resistant, and self-correcting paradigm for autonomous scientific reasoning.
[AI-42] MemPoison: Uncovering Persistent Memory Threats and Structural Blind Spots in LLM Agents
链接: https://arxiv.org/abs/2607.14651
作者: Jifeng Gao,Kang Xia,Yi Zhang,Xiaobin Hong,Mingkai Lin,Xingshen Wei,Wenzhong Li,Sanglu Lu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Persistent external memory enhances agent continuity but introduces persistent security vulnerabilities: adversarial content can be injected via standard interaction channels, retained across turns, and later distort downstream behavior. To address this challenge, we propose MemPoison, a comprehensive benchmark and analysis framework featuring 1227 hand-validated cases across four attack types, three injection channels, and three representative memory substrates, evaluated on seven open-weight and three closed-weight model families. We introduce a three-tier taxonomy: (L1) direct single-record corruption, (L2) compositional multi-record corruption and (L3) context-triggered dormant corruption. Our evaluations reveal a distinct defense frontier: while baseline write-time defenses, such as consistency checks, substantially suppress direct L1 attacks, they fail to reliably suppress L2 and L3 attacks. Through mechanistic influence decomposition (MID), we demonstrate structural blind spots in write-time defenses, which admit seemingly benign records that later become harmful through joint retrieval composition or trigger-conditioned activation. Our findings advocate for shifting from static filtering to adaptive, context-sensitive memory defense strategies.
[AI-43] MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers
链接: https://arxiv.org/abs/2607.14642
作者: Huanxi Liu,Kun Hu,Jiaqi Liao,Qiang Wang,Pengfei Qian,YuanZhao Zhai,Dawei Feng,Bo Ding,Huaimin Wang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:As Model Context Protocol (MCP) servers emerge as the core infrastructure for connecting LLMs with external tools, existing benchmarks leverage real-world MCP servers to evaluate LLM agents’ tool-using capabilities. However, these benchmarks overlook the continuous evolution of tool interfaces and functionalities within MCP servers, resulting in flawed assessments that fail to capture the agent’s adaptability in changing tool landscapes. To bridge this gap, we introduce \textbfMCPEvol-Bench, a novel benchmark for evaluating the task-solving capabilities of LLM agents under dynamic toolset evolution. Inspired by large-scale empirical study, we propose 11 mutation operators to simulate realistic tool evolution within 123 MCP servers. We benchmark 12 state-of-the-art LLMs on multiple versions of MCP servers, revealing that even frontier models struggle to adapt to evolving tools. For instance, GPT-5.4 and Claude-Sonnet-4-6 exhibit performance declines of 13.7% and 14.4% in evolved MCP servers, respectively, accompanied by substantial increases in planning and reasoning errors. These findings highlight the vulnerability of LLM-driven workflows, establishing MCPEvol-Bench as a standard for evaluating agent adaptability in dynamic tool environments.
[AI-44] Analytic Abduction: Causal Decomposition and Governed Commitment for Human–AI Coordination
链接: https://arxiv.org/abs/2607.14641
作者: Remo Pareschi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Abductive reasoning operates in two directions. The synthetic mode builds explanations from available hypotheses; the analytic mode, conversely, identifies the latent factors whose interaction accounts for a complex observed state. This paper develops the analytic mode as a non-greedy, risk-sensitive discipline of commitment, in which candidate factors coexist and interact, resolving into committed conclusions only when explicit governance conditions are met. The formal core is the \kappa - \tau apparatus: \kappa encodes the epistemic interaction among hypotheses, and \tau sets a commitment threshold calibrated to the decision’s stakes. The central contribution is the causal cluster, a structured object recording which latent factors participate in a decomposition, with what weights and interaction structure, together with a two-level architecture (intra-cluster \kappa^* , inter-cluster \kappa^** ) that guards against causal misattribution. Demonstrated in epidemiological crisis decomposition and adversarial cyber threat analysis, the framework’s contribution to human-AI reasoning is the legibility of suspended decomposition as a shared coordination object, providing structural resistance to premature convergence. In practice, the decision-maker is handed not a single imposed answer but the competing explanatory scenarios, weighted by plausibility and paired with the evidence that would resolve between them, so that sound action is possible even before the ambiguity is resolved.
[AI-45] Angular Gaussian Supervised Contrastive Learning for Long-Tailed Electrocardiogram Arrhythmia Diagnosis
链接: https://arxiv.org/abs/2607.14613
作者: Jin Dai,Qiuzhen Zhang,Chenyun Dai,Danmei Lan,Can Han
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Long-tailed label distributions reduce the reliability of deep learning for electrocardiogram (ECG) arrhythmia diagnosis, particularly for clinically important but rare abnormalities. Existing rebalancing and logit adjustment methods mainly address class frequency while overlooking direction-dependent morphological variability across ECG classes. This study proposes Angular Gaussian Supervised Contrastive Learning (AG-SCL) for long-tailed multi-label ECG diagnosis. AG-SCL integrates three components into a unified framework: an Angular Gaussian contrastive branch that models full-covariance class uncertainty on unit-normalized embeddings, Adaptive Logit Adjustment that learns bounded label-state-specific prior corrections instead of fixed frequency-based margins, and tail-aware augmentation that generates morphology-preserving views while protecting the 7-25 Hz QRS-dominant band. The method was evaluated on the public PTB-XL benchmark and a nocturnal ECG dataset comprising 1317 hours of recordings from 141 subjects. AG-SCL achieved the best macro-level performance on both datasets. On PTB-XL, it obtained a balanced accuracy of 0.838, sensitivity of 0.709, specificity of 0.968, mean average precision of 0.495, and TPR at 5% FPR of 0.778. On Noc-ECG, the corresponding values were 0.918, 0.889, 0.947, 0.488, and 0.900. The largest gains occurred in rare or morphologically unstable rhythm classes, while ablation studies confirmed the contributions of full-covariance modelling, Adaptive Logit Adjustment, and tail-aware augmentation. AG-SCL improves long-tailed ECG diagnosis by combining prior calibration with anisotropic representation learning, enhancing sensitivity to rare arrhythmias while maintaining clinically relevant specificity. Our code is available at: this https URL.
[AI-46] Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithms
链接: https://arxiv.org/abs/2607.14607
作者: Umid Suleymanov,Ilhama Novruzova,Khalid Mammadov,Natavan Hasanova,Murat Kantarcioglu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: EuroSP 2026
Abstract:Machine learning (ML) models deployed in sensitive domains such as healthcare, law enforcement, and finance must satisfy not only utility requirements but also fairness and privacy guarantees. While prior work has largely examined how privacy-preserving techniques affect fairness, the inverse question-how fairness-enhancing algorithms influence privacy leakage-remains underexplored. We present the first comprehensive study of how fairness interventions affect membership inference privacy risks at the subpopulation level. By adapting the Likelihood Ratio Attack (LiRA) for subgroup auditing, we uncover privacy disparities that aggregate evaluations obscure. We further analyze how Differential Privacy (DP) interacts with fairness-enhancing methods across different categories, showing that DP’s privacy benefits and utility costs are unevenly distributed across subpopulations. Our results demonstrate that fairness interventions do not uniformly increase privacy risk; their impact depends on model architecture, subgroup size, and mitigation strategy. These findings reveal that fairness, privacy, and utility must be jointly evaluated at the subpopulation level, and we introduce the first unified empirical framework to support such auditing in practice.
[AI-47] MathCoPilot: An Interactive System for Human-AI Symbiotic Paradigm of Mathematical Research
链接: https://arxiv.org/abs/2607.14582
作者: Junjie Zhang,Jiayu Liu,Wenbin Liu,Zhenya Huang,Doudou Wang,Yan Jiang,Leiye Xu,Tao Xiong,Wen Huang,Qi Liu,Guoping Hu,Enhong Chen,Mengping Zhang,Xiangdong Ye
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Existing LLM-based theorem provers have achieved impressive results on formal mathematics benchmarks, yet they remain confined to acting as autonomous agents that prove a stated proposition. In this paper, we propose MathCoPilot, a human-in-the-loop system that embodies a new human–AI symbiotic paradigm for mathematical research, in which the mathematician steers the high-level mathematical direction while AI agents carry out the detailed formalization and proof work under continuous human guidance. MathCoPilot unifies three core capabilities: (1) an interactive workbench where the mathematician and AI agents collaborate through a living proof blueprint that decomposes a proof into navigable steps the human can directly inspect, direct, and refine; (2) automated proving skill orchestration with adaptive knowledge base search and Lean-integrated iterative verification; and (3) topic-driven paper retrieval and automated formalization into a verified Lean knowledge base. Using MathCoPilot, we systematically compare four state-of-the-art LLMs, including Gemini~3.1~Pro, GPT-5.4, and Claude~Opus~4.7, on a FormalMATH subset and on two real PDE theorems requiring deep domain expertise, evaluating their ability to produce verified Lean~4 proofs and to identify errors in deliberately incorrect proofs. Our results show that while current models can handle undergraduate-level problems with high success rates under favorable autoformalization conditions, substantial challenges remain for domain-specific theorems requiring genuine mathematical understanding.
[AI-48] Collaborative Spatial Learning with Multi-LLM Agents in Networked Social Experiments
链接: https://arxiv.org/abs/2607.14574
作者: Hao He,Chris J. Kuhlman,Xinwei Deng
类目: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
备注: Accepted at ASONAM 2026; to appear in the Springer proceedings
Abstract:Collective problem solving often requires that group members consider the tradeoff between exploitation of known solutions and exploration for new ones, where information of known solutions can be disseminated among individual members through communication networks. The Mason–Watts experiment (PNAS 2012) showed that human groups in shorter-path networks outperform those in longer-path networks on a two-dimensional search task. In this work, we focus on the investigation of such network-efficiency effects in the setting of a group of large language model (LLM) agents. Specifically, we consider groups of sixteen LLM agents playing the Mason–Watts experiment on the eight Mason–Watts network topologies. Moreover, we develop mechanistic Bayesian optimization agents such that the performance of LLM agents can be compared with both the mechanistic agents and the human experimental data. Our computational experiments indicate that the LLM agents show a significant network-efficiency effect when instructed to randomize their first-round choices, but not under the default initialization. In this experiment, adding a one-sentence first-round randomization instruction improves collective payoff by more than three times the estimated payoff difference across the eight network topologies. Also, the Bayesian optimization agents obtain higher payoffs than the evaluated LLM agents on this spatial search task. We further compare the agents’ exploration–exploitation behavior, copying, and spatial diversity.
[AI-49] Alipay-PIBench: A Realistic Payment Integration Benchmark for Coding Agents
链接: https://arxiv.org/abs/2607.14573
作者: Shiyu Ying,Xuejie Cao,Yingfan Ma,Yuanhao Dong,Wenyu Chen,Bowen Song,Lin Zhu
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Payment integration is a demanding repository-level software task: agents must select a suitable product, implement coordinated client-server flows, verify payment outcomes, and preserve consistency between transaction and business states. We introduce Alipay-PIBench, a benchmark for evaluating coding agents on realistic Alipay payment integration. It contains nine product-specific projects and 18 task instances, each organized into Basic functional-completion and Advanced risk-aware hardening scenarios. Scenario-specific rubrics support deterministic static, unit, integration, and end-to-end checks, supplemented by LLM-assisted assessment for semantic requirements. We evaluate six coding-agent models and report rubric pass rate (RPR). Under the with-skill condition, mean RPR ranges from 68.58% to 91.37%. Access to the alipay-payment-integration skill improves mean RPR by 10.31 percentage points on average relative to the without-skill condition, with gains varying across models, products, and scenarios. Method-level results distinguish source-level completion, executable payment behavior, and payment-domain requirements. Alipay-PIBench provides a controlled setting for diagnosing model capability and evaluating structured guidance in payment integration.
[AI-50] Gate-Zero Growth: A Geometric Framework for Function-Preserving Continual Learning
链接: https://arxiv.org/abs/2607.14571
作者: Dante Lok
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 24 pages, 2 figures
Abstract:We introduce \emphgate-zero growth, a function-preserving (FP) operator for continual learning that adds new residual blocks through a zero-initialised gate. Under a transversality condition, gate-zero growth induces \emphrank separation in the functional Jacobian: old directions are unchanged, new-weight directions are exactly flat at the growth point, and new gate directions are the only first-order source of new functional variation. As gates open during continual learning, function drift is O(|\boldsymbol\alpha|^2) and Jacobian leakage O(|\boldsymbol\alpha|\infty) , giving a controlled departure from the FP locus. On a 300\mathrmM\to857\mathrmM Transformer adapted from WikiText-103 to BookCorpus, gate-zero growth reaches near-zero old-domain forgetting ( \Delta_A 0.1 ) under both exact-preservation (Isolation) and joint-frontier (Freeze-Nothing) operating points, while a non-FP control ( G\textstack ) suffers an order-of-magnitude larger forgetting under the same recipe. The same geometric analysis covers LoRA, ReZero, and zero-init adapter constructions, establishing gate-zero growth as the canonical instance of a shared local geometry that governs safe capacity activation in CL.
[AI-51] Democratizing Agent Deployment Safety: A Structural Monitoring Approach ICML2026
链接: https://arxiv.org/abs/2607.14570
作者: Preeti Ravindra,Rahul Tiwari,Vincent Wolowski
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Accepted in ICML 2026 Workshops: AI4GOOD and AIWILD
Abstract:AI software development agents are increasingly capable of modifying infrastructure and security critical systems, creating risks where an agent completes its assigned task while covertly weakening safeguards through actions such as broadening permissions, degrading logging, or introducing persistence mechanisms. While frontier laboratories may deploy sophisticated monitoring pipelines, many organizations and individual users adopting coding agents lack the resources and governance maturity required to maintain complex learned monitor ensembles. We study this problem as infrastructure level sabotage under task success in the infrastructure-as-code setting from ControlArena and compare trained and untrained monitors across synchronous and asynchronous deployment modes. We introduce an Information Flow Graph (IFG) monitor that analyzes structural security regressions using control-flow and data-flow graph diffs alongside raw code diffs. In asynchronous evaluation, an untrained git diff monitor misses 11.6% attacks at 1% false positive rate auditing budget, our untrained IFG monitor reduces this to 3.5%, and the trained Async RF monitor achieves below 1%. Unlike the trained Async RF monitor, IFG can also operate synchronously as a pre-deployment safeguard that blocks suspicious infrastructure changes before execution or merge. In synchronous mode, IFG realtime rollback reduces joint main task and covert task success from 74.4% to 0.0% with no measurable reduction in legitimate task completion. These results suggest that untrained structural monitors provide a practical and auditable path toward democratizing deployment safety for organizations seeking trustworthy adoption of advanced AI agents.
[AI-52] Seeing the End at Step Zero: Accelerating Diffusion MLLM s via MLP Sparsity-Aware Truncation ACM-MM
链接: https://arxiv.org/abs/2607.14557
作者: Qicheng Zhao,Qi Sun,Zheyu Yan
类目: Artificial Intelligence (cs.AI)
备注: Accepted to ACM Multimedia (ACM MM) 2026
Abstract:Diffusion Multimodal Large Language Models (DMLLMs) are highly effective for multimodal reasoning, yet their inference efficiency is significantly hindered by fixed-length generation constraints. Since the actual output length is unknown, output sequences are padded to a predefined maximum length, resulting in substantial redundant computation over unnecessary [EOS] tokens. In this work, we discover that DMLLMs implicitly reveal their valid semantic boundary at the very first denoising step through a distinct shift in MLP activation sparsity. Leveraging this observation, we propose Seer, a training-free framework that detects this boundary using a Signal-to-Noise Ratio (SNR)-based criterion and performs one-shot truncation of the redundant suffix for all subsequent computations. To preserve these theoretical gains during batched serving, Seer incorporates a hybrid execution strategy that maximizes throughput while seamlessly accommodating dynamic sequence lengths. Experimental results demonstrate that Seer effectively eliminates padding waste, accelerating throughput by up to \sim 31 \times . Across 9 benchmarks, Seer robustly maintains overall performance and even improves accuracy on complex visual tasks by mitigating noise leakage (e.g., DocVQA score increases from 63.52 to 63.66), offering a highly efficient, plug-and-play solution for DMLLM acceleration.
[AI-53] SafeRelBench: A Spatial-Relation-Aware Benchmark for Process-Level Safety in VLM-Driven Embodied Agents
链接: https://arxiv.org/abs/2607.14543
作者: Huaigang Yang,Ya Li,Min Ren,Bo Dai,Zhenliang Zhang,Zhaofeng He
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Preprint. 10 pages, 6 figures, 4 tables
Abstract:Vision-language models (VLMs) are increasingly used as the reasoning backbone of embodied agents, enabling robots to interpret visual scenes, follow language instructions, and plan multi-step actions. In household environments, however, safety depends not only on recognizing objects, but also on how actions change the physical scene over time. Existing embodied safety evaluations largely focus on static risk recognition, unsafe instruction refusal, or final-state task completion. As a result, process-level safety failures induced by spatial relations such as support, containment, and proximity remain insufficiently studied. To address this gap, we introduce SAFERELBENCH, a spatial-relation-aware safety benchmark with 507 executable evaluation samples, including 248 spatial-relation samples and 259 non-spatial control samples. Using SAFERELBENCH to evaluate seven open- and closed-source VLM-driven embodied agents, we find a substantial gap between task success and process-level safety compliance: models often complete the requested task while violating process-level safety constraints. Unlike prior benchmarks, SAFERELBENCH explicitly tests whether agents satisfy safety conditions before risk-prone actions, making spatial relations a core dimension in embodied safety assessment. More broadly, our results show that safe embodied intelligence requires not only stronger perception and planning, but also reliable reasoning about how object relations shape risk during interaction.
[AI-54] Are LLM -Generated GPU Kernels Production-Ready? A Trace-Driven Benchmark and Optimization Agent
链接: https://arxiv.org/abs/2607.14541
作者: Lingyun Yang,Yuxiao Wang,Shenghao Liang,Linfeng Yang,Daocheng Ying,Chunbo You,Rui Zhang,Luping Wang,Yinghao Yu,Guodong Yang,Liping Zhang
类目: Artificial Intelligence (cs.AI)
备注: Both artifacts are released as open source: Atrex-Bench ( this https URL ) and Atrex-Kernel-Agent ( this https URL )
Abstract:Existing GPU kernel generation benchmarks draw problems from synthetic or curated sources that diverge from deployed workloads. We present Atrex-Bench, a benchmark whose 30 operators and 440 shapes are sampled directly from full-cluster production inference traces of compute-limited, memory-rich GPUs. Each problem carries an importance weight derived from its share of observed GPU time, weighted by application card-hours and computed separately for the serving phases in which it runs, together with a per-problem roofline ceiling, so the aggregate score emphasizes the kernels that consume the most serving time. Evaluating six frontier coding agents on Atrex-Bench shows that even the best vanilla model reaches only \sim10% of the hardware roofline on production operators; and correctness alone overstates capability, since much of the apparent pass rate comes from PyTorch fallbacks rather than kernels the model wrote. To close this gap, we co-release Atrex-Kernel-Agent (AKA), a profile-driven kernel-optimization agent that combines iterative measure-revise search, optimization dropout for escaping stalled search contexts, and a layered GPU-optimization knowledge base (298 reference-kernel files and 244 optimization-knowledge documents, plus external upstream reference projects for API/ISA lookup). In a controlled case study, the agent converts zero-FlyDSL fallbacks into real kernels that match or exceed hand-tuned production baselines.
[AI-55] VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence
链接: https://arxiv.org/abs/2607.14510
作者: Haiteng Wang,Jingheng Yan,Xiaokang Wang,Lei Ren
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 13 figures, and 13 tables, including supplementary material. Haiteng Wang and Jingheng Yan contributed equally to this work
Abstract:Industrial time series serve as the foundation for Prognostics and Health Management (PHM) to ensure the reliability and safety of industrial equipment such as aero-engines. However, existing approaches are typically limited to single-modality modeling, which restricts their generalization in complex scenarios. Although recent advances in large language models (LLMs) provide new opportunities for multimodal learning, bridging continuous time-series signals and discrete textual semantics remains an open challenge. To this end, we propose VLT, a multimodal foundation model that jointly models time-series, frequency-spectrum visual representations, and textual knowledge. A key insight is to utilize the frequency spectrum as a visual bridge to connect continuous temporal signals with discrete semantics. Specifically, a Time-aware Mixture-of-Experts (Time-MoE) is designed to capture heterogeneous temporal dynamics, while a Frequency-Text Augmented Learner enables joint modeling of spectral and semantic features within a shared representation space. Furthermore, a time-centric gradient alignment mechanism is introduced to mitigate cross-modal optimization conflicts via gradient normalization and reliability-aware dynamic reweighting. Extensive experiments on multiple industrial datasets demonstrate that VLT outperforms state-of-the-art methods, achieving superior robustness and generalization under few-shot, noisy, and incomplete-modality settings.
[AI-56] Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards
链接: https://arxiv.org/abs/2607.14506
作者: Yuxuan Zhu,Rohan Alur,Daniel Kang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 22 pages, 7 figures
Abstract:While reinforcement learning with verifiable rewards (RLVR) is widely used to improve the reasoning capabilities of large language models (LLMs), the generalizability of the resulting models remains poorly understood. In this work, we establish the first non-vacuous generalization bounds for parameter-efficient RLVR fine-tuning at the billion-parameter scale. Our approach adapts PAC-Bayes compression bounds to this setting, and addresses the inherent stochasticity of token generation by applying the Gumbel-max reparameterization trick. To operationalize these bounds, we propose the Progressive RLVR framework, which integrates RLVR with on-policy distillation, TinyLoRA, and model quantization. Progressive RLVR empirically retains 84-97% performance of standard LoRA fine-tuning while producing models that are 14,796x more compressible. We show that this framework yields non-vacuous generalization bounds in four domains: mathematical problem-solving, programming, general-knowledge reasoning, and Text-to-SQL. Our bounds exceed the accuracy of the base model by 9-51% and lie within 6-11% of the accuracy of the fine-tuned models.
[AI-57] Contextualized Evaluation of Vision Language Models through Dynamic Multi-turn Interactions
链接: https://arxiv.org/abs/2607.14499
作者: Yijiang Li,Huiqi Zou,Bingyang Wang,Ziang Xiao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-modal Large Language Models (MLLMs) have made substantial advances on benchmarks, yet their real-world effectiveness remains uncertain. This gap stems from the fundamental misalignment between benchmarks in controlled, static settings and the dynamic, interactive, and contextualized nature of real-world applications. To bridge this gap, we propose CEDI (Contextualized Evaluations of MLLMs through Dynamic, multi-round Interactions), a framework that recasts evaluation as a three-party interaction between an evaluatee model, an automated examiner, and a grader. The examiner conducts multi-turn, semi-structured conversation guided by a graph-based representation of the task. By navigating state-space transitions, CEDI deploys diverse strategies, from clarification requests to adversarial probes, to elicit performance evidence. We apply CEDI to visual hallucinations. Empirical results across multiple models, diverse settings, datasets, and domains show that contextualized, interactive evaluations reveal not only significantly more hallucinations than conventional static evaluation but also ones that more closely resemble those arising in practical use cases. We further show that hallucinations often accumulate over long contexts, through self-reinforcing dialogue history, and models are particularly vulnerable to questions requiring premise rejection or refusal. Together, these findings highlight CEDI as a step toward realistic, systematic, and ecologically valid assessments of MLLMs’ capabilities. Code is available at this http URL.
[AI-58] EdgeFaaS: A Function-based Framework for Edge Computing
链接: https://arxiv.org/abs/2607.14489
作者: Neha Vadnere,Yu-Ting Wang,Yitao Chen,Sreehari Sadesh,Ming Zhao
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: 2026 IEEE International Conference on Edge Computing (EDGE)
Abstract:Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world. Existing distributed computing frameworks cannot adequately handle this level of heterogeneity and distribution. This paper proposes EdgeFaaS, a novel function-based edge computing framework to enable edge applications to effectively utilize heterogeneous resources distributed across the Internet of Things (IoT), edge, and cloud for computing. It proposes function virtualization and storage virtualization to abstract distributed and heterogeneous physical resources and provides consistent virtual interfaces for deploying and executing functions and storing and accessing data. EdgeFaaS provides comprehensive support to diverse edge computing workflows, and at the same time allows users to flexibly adjust the configurations and explore various important tradeoffs. To demonstrate its usability, the paper also presents the implementation and evaluation of three representative workflows on EdgeFaaS for video analytics, federated learning, and audio classification, on a real testbed of 100+ geographically distributed IoT devices, edge servers, and cloud services. EdgeFaaS allows users to flexibly explore the deployment configurations of these workflows over distributed and heterogeneous resources. For example, users can easily vary the function placement of the video processing pipeline across IoT, edge, and cloud resources and study the tradeoff between computation and communication costs; users can also flexibly adjust the cluster count and size in the hierarchical federated learning system and explore the tradeoff between training accuracy and speed.
[AI-59] Step-Level Preference Learning for Generative Agents in Social Simulations
链接: https://arxiv.org/abs/2607.14485
作者: Wenchang Gao,Pingyue Sheng,Lanlan Qiu,Yunfei Ma,Jian Zhao,Baicheng Chen,Kangda Wang,Yuyang Tian,Shunqiang Mao,Tianxing He
类目: Artificial Intelligence (cs.AI)
备注: WAICA2026
Abstract:Large language model (LLM)-based generative agents simulate human behavior through long-horizon decision-making processes that comprise intermediate steps such as planning, memory retrieval, reflection, and action selection. However, fine-grained human annotations of these intermediate steps remain scarce, and existing agents are not grounded in human preferences over such intermediate decisions. To address this gap, we introduce \method, an interactive simulation interface that enables us to collect step-level human preference supervision over agent decision trajectories, leading to a dataset of 57K fine-grained annotations. We conduct step-level preference learning on open-weight language models using supervised finetuning and direct preference optimization on this data, consistently improving simulation fidelity, coordination, and interaction quality, and inducing more socially effective agent behavior. Our results show that step-level human supervision is an effective training signal for improving both local decision quality and long-horizon agent behavior.
[AI-60] Can Tokens Compete? Token Representations against Supervised CNN Backbones for BirdCLEF 2026
链接: https://arxiv.org/abs/2607.14474
作者: Anthony Miyaguchi,Murilo Gustineli,Adrian Cheung
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:This paper details the DS@GT ARC team’s approach to BirdCLEF+ 2026, multi-label detection of animal vocalizations in soundscapes from the Pantanal wetlands. The 2026 edition adds about an hour of labeled soundscapes, shifting the task toward supervised pipelines fit to the labeled set. First, we build a competitive supervised baseline that ensembles a frozen Perch v2 backbone, a trained HGNetV2-B0 sound-event-detection network, and a non-bird prototypical head, reaching a private leaderboard score of 0.936 at rank 1894 within a 90-minute CPU budget. Second, we ask whether token-based representations can compete, contrasting codec representations from neural audio codecs against semantic representations from foundational embeddings. We compare two bioacoustic specialist models against four token-based encoders trained on AudioSet. The repository for this work can be found at this https URL.
[AI-61] Beyond Generalist LLM s: Specialist Agent ic Systems for Structured Code Workflow Execution
链接: https://arxiv.org/abs/2607.14456
作者: Harris Borman,Herman Wandabwa,Fusun Yu,Sandeepa Kannangara,Justin Liu,Anna Leontjeva,Ritchie Ng
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) have accelerated the adoption of software development agents, now widely available as Integrated Development Environment (IDE) extensions and standalone applications. While these agents are typically general-purpose, it remains unclear whether specialist agents justify their additional development effort. We investigate this question in the context of business process automation, focusing on the transformation of Business Process Model and Notation (BPMN) diagrams into executable agentic workflows. Since BPMN specifies explicit control-flow semantics, we focus on deterministic workflows in which a fixed process model and inputs uniquely determine the executed path. We introduce a specialist workflow for this task and compare it against generalist agents such as Roo and Cline. Our results show that the specialist solution produces agents that outperform generalist baselines by approximately 9-20 percentage points in tool-use exactness, 2-4x in penalty-adjusted latency, and 3x fewer tool-call errors, while reducing generation token cost by over 95% and eliminating repair iterations. We also find that generalist agents generate code inconsistently in both functionality and quality, limiting their suitability for industrial settings where reliability and maintainability are essential.
[AI-62] actile: Giving Computer-Using Agents Hands and Feet
链接: https://arxiv.org/abs/2607.14443
作者: Yong Liu,Zhenyi Zhong,Zhanpeng Shi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Computer-use agents are becoming capable software operators, but their interface to desktop applications is still often a brittle motor layer: they look at screenshots, predict coordinates, click, and hope that the visible state changed as intended. This collapses target grounding, action execution, and outcome verification into a single ambiguous operation. We present Tactile, an open-source tool layer that gives agents a more reliable “hands and feet” for desktop use. Tactile converts heterogeneous UI evidence–operating-system accessibility semantics, OCR-grounded text, and visual fallback regions–into action-grounded interface states: compact target candidates with source labels, roles or text, state, geometry, executable affordances, and verification cues. Agents operate through an observe-ground-act-verify loop that prefers native semantic actions when available, falls back to OCR-grounded coordinates when visible text is the best evidence, and keeps full provenance for replay and failure attribution. On macOSWorld-style tasks, adding Tactile improves Codex Success@100 from 41.1% to 50.0% overall and from 45.2% to 55.3% on accessibility-adapted tasks; a 96-task cross-agent subset shows consistent gains across Codex, Claude Code, OpenCode, and Goose. These results suggest that reliable computer use requires not only stronger models, but also a reusable execution substrate that exposes software actions as semantic, verifiable, and auditable objects rather than anonymous screen coordinates.
[AI-63] Global drivers and barriers to the public acceptance of autonomous vehicles: Evidence from 17 countries
链接: https://arxiv.org/abs/2607.14436
作者: Antonios Saravanos(1) ((1) New York University)
类目: Emerging Technologies (cs.ET); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:This study investigated the public acceptance of Society of Automotive Engineers Level 3 conditionally automated cars, which can self-drive under certain specified conditions but require the human driver to remain ready to resume control when requested. Previous Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)-based research has focused mainly on European samples, and so it is still unclear whether the same factors shape acceptance across broader world regions. This knowledge gap was addressed using the L3Pilot Global User Acceptance Survey. From an original dataset of 18,631 respondents, the final analytic sample comprised 18,603 respondents from 17 countries across Africa, Asia, Europe, North America, and South America. The data were analyzed using a UTAUT2-based structural equation model to examine how performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation shape the intention to use Level 3 cars. The model showed strong explanatory power. Across the analytic sample, the intention to use Level 3 cars was driven mainly by performance expectancy, social influence, and hedonic motivation. Effort expectancy and facilitating conditions also contributed, but they played smaller direct roles. Age, gender, and previous experience with advanced driver assistance systems were statistically significant, but comparatively weak predictors. Overall, the findings suggest that the acceptance of Level 3 automated cars depends less on demographic characteristics or ease-of-use concerns and more on whether people see the technology as useful, socially supported, and enjoyable to use.
[AI-64] Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers
链接: https://arxiv.org/abs/2607.14427
作者: Joe Logan
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures. Code, configs, experiment log, and checkpoint at this https URL
Abstract:A depth-recurrent transformer applies a weight-tied core a variable number of times, and prior work has shown that training with a randomized recursion count yields one checkpoint usable across a range of inference depths. We ask what such a model actually computes per token, and measure it directly. On a 135M-class model trained on FineWeb-Edu, the recurrent state converges to a per-token fixed point: mean successive-output KL divergence falls from 3.9e-1 at the second loop to 8.5e-6 by the sixteenth, and per-token state change decays in step. Crucially, this convergence is not uniform across tokens. The median token converges by loop six, while approximately 10 percent of tokens continue to update at the training-mean depth of eight, and mean convergence depth is ordered by token type (whitespace shallowest, content words deepest). This per-token variation is the central object of the paper. We show it is directly readable and that reading it outperforms learning to predict it: a training-free rule that halts each token once its output stabilizes attains uniform depth-8 quality at 4.94 average loops (a 38 percent reduction in average depth) and matches uniform depth across the average-depth range, whereas a linear router trained on convergence labels harvested from the same model requires nearly full depth and yields no reduction. The elasticity that makes this possible reproduces here as background (validation loss decreases monotonically from 3.80 at one loop to 3.20 at eight and remains stable to 32 loops). We report average depth as a FLOP proxy with a three-point wall-clock bracket rather than a realized speedup, make no FLOP-matched parity claim, and note that the allocation results are established at a single scale and seed. The complete study runs on a single RTX 4090 in approximately 100 GPU-hours.
[AI-65] ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation
链接: https://arxiv.org/abs/2607.14424
作者: Nutan Chen,Jianxiang Feng,Marvin Alles,Botond Cseke
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: RSS 2026 Workshop on Diffusion for Robot Learning
Abstract:In recent years Flow Matching has become a prominent method for generative modeling robot motion generation. In its generic form Flow Matching is an ODE-based neural sampler that is trained by regressing empirical flow fields associated with motion samples as data. However, in robot motion generation we often have additional constraints that might not be present in the collected data. The majority of current approaches train the flow on the available data and use inference-time guidance to enforce task-specific constraints. To address this mismatch, we propose \textbfConFlow, a constraint-guided flow matching framework that incorporates constraint information directly into the training objective via differentiable barrier or cost functions. To address design specifications such as smoothness and boundary conditions, we propose replacing the standard Gaussian source distribution used in flow matching training with a conditional Gaussian Process. Our approach also uses infeasible demonstrations as negative supervision, improving constraint satisfaction without requiring additional expert data. Experiments on a two-robot navigation task demonstrate that ConFlow achieves lower collision rates and higher trajectory quality than standard flow matching baselines, with or without inference-time guidance. These results validate training-time constraint integration as an effective approach to closing the training–inference gap in generative motion models.
[AI-66] CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment AAAI’26
链接: https://arxiv.org/abs/2607.14416
作者: Rabimba Karanjai,Hemanth Madhavarao,Lei Xu,Weidong Shi
类目: Artificial Intelligence (cs.AI)
备注: Accepted in AAAI’26 Workshop, Agentic AI in Financial Services
Abstract:The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults. Traditional risk models often fail to capture the complex, non-linear dynamics of these networks. While Graph Neural Networks (GNNs) have shown promise in modeling relational data, they primarily learn correlative patterns and function as black boxes, offering little insight into the causal mechanisms of shock propagation. This limitation is critical for regulators who require explainable models to perform stress tests and devise effective interventions. We introduce CausalGraphX, a novel framework that integrates GNNs with counterfactual reasoning to provide explainable assessments of systemic risk. CausalGraphX employs a Graph Attention mechanism to learn representations of institutional vulnerability and uses an adversarial regularization technique to ensure these representations capture causal drivers rather than spurious correlations. Furthermore, we propose an optimization-based approach to generate counterfactual explanations, answering questions such as, “What minimum capital injection would have prevented Bank A’s default under a specific stress scenario?” We validate CausalGraphX on large-scale synthetic financial networks. Our results demonstrate that CausalGraphX significantly outperforms traditional and deep learning baselines in predicting cascading defaults while providing sparse, plausible, and actionable counterfactual explanations.
[AI-67] Reward-Free Evolving Agents via Pairwise Validator
链接: https://arxiv.org/abs/2607.14408
作者: Minghao Liu,Yu Wang,Jiayun Wang,Wei Wei
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality signal. Designing that signal is often the costly part of the project: a reliable scalar reward requires domain expertise and labeled examples that are themselves as expensive to assemble as the agent’s underlying task. We propose replacing the scalar at the accept/reject gate with a pairwise validator: a frozen LLM that, given the parent and child candidate, returns a binary verdict on which is better. Pairwise judgment is generally easier and more stable than absolute scoring, due to its contrastive nature, which mitigates the need for strict scale calibration. The validator also requires no training of its own. We integrate the validator into three published self-evolving engines (GEPA, ADRS, ShinkaEvolve) and report two flavors: Adaptive Focus, which retains the engine’s existing val-set parent selection, and Soft Elo, which lets the validator’s verdicts drive parent selection so that val-set rewards drop as well. Across multiple agents and two artifact substrates (prompt and code), our method matches or exceeds the full-reward baseline on the majority of settings we evaluate, and the pattern survives a cross-family validator swap. The pairwise gate is thus a drop-in replacement for per-step reward design at competitive task accuracy without the labeling cost.
[AI-68] Decision Making Needs Uncertainty Quantification [Lecture Notes]
链接: https://arxiv.org/abs/2607.14407
作者: Osvaldo Simeone
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Many signal processing systems ultimately exist to act. Whenever the state variable that determines the action to be taken by a decision maker, or agent, is uncertain, the way that uncertainty is represented decides how well the agent performs and how much its performance can be trusted. This lecture note develops, from first principles and within a single decision-theoretic setting, the link between the objective and the knowledge of an agent and the form of uncertainty representation that is sufficient to act optimally. To start, assuming a known environment distribution, we show that a risk-neutral agent needs the posterior distribution over the state, whereas a risk-averse agent can rely without loss of optimality on a prediction set and a worst-case decision rule. We then turn to the case in which the environment is unknown, and identify three complementary approaches to address the resulting epistemic uncertainty: calibration of a fixed predictor, credal (ambiguity) sets with distributionally robust optimization, and Bayesian inference over model parameters. The common thread is that reliable decisions require an uncertainty representation matched to the decision objective and to the knowledge profile of the agent, together with a guarantee that certifies the utility the agent will actually obtain.
[AI-69] Integration Matters: Rollout-Based Training for Constrained Diffusion Models
链接: https://arxiv.org/abs/2607.14398
作者: Xiaoxuan Liang,Saeid Naderiparizi,Berend Zwartsenberg,Frank Wood
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution. Existing constrained generation methods typically enforce constraints either through training-time optimization or sampling-time correction. Training-time optimization approaches optimize on states induced by the training distribution, which can differ substantially from those encountered during sampling. Sampling-time correction methods instead modify the sampling process at inference, introducing distribution shift and requiring expensive tuning, particularly for few-step sampling. We propose a fine-tuning framework that incorporates constraint guidance obtained through online rollout into the training process, which aligns training with sampling by differentiating through the fixed noise schedule used to numerically integrate the denoising process. This exposes the model to violations that arise along the denoising trajectory and aligns diffusion learning with the sampling process. Experiments across multiple tasks show that our method improves constraint satisfaction while maintaining competitive sampling quality compared to prior methods.
[AI-70] CatalogAgent : A Supervisor-mediated Self-Learning System Enabling Context Engineering for GenAI Models
链接: https://arxiv.org/abs/2607.14396
作者: Zhu Cheng,Zhenming Wang, Yu (Xuan)Tang,Dan Liu,Bryan Zhang,Athanasios N. Nikolakopoulos,Pranav Souri Itabada,Jing Zhang,Chih-Chi Chou,Peng Gao,Fatemeh Mansoori,Bharat Bojja,Sarath Chander,Sameer Thombare,Umit Batur,Tarik Arici
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Product catalogs are the backbone of e-commerce sites, yet a large number of structured attributes (SAs) – such as material, color, and shape – often have missing values. Typically, SA values are extracted from product information, including titles and descriptions. While LLM-based generator-evaluator frameworks have demonstrated effectiveness for SA prediction – where an LLM generates SA values and another evaluates them – they face challenges when the Generator and Evaluator produce conflicting outputs, as either component can make mistakes. We introduce \textttCatalogAgent, a novel agentic system that continuously improves Generator and Evaluator models for e-commerce catalog enrichment. When disagreements arise from (1) internal conflicts between the LLM-based Generator and Evaluator, or (2) external feedback from sellers on LLM outputs, a Supervisor Agent intervenes to mediate these conflicts and make final decisions. The system also incorporates a Memory Base and a Memory Summarizer that stores Supervisor Agent activities from individual cases and aggregates patterns into learnings. These learnings are fed back to the worker Generator and Evaluator LLMs, enabling self-improvement without human intervention. Through context engineering – injecting learnings and insights into worker LLMs’ contexts – the system successfully transfers the Supervisor’s capabilities to the Generator and Evaluator, improving their performance by 15.24% and 13.98%, respectively. Our experiments demonstrate a new paradigm of Supervisor Agent-mediated self-learning systems for improving generative AI model accuracy.
[AI-71] An offline approach to fNIRS-guided reinforcement learning for robot behavior
链接: https://arxiv.org/abs/2607.14393
作者: Julia Santaniello,Madelaine Brower,Benson Jiang,Donatello Sassaroli,Robert Jacob,Jivko Sinapov
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Preliminary results
Abstract:Human-in-the-loop Reinforcement Learning has become a popular approach to training, finetuning, and aligning robot behavior with user preferences. Our paper explores the feasibility of using brain signals via functional near-infrared spectroscopy (fNIRS) to modulate robot learning in simulation. We compare agents trained on passive (observational) versus active (demonstrative) interaction tasks, and test multiple methods for enhancing the RL algorithm with the neural signal, focusing on parameter augmentation rather than replacement. We further examine how model granularity and noise affect agent learning. Our results show that this framework is effective: the neural signal improves learning when augmenting trajectory priorities and state-action q-values. Additionally, the framework learns successfully from offline data, offering a practical alternative for settings where real-time BCI setups are impractical or only limited data is available.
[AI-72] A Comparative Analysis of Machine Learning Models for Long and Short-Term Forecasting of the Egyptian Stock Market: A Focus on EGX30
链接: https://arxiv.org/abs/2607.14391
作者: Muhammed Walid,Ahmed El-Naeimy,Hosam Moubarak,Walid Gomaa
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This study concentrates on predicting stock prices in the Egyptian market, focusing on the EGX30, an influential financial hub in the Middle East. While most research focuses on global stocks, there’s a growing need to understand stock trends in developing countries like Egypt. The study compares different machine learning models for forecasting EGX30 trends, covering short and long-term predictions. Using historical EGX30 data, including metrics like root mean squared error, Mean Absolute Percentage Error, and coefficient of determination, models like K-Nearest Neighbours, random forest, extreme gradient boosting, long short-term memory networks, and gated recurrent unit networks were evaluated. The goal is to determine the most effective models for EGX30 prediction, considering Egypt’s unique market dynamics. Insights from this study aid investors in making informed decisions. Results show that the Gated Recurrent Unit (GRU) outperformed the other models in the one-week, one-month, and two-months while the eXtreme Gradient Boosting (XGBoost) model outperformed others in the one-day predictions, highlighting their usefulness in predictive analysis for financial markets. The study also showed the importance of using the ensemble techniques, especially in the long-term predictions which proved better results reaching 5 times the GRU in the two-month predictions. Additionally, the study notes the surprisingly good performance of K-Nearest Neighbours (KNN) on long-term predictions, suggesting its enduring relevance and potential for future applications in the fintech domains.
[AI-73] Chat2Scenic: An Iterative RAG -Based Framework for Scenario Generation in Autonomous Driving IROS
链接: https://arxiv.org/abs/2607.14387
作者: Yuan Gao,Wenting Miao,Mattia Piccinini,Haoyu Wang,Qunying Song,Johannes Betz
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Accepted at 2026 IEEE International Conference on Intelligent Robots and Systems (IROS)
Abstract:Validating autonomous driving systems requires diverse, regulation-compliant test scenarios. In simulation-based testing, scenarios are defined as executable scripts. Yet automatically generating such scripts from regulatory descriptions remains an open challenge, and existing approaches face fundamental trade-offs. Retrieval-assemble methods achieve reasonable compilation rates but lack scalability, whereas retrieval-based full-script generation suffers from low compilation success rates. We present Chat2Scenic, the first iterative retrieval-augmented framework to generate scenario scripts in Domain Specific Language (DSL). Specifically, Chat2Scenic provides a chatbot interface that supports interactive scenario refinement and integrates Retrieval-augmented Generation (RAG) to ground scenario generation in regulatory knowledge and DSL syntax. Furthermore, we propose an open benchmark for scenario generation comprising 123 scenarios from various regulations, including NHTSA and United Nations Vehicle Regulations, as well as other sources. Extensive evaluation with State-of-the-Art (SOTA) Large Language Models (LLMs) demonstrates that Chat2Scenic achieves 76.42% Compilation Success Rate (CSR) and 58.17% Framework Accuracy (FA), outperforming existing methods (Retrieval Assemble with 30.08% CSR, 11.03% FA and Retrieval full script generation with 16.26% CSR, 10.86% FA). To facilitate future research, we release our code as open source at this https URL.
[AI-74] CIPHER: A Decoupled Exploration-Selection Framework for Test-Time Scaling of Data Science Agents
链接: https://arxiv.org/abs/2607.14386
作者: Maxime Heuillet,Sharadind Peddiraju
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Data science tasks span from closed-ended information extraction to open-ended analysis, presenting significant challenges for automation. Recent AI agents powered by language models show promise for handling such complex tasks. However, existing agents typically rely on a single initial state that conditions the entire agent’s execution, making them vulnerable to cascading errors initiated by a suboptimal initial state. To mitigate this, we present CIPHER, an automated data science agent that leverages test-time scaling through the generation and selection of multiple initial states for concurrent execution. Unlike existing works on test-time scaling of AI agents, CIPHER explicitly decouples the generation of candidate initial states from their strategic selection for parallel execution. Through extensive evaluation on two benchmarks (closed-form and open-form tasks), we demonstrate that CIPHER exceeds state-of-the-art performance in matched-model comparisons, and remains competitive against larger-model baselines despite relying on a substantially smaller base LM. Our empirical study characterizes the design space of the Decoupled Exploration-Selection (DES) framework: we quantify how generation strategy, selection strategy, and aggregator model capacity contribute to overall performance, and derive actionable design recommendations for practitioners.
[AI-75] Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI
链接: https://arxiv.org/abs/2607.14353
作者: Joshua A. Kroll,Andrew Smart,R. Stuart Geiger,Abigail Z. Jacobs
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: Accepted to the Harvard Data Science Review, Volume 8(3), Summer 2026
Abstract:As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology’s capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems. Sociotechnical analysis of risks in highly complex systems provides clear lessons for the design and evaluation of AI systems, transcending a technical focus on reliable or “responsibly designed” components to understand risks at a systems level. Human-made catastrophes have been studied for decades because of the severity of these events: consider Chernobyl, Three Mile Island, Fukushima-Daiichi, Bhopal, the Challenger disaster. A common misconception is that these kinds of events are freak accidents, resulting from the inherently unforeseeable interactions in complex systems. Closer examination reveals that the risks and hazards were well-known beforehand but not acted upon due to social structural, political and economic factors. We outline several areas where the development and use of AI can benefit from learning these unlearned lessons: improved risk perception, communication, and analysis at the organizational level; traceability of requirements and responsibilities; and holistic approaches to responsibility and safety that include social and organizational dynamics as first-order engineering concerns. For each area, we offer concrete unlearned lessons and exemplify how they led to failure in prior accidents as well as examples of how these lessons remain unlearned for modern computing systems, particularly AI. Comments: Accepted to the Harvard Data Science Review, Volume 8(3), Summer 2026 Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) ACMclasses: K.4.0; H.0; I.2.0; H.5.3; J.7 Cite as: arXiv:2607.14353 [cs.CY] (or arXiv:2607.14353v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2607.14353 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Harvard Data Science Review, Volume 8(3), Summer 2026 Related DOI: https://doi.org/10.1162/99608f92.91b8834f Focus to learn more DOI(s) linking to related resources
[AI-76] Value Leakage: An LLM s Answers Are Silently Shaped by Its Own Values
链接: https://arxiv.org/abs/2607.14345
作者: Jan Betley,Johannes Treutlein,Jan Dubiński,Harry Mayne,Karol Gałązka,Niels Warncke,Anna Sztyber-Betley,Owain Evans
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:People use language models for practical questions whose answers are difficult to verify. We show that models exhibit covert value leakage: the information they provide is influenced by their own values, without this influence being disclosed to the user. In one of our evaluations, the user is considering investing in an AI company and wants to know how likely the AI bubble is to pop. Claude Opus 4.8 gives a lower probability when the company under consideration is Anthropic rather than OpenAI. Yet Claude mostly fails to disclose this influence to the user. Covert value leakage is a form of misalignment because it goes against the user’s preferences and is likely to mislead them. To investigate this phenomenon, we introduce a suite of evaluations to quantify value leakage and whether models disclose it. We find that models are influenced by different types of values, including preferences for morally good outcomes, for the company that developed them, and for some human leisure activities over others. We often observe large differences among frontier models on the same evaluation. For example, on a Fermi-estimation task, Claude models falsely claim to give unbiased answers in their chain-of-thought, while Qwen models explain how their values bias their answers. Value leakage is a failure mode distinct from sycophancy and reward hacking, and current alignment training and evaluations do not adequately address it. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) Cite as: arXiv:2607.14345 [cs.LG] (or arXiv:2607.14345v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.14345 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Johannes Treutlein [view email] [v1] Wed, 15 Jul 2026 20:19:17 UTC (4,247 KB)
[AI-77] Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution
链接: https://arxiv.org/abs/2607.14341
作者: Hanyi Zhang,Khang Nguyen,Charith Munasinghe,Basu Hela,Tianyu Li,Zihong Luo,Hoan Nguyen,Hans Wernher van de Venn,Yalin Zheng,Ravi Prakash,Tung D. Ta,Anh Nguyen,Baoru Huang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Robust robotic grasping remains a fundamental challenge for complex real-world applications. Recent advances in large-scale models demonstrate promising capabilities for reasoning in robotic tasks. However, existing benchmarks for grasping primarily focus on isolated, visual-based grasp pose detection, failing to capture the complexity of grasping tasks that require multi-step reasoning and semantic understanding during execution. To address this gap, we propose GCA-Bench, a benchmark featuring challenging \textitgrasping with complex action scenarios that involve both scene-level reasoning and semantic constraints. GCA-Bench enables the evaluation of recent large foundation models under the same settings. To demonstrate the effectiveness of our new benchmark, we implement a diverse set of baselines, ranging from traditional grasp detection pipelines to end-to-end learning methods. Empirical studies achieve success rates below 70% on complex grasping scenarios, underscoring critical limitations. In addition, we propose new evaluation metrics, analyze critical failure models, and provide insights to guide the development of more robust and generalizable grasping strategies.
[AI-78] he Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK
链接: https://arxiv.org/abs/2607.14340
作者: Tobias Philipp
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:AI coding agents produce code faster than humans can review it. In our approach, the prover is the judge of whether the code is correct. Under a verifier-driven loop, AI agents wrote and verified bare-metal security software in Ada/SPARK spanning classical and post-quantum cryptography, TLS 1.3, IKEv2, X.509, and a Matrix client. GNATprove discharged 49,280 proof obligations, established functional correctness for selected primitives, and proved the absence of run-time errors for the rest, at roughly 20-40 times lower supervision cost than comparable hand verification. GNATprove alone was insufficient: some defects could not be detected and were resolved using known-answer tests, interoperability, or human review of specifications. Given weak checks, the agent tried to bypass them and reported success. We report where each layer caught faults and draw the central lesson: what an agent can be trusted to establish is bounded by the strength of its feedback.
[AI-79] Copy-on-Write Scoring: Application-Specific Agent Evaluations ICML2026
链接: https://arxiv.org/abs/2607.14336
作者: Joanna Roy,Sven Hoelzel
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 15 pages, 11 figures, accepted at ICML 2026 Second Workshop on Agents in the Wild: Safety, Security, and Beyond
Abstract:Trustworthy deployment of LLM-based agents in software systems requires evaluating how they perform on application-specific workflows, with enough granularity to localize where they succeed and fail. Yet existing agent evaluation mechanisms are limited: benchmarks have low construct validity for application-specific workflows and environments, and replica evaluation environments are expensive and prone to drift. We propose Copy-on-Write (CoW) Scoring, a framework that evaluates agent operations directly within application environments using a PostgreSQL-level Copy-on-Write mechanism to isolate agent writes. CoW Scoring produces session- and operation-level scores that highlight where agents’ database write operations succeed and fail in a given application environment, enabling inexpensive evaluation and iteration on agent harnesses and tool surfaces. We demonstrate the framework on Plane, an open-source project-management platform, where analysis surfaced specific issues in the tool surface, and corresponding fixes produced measurable improvements on affected models. Python library: this https URL Comments: 15 pages, 11 figures, accepted at ICML 2026 Second Workshop on Agents in the Wild: Safety, Security, and Beyond Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.14336 [cs.SE] (or arXiv:2607.14336v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2607.14336 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-80] Accounting for Hysteresis and Eddy Currents in Finite Element Simulations of Ferromagnetic Laminated Cores using a Recurrent Neural Network
链接: https://arxiv.org/abs/2607.14321
作者: Florent Purnode,Louis Denis,François Henrotte,Gilles Louppe,Christophe Geuzaine
类目: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI); Numerical Analysis (math.NA)
备注:
Abstract:Incorporating hysteresis and eddy currents into finite element simulations of laminated-core electrical machines is computationally challenging. Resolving the fields inside the laminations at each integration point and at every nonlinear iteration leads to computational costs several orders of magnitude higher than anhysteretic simulations, making such approaches impractical for design applications. Conversely, simplified models accounting only for magnetic saturation are becoming increasingly inadequate as electrical machine topologies and operating conditions grow in complexity. In this context, machine learning surrogate modeling has emerged as a promising alternative, offering efficient and accurate approximations of complex electromagnetic behaviors. In this paper, a recurrent neural network is trained as a surrogate of a laminated-core material model for an isotropic laminated core, and is integrated into realistic two-dimensional magnetodynamic finite element simulations based on a magnetic vector potential formulation. The proposed approach achieves excellent agreement with the reference laminated-core model while limiting the computational cost to about twice that of an anhysteretic simulation. By training the recurrent neural network on a sufficiently diverse set of artificially generated magnetic field sequences designed to mimic those encountered in electrical machine simulations, the proposed approach can be readily applied across a wide range of finite element simulations. Furthermore, the trained surrogate model is provided as a standalone component that can be easily incorporated into existing computational frameworks. It is publicly available at this https URL.
[AI-81] owards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models
链接: https://arxiv.org/abs/2607.14315
作者: Georgios Makridis,Georgios Fatouros,Athanasios Kiourtis,Dimitrios Kotios,Vasileios Koukos,Dimosthenis Kyriazis,Jonh Soldatos
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning models, with the ultimate goal of creating a unified multidimensional explainability score. Our methodology focuses on three key aspects of explainability: fidelity, simplicity, and stability. We leverage benchmarking experiments to systematically evaluate these aspects and use the insights gained to construct an offline knowledge base. This knowledge base captures the explainability scores for each registered model and serves as a valuable resource for context-dependent evaluation of explainability. By analyzing the complementary characteristics and metadata of AI models, datasets, and XAI methods, the knowledge base will enable the estimation of explainability scores for previously unseen datasets and models. Properties like fidelity, simplicity, and stability may vary significantly based on the dataset, underlying model, and domain expertise of the end user. We demonstrate our framework by applying it to three open-source datasets, discussing the implications of the obtained results in relation to the characteristics of the datasets. Our work contributes to the growing field of XAI by providing a robust and versatile tool for evaluating and comparing the explainability of various XAI methods, ultimately supporting the development of more transparent and trustworthy AI systems.
[AI-82] raccia: An OpenTelemetry-Based Governance Platform for AI Systems
链接: https://arxiv.org/abs/2607.14309
作者: Nutan Kumar Naik,Aditya Kumar Saroj,Vijay Prasad Poudel,Saurav Samantray,Abhishek Patel
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 26 pages, 2 figures, 3 tables, Declaration of generative AI and AI-assisted technologies in the writing process, Declaration of competing interest
Abstract:The rapid development of Large Language Models (LLMs) and Artificial Intelligent (AI) powered autonomous agents has fundamentally changed the existing forms of software governance. In spite of the rigorous standards of transparency and account ability required according to the international frameworks such as the European Union’s AI Act, there is a considerable gap between theory and reality. The present study discusses the inherent drawbacks of currently utilized platforms for LLM evaluation, machine learning workflow, and application performance monitoring in general. It has been shown that current disjointed solutions fail to protect unbound state space agentic architecture from serious threats such as alignment drift, SaaS security concerns, and unauthorized deployment of shadow AI systems. Moreover, a solution is proposed for overcoming the discussed challenges in form of a coherent multi-level AI governance stack Traccia built on the top of OpenTelemetry infrastructure platform. Traccia resolves the last mile for AI Alignment by adding the telemetry data, passive semantic guardrail assessment, and execution lineage into a hashed trace ledger. Traccia automatically creates compliance evidence packages by appending tamper-resistant fingerprints and SHA-256 content hash, that map to regulatory requirements (Articles 12, 14, 19, 26(6), and 50 of the EU AI Act) without invading any data privacy. By performing this evaluation in a methodical manner, a solid machine-readable base has been created for enterprise-wide management of autonomous AI systems.
[AI-83] racing LLM Behavior to the Training Data with Empirical Next-Token Distributions
链接: https://arxiv.org/abs/2607.14306
作者: Zachary Izzo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In this paper, we study the connection between an LLM’s output distribution and the data used to train it. Specifically, we study the degree to which an LLM’s next-token distribution agrees with the empirical next-token distribution (ENTD) given the context in the training data. The ENTD is an appealing target because it is the unrestricted global minimizer of the next-token cross entropy loss used for pretraining, as well as an easily interpretable function of the pretraining corpus. We find that for a significant fraction of inputs, the LLM’s distribution agrees with the ENTD almost perfectly, and the average agreement increases with model scale and training compute. Nevertheless, there is a long tail of input sequences where the LLM and ENTD differ significantly, and we examine several possible sources of this discrepancy across the transformer architecture, training procedure, and finite-sample noise in the ENTD estimate itself. More broadly, we hope our findings will encourage more work on ``data-centric mechanistic interpretability,‘’ a complement to standard mechanistic interpretability that opens the black box of how model behaviors arise from the data, rather than how they are encoded in the learned weights.
[AI-84] oolAlignBench: Investigating Alignment Conflicts in Tool-Calling Enabled LLM s ICML2026
链接: https://arxiv.org/abs/2607.14285
作者: Aryan Keluskar,Amrita Bhattacharjee,Huan Liu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted to the Pluralistic Alignment Workshop at ICML 2026
Abstract:Safety alignment in LLMs aims to align models with human values, but which values take precedence when they conflict? We investigate this question in the context of tool-calling LLM agents deployed in regulated industries, where agents processing confidential documents may encounter content that triggers safety-trained values (e.g., public welfare) that conflict with deployment-context instructions (e.g., internal logging). To empirically verify this phenomenon, we build a benchmark of 128 scenarios across 16 domains. We find that safety-aligned open-source models override their deployment instructions up to 43.4% of the time, engaging in whistleblowing, data exfiltration, and evidence tampering when processing documents that suggest organizational wrongdoing. We also find that abliteration reduces rates of external whistleblowing. These results reveal a fundamental tension in pluralistic alignment, where the same safety training that protects users can cause agents to act against deployment instructions in ways that create unpredictable liability risks. We release our benchmark as a framework to support evaluation of agent behavior under competing legitimate interests.
[AI-85] Local Additive Feature Attribution: A Mathematical Taxonomy and Reporting Checklist
链接: https://arxiv.org/abs/2607.14271
作者: Rebecca Afriyie Sarpong,Daniel Commey
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 35 pages, 7 figures, and 19 tables. Ancillary files include the axiom matrix, reporting checklist, related-survey scoring, and review-corpus summary
Abstract:Feature-attribution methods are central to explainable artificial intelligence. Their assumptions are expressed in several mathematical languages: cooperative-game values, path integrals, gradient operators, perturbation distributions, and backpropagation rules. This survey proposes a common framework for local additive feature attribution. It organizes Shapley, path-based, gradient/backpropagation, perturbation, and CAM-style methods around five specification choices: value function, reference, path, perturbation distribution, and conservation rule. It then compares these methods through an axiom-by-method matrix and links common failure modes, including baseline sensitivity, off-manifold perturbations, sanity-check failures, adversarial manipulation, and method disagreement, to the assumptions that produce them. Finally, the survey proposes a ten-item reporting checklist for studies that use local additive attributions. The central message is that attribution results are meaningful only relative to the mathematical assumptions under which they are defined, and that those assumptions should be reported.
[AI-86] he Steering Budget: Examples beat Knobs
链接: https://arxiv.org/abs/2607.14246
作者: Raj Kumar Rajendran
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 39 pages, 14 figures
Abstract:Generative models are steered with knobs – prompts, guidance scales, property tags. Turn one as hard as you like and, past a point, it stops moving the property you care about. We find that ceiling is not a shortcoming of the model but a budget, set by the training data before the model is trained: a property’s movable range splits in two – the part a knob can reach, and a second, significant part that only examples – concrete instances of what you want more of – can reach. That second part is usually much larger, but not always, and the same budget says so in advance. Reaching that second part takes a different move: instead of turning a knob, you show the model examples, composed from what it already learned rather than added to its training. A cheap audit of the training data measures the budget; we give a recipe for building the example set that reaches all of it. This buys two things a knob can’t. Reach: it moves a property across the whole budget, not just the part a knob reaches. Expressiveness: it steers toward targets you can only specify by example – including ones you can’t put into words. We turn these into a handful of falsifiable claims and verify them in two unrelated domains, image and crystal-structure generation – marking where a knob is enough, and where only examples will do. Comments: 39 pages, 14 figures Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2607.14246 [cs.AI] (or arXiv:2607.14246v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.14246 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-87] Align AI to Dynamic Human-AI Workflows
链接: https://arxiv.org/abs/2607.14240
作者: Valerie Chen,Cleotilde Gonzalez,Anita Williams Woolley,Michael Lee,Tongshuang Wu,Vincent Conitzer,Aarti Singh
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions. In this paper, we argue for a shift from static and emulative to interactive and complementary alignment, where preferences emerge through interaction and alignment is defined not by satisfying preferences alone. We first formalize this gap by contrasting existing alignment with a trajectory-level view in which human and model behavior co-evolve over time. Because these interaction dynamics have not been adequately captured within existing ML formulations, we ground this perspective in insights from an interdisciplinary workshop. We draw on lessons from social-science accounts of human-human collaboration and then argue that human-AI systems amplify these dynamics, introducing new asymmetries that make reasoning about uncertainty harder and introduce new coordination challenges. Based on these lessons and new challenges, we conclude by outlining a research agenda for developing AI systems that align with humans in interaction, requiring an interdisciplinary synthesis of machine learning and the social and decision sciences.
[AI-88] Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection
链接: https://arxiv.org/abs/2607.14236
作者: Yi Wang,Wendi Chen,Zimo Wen,Han Xue,Xueqi Li,Wenye Yu,Zhijie Chen,Hao Yang,Jun Lv,Chuan Wen,Cewu Lu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Project page: this https URL
Abstract:Pretrained vision-language-action (VLA) policies provide strong language-conditioned manipulation knowledge, but they remain largely vision-driven and can struggle once manipulation enters contact states where the scene is occluded, depth is ambiguous, or small force errors push execution off the offline demonstration distribution. We present LIFT (Late Reactive Injection of Force for VLA Post-Training), a force-aware post-training framework that adds contact reactivity to a pretrained VLA policy while preserving its general manipulation knowledge. LIFT grafts a reactive action expert beside the original action expert, initializes it from pretrained action weights, and injects recent 6D end-effector force through causal force memory and zero-initialized cross attention, enabling actions to be refreshed during execution. To address the policy-dependent distribution shift of contact feedback, LIFT further couples reactive force injection with an online DAgger loop that trains on a mixture of offline task-alignment data and human-corrected online rollouts. Across towel folding, book insertion, and Hanoi ring placement, LIFT learns faster and reaches higher performance than vision-only post-training, while ablations show that reactive force memory and online corrective data are both important for robust contact-rich manipulation. Our code and data will be publicly available.
[AI-89] LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks
链接: https://arxiv.org/abs/2607.14233
作者: Nilay Anurag,Shital Adhikari,Taniya Kapoor,Nikhil Muralidhar
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 28 pages, 10 figures, 13 tables. Code available at this https URL
Abstract:Physics-informed neural networks (PINNs) have had a broad research impact in modeling domains governed by partial differential equations (PDE). However, PINNs have been shown to perform poorly, sometimes even converging to trivial solutions, in challenging PDE domains, or when generalizing to unseen but related PDE domains. Previously proposed solutions detail hyperparameter tuning to reduce loss imbalance between data-driven and physics guided losses, curriculum learning based training strategies, or dynamic re-sampling of hard collocation points. These methods face certain pitfalls: hyperparameter tuning is expensive, designing a training curriculum is ambiguous in multi-parameter PDE settings, and dynamic resampling still fails in complex PDE settings. Complementary to this line of thinking, we believe the initial PINN network weights also play a crucial role in the emergence of catastrophic failures during training, yet the effect of PINN weight initialization has been surprisingly under-investigated. To this end, we propose a framework for Learned Initialization via Gated Layerwise Optimization (LIGO-PINN) to overcome PINN convergence failures. Through rigorous evaluation on 1D and 2D PDE domains, including a challenging 2D fluid dynamics setting, we demonstrate that our methodology outperforms state-of-the-art methods designed to alleviate PINN failures, achieving a 91.5% average performance improvement across six baselines and 81% over the strongest baseline. We also verify that LIGO-PINN generalizes to 3D unstructured domains. Finally, we analyze training dynamics across all three PDE domains to explain both LIGO-PINN’s improvement and the convergence failure of traditional PINNs. Code: this https URL Keywords: Machine Learning, Physics-Informed Neural Networks, Deep Learning, PDE Modeling Comments: 28 pages, 10 figures, 13 tables. Code available at this https URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) MSC classes: 68T07, 65M99, 65N99 ACMclasses: I.2.6; I.5.1; G.1.8 Cite as: arXiv:2607.14233 [cs.LG] (or arXiv:2607.14233v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.14233 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-90] How Artificial Intelligence LLM Engines Shape the Global Conflict Information Environment
链接: https://arxiv.org/abs/2607.14197
作者: Jason Miklian
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial Intelligence (AI) answer engines now field a growing share of the questions that analysts, scholars, and the public ask about issues of peace and conflict. Large Language Models (LLMs) are known to hallucinate under certain conditions, but do these errors have discernible patterns when they are asked about conflicts, and if so what can that teach us about the changing global conflict information environment? To answer, we first asked a battery of questions about 28 conflicts to five leading answer engines and scored their 5,460 answers against documented evidence. We found that the thinner the retrievable record around a given conflict, the more the engines invent, misattribute, and miscount. Thin records don’t just encourage hallucination, but create structural exposure to mis- and disinformation, because they are the easiest records to warp through Generative Engine Optimization (GEO) to bias engine responses. Through an analysis of 1,048 websites that the AI LLMs pulled conflict facts from, we found that GEO source optimization is already happening, and while state-partisan digital capture remains incipient it is rapidly growing. We explain what these findings mean for scholarship with the rise of GEO information warfare, and for policy argue for a return to the deep local monitoring and translation-based research that AI tools cannot replicate, closing with a discussion of future research opportunities and challenges in this fast-moving space.
[AI-91] RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination
链接: https://arxiv.org/abs/2607.14187
作者: Haotian Liang,Mingkang Chen,Yufei Huang,Yuchun Guo,Xiaomeng Zhu,Xiangli Shi,Kaixuan Wang,Yunxuan Mao,Weijie Zhou,Ling Chen,Shirong Zeng,Yueyu Long,Yuchen Si,Yajuan Zhu,Xingyu Zhou,Minghui Wang,Wanjia He,Xin Yang,Lingzhu Xiang,Zhiqing Liu,Bohan Ma,Xiran Huang,Tianshuo Yang,Zhiheng Liu,Xuantang Xiong,Zisheng Lu,Ping Luo,Yao Mu,Han Hu,Zhengyou Zhang
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination. Unlike vision-language models that emphasize scene understanding and textual decision making, or generative world models that mainly predict future visual states, RxBrain represents embodied plans in a single planning sequence where language and visual imagination play complementary roles. Language provides the abstract structure of a plan, including task decomposition, planning primitives, constraints, temporal order, and decision logic, while visual imagination grounds this structure through world state prediction and joint subgoal planning, associating each planning step with intermediate and final physical states. RxBrain adopts a unified multimodal Mixture-of-Transformers architecture that supports language, image, and video understanding and generation within one model. To train this capability, we build an automatic pipeline that converts embodied videos into joint text-visual planning supervision by decomposing videos into planning steps and aligning them with visual state transitions. We further introduce RxBrain-Bench to evaluate whether models can represent embodied plans through joint textual and visual components rather than separate understanding or generation. Experiments show that RxBrain maintains embodied understanding and generation abilities, and produces plans with coupled textual reasoning, world state prediction, and joint subgoal planning. We also extend RxBrain to continuous robot action generation, where it shows promising real-robot performance without large-scale action-data pretraining. These results provide an initial step toward foundation models for embodied cognition.
[AI-92] NexForge: Scaling Executable Agent Tasks via Requirement-First Synthesis
链接: https://arxiv.org/abs/2607.14186
作者: Jiarong Zhao,Zhikai Lei,Zhiheng Xi,Rui Zheng,Hang Yan,Jie Zhou,Qin Chen,Liang He
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Scaling executable agent training data is bottlenecked by substrate-first methods that tie task generation to predefined tools, repositories, or skill graphs: expanding coverage requires manual expansion of the substrate, each new domain demands a bespoke pipeline, and the resulting task distributions often reflect substrate convenience rather than real-world demand. We introduce NexForge, a requirement-first framework that compiles free-form capability requirements into executable agent training data. NexForge first performs research-based demand discovery to identify representative task forms, realistic scenarios, and their relative prevalence. It then applies distribution-aware task compilation and automatically retrieves or constructs the files, repositories, dependencies, and runtime configurations required to materialize each task, followed by teacher rollout collection and trajectory distillation. The same pipeline, without any domain-specific infrastructure, produces 3,600 terminal tasks and 2,000 office tasks, improving Qwen3.5-35B-A3B Base from 22.5% to 52.0% on Terminal-Bench 2.0 and from 813 to 1338 Elo on GDPval; scaling to 43.2K terminal tasks reaches 58.4%, surpassing Claude Opus 4.6. Scaled further, NexForge-synthesized data contributes to the training of Nex-N2, a family of publicly available agent models that lift Qwen3.5-35B-A3B to 75.3% on Terminal-Bench 2.1 and to 1585 Elo on GDPval – achieving state-of-the-art open-source performance and surpassing several frontier proprietary systems. Nex-N2 models are available at this https URL
[AI-93] Closed-Loop Knowledge Dynamics: An Operational Framework for Saturation and Escape
链接: https://arxiv.org/abs/2607.14185
作者: Xuening Wu,Shan Yu,Shenqin Yin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Feedback-driven loops support iterative improvement in large language models, reinforcement learning, and autonomous discovery, yet their gains often diminish under repeated internal feedback. We study why closed-loop knowledge systems saturate and what external information can move them beyond their current attractors. We introduce a three-level operational framework in which knowledge states x_t evolve through transition kernels K_\theta indexed by a structural parameter \theta . The governing structure is defined as the observational equivalence class of \theta induced by these kernels, while attractors and basins are properties of the fixed- \theta dynamics. A structural intervention changes \theta and produces a detectable kernel discrepancy on pre-specified probe states, making structural change falsifiable. Using a Lyapunov drift condition, we show that stable internal dynamics approach bounded stability regions with exponentially attenuated transients and a noise-controlled residual floor. We characterize escape through a metric condition on intervention-induced attractor displacement and a baseline-relative KL lower bound for increasing escape probability. This analysis also explains why conditional mutual information alone cannot certify escape: it measures variation among intervention-conditioned updates rather than departure from the no-intervention law. Case studies in LLM code repair, sparse-reward reinforcement learning, and Bayesian optimization use matched continuation controls to illustrate how feedback strength and alignment affect quality-improving escape. Our contribution is an operational connection among stability tools, measurable intervention effects, and cross-domain diagnostics.
[AI-94] Semantic Audio-driven Understanding for Dynamic Humanoid Whole Body Control
链接: https://arxiv.org/abs/2607.14182
作者: J. M. A. Marcelo,M. Brienza,E. Bugli,L. Comito,D. Nardi,D. D. Bloisi,V. Suriani
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Accepted at 29th Robocup International Symposium, held on July 6th, 2026 in Incheon, Republic of Korea
Abstract:Recent advances in humanoid robotics and reinforcement learning have enabled the acquisition of highly expressive whole-body motion policies. However, most robotic performances remain based on pre-scripted sequences or externally triggered behaviors, limiting autonomy and responsiveness to dynamic environments. In this work, we introduce a novel multi-modal orchestration framework for semantic audio-driven humanoid control, enabling robots to autonomously select and execute appropriate motion skills in real time. The system processes continuous audio streams and routes them into music or speech branches. Music input is handled via audio fingerprinting and semantic embeddings to retrieve track identity and temporal alignment, allowing dynamic mapping between musical segments and motion policies. Speech input is grounded into a discrete library of imitation-learned skills, enabling direct human-robot interaction. Both modalities share a unified interface that schedules skill execution over a reinforcement learning control pipeline. We validate the approach in simulation and on a Unitree G1 humanoid, showing robust sim-to-real transfer and consistent audio-conditioned policy selection. Supplementary materials are available at the following site: this https URL
[AI-95] RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences
链接: https://arxiv.org/abs/2607.14180
作者: Logan Mondal Bhamidipaty,Mykel Kochenderfer,Subramanian Ramamoorthy
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:World models are widely used in offline reinforcement learning (RL) to improve sample efficiency and generate experience beyond a fixed dataset. However, they are vulnerable to model exploitation where data coverage is thin. Prior work addresses this either by collecting more expert demonstrations, which is often expensive, unsafe, or unavailable, or by conservative algorithms that avoid uncertain regions, which limits generalization. We propose instead to repair exploitation directly using human preferences over imagined rollouts, leveraging the strong intuitive physics that allows humans to easily spot egregious dynamics hallucinations. We formalize this as Dynamics Learning from Human Feedback (DLHF), a Bradley-Terry preference loss over trajectory log-likelihoods under a learned dynamics model. Unfortunately, naive DLHF is sample inefficient, so we introduce RENEW, which uses epistemic uncertainty to focus finetuning where the model is most exploitable. We evaluate on several Jumanji and classic control environments and find that while naive DLHF requires an outsize preference budget, RENEW makes the framework practical by improving sample efficiency, limiting catastrophic forgetting, and reducing exploitation in pretrained world models. Taken together, our results provide initial evidence that preferences can supervise world model dynamics directly, offering a new approach to addressing exploitation in offline model-based RL.
[AI-96] he Cost and Network Limits of Space-Based AI Compute
链接: https://arxiv.org/abs/2607.14172
作者: Kees van Berkel
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper evaluates whether large-scale AI data centers deployed in low-Earth orbit (LEO) could become a cost-effective alternative to terrestrial facilities. The analysis compares orbital and ground-based systems across launch cost, power generation, cooling, radiation exposure, and atmospheric reentry, as well as compute-network performance. A key distinction is the shift from terrestrial Clos networks to space-based mesh networks using laser inter-satellite links. Using bisection bandwidth, bisection intensity, and roofline-style models, we show that while LEO-based inference may be feasible, training frontier-scale LLMs in orbit is unlikely to be competitive with terrestrial data centers.
[AI-97] When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM -Synthesized Code World Models
链接: https://arxiv.org/abs/2607.14169
作者: Javier Aguilar Martín
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 41 pages, 4 figures. Code and reproduction log: this https URL
Abstract:Large language models can synthesize a game’s rules as executable code - a Code World Model (CWM) - which a classical planner then searches over. Such models are typically accepted when they reach high transition accuracy on sampled trajectories. We argue this is the wrong notion of adequacy for planning. We show four things. (1) An LLM-synthesized CWM can pass a sampling gate at 100% transition accuracy and be \geq 98% state-accurate on the planner’s own search distribution, yet lose systematically at play, because the 1% it gets wrong is exactly the pivotal dynamics; the play cost of the omitted rule is 0.091 (seed-clustered 95% CI [0.065,0.117] , n=4800 ). We call this the verified-vs-correct gap, and confirm it end-to-end through the synthesis pipeline. (2) The harm follows a quantitative law, \mathrmdanger=\mathrmplay_cost\times(1-\mathrmrarity)^N , whose (1-\mathrmrarity)^N gate-miss factor is proven exact and whose play cost is empirically bounded. (3) The failure is not repaired by more data: LLM synthesis behaves as rule translation, not rule inference, and did not infer the omitted rule across models (GPT-5.x) and data regimes (including DAgger and targeted examples). (4) The same mechanism recurs on the belief-inference function of imperfect-information CWMs: we prove a coverage bound (a size- N gate is identifying when N\gtrsim b^d_\max ), explaining why shallow games such as Kuhn poker show no gap, and hand-construct Beacon, a verified-but-wrong inference function that passes the gate yet loses every game. These results suggest adequacy for planning-oriented world models should be measured on the search distribution or by play directly, not by prediction accuracy on sampled transitions. Comments: 41 pages, 4 figures. Code and reproduction log: this https URL Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) MSC classes: 68T05, 91A05 ACMclasses: I.2.6; I.2.8; I.2.7 Cite as: arXiv:2607.14169 [cs.AI] (or arXiv:2607.14169v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.14169 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-98] Structured Feedback Improves Repair in an LLM Agent Loop
链接: https://arxiv.org/abs/2607.14167
作者: Jaideep Ray,Ankit Goyal
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM agents often retry after external validation rejects a candidate, but the interface between validation and the next model call remains underspecified. We introduce VeriHarness, a code-controlled agent loop in which models generate candidates while external validators control acceptance, budgets, and traces. We use it to compare raw diagnostics with feedback that identifies the failure location, observed value, and admissible alternatives. Across 50 paired TextWorld games under a four-call cap, feedback containing all three fields raises terminal success from 14/50 to 36/50 for Qwen2.5-Coder-14B (+44 percentage points) and from 8/50 to 29/50 for Llama-3.1-8B (+42 points). Ablations locate most of the gain in the admissible alternatives: feedback containing only the location and observed value remains near the raw diagnostic baseline. Presenting the complete repair information in prose instead of a keyed JSON record yields nearly the same success, providing no evidence that JSON syntax itself improves repair. The ordering persists across the tested call budgets and one sampled-decoding setting.
[AI-99] owards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation
链接: https://arxiv.org/abs/2607.14165
作者: Dimple Vijay Kochar,Hae-Seung Lee,Anantha P. Chandrakasan
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Machine Learning (cs.LG)
备注:
Abstract:While Large Language Models (LLMs) have demonstrated significant capability in software code generation, their application to analog Electronic Design Automation (EDA) is bottlenecked. Owing to limited circuit topology understanding and data, directly prompting LLMs and multimodal models leads to hallucinations and failure to produce schematics capable of passing rigorous SPICE simulations, as we show in our work. Instead, we propose an end-to-end, multi-step LLM agentic framework ATLAS, capable of generating a functional Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) that successfully passes simulation validation. To adhere to the rigid constraints of analog design, we utilize expert knowledge to ground the LLM in its planning, selection, parameterization, and iterative modification. As part of ATLAS, we introduce Template-Constrained Generation - which unlike other template-based works - builds towards a more generalized SAR ADC generation flow. We demonstrate a strong proof-of-concept of our framework by developing SAR ADCs across technology nodes and input specs. Overall, our expert-knowledge grounded multi-step agentic ATLAS establishes a pragmatic foundation for integrating LLMs into reliable analog design methodologies.
[AI-100] Enhancing Small Language Models Reasoning through Knowledge Graph Grounding ESWC2026
链接: https://arxiv.org/abs/2607.14149
作者: Dimitrios Kelesis,Konstantinos Bougiatiotis,Georgios Paliouras
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Presented at the 2nd Causal Neuro-symbolic Artificial Intelligence (Causal NeSy): Toward Agentic LLMs with Neuro-Symbolic and Graph Based Reasoning Workshop @ ESWC2026
Abstract:Although large language models (LLMs) have set benchmarks for zero-shot reasoning, their deployment remains cost-prohibitive and environmentally taxing. Small Language Models (SLMs) offer a sustainable alternative, but prone to errors, on tasks requiring complex, multi-hop logical grounding. We investigate a neuro-symbolic agentic framework to enhance the reasoning capabilities of SLMs, specifically Gemma 3 (1B, 4B) and Llama 3.2 (3B), using the CLUTRR kinship benchmark. Our approach transforms the SLM into a minimalist agent utilizing two specialized tool calls: extract_facts for symbolic triplet extraction and get_hint for expert reasoning via a Relational Graph Convolutional Network (RGCN). We evaluate these models across two configurations, both in an Oracle scenario with ground-truth triplets and a Realistic scenario relying on self-extracted knowledge. Our results reveal that while RGCN-derived hints provide a 1.5 - 2x performance gain over story-only baselines, the system is constrained by the extraction bottleneck and sequential deductive fragility, where early extraction errors compound over multi-hop chains. Furthermore, we identify a “distraction effect” in specific architectures where noisy, self-generated facts degrade performance despite the presence of expert hints. This work characterizes the challenges of symbolic grounding in low-resource agentic systems and provides a roadmap for iterative verification in neuro-symbolic agentic pipelines.
[AI-101] Capability from Access Structure Not Scale: Lower Bounds and Pre-Registered Tests for Hybrid Sequence Models
链接: https://arxiv.org/abs/2607.14144
作者: Wenhui Chen,Jianlin Chen,Ziyao Lin,Chi Man Vong
类目: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注: 41 pages, 16 figures. Pre-registered small-scale experiments (scorecard: 11 supported, 7 partial, 1 failed). Information-theoretic lower bounds plus controlled trained witnesses. Jianlin Chen and Ziyao Lin contributed equally. Corresponding author: Chi Man Vong (cmvong@um. this http URL )
Abstract:The Platonic Representation Hypothesis (PRH) holds that as models scale, representations of heterogeneous networks converge toward a shared model of reality. We propose its sequel and boundary, the Capability Convergence Hypothesis (CCH): under a fixed per-token inference budget, representational convergence does not entail capability convergence. Capability instead converges toward a class, the access-complete hybrid: any architecture holding both a compressive O(1)-state channel and a scalable verbatim-index channel. We anchor it on a witness task, the Newton’s-apple problem in an infinite stream, and name three resource walls: a Shannon wall barring any o(Nb)-state architecture, a horizon wall barring any fixed window, and a circuit wall barring fixed-depth attention-only composition (conditional on TC0 != NC1). Under an explicit separability assumption a hybrid crosses all three by paying each wall’s price, so capability is strictly super-additive under composition. We separate what we prove from what we conjecture: the access-completeness principle rests on information-theoretic lower bounds and pre-registered experiments, while the field-level convergence trend is an economics-motivated conjecture. We report the first pre-registered small-scale tests under criteria frozen before the data: the predicted scissors gap is measured (exact-retrieval error 0.994 vs. 0.000 once a 64-scalar state gains one global-attention layer), the state-tracking bifurcation lands at the registered boundary, and a conjunction witness shows an irreducibly two-channel solution; one prediction failed with its direction reversed and is reported as such. Representational convergence is given freely by scale; capability convergence must be purchased by access structure.
[AI-102] Human AI Construction of Bayesian Networks for Operational Decision Support – A Virtual Survey Approach
链接: https://arxiv.org/abs/2607.14141
作者: Kumar Rahul(1),Shovan Chowdhury(2) ((1) Indian Institute of Management Kozhikode, Kerala, India, (2) Indian Institute of Management Kozhikode, Kerala, India)
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Bayesian Belief Networks (BBNs) are powerful tools for decision-making under uncertainty. However, building their structures and estimating parameters are difficult. Currently, researchers must choose between relying on expert judgement or using large datasets to learn the structure and parameters of the network. We propose a new methodology using Large Language Models to bridge the gap between expert opinion and data-driven learning. This approach uses a panel of AI agents to estimate probabilities based on specific personas and context. We then apply a trimmed-mean rule to remove noise from these responses. We develop a six step BBN framework and illustrate it to model customer intention to consult a doctor in an alternative healthcare system. The model reveals that while self efficacy appears to be a major factor, its actual causal impact is small. In contrast, subjective norms have a much stronger effect in modelling customers’ intention. The most effective strategy is to improve both confidence and community norms simultaneously.
[AI-103] Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence
链接: https://arxiv.org/abs/2607.14127
作者: Shohini Sarkar,Smithi Mahendran,Rishi Chudasama,Varun Mannam,Arav Luthra,Yuvraj Rekhi,Vivek Nadig,Arsh Goenka
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: Accepted at the 2026 IEEE 22nd International Conference on Automation Science and Engineering (IEEE CASE 2026)
Abstract:Representative clutter height (RCH) is a key parameter in radio propagation and interference analysis because it captures the dominant height of local obstructions that drive terminal clutter loss. Current practice often relies on fixed clutter heights assigned to land use classes in Recommendation ITU-R P.452-18, but this misses within class variation and can lead to conservative exclusion zones and poor site ranking for low Earth orbit ground station siting and spectrum coordination. We present an interpretable, globally deployable machine learning framework for predicting RCH from open geospatial data. The model is trained using LiDAR derived labels from the U.S. Geological Survey 3D Elevation Program and inference time features from global land-cover, terrain, demographic, thermal, and optical remote sensing products. We define RCH using a robust 75th percentile clutter height statistic, evaluate multiple regressors, and select LightGBM for its accuracy, efficiency, and compatibility with feature attribution analysis. The final model achieves a mean absolute error of 1.79m and an R^2=0.765, reducing absolute error by more than 60% relative to the ITU baseline. Beyond aggregate fit, we evaluate domain facing criteria relevant to RF planning, including meter scale error, tolerance band accuracy, over and under estimation tails, agreement with ITU clutter height regimes, and SHAP-based physical plausibility. SHAP identifies tree canopy cover, land-cover semantics, and spectral reflectance as the most influential predictors. Studies on segmentation derived features, non-forest ablations, and land-cover matched international validation show that open geospatial data can improve clutter modeling at scale without sacrificing interpretability or deployability.
[AI-104] Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods ICML2026
链接: https://arxiv.org/abs/2607.14123
作者: Michal Moshkovitz,Suraj Srinivas,Lesia Semenova,Nave Frost,Cyrus Rashtchian,Valentyn Boreiko,Shichang Zhang,Himabindu Lakkaraju,Cynthia Rudin,Jennifer Wortman Vaughan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to ICML 2026 Position Paper Track
Abstract:Despite the proliferation of Explainable AI (XAI) techniques – from feature attributions to sparse autoencoders – explanations rarely influence real-world workflows. In practice, they are often generated and discarded without guiding meaningful action. This gap reflects foundational shortcomings: research has not yet established methodologies for integrating explanations into end-to-end, human-in-the-loop systems. This position paper argues that the machine learning community must pivot from ad-hoc XAI methods toward addressing foundational structural challenges, including unclear problem formulations, underspecified evaluation objectives, and the absence of pipelines for explanation-driven feedback. We support this claim through an analysis of recent ICML, NeurIPS, and ICLR papers and a survey of XAI practitioners, revealing recurring issues that limit cumulative progress. We conclude by outlining a practical checklist designed to shift XAI toward a more human-centered, action-oriented paradigm. By emphasizing foundational clarity over the development of ad-hoc methods, we hope to provide a roadmap for integrating explanations into actionable, feedback-driven AI systems.
[AI-105] RegNetAgents : A Multi-Agent Framework for Cross-Network Regulatory Driver Identification in Cancer Genomics
链接: https://arxiv.org/abs/2607.14097
作者: Jose A. Bird
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce RegNetAgents, an AI-oriented multi-agent framework for structured, query-driven regulatory candidate identification across heterogeneous gene regulatory networks. The system enables unified analysis of bulk tumor and single-cell-derived ARACNe networks by integrating TCGA-derived cancer networks with large-scale single-cell regulatory networks from the GREmLN project. For a given focal gene, the framework performs dual-network classification, cancer gene filtering using OncoKB annotations, and mode-of-action (MoA) assignment for tumor-derived regulatory relationships. Candidates are ranked by evidence consistency across networks (Both, TCGA-only, GREmLN-only). The system is implemented as a multi-agent LangGraph DAG workflow, accessible through a unified Python API and Model Context Protocol (MCP) client, operating as a downstream analytical layer over precomputed regulatory networks rather than a network inference method. Across eleven breast cancer (BRCA) and twelve colorectal cancer (COAD) focal genes, RegNetAgents identifies candidate regulators significantly enriched for OncoKB-annotated cancer genes. TCGA-derived candidates show strong enrichment (Stouffer Z = 6.69 for BRCA and 6.95 for COAD), while GREmLN-derived candidates also demonstrate significant enrichment (Z = 5.51 for BRCA and 7.06 for COAD; all p 0.0001). No enrichment is observed in housekeeping or non-driver control gene sets, supporting signal specificity. An extended module enables structured evaluation of oncogenic potential, druggability, clinical relevance, and network vulnerability, supporting end-to-end interpretation from candidate identification to biological hypothesis generation. RegNetAgents establishes an interpretable AI framework for cross-network regulatory candidate identification in cancer genomics.
[AI-106] IMEX Interaction-Based Model Explanation
链接: https://arxiv.org/abs/2607.14096
作者: Emiliano Massi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In predictive modeling, the ability to explain why a model produces a given target prediction has become increasingly important [5, 10]. Black-box models do not provide a transparent description of the internal mechanisms that generate the prediction, making even accurate predictions difficult to interpret and validate. In critical contexts, predictive accuracy alone is not a sufficient validation metric if the reasons underlying model decisions remain unexplained. The IMEX (Interaction-Based Model Explanation) approach represents a methodological direction within explainable predictive modeling. IMEX is designed to identify which variables contribute most to the target prediction and which interactions among variables are significant in determining the target. The method does not impose limitations on higher-order interaction analysis, allowing the investigation of feature subsets with cardinality greater than two. Beyond the identification of feature importance, IMEX enables the exploration of interaction patterns that may be consistent with latent mechanisms influencing the outcome. Through the application of the IMEX algorithm, it is possible to construct an interpretability map of the predictions. The IMEX framework is built on two complementary metrics: Static Correlation Power (PCS), which quantifies the contribution of individual features, and Interaction Correlation Power (PCI), which captures non-additive effects among features. In the present work, the PCS component is experimentally validated through a comparison with INVASE [18] on three synthetic datasets with known structures. The results indicate that IMEX can recover relevant feature-level structures in the presence of non-linear, conditional, and multicollinear relationships between input features and prediction targets.
[AI-107] HG-RAG : Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs
链接: https://arxiv.org/abs/2607.14095
作者: Pranav Yadav
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures
Abstract:Retrieval Augmented Generation (RAG) has proven to be a widely successful process at improving the quality of outputs from a Large Language Model (LLM) for wider context. However, RAG systems typically retrieve context from flat document stores, which struggles when queries require hierarchical or relational reasoning across structured knowledge. I present HG-RAG (Hierarchy-Guided RAG), a framework that performs graph-traversal over a hierarchical knowledge graph to deliver structured context to a language model. My retrieval pipeline resolves a named entity anchor from the query, then expands context upward through parent nodes, laterally through relational neighbors, and downward through child nodes when needed. I evaluate HG-RAG against a dense retrieval baseline across three world scales (18-800 nodes) with four query types: local fact, hierarchical, neighborhood, and multi-hop. Results show HG-RAG consistently outperforms the flat baseline on hierarchical, relational, and multi-hop reasoning tasks, while reducing hallucination and maintaining locality coherence.
[AI-108] Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue
链接: https://arxiv.org/abs/2607.14093
作者: Oleksii Bychkov
类目: Artificial Intelligence (cs.AI)
备注: 9 pages
Abstract:This paper presents a novel three level hierarchical learning architecture for autonomous UAV swarms performing search and rescue operations. Unlike conventional approaches that apply a single learning paradigm across all hierarchy levels, the proposed architecture integrates three qualitatively different learning mechanisms corresponding to the biological hierarchy of reflexes, skills, and reasoning such as Hebbian neuroplasticity for individual agent adaptation, multi agent reinforcement learning with graph neural networks and behavior trees for tactical coordination, and model agnostic meta learning with BDI reasoning and a digital twin for strategic decision making. The architecture is formalized through twenty two architectural contracts organized across six components such as BDI, Behavior Trees, GNN, MARL, Neuroplasticity, Meta Learning that collectively provide six classes of formal guarantees such as safety, budget correctness, optimality, liveness, starvation freedom, and inter level consistency. We introduce Swarm Meta Cognition as a compositional property arising from the structured interaction of all three levels, enabling the swarm to monitor its own cognitive state and switch between cognitive strategies. Five constructive progress functions for SAR task types bridge the gap between abstract optimization theory and concrete operational scenarios. The main integration theorem establishes that when all contracts are satisfied, the hybrid neuro-symbolic system preserves all six guarantee classes. For the dynamic case with active learning, five new contracts extend the framework with three additional guarantees such as cognitive resilience, graceful degradation, and monotonic meta improvement. Theoretical analysis demonstrates that the architecture addresses five fundamental limitations of existing hierarchical RL approaches. Comments: 9 pages Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2607.14093 [cs.AI] (or arXiv:2607.14093v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.14093 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Oleksii Bychkov S. [view email] [v1] Thu, 9 Apr 2026 12:12:05 UTC (344 KB)
[AI-109] Falsifiable Release Gates for Self-Improving Systems
链接: https://arxiv.org/abs/2607.13070
作者: Deepak Soni
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 14 pages, 8 figures. Software, gate suite, run artifacts, and TLA+ specification are open source
Abstract:Safety claims on self-improving agent runtimes are almost always self-graded: a policy file, a guardrail, or a README commitment. We describe falsifiable release gates, and a methodology to build and validate such systems, such that every new capability must pass a pre-specified, machine-verifiable acceptance suite before it ships, and a fixed set of standing invariants is preserved at each gate. I think we applied the method in the Antahkarana, an open runtime, via seven gates from basic observability into a self governing loop that suggests changes to its own policy. no action goes to an effector without a safety-critical property capability token minted by a control ring is exhaustively machine-checked over the one million recorded reachable state space of a bounded model and re-checked against execution traces. A purposely broken model gives the shortest counterexample, so the checker has teeth, is apparent. The self-enhancement loop is positively Constrained: the entire write surface is policy rules, tightening changes. may auto-apply loosening changes always require a human merge and a proposal autoclosed is one that mispredicts its own effect; We publish the acceptance measured results for all the seven gates, define precisely the scope of each claim (a bounded of the coordination skeleton (not the learned components) and free the runtime, either command line tools and the gate suite, so the results reproduce, and gates can run against other agent frameworks. Reviewers may repeat the single command central non-bypass in seconds.
[AI-110] Subjective Risk Decomposition: A New View for Uncertainty Quantification
链接: https://arxiv.org/abs/2607.15196
作者: Raghad Alamri,Michele Caprio,Gavin Brown
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 27 pages (including bibliography/appendix)
Abstract:We present a novel viewpoint for uncertainty quantification. Uncertainty measures are not primitives, in need of axioms and argumentation, but instead consequences, of higher-level modelling decisions. We show how epistemic and aleatoric uncertainty measures can be derived via decomposition of a subjective risk, based on a strictly proper loss. Reverse cross-entropy provides a prominent example, where decomposition recovers the classic information-theoretic uncertainty terms. The same approach recovers numerous measures previously proposed across the UQ literature, providing them a common theoretical foundation. From a practical point of view, this suggests a new approach to UQ: given a modelling scenario and strictly proper loss, the corresponding epistemic and aleatoric terms are induced by the subjective-risk decomposition. We then extend our view to learning theory: we introduce and analyse subjective risk analogues of excess risk, approximation error, and estimation error, and identify the connections to UQ. We consider this a first step towards a full learning-theoretic framework for uncertainty quantification.
[AI-111] LQCDMaster: Agent ic Scientific Computing for Lattice Quantum Chromodynamics Research
链接: https://arxiv.org/abs/2607.15001
作者: Haofei Gao,Tingjia Miao,Wenkai Jin,Muhua Zhang,Hanzhang Wang,Jie Ran,Jinxin Tan,Zhentao Zhang,Bo Tang,Leiyi Li,Jun Hua,Xiangyu Jiang,Qi-An Zhang,Siheng Chen,Wei Wang
类目: High Energy Physics - Lattice (hep-lat); Artificial Intelligence (cs.AI); High Energy Physics - Phenomenology (hep-ph)
备注: 17 pages, 4 figures
Abstract:Lattice quantum chromodynamics (LQCD) provides a first-principles framework for computing hadronic observables, but its practical use remains limited by the substantial expertise required to turn research motivation into reliable computing workflows. Here we present \textscLQCDMaster, a tool-augmented, skill-guided and domain-specialized scientific computing agent that converts natural-language LQCD research tasks into executable PyQUDA computing workflows, including measurement scripts, job-submission artifacts, execution logs and numerical outputs. The system combines agentic planning, expert-annotated LQCD skills and a deterministic Wick-contraction tool to constrain the algebraically fragile components of code generation. We evaluate \textscLQCDMaster on a benchmark at the forefront of scientific research, comprising 70 LQCD computing tasks, with observables covering local and nonlocal two-point functions, Wilson loops, meson and baryon three-point functions. The generated workflows exactly reproduce expert-written implementations in 63 of 70 tasks at machine precision, with three additional discrepancies attributable to convention mismatches. Across representative observables, the agent reduces implementation time from hours to minutes while preserving end-to-end numerical validation. Further, we present a typical case of \textscLQCDMaster-driven exploration: a lattice computation of light-cone distribution amplitudes with diagonal Wilson-line, a quantity accessible with standard methods but never before computed, and computation of the spectrum of proton, deuteron, triton, hyperon, hyperdeuteron and hypertriton. This work pioneers the paradigm of agentic scientific computing by automating the end-to-end scientific computing workflows in lattice QCD research, lowering its barrier and facilitating the exploration and verification of non-standard scientific ideas.
[AI-112] Governing Artificial Intelligence: Public Preferences and Regulatory Options
链接: https://arxiv.org/abs/2607.14585
作者: Magnus Lundgren,Jonas Tallberg
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI)
备注: Main paper 16 pages; supplementary materials 51 pages
Abstract:Artificial intelligence (AI) is rapidly transforming economies, societies, and polities, raising fundamental questions about how it should be regulated. Policymakers face choices over whether to prioritize innovation or safety, rely on public oversight or private self-regulation, and govern nationally or internationally. Yet little is known about how citizens evaluate these competing priorities. Here we report a conjoint survey experiment conducted in seven countries with diverse political and economic profiles. We find that citizens strongly support regulating AI and generally prioritize safety over innovation, public governance over private self-regulation, and international over national approaches. The preference for safety is strongest among those who perceive AI as risky, unpredictable, and personally consequential. These findings reveal a systematic misalignment between dominant regulatory approaches and citizen preferences.
[AI-113] A Modern Multimodal Assistant on a 6 GB 2011 GPU: Stage-Validated All-GPU CUDA Inference for Fermi
链接: https://arxiv.org/abs/2607.14568
作者: A. C. Opus,J. Q. Lu
类目: Other Condensed Matter (cond-mat.other); Artificial Intelligence (cs.AI)
备注:
Abstract:A companion study ran a 35B mixture-of-experts model on a 2011 NVIDIA Tesla C2075 (Fermi, sm_20, 6GB) as a GPU-prefill/CPU-decode hybrid, because the 4-bit model did not fit in device memory (arXiv:2606.24031). This report keeps the hardware and asks what a model that fits can do: we deploy MiniCPM-V-4.6, a modern multimodal assistant pairing a SigLIP2 vision encoder and window-attention merger (16x visual token compression) with a compact hybrid gated-delta-net backbone, entirely on the GPU. Three results. (i) An all-GPU engine built on measured foundations: projections that dequantize 8-bit weights once and call the vendor SGEMM still in the last Fermi toolchain (64% of FP32 peak; our best hand-written GEMM hit 37%, wrongly called the ceiling); a chunked delta-rule rewrite of the recurrent layers, 2.8x faster than the sequential scan once attribution exposed one bad kernel; and a measured negative: 4-bit weights make decode slower than 8-bit here, since Fermi issues nibble-unpacking shifts at half rate. (ii) The vision side is a port with a proof obligation: we translate tower, merger, and projector to sm_20 CUDA, validating every stage against a locally generated reference forward (full tower 1.4e-5). One failure, position-embedding bucketization differing on exact rational ties, generalizes to a rule: float tie-breaking in index arithmetic is implementation-defined; call the reference operator, do not reimplement it. (iii) Long context exposes an O(N^2) wall short benchmarks hide: prefill falls from 114 tok/s at 2k tokens to 21 at 10k in a naive attention kernel; per-head vendor-GEMM calls writing into the existing score buffer (zero extra memory) restore a flat profile (408 at 2k, 361 at 10k; 17x), verified by exact needle retrieval from 60% depth. The same rewrite cuts image encoding 6x, to 0.93s. The system answers an image question end-to-end in 1.7s.
[AI-114] Assessing AI in Introductory Physics Problem Solving
链接: https://arxiv.org/abs/2607.14303
作者: Amir Bralin,N. Sanjay Rebello
类目: Physics Education (physics.ed-ph); Artificial Intelligence (cs.AI)
备注: Working paper, submitted as a placeholder
Abstract:Reasoning or inference-scaling models are the new generation of Large Language Models (LLMs) capable of complex problem solving. To investigate their problem-solving capability in physics, we evaluated model o4-mini by OpenAI on solving traditional, end-of-chapter problems from Halliday and Resnick’s “Fundamentals of Physics,” spanning core topics in the undergraduate physics curriculum. Performance was analyzed across modality and problem difficulty. The model solved the problems with overall accuracy of about 90%, but performance depended strongly on representation: accuracy was much higher on text-only problems (96%) than on problems requiring coordinated interpretation of text and images (79%). Accuracy also declined significantly as the problem difficulty increased from low to medium to high. These results show that state-of-the-art LLMs can solve much of the standard introductory physics problems, but that their performance remains uneven and constrained by problem modality and problem difficulty.
[AI-115] he Planar Case of Thomas Positive Circuits Conjecture
链接: https://arxiv.org/abs/2607.14143
作者: Natan Katz
类目: Dynamical Systems (math.DS); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The notion of circuit refers to a cyclic oriented influence between the elements of a dynamical system. There are two classes of circuit: positive and negative. R. Thomas conjectured that a necessary condition of multi stationarity is the existence of positive circuits. In this paper we use dynamical system tools and planar analysis to find conditions for which the conjecture holds for planar systems.
[AI-116] Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network
链接: https://arxiv.org/abs/2406.04737
作者: Honglei Ma,Erwu Liu,Zhijun Fang,Rui Wang,Yongbin Gao,Wenjun Yu,Dongming Zhang
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: This work has been accepted by the IEEE TWC for publication
Abstract:The cellular network of magnetic Induction (MI) communication holds promise in long-distance underground environments. In the traditional MI communication, there is no fast-fading channel since the MI channel is treated as a quasi-static channel. However, for the vehicle (mobile) MI (VMI) communication, the unpredictable antenna vibration brings the remarkable fast-fading. As such fast-fading cannot be modeled by the central limit theorem, it differs radically from other wireless fast-fading channels. Unfortunately, few studies focus on this phenomenon. In this paper, using a novel space modeling based on the electromagnetic field theorem, we propose a 3-dimension model of the VMI antenna vibration. By proposing ``conjugate pseudo-piecewise functions’’ and boundary p(x) distribution, we derive the cumulative distribution function (CDF), probability density function (PDF) and the expectation of the VMI fast-fading channel. We also theoretically analyze the effects of the VMI fast-fading on the network throughput, including the VMI outage probability which can be ignored in the traditional MI channel study. We draw several intriguing conclusions different from those in wireless fast-fading studies. For instance, the fast-fading brings more uniformly distributed channel coefficients. Finally, we propose the power control algorithm using the non-cooperative game and multiagent Q-learning methods to optimize the throughput of the cellular VMI network. Simulations validate the derivation and the proposed algorithm.
机器学习
[LG-0] Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier
链接: https://arxiv.org/abs/2607.15258
作者: Arthur G. Bubolz,Abreu Quevedo,Giancarlo Lucca,Rafael A. Berri,Eduardo Borges,Bruno L. Dalmazo
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
*备注: This manuscript has been accepted for presentation at the IEEE International Symposium on Computers and Communications (ISCC 2026)
Abstract:The growing use of Bitcoin as a decentralized digital asset and investment tool has sparked strong interest in understanding its market behavior. This study presents a new approach to analyze Bitcoin market sentiment by combining on-chain and financial data with social media posts. Unlike models that aim to predict prices, this work focuses on explaining market sentiment using blockchain transactions, historical price data of Bitcoin, and daily Twitter sentiment classifications. The method merges sentiment trends with on-chain and financial metrics, normalized into a dataset for detailed market analysis. Multiple machine learning models were tested using cross-validation, with Gradient Boosting (XGBoost) emerging as the most reliable model for classifying sentiment, achieving an average F1-score of about 0.84. SHAP (SHapley Additive exPlanations), a game theory-based method for model interpretability, was used to quantify the contribution of on-chain features to the model’s predictions, improving transparency. The results indicate that this data combination yields meaningful predictive signals and insights, supporting data-driven cryptocurrency analysis and future improvements with deep learning.
[LG-1] Mutable Low-Rank Sketches for Retrain-Free Recommendation
链接: https://arxiv.org/abs/2607.15242
作者: Hector J. Garcia,Nick Clayton
类目: Machine Learning (cs.LG)
*备注: 6 pages, 3 figures, 8 tables
Abstract:A common bottleneck in two-stage recommendation is embedding staleness: when a user rates a new item, their embedding remains fixed until the next retrain cycle. We propose mutable sketches, which store each user’s preferences in a KP-tree (a sparse segment tree with sum aggregation), fit a low-rank projection once, and recompute embeddings on-the-fly as ratings arrive. We prove that each new observation monotonically tightens the prediction error envelope (Theorem 1), a guarantee that FunkSVD and eALS lack. On KuaiRec, the mutable sketch achieves 0.810 RMSE at 1.8% data read vs. ALS 0.822 at 100%, with 8x faster per-batch updates. A new user receives personalized recommendations in 1 ms after their first rating, with no model retraining required. A comparison of sampling strategies across density regimes shows that the KP-tree’s norm-proportional sampling provides 40-130% better item coverage on sparse data (1% density), while uniform sampling suffices on dense matrices.
[LG-2] Data Driven Block Replacement Scheduling
链接: https://arxiv.org/abs/2607.15229
作者: Aniruddhan Ganesaraman,VIdyadhar Kulkarni
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Applications (stat.AP); Machine Learning (stat.ML)
*备注: 36 pages, 4 figures
Abstract:We develop data-driven algorithms for maintaining N independent identical machines under a \textitblock replacement policy, in which each machine is replaced upon failure and all machines are jointly replaced at regular intervals of length k . The goal is to learn the cost-minimizing interval k^* from operational data when the lifetime distribution is unknown. At each decision epoch, the operator selects k \in \1, 2, \ldots, K\ , observes the resulting failure history (a mixture of complete and right-censored lifetimes) and incurs a per-unit-time cost governed by the renewal function. We formulate this as a stochastic multi-armed bandit and propose Hoeffding- and Bernstein-based lower-confidence-bound algorithms achieving O(K \log T) regret, matching the Lai–Robbins lower bound. Exploiting a nested observation property unique to block replacement, correlated variants attain O((K-k^)\log T) regret and require only O(1) direct pulls of suboptimal arms k k^ . A complementary Kaplan–Meier renewal algorithm estimates the lifetime distribution nonparametrically from censored data, achieving almost-sure policy consistency and empirically near-zero incremental regret at long horizons. We additionally analyze two average-cost MDPs: a time-elapsed formulation establishing that block replacement is optimal within its policy class for any lifetime distribution, and an age-vector formulation proving a monotone threshold structure under increasing failure rate distributions and providing a gold-standard cost benchmark. Numerical experiments confirm the theoretical ordering and reveal structural cost gaps between optimal block and age-dependent replacement.
[LG-3] NeuronSoup: Evolving Asynchronous Shared-Neuron Temporal Graphs without Backpropagation
链接: https://arxiv.org/abs/2607.15217
作者: Subodh Kalia
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注:
Abstract:We present NeuronSoup, a neural computation architecture that replaces synchronous layer-by-layer processing with asynchronous, delay-mediated signal propagation through a pool of shared neurons. Each path in the network routes a continuous-valued signal from one input neuron to one output neuron through a variable number of intermediate hidden neurons. Hidden neurons are physically shared across paths: when two paths pass through the same neuron, the second arrival encounters the accumulated state left by the first, producing constructive or destructive interference that depends on signal polarity and arrival timing. The entire architecture – topology, weights, delays, and connectivity – is co-evolved by a genetic algorithm operating on a flat real-valued genome of 14,602 genes. On 10-class MNIST digit classification using frozen ResNet18 features as input, the system evolves a network of 204 active paths through 266 hidden neurons (156 shared across multiple paths, with one neuron participating in 11 distinct paths) and achieves 85.9% test accuracy after 10,000 generations. The trained model occupies 115 KB. We argue that this architecture addresses fundamental limitations of current deep learning: it requires no differentiable computation graph, adapts its computation depth per-sample, and discovers lateral interactions between processing pathways that current architectures must engineer explicitly. We discuss why genetic algorithms are the correct optimization tool for this problem class, why CMA-ES fails at this scale, and how the architecture generalizes to arbitrary domains by substituting the encoder and output structure.
[LG-4] BadWAM: When World-Action Models Dream Right but Act Wrong
链接: https://arxiv.org/abs/2607.15207
作者: Qi Li,Xingyi Yang,Xinchao Wang
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:World-action models (WAMs) are emerging as a promising foundation for embodied control: rather than predicting actions alone, they learn representations that couple action generation with future world prediction. This coupling is often viewed as a source of robustness, interpretability, and safety, as a robot’s action can in principle be checked against its imagined future. In this paper, we show that this assumption is fragile. We introduce BadWAM, a unified framework for modeling and evaluating World-Action Drift Attacks: a new class of WAM-specific adversarial attacks that use small visual perturbations to break the alignment between what a WAM imagines and what it executes. BadWAM characterizes this attack surface along two natural criteria: attack strength and stealthiness. When the adversary prioritizes disruption, BadWAM instantiates an action-only adversarial attack, which directly drives the model toward task-failing actions. When the adversary additionally prioritizes stealth, BadWAM instantiates an imagination-preserving adversarial attack, which seeks to induce harmful action shifts while keeping the model’s predicted future close to its clean imagination. Together, these two attacks capture a spectrum of WAM-specific failures: from overt action hijacking to stealthier cases where the model appears to imagine a plausible future but executes a desynchronized action. We evaluate BadWAM across different variants of WAMs. Results show that our attacks substantially reduce task success rates under closed-loop execution. For example, our action-only attack reduces the model performance from 96.5% to 43.1% success. The results of our imagination-preserving attack further exposes a WAM-specific vulnerability: moderate future-preserving regularization can maintain strong attack performance while reducing future imagination drift.
[LG-5] RTS Smoother-Guided Learning of Physics-Based Neural Differential Models
链接: https://arxiv.org/abs/2607.15180
作者: Ahmet Demirkaya,Georgios Stratis,Tales Imbiriba,Zachary D. Danziger,Deniz Erdogmus
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Ordinary differential equations (ODEs) are widely used to model dynamical systems in physics, biology, neuroscience, and physiology, but in many applications some equations of the dynamics are unknown and only a subset of the state variables are measured. We propose a hybrid neural–physics framework in which the known components of the ODE are kept explicit and the missing components are represented by a neural network. The proposed method consists of two stages where we alternate between state and parameter estimation and iterate until a predetermined criterion is met. Specifically, in the first step, we treat the model parameters as being known and we infer the latent states from the available measurements using a Rauch–Tung–Striebel (RTS) smoother. In the second stage, we treat the smoothed trajectories as being known and use them to estimate the neural networks’ parameters through backpropagation. We evaluate the method on benchmark systems spanning linear, nonlinear, and stiff dynamics under partial state observation. Across these settings, the proposed method learns missing ODE components from incomplete measurements while exploiting and retaining interpretable mechanistic structure and improving latent-state reconstruction and long-horizon prediction.
[LG-6] Learning in Infinitesimal Non-Compositional Sketches
链接: https://arxiv.org/abs/2607.15107
作者: Sridhar Mahadevan
类目: Machine Learning (cs.LG); Category Theory (math.CT)
*备注:
Abstract:This paper develops a categorical framework – Learning in Infinitesimal Non-Compositional Sketches (LINCS) – as the repair of non-compositionality: failures of diagrams to factor through quotient sketches lifted to the tangent category setting. Machine learning problems are specified as sketches: graphs with commutativity conditions \mathcal D , limit cones \mathcal L , and colimit cocones \mathcal K , generalizing the usual scalarization of loss functions or vector space assumptions. Non-compositionality is defined purely as failure of a universal factorization problem, not as arithmetic error between the desired and actual predictions. Given a learning sketch \mathbb S=(S,\mathcal D,\mathcal L,\mathcal K) , whose underlying graph is S , and a model D:J \rightarrow C , the base defect is the obstruction to factorization \mboxObs(\mboxFact_\mathbb S(D)) . The tangent lift applies the tangent functor T to obtain TD:J \rightarrow C , and LINCS is defined as the obstruction \mboxObs(\mboxFact_\mathbb S(TD)) – asking whether infinitesimal perturbations preserve the compositionality this http URL paper also introduces Tangent Learning Sketches, which are sketches equipped with Cockett-Cruttwell tangent structure. The paper defines the INC endofunctor, which iterates the tangent lift, producing a tower D,TD,T^2D, \cdots of factorization problems. ML is thereby formulated as the search for a coalgebraic fixed point where successive tangent unfoldings stabilize ( \nu T_\mboxINC ). Using the Aczel–Mendler theorem, we prove existence of a final INC coalgebra whenever T_\mboxINC admits a set-based class realization that creates its final carrier. A detailed experimental evaluation of LINCS is underway in a number of concrete ML settings, including deep learning, large language models, and reinforcement learning, and is described in companion papers.
[LG-7] Evaluating covariate balance for long time horizon Markov decision processes
链接: https://arxiv.org/abs/2607.15080
作者: Joshua Spear,Rebecca Pope,Neil J Sebire
类目: Machine Learning (cs.LG)
*备注:
Abstract:This article explores the application of covariate balance diagnostics for detecting the presence of hidden confounding/model miss-specification in studies applying offline reinforcement learning (RL) to deriving optimal treatment recommendations. The results demonstrate that, either there is a high risk of bias within existing offline RL studies for treatment recommendations or, existing covariate balance metrics are not sufficient to assess such studies. Regardless, existing offline RL studies cannot be concluded as being statistically robust. The conclusions propose future research directions for obtaining more methodologically robust applications of offline RL to treatment recommendation problems.
[LG-8] An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications
链接: https://arxiv.org/abs/2607.15077
作者: Yao Cheng Li,Ana Larrañaga,Steven L. Brunton,Urban Fasel
类目: Machine Learning (cs.LG)
*备注: 15 pages, 4 figures
Abstract:Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known. Surrogate modeling techniques such as neural networks can capture the behavior of these systems, but they typically demand large training datasets that are difficult to obtain in engineering contexts and yield models with limited physical interpretability. The Sparse Identification of Nonlinear Dynamics (SINDy) method addresses both limitations by performing sparse regression over libraries of candidate nonlinear terms, recovering interpretable governing equations from comparatively small datasets. Although SINDy has been demonstrated extensively on canonical benchmark systems, its application to practical engineering problems is less widely documented. This tutorial introduces the SINDy method and progressively builds toward its main extensions, from noise-robust weak-form and ensembling-based variants to constrained and parametrizable formulations. The paper and the accompanying tutorial (available at this https URL) is organized in three parts: the first introduces the standard SINDy algorithm and progressively extends it, inviting readers without prior knowledge to follow each step and adapt the methods to their own problems; the remaining two parts present detailed case studies on (1) the system identification of an unmanned aerial vehicle and (2) a chaotic thermosyphon heat exchanger. Through these examples, we aim to demonstrate that SINDy is simple to implement yet flexible enough to serve as a valuable identification tool for advanced engineering applications.
[LG-9] Kernel weighted importance sampling for off-policy evaluation in contextual bandits
链接: https://arxiv.org/abs/2607.15067
作者: Joshua Spear,Matthieu Komorowski,Rebecca Pope,Neil J Sebire,Erica E.M. Moodie
类目: Machine Learning (cs.LG)
*备注:
Abstract:This article presents a novel estimator for performing off-policy evaluation using only offline data for contextual bandits. The proposed estimator, Kernel-WIS is demonstrated to be asymptotically consistent and to empirically outperform strong baselines (including vanilla weighted importance sampling), particularly under complex conditions including behaviour policy miss-specification. The benefit of Kernel-WIS is derived from combining the bounded property of vanilla weighted importance sampling with the linearity of vanilla importance sampling.
[LG-10] Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging
链接: https://arxiv.org/abs/2607.14995
作者: Sara Ketabi,Matthias W. Wagner,Cynthia Hawkins,Uri Tabori,Birgit Betina Ertl-Wagner,Farzad Khalvati
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:Multimodal Contrastive Learning (CL) has shown significant performance in aligning representations across various data modalities and improving downstream tasks, especially in healthcare. It works by minimizing the distance between matched (positive) data modalities, while maximizing the distance between mismatched (negative) samples. Traditional CL frameworks typically assume instance-based correspondence within data batches, treating all non-paired samples as negatives. However, this assumption often fails in medical settings, where samples may share high-level semantic attributes, leading to false negatives that degrade representation quality. In this paper, we propose Multimodal Semantic-Aware Contrastive Learning (MseaCL), a CL framework trained on a pediatric cohort of 3D brain magnetic resonance imaging (MRI) scans and radiology reports. The goal of this framework is to mitigate the impact of semantically similar false negative samples by incorporating semantic similarity between radiology reports, as a guiding signal during the learning process. Our results indicate that applying this framework as a pretraining stage can achieve notable improvements in downstream tasks, e.g., at least a 22.6% increase in the area under the receiver operating characteristic curve (AUC) of pediatric brain tumor molecular classification, demonstrating its potential for more robust and semantically aligned multimodal representations in clinical applications.
[LG-11] LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget
链接: https://arxiv.org/abs/2607.14952
作者: Changhai Zhou,Kieran Liu,Yuhua Zhou,Qian Qiao,Jun Gao,Harry Zhang,Irvine Lu,Nolan Ho,Lucian Li,Andrew Lei,Cleon Cheng,Steven Chiang,Yihang Zeng,Di Zhang,Rio Yang,Kaijie Chen,Andrew Chen,Pony Ma,Weizhong Zhang,Cheng Jin
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 41 pages, 10 figures, 11 tables. Code: this https URL
Abstract:A growing gap separates inference context lengths from RL post-training: inference systems are approaching million-token contexts, while post-training workloads often remain at 256K tokens or below and rely on length generalization at deployment. The gap is especially important for AI agents, whose observations, tool outputs, documents, and prior decisions accumulate over long trajectories. LongStraw is an architecture-aware execution stack for million-token RL post-training under a fixed GPU budget, instantiated with Group Relative Policy Optimization (GRPO). It evaluates the shared prompt without autograd, retains only model-specific state needed by later tokens, and replays short response branches one at a time, reducing the live training graph at the cost of additional replay time. We implement it for the hybrid recurrent and full-attention Qwen3.6-27B and the compressed-attention mixture-of-experts GLM-5.2. On eight H20 GPUs, LongStraw completes grouped Qwen scoring and response backward at 2.1M positions for groups of 2 and 8; increasing the group size adds only 0.21 GB of peak allocated memory, while a separate stress test reaches 4.46M positions. On 32 H20 GPUs, we validate the end-to-end LongStraw execution path for a 2.1M-token prompt across all 78 layers of GLM-5.2. These experiments establish execution capacity rather than complete training correctness because the captured prompt state is detached and some distributed forward and gradient composition paths remain incomplete.
[LG-12] Causal Inference for Sequential Settings under Interference and Latent Confounding
链接: https://arxiv.org/abs/2607.14940
作者: Phevos Paschalidis,Constantinos Daskalakis,Devavrat Shah
类目: Machine Learning (cs.LG); Probability (math.PR)
*备注:
Abstract:We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian across T time steps. At each time step, the outcomes of N units have dependencies captured through an Ising model; each outcome is also impacted through an external field capturing the effects of its treatment as well as latent confounders. Similar to panel data literature, these latent confounders are modeled to have a low-rank factor structure. Our data is a single sample from this high-dimensional distribution. To estimate causal quantities of interest, we provide a computationally efficient method based on Maximum Pseudo-Likelihood Estimation (MPLE) for learning the model parameters. Under mild assumptions, we establish non-asymptotic consistency for parameter estimation and show this translates to faithful estimation of causal quantities of interest after sampling from the learned model. We demonstrate the efficacy of the method through synthetic experiments as well as a real-world case-study investigating causal effects of vaccine rates on COVID-19 death rates within US counties nationwide.
[LG-13] Analytical study of the optimal combination of binary classifiers based on classifiers-induced partitioning of the training set
链接: https://arxiv.org/abs/2607.14889
作者: Jean-Marc Brossier,Olivier Lafitte
类目: Machine Learning (cs.LG)
*备注: 35 pages
Abstract:This paper studies an optimal linear combination of binary classifiers based on a logical structuration of the dataset via truth tables. The given classifiers partition data into equivalence classes, allowing for a rigorous analysis of the convexified empirical risk through a multidimensional generalization of classification calibrated functions. We establish sufficient conditions for the existence and uniqueness of the (global) point of minimum of the convexified empirical risk for any list of classifiers (when the number of classifiers is large, there frequently could be no point of minimum). In the case of three classifiers, our analysis allows to list all the configurations leading to either a unique solution, infima or non-unique points of minimum. Furthermore, we derive explicit analytical formulae for optimal weights using Exponential (Boost) and Logistic (Logit) loss functions, bypassing iterative optimization. The stability of the resulting classifier and the analysis of data quality can be evaluated through the introduction of the notion of \phi -frontiers. Comments: 35 pages Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.14889 [cs.LG] (or arXiv:2607.14889v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.14889 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-14] PAC Learning in Turn-Based Stochastic Games with Reachability Objectives: A Decentralized Private Approach via Expected Conditional Distance
链接: https://arxiv.org/abs/2607.14877
作者: Ali Asadi,Krishnendu Chatterjee,Pavol Kebis
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT)
*备注:
Abstract:Reachability is the most fundamental logical objective, yet it is notoriously difficult to learn in reinforcement learning settings: even for Markov decision processes, PAC learning of reachability is impossible without additional assumptions. This difficulty also holds in turn-based stochastic games (TBSGs), where two adversarial players interact on a finite state space. In this work, we consider turn-based stochastic games with reachability objectives. For such settings, adversarial learning, in which players are adversarial even in the learning phase, is impossible. Therefore, the goal is to consider learning, in which both players learn the unknown model together. In this spirit, previous literature on PAC learning in TBSGs considers (a)~public information shared by both players; and (b)~centralized learning, which means that players share the same learning algorithm. In this work, our contribution is two-fold. First, we relax these strong assumptions and ensure learning: (i)~with private information not shared with the other player; and (ii)~decentralized learning where the players do not share the same learning algorithm. To the best of our knowledge, this work is the first positive result for decentralized and private information learning of TBSGs with reachability objectives. Second, we introduce a game-theoretic generalization of the Expected Conditional Distance (ECD) parameter, which measures the expected length of reaching the target set. We establish a polynomial-sample complexity bound with respect to the number of states, actions, ECD parameter, and inverses of error tolerance and failure probability.
[LG-15] Subgrid-Scale Parameterization in Burgers Equation Using Structure-Preserving Neural Networks and Entropy Variables
链接: https://arxiv.org/abs/2607.14855
作者: Aijaz Nazir,Ilya Timofeyev
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:We present a machine learning approach for developing subgrid-scale (SGS) parametrizations in coarse simulations of partial differential equations. We utilize structure-preserving neural networks and entropy variables to learn subgrid fluxes in coarse simulations of the Burgers’ equation. In particular, we employ a decoupled neural network architecture explicitly separating the subgrid corrections into two distinct components: a conservative Flux Potential network and an Eddy Viscosity network. We demonstrate that this reduced-order framework maintains high physical fidelity, accurately reproducing the energy spectrum, spatial and temporal correlation functions, and dynamical characteristics of the full-scale system. Furthermore, we show that our approach is robust and applicable to parameters outside the training regime.
[LG-16] ChronoQG: Towards a Temporally Expressive and Hop-Bounded Benchmark for Temporal Knowledge Graph Question Generation
链接: https://arxiv.org/abs/2607.14770
作者: Xuemeng Liu,Zhengpin Li,Wanpeng Tang,Haotong Xie,Wentao Zhang
类目: Machine Learning (cs.LG)
*备注: Preprint
Abstract:Knowledge graph question generation (KGQG) aims to generate natural-language questions from structured graph evidence. Existing KGQG benchmarks, however, are mostly built on static knowledge graphs and do not encode the temporal scopes of graph facts. As a result, they cannot evaluate whether generated questions faithfully preserve temporal validity, event ordering, and answer-determining temporal constraints. In this paper, we study temporal knowledge graph question generation (TKGQG), where a generated question must be faithful to both the support subgraph and the temporal constraints required to identify the target answer. We propose ChronoQG, the first temporally expressive and hop-bounded benchmark construction framework for TKGQG. ChronoQG integrates a comprehensive temporal-constraint taxonomy, topology-temporal subgraph sampling, and trace-grounded question generation to construct temporally faithful questions. The framework produces four benchmark datasets from heterogeneous temporal knowledge graphs, totaling 16,011 verified questions. We evaluate representative LLM-based KGQG methods and prompting baselines across diverse TKGQG settings, including temporal-constraint counts, topological templates, and temporal-constraint types. The results show that existing methods struggle to preserve temporal constraints, especially under multi-constraint settings and harder temporal-constraint types. These findings reveal a clear gap between static KGQG and TKGQG, and establish ChronoQG as a challenging testbed for temporally faithful question generation.
[LG-17] oward Energy-Efficient and Low-Power Arrhythmia Detection for Wearable Devices
链接: https://arxiv.org/abs/2607.14747
作者: Floriaan Bulten,Yawar Rasheed,Arlene John,Vincenzo Stoico,Ghayoor Gillani
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Performance (cs.PF)
*备注:
Abstract:Cardiovascular diseases are the leading cause of death worldwide, and conditions such as arrhythmia often require long-term monitoring for effective detection and diagnosis. However, current wearable monitoring devices are bulky, uncomfortable, and typically rely on clinicians to manually evaluate electrocardiograms (ECGs). While Deep Learning (DL) algorithms have shown superior performance in arrhythmia detection and classification, their computational complexity coupled with high power consumption limit deployment in wearable devices. To address this challenge, this paper investigates the use of approximation techniques to reduce the power and energy consumption of DL architectures while maintaining acceptable classification performance. Specifically, techniques such as data precision reduction and approximate multiplication are investigated in a state-of-the-art DL model and its corresponding hardware architecture. The model is trained and validated using the MIT-BIH Arrhythmia Database, and hardware implementations employing various approximate multipliers are synthesized and evaluated. Compared with the state-of-the-art 8.75 \muW (and 2.08 \muJ) reference architecture, our proposed architecture consumes 3.07 \muW (and 2.17 \muJ) at 12 kHz, showing 64.9% reduction in power consumption while providing an acceptable output quality, i.e., 93.7% classification accuracy and 92.1% sensitivity. At 100 MHz, our proposed architecture consumes 9.45 mW (and 0.8 \muJ), showing 61.5% reduction in energy consumption as compared to the state-of-the-art architecture. These results demonstrate that our proposed approximations significantly extend wearable device battery life while preserving the required arrhythmia classification performance.
[LG-18] GAttNHP: Group Attention Neural Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs
链接: https://arxiv.org/abs/2607.14733
作者: Xiangni Tian,Kaixian Yu,Runpeng Dai,Niansheng Tang,Hongtu Zhu
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Temporal Knowledge Graphs (TKGs) record how facts evolve over time, but forecasting future events on a TKG remains difficult for three reasons: (i) long-range temporal dependencies are hard to encode; (ii) events on different chains mutually excite or inhibit one another in ways that snapshot-level models cannot express; and (iii) inter-arrival times are heavy-tailed and statistically sparse, so deterministic time predictors are unreliable. We address these three issues with a single framework, the \textbfGroup Attention Neural Hawkes Process (GAttNHP), built around three matched components. First, a self-attention encoder casts each subject–relation chain as a continuous-time point process and captures the lingering excitation of distant history. Second, a semantic soft-grouping module turns globally learnable Hawkes priors into an analytical cross-attention mask, so chains share excitation patterns through their latent group memberships rather than through exhaustive pairwise computation. Third, a Non-Crossing Quantile (NCQ) regression head replaces mean-based time prediction, providing calibrated, monotonically ordered quantile estimates that remain stable under heavy-tailed inter-arrival distributions. On six benchmark TKG datasets, GAttNHP improves over state-of-the-art baselines on both entity prediction and time prediction, and ablations confirm that its largest gains arise on the long-tail event chains where existing models fail most severely.
[LG-19] Whats in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity
链接: https://arxiv.org/abs/2607.14731
作者: Kumar Kshitij Patel,Rustem Islamov,Sebastian U Stich,Aurelien Lucchi,Eduard Gorbunov,Lingxiao Wang
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注:
Abstract:Local SGD, also known as Federated Averaging, is a widely used distributed optimization algorithm. Although Local SGD often outperforms alternatives such as Mini-batch SGD in practice, theory still only partially explains when and why local updates help under realistic data heterogeneity. Recent work by [Patel et al., 2025] shows that a bounded second-order heterogeneity assumption captures the efficiency of Local SGD for strongly convex objectives, and conjectures that the same principle extends to the general convex setting. In this paper, we prove this conjecture by establishing an improved convergence guarantee for Local SGD on general convex objectives under bounded second-order heterogeneity. We also improve the best-known lower bounds for Local SGD in this setting, showing that our upper bounds are nearly tight. Together, these results provide a sharper, more fine-grained convergence theory for Local SGD. As a further application of our techniques, we provide a lower bound for serial SGD with replacement, showing how second-order heterogeneity captures the impact of rare high-curvature clients.
[LG-20] Counterfactuals for Feature-Weighted Clustering
链接: https://arxiv.org/abs/2607.14719
作者: Richard J. Fawley,Renato Cordeiro de Amorim
类目: Machine Learning (cs.LG)
*备注:
Abstract:Counterfactual explanations provide local, interpretable insight by identifying changes to an input that would alter its assigned outcome. Although well established in supervised learning, their extension to clustering is less direct, since cluster assignments are unlabeled and governed by the geometry of the partition. This paper introduces VoICE, a Voronoi-Induced Counterfactual Explainability framework for feature-weighted k -means clustering. Rather than treating cluster change as a crossing of a single pairwise centroid boundary, VoICE formulates counterfactual generation as projection onto the full weighted Voronoi region of a target cluster, incorporating feature weights directly into both the clustering geometry and the counterfactual objective to yield least-cost and parsimonious explanations under actionability constraints. Target regions are further intersected with data-derived bounds and homothetically contracted towards their centroids, limiting extrapolation and boundary sensitivity. VoICE consistently produces valid target-cluster membership, across several benchmark datasets, where the leading pairwise baseline does not.
[LG-21] MESHA: Mechanism-Enforced Sequential Halving for Strategic Linear Bandits
链接: https://arxiv.org/abs/2607.14706
作者: Xin Li,Zixin Zhong
类目: Machine Learning (cs.LG)
*备注:
Abstract:We design and analyze \underlineMechanism-\underlineEnforced \underlineSequential \underlineHAlving (MESHA), an algorithm for Best Arm Identification (BAI) in strategic linear bandits. In this setting, each arm may strategically misreport its feature vector to maximize the probability of being identified as the best arm, when rewards are generated from the arms’ true but unobservable features. The design of MESHA applies the naïve uniform sampling rule and an epoch-wise Grim Trigger Condition (GTC): the former reduces the impact of arms’ strategic behaviours and the latter eliminates arms whose reported features severely deviate from the ground truth. Considering an arbitrary Nash Equilibrium, we prove that any arm would attempt to pass the GTC check to maximize its identified probability and derive an upper bound on the failure probability of MESHA within a fixed budget T . We also show that state-of-the-art linear BAI algorithms with G -optimal design would fail in such strategic environment, as the optimal design (OD)-based sampling rule based on strategically reported features may \it starve the optimal arm of any sampling budget. Finally, extensive numerical experiments indicate that MESHA outperforms baselines that rely on OD-based sampling rules as well as the feature-agnostic baselines, corroborating the efficacy of MESHA. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.14706 [cs.LG] (or arXiv:2607.14706v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.14706 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-22] Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
链接: https://arxiv.org/abs/2607.14705
作者: Yuchang Zhu,Zezhong Xie,Huizhe Zhang,Huazhen Zhong,Jintang Li,Liang Chen,Zibin Zheng
类目: Machine Learning (cs.LG)
*备注: Under Review
Abstract:Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such as gender or race. While this challenge has motivated extensive research, most existing solutions rely on the strong assumption that demographics are fully available. To bypass this strict requirement, a few recent studies have attempted to use predicted demographics as proxies to enforce fairness constraints. However, predicted demographics may be inaccurate, resulting in the failure to improve fairness. In this work, we investigate the problem of graph fairness without demographic information and avoid the utilization of predicted demographics. Motivated by our observation that the gradient distributions of misclassified nodes implicitly encode demographic information, we first propose GradDist, a gradient-based metric that quantifies bias by measuring the distance between local modes within these distributions. To mitigate this bias, we propose Gradient-to-Fairness (Grad2Fair), a gradient-guided approach for group fairness without demographics. Due to the potential demographics in gradients, Grad2Fair directly leverages gradients to debias and eliminates demographic prediction, thereby enabling stable fairness performance. Experiments on several real-world datasets demonstrate the effectiveness of Grad2Fair, as evidenced by superior performance over baselines in most cases. Our code is available at this https URL.
[LG-23] Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization
链接: https://arxiv.org/abs/2607.14672
作者: Yusuke Sakemi,Tomoya Takeuchi,Takeo Hosomi,Kazuyuki Aihara
类目: Machine Learning (cs.LG)
*备注:
Abstract:Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware. However, training deep continuous-time SNNs is severely constrained by the memory required for exact spike-time computation, which evaluates and retains candidate firing times over intervals determined by presynaptic spike ordering. Here we introduce a memory-efficient training framework based on differentiable spike-time discretization (DSTD) for leaky integrate-and-fire neurons with general membrane and synaptic time constants. DSTD maps irregular presynaptic spikes onto differentiable weighted events at fixed time points, replacing the input-dependent candidate dimension with M fixed time intervals while accurately approximating continuous-time membrane-potential dynamics. This reduces candidate-related activation memory from O(N_\mathrmoutN_\mathrmin) to O(N_\mathrmoutM) in the case of time-to-first-spike (TTFS) coding, where N_\mathrmin and N_\mathrmout denote the numbers of presynaptic and postsynaptic neurons, respectively. We further introduce synfire-chain-inspired temporal regularization that organizes layer-wise firing windows, mitigates dead-neuron failures, and enables pipeline-like processing. In dense LIF layers, DSTD reduced peak memory consumption by up to approximately 100-fold and training time by up to approximately 20-fold compared with exact spike-time computation. Together, these methods allowed us to train 9-layer convolutional SNNs on CIFAR-10 and 20-layer convolutional SNNs on Fashion-MNIST on a single GPU.
[LG-24] rajectory-Aware Flow Matching for Topology Optimisation
链接: https://arxiv.org/abs/2607.14652
作者: Shusheng Xiao,Jinshuai Bai,Hyogu Jeong,Yunfei Xi,Yilin Gui,YuanTong Gu
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:Topology optimisation (TO) often requires repeated finite element analysis and sensitivity-based material updates, which can be costly when multiple candidate designs are needed under varying physical and design conditions. Generative TO offers a route to rapid design exploration, but existing models may rely on adversarial training, long reverse-diffusion sampling, or external guidance to maintain structural feasibility and physical consistency. This study develops a flow matching-based topology optimisation (FMTO) framework for conditional topology generation. Linear FMTO is first formulated as an endpoint-based baseline by interpolating between a Gaussian source field and the BESO reference topology. To introduce mechanically meaningful intermediate states, a trajectory-aware FMTO formulation is proposed, where volume-fraction-indexed BESO states are used to construct the probability path and target velocity field. This incorporates physics-guided optimisation history into generative flow learning without adding inference-time optimisation. A path–velocity mismatch analysis explains why moderate trajectory weighting can improve generation stability, whereas excessive guidance may over-constrain the learned transport. Numerical examples show that FMTO generates diverse topology candidates with improved compliance-related performance, volume-fraction satisfaction, topology fidelity, and substantially fewer sampling steps than a diffusion-based baseline. Under limited training data, trajectory-aware FMTO achieves the best overall performance with a moderate trajectory weight. Studies on trajectory-anchor density and three-dimensional topology generation further demonstrate the influence of path design and the applicability of the proposed framework beyond two-dimensional problems.
[LG-25] IDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation
链接: https://arxiv.org/abs/2607.14640
作者: Wen Yang Tan,Jiawei Li,Fang Liu,Wei Zhang,Sumei Sun,Peng Cheng Wang,Elisa Y. M. Ang
类目: Machine Learning (cs.LG)
*备注: 8 pages, 11 figures, WI-IAT 2026
Abstract:Battery health estimation is fundamental for battery management in battery-powered systems, where inaccurate health states may affect control, maintenance, and service life. It becomes even more critical in intelligent connected systems, where estimation errors can propagate across interconnected devices and downstream decisions. In this paper, we propose TIDE, a trustworthy and interpretable battery degradation estimator for reliable battery health estimation. TIDE jointly considers accuracy, trustworthiness, and interpretability, which are all essential for practical deployment and downstream decision making. To realize these objectives, TIDE combines battery-domain knowledge with operational measurements in a three-component backbone. A knowledge-guided degradation prior promotes trustworthy estimation, a monotone residual component provides interpretable aging-consistent refinement, and a contextual learning component captures battery-specific operational effects for improved accuracy. The trained backbone is then distilled into a compact symbolic surrogate that provides a concise model-level interpretation of its learned estimation logic. Experiments show that TIDE achieves strong estimation accuracy, improving overall estimation fidelity by an average of 19.7% over representative baselines. Its knowledge-guided prior and monotone residual modelling substantially reduce aging-consistency violations, supporting trustworthy estimation. Meanwhile, the backbone enables component-level interpretation, while symbolic distillation provides a compact model-level representation of the learned estimation logic. These results support the practical use of TIDE for battery health monitoring and decision support in intelligent connected systems.
[LG-26] ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM
链接: https://arxiv.org/abs/2607.14622
作者: Hyunwoo Oh,Suyeon Jang,Hanning Chen,Sanggeon Yun,Ryozo Masukawa,Mohsen Imani
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG); Operating Systems (cs.OS)
*备注: Accepted to ICCAD 2026
Abstract:Low-bit GEMM is increasingly central to efficient ML inference, yet very-low-bit execution remains a poor fit for conventional CPUs. Practical deployment spans fragmented regimes-from 1/2/4-bit weights to varying activation precision-whose feasibility, reuse opportunity, and support cost differ under fixed SIMD and register-file budgets, making lightweight CPU support selection a first-class design problem. We present ExaGEMM, a workload-aware codesign and exploration framework for CPU-native low-bit GEMM via register-resident LUT execution. The key insight is that existing SIMD datapaths already cover table generation and accumulation; the only new hardware is an in-register select/feed mechanism with explicitly modeled cost. ExaGEMM co-explores parameterized kernels and lightweight SIMD ISA support using analytical models of register feasibility, compute cost, memory traffic, and hardware overhead, pruning the candidate space by 99.2% before simulation. It then identifies non-dominated support points and generates ISA specs, gem5 patches, and GEMM kernels for validation. Across representative ML models and CPU targets, ExaGEMM improves latency by 13.29x over software-only baselines, while showing that workload-aware frontier selection is especially important for mixed-precision LLM workloads.
[LG-27] PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference
链接: https://arxiv.org/abs/2607.14618
作者: Hyunwoo Oh,Suyeon Jang,Hanning Chen,KyungIn Nam,Sanggeon Yun,Ryozo Masukawa,Mohsen Imani
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR); Operating Systems (cs.OS)
*备注: Accepted to ICCAD 2026
Abstract:CPUs are the most universal target for on-device LLM inference, but existing low-bit quantization methods offer either coarse operating points or fine-grained mixed precision that is difficult to execute efficiently on CPUs. We present PolyQ, a CPU-oriented compiler/quantization co-design for activation-aware channel-wise bit allocation under a user-specified average-bit budget. PolyQ assigns per-channel bit-widths from \2,3,4,8,16\ , then uses a compile-time model compiler to permute and cluster channels into bit-homogeneous blocks, generate SIMD- and LUT-compatible kernels, and merge compatible permutations across operators to keep layout regularization off the runtime path. This turns fine-grained budget fitting into a practical fractional-bit deployment method for CPU-only inference. Across Falcon-H1-3B, Llama2-13B, and Qwen3-32B on WikiText-2, PolyQ provides stable quality scaling from 3–6,b and improves perplexity by 2.4–32.1% over prior methods at a 3,b target. End-to-end measurements on three representative CPUs – workstation, laptop, and mobile – show that compiler layout regularization reduces activation reorder traffic by up to 70.8%, prefill latency and decode throughput scale nearly proportionally with the configured bit budget, and energy/token overhead stays below 2% relative to an optimized LUT-based back-end. These results show that fractional-bit CPU deployment is practical, predictable, and energy-efficient across diverse edge targets.
[LG-28] Sharp Stability Threshold and Certification for Designing Stable Residual Architectures
链接: https://arxiv.org/abs/2607.14576
作者: Hyemin Gu,Michael Tyrrell,Tuhin Sahai,Markos A. Katsoulakis
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We propose \emphthe sublinear-growth principle for deep residual architectures – a sharp stability threshold on the input-magnitude exponent of every residual block’s velocity field: |v(x, t)| \leq c,|x|^q + b, \qquad q \in [0, 1]. The threshold q = 1 is established via two independent arguments. Classical ODE theory gives a global forward flow on [0, T] at q \le 1 and exhibits divergent velocity fields at any q 1 . The optimal-control analysis, via the Hamilton-Jacobi-Bellman equation, sharpens this to a selection statement: the training optimum is bang-bang on the boundary of the admissible class, so the optimum at q 1 blows up while the optimum at q \le 1 is safe by construction. The exponent criterion q \le 1 is thereby a necessary and sufficient condition for stable training. It clarifies architectural placements that ensure the stability of training and inference, explaining, for instance, the stabilizing role of layer normalization. The sublinear-growth velocity fields form \emphthe right function space on which forward dynamics, adjoint sensitivity, and architectural composition are all well-controlled. An arithmetic of input-magnitude exponents under the five operations that build residual blocks enables efficient certification of q_k \le 1 at the level of architectural primitives, in place of ad hoc trial and error in the search for stable neural architectural designs. A parameter-free modification reduces the supercritical Mamba block from q = 5 to q = 1 without layer normalization, demonstrating this point. Experiments on Mamba and PatchTST confirm that the q \le 1 variants train stably: the criterion is the input-magnitude exponent, not the presence of a normalization layer.
[LG-29] Probabilistic Physics-Informed Neural Networks for Estimating Heterogeneous Elastic Properties from Low-Resolution and Noisy Displacement Data
链接: https://arxiv.org/abs/2607.14563
作者: Tatthapong Srikitrungruang,Jaesung Lee
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Estimating spatially heterogeneous elastic properties from low-resolution displacement measurements is a severely ill-posed inverse elasticity problem because low resolution obscures spatial details needed to distinguish heterogeneous property variations, and small measurement perturbations or fitting errors are amplified through inverse estimation. Existing inverse methods often rely on high-fidelity observations and manually prespecified loss weights, limiting their adaptability and making them sensitive to noise and resolution degradation. We propose a Probabilistic Inverse Elasticity Physics-Informed Neural Network (PIE-PINN) framework for robust estimation of Young’s modulus and Poisson’s ratio from noisy, low-resolution displacement data. PIE-PINN models displacement observation, strain-discrepancy, and equilibrium residuals using Laplace distributions within a unified probabilistic model. To improve robustness, the framework combines a B-spline-guided displacement network with a hierarchical half-Cauchy model for displacement residual scales. The B-spline provides a smooth global representation of the displacement field, while the neural network correction captures local variations. The hierarchical scale model adaptively downweights severe displacement fitting errors, enabling more robust recovery of the latent mean displacement field. An alternating maximum-likelihood training strategy updates the mean through weighted residual minimization and updates the scales to adjust the loss weights. Systematic case studies across varying noise levels and observation resolutions demonstrate the robustness of PIE-PINN.
[LG-30] CASP: Learning-Augmented Offline Approximation with Verifiable Certificates and Bounded-Loss PAC Guarantees
链接: https://arxiv.org/abs/2607.14545
作者: Haifeng Li,Mo Hai
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine-learned predictions can speed up offline NP-hard optimization, but asking a predictor what to do amounts to asking it to solve the problem, and committing an unchecked prediction forfeits every worst-case guarantee. CASP (Certificate-Augmented Solution Pruning) instead asks which parts of the search space may be ignored, and accepts each answer only after a sound polynomial-time verifier has checked it, so correctness never depends on prediction quality. We develop the learning theory of this design. The verifier makes the induced loss class uniformly bounded, so certificate parameters are learnable from \tilde O(\varepsilon^-2\log K) samples ( K the maximum instance size), whereas the unverified commitment class admits no distribution-free rate and, under cost spread R , none below \Omega(R/\varepsilon^2) . Filtering noisy predictions by verifiable confidence dominates the standard min-combiner, with a margin we compute in closed form, and the prediction stays useful even given the LP, because it breaks ties on degenerate optimal faces, where every symmetric LP policy, meaning one whose commitments depend on the instance only through the verifiable confidence values, provably stalls. Experiments on five problems test the theory’s quantitative predictions. With trained predictors, unverified pruning loses up to 26% of the optimum under distribution shift, while the verified deployment of the same predictions loses nothing.
[LG-31] MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music
链接: https://arxiv.org/abs/2607.14537
作者: Scott H. Hawley
类目: ound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
*备注: 8 pages, 8 figures
Abstract:Rich internal representations of musical structure are essential for music understanding tasks such as machine-assisted music co-writing, yet self-supervised approaches for symbolic music representation remain underexplored, particularly those that encode the hierarchical multiscale nature of musical structures. We present MIDI-RAE-JEPA, combining a pitch- and time-shift equivariance objective with LeJEPA and a Swin Transformer V2 encoder to learn such hierarchical representations of symbolic music encoded as piano roll images. The time-shift equivariance objective encourages the model to internalize temporal musical relationships. The encoder is trained purely on self-supervised objectives – including a masked embedding predictor (MEP) – with collapse prevented via SIGReg. A separate decoder trained on the frozen encoder embeddings achieves reconstruction F1 of 0.995, and a flow matching generative model conditioned on those embeddings produces generations that closely match the pitch register and rhythmic density of the conditioning excerpt, while mismatched conditioning yields unrelated but musically plausible output. Learned representations outperform a Haar scattering transform baseline on a downstream emotion classification task, and embedding distances increase monotonically with pitch and time shift magnitude, confirming measurable equivariance. These results suggest that equivariance-based SSL objectives, combined with sufficient fine-level encoder capacity, provide a viable path toward semantically rich, generatively useful representations of symbolic music.
[LG-32] Muse: Representation Geometry of Muon Beyond Normalized Momentum
链接: https://arxiv.org/abs/2607.14536
作者: Da Chang,Qiankun Shi,Lvgang Zhang,Di He,Yaoshuai Ma,Ganzhao Yuan,Yongxiang Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Muon-style optimizers apply a polar map to matrix momentum, but their updates also depend on the representation of each parameter block before orthogonalization. We study this representation choice as a form of optimizer geometry and introduce \method, a family of Muon-style optimizers that shares the same momentum rule and Newton–Schulz backend across native, nearest-square, skinny, and vector representations. Each Frobenius-isometric representation induces a distinct polar steepest-descent geometry, in which the shorter matrix dimension determines the number of supported singular channels, the pullback scaling, and the constants in stochastic nonconvex convergence bounds. In a teacher–student model, curvature collapse and an isotropic Marchenko–Pastur spectral profile connect early-stage dissipation to the represented nuclear-to-squared-Frobenius norm ratio. Pretraining experiments on LLaMA2-130M and LLaMA2-600M, together with fixed-momentum diagnostics, show that balanced non-native representations can match the performance of the native representation, whereas reducing the shorter dimension weakens the scaling and singular-channel support, leading to behavior that increasingly resembles normalized momentum.
[LG-33] A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models
链接: https://arxiv.org/abs/2607.14522
作者: Zikun Zhang,Jiayuan Sheng,David D. Yao,Wenpin Tang
类目: Machine Learning (cs.LG)
*备注: 36 pages, 5 figures
Abstract:We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control approach, where the state dynamics are modeled as a controlled continuous-time Markov chain (CTMC). We consider policy optimization problems and derive the corresponding policy gradient methods, leading to continuous-time variants of proximal policy optimization (PPO) and group relative policy optimization (GRPO). As a primary application, we develop a complete continuous-time RL framework for fine-tuning score-based discrete diffusion models. The proposed framework enables reward-driven optimization without requiring differentiability on the reward signals. In contrast to the existing GRPO-based approaches that only rely on terminal rewards, our formulation allows intermediate reward or advantage signals to be incorporated throughout the denoising trajectory. Importantly, when specialized to masked diffusion models (MDMs), our framework encompasses a rich class of policy parameterizations over the vocabulary simplex with analytically tractable probability ratios, providing a unified perspective on exploration and policy optimization in MDMs. For masked diffusion large language models (dLLMs), we further propose trajectory subsampling techniques to efficiently estimate computationally prohibitive trajectory likelihoods, reducing the computational cost of computing per-position probability ratios. We showcase the effectiveness of our methods on both low-dimensional entropy-regularized optimization problems and RL post-training of dLLMs on mathematical reasoning and coding tasks.
[LG-34] Adaptive Runge-Kutta Step Control Buys Training Loss Not Generalization: An Honest Compute-Matched Study of RK-Adam Optimizers
链接: https://arxiv.org/abs/2607.14516
作者: Akhilesh Gogikar
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: 10 pages, 4 figures. Code, logs, and result JSONs: this https URL
Abstract:Interpreting optimizers as gradient-flow discretizations has motivated applying higher-order Runge-Kutta (RK) integrators to neural networks. We build a representative Adam variant (Bogacki-Shampine 3(2) RK pair, FSAL reuse, local-error step control) and evaluate it under a strict compute-matched protocol giving every method the same gradient-evaluation budget - an accounting this literature rarely enforces. Under it the RK variant loses to plain Adam on training loss in both minibatch and full-batch (RK’s best-case) training. Instrumenting it shows the “adaptivity” is illusory: normalized error stays far below tolerance, the step size pins at its growth cap from step one (98-100 percent of steps), and no rtol x hmax x h0 setting makes it act; tolerances spanning 100x give bit-identical trajectories. The method is exactly fixed-step Adam with an averaged gradient at 3-4x cost. Repairing it (true reject branch; error on the applied map) reverses the full-batch result - about 40x lower training loss than tuned Adam - and a fixed-step control isolates adaptivity (an emergent warmup-and-growth schedule) as the mechanism. But the gain is fragile to the initial step size and does not reach test accuracy. A pre-registered follow-up rules out the obvious explanations: deeper minimization does not overfit, and an explicit temperature knob only hurts - leaving a trajectory effect, the controller selecting a minimum generalizing 1.3-3.4 points below first-order descent at equal depth. An n=10 study confirms one secondary effect: gradient averaging is a genuine implicit regularizer, beating lr-matched Adam and AdamW on 10/10 seeds - yet RMSprop and NAdam match or beat it at a third the per-step cost. Higher-order adaptive integration buys deeper deterministic minimization and a small regularization effect, but nothing a cheaper, well-tuned first-order baseline does not already provide.
[LG-35] One-Shot Generative Design for Disordered Metamaterials via Self-Organizing Neural Cellular Automata
链接: https://arxiv.org/abs/2607.14475
作者: Yujie Xiang,Liwei Wang
类目: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
*备注:
Abstract:Disordered metamaterials feature microstructures with inherent randomness and irregularity, enabling them to achieve broader property coverage and superior performance unavailable in their regular counterparts. Despite their promise, designing disordered microstructures is substantially harder than designing regular ones. Their design remains trapped between manual parameterizations with limited expressiveness, and generative AI that is data-hungry and struggles to generalize. To address these limitations, we propose a generative design framework based on Neural Cellular Automata that dynamically grows complex microstructures through learned local interaction rules, inspired by the self-organizing processes in natural materials. This framework requires only a single training template, yet accommodates diverse disordered microstructures and adapts to irregular domains and arbitrary discretizations. By manipulating the learned local rules, we can steer the growth process to generate microstructures unseen during training, providing control over orientation, anisotropy, and directional thickness without retraining. As a dynamic, local growth process, it naturally produces spatially varying microstructures that transition smoothly to enable location-specific mechanical properties. We demonstrate this in a multiscale mechanical cloaking design, where microstructures vary across the space to meet an optimized heterogeneous property distribution. Our design enables excellent cloaking performance without complicated post-processing and incompatible assembly common in existing methods. This data-efficient, generalizable approach opens access to previously intractable disordered materials for biomedical implants and soft robotics.
[LG-36] Interleaved Noise Injection Improves Clean Corrupted and OOD Performance
链接: https://arxiv.org/abs/2607.14466
作者: Matt L. Wiemann,Peter Melchior,Andrew K. Saydjari
类目: Machine Learning (cs.LG)
*备注: 24 pages, 11 figures
Abstract:Noise injection is a well-known technique in stochastic optimization. We report its surprising effectiveness with an interleaved (on-off-on-off…) rather than the usual monotonic decay schedule. We present a theoretical analysis of noise injection, which confirms that corruption by impulse noise approximates a Jacobian regularization, whereas Gaussian noise acts as a curvature penalty. This regularization behavior has been invoked to explain why noise injection increases model robustness. But the interleaved nature of our proposed schedule produces superior results even for the optimization objective: mixing phases of noisy data permits the optimizer to escape local minima and increase exploration without the risk of catastrophically forgetting the important features from the clean data. To stabilize this training scheme against the rapid changes of the loss when switching between clean and noisy data, we introduce a gradient-norm stabilization technique that scales noisy updates based on clean gradient magnitudes. We compare this method with other common augmentation methods and find substantial improvements in corruption tolerance and robustness to real-world distribution shifts on CIFAR-100-C, ImageNet-C, and ImageNet-R for ResNet and ViT architectures, with the best results being achieved by stacking our method on top of other augmentations. Through saliency and attention maps we show that the effect of interleaved noise injection stems from penalizing the failure modes encouraged by the inductive bias of the models: impulse noise works against the locality bias of convolutional (ResNet) architectures, and Gaussian noise reduces the tendency of attention-based models to pick up large-scale spurious features. Interleaved noise injection is therefore an effective tool to improve the test performance on clean, noisy, and out-of-distribution data at essentially zero computational cost.
[LG-37] Active Real-World Factor-Based Evaluation for Generalist Robot Policies
链接: https://arxiv.org/abs/2607.14439
作者: Andrew Liao,Hanchen Cui,Karthik Desingh,Aryan Deshwal
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Generalist robot manipulation policies trained on large, diverse datasets have shown remarkable promise across a wide range of tasks. However, rigorously evaluating these policies remains a fundamental challenge. Real-world performance depends on a large combinatorial space of task factors including object poses and camera viewpoints, making full, exhaustive evaluation intractable. Additionally, real hardware evaluation is slow and resource-intensive, so current practice is to use narrow test suites that can miss critical failure modes and misrepresent true deployment readiness. We propose an active evaluation framework that addresses this challenge by treating policy evaluation as a sequential experimental design problem. Our approach fits a probabilistic surrogate model over a structured space of task factors and adaptively selects evaluation configurations to maximize information gain over the policy’s performance distribution, allowing for sample-efficient characterization of policy behavior across unseen conditions and a systematic identification of failure-prone regions. We conduct 2331 real-world evaluations across 3 tasks with 3 factor variations and find that our approach typically saves the evaluator at least 20-40% of trials compared to typical random testing.
[LG-38] HyperShadow: A Benchmark for Detecting 3D Projections of Higher-Dimensional Spatial Objects
链接: https://arxiv.org/abs/2607.14419
作者: Akshay Sasi
类目: Machine Learning (cs.LG); Computational Geometry (cs.CG)
*备注: 8 pages, 5 figures. Code: this https URL . Dataset: this https URL
Abstract:Machine-learning datasets labelled “4D” universally denote three spatial dimensions plus time. We introduce HyperShadow, the first public benchmark in which the fourth, fifth, and sixth dimensions are spatial: the task is to decide whether a 3D point cloud is a native three-dimensional shape or the projection, the “shadow”, of a rigid object living in R^N (N = 4-6). We show this task is fundamentally distinct from intrinsic-dimension estimation: a shadow is still at-most-3-dimensional data, and standard estimators (TwoNN, Levina-Bickel MLE) reach only 71-73% accuracy. Detection instead requires projection signatures, density folds, filled volumes with characteristic radial profiles, and topology changes, which a 190k-parameter point network recovers at 96.6% accuracy across four corruption tiers, generalizing at 79-91% to object families never seen in training. On a temporal track of rigidly rotating objects we introduce a zero-parameter rigidity witness: the residual of the optimal rigid 3D alignment (Kabsch) between consecutive frames, which must vanish for any rigid 3D motion but cannot vanish for the shadow of a rigid rotation in R^N. This single interpretable statistic separates the classes at AUROC 0.982. All data are generated reproducibly from seeds; the dataset, models, and code are released publicly. HyperShadow makes no claim about physical reality; it is a controlled instrument for studying which observable statistics can certify incompatibility with a purely three-dimensional explanation.
[LG-39] Adaptive Ad Load Design for Sponsored Search Markets: Evidence Theory and Deployment
链接: https://arxiv.org/abs/2607.14418
作者: Mohammad Rashid,Hema Yoganarasimhan
类目: Machine Learning (cs.LG); General Economics (econ.GN)
*备注: 54 pages
Abstract:Ad-load design is a central supply-side decision in sponsored search: more sponsored slots can raise revenue, but may crowd out organic results and degrade user outcomes. We study this trade-off using a large-scale randomized field experiment on an Android app store, where over five million users are exposed to one through six sponsored slots. Increasing ad load raises revenue by up to 43%, but reduces total search conversions by up to 5% and daily engagement by up to 2.2%. These average effects mask substantial heterogeneity: additional slots generate large revenue gains for high-ad-conversion queries, but little or negative marginal revenue for low-conversion queries. The trade-off also shifts within query as advertiser composition changes, such as brand-advertiser presence. Motivated by these findings, we design and deploy a novel adaptive algorithm – exploration-augmented Locally Adaptive Ad Load (e-LAAL). e-LAAL combines LAAL, a model-free query-level decision rule that updates ad-load recommendations using recent outcomes, with static exploration arms that maintain support and provide fixed-policy counterfactual benchmarks. We provide a finite-time dynamic-regret guarantee for the e-LAAL architecture. In a platform-level production deployment serving 22.3 million users and 77.6 million searches, e-LAAL improves the empirical revenue–conversion trade-off relative to deployed static benchmarks and outperforms uniform and historical query-dependent static benchmarks.
[LG-40] LATTICE: Graph Self-Supervised Learning for Multimodal Spatial Omics Integration
链接: https://arxiv.org/abs/2607.14410
作者: Jagan Mohan Reddy Dwarampudi,Veena Kochat,Suresh Satpati,Kunal Rai,Tania Banerjee
类目: Machine Learning (cs.LG); Genomics (q-bio.GN)
*备注:
Abstract:Spatially resolved omics studies increasingly combine transcriptomic and epigenomic assays, yet downstream analysis is often still performed using single-modality pipelines. We present LATTICE (Latent Alignment of Tissue-level and Transcriptomic Information for Cross-modal Embedding), a graph-based self-supervised framework that learns spot-level representations from harmonized multimodal features. LATTICE integrates five aligned modality blocks per Visium spot: Visium RNA, scMultiome RNA, scMultiome ATAC, spatial ATAC, and spatial CUT\Tag. These modalities capture spatial transcriptomic measurements, single-cell inferred regulatory activity, and in situ chromatin and histone states within a unified lattice representation. LATTICE constructs a spatial neighborhood graph and trains a TransformerConv encoder using masked reconstruction, cross-modal alignment, and spatial smoothness objectives. On a private 11-sample melanoma cohort from an anonymized clinical collaborator comprising 54,912 total spots, LATTICE demonstrated stable optimization behavior, reproducible embeddings across analysis seeds, and complete multimodal integration across all samples. Adding scMultiome RNA to Visium RNA alone substantially improved concordance with Space Ranger clusters across 11 runs (adjusted Rand index [ARI] +0.157, normalized mutual information [NMI] +0.143, and spatial contiguity +0.174). Additional modalities further improved spatial contiguity and multimodal utility score (MUS), although they sometimes reduced agreement with RNA-derived reference labels, likely because the learned embeddings captured chromatin and regulatory structure beyond transcriptomic similarity alone. These results position LATTICE as a practical and empirically grounded framework for multimodal spatial omics integration, while also highlighting the need for stronger supervision and broader external benchmarking.
[LG-41] Random Parameter Noise Does Not Make Exact ReLU Verification Easy
链接: https://arxiv.org/abs/2607.14375
作者: Mojtaba Soltanalian
类目: Computational Complexity (cs.CC); Machine Learning (cs.LG)
*备注: 16 pages, 2 figures. Includes full bit-complexity details and computational checks. Reproducibility code and data are included as ancillary files
Abstract:We study exact verification of ReLU networks in an adversarial smoothed model. Every network weight and bias is independently perturbed by Gaussian noise, clipped to [-2,2] , and rounded to the exact dyadic grid determined by the input bit complexity. We show that, under the standard assumption \mathrmNP\not\subseteq\mathrmBPP , there is no sound and complete verifier whose expected running time is polynomial in network size, bit complexity, and inverse noise level for every base instance. The conclusion already holds at the fixed noise level \sigma_\star=2^-11 for one-hidden-layer networks over a unit box, with hidden fan-in at most three and base coefficients in [-1,1] . The proof combines an exact gap embedding with a quantitative robustness argument. For every E3SAT formula \Phi with m clauses, a four-ReLU-per-clause construction satisfies \max_x\in[0,1]^n g_\Phi(x)=(m-\operatornameunsat(\Phi))/3 , and coordinatewise threshold rounding never decreases the objective. A weighted parameter-sensitivity inequality and Gaussian concentration then show that a verification gap linear in m survives the aggregate perturbation of all coefficients with probability at least 1-e^-m/8 . The proof includes clipping, exact dyadic rounding, output-layer perturbations, polynomial-bit sampling of the rounded Gaussian law, and the conversion from expected smoothed running time to a BPP algorithm. Computational checks test the exact identity and illustrate the different scaling of extensive and constant gaps; they are diagnostics rather than evidence for the complexity theorem. The result concerns worst-case base networks in the stated absolute-noise model, but it shows that parameter nondegeneracy alone does not yield a universal smoothed-polynomial guarantee for exact verification. Comments: 16 pages, 2 figures. Includes full bit-complexity details and computational checks. Reproducibility code and data are included as ancillary files Subjects: Computational Complexity (cs.CC); Machine Learning (cs.LG) Cite as: arXiv:2607.14375 [cs.CC] (or arXiv:2607.14375v1 [cs.CC] for this version) https://doi.org/10.48550/arXiv.2607.14375 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-42] A Noise-Robust Elicit-to-Optimize Framework for Distortion Riskmetrics via Inverse Reinforcement Learning
链接: https://arxiv.org/abs/2607.14373
作者: Yang Liu,Yuhao Liu,Yunran Wei
类目: Machine Learning (cs.LG); Risk Management (q-fin.RM)
*备注:
Abstract:We propose a noise-robust elicit-to-optimize framework that integrates inverse reinforcement learning (IRL) and reinforcement learning (RL) for eliciting agents’ risk preferences and optimizing policies under a broad class of risk objectives characterized by distortion riskmetrics. On the elicitation side, we propose an adaptive Bayesian IRL method that infers agents’ latent risk objectives from their noisy observed decisions, explicitly allowing agents to take stochastic and suboptimal actions. We establish the existence of a finite set of distinguishing questions that identifies the preferred distortion riskmetric within the candidate class and prove that the convergence rate of the algorithm is of order O(\exp(-cm+O(\sqrtm\log m))) under general settings, where c0 is a constant and m denotes the number of algorithm iterations. On the optimization side, we develop a model-free RL algorithm for optimizing policies under conditional distortion riskmetrics. By representing the objective as an integral of the conditional cost quantile function with respect to the distortion function, the method unifies distortion-riskmetric objectives. We optimize diverse risk objectives by extending the Proximal Policy Optimization (PPO) algorithm with policy, value, and quantile neural networks, where the quantile network estimates the full conditional cost quantile function and enables numerical evaluation of general risk objectives. A comprehensive empirical study demonstrates the framework’s elicitation accuracy and effectiveness in complex financial environments.
[LG-43] Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion
链接: https://arxiv.org/abs/2607.14371
作者: Fengzhuo Zhang,Zhuoran Yang,Dirk Bergemann
类目: Machine Learning (cs.LG); Theoretical Economics (econ.TH); Machine Learning (stat.ML)
*备注:
Abstract:Large Language Models (LLMs) have revolutionized AI services, but a critical tension emerges: while personalization improves model performance, it consumes scarce computational resources that users must share. When should a user invest in expensive Supervised Fine-Tuning (SFT) versus lightweight In-Context Learning (ICL)? How does congestion from other users’ personalization choices reshape these incentives? And what strategies should platforms adopt when offering multiple personalization algorithms? We develop a tractable framework for LLM serving that captures the statistical-economic trade-offs users face. Our analysis yields several surprising insights. First, we show that ICL and SFT dominate in different regimes, determined by an interplay between pretraining coverage and data signal-to-noise ratios, but congestion can flip these rankings. Second, equilibrium resource consumption exhibits pronounced non-monotonicity: improving pretraining precision reduces the congestion, while broader pretraining coverage and harder tasks sometimes increase it. Third, we prove that offering both personalization methods never hurts the platform’s maximal profits, despite potentially increasing computational load. Experiments with GPT-2 on linear regression tasks validate our theoretical predictions about algorithm performance. Complementing these results, our review of documentation from 21 major AI platforms shows that the share offering both SFT and ICL increased from 9.5% in 2021 to 71.4% in 2025, consistent with our platform-design implications. Subjects: Machine Learning (cs.LG); Theoretical Economics (econ.TH); Machine Learning (stat.ML) Cite as: arXiv:2607.14371 [cs.LG] (or arXiv:2607.14371v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.14371 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-44] Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning
链接: https://arxiv.org/abs/2607.14367
作者: Haobo Zhang,Jiankun Wang,Suraj Rajendran,Weishen Pan,Lam Tsoi,Yong Chen,Fei Wang,Jiayu Zhou
类目: Machine Learning (cs.LG)
*备注: 33 pages, 10 figures, 11 tables
Abstract:Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable. We identify the data-parameter interference as a geometric source of this instability. This interference is controlled by the alignment between LoRA update subspaces and client activations, suggesting that federated LoRA aggregation should be viewed not only as parameter averaging but also as subspace allocation. We propose Dynamic Subspace Boosting (Dysco), a plug-in method that allocates client-specific LoRA subspaces in a federated and dynamic manner. In each round, clients compute activation-insensitive subspaces from local representations and transmit only the resulting bases; the server then constructs client-specific merged subspaces through a closed-form solution that maximizes compatibility with other clients’ insensitive directions. To handle representation drift, Dysco performs multi-round subspace boosting to preserve past update directions while adapting to future representations. We provide a convergence analysis that embeds the data-parameter interference as an aggregation-error term in a standard federated optimization bound, and prove that Dysco’s server-fixed merged subspaces yield a tighter upper bound on this error. Experiments on controlled synthetic federated tasks and on MIMIC-IV clinical-note classification with Llama-3.2-1B show that Dysco substantially reduces interference, reduces the final-round synthetic training loss by up to 9 times relative to baselines under the orthogonal-subspace partition the theory identifies, improves all five tested FL algorithms by up to 4.3% on MIMIC, outperforms recent federated LoRA methods, and adds only 0.9% wall-clock overhead. Our code is available at this https URL.
[LG-45] Learning Who to Treat When Treatment is Missing
链接: https://arxiv.org/abs/2607.14346
作者: Johnna Sundberg,Rayid Ghani,Eli Ben-Michael,Edward Kennedy
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Policy learning methods are increasingly used to inform treatment allocation under budget constraints. Most proposed methods assume complete treatment data, yet applications frequently suffer from missingness that can bias estimates and lead to suboptimal policies. We address this gap by extending efficient estimators for average treatment effect (ATE) estimation to policy value and conditional average treatment effect (CATE) estimation under missing at random (MAR) and missing completely conditionally at random (MCCAR) treatment data. Through asymptotic efficiency analysis, we prove that the MAR estimator, which leverages partially-observed units, is both valid and more efficient than the MCCAR estimator when MCCAR assumptions hold. This result provides formal justification for preferring MAR-based estimation in policy learning under both missing data settings. Our comprehensive experiments using synthetic and semi-synthetic datasets confirm that correctly specifying the missingness mechanism is crucial: misspecified estimators remain biased regardless of sample size, while our estimators achieve near-oracle performance when assumptions are satisfied. Our work provides practitioners with theoretically grounded, empirically validated tools for robust policy learning in the presence of missing treatment data.
[LG-46] Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data
链接: https://arxiv.org/abs/2607.14318
作者: Youssef Drissi,Markus Ettl,Shivaram Subramanian,Wei Sun,Zack Xue
类目: Machine Learning (cs.LG)
*备注:
Abstract:We introduce COAT (Counterfactual Optimal Action Tree), a framework for learning interpretable prescriptive policies from observational data. COAT combines counterfactual outcome estimation with large-scale mixed-integer optimization, using column generation to translate causal predictions into feasible, transparent decisions under business and regulatory constraints. We apply COAT to airline ancillary pricing, a setting characterized by complex business rules and limited experimental flexibility. In a 17-week field pilot with a major global airline, COAT increased upsell revenue per booking by 6.9%, with the airline projecting \ 50-\ 150 million in incremental annual premium seat revenue across eligible domestic markets. The success of the pilot led to scaled adoption and informed broader AI-driven decision initiatives within the organization.
[LG-47] NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis
链接: https://arxiv.org/abs/2607.14314
作者: Lincan Li,Zheng Chen,Yushun Dong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling. While spatial-temporal graph neural networks (STGNNs) have advanced EEG brain network representation learning, the resulting graph structures suffer from low clinical plausibility and limited interpretability due to their purely data-driven nature. To this end, we introduce NeuroGRIP, a retrieval-augmented graph refinement framework that incorporates external medical knowledge to calibrate noisy EEG graphs. We first construct a large-scale, domain-specific knowledge base derived from authoritative clinical guidelines. Leveraging large language models, we extract structured biomedical entities and relations to form a textual knowledge graph (KG), which serves as external knowledge source of clinical priors. Our framework performs alignment-aware query construction by projecting STGNN-generated EEG node embeddings into the semantic space of KG. Semantic queries are then executed via FAISS-based similarity search over knowledge triplets to retrieve relation evidence. Each predicted edge is assigned a confidence score based on retrieved similarity, relation type, and source reliability, enabling us to prune medically implausible edges from the originally predicted graph. Extensive experiments on TUSZ and CHB-MIT demonstrate that NeuroGRIP not only improves seizure detection accuracy but also enhances interpretability by grounding each prediction in clinically validated knowledge. This work provides the first unified framework that tightly couples brain dynamics with external medical expertise via retrieval-augmented reasoning, paving the way for knowledge-enhanced, explainable clinical diagnosis. The code is available at: this https URL.
[LG-48] DiMaS: Distribution Matching for Steering Vision-Language-Action Models
链接: https://arxiv.org/abs/2607.14280
作者: Pegah Khayatan,Sara Meziane,Jayneel Parekh,Matthieu Cord
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Flow-matching-based vision-language-action (VLA) models have emerged as powerful policies for robotic manipulation, yet a critical capability remains underexplored: fine-grained behavioral control, the ability to govern how a robot performs a task by intervening on its internal representations. Representation steering is a well-established interpretability tool for language and vision-language models, where behavioral features are typically encoded as linear directions, but we show that these classic methods fall short in VLAs. We propose DiMaS, a Distribution-Matching Steering strategy tailored to flow-matching VLAs, which transports between representation distributions rather than shifting along a fixed direction, and show that it effectively controls behavior across two state-of-the-art VLAs. We further examine the generalizability of this strategy as the tasks it is learned from and evaluated on grow increasingly dissimilar, characterizing where behavioral control transfers and where it weakens. Finally, through an analysis of the representation structure of the action expert, we explain why classical linear steering falls short in the visuomotor setting: behavioral features are linearly decodable but not linearly steerable, which motivates the distribution-matching design of DiMaS. Our code is publicly available at this https URL, with additional results and videos at this https URL
[LG-49] Lyapunov Guidance: A Unified Framework for Stabilizing Generative Flows
链接: https://arxiv.org/abs/2607.14272
作者: Jingdong Zhang,Xinze Li,Yize Jiang,Luan Yang,Minkai Xu,Junhong Liu
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS); Optimization and Control (math.OC)
*备注: 25 pages, 13 figures
Abstract:Flow matching has emerged as an effective framework for learning complex data distributions, but adapting pretrained flow models to new tasks often requires computationally expensive retraining. Post-training guidance provides a more efficient alternative, but existing methods are largely heuristic and offer no explicit stability guarantees. We address this limitation by proposing LyaGuide, a unified Lyapunov-guided framework that formulates flow guidance as a Lyapunov control problem. Our main theoretical result establishes an equivalence between guided flow matching and Lyapunov control, thereby unifying common guidance strategies, such as classifier guidance, reward guidance, and energy-based guidance, within a single control-theoretic framework. To enforce the Lyapunov condition, we introduce a pseudo-projection operator with a closed-form expression that endows learned or heuristic guidance terms with explicit stability guarantees. LyaGuide supports two practical settings: a model-driven setting, where the target guidance distribution is specified through a known Lyapunov function, and a data-driven setting, where the guidance is adapted from task-specific downstream data. LyaGuide is compatible with existing guidance methods, introduces minimal additional computational overhead, and is straightforward to integrate in practice. Extensive experiments on synthetic benchmarks, image inverse problems, reinforcement learning planning, and energy-based modeling demonstrate consistent improvements in sample quality, guidance fidelity, and robustness, while maintaining computational efficiency.
[LG-50] MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion
链接: https://arxiv.org/abs/2607.14249
作者: Yilai Liu,Shiyuan Zhang,Hongyang Du
类目: Machine Learning (cs.LG)
*备注:
Abstract:Mobile usage traces are critical for tasks such as user behavior prediction and app recommendation, yet their use is constrained by privacy restrictions and costly large-scale data collection. Although generative models perform well on general time series, their application to mobile usage data remains challenging because (i) limited user activity causes severe sparsity, (ii) heterogeneous variable types complicate joint modeling, and (iii) functional differences across apps create pronounced usage imbalance. To address these challenges, we propose Multivariate-Imaging Diffusion (MIDiff), a diffusion-based framework operating in an imaging space defined by Cross-Gramian Angular Sum Field (C-GASF). C-GASF transforms sparse multivariate sequences into correlation images, while MIDiff employs Triple Attention in a U-Net to preserve temporal consistency and variable dependencies. Experiments show that MIDiff achieves state-of-the-art performance across fidelity metrics. In particular, it obtains a Discriminative Accuracy (DA) of 0.1526, compared with 0.3476 for the strongest baseline, ZITS-VAE, demonstrating its effectiveness in generating realistic and diverse mobile usage traces. Our code is available at this https URL.
[LG-51] Augmentations for Robust and Efficient Imitation Learning in Streamed Video Games
链接: https://arxiv.org/abs/2607.14200
作者: Somjit Nath,Abdelhak Lemkhenter,Pallavi Choudhury,Chris Lovett,Katja Hofmann,Sergio Valcarcel Macua,Lukas Schäfer
类目: Machine Learning (cs.LG)
*备注: To be published in proceedings of IEEE Conference on Games (CoG), 2026
Abstract:Imitation learning is an appealing way to scale game-playing agents to complex 3D environments by training policies to map visual observations to actions from human demonstrations. However, these demonstrations are expensive to collect and modern game-playing is often done through streaming in which network delay and compression introduce spatiotemporally correlated visual artifacts that can cause a covariance shift at test time. To address these challenges, we propose streaming augmentations that mimic four types of artifacts commonly encountered during streaming with low-bandwidth network connection: pixelated blocks and scrubs, global blur, and ghosting. We instantiate our approach on top of predictive inverse dynamics models (PIDM), which combine future-state conditioning with an inverse dynamics policy in a learned latent space, and evaluate the impact of our augmentations across three tasks in modern 3D video games. Under stable streaming conditions, agents trained with spatiotemporal augmentations achieve up to 41% higher evaluation performance compared to agents trained without augmentations under an identical data budget. When network lag is introduced, agents trained with augmentations degrade by only 7.45% vs 49.82% of the original performance for agents trained only with the original data. These results clearly indicate that spatiotemporal augmentations tailored for the streaming setting are a simple yet powerful tool to train robust and efficient game-playing agents.
[LG-52] EDDY: A Pediatric Foundation Model for Risk Forewarning from ICD-Coded Diagnostic Histories
链接: https://arxiv.org/abs/2607.14191
作者: Matthew Brady Neeley,Jorge Botas,Johnathan Jia,Lin Yao,Daniel Palacios,Benjamin Choi,Zhandong Liu,Hyun-Hwan Jeong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Pediatric electronic health records capture developmentally structured clinical trajectories, yet their potential for generative healthcare foundation models remains largely unexplored. Here we present TEDDY (Temporal Event Decoder for Disease in Youth), a 1.84-million-parameter decoder transformer trained on approximately 73 million ICD-10 diagnoses from 1.6 million children at a single pediatric institution. TEDDY models longitudinal diagnosis trajectories and visit timing. Predictions were made before visit codes were revealed, limited to first occurrences, and evaluated against sex- and age-matched controls. Across 797 disease-onset prediction tasks spanning 16 ICD-10 chapters, TEDDY achieved a median AUC of 72.0%, outperforming same-data DenseNet (50.0%), CNN (57.2%), RNN (60.1%), and LSTM (62.7%) baselines on 96-99% of tasks. Performance held across sex and age and was strongest among lower-prevalence diagnoses; 202 of the 225 rarest conditions (90%) had 95% confidence intervals above chance. Predictive signal remained detectable more than two years before first recorded diagnosis, with median AUCs of 59.7% in the unrestricted analysis and 64.4% in a fixed-cohort sensitivity analysis. In asthma and attention-deficit/hyperactivity disorder benchmarks, AUCs were 79.3% and 84.7%, compared with 62.7% and 71.7% for the strongest comparators, including a general-purpose language model three orders of magnitude larger. Visit-timing predictions had a 3.0-day mean absolute restricted mean survival-time error over 365 days, although median and long-tail return intervals remained miscalibrated. Together, these results establish pediatric diagnostic histories as a substrate for compact generative models supporting broad, rare-disease, and long-horizon risk forecasting without population-scale data or billion-parameter models.
[LG-53] Quantize with Confidence? An Empirical Study of Quantization for Code Generation
链接: https://arxiv.org/abs/2607.14181
作者: Saima Afrin,Md. Zahidul Haque,Antonio Mastropaolo
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG); Programming Languages (cs.PL)
*备注:
Abstract:The growing adoption of local inference frameworks such as Ollama has made it increasingly common for developers to run large code models on laptops and other resource-constrained hardware. In these settings, post-training quantization is essential for reducing memory footprint and enabling practical deployment, yet its impact on generated code remains insufficiently understood. We empirically evaluate six state-of-the-art quantization methods (GPTQ, AWQ, QuIP#, AQLM, BitsAndBytes, and GGUF) on two representative large code model families, Qwen2.5-Coder and CodeLlama, using the multilingual McEval and CoderEval benchmarks for Python and Java. We assess functional correctness (pass@1) together with maintainability, reliability, security, and structural complexity. We also introduce a novel analysis of robustness under varying prompt complexity, characterized by Shannon entropy and token length. Our results show that quantization techniques differ meaningfully in their impact on correctness and code quality. AQLM consistently matches or exceeds the full-precision baseline, whereas QuIP# exhibits the largest correctness degradation, particularly on complex prompts. Security attributes remain stable across models, benchmarks, and programming languages, while robustness to prompt complexity varies across techniques. These findings provide practical guidance for selecting quantization strategies for deploying large code models on resource-constrained hardware and highlight the importance of evaluating quantized models beyond functional correctness.
[LG-54] Low-Latency Relay Selection in NR-V2X Vehicular Communications via Graph Isomorphism Networks with Edge Features
链接: https://arxiv.org/abs/2607.14176
作者: Giambattista Amati,Federica Mangiatordi,Emiliano Pallotti,Simone Angelini,Pierpaolo Salvo,Paola Vocca
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: 6 pages, conference
Abstract:Reliable, low-latency uplink connectivity is a key requirement for C-V2X networks in dense urban environments, where fast channel variations and blockages often degrade direct vehicle-to-infrastructure links. Multi-hop relaying can restore coverage, but relay-link activation under radio, capacity, and routing constraints results in an NP-hard optimisation problem, typically solved via Mixed-Integer Linear Programming (MILP), whose runtime scales poorly with graph size. This paper introduces an edge-aware Learning-to-Optimise framework for real-time relay selection. Each V2X snapshot is modelled as a directed graph: node features encode vehicle state and traffic demand, while edge features capture radio-link capacity. An offline MILP oracle generates optimal relay configurations that supervise a Graph Isomorphism Network with Edge Features (GINE), enabling edge-level relay activation through a single forward pass, with tightly bounded inference latency. To bridge learning and exact optimisation, we also propose a hybrid GINE-Pruned MILP (GP-MILP) strategy in which GINE predictions prune the MILP search space. Experiments on a large-scale dataset generated via an OSM-SUMO-GEMV ^2 pipeline show that GINE closely matches MILP decisions at the link level (accuracy 0.9589), F1-score (0.9544) on validation) and yields consistent end-to-end connectivity gains over a 1-hop MILP baseline (up to 9.2% with four RSUs and 12% with two RSUs). Inference latency remains tightly bounded, with all evaluated instances completing within 5~ms. Moreover, GP-MILP preserves MILP-equivalent solutions (same objective value) while achieving solver runtimes below 30~ms for more than 98%) of the graph instances, making MILP-grade optimisation compatible with stringent NR-V2X latency budgets.
[LG-55] How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentiment
链接: https://arxiv.org/abs/2607.14174
作者: Sanggyu Sean Choi
类目: Machine Learning (cs.LG); Computational Finance (q-fin.CP); Mathematical Finance (q-fin.MF); Statistical Finance (q-fin.ST)
*备注:
Abstract:Financial sentiment extraction has largely relied on news text and supervised extraction against return labels alone, leaving 10-K filings – and volatility, the target risk disclosure is arguably best suited to informing – comparatively unexplored. We extend a supervised lexicon-learning approach to 10-K filings and their Item 1A risk-factor sections, training sentiment scores against both return and volatility labels at three levels of aggregation: sector, portfolio, and individual firm. Across 1,383 filings from 94 Nasdaq-100 technology constituents (2006–2023), we evaluate the resulting twelve sentiment metrics on classification accuracy, correlation with realised market outcomes, and qualitative lexical content. Full-filing text produces more accurate sentiment at the sector and portfolio level for both targets, but this reverses at the individual-firm level, where the narrower Item 1A section performs better – an effect we attribute to the interaction between document volume and the amount of independent training signal available at each level of aggregation. A Loughran-McDonald dictionary baseline is consistently, strongly negatively correlated with price at every level tested, underscoring the value of a supervised approach for regulatory disclosure text. These findings, and the design choices they motivate, establish the sentiment-generation methodology underlying a subsequent, larger-scale, multi-source system.
[LG-56] ITGPT : A Transformer Based Architecture for the Generation of Dance Dance Revolution and In the Groove Charts
链接: https://arxiv.org/abs/2607.14148
作者: Miguel O’Malley
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注: 14 pages, 11 figures, 2 tables + appendix
Abstract:Dance Dance Revolution and In the Groove are rhythm games consisting of songs and accompanying choreography, referred to as charts. Players press arrows on a device referred to as a dance pad in time with steps determined by the song’s chart. The process of manual chart generation is timestaking and difficult, motivating interest in automation. We propose ITGPT, a new transformer based architecture for the generation of DDR/ITG charts, and demonstrate significant improvements to generation accuracy and computational cost in comparison to predecessor work.
[LG-57] Delocalization of bias in unadjusted Hamiltonian Monte Carlo and underdamped Langevin
链接: https://arxiv.org/abs/2607.15208
作者: Yifan Chen,Xiaoou Cheng,Jonathan Niles-Weed,Jonathan Weare
类目: Computation (stat.CO); Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注:
Abstract:Unadjusted samplers such as unadjusted Hamiltonian Monte Carlo and underdamped Langevin are well-known to be biased. Metropolis–Hastings adjustment has been conventionally incorporated into Hamiltonian Monte Carlo to eliminate the bias. However, this adjustment can significantly increase the iteration complexity due to the small step size required for reasonable Metropolis acceptance rates. In this work, we extend the \emphdelocalization of bias phenomenon, previously established for the overdamped Langevin algorithm, to these two unadjusted algorithms. We show that to control the W_2 bias of any K -dimensional marginal of a high-dimensional distribution, O(\sqrtK) integration steps suffice up to \log d terms, assuming either weak or sparse interactions among variables. The discrete-time integrators here introduce technical difficulties beyond those of the overdamped setting, which we address through a broadly applicable matrix-polynomial framework that characterizes their propagators. Our result for the underdamped Langevin algorithm is valid for all large friction parameters, implying that the Leimkuhler-Matthews integrator for the overdamped Langevin dynamics also exhibits delocalization of bias.
[LG-58] cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data
链接: https://arxiv.org/abs/2607.15018
作者: Chun-houh Chen,Shun-Chuan Chang,Chiun-How Kao,Yi-Ju Lee,Shang-Ying Shiu,Yin-Jing Tien,ShengLi Tzeng,Han-Ming Wu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Computation (stat.CO); Methodology (stat.ME)
*备注: 23 pages, 9 figures, 3 tables
Abstract:High-dimensional categorical data arise in genetics, biomedicine, and the social sciences, yet visualization tools for such data remain far less developed than those for continuous variables. Existing methods either scale poorly, rely heavily on low-dimensional displays detached from the original data matrix, or prioritize predictive accuracy over interpretability. To address this gap, we introduce categorical Generalized Association Plots (cGAP), a visualization framework for nominal, ordinal, and binary data that preserves the original data matrix while augmenting it with interpretable geometric structure. cGAP uses Homogeneity Analysis (HOMALS) to embed subjects and category levels in a three-dimensional Euclidean space and maps the embedding to red-green-blue coordinates so that similar patterns receive similar colors. The framework integrates three coordinated views: a HOMALS-guided heatmap of the raw data matrix, a subject proximity matrix, and a variable proximity matrix. Seriation algorithms are then used to reorder rows and columns to reveal coherent clusters, outliers, and local-to-global structure. We also derive barycentric traceability, projection-distortion, and contrast-preservation properties that clarify how embedding geometry is transferred to the display. We demonstrate the versatility of cGAP through applications to student-animal classification data, mammalian dentition profiles, mushroom records from the UCI Machine Learning Repository, and the Clusters of Orthologous Genes database. These examples show that cGAP supports transparent exploratory analysis by maintaining traceability between derived visual structure and the original categorical observations. cGAP provides a full-matrix, heatmap-based visualization environment for investigating complex categorical datasets across scientific domains.
[LG-59] Optimal Self-Distillation for Rectified Flow via Linear Probing
链接: https://arxiv.org/abs/2607.14947
作者: Saptarshi Roy,Debepsita Mukherjee,Pratik Patil
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 29 pages
Abstract:Modern generative models are increasingly trained using model-generated signals, creating both opportunities for self-improvement and risks of collapse. We study optimal self-distillation (SD) for rectified flow (RF): given a suboptimal teacher velocity field, can a student trained on a mixture of true RF velocities and teacher velocities provably improve the teacher? For linear RF with ridge regularization on fixed interpolation pairs, we prove an exact affine path identity, derive the optimal mixing coefficient in closed form, and show strict improvement in integrated velocity risk whenever the teacher risk is nonstationary along the regularization path. The optimal coefficient obeys a sign rule: positive mixing corrects under-regularized teachers, while negative mixing corrects over-regularized teachers. We also give one-shot generalized cross-validation (GCV) and validation tuning procedure that avoids grid search over mixing weights and repeated refitting. Combining this theorem with RF Wasserstein convergence bounds, we show that optimal self-distillation improves the velocity estimation terms controlling continuous-time and finite-step generation error. Experiments with Gaussian models, Gaussian mixtures, and image data show that optimal self-distillation improves velocity risk, mode recovery, and finite-step generation relative to both the teacher and pure distillation.
[LG-60] Domain Adaptation of Mismatched Proximal Denoiser for Plug-and-Play Image Reconstruction
链接: https://arxiv.org/abs/2607.14894
作者: Guixian Xu,Jinglai Li,Junqi Tang
类目: Image and Video Processing (eess.IV); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 33 pages
Abstract:Plug-and-play proximal gradient descent (PnP-PGD) enables flexible image reconstruction by using denoisers as implicit priors. In practice, these denoisers are often deployed outside their training domains. Existing analyses establish convergence under structural assumptions on the deployed denoiser, such as requiring it to be a proximal map or a contraction. However, they do not measure how domain mismatch affects convergence of PnP-PGD. We define this effect as \emphproximal mismatch: the discrepancy between a deployed denoiser \widehat\mathsf D and a target-domain reference map \mathsf D_\star=\operatornameprox_R_\star associated with the underlying regularizer R_\star . Under this mismatch, each denoising update becomes an inexact proximal step for the target objective. We further derive a stationarity bound that decays at a rate of \mathcalO(1/K) , with an additive term proportional to the average squared proximal mismatch. This result motivates adaptation via proximal matching rather than MSE-based adaptation alone. We study this approach with two established denoiser families: learned proximal networks and gradient-step denoisers. Experiments on Gaussian deblurring and super-resolution under substantial domain shift show that proximal matching adaptation improves reconstruction quality significantly over MSE-based adaptation, yielding the largest numerical gains in the few-shot regime.
[LG-61] Measuring Spatial Clustering via Metropolis-Hastings Diffusion Distance
链接: https://arxiv.org/abs/2607.14880
作者: Thomas Weighill,Chidinma Williams
类目: atistics Theory (math.ST); Machine Learning (cs.LG)
*备注:
Abstract:We propose a novel measure of the discrepancy between two probability distributions f and g on a graph - which we call the diffusion distance - that measures the rate of convergence of f to g under a graph-constrained Markov chain with stationary distribution g . As a default choice for this Markov chain, we use the Metropolis-Hastings transition matrix targeting g with proposals given by a random walk on the graph. Our primary case of interest is when the second distribution g is uniform, in which case the diffusion distance becomes a measure of spatial clustering in f . Used in this way, (Metropolis-Hastings) diffusion distance to uniformity extends Moran’s I -type measures of spatial autocorrelation by incorporating global graph geometry rather than just local patterns. Indeed, Moran’s I , the most well-known measure of spatial autocorrelation, can be viewed as a one-step heuristic for diffusion distance, so long as specific spatial weights are used. We establish theoretical bounds and a stability result for our measure, connecting it to graph spectra and optimal transport. We then turn our attention to outlining a statistical test for spatial clustering using diffusion distance. Under permutation null models, we derive high-probability bounds on diffusion distance underpinned by exact spectral formulas for convergence of distributions, enabling an efficient statistical test for spatial clustering on large datasets. We empirically compare diffusion distance to Moran’s I both as a numerical measure and as a statistical test. We show that diffusion distance exhibits higher power on synthetic data using a stochastic block model. Empirical analysis of Black population distributions for 100 U.S. cities shows that diffusion distance detects subtle differences in urban segregation patterns that Moran’s I does not.
[LG-62] Riesz-Kernel Stein Variational Gradient Descent: Renormalized Entropy and Long-Time Particle Limits
链接: https://arxiv.org/abs/2607.14527
作者: Trevor Teolis,Maarten V. de Hoop
类目: Analysis of PDEs (math.AP); Machine Learning (cs.LG)
*备注: 31 pages. No figures. Many-particle and long-time convergence for Stein variational gradient descent with singular periodic Riesz kernels
Abstract:Stein variational gradient descent (SVGD) transports interacting particles toward a target distribution through deterministic kernelized dynamics. Singular Riesz kernels are attractive because they can provide quantitative population-level convergence, but at the finite-particle level the corresponding Stein energy has infinite self-interaction. We study periodic Riesz SVGD with self-interaction removed and prove a many-particle, long-time sampling theorem. Throughout the range in which the singular Stein energy is locally integrable, under a uniform bound on the initial relative entropy per particle, the time-averaged empirical-measure law converges weakly to the point mass (\delta_\pi) at the target as the particle number and any diverging averaging horizon tend to infinity. We also show that the empirical-measure laws induced by invariant particle laws of finite relative entropy converge weakly to (\delta_\pi), without a uniform entropy bound. Below the logarithmic singularity threshold, we obtain an explicit algebraic finite-particle error bound. These results extend the joint-entropy approach for smooth-kernel SVGD to singular interactions.
[LG-63] Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields
链接: https://arxiv.org/abs/2607.14486
作者: Sheng Bi,Yi-Ze Wang,Jun Cheng
类目: Chemical Physics (physics.chem-ph); Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注: 23 pages, 5 figures
Abstract:Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows. Active learning can reduce this cost, but standard model-committee uncertainty is impractical for foundation MLFFs because each committee member requires a separate fine-tuning run. We present an active-learning workflow based on last-layer-projection regression (LLPR), a forward-pass-cheap per-configuration uncertainty estimator. Across molecular, condensed-phase, and electrolyte systems, LLPR identifies compact, high-value training sets that recover full-data accuracy using only a small fraction of electronic-structure labels. In foundation-model fine-tuning, LLPR-selected configurations reach the full-pool fine-tuning ceiling with substantially fewer labels than random selection. In iterative electrolyte fine-tuning, LLPR detects unphysical local coordination before DFT labelling, provides an absolute force-error threshold, and enables automatic termination of the learning loop. The resulting models reproduce reference density and ion-coordination structure, providing a scalable uncertainty-quantification strategy across MLFF training regimes.
[LG-64] NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processes
链接: https://arxiv.org/abs/2607.14361
作者: Anastasis Kratsios,Giulia Livieri,Philipp Schmocker
类目: Probability (math.PR); Machine Learning (cs.LG); Computational Finance (q-fin.CP); Machine Learning (stat.ML)
*备注:
Abstract:We address fundamental challenges in representing and computing \mathbbR^d -valued predictable square-integrable processes over [0,T] , collected in the space \mathcalH^2_T(\mathbbR^d) . These processes are central to continuous-time stochastic control, reinforcement learning, and mathematical finance. Although Wiener-chaos expansions offer strong theoretical tools, traditional computational methods are hindered by the need for large chaos dictionaries and high-order iterated integrals. To overcome these obstacles, we introduce NeuralChaos – a neural operator architecture that produces elements of \mathcalH^2_T(\mathbbR^d) using only finitely many evaluations of the driving Brownian motion, while preserving predictability and square-integrability. We prove that NeuralChaos is dense in \mathcalH^2_T(\mathbbR^d) and achieves the best N -term chaoslet approximation rates for compressible and Malliavin–Sobolev regular processes. Moreover, compressibility is shown to be typical for processes from \mathcalH^2_T(\mathbbR^d) under non-degenerate sub-Gaussian sampling. In contrast, we show that finite-dimensional Markovian neural SDE models constitute a meagre and Gaussian-null subset in \mathcalH^2_T(\mathbbR^d) , regardless of discretization, whereas compressible processes are generic. Numerical experiments on a stochastic optimal control problem and dynamic hedging highlight the practical effectiveness of our approach. Our results enable more efficient and expressive modelling in stochastic analysis and mathematical finance.
[LG-65] Spectral Concentration and Recovery in Sparse High-Dimensional Random Geometric Graphs
链接: https://arxiv.org/abs/2607.14304
作者: Manuel Fernandez V,Yizhe Zhu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST)
*备注: 62 pages
Abstract:We study sparse random geometric graphs generated by connecting pairs of high-dimensional vectors whose inner product exceeds a threshold. The latent vectors are sampled either uniformly from the sphere or from a standard Gaussian distribution. Although every edge appears with probability p , the edges are dependent through their shared latent vectors. For the spherical model, at the connectivity scale np=\Omega(\log n) , we prove |A-\mathbb E A|=O\left(\sqrtnp\log n+np\tau\right) , with high probability, where \tau is the cap threshold. This sharpens the spectral norm bound of Liu, Mohanty, Schramm, and Yang (2023) under weaker assumptions. An analogous result holds for the Gaussian model after removing the fluctuations of the vector norms, yielding improved global synchronization guarantees for the homogeneous Kuramoto model. We then recover the latent geometry from the leading eigenspace. When np\gg\log n , both the latent vector and relative Gram matrix errors vanish provided d\ll np\log(1/p)/\log n . The required lower dimension is only d\gg\log(1/p) for the spherical model and d\gg\log^2(1/p)\log n for the Gaussian model, improving the recovery guarantees of Li and Schramm (2023). Finally, we prove the first exact recovery result for the Gaussian mixture block model of Li and Schramm (2023). At the optimal connectivity scale np=\Omega(\log n) , a polynomial-time semidefinite program exactly recovers all labels in a moderate-separation regime, whereas larger separation makes exact recovery impossible because isolated vertices appear with high probability. Our proofs combine orthogonal polynomial expansions, decoupling, and matrix concentration, avoiding the trace-moment arguments used in previous work.
[LG-66] Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network
链接: https://arxiv.org/abs/2607.14293
作者: Daniel Gaytan-Villarreal,Peter Meiring,Daniel Baxter,Daniel Bowring,Grace Bratrud,Matteo Cremonesi,Giuseppe Di Guglielmo,Grace Wagner,Bowen Xiao
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:
Abstract:Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, introducing latency incompatible with in-the-loop qubit control. In this paper, an online detector of charge jumps for superconducting qubits, based on a dilated causal convolutional neural network (DCCNN) designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, is presented. The network is trained on synthetic Ramsey tomography scans generated from qubit templates measured at the Northwestern Experimental Underground Site (NEXUS) at Fermilab, and translated to FPGA firmware via hls4ml with ap_fixed \langle 16,6 \rangle quantization, reaching a per-inference latency of 6.19 \mu s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline \chi^2 algorithm ( 0.843 \pm 0.022 vs. 0.866 \pm 0.020 on |\Delta q| \in [0.1, 0.5] e at matched false-positive rate), while requiring no per-qubit hyperparameter tuning. This shifts charge-jump detection from a post-hoc diagnostic to a control-loop primitive, enabling adaptive protocols that respond to radiation-induced events in situ, with applications to quantum-computing error mitigation and to the use of superconducting qubits as particle detectors.
[LG-67] Operator-Informed Gaussian Processes for Complex Helmholtz Wavefields: From Synthetic Benchmarks to In Vivo Brain Elastography
链接: https://arxiv.org/abs/2607.14193
作者: Boyuan Deng,Kshitiz Upadhyay,Michael Shields
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Numerical Analysis (math.NA); Medical Physics (physics.med-ph)
*备注: 26 pages, 7 figures
Abstract:The Helmholtz equation governs time-harmonic wave propagation, and in dissipative media a complex modulus renders its squared wavenumber \kappa^2 complex. Inferring such fields from sparse, noisy data calls for solvers that also quantify their own uncertainty. Physics-informed Gaussian-process (GP) regression supplies this by returning a posterior over the solution, yet operator-conditioned formulations have been developed almost exclusively for real-valued fields. We extend operator-informed GP regression to complex-valued Helmholtz problems by realifying the complex operator into an equivalent coupled real block, which enables inference with standard real-valued GP conditioning. The construction admits a family of priors, from a proper diagonal prior to coregionalized and multiscale variants, and conditions on PDE residuals and boundary traces. On benchmark problems in one to three dimensions, the solver is competitive with finite-difference and neural-network baselines at a far smaller interior-constraint budget. Unlike those deterministic baselines, it returns a posterior over the complex wavefield rather than a point estimate. Applied to \textitin vivo brain magnetic resonance elastography, a proper multiscale prior reconstructs the shear curl field to a correlation of 0.77 with measurement, above a 0.75 target. The gain arises from the multiscale kernel rather than from real–imaginary coupling. We further identify a low-frequency accuracy ceiling set by model mismatch and a posterior uncertainty that is not yet calibrated. Calibrated uncertainty therefore emerges as the central next step for probabilistic wavefield inference in dissipative media.
[LG-68] Analysis of Public Schools Educational Performance Based on Causal Models and Hierarchical Clustering
链接: https://arxiv.org/abs/2607.14124
作者: Anderson L. de Paula,Pedro C. dos Santos,Renato A. Krohling
类目: Applications (stat.AP); Machine Learning (cs.LG)
*备注: 15 pages
Abstract:The increasing availability of large-scale educational datasets has expanded the use of quantitative methods for investigating school performance. However, institutional heterogeneity among schools and the structural complexity of educational data pose substantial challenges to traditional statistical modeling approaches. This study investigates the existence of school typologies based on structural, pedagogical, and demographic characteristics, and examines how these typologies relate to performance in the Brazilian Basic Education Assessment System (Saeb). Using data from the Brazilian School Census and Saeb, data preprocessing and normalization procedures are applied followed by hierarchical clustering to identify groups of schools with similar structural profiles. After the identification of these typologies, causal analysis techniques are employed to investigate potential causal relationships between school characteristics and educational outcomes. The results reveal the presence of distinct school profiles and statistically significant differences in average performance among them. The causal analysis provides insights into the structural and contextual factors that may influence educational performance, contributing to a better understanding of the mechanisms associated with school effectiveness.
[LG-69] Generalized Neural Distributional Regression
链接: https://arxiv.org/abs/2607.14122
作者: Natan Hilario da Silva,Vicente Garibay Cancho,Adriano Kamimura Suzuki
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Applications (stat.AP); Computation (stat.CO); Methodology (stat.ME)
*备注: 51 pages, 10 figures, 5 tables
Abstract:We introduce the Generalized Neural Distributional Regression (GNDR) framework, which seamlessly embeds deep neural networks into the parameter space of classical probability distributions. To reconcile the inherent non-identifiability of deep architectures with maximum likelihood theory, we propose a two-step semi-parametric estimation procedure. By isolating the terminal prediction heads and treating the upstream network as a fixed, non-linear basis expansion, GNDR enables the extraction of analytical Fisher Information matrices. This facilitates rigorous uncertainty quantification, generating observation-specific confidence bands and tolerance intervals via the multivariate Delta method. We demonstrate the framework’s versatility and superior distributional calibration across diverse data modalities, including overdispersed clinical counts, right-censored transcriptomic survival profiles under a mixture cure framework, and zero-truncated age distributions derived directly from unstructured facial images. The methodology is natively implemented in the open-source Python package \textitthetaflow.
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